University of Ghana http://ugspace.ug.edu.gh FACTORS INFLUENCING LECTURERS’ ADOPTION AND USE OF AN OPEN SOURCE LEARNING MANAGEMENT SYSTEM IN UNIVERSITIES IN GHANA ASAMAOH, MOSES KUMI (10363642) THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE AWARD OF DOCTOR OF PHILOSOPHY DEGREE IN ADULT EDUCATION AND HUMAN RESOURCE STUDIES JULY, 2017 University of Ghana http://ugspace.ug.edu.gh DECLARATION I declare that with the exception of references made from other authors' works which have been properly acknowledged, this research work carried out in the department of Adult Education and Human Resource Studies, University of Ghana, Legon, under the supervision of Professor Yaw Oheneba-Sakyi, Professor Michael Tagoe and Dr. Samuel Badu-Nyarko, is the result of my own research work and that it has neither in part nor in whole been presented in this University or elsewhere for another degree. Candidate: …………………………………………. ASAMOAH, MOSES KUMI This thesis has been submitted for examination with the approval of Principal Thesis Supervisor: ………………………………………………… Professor Yaw Oheneba-Sakyi Date:……………………… Second Thesis Supervisor:…………………………………………………… Professor Michael Tagoe Date: ………………………… Third Thesis Supervisor :………………………………………………………… Dr. Samuel Badu-Nyarko Date:……………………………… i University of Ghana http://ugspace.ug.edu.gh DEDICATION This thesis is dedicated to all the lecturers of School of Continuing and Distance Education who taught me during Master of Philosophy and Ph.D. course work. ii University of Ghana http://ugspace.ug.edu.gh ACKNOWLEDGEMENTS I express much gratitude to the Most High God for His incredible interventions in my life. To Him alone be all the glory and honour. I thank my supervisors, Prof. Yaw Oheneba-Sakyi, Prof. Micheal Tagoe and Dr. Samuel Badu-Nyako of the University of Ghana (UG), for their time, devotion, academic advice and skillful supervision. I thank the top Management of the University of Ghana for their ability to manage the affairs of the University in a peaceful manner throughout our enrolment in the University till our successful conclusion. I thank Central University Management for giving me a partial scholarship to pursue the Ph.D. programme. I appreciate the assistance of heads of department, deans of schools in the various universities who were consulted and who allowed me to collect data within their jurisdictions. Much appreciation goes to all respondents who faithfully completed the questionnaires and responded to my interviews. My wife and children are also given much recognition for their support. I also express gratitude to the following persons for the diverse roles they played to support me during the Ph.D. work: Prof. Dampari, Prof. Charity Akotia, Dr. Francis Annor, Prof. Edward Amponsah-Nketiah, Dr. Joseph Osafo, Dr. Oppong Asante, Dr. Charles Asante, Prof. Olivia Kwapong, Dr. Daniel Oduro- Mensah, Dr. Clara Benneh, and Prof. Christina Nti, all of the University of Ghana. I also appreciate Mr. Jones Apawu, Mrs. Patricia Appiah-Boateng and Prof. Owusu Mensah, all of the University of Education, Winneba; Dr. Raymond K. Dziwornu, Dr. Emmanuel S. Asamoah and Mr.. Maxwell of University of Professional Studies Accra (UPSA). The Management of Ghana Technology University College are also acknowledged for allowing me to collect data in the University. iii University of Ghana http://ugspace.ug.edu.gh TABLE OF CONTENTS DECLARATION ...................................................................................................... i DEDICATION ........................................................................................................ ii ACKNOWLEDGEMENTS ................................................................................... iii TABLE OF CONTENTS ....................................................................................... iv LIST OF TABLES .................................................................................................. v LIST OF FIGURES ................................................................................................ vi ABSTRACT ......................................................................................................... viii CHAPTER ONE ...................................................................................................... 1 INTRODUCTION ................................................................................................... 1 1.1 The Background to the Study ........................................................................ 1 1.2 The Statement of the Problem ....................................................................... 5 1.3 The Objectives of the Study .......................................................................... 6 1.4 Hypotheses of the Study .............................................................................. 7 1.5 Research Questions (Qualitative) .................................................................. 9 1.6 The significance of the study ......................................................................... 9 1.7 The scope of the study ................................................................................. 10 1.8 Operational definition of concepts and terms .............................................. 10 1.9 The structure of the thesis ............................................................................ 10 CHAPTER TWO ................................................................................................... 12 RELATED LITERATURE REVIEW AND THEORETICAL FRAMEWORK . 12 2.0 Chapter Overview ........................................................................................ 12 2.2.0 Theoretical Framework ............................................................................. 35 2.3 Summary of Literature and Theoretical Review ......................................... 61 CHAPTER THREE ............................................................................................... 62 METHODOLOGY OF THE STUDY ................................................................... 62 3.0 Chapter Overview ........................................................................................ 62 3.1 Mixed Methods Approach ........................................................................... 62 3.3 Ethical consideration ................................................................................... 72 3.4 Data collection challenges ........................................................................... 74 3.5 Quantitative Study ....................................................................................... 76 iv University of Ghana http://ugspace.ug.edu.gh 3.7 Summary of Methodology ......................................................................... 100 CHAPTER FOUR ............................................................................................... 101 PRESENTATION OF RESULTS ....................................................................... 101 4.1 Results for Quantitative Study ................................................................... 101 4.2 Presentation of Results for Qualitative Study ............................................ 118 CHAPTER 5 ........................................................................................................ 153 DISCUSSION OF RESULTS ............................................................................. 153 5.0 Chapter Overview ...................................................................................... 153 5.1.0 Discussion of Quantitative Results ......................................................... 153 5.2 Discussion of Qualitative Results- Overview ............................................ 164 5.3 Corroboration of the qualitative phase to the quantitative results ........... 172 5.4 Summary of Discussion Chapter ............................................................... 175 CHAPTER SIX ................................................................................................... 176 SUMMARY, CONCLUSION AND RECOMMENDATIONS ......................... 176 6.1 Contributions of the thesis to knowledge .................................................. 178 6.2 Contributions to Literature ........................................................................ 179 6.3 Methodological additions .......................................................................... 180 6.4 Policy implications for higher education studies and researchers ............. 180 6.5 Limitations and future directions ............................................................... 181 6.6 Conclusion ................................................................................................. 182 6.7 Recommendations ...................................................................................... 183 REFERENCES .................................................................................................... 189 APPENDICES ..................................................................................................... 218 LIST OF TABLES Table 4.2. Confirmatory Factor Analysis and Reliability Test ........................... 101 Table 4.3 SEM Analysis Results .................................................................... 102 Table 4.4 Multivariate Normality of Observed Variables ................................... 104 Table 4.5. Correlations among study variables ................................................... 106 Table 4.6. SEM Analysis (Test of Hypotheses) ................................................ 109 Table 4.7. Bootstrapped estimates and Confidence interval for indirect effect .. 111 v University of Ghana http://ugspace.ug.edu.gh LIST OF FIGURES Figure 2.1. The Technology Acceptance Model (TAM) ....................................... 36 Figure 2.2. Technology Adoption Model .............................................................. 43 Figure 2.3 Proposed Theoretical Model ............................................................. 58 ................................................................................................................................... Figure 4.1 Observed Theoretical Model ............................................................. 110 Figure 4.2. Diagrammatical representation of themes and sub-themes .............. 119 Figure 4.4. Open source LMS Usage Conceptual Framework ............................ 129 Figure 4.5. Open Source Learning Management System Uses ........................... 134 Figure 4.6: Educational Technology relevance model ........................................ 136 Figure 4.7. Non-Use of Open Source LMS ......................................................... 149 ............................................................................................................................. 170 Figure 4.8 Open Source LMS adoption and use Thematic Mapping Model ...... 170 LIST OF ABBREVIATIONS ADV: Relative Advantage BIU: Behavioral Intention to Use CPA: Compatibility CPL: Complexity IMG: Image LMS: Learning Management System OBS: Observability TRI: Trialability FC: Facilitating Condition vi University of Ghana http://ugspace.ug.edu.gh PU: Perceived Usefulness PEU: Perceived Ease of Use AU: Actual Use SUBN: Subjective Norm SEM: Structural Equation Model GFI: Goodness of Fit Indices CFI: Comparative Fit Indices SRMR: Standardized Root Mean Squared Residual TLI: Tucker-Lewis Index RMSEA: Root Mean Square Error of Approximation vii University of Ghana http://ugspace.ug.edu.gh ABSTRACT The deployment of ICT as a teaching and learning tool has long been acclaimed as a catalyst for educational transformation. As a result, universities have invested and continue to invest in e-learning infrastructure including Learning Management Systems (LMS). This thesis identifies the factors influencing lecturers’ adoption and use of an Open Source Learning Management System (LMS) in four universities in Ghana. The overall objective is to analyze factors that influence lecturers’ adoption and use of an Open Source Learning Management System (LMS) in Universities in Ghana in order to enhance e-learning education in the country. The study first, quantitatively examined Innovation Diffusion Theory (IDT), Technology Acceptance Model (TAM), Image, Subjective Norm and Facilitating Conditions variables, and their direct and indirect influence on Actual Use of an Open Source LMS; and second, used a qualitative approach to identify factors that determine lecturers’ adoption and use, and non-use of Open Source LMS. Hypotheses were formulated and tested using Structural Equation Modelling (SEM) to determine whether the predictive generalizations of the theoretical model are valid or not. The study has relevance for e-learning policy formulation and blended learning practice, and adds new knowledge to the existing literature. Theories such as TAM and IDT, related literature on the deployment of ICT in teaching and learning, issues about e-learning education and the use of an Open Source LMS in Ghana were reviewed. Ethical clearance was acquired from the Ethics Committee under the College of Humanities, University of Ghana, Legon. The instrument was piloted with 25 lecturers from University of Ghana and the reliability test results of the pilot study viii University of Ghana http://ugspace.ug.edu.gh were favourable. For this study, the target population was lecturers of the University of Ghana (Legon); University of Education (Winneba) University of Professional Studies, Accra and Ghana Technology University College, who were aware of, and had been trained for the use of an Open Source LMS such as MOODLE or Sakai. Out of the target population, an accessible population of 435 (consisting of lecturers within the purposefully sampled departments of schools/faculties deemed accessible (by the researcher) in the four Universities) was used for the study. Copies of the modified questionnaire from the pilot study were administered to the accessible population of 435 of which 283 participants fully returned their questionnaire. Homogenous sampling and census were used. Additionally, 20 lecturers were judgmentally sampled and interviewed for the qualitative study and thematic analysis was used to analyse the data. Confirmatory Factor Analysis, Reliability Test and Structural Equation Modelling were used for the analysis of numeric data. Almost all the constructs recorded favourable reliability scores, and convergent, and discriminant validity were also achieved. The study variables were subjected to descriptive analysis for a normality test and an inter-correlation matrix. The mean values, skewness and kurtosis were within the standard. Hypothesized structural relationships among the study variables were tested by conducting SEM with maximum likelihood estimation in IBM AMOS 21.0. The estimated indirect effects were cross-validated using bootstrapping to compute the confidence intervals associated with each indirect effect. Paths that were not significant and those that were significant were discussed in line with other studies. Some key findings were as follows: The fit indices suggested that the 8-factor model showed a good fit to the data: (χ2 = 383.23, df = 271, p < .05; SRMR = .046; ix University of Ghana http://ugspace.ug.edu.gh TLI = .965; CFI = .971; RMSEA = .038. The SEM results also showed that the hypothesized structural model fitted the data well: (χ2 = 360.39, df = 253, p < .05; SRMR = .048; CFI = .971; TLI = .964; RMSEA = .040. The test of hypotheses showed that Perceived Ease of Use had a positive and significant relationship with Actual Use (β = .45, p < .05). Compatibility had a significantly positive relationship with Perceived Usefulness (β = .62, p < .05) as well as Perceived Ease of Use (β = .43, p < .05).Trialability was found to be positively and significantly related to Perceived Ease of Use (β = .12, p < .05). Subjective Norm was found to be positively related to Perceived Usefulness (β = .17, p < .05). Also, the path from Facilitating Condition to Perceived Ease of Use was positive and significant (β = .36, p = .05). Compatibility and Facilitating Conditions were indirectly and significantly related to Actual Use. Under the qualitative study, Utility of Use, Simplicity of Use, Self-Efficacy, Enablers, Institutional Policy were found to exert influence on Actual Use of Open Source LMS. Factors that determined Non-use of the Open source LMS also included nature of course, restrictive nature of the LMS, difficulty in blending face– to-face teaching with online and slow Internet connectivity. The qualitative study augmented the quantitative results. Implications of the study for future research, e- learning policy, higher education practice, management of lecturers, and methodological additions were highlighted. The limitations of the study included the small sample size (n=20) for the qualitative study and the cross-sectional nature of the quantitative survey. Future studies must include a larger sample size and a longitudinal study for better results. The conclusion was that: the qualitative study corroborated the quantitative study which found out that factors that influence the use of the Open Source Learning Management Systems (LMS) included Simplicity x University of Ghana http://ugspace.ug.edu.gh of Use, Utility, Institutional Policy and Enablers. Compatibility and Facilitating Conditions indirectly, positively and significantly influenced Actual Use of Open Source LMS. Perceived Ease of Use had a significant positive relationship with Actual Use. Recommendations made from the study included: regular training and education be organised for faculty (especially laggards and non-users) to appreciate the usefulness of the LMS for possible adoption and use. Universities must make sure that the LMS is made simple to use by ensuring that power, Internet connectivity, appropriate software and technical staff are always available. xi University of Ghana http://ugspace.ug.edu.gh CHAPTER ONE INTRODUCTION 1.1 The Background to the Study The use of Information and Communication Technology (ICT) in the life of individuals and institutions has increased remarkably. ICT has made inroads into the field of education. Teaching by the use of educational technology (ICT), which was unknown a few decades ago, has now become a reality globally, and universities that want to remain relevant and competitive are adopting and using educational technologies as teaching and learning tools. Some of the educational technology tools are YouTube, Twitter, Video conferencing, Massive Open Online Courses (MOOCS), Emails, Short Message Service (SMS), Whatsapp and Learning Management Systems (LMS). ICT-mediated teaching and learning has caused a paradigm shift in higher education with regards to the mode and method of teaching, flexibility, convenience, comfortability, effective interaction among learners, and between learners and lecturers without distance barriers, provided power, Internet connectivity and other facilitative conditions are available. ICT has helped augment access to education, reduced cost, and improved quality of education; it has facilitated a market for different types of students, improved inter-university collaboration and global competition among universities (Kasim & Khalid, 2016; Chandra & Briskey, 2012; Daniel, Kanwar & Uvalic-Trumbic, 2009). For example, Saudi Arabian and South Korean universities have used e-learning to widen access to higher education significantly (Asiri, Mahmud, Bakar & Ayib, 2012; Lee,Yoon & Lee, 2009). However, ICT-based teaching and learning in a number of African countries including Ghana has been very low. 1 University of Ghana http://ugspace.ug.edu.gh It is expedient to mention that alienation, superficial communication and less human contact have been observed as negative factors of ICT use in teaching and learning (Vilhelmina, 2012). It is also argued that over-reliance on ICT use for teaching and learning is negatively affecting the writing skills of students. Additionally, power and Internet connectivity inhibitions are a big challenge to the use of ICT for educational purposes. The challenges associated with using educational technology for teaching and learning must be managed well in order to ensure successful e-learning educational programmes in Africa. Within the last two decades, several factors have propelled and dictated the pace of e-leaning education globally, and they are: world-wide expansion of telecommunication infrastructure and emerging digital technologies, the strong presence of the Internet, demand for learner-centred education as opposed to teacher-centred, growth in digital-oriented students (digital natives), the need to widen access to higher education and the availability of Open Source Learning Management Systems (LMSs) for teaching and learning (Grgurović, 2014; Colvin & Bullock, 2014). Since Africa and for that matter Ghana, is part of the global community, it must take advantage of this global technological wave in order not be left behind. In organising for e-learning education, an important tool that is popular for use is the LMS. Learning Management Systems (LMSs), also referred to as Course Management System, have been the principal vehicle for mounting e-learning courses, providing course material online, lecturing, engaging in synchronous and asynchronous discussions, delivering quizzes and grading in both hybrid and full online learning settings (Coates, James & Baldwin, 2005; Alghamdi & Bayaga, 2016). 2 University of Ghana http://ugspace.ug.edu.gh There are two types of LMSs, namely Proprietary and Open Source. The Proprietary LMS is the Cathedral type which is also referred to as Closed-Source Software or Commercial Systems, which can be public domain software designed for sale to serve a commercial need. It has restrictions on any combination of the usage, revision, copying or distribution of modified versions of the software. The Proprietary software is very expensive due to the complexity of the software and other customizations or services that come with it. Proprietary software providers do not allow users to alter or view the source code of its software products. Examples of Proprietary LMSs are WebCT, Blackboard, ANGEL, Apex Learning (CoreDNA, 2009; Coates, James & Baldwin, 2005). The Open Source LMS (also known as the Bazaar LMS), on the other hand, is a conventional designation that refers to practices in production and development of software where the source code is made publicly available for extension and modification depending on the user’s needs (Conole & Oliver, 2007). Items generally accepted by the Open Source Community include free-distribution source code, derived work-allowing modification, distribution of licence, non- specification of licence to a product, integrity of authors’ source code, technological neutrality of licence, non-restriction of the software by license, and lack of discrimination against persons or groups (Cavus & Zabadi, 2014). Examples of Open Source LMS are Sakai and MOODLE, whose adoption and use have been reviewed under this study. The use of LMSs has encouraged self-study, independent learning and learner-centred education. Students who are workers can also have education without commuting to campus. Thus it has widened access to education. The use of LMS as a tool for e-learning education has removed the issue of time and space 3 University of Ghana http://ugspace.ug.edu.gh limitations in education. Universities can double their students’ intake when technology (e.g. LMS) is used in delivering teaching or presenting courses. Although several universities in Ghana have invested in e-learning and improved upon their e-learning needs, there are still several lecturers who are struggling with the adoption and use of the educational technology for teaching. Besides, despite the advantages associated with the use of an Open Source LMS and the huge investments committed to the acquisition and use by many universities in Ghana and other countries, there is evidence of a lot of faculty members who are still not adopting or using the Open Source LMS. In some instances, only minimal features on the platform are used, for example uploading slides into the resource tool and engaging the chat room (Jaschik & Mishram, 2007; Dahlstrom et al., 2014). It was worthwhile conducting the current study because universities have made major investments in e-learning infrastructure and have developed comprehensive policies to guide and manage the e-learning educational process and yet not much has been gained due to slow adoption, under-utilization and abandonment of the system (University of Ghana, 2009, 2012; Sunkwa, 2012; Tagoe, 2013; Fathema, Shannon & Ross, 2015). Additionally, there are scanty studies focusing on e-learning education (Awidi, 2008; Tagoe, 2012) and specifically, Open Source LMSs, e.g., Sakai/MOODLE for teaching and learning in Ghana (Dadzi, 2009; Budu & Ackah, 2016). Studies on the educational technology users’ perceptions, beliefs and attitude have become increasingly necessary to improve understanding and prediction of adoption and use of technology for teaching (Lee, Hsieh & Hsu, 2010). Thus, it is critical to identify which factors contribute to users’ Open Source LMS adoption and use behaviour (Koça, Turana & Okursoyb, 2016). 4 University of Ghana http://ugspace.ug.edu.gh Sarfo & Yidana (2001) found in their studies that some teachers/lecturers feel threatened when technological innovation is introduced for teaching and learning. This behaviour could hinder adoption and use of educational technology and thus must be investigated. Besides, lecturers play a pivotal role in the effective use of e-learning education; thus lecturers’ non-use of the Open Source LMS is a worry and needs to be investigated and mitigated. Furthermore, an awareness by Management of universities of lecturers’ perspectives on systemic inhibition debarring them from effective use of the Open Source LMS is also important to help address challenges faced by the users. It is expedient to mention that this research was conducted using a mixed methods approach, with explanatory sequential design. The quantitative part dominated the study although the methods were discussed separately due to their distinctiveness. 1.2 The Statement of the Problem Many studies conducted on technology adoption or use focused on areas including marketing, mobile services usage, instant messaging, Internet banking adoption (Lu, Deng, Wang, 2010; Yang, 2010). Few studies focused on e-learning education, especially in Ghana. A few studies conducted on e-learning education (Buabeng-Andoh, 2012; Ngololo, Howie & Plomp, 2012) focused on investigating the advantages and disadvantages of e-learning, and examining students’ perspectives and experiences with regards to e-learning education. Those studies did not look at factors influencing adoption and use of an Open Source LMS for teaching and learning. 5 University of Ghana http://ugspace.ug.edu.gh However, studies on Open Source LMS’ adoption and use are very essential in order to develop the descriptive and prescriptive findings to guide stakeholders of LMS application. It is the instructional implementation of educational technology that determines the success of e-learning programmes and not merely the presence of the technology. Thus examining factors that influence adoption and use of Open Source LMS is very important. Although variables in Technological Acceptance Model such as Perceived Ease of Use, Perceived Usefulness and Actual Use, and Innovation Diffusion Theory variables such as Trialability and Compatibility are popular for determining technology adoption, acceptance and use, there is very little study of them in Ghana. This applies to other constructs such as, Image, Subjective Norm and Facilitating conditions. The goal of the present study, therefore, is to investigate what factors influence lecturers’ adoption and use of an Open Source LMS (Sakai/MOODLE) in Universities in Ghana. The thesis is anchored on the premise that identifying and taking appropriate action on factors that influence lecturers’ Adoption and Use, and Non-use of an Open Source LMS for teaching and learning could positively cause a paradigm shift in the Ghanaian higher educational landscape. 1.3 The Objectives of the Study The overall objective is to analyze factors that influence lecturers’ adoption and use of an Open Source Learning Management System (LMS) in Ghanaian universities. 6 University of Ghana http://ugspace.ug.edu.gh The following are specific objectives of the study: 1. To investigate the relationship between Perceived Usefulness, Perceived Ease of Use, and Actual Use of Sakai/MOODLE LMS; 2. To ascertain the extent to which Compatibility, Trialability and Image influence Perceived Usefulness, Perceived Ease of Use and Actual Use of Sakai/MOODLE LMS. 3. To find out the degree to which Perceived Usefulness and Perceived Ease of Use mediate between ‘Compatibility, Trialability, Image’ and Actual Use of Sakai/MOODLE LMS. 4. To examine how Subjective Norms and Facilitating Conditions influence Perceived Usefulness, Perceived Ease of Use and Actual Use of Sakai/MOODLE LMS. 5. To ascertain the degree to which Perceived Usefulness and Perceived Ease of Use mediate between ‘Subjective Norm, Facilitating Condition’ and Actual Use of Sakai/MOODLE LMS. 6. To find out lecturers’ experiences in using Open Source LMS for teaching and students learning; 7. To investigate the reasons why other lecturers are not using Sakai/MOODLE for teaching. 1.4 Hypotheses of the Study Based on the statement of the problem, the objectives of the study, the related literature review and the theoretical framework for the study, the following hypotheses were formulated and tested: 1. Hypothesis 1: Perceived Usefulness and Perceived Ease of Use would have positive relationships with Actual Use of Sakai/MOODLE LMS. 7 University of Ghana http://ugspace.ug.edu.gh 2. Hypothesis 2a: Compatibility has a positive relationship with Perceived Usefulness, Perceived Ease of Use and Actual Use of Sakai/MOODLE LMS. Hypothesis 2b: Compatibility would be indirectly related to Actual Use via Perceived Usefulness and Perceived Ease of Use. 3. Hypothesis 3a: Trialability has a positive relationship with Perceived Usefulness, Perceived Ease of Use and Actual Use of Sakai/MOODLE LMS. Hypothesis 3b: Perceived Usefulness and Perceived Ease of Use would mediate the relationship between Trialability and Actual Use of Sakai/MOODLE LMS. 4. Hypothesis 4a: Image would be positively related to Perceived Usefulness, Perceived Ease of Use and Actual Use of Sakai/MOODLE LMS. Hypothesis 4b: Perceived Usefulness and Perceived Ease of Use would mediate the relationship between Image and Actual use of Sakai/MOODLE LMS. 5. Hypothesis 5a: Subjective Norms would be positively related to Perceived Usefulness, Perceived Ease of Use and Actual Use of Sakai/MOODLE LMS. Hypothesis 5b: Subjective Norm would be indirectly related to Actual Use of Sakai/MOODLE LMS via Perceived Usefulness and Perceived Ease of Use. 6. Hypothesis 6a: Facilitating conditions would be positively related to Perceived Usefulness, Perceived Ease of Use and Actual Use of Sakai/MOODLE LMS. 8 University of Ghana http://ugspace.ug.edu.gh Hypothesis 6b: Facilitating Conditions would be indirectly related to Actual Use of Sakai/MOODLE LMS via Perceived Usefulness and Perceived Ease of Use. 1.5 Research Questions (Qualitative) 1. What are lecturers’ experiences in using Open Source LMS for teaching? 2. Why are some lecturers NOT using the Open Source LMS? 1.6 The significance of the study The study seeks to explore the adoption and use of an Open Source LMS for teaching and learning in universities in Ghana. Thorough engagement with relevant literature has revealed very scanty research on lecturers’ adoption and use of an Open Source LMS (e-learning) in universities in Ghana. The findings from this work should contribute to an in-depth understanding and appreciation of factors influencing lecturers’ adoption and use of Open Source LMS. The study adds to scholarship on the global and sub-regional literature and provides useful lessons for other developing countries. Embracing Open Source LMS for teaching and learning is very critical and timely and serves as a base for educational policy formulation and planning by education providers and educational policy makers in Ghana. For example, recommendations emerging from the findings could help higher education institutions formulate an ICT- mediated teaching and learning policy that focuses on how to motivate lecturers to adopt and use Open Source LMS for teaching and learning. Recommendations from this study could also enable higher education institutions to develop policies and programmes that will enhance learner and teacher support for effective teaching and learning. Additionally, this work adds to the IDT and TAM literature by examining the relationships between IDT and TAM 9 University of Ghana http://ugspace.ug.edu.gh variables in the same model; and this might be helpful in developing and testing theories associated with e-learning system adoption and use as well as enabling practitioners to understand the ways for designing and promoting e-learning systems. Using the new hybrid theoretical framework and the qualitative findings, this study helps practitioners and researchers to better understand what variables directly and indirectly influence actual use of Open source LMS. Finally, the study provides a foundation upon which further studies can be carried out to help widen the scope of research-based information in the subject area. 1.7 The scope of the study Only four universities in Ghana using an Open Source LMS were chosen as study areas. The research participants were full-time lecturers within the selected universities who were available for the study. In terms of the data collection methods, the study was limited to the use of interviews and questionnaire administration. 1.8 Operational definition of concepts and terms External factors are independent variables that influence the dependent variable. Effect variable is the outcome variable. Mediating variables are those variables that intervene between the external factors and the effect variable. Control variables in this study are the bio-data variables. 1.9 The structure of the thesis The study was organised into six (6) chapters. Chapter One looked at the background of the study, the statement of the problem, the objectives of the study, the hypotheses of the study, research questions, the scope of the study, the 10 University of Ghana http://ugspace.ug.edu.gh significance of the study, definition of words, terms and concepts and the structure of the study. Chapter Two reviewed literature on ICT, e-learning, LMS and lecturers’ adoption and use of educational technology for teaching and learning. It discussed the various theories that determine the adoption and use of technology for various purposes and also highlighted in detail the Innovation Diffusion Theory and Technology Acceptance Model. It also shared light on the factors considered when the researcher developed his own proposed theoretical model. Chapter Three examined the methodology of the study. It highlighted the research approach (Mixed Methods) and the related philosophy, population of the study, the sample size, the study area, inclusion and exclusion criteria, tools for data analysis, ethical considerations and methodological challenges. It examined in- depth the methodology of the quantitative and the qualitative phases of the research. Chapter Four covered the presentation of results of the study. Chapter Five discussed the results of the study and also demonstrated how the qualitative results corroborated the quantitative results. Chapter Six contained the summary, key contributions of the study to knowledge, limitations of the study and future direction, conclusion and recommendations. 11 University of Ghana http://ugspace.ug.edu.gh CHAPTER TWO RELATED LITERATURE REVIEW AND THEORETICAL FRAMEWORK 2.0 Chapter Overview This chapter reviews related literature and relevant theories on adoption and use of technology for several purposes but with particular reference to using it for an educational purpose. Related literature on ICT and education, e-learning, LMS and factors that influence lecturers’ adoption, and the use of ICT for teaching purposes, have also been reviewed. 2.1 Related Literature Review The adoption and use of technology has been studied in such areas as marketing (Carlsson, Carlsson, Hyvönen, Puhakainen & Walden, 2006; Schierz, Schilke & Wirtz, 2010; Wang & Li, 2012), mobile services usage (Yang, 2010; Zhou, 2011) and instant messaging (Lu,Deng,Wang, 2010) to mention just a few. This work is special in that it examined Lecturers’ adoption and use of Open Source LMS for teaching in universities in Ghana. Rangaswamy & Gupta (2000) explain adoption as the decision that prospective users of innovation make when they make up their minds to embrace an innovation. Similarly, Rogers (2003) describes adoption as the intention of a person to make use of an innovation as the best course of action. ‘Actual Use’ of technology implies the practical deployment of the technology to a given task. The world has become a cyber-community because the dramatic growth and sophistication of ICT has greatly influenced human interactions and provided new metaphors for understanding the world (Colvin & Bullock, 2014). ICT has been deeply incorporated into our modern global society, changing how we work and engage with one another, and it has positively impacted on economic activities, 12 University of Ghana http://ugspace.ug.edu.gh teaching and learning (Yang, 2012). Several studies have also revealed that the popularization of ICT into lifelong learning as the ultimate goal of education supports students to become autonomous learners who actively utilize technologies to construct personalized learning spaces and experiences from ‘a complex network of people and technologies (Lai, Yeung & Hu 2015; Sims 2008). Dobre (2015) asserts that ICT also provides a window of opportunity to higher educational institutions to overcome the limitations associated with students’ enrolment due to the infrastructure inadequacies in accommodating students. ICT plays a pivotal function in facilitating teaching and learning at all levels of education. Technology deployment in teaching and learning increases access to world markets, cuts down cost to students and has an inherent tendency to augment and deliver more flexible learning (Singh & Hardeiker, 2012). Several authors have reported that the deployment of ICT in teaching can influence students’ learning outcomes (Chandra & Briskey, 2012; Chandra & Watters, 2012; Halabi, Essop, Carmichael & Steyn, 2014). ICT use is able to guide students in the pursuit of modern, rationalistic, democratic values such as critical thinking and the search for democracy, freedom and social responsibility (Aviram & Tami, 2004). Repositioning education in the light of social and technological changes has always been a valid aim of pedagogy (Ingram, 2014). ICT can be used to enhance teaching, for on-campus students and also for fully online learning and blended learning education. It can offer opportunities for students to learn at their own pace, encourage collaborative work, focus on problem-solving and involve students in the assessment of their learning. The lecturers also do not need to commute to campus in order to teach a class. Embracing and using ICT-mediated teaching and learning 13 University of Ghana http://ugspace.ug.edu.gh in universities in Ghana will bring great relief to students and lecturers and widen access to university education. Buabeng-Andoh (2012) examined the factors that influence teachers’ adoption and integration of ICT into teaching. He argues that although at a global level, several governments have invested in infrastructure, equipment and training of teachers to improve education in various countries, ICT adoption and integration in teaching and learning have been limited. The reasons for the limited adoption of ICT include: lack of teachers’ ICT skills; teachers’ lack of confidence; teachers’ lack of pedagogical training; lack of suitable educational software; limited access to ICT; rigid structure of traditional education systems; and restrictive curricula. Buabeng-Ando (2012) concludes that knowing the extent to which these barriers affect individuals and institutions may help in taking a decision on how to tackle them. Gautreau (2011) points out that a significant number of lecturers prefer the face-to-face classroom-based teaching and learning, and are demotivated to use ICT for teaching due to several factors including attitudes, poor preparation for a change, lack of prior knowledge in ICT as well as poor technical support provided by the University. Sherry & Gibson (2002) contend that the nature of technology, users and institutional elements ought to be factored in when investigating ICT adoption and integration. Instructors’ incorporation of ICT into teaching design is also influenced by behaviour towards technology and some institutional factors (Tondeur; van Braak & Valcke, 2008; Lim & Chai, 2008). Several studies have found that intrinsic factors such as the attitude towards the use of computers, preparedness for a change, prior knowledge and confidence 14 University of Ghana http://ugspace.ug.edu.gh to use ICT influence technology adoption and use (Sarfo & Yidana, 2016; Ngololo, Howie & Plomp, 2012; Buabeng-Andoh,2012). Ngololo, Howie & Plomp (2012) examined ICTs’ deployment in Namibian schools. Their work was discussed with reference to Chile and countries in the Sub- Saharan African Region including Ghana and South Africa. They found that although computers were made available at the schools, teachers were not using them because they had a negative attitude towards them and lacked training in the use and incorporation of technology. Mukoko (2012) finds that educational background, age, lifestyle, perceived usefulness and perceived ease of use, play a crucial role in the adoption and use of computers and the Internet. Asiedu-Asante & Temeng (2010), who were part of the team that introduced ICT in the University of Mines and Technology, found that the adoption of ICT in the University was not a straightforward issue. There are problems such as lack of personnel who actually understand ICT to advise on selection and building of the appropriate infrastructure, lack of consultants to design and build the infrastructure, lack of personnel to manage and teach others how to use the ICT infrastructure, and problems associated with funding. What is inferred from the above research finding is that adopting ICT for teaching and learning at the institutional level also faces a lot of constraints that must be managed effectively in Ghana. The adoption of technology by teachers is conceived as a process that develops through different stages: from being aware and informed about the possibilities of ICT in education, to a more routine utilization of ICT in classroom practice, and finally to creative uses of the educational technology for teaching and learning (Christensen & Knezek, 2008). Teachers’ mind-sets, impressions and choices such as readiness and desire to learn how to use ICT, their competencies in 15 University of Ghana http://ugspace.ug.edu.gh terms of knowledge and skills that enable them to easily learn the ICT device, and their access to technology (through institutional support or government or donor support) together inform their adoption and use of educational technology. Specific technical requirements that also help teachers in using technology include computer access, a fast Internet connection and software availability (Kearsley, 2002). The adoption of ICT by teachers also depends on their values and beliefs about the importance of ICT for learning and teacher knowledge of both learners and content. The effective integration of ICT in schools should include policies and practices that simultaneously consider the processes by which teachers learn technical skills as well as the cultural, social and historical contexts of learning (MacCallum, 2008). The role of teachers/lecturers is changing as ICT develops. The influx of educational technologies allows the use of the Internet and other technologies in the physical and online classroom and this has enabled teachers to incorporate activities that tap the resources of the World Wide Web outside the class time. Individual teachers could upload video or audio versions to YouTube, post PowerPoint presentations to Slide Share, and share Open Educational Resource(OER) (Loftstrom & Nevgi, 2008). Unfortunately, this is not the norm in Africa, including Ghana, a low technological context. Meanwhile, technology developments are on-going, and we have no alternative than to accept and use them. That is why this study is very important. 2.1.1 E-learning in Education Several studies have highlighted methods integrating technology into teaching and learning (Henriksen, Mishra & Fisser, 2016; Chai & Fan, 2016; 16 University of Ghana http://ugspace.ug.edu.gh Adedoja & Abimbade, 2016). Despite the fact that e-learning education provides many benefits for institutional managers, lecturers and students, there are still several lecturers who are struggling to adopt it. Owing to this observation, the adoption and use of e-learning education by teachers and students have been the subject of many studies (Chiu & Churchill, 2016; Khan & Adams, 2016; Cowan & Earls, 2016). There are also some studies explaining why some university lecturers abandon the use of technology for teaching and learning (Chris, 2016). We find in Ghana a situation where several faculty members have also abandoned the use of LMSs for teaching and learning, which calls for attention. E-learning education is a collection of useful web and social media applications in education that involve web-based learning, distributed learning, computer-assisted instruction or Internet based learning (Abdallah, 2009). It is a broad concept, typically encompassing the expansion and effective use of digital technologies to support learning and teaching in schools, colleges and universities, aimed at providing students with an opportunity to enjoy a more flexible learning experience (Mykhnenko, 2016). A more detailed definition of e-learning education is given by Selim, (2007). He sees e-learning as the delivery of course contents via electronic media such as Internet, intranet, extranet, satellite broadcast, audio-video tapes, interactive television and CD-ROM. E-learning education can be classified under four main divisions as follows: Web-based learning, web-dependent learning, blended mode courses and fully online courses (OECD, 2005, cited in Tagoe, 2012). Picciano (2009) describes blended learning courses as classes where there is face-to-face teaching and learning supported by online aspects which are delivered through a Learning Management System and other ICT devices integrated 17 University of Ghana http://ugspace.ug.edu.gh in a planned and pedagogically valuable manner. With the influx of Internet and digital technologies, e-learning education has been growing remarkably and this has transformed higher education on the global landscape. For many higher educational systems, the incorporation of technology into teaching and learning has been recognised as one of the key drivers to the improvement of teaching and learning, thus influencing governments to launch major initiatives through considerable capital investment to build ICT infrastructure in the schools (Teo, 2012; Pelgrum, 2001). The e-learning enterprise is found to be the means for reimagining higher education and making it sustainable. Several studies have catalogued the benefits of e-learning (Makure, 2014; Tok & Sora 2013; Pretorius, Steyn & Johnson 2012; Mullamaa, 2010; James, 2008). Such benefits include, facilitating teaching practice and thought, motivating learners, boosting students’ academic performance and enhancing pedagogy, facilitating students’ critical thinking, knowledge construction and improving students’ relationships (Jaffer, Ng’ambi & Czerniewicz 2007; Marra, Moore & Klimczak, 2004). E-learning education promotes active, collaborative, interactive and lifelong learning environments. It offers shared working resources and better access to information; it increases students’ motivation and deepens their understanding thus helping them to think and communicate creatively, enrich their learning and stimulate interaction and higher‐level thinking and reasoning among and greater cognitive and explanatory learning (Borokhovski, Tamim, Bernard, Abrami & Sokolovskaya, 2012; Marra, Moore & Klimczak, 2004). E-learning provides flexible and convenient access to learning material resources, thus improving learning outcomes and increasing quality of teaching and 18 University of Ghana http://ugspace.ug.edu.gh administrative services; it helps to design varied learning activities around group work, problem based learning, self-testing, video lectures and forum discussions (Boateng, 2015). Furthermore, e-learning allows the re-use of study contents by students and lecturers; makes room for easy storage of materials in digital format and permits retrieval of information; deletion of irrelevant materials; digital assessment; auto marking and grading. It enables information sharing in general; permits practical teaching design with regular follow ups on students; improves internet skills of learners and lecturers (Lwoga, 2012; Kwofie & Henten, 2011; Majhi &Mahrana, 2010; Marfo & Okine, 2010). Sun (2011) asserts that whether it is face-to-face, blended, or fully online, learner interaction and participation, group communication and information sharing are of crucial relevance to successful teaching and learning. The interactivity in a learning environment can therefore be simplified into learner–content, learner– people, and learner–interface interaction (Liaw & Huang, 2000). Norris & Lefrere (2011) also maintain that the traditional higher education system focuses on a predetermined, structured, defined and ordered roadmap and learning path where teachers deliver knowledge to the learners who sit passively to receive it and make efforts to apply it to their own pre-existing knowledge in the subject. To them, this system of education is good for early stage learners who need to finish a prescribed course of student study, but not for adults who frequently, encounter dynamic knowledge flows to fill a specific knowledge gap rapidly for immediate application, using stored knowledge, intuition, problem-solving, creativity and adaptability. This implies that e-learning in education is essential for higher education today. 19 University of Ghana http://ugspace.ug.edu.gh How can these adults be catered for in terms of accessing higher education without competing with the young ones, and without enrolling for campus-based teaching and learning since their family and work life will not permit them? This calls for higher education to realign, redesign, restructure, re-engineer and reorganise itself to link work to learning (Norris & Lefrere, 2011; Norris & Dolence, 1995) through the use of the e-learning system. For this change to work effectively, it is expected that lecturers must adopt and use educational technologies for teaching. The current study therefore examines the factors that influence lecturers’ adoption and use of Open Source LMS for teaching and students’ learning, a foundation for full embracement of ICT-mediated teaching and learning. 2.1.2 E-learning Policies in Ghanaian Universities E-learning has been primarily adopted by several universities in Ghana in order to widen the provision of higher education to applicants who have qualified for higher education but are not admitted for a campus-based option due to high costs, infrastructure deficiency and poor lecturer-students’ ratios and for campus base teaching and learning (Kwofie & Henten, 2011). University of Ghana’s ICT policy framework promotes e-learning education (University of Ghana, 2009). The policy ensures that students and lecturers are offered training to acquire skills in the use of educational technology tools (e.g. LMS) in order to use them effectively. The University of Ghana (2012) Policies and Procedures on Technology- Mediated Courses and Programmes on e-learning also ensures that students and 20 University of Ghana http://ugspace.ug.edu.gh lecturers of the university are abreast of technological skills needed to meet the demand of the higher education market, promote the quality of teaching and learning and to solve the problem of infrastructural deficiency on campus. Similarly, the University of Education, Winneba (UEW) Strategic Plan 2014-2018 outlines issues relating to ICT infrastructural development and e- learning enhancement strategies of the University. The strategies focus on the provision and development of ICT infrastructural facilities, enhancing manpower capacity building, introducing new e-learning programmes and putting structures in place for an effective student support services. The University of Professional Studies, Accra (UPSA) has a policy to enhance access to higher education through the use of innovative modes including an Open Distance Learning School (DLS) with the use of MOODLE as an Open Source LMS. This is in line with one of the strategic objectives of UPSA that aims at establishing a Virtual life-long learning Centre to meet the diverse needs of non- traditional and off-campus learning. Ghana Technology University College (GTUC), Kwame Nkrumah University of Science and Technology (KNUST), University of Cape Coast (UCC), Central University (CU) and several other universities in Ghana have a policy for their e-learning education (even if they are unwritten guidelines and practices). Despite the provisions of these policies on e-learning education, not much has been achieved in the use of e-learning to enhance teaching (Tagoe, 2012; 2013). Besides, matching the policy provisions to what pertains on the ground, there are still gaps that need to be bridged. Several faculty members remain laggards, struggling to apply the technology (LMS) for teaching. Besides, little research has 21 University of Ghana http://ugspace.ug.edu.gh been conducted on lecturers’ adoption and use of E-learning in the University of Ghana (Awidi, 2008; Dadzi, 2009; Tagoe, 2012). Additionally, in the University of Education, Winneba, several items in the strategic plan have not been realised yet. Lecturers’ adoption and use of the technology is still at nascent level. Several facilitating conditions (enablers) for smooth adoption and use of technology are still lacking: Internet connectivity is slow due to low bandwidth and there are also some challenges relating to technical support. Besides, irrespective of acquiring e-learning infrastructure in the UPSA for blended learning, several lecturers who were offered training in the use of MOODLE LMS are still battling to adopt and use it for teaching and students’ learning. Although higher education institutions in Ghana have initiated reforms geared towards e-learning, these initiatives have not been without challenges. There are inadequate infrastructural facilities such as computers, projectors, wifi facilities and Internet connectivity. Additionally, culturally-driven attitudes, beliefs and traditional education practices as well as lack of strategic planning and effective management are key barriers to e-learning in education. Other challenges include poor learner support services and lack of training for lecturers and students to promote the eagerness to use technologically-based pedagogy (Sarfo & Yidana, 2016; Boaten, 2015; Awidi, 2008). Some studies have been conducted on e-learning in Ghana around the themes: Advantages and Challenges Affecting E-learning; and Students’ Perception of E-learning. Investigating challenges affecting e-learning in Ghanaian tertiary education, Budu & Ackah (2016) studied four tertiary institutions in Ghana. 22 University of Ghana http://ugspace.ug.edu.gh Questionnaires and interviews were used for data collection. In the article, they highlighted the good efforts made by some higher education institutions in Ghana by introducing an e-learning policy. They found that there was a number of challenges bedeviling their potential to succeed. Nevertheless, they concluded that the advantages associated with e-learning far outweigh the challenges and therefore recommended that much effort must be made toward accepting e-learning. Asabere & Enguah (2012) also elaborated on the essence of e-learning in the tertiary educational sector of Ghana as well as the issues that needed to be addressed in implementing an e-learning system. The authors maintain that e- learning could help prospective students including those who are working but do not want to be on-campus to gain access to higher education without being on campus. Thus the authors recommended that all higher education institutions should embrace e-learning education in their educational delivery Awidi (2008) explored issues revolving around ICT applications into teaching and learning in the public universities thus requiring rethinking institutional policy that will boost pedagogy-supporting technology in higher education. He found that the main challenges confronting Ghanaian Public Universities include low teacher/student ratio, overburdened teaching loads, learning and residential facilities, aging faculty members and increasing industry demand for market-driven curriculum. In his view, the use of educational technology will help address some of the problems facing higher education institutions. It could be seen from the information above that all the authors were looking at the challenges of e-learning and its advantages. None of them specifically looked 23 University of Ghana http://ugspace.ug.edu.gh at the issue of the Learning Management System as a tool of e-learning in Education. Besides, none of them looked at the factors influencing the adoption and use of an LMS in the Universities in Ghana. Again, none of them used referential statistical tool such as Structural Equation Modelling. The current research work bridges that gap. On students’ perception of e-learning, Boateng et al. (2016) employed survey research, TAM and Attitude Towards Use as theoretical framework and structural equation modelling as data analysis tools to examine the topic: Determinants of E-learning Adoption among students of developing countries. The results showed that Perceived Usefulness and Attitude Towards Use had a direct effect on E-learning Adoption, while Perceived Usefulness and Perceived Ease of Use also had a direct relationship on Attitude Towards Use. Also, Sunkwa (2008) studied e-learning in the sub-Saharan Africa, Ghanaian universities students’ experiences and perceptions. This qualitative enquiry investigated the attitudes, experiences and perceptions of undergraduate students who were enrolled in online collaborative learning in Ghanaian Private Universities. They found that students did not respond favorably to online asynchronous discussions and ill-structured project-based learning activities, and perceived collaborative online learning as challenging, more task- involving and time-consuming experience. While the author above used qualitative approach and focused on private universities, the current work used mixed methods and used both public and private universities. Boateng et al (2016) also researched into videos in learning in higher education: assessing perceptions and attitudes of students at the university of 24 University of Ghana http://ugspace.ug.edu.gh Ghana. The study focused on students’ attitudes and perceptions regarding the use of videos as a medium for teaching and learning. The results revealed that students found videos as a medium of teaching and learning to be positive. However, the students complained about the nature of videos they watched in terms of the contents and the format. The above study did not focus on the adoption and use of LMS as a tool for e-learning. The current work is on Open source LMS. Furthermore, Tagoe (2012) examined students’ perception on incorporating e-learning into teaching and learning at the University of Ghana. He remarks that some strides have been made by African universities using e-learning to address access, quality and cost of education. The author found that students entered the university with computer skills which are very important for e-learning embracement, and males were found to be favourably exposed to the Internet than their female counterparts. Additionally, students preferred blended and web- supplemented courses in the immediate future to web-based or fully online courses. He however, points out that regarding the University of Ghana, very little information has been provided on its e-learning policy direction. Among the considerable number of articles on e-learning cited above, not even one paper addressed any issue on LMS as an e-learning tool, let alone addressed the issue of lecturers’ adoption of Open Source LMS for teaching and learning. The majority of the studies on e-learning in Ghana used students as participants and focused on single study sites. The current study used lecturers as participants and used multiple sites. There is also a difference in theoretical model and data analysis techniques used concerning the current study and similar studies in the existing literature. Although several universities have improved upon their e- learning needs, there are still several lecturers who are struggling with the adoption 25 University of Ghana http://ugspace.ug.edu.gh and use of the educational technology for teaching. The current study identified the factors that influence lecturers’ adoption and use of an Open Source LMS in universities in Ghana. 2.1.3 Learning Management System 2.1.3.1 Open Source LMS in Ghanaian Universities University of Ghana In 1997, the University of Ghana used the African Virtual University ICT infrastructure to run distance learning, although on a small scale. However, the University’s interest in deploying ICT to augment its work on a grand scale dates back to 2004 when Professor Mumuni Dakubu, who was the then head of the ICT Services, introduced a web-based Learning Management System called Knowledge Environment for Web-based Learning (KEWL) in the University (University of Ghana, 2014). KEWL was poorly patronised because of the difficulties and gaps in its usage, ICT infrastructure deficiency, and inadequate training for lecturers to develop learning materials. It is enlightening to mention that in 2010, the University of Ghana Council approved the ICT Policy which was meant to enhance e-learning and faculty effectiveness. Following ICT infrastructure developments resulting in Chinese Phases 1 and 2 projects in 2009 and 2010 respectively, the Business and Executive Committee (BEC) of the University of Ghana in March 2012, approved for implementation, the report of the committee set up by the then Pro-Vice-Chancellor in charge of Academic and Students Affairs on Policies and Procedures on ICT- Driven Courses and Programmes in E-learning. Owing to financial constraints, the 26 University of Ghana http://ugspace.ug.edu.gh implementation of the roadmap of the committee’s report was forestalled (University of Ghana, 2014). That notwithstanding, some senior members of the University’s Institute of Continuing and Distance Education led by Professor Yaw Oheneba-Sakyi, who was then the Director, and the School of Public Health led by the Dean, Professor Richard Adanu as well as some staff from University of Ghana Computing Systems (UGCS), employed trial version of a new LMS called Sakai to examine its functionality and user-friendliness as against KEWL to create online classes for students in the University of Ghana (University of Ghana, 2014). After Sakai was piloted from 2010-2013, it was found very useful by several stakeholders including students, staff and faculty members who were engaged in the pilot. A recommendation was then made to the University Management that Sakai be adopted and adapted as the LMS of choice in place of KEWL. In May, 2014, the University installed and commissioned the Sakai LMS and hosted by the Longsight Group, a Commercial Affiliate of the Sakai Community. University of Education, Winneba (UEW) In 2003-2005, the University of Education, Winneba made attempts to create awareness and offered training to academics to develop basic technology competencies in the area of using multimedia tools, such as PowerPoint and word- processing for teaching through a series of workshops and demonstration sessions organised by foreign experts, although it was just for a short while. However, the lack of regular training for lecturers, poor Internet access, lack of ICT equipment in the classroom, to mention just a few, killed the technology embracement (University of Education, 2012). Before 2010, UEW had also mounted three 27 University of Ghana http://ugspace.ug.edu.gh courses for online delivery on MOODLE. These courses were mounted and developed externally by a South African organisation, eDegree. Lecturers in the University did not take part in the courseware development but only facilitated the courses after their development. However, pedagogical deployment of ICTs was limited (University of Education, 2012) owing to the lack of instructional technologies in classroom settings, poor Internet connectivity, frequent power outages and limited knowledge and skills in pedagogical integration of ICTs by lecturers. However, from 2009/2010 the University collaborated with Partnership for Higher Education in Africa Educational Technology Initiative (PHEA ETI) to introduce MOODLE LMS in the University of Education, Winneba (University of Education, 2012). As a result, in 2011, a MOODLE learning platform was set up to supplement face-to-face lectures and practical classes. MOODLE was used to extend students’ access to learning resources and activities online and for that matter to address the overcrowding, reduce the cost of education, engage students in a diversity of learning styles and needs and ensures interactivity among students and between the learners and instructors and contents. University of Professional Studies, Accra University of Professional Studies Accra attempted to expand the use of the MOODLE platform and add other Open Source Software plug-ins to enhance the usage of the platform. An Ultra-Modern Distance Learning Centre has also been considered, which both faculty and students could use to enhance teaching and learning. A visit to UPSA for data collection and some interactions with some faculty members revealed that UPSA lecturers had declined in their zeal to use 28 University of Ghana http://ugspace.ug.edu.gh MOODLE for teaching and learning. One of the general challenges they faced was poor Internet connectivity owing to low bandwidth. The current work investigated the factors that influence or affected their adoption of the Open Source Learning Management System. Ghana Technology University College E-learning with the use of a videoconferencing ICT facility, video links and use of Distance Learning tools in GTUC could be traced to 2007 when the University started running its Master of Information Communication Technologies (MICT) programme in partnership with Aalborg University, Denmark. Students were able to communicate with their instructors and professors at any time and place without barriers and they were able to finish the coursework from the comfort of their homes. They could also access both the course presentation and learning materials on personal computers (Asabere & Mends-Brew, 2012). 2.1.3.2 Studies on Learning Management System Learning Management Systems (LMSs) have been widely adopted by higher educational institutions globally for over two decades. Effective deployment of e-learning systems is connected to the availability of LMS. Studies have been conducted to explore commercial and self-developed LMS in the United States of America (USA). Until the use of LMS in the USA, distance education programmes were organised using face-to-face sessions, live satellite or closed-circuit television. The Internet as a technology device became popular and was employed broadly in academia to deliver education in three modes namely full online, blended learning (hybrid of online and face-to-face) and face-to-face courses with integrated web- based support materials and activities (Falvo & Johnson, 2007). 29 University of Ghana http://ugspace.ug.edu.gh The popularity and availability of computers in individuals’ homes or offices and online interactions have drawn more teachers and learners into the online or e-learning environment. For example, in the USA, it was reported in 2001 by the Census Bureau Report that, 54 million households (51 %) had one or more computers at home (Falvo & Johnson, 2007). Also, the 2003 Sloan Survey of Online Learning reported that 81% of all tertiary education institutions run at least one fully online or blended course and 34% of the institutions offer one or more complete online degree programme. In 2003, an analysis conducted by Paulson in six regions in Europe revealed that there was a plethora of commercial and self-developed LMSs that worked effectively for educational institutions in Europe (Falvo & Johnson, 2007). Fathema, Shannon & Ross (2015) maintain that although universities have invested considerably in Learning Management Systems (LMSs) to facilitate their teaching and learning processes, these systems are not used by the faculty members to their fullest capabilities (Jaschik & Lederman, 2014; Dahlstrom, et. al, 2014; Allen & Seaman, 2010). For example, in a study on faculty attitudes to the use of technology conducted by Jaschik & Lederman (2014) & Fathema, Shannon & Ross (2015) only 20% of faculty members reported using the LMS mostly for recording lecture content, uploading course syllabuses, maintaining online grade book and interacting with students. Weaver, Spratt & Nair (2008) also affirm that not all the features on the LMSs are used by the faculty members. Effective incorporation of technology into teaching depends not only on the availability of educational technology but also on how instructors adopt and use it. Lack of adoption and use of an Open Source LMS among the faculty members have a number of implications, 30 University of Ghana http://ugspace.ug.edu.gh since their negative attitudes could also affect students’ effective use of these systems (Teo, 2009; Wang & Wang, 2009). Unwin et al (2010) conducted a survey involving 358 participants in 25 countries in Africa regarding their usage of LMS. They found that while there are some who strongly advocate LMS, many African educators/lecturers have either little knowledge or have no interest in the use of the LMS. The study showed other constraints such as infrastructural deficiency and unwillingness on the part of many institutions to develop a system that is congenial for learning resources to be made available in this way. They indicated that the use of the LMS should not be ignored in Africa but, consistent human capacity development and enabling conditions, must be put in place if African learners are to benefit from the interactive learning experiences that such systems can offer. The author hits the nail right on the head. The constraints hindering adoption of LMS in Africa must be addressed, including building faculty capacity to use the LMS, psyching their minds to appreciate the embracement and the use of the LMS and also providing the facilitating conditions necessary for engaging an effective e- learning programme. Additionally, universities in the sub-Saharan Africa consistently inject a lot of money from their limited financial resources to acquire and maintain an LMS to facilitate teaching and learning activities synchronously and asynchronously. Universities offering distance education also blend LMS with traditional classroom- based modes in order to reach more learners who are geographically dispersed. Questions are now raised as to whether it is worthwhile using LMS- that is, whether it is able to address the intentions for which it is installed. Mtebe (2015) examined the questions above by analysing literature on LMS in the sub-Saharan African 31 University of Ghana http://ugspace.ug.edu.gh region. The author concluded by suggesting strategies, including effective management policy that can help academic institutions to optimize the use of an LMS. Sunkwa (2012) used a participatory research to examine cultural barriers inhibiting faculty adoption and use of LMS for online collaborative learning (OCL) at a private university in Ghana. He found that an LMS that had been installed five years earlier for online learning was not used by the lecturers although they had all been trained and motivated to use it. The author concluded that removal of cultural barriers will help the adoption and the use of LMS. Rewards and incentives, technical and administrative support, policy, sound working conditions, teaching load etc. have been found to impact on faculty LMS uptake. Authors suggest that by collectively identifying the cultural underpinnings, and conscientiously working on them, faculty members can ultimately change their attitudes (as well as those of their other colleagues) significantly, and be better predisposed to using online collaborative tools and resources for OCL. It is likely that the lecturers were not using the system because they had not found the LMS easy to use despite the training given to them or because they preferred the face-to-face mode of teaching to the technology or there was no policy for mandatory technology use for teaching and learning. Thus the author was right suggesting that cultural barriers that hinder LMS uptake must be eliminated. Sarfo & Yidana (2016) examined how lecturers engaged in the design and development of courses at the University of Education, Winneba using the MOODLE LMS to promote teaching and learning in the classroom and what factors motivate or inhibit the design of MOODLE by the lecturers. They found that 32 University of Ghana http://ugspace.ug.edu.gh lecturers used the MOODLE LMS to prepare and present lessons and to chat with students before and after the face-to-face lesson in the classroom. Additionally, the authors found that training programmes, incentives and motivational packages were necessary for lecturers to adopt the LMS. Finally, their results showed that challenges faced by the lecturers included low technology competencies, institutional cultural barriers, and lack of adequate ICT facilities. They concluded that although the integration of LMS into traditional face-to-face teaching is effective and useful for developing countries like Ghana, there were challenges that needed to be addressed in order to exploit its full potential to promote the development of the 21st century competencies. Since Africa is within a low technology web, much effort is needed by institutions to enable lecturers develop an interest in using LMS. This means all possible barriers to adoption and use of a LMS must be eradicated. This calls for studies such as the current one. Dadzi (2009) investigated the prospects and challenges of using an Open Source platform: Knowledge Environment for Web-based Learning (KEWL) by the teaching staff of the Faculty of Social Studies, University of Ghana. The researcher employed interviews and questionnaires to collect data from faculty members concerning their opinion, expectations and capabilities in using KEWL for teaching and learning. This study is one of the foremost, if not the first, to examine teachers’ adoption of an Open Source LMS for teaching in the Universities in Ghana. The author found out that the adoption of this Open Source LMS was a problem due to poor internet connectivity, training and lack of computers. 33 University of Ghana http://ugspace.ug.edu.gh Although, Dadzi’s study focused on an Open Source LMS, the type was not MOODLE or Sakai as being used in the current study. Nevertheless, issues such as fast Internet connectivity, training faculty members to acquire the relevant LMS skills and providing the relevant software are still prerequisites to enhancing the success of e-learning education in Ghana. Asamoah & Mackin (2016) used interviews to explore first year Ph.D. students’ experience in taking ADLT 704, Educational Technology and Innovation course using Sakai LMS in the University of Ghana. The students accepted the fact that there are several benefits using the Sakai LMS for teaching and learning and that Sakai LMS contributes to a better learning outcome than face-to-face teaching and learning. However, they also raised the point that they faced a lot of difficulties using the educational technology (Sakai LMS) for their learning. The challenges include inadequate student support services, poor Internet connectivity and power fluctuations. The authors therefore recommended faster and cheaper Internet connectivity, the availability of stand-by power generators and effective student support services among others to help enhance the use of the technology (Sakai LMS) for teaching and learning. Although, the participants for the above study were students, the same challenges would have confronted lecturers too. Working on factors hindering LMS uptake is a core issue. One of the objectives of the study was to address that. 34 University of Ghana http://ugspace.ug.edu.gh 2.2.0 Theoretical Framework For over two decades, ICT adoption/acceptance studies have focused on investigating the determinants of users’ intentions to adopt and use new technologies for their work. As a result, several theoretical models have been developed (Venkatesh, Morris, Davis & Davis, 2003). These include Technology Acceptance Model (TAM) (Davis, Bagozzi & Warshaw,1989) or some extension of that model; the Innovation Diffusion Theory (Rogers,1983,1995); the Theory of Planned Behaviour (TPB) (Ajzen,1991); the Expectation-Confirmation Theory (ECT) (Bhattacherjee, 2001); the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh, et al., 2003); and the Structuration Theory (ST) (Habib & Johannesen, 2014). These theories mainly focus on the exploitation stage and deal with both prediction and modelling of the behaviour of users who make the decision to either adopt and use technology or reject it. The current study was built on the foundation of two main theories: Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT). 2.2.1 Technology Acceptance Model (TAM) The Technology Acceptance Model (TAM) is originally traced to the theory of Social Psychology propounded by Fisbein & Ajzen (1975) as the Theory of Reasoned Action (TRA) (Davis, et al., 1989). TRA maintains that based on a certain belief, a person forms an attitude about a certain object, on the basis of which he or she forms an intention to behave with respect to the object. The intention to behave is the sole determinant of actual behaviour (Fisbein & Ajzen, 1975; Bagozzi, Davis & Warshaw 1992). TAM posits that Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) influence one’s attitude towards usage, which also influences one’s behavioural intention to use a technology (a system) and in turn determines actual 35 University of Ghana http://ugspace.ug.edu.gh use of a technology (Li & Huan 2009; Carter & Belanger, 2005; Davis, Bagozzi & Warshow, 1989). Figure 2.1. The Technology Acceptance Model (TAM) The variables in the Technology Acceptance Model are explained below: Perceived Usefulness (PU) According to Davis (1989) & Davis et al., (1989), Perceived Usefulness (PU) is defined as ‘the user’s impression that using a specific technology will increase his/her job performance in an organisational environment. Venkatesh & Davis (2000) posit that PU is positive in predicting a person’s acceptance and use of various technologies. Also PU positively affects user’s behavioural intention to use a technology (Chin & Todd, 1995). In Tung, Lee, Chen & Hsu’s (2009) research, PU registered a significant positive effect on behavioural intentions to use a technology-based product. PU was also found to predict students’ behavioural intentions to use E-portfolio (Abdulah, Ward & Ahmed, 2016). 36 University of Ghana http://ugspace.ug.edu.gh Additionally, Calisir, Gumussoy, Bayraktaroglu & Kara (2014) have undertaken a study using an extended TAM to determine the factors influencing blue-collar workers’ intention to use a web-based learning system in the pre- implementation phase in the automotive industry. PU was found to be the strongest predictor of behavioural intention to use a web-based learning system. We find from the above studies that PU’s influence on Actual was not tested. The current work has examined that relationship. Šebjan & Tominc (2015) in their research attempt to expand TAM in the studies of different aspects of acceptance of software packages, used statistical analysis (SPSS) and added three factors: Perceived Support from the teacher, Perceived Compatibility with the academic needs of students of economics and business and Perceived Usefulness of statistics. Their findings reveal that (i) PU of statistics plays an important role in using SPSS and Perceiving its ease of use. These results are consistent with the previous studies (Davis et al., 1989) on TAM, which show significant effects of PU and attitude toward technology on Behavioural Intention to Use (BIU). We find from the above study that PU’s influence on Actual has was not tested. The current work has examined that relationship. van Raaij & Schepers (2008) built a conceptual framework to explain the difference between individual students in the level of acceptance of the Virtual Learning Environment (VLE). They collected data from 45 Chinese participants in an executive MBA programme. The result showed that PU has a direct effect on VLE use. Perceived Ease of Use (PEOU) Perceived Ease of Use (PEOU) is the extent to which a person holds the view that using a particular technology could be done effortlessly (Davis et al.,1989). An 37 University of Ghana http://ugspace.ug.edu.gh individual’s perception that not much effort is needed to use a technology has a positive effect on his/her attitude and intention to adopt or use that technology. Chin & Todd (1995) noted that PEOU has a positive effect on the person’s Behavioural Intention to Use and PU to use a system. Tung, Lee, Chen,& Hsu’s (2009) study also shows that PEOU has a significant positive effect on behavioural intentions to use technology-based products. Oha & Yoon (2014) in their studies on consumers’ adoption of innovation products like the Haptic Enabling Technology (HET) product noticed that PEOU has a significant impact on PU. PEOU was also found to predict students’ BI to use E-portfolio (Abdulah, Ward & Ahmed, 2016). Šebjan & Tominc (2015) found in their study that the higher the PEOU of SPSS, the higher, on average, its PU. Wu, Wu & Chang (2016) on the other hand, found in their investigation relating to intentions of using a smartwatch from the consumer perspective that, the influence of Perceived Ease of Use on the acceptance of a system was not significant. Several of the above studies did not look at the influence of PEOU on Actual Use, but the current study did. Additionally, the adoption and use of Open Source LMS was not the focus of those studies, while the current study emphasizes Open Source LMS. Attitude Towards Use (ATT) Attitude Towards Use (ATT) is defined as, “one's positive or negative feeling about performing the target behavior (e.g., using a LMS) (Fishbein & Ajzen 1975). It asserts that if users find a system beneficial and simple to use then they develop a positive attitude toward this system. Ajzen (1991), Davis (1989), Fisbein & Ajzen (1975) maintain that Attitude Toward Use is capable of influencing Behavioural Intention to Use. Wu, Wu & Chang (2016) in investigating the 38 University of Ghana http://ugspace.ug.edu.gh intentions of using a smartwatch found that ATTU, although often identified as a weak mediator, was significant in determining influence on the use of smartwatch (technology). Behavioural Intention to Use Behavioral Intention to Use (BIU) is defined as the extent to which a person has planned consciously to perform or not perform some specified future behavior (Davis, 1989). TAM maintains that, PU, PEOU and ATT directly influence BIU. If users find a specific technology useful (PU), then they develop a positive intention of using it. Extant literature also provides evidence that Behavioural Intention to Use has a positive influence on Actual Use of a System. If users have intention to use a specific system, then they use it. Cigdem & Topcu (2015) embarked on a study by administering a questionnaire to 115 instructors to test a framework that had the following constructs: PU, PEOU, BIU, Self-Efficacy (SE), Technology Complexity (TC), Subjective Norm (SN) and other control variables such as Age, Prior knowledge, Experience and Course Relevance. Using t-test, regression and correlation analytical tools, the results showed that all the variables directly or indirectly affect Behavioural Intention to Use LMS. Again, this study was not on Actual Use of a System while the current study is on Actual use. Actual Use (AU) Actual Use (AU) implies the adoption and practical integration of the system to one’s job. The causal linkage among PU, PEOU, ATTU, BIU and AU of technology are reflected in the Technology Acceptance Model (TAM) (Tung, Lee, Chen & Hsu, 2009). 39 University of Ghana http://ugspace.ug.edu.gh TAM has been used intensively and extensively in research due to its simplicity and reliability (Legris, Ingham & Collerette, 2003). It has been applied in a broad and varied context to examine the acceptance and use of information technologies, including telemedicine software (Chau & Hu, 2002), the Internet (Shih, 2004), virtual reality (Bertrand & Bouchard, 2008) and multimedia learning systems (Saade, Nebebe & Tan, 2007), and has been found to be a reliable and robust model. TAM has been considered in this study because previous studies have found TAM to be most influential, a commonly used and highly predictive model of IT (technology) adoption (Fathema, Shannon & Ross, 2015; Venkatesh & Davis, 2000; Davis et al., 1989). Venkatesh & Davis (2000) developed and tested a theoretical extension of the Technology Acceptance Model (TAM) referred to as TAM 2 that explains Perceived Usefulness and Usage Intentions with reference to Social Influence (Subjective Norm, Voluntarism, and Image) and Cognitive Instrumentality (Job Relevance, Output Quality, Result Demonstrability, and Perceived Ease of Use). They found that both Social Influence Processes, such as Subjective Norm and Image and Cognitive Instrumental Processes, such as Perceived Ease of Use significantly influence user acceptance of technology. Agarwal & Karahanna (1999) have also modified the Technology Acceptance Model by adding Cognitive Absorption, Product Involvement and Perceived Enjoyment. Applying TAM, Venkatesh & Morris (2000) explore sex differences in the neglected context of individual adoption and continuous employment of technology in the work environment. Though TAM was formulated to study technology acceptance decisions across various organisational environment and users globally, its application in the 40 University of Ghana http://ugspace.ug.edu.gh field of education as an explanatory tool in examining e-learning processes is gradually becoming a practice (Park, 2009; Teo, Lee & Chai, 2008). Using TAM alone for research has not proven to be adequate for a strong predictive power. This position is derived from the statement made by Legrisa, Inghamb & Collerette (2003), that PU and PEOU explain about 40 per cent variance of the use of technology. The authors also note that an examination of empirical studies using TAM indicates that findings are not fully consistent, meaning that key factors are not included in the model. They then concluded that, although TAM is a good model, it cannot fully reflect the specific influences of technological and usage-context factors that may influence users’ technology adoption or use. TAM, therefore needs to be incorporated into other theories which would include factors relating to both human and social change processes, and to the adoption of the innovation model. 2.2.2 Innovation Diffusion Theory (IDT) The other theory used in combination with TAM in this study is the Innovation Diffusion Theory (IDT). IDT was popularized by Everett M. Rogers in the 1960s. The theory seeks to explain how social systems adopt new information or technology (Barker, 2004). Diffusion is the process by which an innovation (creative thought, new idea, invention, or new practice that is obviously better than the existing one) is communicated through certain channels over time among the members of a social system (Fache, 2000; Rogers, 2003). It is the totality of the adoption process whereby an individual progresses from knowledge to persuasion, to a decision to either adopt or reject, then to implementation of the innovation, and finally to confirmation of the decision within a time frame in a social system (Chang, 2010; Rogers, 2003). 41 University of Ghana http://ugspace.ug.edu.gh The objective of IDT is to examine the level of spread of an innovation among a given set of potential adopters in a given time frame that has elapsed since the introduction of the innovation. One reason accounting for so much interest in the IDT is that getting people to adopt and accept an innovation is very difficult, more so in developing countries even if they perceive the innovation to be useful (Rogers, 1983). The Elements of the IDT There are four main elements of IDT. These are: (1) Innovation Characteristics (2) The Communication Channel (3) The Timeline of Diffusion and Adoption and (4) the Social System. 1. Innovation Characteristics There are five innovation characteristics employed to explain the user adoption and decision-making process. They are also used to predict the execution of technological innovations, and to clarify how those variables interact with one another. The innovation characteristics that may influence adoption and diffusion of an innovation are Relative Advantage, Compatibility, Complexity, Observability and Trialability (Suh, 2004; Rogers, 2003). These innovation characteristics are used to explain the adoption of technology and the decision-making process an individual goes through in order to adopt a technology (Lee, Hsieh & Hsu, 2011). More & Benbasat (1991) have also designed a tool meant to test the kinds of perceptions that an individual may have for initial adoption and eventual diffusion of an information technology (IT) innovation in an organisation. 42 University of Ghana http://ugspace.ug.edu.gh Figure 2.2 below is Technology Adoption Model showing innovative characteristics mentioned earlier. Rogers 1983; 1995 F i g u re 2.2. Technology Adoption Model The variables in the model are explained below: Relative Advantage Relative Advantage is the extent to which an individual views a technology innovation as useful and a better idea than the existing one being used regarding its functionality, economic viability and technical feasibility (Rogers, 1983). The more individuals perceive advantages associated with a new technology (innovation) than the existing one the more they adopt it. This variable is deemed to be one of the most significant predictors of the adoption of an innovation (Tung, Chang & Chou, 2008; Wu & Wang, 2005). Some studies have demonstrated that the Relative Advantage associated with the use of technology exerts a positive effect on users’ intention to use technology across different participants (Lee, 2007). Previous 43 University of Ghana http://ugspace.ug.edu.gh studies have found that the Relative Advantage construct in IDT is similar to the PU variable in TAM (Taylor & Todd, 1995; Moore & Benbasat, 1991). Complexity Complexity refers to how difficult it is for the end-user to understand and use or implement an innovation (Rogers, 1983). The more complex the use of an innovation, the less likely it is be to adopted and vice-versa (Rogers, 1995). Empirical research reveals that Complexity has a significantly inverse relation on the intention to use (Lee, 2007). Hardgrave, Davis, Remenschneider (2003) also found an inverse relationship between Complexity and PU. Previous studies have found that Complexity construct in IDT is also similar to the Perceived Ease of Use in TAM (Moore & Benbasat, 1991; Taylor & Todd, 1995). The PU and PEOU variables in TAM are also professed to be a subsect of the innovation characteristics found in IDT. Compatibility Compatibility looks at the degree to which an innovation (e.g. adoption of a new technology) is applicable to or consistent with one’s job due to one’s prior experiences, beliefs, values and needs. It measures the degree to which a technology innovation is suitable for one’s job (Rogers, 1983). While some studies show a significant and positive exertion of Compatibility on PU only (Chau & Hu, 2001), others like Chang & Tung (2008a) and Wu & Wang (2005) affirm that Compatibility positively exerts a significant effect on both PU and the BIU. Some authors also find a positive effect of Compatibility on all the variables within the TAM model: PU, PEOU, BIU and AU (Hardgrave et al., 2003). Compatibility is also found to be one of the most important constructs to adoption research (Tornatzky& Klein, cited in Carter & Belanger, 2005). Carter & Belanger (2005) 44 University of Ghana http://ugspace.ug.edu.gh find Compatibility as one of the significant predictors of citizens’ intention to use an e-government service. Tung, Lee, Chen & Hsu (2009) in their study found that Compatibility had a significant positive effect on BIU to use a technology-based product. Oha & Yoon (2014) in their studies on consumers’ adoption of an innovative product like the Haptic Enabling Technology (HET) product noticed that Compatibility had a significant impact on PU. Additionally, Schierz, Schilke & Wirtz, (2010) in their studies also noted that Compatibility seemed to affect the users’ BIU. Šebjan & Tominc (2015) found in their studies that compatibility with the academic needs of students positively impacts on the PU of the Statistical Package for Social Sciences and the intention to use it in the future. Observability Observability applies to the degree to which an innovation can be observed before adoption. The results and benefits of the innovation’s use should be easily observable and communicated to others. It refers to how visible the innovation is to other people. Lee (2007) learned in his studies that observability has a positive effect on the users’ ATTU and BIU. Huang (2004) and Yang (2007) also found in their research that when employees realise that technology is easier to describe its functionality or observe its functional output, the technology is found useful and easier to use. However, Tung & Chou (2008) ignored observability because they reported that previous studies never found a strong correlation between it and attitude towards use and for that matter behavioural intention to use or actual use. Trialability Trialability measures the extent to which the innovation can be tested, experimented with, or piloted with ease or on a limited basis before being adopted. 45 University of Ghana http://ugspace.ug.edu.gh If it is easy to try an innovation, it is more likely to be adopted (Rogers, 1983, 1995). A positive relationship between Trialability and intention to use technology has been observed (Lee, 2007). A study also revealed that when users perceive higher Trialability, they perceive higher levels of Usefulness and Ease of Use of the system (Yang, 2007). Allen & Seaman (2007) argue that an inadequate level of e-competence of the majority of faculty members is one reason for the slow adoption of e-learning in higher education. Agarwal & Prasad (1999) attest to this assertion when they maintain that there is a positive relationship between prior knowledge in computers and acceptance and use of new technologies. 2) The Communication Channel The next element in the IDT is the Communication Channel. A communication Channel includes the use of the Internet, radio, television, newspapers, face-to-face- meetings and durbars for sharing of information in order to reach or create mutual understanding (Vreese & Boomgaarden, 2006; Wright, 2003). 3) Timeline of Diffusion and Adoption The next important elements in the IDT is the relative time of diffusion of adoption, as the adoption of an innovation requires time. The characteristics of those who adopt an innovation in early stages could be examined in order to determine how to influence and help others to adopt and practise the innovation in time (Rogers, 2003). The innovation adoption decision process describes the stages one goes through from the initial contact with an innovation to its absolute adoption. These include Knowledge, Persuasion, Decision, Implementation, and Confirmation (Rogers, 1995). 46 University of Ghana http://ugspace.ug.edu.gh Knowledge stage- This is where an individual is exposed to or made aware of an innovation (through sensitization, awareness creation and training). The individual therefore learns about its usefulness, its functions and the need for its adoption. In adopting a technological innovation, the number one question is: Are the people provided adequate information about the change and the stages involved and have they been trained or mentally prepared for the adoption? Again, the previous knowledge, experience and attitude of the expected adopter of the new technology will determine the time of adoption if the innovation will be adopted at all. Persuasion stage- This is where a person is either convinced or not convinced about a belief regarding the need to adopt innovation, the person thus forms a positive or negative opinion about the innovation, leading to acceptance. Positive perceptions of Relative Advantages of that innovation may be of use in solving known and potential problems because Compatibility, Simplicity and Trialability inform beliefs to proceed to the next step. Decision stage - In taking a decision to adopt an innovation, one finds out the workability of the innovation in terms of context-specific relevance and usability. One goes through this by soliciting information from different reliable sources. One test the outcome or the Usefulness of the innovation in the pilot stage and during the Actual Use of the technology. Implementation stage - Innovation is executed in the implementation stage. The adopters continually look for support from others in the form of technical assistance or positive motivation or reinforcement. 47 University of Ghana http://ugspace.ug.edu.gh Confirmation stage: At this stage, the innovation becomes completely integrated into the ongoing routine, while the adopter may start promoting the innovation to others (Grgurović, 2014). 4) The Social System Rogers (1983) explains five adopter groupings with regards to the time one adopts an innovation in relation to other individuals. The groups are Innovators, Early Adopters, Early Majority, Late Majority and Laggards. Innovators are the members of the social system who import and launch the innovation from outside the boundaries of the social system and, as gatekeepers, play a very significant role in the diffusion process. Innovators must control adequate financial resources and emotional strength in order to be able to contain any financial risk and other uncertainties and must also be able to understand and apply complex technical knowledge. The Innovators could be top management or their representatives, opinion leaders etc. They are risk takers who try new things (Lustig & Koester, 2006). They have innovation skills, multi-cultural relationship talents and sufficient innovation capital. They are also risk-tolerant and believe in high returns on investment (Fill, 2005; Rogers, 1983). They are the first point of reference if innovation will be integrated or embraced in the system at all. Their support to potential adopters of the innovation is another determinant for a complete adoption of an innovation. Early Adopters Early adopters are the first members who easily embrace the innovation and put it on trial. They serve as role models, experts to other individuals, and also are change agents who influence the innovation- decision process in the social system (Rogers, 2003). They are normally available to share their perspectives with 48 University of Ghana http://ugspace.ug.edu.gh potential adopters with regards to the innovation (Fill, 2005; Rogers, 1983). In this way, the insecurity about the innovation is lessened and its evaluation given through interpersonal networks with other potential adopters. Early Majority They adopt just before the average member of the system has had contacts with peers, but rarely lead except to strengthen connections in the social system. The early majority adopters rely on informal sources to gain information about the innovation. They might take a relatively long decision period to adopt the innovation (Rogers, 1983). Late Majority This category adopts after the mean (average) part of the population has adopted the innovation, their main characteristics being that they are sceptical and cautious in the adoption decision process. Laggards Laggards are the last group to adopt innovation. They are seen as “Traditionalists” who are often content with what they have. They would unenthusiastically and reluctantly adopt an innovation only because they feel they have to or are compelled to do so (Rogers, 1983). Alias & Zainuddin (2005) point out in their studies that Rogers theorised that individual adoption rates of innovation are usually distributed along a bell-shaped curve and can be grouped under five categories: innovators, representing 2.5% of the population; early adopters, representing 13.5% of the population; early majority, representing 34% of the population; late majority, representing 34% of the population, and laggards, representing 16% of the population. 49 University of Ghana http://ugspace.ug.edu.gh 2.2.3 Integrating Technology Acceptance Model (TAM) and Innovation Diffusion (IDT) Many studies have attempted to integrate TAM and IDT in the past. For example, Oh & Yoon (2014) combined TAM and IDT to investigate the variables influencing the adoption of Haptic Enabling Technology (HET) based products. Additionally, the concepts of Presence and Perceived Enjoyment were applied to highlight the hedonic aspect of consumers’ adoption of innovation product like HET product. Using structural covariance analysis for data analysis, they found that PEOU, Compatibility, Relative Advantage and Presence had a significant impact on PU. The studies also found that Relative Advantage, Compatibility, PEOU and Presence were all found significant in their effects on Perceived Enjoyment with PEOU and Relative Advantage being relatively more significant. Finally, the results showed that PU and Perceived Enjoyment had a significant impact on the adoption intention of HET products, with Perceived Enjoyment being more significant than PU, which explains hedonic motives of HET product use. Zhang, Guo & Chen (2008) also blended TAM and IDT to examine individual information technology (IT) adoption behavior. Subjective Evaluation, Objective Conditions and Interaction Factors were classified as Innovation Characteristics. The cognitive mechanisms that drive user IT acceptance were analyzed based on the patterns through which these characteristics influence individual user IT adoption behaviour. The survey was conducted on the adoption and use of emails in China and the structural equation model analysis was employed for data analysis. The authors found that the model provided meaningful insights 50 University of Ghana http://ugspace.ug.edu.gh for understanding, explaining, and predicting the IT adoption behavior of Chinese users. Tung, Chang & Chou (2007) integrated TAM and IDT, and added two research constructs (Trust and Perceived Financial Cost) to examine how nurses’ acceptance of the e-logistics information system has been affected by their attitude in the medical industry. The structural equation modeling data analysis tool was employed to assess the causal model. Confirmatory factor analysis was also conducted to test the reliability and validity of the measurement model. The results of the survey strongly support the new hybrid technology acceptance model in predicting nurses’ intention to use the electronic logistics information system. Compatibility’, ‘PU’, ‘PEOU’, and ‘Trust’ were all found to have a great positive influence on ‘BIU’. However, ‘Perceived Financial Cost’ was found to have a great negative influence on BIU. Lee, Hsieh & Hsu (2011) integrated TAM and IDT to evaluate factors influencing business employees' behavioral intentions to use the e-learning system. The proposed theoretical model was tested with data collected from 552 business employees using the e-learning system in Taiwan. It was found that five perceptions of innovation characteristics significantly influenced employees' e-learning system behavioral intention. The effects of the Compatibility, Complexity, Relative Advantage, and Trialability on the PU and PEOU have a significant influence. The findings suggest an extended model of TAM for the acceptance of the e-learning system, which can help decision makers in planning, evaluating and executing the use of e-learning systems. Giovanis, Binioris & Polychronopoulos (2012) administered a survey on 212 cases involving offline banking customers that are familiar with the Internet. 51 University of Ghana http://ugspace.ug.edu.gh The authors validated a causal model linking the constructs of the proposed Service's Compatibility, PEOU, PU, Perceived Security and Privacy Risk, Customers’ Demographics and IT Competences, with customers’ intentions to adopt Internet banking services in the future. The findings were that Service Compatibility was the key factor, which mostly shapes customers’ behavioural intentions toward Internet banking adoption, followed by TAM constructs and Perceived Risk elements. Moreover, TAM and Perceived Security and Privacy Risk constructs partially mediate the relationships between Compatibility and Customers’ Behavioural Intentions, while PU partially mediates the relationship between PEOU and customers’ BIU. In terms of the impact of individual differences on customers’ beliefs about internet banking, Compatibility, Value and Risk elements, younger, mostly male customers, with adequate previous IT experience who find themselves to be compatible with the new service, are a more promising target group to use Internet banking, as an alternative channel to perform their financial transactions in the future. Carter & Belanger (2005) integrated TAM and IDT constructs and web trust models to form a parsimonious yet detailed model of factors that influence citizens’ adoption of e-government initiative. They found that PEOU, Compatibility and Trustworthiness are significant predictors of citizens, intention to use an e- governance service. Chen, Gillnson & Sherrel (2002) also blended TAM and IDT in order to elucidate and assess consumer behaviour in an online store environment. Suh (2004) also employed a combination of TAM and IDT models to enquire the 52 University of Ghana http://ugspace.ug.edu.gh adoption of a supply-chain management system by small and medium enterprises. Additionally, Hong, Shin & Kang (2008) deployed a TAM and IDT integrated model to predict the adoption intention of an intelligent robot for home use. Wu & Wang (2005) blended the IDT, Perceived Risk and Cost into Technology Acceptance Model to investigate the factors that influence mobile commerce acceptance. The study surveyed mobile commerce consumers. They found that all the variables except PEOU significantly influenced users’ BIU. Chang & Tung (2007) integrated IDT and TAM, and added two research variables, Perceived System Quality and Computer Self-efficacy to propose a new hybrid TAM to study students' Behavioural Intentions to Use the online learning course websites. The study found among others that Compatibility, PU and PEOU were critical factors for students' Behavioural Intentions to Use the online learning course websites. Wu, Wu & Chang (2016) investigated the intentions of using a smartwatch from the consumer perspective by combining the IDT, TAM, UTAUT, and Perceived Enjoyment. Additionally, by applying TAM and IDT, Chena, Gillenson, Sherrell (2002) took an extended perspective to examine consumer behaviour in the virtual store. López-Nicolása, Molina-Castilloa & Bouwmanb (2008), using TAM and IDT theories examined an assessment of advanced mobile services acceptance. Also Wu & Wang (2005) in blending TAM and IDT to investigate the determinants of mobile commerce acceptance, observed that Compatibility has a direct influence on PU and BIU. In sum, the literature reviewed above is flooded with studies assessing the factors that influence the use of technology in various areas such as: factors influencing adoption of Haptic Enabling Technology (HET) based products, 53 University of Ghana http://ugspace.ug.edu.gh information technology (IT) adoption behavior, nurses’ acceptance of e-logistics information system, Internet banking adoption, E-mail adoption, adoption of e- government, assessing consumer behaviour in an online store environment, examining factors influencing mobile commerce acceptance, looking at consumers’ intentions of using a smartwatch, examining consumer behaviour in the virtual store, identifying behaviours on mobile commerce acceptance, business employees' behavioural intentions to use the e-learning system and assessing students' Behavioural Intentions to Use the online learning. Clearly, none of the studies above triangulated TAM and IDT to examine factors influencing Open Source LMS in Universities. This is a clear gap that the current study bridges. The current study investigated factors influencing lecturers’ adoption of Sakai or MOODLE LMS among a cross-section of universities in Ghana using some constructs in TAM, IDT and other parameters such as Image, Subjective Norm and Facilitating Conditions. Blending these two theories and three other parameters provided stronger and more credible results than each theory used in isolation (Wu & Wang, 2005). Image Image is defined as ‘the extent to which the use of technology innovation is perceived to enhance one’s Image or status in one’s social systems’. Rogers (1995) originally incorporated Image as a component of Relative Advantage, but later on, Moore & Benbasat (1991) captured Image as an isolated factor from a Relative Advantage so as to stress the relevance of users’ perceptions of the tendency that the action to adopt and use a technology may raise their position in the social structure. Image was also included in Ajzen & Fishbein’s Theory of Reasoned Action (TRA) which is the root of TAM, as a Subjective Norm (Carter & Belanger, 54 University of Ghana http://ugspace.ug.edu.gh 2005). Image was found to be one of most important innovation characteristics in predicting users’ intentions to use technology (Yi et al., 2006). Venkatesh & Davis (2000) have demonstrated in their study that Image can influence intention to use technology. Image’ s influence on Actual Use of an Open Source LMS was not found in the literature reviewed. The current study bridges that gap. Subjective Norm Subjective Norm is a person’s perception that a majority of the people who care very much for him/her, think he/she should perform a behaviour or should not perform a behaviour in question. Taylor & Todd (1995) used the term ‘Subjective Norm’ to refer to a person’s perception of the social pressures put on him or her to perform or not to perform a behaviour in question. Fong & Wong, (2015), Venkatesh et al (2003), Venkatesh & Davis (2000) and Taylor & Todd (1995a) have all argued that a Subjective Norm can impact on Behavioural Intention to Use technology. Venkatesh & Davies (2000) observed that Subjective Norm has an indirect effect in predicting an individual’s intention to use computer technology. However, Abbad & de Nahlik’ (2009) investigation revealed that Subjective Norm was not significant at all. Venkatesh & Davies (2000) found out that the influence of Subjective Norm deteriorates over time and only remains significant in a mandatory technology use environment. Abdulah, Ward, and Ahmed (2016), for example, used the extended Technology Acceptance Model and survey research to examine the influence of Self-efficacy, Subjective Norm, Enjoyment, Computer Anxiety and Experience on students’ PEOU and PU of an e-portfolio system and their Behavioural Intention (BI) to use the system for learning. They found that the predictors of students’ PEOU of the e-portfolio included Subjective Norm among other variables. Schepers 55 University of Ghana http://ugspace.ug.edu.gh & Wetzels (2007) embarked on a quantitative meta-analysis of a previous study on the Technology Acceptance Model (TAM) in an effort to make a research-based assertion on the role of Subjective Norm. The results indicated a significant influence of Subjective Norm on PU and BIU. Schierz, Schilke & Wirtz (2010) found that Subjective Norm seems to be affecting users’ intentions to use technology. Subjective Norm influences technological adoption (Kreijnsa et. al., 2013). Subjective Norm and Self-efficacy mediated the impact of three distal variables on intention: Previous Experience with the Use of Technology, Perceived Knowledge and Skills to Use Technology, and Colleagues’ Usage of Technology (Kreijnsa et. al., 2013). Yuen & Ma (2008) explored the Technology Acceptance Model to understand teachers’ acceptance of e-learning. Using survey, they collected data from 152 in-service teachers in Hong Kong. Variables such as BIU, PU, PEOU, Subjective Norm and Computer Efficacy were tested by using LISREL for data analysis. They found that Subjective Norm and Computer Efficacy were significant perception anchors regarding the fundamental constructs in TAM. Subjective Norm, Computer Efficacy and PEOU explained 68% of the users’ intention to use the e-learning system. Lee (2006) found that Subjective Norm significantly influenced PU. The relationship between Subjective Norm and Actual Use of Technology for whatever purpose was not tested per the reviews above. Only Subjective Norm and Behavioural Intention to Use Technology were tested. Besides, the authors’ focus was not on Open Source LMS. However, reviewing them has been helpful as it reveals the literature on factors determining technology adoption and use in 56 University of Ghana http://ugspace.ug.edu.gh general. Looking at the adoption and use of an Open Source LMS in the Ghanaian context is worthwhile. Facilitating conditions (FC) Facilitating conditions are seen as the “perceived enhancing organisational support factors or hurdles in the work setting that affect a person’s perception of simplicity or difficulty of performing a job” (Teo, 2010). Teo (2010) revealed FC’s significant effect on PEOU. Teo (2010) reported FC’s significant effect on teachers’ Attitudes (ATT) towards using computer technology. Panda & Mishra (2007) found that the significant barriers for e-learning adoption, as perceived by faculty members, were deficiency in facilitating conditions (FC) such as poor Internet access, lack of training and institutional policy. Bergeron, Rivard & DeSerre’s (1990) results show that FC has a significant effect on PEOU and it is consistent with other studies such as Li & Huang, (2009). Teachers’ acceptance of various technologies is based on Facilitating Conditions such as technical help, Internet infrastructure, hardware and software availability, and training. However, an in-depth enquiry into assistive technology applications by teachers and occupational therapists revealed that a high level of training and education do not necessarily lead to better technology acceptance (Hutinger, Johansen & Stonburger, 1996). By reviewing the two models of (TAM) and (IDT), the researcher found it necessary to add three more parameters of Image, Subjective Norm, and Facilitating Conditions to the body of knowledge in this domain. He showed their direct and indirect relationships with the Actual Use of an Open Source LMS, thus allowing a strong predictive power on the use of the Open Source LMS in the African context and filling a gap currently exiting in the literature. This enabled the researcher to 57 University of Ghana http://ugspace.ug.edu.gh develop and generate hypotheses which were formulated and measured using Structural Equation Modelling (SEM) to determine whether the predictive generalizations of the theoretical model were valid or not. 2.1.7 Proposed Integrated Theoretical Model Based on the statement of the problem, the related literature review and theoretical framework, the following proposed model is formulated for the study. Figure 2.3 Proposed Theoretical Model 2.2.4 Theoretical Model Formulation Innovation Diffusion Theory (IDT) and Technology Acceptance Model (TAM) are generally regarded as the most influential models among the technology adoption/ acceptance models (Zhang, Guo & Chen, 2008). As a result, the current study used the researcher’s own modified traditional IDT and TAM, Image, 58 University of Ghana http://ugspace.ug.edu.gh Subjective Norm and Facilitating Conditions theoretical model in order to increase the predictive power of factors influencing Adoption and Actual Use of Open Source LMS in the Universities in Ghana. The variables within the models under review were derived from Rogers (1983) Davis, Bagozi & Warshaw (1989), Moore & Benbasat (1991), Taylor & Todd (1995), Karahanna, Straud & Cher (1999) and Wu & Wu (2005). The essence of the theoretical model was twofold: (i) to identify the factors that influence lecturers’ Open Source LMS adoption and use and (ii)) to ascertain the underlying causal relationships or links among the factors - that is, independent variable and output variable via the mediating variables (Wu & Wang, 2005). The independent variables under Innovation Diffusion Theory (IDT) used in these study were Compatibility and Trialability. Other independent variables used in the study were, Image, Subjective Norm and Facilitating Conditions. The variables used under the Technological Acceptance Model (TAM) were Perceived Usefulness, Perceived Ease of Use (mediating variables) and Actual Use (an output variable). Compatibility was found to have a direct influence on PU and BIU (Wu & Wang, 2005). This work tested how compatibility exerts influence on the mediating variables of TAM and Actual use of a system instead of BIU. Trialability had been dropped by several studies because they found no link between it and Attitude towards system adoption (Tung & Chou, 2008). The researcher of this study also thought that if the lecturers had the opportunity to try using the Sakai or the MOODLE LMS then it would influence their use. Since the facility is available and several lecturers can try it, it is reasonable to include it in the study and test it. Thus, the study considered Trialability and examined the positive relationship between it 59 University of Ghana http://ugspace.ug.edu.gh and the variables of TAM. It also looked at how the mediating variables mediate Trialability and Actual Use. Since in academia, Image building is critical and desirable, the researcher considered Image in the model to test it. The researcher also thought that in an academic environment like any other social environment, human beings are normally gregarious and influence one another. Influence from peers and superiors can impact on the decision people take. Therefore, the researcher found it important to test the influence of the Subjective Norm that is, the social influence on PEOU, PU and AU. Facilitating Condition was considered because it was found to have significant effect on PEOU (Bergeron, Rivard & DeSerre,1990; Li & Huang, 2009) and the researcher found it to be one of the very important variables tested in the study. Relative advantage variable was found to be one of the most significant predictors of the adoption of an innovation (Wu & Wang, 2005; Tung, Chang & Chou, 2008). However, studies have shown that it is the same as PU. Therefore, Relative Advantage has been dropped. Complexity has also been dropped because similarly, studies have shown that it is the same as PEOU. Attitude variable within TAM has been eliminated in the model because it was found as a weak predictor of either BIU or AU of technology (Venkatesh & Davis, 2000). In the researcher’s view it is prudent to use either BIU or AU as an effect variable. BIU has also been removed from TAM variables because the study is a cross-sectional survey and not a longitudinal study. Some studies have also used 60 University of Ghana http://ugspace.ug.edu.gh either BIU or AU. Since the work looked at the AU of the Open Source LMS, BIU was dropped in favour of Actual Use. 2.3 Summary of Literature and Theoretical Review This chapter has intensively reviewed the existing related literature and relevant theories on adoption and use of Learning Management System (LMS) in higher education. Related literature on ICT and education, e-learning in education, LMS and factors that influence lecturers’ adoption and use of technology for teaching, have been reviewed. It has addressed the theories underpinning the thesis: Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT). Combining the two models and adding three more parameters allowed a strong predictive power on the use of the Open Source LMS in African context to fill a gap that exists in the literature. The next chapter looked at the methodology of the study, with an analysis of quantitative and qualitative methods due to the rules for using an explanatory sequential mixed method design. 61 University of Ghana http://ugspace.ug.edu.gh CHAPTER THREE METHODOLOGY OF THE STUDY 3.0 Chapter Overview This chapter highlights steps, strategies, techniques followed and the tools used in conducting the study. It encompasses decisions and activities undertaken during the data collection and analysis procedure. The chapter also incorporates in- depth descriptions of the quantitative data collection method and procedure phase, as well the qualitative data collection methods and procedure phase. There are three research paradigms that guide researchers from which one was selected for this study. These are positivism, interpretivism and pragmatism. These represent quantitative, qualitative and mixed methods research approaches (Venkatesh, Bown & Bala, 2013). This thesis employed the mixed methods approach and for that matter quantitative and qualitative strands were combined in the study. Data for both quantitative and qualitative strands were collected, analysed and blended, and inferences were drawn from them. 3.1 Mixed Methods Approach In the Social Sciences in general, mixed methods have experienced growth in their use and have also been considered a legitimate stand-alone research design. A mixed method is generally defined as an approach that uses both quantitative and qualitative methodologies or methods to collect data, analyse data, report findings, and draw inferences in a single study (Long, 2017; Tashakkori & Creswell, 2007). Mixed methods could also be explained as the collection and analysis of both quantitative and qualitative data in a single study in which the data are collected either sequentially or concurrently and are given priority in the research process (Creswell, Plano Clark, Guttmann & Hanson, 2003). The key element of the 62 University of Ghana http://ugspace.ug.edu.gh definition is the methodological pluralism; indicating that it is doable and legitimate that research that involves multi-methodology and paradigms be conducted in one study. The conceptual difference between mixed methods and multi-methodology is that the former strictly involves integration of multiple paradigms within a study while multi-methodology may include using methods within a particular worldview (paradigm) or multi-paradigms. The origins of mixed methods research can be traced to its use among fieldwork Sociologists and Cultural Anthropologists early in the 20th century (Johnson et al., 2007) and has emerged as the third methodological movement or the ‘third research paradigm’ (Tashakkori & Teddlie, 2003; Johnson & Onwuegbuzie, 2004) and the approach has also been adopted for educational research (Long, 2017). Pragmatism, a philosophy governing mixed methods is the belief that one can apply several methods for any study provided they are suitable. It endorses the use of different but appropriate approaches, recognising both objectivity and subjectivity of knowledge. It asserts that the use of the mixed methods approach is possible irrespective of the situation (Hanson et al, 2004). The researcher assumed the position of immersing and detaching from participants in data collection and interpretation based on the study, either quantitative or qualitative. Ontologically, the researcher observed multiple forms of realities per Dewenyan’s ‘what works action oriented view of reality’. Epistemologically, the researcher conceived that both etic and emic perspectives could co-exist in a single study whereby each tradition generates knowledge that could be matched and blended, thus increasing the credibility and the utility of the 63 University of Ghana http://ugspace.ug.edu.gh study’s findings. Multiple stances of values were brought into the limelight and recognised as influencing the research process. 3.1.2 Rationale for Mixed Methods Approach Mixed methods research ensures methodological rigour and is better able to answer certain complex research questions/objectives than what qualitative or quantitative research methods could do independently. In using mixed methods, quantitative and quantitative results may corroborate or be divergent or contradictory, and this can lead to extra reflection, revised hypothesis, and further research (Thorleif, 2012; Fraser, 2014). 3.1.3 Research Design The researcher applied the quality criteria for a mixed methods research design proposed by Creswell & Plano Clark (2011). The researcher ensured that: a. Both quantitative and qualitative data were collected; b. Rigorous procedures for data collection and analysis were followed; c. The study used one of the appropriate designs namely: explanatory sequential design; d. A theoretical lens was used; and e. Terminology consistent with mixed-methods such as triangulation, qualitative and quantitative research, dependability, confirmability, transferability, validity and reliability were used. 3.1.4 Mixed Methods Design This study employed the Explanatory Sequential mixed methods design. This is useful for explaining relationships of variables and study findings. 64 University of Ghana http://ugspace.ug.edu.gh 3.1.5 Data collection procedure The quantitative and qualitative data were collected sequentially. Priority was distributed unequally between the quantitative and the qualitative data. Priority (weight or more emphasis) was given to the quantitative study. What it means is that the quantitative data collection preceded the qualitative data collection in the sequence and also represents the major aspect of the mixed methods data collection process. The intention was first to examine with a large sample size to test the variables (the independent and mediating variables) that intend to predict Actual Use of the LMS, and then explore in-depth with a few cases during the qualitative phase. Quantitative survey data on factors influencing lecturers’ adoption and use of Open Source LMS was collected (this phase was meant to identify the potential predictive power of the selected variables on the actual use of the LMS) followed by qualitative interviews data collection. The interview data were used to corroborate and augment the findings from the survey data. 3.1.6 Quantitatively driven sequential design notation In mixed methods research, it is important to use procedural notation to illustrate the design. Q U A N q u al 65 University of Ghana http://ugspace.ug.edu.gh QUAN QUAN qual qual interpretation DATA DATA data data of entire work COLLECTION ANALYSIS collection analysis The use of the capital letter word QUAN means priority is given to the quantitative study and the lower case word qual means lower priority is given to the qualitative study. It also implies that the qualitative study is embedded within the quantitative design and is connected. Therefore, the qualitative phase was used to explain the quantitative phase. 3.1.7 Integration stage of quantitative and qualitative methods This integration aspect of the design was also based on the type of data collected, the sample size, the research design and the purpose of the study. Integration refers to the stage or stages in the research process where mixing of the quantitative and the qualitative data occurs. The two phases were connected while selecting the interviewees for the qualitative follow-up analysis based on the quantitative results from the first phase. Another point of link was the development of the qualitative data collection protocols based on the results from the quantitative phase, to investigate those results in more depth through gathering and analyzing the qualitative data in the second phase of the study. The mixing of the two research approaches occurred at the study design stage by introducing both quantitative hypotheses and qualitative research questions and the integration of results of the two data sets occurred at the interpretation in the discussion chapter. 66 University of Ghana http://ugspace.ug.edu.gh 3.1.8 Mixed methods design procedure (continuation) TIMING WEIGHT MIXING THEORIZING EXPLANATORY Quantitative Embedding Implicit SEQUENTIAL The researcher also considered a theoretical perspective to guide the study. This theory is strategically positioned in the literature review and theoretical framework chapter as an orienting lens that shapes the types of hypotheses formulated, who participates in the study, how data are collected, and the implications made from the study. 3.1.9 Sampling schemes for mixed methods Sampling is an important step in the research process because it helps to determine the quality of inferences made by the researcher (Collins, Onwuegbuzie & Jiao, 2006). Census; Judgemental and homogenous sampling were used. 3.2 Study settings Four universities in Ghana were purposefully chosen as the study bases for this research. These were, University of Ghana, Legon; University of Education, Winneba; University of Professional Studies, Accra; and Ghana Technology University College. They were carefully selected based on the fact that they have similar characteristics and uniqueness. All the four universities use an Open Source LMS. The University of Ghana uses Sakai LMS and the rest use MOODLE LMS. With the exception of Ghana Technology University College, all others are Public Universities. The issue of proximity to the base of the research was also considered. 67 University of Ghana http://ugspace.ug.edu.gh However, the reasons above do not in any way suggest that other universities in Ghana do not equally have similar and peculiar characteristics or are not equally close to the base of the researcher. Therefore, the key underlying factor for the choice of these study areas was the hope that the sample size-the number of lecturers required to participate fully in the survey would be achieved. 3.2.1 University of Ghana The University of Ghana, Legon is about 12 kilometres northeast of the centre of Accra. Formerly called the University College of the Gold Coast, it is the premier and leading University in Ghana and was established by Ordinance on August 11, 1948 for the purpose of providing for and promoting university education, learning and research. The first Principal of the University of Ghana was the late David Mowbray Balme. The University of Ghana adopted Collegiate System from 2014/2015 academic year. The Colleges are: College of Health Sciences, College of Basic and Applied Sciences, College of Humanities and College of Education1. Undergraduate and post-graduate programmes are run by the various Schools and departments under these Colleges. The University of Ghana has over 45,000 students made up of both local (dominant) and international students (currently, the number of international students is close to 1,500, drawn from over 71 countries)2. 1 http://www.ug.edu.gh/news/university-ghana-adopts-collegiate-system- 20142015-academic-year retrieved 15/07/2017 2 http://admission.ug.edu.gh/applying/ retrieved 28/06/2017. 68 University of Ghana http://ugspace.ug.edu.gh While the University of Ghana’s face-to-face teaching and learning still dominates, its Distance Education programme is also thriving steadily. The University also runs Sandwich and Weekend programmes. Aside from the main campus, the University of Ghana has 10 Learning Centres located in each of the 10 regions of Ghana with the head of the Learning Centres located at the main campus (Legon). The University also has the Accra City and Korle-bu campuses and has 3 reputed Research Centres (based at Nungua, Kpong and Kade). Additionally, it has an Office for Research, Innovation and Development. On the African universities front, it was placed 10th, 7th and 6th best University in the African universities rankings in 2015, 2016 and 2017 respectively. The University is a leading University in Ghana in relation to faculty research output, global visibility and employability status. University of Ghana uses Sakai LMS to support its face-to-face teaching and learning and for its Distance Education programme. 3.2.2 University of Education, Winneba The University of Education, Winneba (UEW) was founded in 1992 as a University College that served as the centre of seven Training Colleges. University of Cape Coast served as a mentor having an oversight responsibility over UEW to ensure that it could run effective and efficient academic programmes (University of Education, 2014). UEW was eventually chartered in 2004, to become a full-fledged independent, public university running both undergraduate and post-graduates programme. The main aim is to run programmes in education for teachers or those aspiring to be teachers. At present, the University has about 50,000 students, some enrolled in face-to-face programmes and others studying at a distance (University of Education, 2014). 69 University of Ghana http://ugspace.ug.edu.gh UEW has four campuses across Ghana, with the main campus at Winneba in the Central Region where it operates its central administration. The various Colleges and associated campuses are: College of Languages Education, Ajumako; the College of Technical Education, Kumasi: and the College of Agriculture Education, Mampong3. Besides, it has 20 service centres that provide the needs of the University’s distance education students; and also runs Sandwich and Weekend school programmes (University of Education, 2014). As the University was growing, it factored the need to embrace technology into facilitating teaching and learning. Owing to this, a computer service centre was established in 1994 to train first-year students in computer literacy, and advising management on policy and procurement issues (University of Education, 2014). ICT in the Education Department was later established as an academic entity in 2008. With increasing student numbers, Management was challenged to find ways and means to embrace the additional students. This led to the commencement of an e-learning programme with the use of the MOODLE LMS and other applicable technologies to support its face-to-face teaching and for its Distance Education programme (University of Education, 2014). 3.2.3 University of Professional Studies, Accra The University of Professional Studies, Accra (UPSA) is located at Madina in the Greater Accra Region of Ghana. UPSA with a current population of 11,300, was established in 1965. It has the reputation of being the first and the only public institution in Ghana with the mandate to offer both academic degrees and provide 3 http://www.uew.edu.gh/campuses/college-technology-education 70 University of Ghana http://ugspace.ug.edu.gh training for higher Professional Education in Ghana. It is the oldest professional accountancy and management tuition provider in Ghana but was taken over by government in 1978 through the Institute of Professional Studies Decree, 1978 (SMCD 200). It was later converted into a higher education institution with the mandate to provide tertiary and professional education in the academic disciplines of Administration, Banking and Finance, Accounting and Marketing for undergraduate and graduate levels and providing professional Accountancy, Management and other related areas of study4. UPSA also runs both under-graduate and post-graduate studies. It also runs Evening and Weekend schools. In 1999, the University attained tertiary institution status and has been given a presidential harter to award degrees since 2005. UPSA also runs campus-based teaching and learning programmes and distance learning by using MOODLE LMS. 3.2.4 Ghana Technology University College Ghana Technology University College (GTUC) is one of the accredited private universities in Ghana. When it was established in 1948, it was designated as Ghana Telecom’s flagship Training Centre, Royal Air Force (RAF) Training School. GTUC, formerly known as Ghana Telecom University College was established in 2005 by Ghana Telecom (the national telecommunications company) and was granted accreditation by the National Accreditation Board (NAB) on March 30, 2006. GTUC, being then the only school of its kind in the country and the sub-region, provided training services for other establishments such as the maritime industry, meteorological services, the military, the police and civil aviation. 4 http://www.upsa.edu.gh/about.php retrieved 8 July, 2017 71 University of Ghana http://ugspace.ug.edu.gh It is affiliated to the Kwame Nkrumah University of Science and Technology (KNUST) in Ghana, Aalborg University in Denmark and University of California (USA) to mention just a few. Its mission is "to be a centre of excellence in education, research, teaching, intellectual creativity and innovation5. Its student population is over 8,000. It works through three faculties, namely Faculty of Engineering; Faculty of Informatics and Faculty of IT Business. It has campus- based and distance learning programmes. It runs certificate, diploma, bachelor’s, master’s and doctoral level programmes. It uses the MOODLE LMS for both its distance and E-learning programme. GTUC operates from 6 campuses. The main campus is located at Tesano, on the Accra-Kumasi highway next to the Ghana Police Training College. Its second campus is at Abeka, about 1.5 kilometers from the main campus. A third campus in Accra is meant for the school of tourism, yet to be opened. The other campuses are in Koforidua (Eastern Region), Kumasi (Ashanti Region), Takoradi (Western Region) and Ho (Volta Region)6. 3.3 Ethical consideration Prior to the commencement of the research, ethics clearance was obtained from the Ethics Committee for Humanities (a segment of Office of Research, Innovation and Development) in the University of Ghana. The researcher’s School wrote an introductory letter addressed to the various heads of department and schools whose outfits the data were to be collected. Informed consent was also requested from the respective heads of department, and all the 5 http://www.coursesghana.com/universities/763-ghana-technology-university- college.aspx retrieved 04/07/2017 6 http://gtuc.edu.gh/site/images/uploads/studentaffairs/gtucstudentshandbook.pdf retrieved 04/07/2017 72 University of Ghana http://ugspace.ug.edu.gh respondents/participants agreed before they were engaged in the survey or the interview exercise. The time needed to complete the questionnaire and to conduct interview was made known to the respondents. Several respondents who were unable to complete the questionnaire within time were allowed to complete it at their own pace. Where a participant needed clarification on an issue, it was respectfully done by the researcher. The participants were also assured of anonymity and the confidentiality of their responses. Appreciation and gratitude were extended to all the participants. The work was also subjected to Turnitin (plagiarism detection software) to check if sources of data had been properly credited and used. Quotes from participants were also anonymously stated. Acceptable behaviours required by research ethics were conformed to. An atmosphere of respect was maintained to enhance a dialogical relationship between participants and the researcher. Being a student of University of Ghana, the researcher was careful about reflexivity, in order not to be biased in terms of the data organisation. The questionnaire and the interviews did not involve any sensitive topics such sexual activity, illegal drug use, illegal activities, or whistle-blowing. The study did not involve participants such as people under 18 years or people with disabilities. No financial inducement was offered to any of the participants to influence their participation in the study. The study did not involve any means of physical torture or torment or psychological trauma to the participants. Again, the study did not involve any potentially harmful process. The responses from participants were digitally recorded based on the participants’ approval, and they were deleted after the data were transcribed and analysed. Participants who were exhausted in the course of the interview were 73 University of Ghana http://ugspace.ug.edu.gh released and those who decided to abruptly end the interview entirely were permitted to do so. Participants were given prior notice that if they flouted the interview meeting data, time and venue for more than two times, then their participation would be revoked. The researcher alone had direct access to the interview data at any particular time. The researcher went to each interviewee’s office by appointment. Others who were interviewed by phone suggested the date, time and the interview period. Many participants who voluntarily participated in the study were not interested in signing the ethics consent form. They found the oral consent to be adequate. 3.4 Data collection challenges At the University of Ghana, not all the lecturers who were contacted, consented to participate in the survey. Additionally, not all those who participated also faithfully completed and returned their questionnaire. Only five (5) out of the thirty (30) copies of questionnaire administered through email format were returned to the researcher despite regular email reminders and a plea to participants to respond. The researcher had to find a way to redistribute new set of questionnaires to a number of these faculty members, entailing further cost and delays. It is worthy of note that administering questionnaires to lecturers through the email format is inefficient, at least in the University of Ghana. Again, when some qualified lecturers were not found in their offices or around campus, a number of questionnaires were left with faculty officers or administrative staff to be placed in their pigeon-holes. Although, the faculty officers said that the lecturers collected them from their pigeon-holes, all such questionnaires (14 in number) were never returned/retrieved. A number of lecturers were given new questionnaires to complete due to misplacement of the previous 74 University of Ghana http://ugspace.ug.edu.gh ones. Some lecturers (respondents) failed to honour their promises regarding the questionnaire retrieval date or time. Some of these lecturers were followed up as many as eight (8) times and yet some of them failed to honour their promise to complete the questionnaire. The best approach to select the prospective participants was to obtain direct help from the heads of department or lecturers who were aware of those who have had training in the use of Sakai/MOODLE LMS in their respective departments. In UPSA, the Human Resource Office was not interested in giving out the faculty members’ list to the researcher and did not have any list of those who had gone through the training for the use of the MOODLE LMS. It is also worthy of note that some faculty members who qualified to be included in the survey were not available to the researcher, while others were not interested in participating in the study although they had received training in the use of the MOODLE LMS. The researcher was thus compelled to make frequent visits to the heads of department for intense discussions about ways of getting qualified lecturers participate and provide the needed information. Eventually, there was a suggestion to use the lecturers’ WhatsApp contact to urge them to kindly respond to a researcher who would be visiting their offices. After numerous exchanges and follow-up visits while patiently enduring breaches of promises, a reasonable number of fully completed questionnaires were obtained. The case of University of Education, Winneba, was most disappointing considering the distance from the base of the research. Travelling there on several occasions to collect completed questionnaires did not yield much result. The suggestion from one head of department that completed questionnaire would be scanned and emailed to the researcher as an attachment later also did not happen. 75 University of Ghana http://ugspace.ug.edu.gh The researcher had to work with the only few he got from them. Overall, data collection at GTUC was most encouraging. 3.5 Quantitative Study This aspect of the study presents and reports on the quantitative study based on empirical data. It highlights the specific research approach and procedure that the researcher used to achieve the quantitative research objectives. This chapter sheds light on quantitative research strategy, procedures and the reasons for the choice of the quantitative approach. The chapter discusses why the quantitative approach was used, the philosophical stance of the researcher, research design, target population, accessible population, sample size, sampling strategy, instrument for data collection, data collection procedure, inclusion and exclusion criteria, validity reliability, pilot test, administration and preliminary analysis and data analysis technique. 3.5.1 Research design Cross-sectional descriptive survey The researcher used a cross-sectional descriptive survey for the quantitative study. Survey research, either cross-sectional or longitudinal, provides a quantitative or numeric description of trends, attitudes, opinions and behaviours of a population by studying at least a fairly representative sample of that population. The current survey research used a questionnaire instrument to gather data with the intention of generalizing from a representative sample or representative participants of a population (Babbie, 1990 cited in Creswel, 2003). A cross-sectional design as posited by Calder, Philip & Tybout (1981) was used due to the fact that data collection was conducted within a single specified time span. The characteristics of a cross-sectional study making it reasonable for use in this study (Heiman, 2002) 76 University of Ghana http://ugspace.ug.edu.gh include soliciting information from a sample of population about their views, opinions and behaviours towards the adoption of Open Source LMS. There are scanty studies of these characteristics in the existing literature on Ghana and sub- Saharan Africa. A survey is noted for enhancing external validity better than laboratory experiment (Sekaran, 2000). Inferences could be extended from the research environment to the environment of the decision and policy maker. 3.5.2 Target population and accessible population The target population of this study was entirely made of lecturers of the University of Ghana (Legon); University of Education (Winneba) University of Professional Studies, (Accra) and Ghana Technology University College (who were aware of, and had been trained for the use of an Open Source LMS such as MOODLE or Sakai which had been acquired and installed by the respective Universities for adoption and use by lecturers and students for teaching and learning. On the other hand, accessible population in this context, consists of lecturers within the purposefully sampled departments of schools/faculties deemed accessible (by the researcher) in the four Universities under review. The accessible population has been used for this study. Homogenous and census sampling methods were used to sample and access information from respondents belonging to the selected departments within different schools/faculties. 3.5.3 Census The researcher reasoned that all lecturers forming the accessible population in the schools/faculties and departments selected based on the inclusion criteria, 77 University of Ghana http://ugspace.ug.edu.gh should be served with questionnaires in order to be able to achieve a good number of cases for the study. Therefore, it turned out to be a census survey, that is, an attempt was made to ensure that copies of the questionnaire were administered and distributed to all the accessible population. Although efforts were made to reach out to all the accessible population, not all of the qualified participants were available for the exercise. Also, not all who were contacted consented to participate in the survey. Additionally, not all those who consented also faithfully completed and returned their questionnaire. 3.5.4 Homogenous sampling The researcher chose settings, groups and/or individuals based on similar or specific characteristics: a. The faculty members from all the selected universities are all into teaching, research and community service. b. All the universities run campus-based education, blended learning, and online learning using an Open Source LMS. c. With the exception of one private university, the rest are all public universities. d. All the universities have a track record of offering education/training programmes for several years. A number of qualified lecturers from various departments in more than 20 schools/faculties in the four universities were involved in the survey. The minimum sample size based on the standard for the use of structural equation modelling (over 200) was realised thus making the findings from the participating population sample generalizable. 78 University of Ghana http://ugspace.ug.edu.gh 3.5.5 Sample size Sample size is critical in quantitative research. A large and properly sampled population ensures better representativeness and generalizability of findings as well as proper use of statistical tools. Although a rich toolbox of mathematics is available to guide sample size determination, other factors are important such as ethical concerns, availability of participants, researcher’s time constraints, research objectives, contextual situations etc. (Cocks & Torgerson, 2013). The current study fixed a sample size of 300 but 283 participants out of the total accessible population of 435 was realised from more than 20 schools (faculties) of the 4 universities. Considering the literature on sample size, with the selection of numerous departments from over 20 schools/faculties from 4 universities, the sample size for the current study is representative of the total population. 3.5.6 Research participants Participants were made up of assistant lecturers/assistant research fellows, lecturers/research fellows, senior lecturers/senior research fellows, associate professors and full professors, both male and female in the respective universities under review. In the case of the University of Ghana, the names of some participants were identified by the use of the Lecturers’ List from the Research and Planning Office (although not reliable), the University of Ghana Computer Systems, and from information provided by the deans of schools and heads of department regarding those who have had training in the use of MOODLE/Sakai LMS. However, it is also important to mention that lecturers’ lists were not available in the other universities but the total number of lecturers in each school or department was made available to the researcher from which those who were qualified and ready to participate in the exercise were engaged. After the initial screening to 79 University of Ghana http://ugspace.ug.edu.gh determine those who were within the inclusion criteria, it was found necessary to administer the questionnaire to all of them in order to obtain a minimum sample size of at least 300. 3.5.7. Profile of participants Table 3.1: Demographic characteristics of Respondents Variable Frequency (N) Percentage (%) Age: Less than 49 years 247 83.7 50 years and above 46 16.3 Sex: Male 197 69.9 Female 86 30.1 University: University of Education 19 6.7 University of Ghana 179 63.3 Univ. of Professional Studies Accra 60 21.2 Ghana Technology University College 25 8.8 Educational Level: MA/Msc/MBA/MPhil 121 42.8 Ph.D.. 162 57.2 Rank: Lecturer/Snr Lecturer 268 94.6 Professor 15 5.3 Years of Teaching: Less than 11 years 221 78.1 11 and above years 62 21.9 Table 3.1 above provides the demographics of the participants of the study. The essence is to provide the background information of the participants to readers. Therefore, the age range, sex, university affiliation, faculty/school, educational 80 University of Ghana http://ugspace.ug.edu.gh level, rank and number of years of service in their respective universities have been captured. Most of the participants were in the age range 40-49 years representing 41% of the total number of lecturers who participated in the study (n=283). Males constituted 69.9% while females were 30.1%. Many of the participants were at the rank of lecturer and held a doctorate degree. A majority of the participants were from the University of Ghana, representing 63.3% and the least was from University of Education, constituting 6.7% of the total number of population who participated in the study. The differences in background were as a result of the inclusion criteria and the willingness of the participants to participate in the study 3.5.8 Data collection methods/ procedure The researcher personally met and delivered an introductory letter from his School, in addition to his own letter, to all the relevant heads of departments/deans of various Schools in the universities (those available) under study, inviting lecturers to participate in the survey. In the case of the University of Ghana, the heads of schools/departments that were purposefully sampled and engaged in the studies are from: School of Public Health, School of Nursing, Business School, School of Social Sciences, School of Languages, Institute of African Studies, Regional Institute for Population Studies, School of Performing Arts, Centre for Migration Study, School of Continuing and Distance Education, School of Information and Communication Studies, School of Education and Leadership, School of Nuclear and Allied Sciences, School of Agriculture and the School of Physical and Mathematical Sciences. The extensive list above is representative of all the Colleges of University of. 81 University of Ghana http://ugspace.ug.edu.gh Approval was given to the researcher before the survey started in those departments under the selected schools. To facilitate effective participation of lecturers in the survey, some of the deans of Schools also wrote letters, scanned and attached the introduction letter from the researcher’s School, together with the researcher’s own letter, and sent them as email attachments to all the heads of departments under such schools asking all the faculty members to voluntarily help the researcher to achieve his data collection goal. Also, some of the deans directly distributed copies of the questionnaire to the various heads of department under them as well as to the faculty members based on the list of members who have gone through the Sakai LMS training in the School. It is also important to state that in some cases, not all the departments under each of the selected schools were contacted, especially if the criteria for inclusion were lacking and the lecturers in such departments were difficult to reach (unavailability). At the University of Education, Winneba, before the questionnaires were distributed to the heads of department, the coordinator of e-learning and lecturers in the departments such as Mathematics Education, Chemistry Education; Information and Communication Technology Education (under the Faculty of Science Education) and departments such as Social Studies Education, Social Science Education (under Faculty of Social Education) who had had training in the use of the MOODLE LMS were all contacted and briefed on the survey exercised. At UPSA, the introductory letter was given to the Registrar of the University, who wrote some remarks on it and sent it to the director of Research of the same University to help the researcher achieve his data collection goal. The researcher also contacted the heads of department of all the faculties: Accounting, Banking and Finance, (Faculty of Accounting and Finance); the departments of 82 University of Ghana http://ugspace.ug.edu.gh Business Administration and Marketing (Faculty of Management Studies), the departments of Public Relations Management and Information Technology Management Studies (Faculty of Information Technology and Communication Studies) in order to distribute questionnaires to those who qualified and were willing to participate in those departments. In the case of Ghana Technology University College, the director of e- learning and distance learning was served with the introductory letter from the researcher’s school and personal letter from the researcher requesting to administer questionnaire to the lecturers on that programme. This was approved. 3.5.9 Ensuring a good response rate To ensure a good response rate, the researcher persisted in knocking on doors several times each day for several months until he found qualified lecturers/research fellows who were willing to complete the questionnaire. If a lecturer was willing to participate then a copy of the questionnaire was given to him or her. If a lecturer told the researcher he/she would not be able to participate due to time constraint, the researcher thanked the person and left. Those who were interested in completing the questionnaire asked the researcher to sit down a while in their offices until they completed the questionnaire and handed them immediately to the researcher. Some asked the researcher to come back for them in 30 minutes or an hour’s time. Others told the researcher the day on which they could come for the completed questionnaire or told the researcher the place it would be left for him to pick up. While completing the questionnaire, others asked for clarification of some of the questions, and this was courteously responded to. The response rate at GTUC was 69 per cent. 83 University of Ghana http://ugspace.ug.edu.gh The total accessible population of all the study area (all served with questionnaires) is 264+108+27+36 = 435. For this study in particular, expected sample size was equated to the accessible population of 435 (census population) of which 283 questionnaires were received, representing 65% of the accessible population. This figure far exceeds the sample size requirement prescribed in previous studies as stated earlier. 3.5.10 Questionnaire administration, distribution and collection The questionnaire was significantly self-administered and distributed to the accessible participants in all the universities. Those who preferred an electronic format were sent copies by email. Several participants were not interested in signing the ethical consent form as they found the oral consent adequate. The data administration, distribution and collection spanned October, 2016 to April, 2017. 84 University of Ghana http://ugspace.ug.edu.gh Table 3.2: Distribution of Accessible Participants Who Returned Their questionnaires Name of Unit Number of Number Accessible Returning Participants Questionnaire University of Ghana, Legon 264 178 Response rate 67% University of Education, Winneba 2 7 19 Response rate 70% UPSA 108 60 Response rate 56% GTUC 36 26 Response rate 69% Total 435 283 85 University of Ghana http://ugspace.ug.edu.gh The total accessible population of all the study area was 435 out of which 283 returned their completed questionnaires in good condition. Thus the response rate was 65% response rate. 5.11 Instrument for collecting data The study deployed a structured questionnaire (completely closed-ended questions) administered to the lecturers to examine factors that influence their Open Source LMS use behaviour. The design of the instrument (scale) was made in the light of the research objectives and for ease of coding. In order to enhance the validity of the instrument, the researcher employed existing multi-item scales adopted from the extant literature (Davis et al., 1989; Moore & Benbasat, 1991; Taylor & Todd, 1995; Karahanna et al., 1999; Wu & Wu, 2005) and modified them to fit the specific area of interest. The instrument, consisting of 53 questions in all, was structured in three parts. Section ‘A’ was made up 12 multiple choice questions (nominal/categorical data/control variables) that address the demographic characteristics of respondents, such as age, sex, type of university, faculty/school, educational level, rank, number of years served in the university, religious affiliation, marital status, nationality, awareness of open source LMS and whether one has been trained in the use of Sakai/MOODLE LMS. The reasons for including the control variables are to provide information about the respondents in general and to select some of them to find out the degree of association with other factors and mediating variables. Section ‘B’ was a set of 39 questions based on IDT, TAM, Image, Subjective Norm and Facilitating Condition variables that were tested. The 86 University of Ghana http://ugspace.ug.edu.gh instrument was structured to have a number of questions under each factor or variable as follows: 3 questions for Complexity (CPL), 5 questions for Relative Advantage (ADV.), 4 questions for Compatibility (CPA), 3 questions for Observability (OBS), 3 questions for Trialability (TRI), 3 questions for Image (IMG), 3 for Subjective Norm (SBBN), 3 for Perceived Usefulness (PU), 4 for Perceived Ease of Use (PEU), 4 for Behavioural Intention to use (BIU). Facilitating Condition (FC) set of questions was wholly self-constructed after reviewing the literature and considering the objectives of the studies. All the section ‘B’ questions constituted five-point Likert-scale questions ranging from (1) “strongly disagree”; (2) ‘’disagree”; (3) Neutral; (4) ‘’agree’’ and (5) strongly agree. The last set of questions was made up of 3 items that focused on ‘Actual use’ of Open source LMS. There were also likert-scale type questions ranging from 1-7(8 items) on a scale. The draft set of the questionnaire in the English language was reviewed by supervisors and colleagues. Comments were considered before it was eventually approved by the researcher’s supervisors and piloted by 25 lecturers of the University of Ghana who also used the Sakai LMS. The modified questionnaire, after the piloting, was also communicated to the researchers’ supervisors before being administered to the sampled population (accessible population). The importance of using a questionnaire is that it provides a systematic way to solicit information. It is cost effective and easy to analyse. 3.5.12 Enhancing rigour (Reliability and validity test) In quantitative research, rigour is established in reference to internal and external validity as well as reliability. Reliability implies consistency and trustworthiness of the data. It measures the extent of consistency between multiple 87 University of Ghana http://ugspace.ug.edu.gh measurements (scale) of a related construct (Hair, Black, Babib, Anderson, 2007) and the stability of the measure within a period of time. Reliability measures the firmness of the proposed measure. A reliability test using Cronbach’s alpha to examine the internal consistency of the measurement scale was used (Hair et al., 2007). Validity is the correctness, accuracy, strength of statement or credibility of the data. For a scale to be applied with confidence, it must possess validity, it must be able to measure what it intends to measure (Berthon, Wing & Hah, 2005). Types of validity implied in this study are content validity, convergent validity and discriminant validity. A mini research (pilot) test was carried out on some lecturers (25 in all) in the School of Nursing and the School of Continuing and Distance Education, (University of Ghana) who shared similar characteristics with the accessible population in order to pre-test the instrument to enhance its validity and reliability. The pilot test was conducted in preparation for the mainstream study. The schools that the respondents belonged to were selected through an intentional sampling scheme because several of lecturers in these schools were aware of the availability of the Sakai/MOODLE which had been acquired by their University, and additionally, had had training in the use of Sakai and were using it. The pre-testers fully completed the questionnaire in with each question in the pre-test assessed for explicitness, ease of understanding, utility and appropriateness in terms of the structure of questions, suitability of time allotted to complete the questionnaire, language construction and grammar. The piloting of the instrument was to help evaluate the questionnaire, find out if the instrument was able to measure with the required precision and accuracy what it intended to measure in order to attain the goals of the study. The pilot study 88 University of Ghana http://ugspace.ug.edu.gh also helped to refine the data collection plans and procedures. It was also a way to do a preliminary analysis of lecturers’ adoption of an Open Source LMS such as Sakai for teaching and learning. The questionnaire confirmed the relevance and design of the research plan and helped discern areas that needed further refinement. Cronbach’s Alpha was employed to measure the internal consistency of the instrument. 89 University of Ghana http://ugspace.ug.edu.gh Table 3.3: A summary of the reliability statistics for the study variables Item-Total Statistics Scale Mean Scale Variance if Corrected Item- Cronbach’s if Item Item Deleted Total Correlation Alpha Deleted Compatibility .903 CPA1 10.7815 5.877 .759 CPA2 10.7966 6.085 .813 CPA3 10.8314 5.802 .817 Trialability .819 TRI1 5.3299 4.964 .627 TRI2 5.0314 4.201 .745 TRI3 5.0547 4.461 .649 Image: .871 IMG1 4.7361 3.055 .727 IMG2 4.7148 2.918 .831 IMG3 4.7802 3.094 .703 Subjective .850 norm: SUBN1 5.4540 3.617 .774 SUBN2 5.3550 3.496 .767 SUBN3 5.2125 4.020 .622 Perceived .921 use: PU1 7.2617 3.572 .818 PU2 7.3711 3.296 .840 PU3 7.3508 3.391 .865 Perceived .801 ease of use: PEU1 10.4833 5.426 .789 PEU2 10.5045 5.820 .711 PEU3 10.5152 6.046 .666 PEU4 10.8297 7.333 .336 Facilitating .690 Conditions FC1 9.9319 6.082 .483 FC2 10.7429 6.892 .405 FC3 10.0316 6.666 .461 FC4 9.6052 6.787 .568 Actual use: .808 AU1 10.1466 9.942 .664 AU2 9.0396 8.194 .716 AU3 8.0176 10.820 .605 90 University of Ghana http://ugspace.ug.edu.gh 3.5.12 Data analysis Data were compiled, coded, cleaned, edited, classified and entered into a computer for analysis using statistical packages for social scientists (SPSS) version 21.0 and AMOS 21 software. Statistical analysis tools used included descriptive statistics - frequency, mean, standard deviation, skewness, kurtosis, minimum and maximum. Confirmatory Factor Analysis and Structural Equation modelling were used for data analysis. Primarily, the collected data (from 283 usable cases) were analysed using a reliability test coefficient - (Cronbach’s alpha), Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM). The validity and reliability of the measurement model as well as normality of the data were examined prior to modelling the structural model. Cronbach’s alpha and confirmatory factor analysis were used to test the validity and reliability of the measurement model at the pilot and main work stages. CFA has been used to test the psychometric features of measurement scale in a number studies (Rieflier, Diamantopoulos & Siguaw, 2012) and it is believed to improve congeneric measurement characteristics of scale during purification level (Anold & Reynolds, 2003). It is theory-driven, finding the relationship among the observed and unobserved variable (Schneider, Nora, Stage, Balow & King, 2006). CFA is seen as the most reliable statistical procedure for testing a hypothesized factor structure (Bryne, 2010). AMOS 21 was employed to conduct CFA. In order to achieve the quality of the construct’s item, any item with factor loading less than 0.5 was to be eliminated except when it equally had adequate reliability, in which case it was retained (Awang, 2012). 91 University of Ghana http://ugspace.ug.edu.gh 3.5.13 Structural Equation Model Structural Equation Modeling (SEM) was used principally to analyze the factors that determine lecturers’ adoption and use of an Open Source LMS in four universities in Ghana using TAM, IDT, Subjective Norm and Facilitating Condition constructs. SEMs are renowned for their broad use in the educational, behavioural, psychological, and social sciences (Zhang, Tian & Tang, 2016). Several authors have developed many methods to fit SEM, including, Song, Lu, Hser, Lee (2011), Song & Lee (2002), and Lee & Zhu (2002) covering the Bayesian method for SEMs factoring ignorable missing continuous and polytomous data; expectation– maximization algorithm including Metropolis–Hastings algorithm for maximum likelihood estimation (MLE) of a general nonlinear SEM; Bayesian approach for analysis of nonlinear SEMs with variables either categorically ordered or from an exponential distribution family, and the use of Bayesian method for the analysis of longitudinal data in SEMs; and development of a general SEM in which the latent variable model is exploratory. A Structural Equation Modelling (SEM) technique was found suitable for complex variables. SEM was chosen because it simultaneously analyses the paths in the model and tests the goodness of fit of the model; that is, it is able to ascertain adequate fit with the observed data, compared with the standard fit index (Bollen & Noble, 2011). This statistical tool helps to study the causalities among all parameters within predictive model. Causal relationships among all constructs and the proposed structural model were tested using SEM. The causal relations of latent exogenous variables and latent endogenous variables were measured through standard co-efficient and significance value using AMOS. In the current study, fit indices were reviewed in order to find out how well the fit between the data and the proposed structural model was. Feasibility of each path in the model was evaluated by examining whether the weights are statistically and practically significant. 92 University of Ghana http://ugspace.ug.edu.gh Where the fitness index was satisfied, no modification indices (MI) were examined. The individual measurement models were evaluated by the Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Squared Residual (SRMR) and Tucker-Lewis Index (TLI). The threshold values for all these fit indices were considered when evaluating the measurement model. The cut-off values are greater than 0.90 for CFI, greater than .09 for TLI and less than 0.08 for RMSEA (Awang, 2012). The next step in the data analysis was to examine the significance and strength of hypothesized relationships in the research model. The hypotheses were either validated or unsupported based on the results of data analysis (Oruça & Tatarb, 2016). 3.6.0 Qualitative Study 3.6.1 Background overview This section presents the philosophical assumptions, research design, population, instrumentation/research questions, participant and sampling size, data collection methods, inclusion and exclusion criteria, data analysis and procedures followed to ensure trustworthiness of the qualitative study. Qualitative study is a context-specific study that describes the real world setting of people so that social realities, structural characteristics and qualities can be understood (Golashani, 2003). The current study is pivotal because qualitatively, it highlights the meanings lecturers give to some specific experiences, and hence the factors that influence their use of an Open Source LMS for teaching and students’ learning. It also explores the reasons why some of them are not using it for such purposes (Willig, 2001) . This is an area that apparently no studies have been conducted, thus calling for an enquiry. Risjord, Moloney & Dumba (2001) argue that, a qualitative study is used to explore a phenomenon that has not been properly described. 93 University of Ghana http://ugspace.ug.edu.gh 3.6.2 Philosophical assumptions The philosophy of the qualitative approach is an interpretivist (constructivist) stance which emanates from idealism (Sale, Lohfeld & Brazil, 2002). Ontologically, it is emic and asserts that reality depends on one's mental structure and activity; that is, there is no single reality but multiple realities based on an individual’s construction or interpretation of reality. Thus reality is viewed as an inter-subjective creation (Hellström, 2008). Epistemologically, the researcher asserts to the philosophy that ‘truth’ is multi-various, subjective and there also a co- construction of knowledge on a phenomenon by the researcher and the interviewee. Besides, the researcher holds that the qualitative method does not pursue objectivity and generalizability in a broader sense because both conditions are viewed as unachievable. Instead of generalizability, the qualitative method emphasises “transferability”, that is the extent to which readers can transfer described experiences of the phenomenon to other settings based on the depth and vividness of the descriptions (Sale et al., 2002). Axiologically, values may be engrained in the data. Aesthetically, ornaments, pictures or images could be used in the reporting of research results. 3.6.3 Research design An explorative and multiple case study designs were used for this study. It is an explorative case study because very scanty work had been done in the study area in Ghana, and for that reason further exploration into the area was required. It is a multiple case study because several different cases were used: cases were selected from different departments of different universities. 94 University of Ghana http://ugspace.ug.edu.gh 3.6.4 Population The population constitutes lecturers from selected departments under the School of Nursing, School of Continuing and Distance Education, School of Education and Leadership and Regional Institute for Population Studies (all at the University of Ghana) who used Open Source LMS for teaching and learning. It also includes lecturers from departments under the Faculty of Science Education from the University of Education, Winneba, who equally used Open Source LMS; and those within the same departments who were aware of the availability of the Open Source LMS in the university but for some reasons were not using it. 3.6.5 Instrumentation Research instruments used for this explorative aspect of the study were a semi- structured interview guide. The semi-structured interview guide was designed in accordance with the purpose of the study. The researcher was interested in knowing the factors that influence lecturers’ use of an Open Source LMS as well as others who do not use it although the LMS has been acquired already by the respective Universities. Two separate interview guides were developed by the researcher for the study: one for Users of Sakai/MOODLE LMS, and another for the Non-users. The interview guide for lecturers who use Sakai/MOODLE LMS for teaching contained the following interview questions: 1. Can you tell me about the factors that influenced your decision to adopt and use Sakai/MOODLE LMS? 2. How do you use Sakai/MOODLE in the teaching profession? 3. How do you think Sakai/MOODLE LMS could be upgraded to broaden its uses? Interview questions for lecturers who do not use Sakai/ MOODLE LMS are: 1. What do you know about Sakai/MOODLE LMS? 2. Why are you not using Sakai/MOODLE LMS? 95 University of Ghana http://ugspace.ug.edu.gh 3. What do recommend to facilitate the use of the Sakai/MOODLE LMS? Fourteen (14) lecturers, 7 each from the University of Ghana and the University of Education were selected to participate in the study. They consisted of 8 males and 6 females who used Sakai or MOODLE to teach. Additionally, 6 Non-users, 3 each from the University of Education and the University of Ghana were also recruited. These Non-users were composed of 3 males and 3 females. All the lecturers were between the ages of 36 and 55 years, had taught in their respective universities for over 5 years, were married. And all except 2 were non- Ghanaians. 3.6.6 Sample size and sampling method In qualitative studies, samples are based on the ability of respondents to provide important and rich information, not because they are representative of a larger group. An attempt is usually made to understand a small number of participants’ own frames of reference and worldviews, rather than to test a hypothesis on a large sample (Hellström, 2008). The 20 sampled cases were guided by such understanding and the principles of saturation. With saturation, interviews were discontinued when participants were no longer adding any new information to the discourse. A homogeneous sampling scheme was selected. The researcher chose settings, groups and/or individuals based on similar characteristics. The lecturers were in the same general profession (teaching). Besides, both universities run campus-based teaching and learning, and blended learning including technologically-based distance education. Additionally, judgmental sampling was utilized as it was found to be suitable for the research context. This is noted for collecting perceptions and experiential data. A purposive sampling technique is used to select participants for a study to ensure that the information collected is rich and specifically addresses the issues being reviewed. This was chosen based on the participants’ willingness and availability. The data size was not too 96 University of Ghana http://ugspace.ug.edu.gh small or too large (Smith, Flowers & Larkin, 2009). The sample size was small enough to handle the material, and large enough to yield ‘a new and richly textual understanding of experience’ (Sandelowski, 1995) having enough data to demonstrate patterns while ensuring there is not too much data to manage, which involves subjectivity and guided is by the researcher’s experience, assessing the data as it is analysed in relation to the goals of the research (Barker, Pistrang & Elliot, 2002). 3.6.7 Inclusion and exclusion criteria Only some lecturers from University of Ghana and University of Education who were full-time and used Sakai/MOODLE in one form or the other were interviewed. Also, it was guided by a search for only information-rich cases, thus those who were using the LMS system and had acquired some level of experience. Further, the selection also included those who were not using the system at all. Their inclusion was important for exploring reasons underlying their non-use of the Open Source LMS. Those excluded were retired professors, tutors and administrative staff. 3.6.8 Data collection procedure and methods Participants were contacted at least a week to the interview time to schedule them for a session. Those who agreed with the schedule were booked and reminded two days prior to the impeding engagement. Interviews were conducted face-to-face at times convenient to the participants and at their respective campuses using semi-structured interviews. Some were then followed up with telephone interviews for a further probe. The advantage associated with the semi-structured interview is its flexibility and the ability to facilitate a deep exploration of social and personal worldview of the participants. It also allows effective dialogue and engagement with the participants. Finally, it allows adjustments of the interview questions and further probes based on the participants’ responses, and permits co-creation of meaning (Smith 97 University of Ghana http://ugspace.ug.edu.gh & Orsbon, 2003; DiCicco-Bloom & Crabtree, 2006). Interviews were conducted in English. Each interview took 15 to 30 minutes either in the participant’s office or by telephone. Participants agreed that the interviews could be tape-recorded. Ethical clearance approval and informed consent issues have been discussed earlier in this chapter under the sub-heading: ethical consideration. 3.6.9 Data analysis Thematic analysis Thematic analysis was used to analyse the data. Thematic analysis is a qualitative approach for unravelling ‘some level of patterned response or meaning’ within a data-set (Braun & Clarke, 2006). It transcends word or phrase counting to analyses encompassing ‘identifying and describing both implicit and explicit ideas’ (Guest, MacQueen & Namey, 2012). The use of thematic analysis helped to single out, classify and delineate trends within data as well as interpreting various aspects of the research topic (Braun & Clarke, 2006). Data analysis began in the field just after finishing in-depth interviews. The digitally recorded interviews were played back and listened to critically several times from beginning to end by the researcher. The digitally recorded information was transcribed verbatim; and the transcription was very significant in the interpretive paradigm. Additional reading was done to identify prominent themes from each interview and how it related with other interviews. Typical statements that reinforced a specific emerging theme were recognised and linked to the developing themes in order to allow narratives to flow naturally from the perspectives of the interviewees, and the depth of such narratives was explored through interpretation by the researcher (Smith& Osborn, 2003). Eventually, when themes were assembled, logical links were established between them in a more theoretical and coherent ordering and general classifications were obtained (Smith & Osborn, 2003). The 98 University of Ghana http://ugspace.ug.edu.gh analytical approach is interpretive. One of the benefits of using this method is its flexibility in applying it across a range of theoretical and epistemological approaches (Brown & Clarke, 2006). 3.6.10 Ensuring trustworthiness To ensure trust in the qualitative research data collection, analysis and reporting, the researcher took steps to enhance credibility, transferability, dependability and confirmability. Regarding credibility, the researcher made sure that the findings and explanations were consistent with the opinions and thoughts of the participants. Thus the principle of member check was deployed (some participants were consulted to read and affirm the content of information solicited from them). Also, the triangulation of data from different participants and sources enhanced credibility (Yin, 2006). Additionally, an internal peer review process and group interpretation were engaged. Supervisory team members’ perspectives and interpretations on the analysis and report helped in the process of cross-validation and ultimately improved the trustworthiness of the analysis (Smith, Flowers & Larkin, 2009). The efforts were meant to establish correctness, accuracy, strength of statement and credibility. Transferability coincides with the term ‘generalizability’ of findings to other settings in quantitative research and also refers to as external validity. Describing in-depth the contextual situation where the study took place, the reasonable sample size and the associated rich findings put the whole work in a conspicuous domain where readers with good understanding capabilities could determine the applicability of the findings in other contexts. Dependability describes the degree to which if the study were carried out in a different geographical location using the same method of data collection and analysis and similar participants, would yield the same results. This was achieved by triangulating data from 99 University of Ghana http://ugspace.ug.edu.gh different sources and using a thick description for analysis and interpretation. The consensus arrived by both the researcher and peer reviewers makes the thesis dependable. 3.7 Summary of Methodology The Mixed Methods approach has been explained in detail. Since it is a blend of two research paradigms (quantitative and qualitative), each paradigm has been discussed in detail regarding its philosophy, research design, data collection methods, sampling strategy, data analysis and rigour. The next chapter examined the results of the quantitative and qualitative studies. 100 University of Ghana http://ugspace.ug.edu.gh CHAPTER FOUR PRESENTATION OF RESULTS 4.0 Chapter Overview This chapter analyses both the quantitative and the qualitative results of the study. Regarding the quantitative data, confirmatory factor analysis, multivariate normality, inter- correlation matrix, structural equation modelling and test of hypotheses were conducted. 4.1 Results for Quantitative Study Table 4.2. Confirmatory Factor Analysis and Reliability Test Factor Item Factor Construct AVE MSV ASV number Loading Reliability Compatibility CPA1 .82 .91 .71 .44 .16 CPA2 .87 CPA3 .87 CPA4 .79 Trialability TRI1 .72 .83 .61 .06 .04 TRI2 .89 TRI3 .73 Image IMG1 .80 .88 .71 .26 .08 IMG2 .94 IMG3 .77 Subjective norms SUBN1 .89 .86 .67 .26 .07 SUBN2 .88 SUBN3 .66 Perceived usefulness PU1 .86 .92 .80 .44 .17 PU2 .89 PU3 .93 Perceived ease of use PEU1 .89 .88 .57 .31 .18 PEU2 .88 PEU3 .77 Facilitating conditions FC1 .57 .70 .38 .25 .10 FC2 .45 FC3 .66 FC4 .76 Actual use AU1 .73 .68 .52 .32 .15 AU2 .70 101 University of Ghana http://ugspace.ug.edu.gh Note: AVE = Average Variance Extracted; MSV = Maximum Shared Variance; ASV = Average Shared Variance Table 4.3 SEM Analysis Results χ2 df p SRMR CFI TLI RMSEA Measurement model 383.23 271 < .05 .046 .971 .965 .038 Hypothesized structural model 360.39 248 < .05 .048 .971 .964 .040 Note. SRMR = Standardized Root Mean Square Residual; CFI = Comparative Fit Index; TLI = Tucker-Lewis Index; RMSEA = Root Mean Standard Error of Approximation 4.1.3 Confirmatory factor analysis Confirmatory factor analysis (CFA) was conducted to examine the factor structure and distinctiveness of the study variables. An 8-factor measurement model was estimated for all the latent variables in the study. Each scale item (indicator variable) was estimated to load onto its respective latent construct in the measurement model. The results showed chi-square value of 383.23 with a degree of freedom (df) of 271, which was significant at a probability of less than .05. This indicates that the model does not adequately account for the observed covariation among the variables. However, the chi-square statistic has been noted to be sensitive to sample size. As such alternative fit indices including Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Standardized Root Mean Square Residual (SRMR), and Root Mean Square Error of Approximation (RMSEA) were used to examine the fit of the model. These indices showed that the measurement model fit the data fairly well (SRMR = .046; TLI = .965; CFI = .971; RMSEA = .038), per Awang (2012) recommendation. In addition, the modification indices did not suggest any major misspecifications in the model. The standardized regression weights provided estimates of the factor loadings of the items to their respective constructs in the measurement model. Hair et al. (2006) suggest "standardized loading estimates should be 0.5 or higher, and ideally 0.7 or higher" (p. 779). As shown in Table 4.2 above, the standardized factor loadings for the items ranged from .45 to .94. Of the 26 items, 21 had factors loadings ranging from .70 to .94; 3 items had factor loadings 102 University of Ghana http://ugspace.ug.edu.gh between .50 and .70. Only one item had a factor loading below 0.5. It is important to note, however, that the affected item loaded significantly on it respective latent construct. Taken together, the results indicate that the items loaded strongly and significantly on the respective constructs. Convergent validity and discriminant validity were assessed for each construct based on their respective average variance extracted (AVE) and the maximum shared variance (MSV) (Hair et al., 2010). The AVE reflects the amount of variance an observed variable (item) explains its underlying latent construct, an indication of the construct’s convergent validity. The MSV reflect maximum variance an observed variable explains in a construct other than its underlying latent construct. Hair et al. (2010) recommends that for a construct to demonstrate convergent validity, its AVE must be at least 0.50. The analysis showed that all the constructs in the present study, with the exception of Facilitating Condition, had AVE greater than the recommended 0.50. The AVE for Facilitating Condition was 0.381. However, given that the construct had adequate reliability it was retained in the model. Discriminant validity was assessed by comparing each construct’s AVE with its respective MSV (Fornell & Larcker, 1981). According to Hair et al. (2010) discriminant validity is supported when the AVE for a construct is greater than the variance it shares with other construct (i.e., MSV). As shown, in Table 4.2, the AVE for the constructs in the study were greater than their respective MSV, which demonstrates that the constructs had adequate discriminant validity. Construct reliability was estimated using coefficient alpha (i.e., Cronbach’s alpha). Generally, the constructs in the study had adequate levels of reliability. With the exception of Actual Use, which had a construct reliability of .68 (Table 4.2), the reliability estimates for the remaining constructs exceeded the conventional level of .70 suggested by Nunnally (1978). 103 University of Ghana http://ugspace.ug.edu.gh Table 4.4 Multivariate Normality of Observed Variables SE SE Variables Mean SD Skewness skewness Kurtosis Kurtosis Min. Max. Compatibility 3.61 .80 -.49 .14 .10 .29 1.00 5.00 Trialability 1.0 2.57 .29 .14 -.64 .29 1.00 5.00 2 Image 2.37 .84 -.03 .14 -.50 .29 1.00 4.67 Subjective norms 2.67 .93 -.10 .14 -.66 .29 1.00 5.00 Perceived usefulness 3.66 .91 -.84 .14 .71 .29 1.00 5.00 Perceived ease of 3.53 .80 -.77 .14 .85 .29 1.00 5.00 use Actual Use 1.5 3.60 -.26 .14 -.91 .29 1.00 6.50 7 The table 4.4 above shows that the Mean values range between 2.37-3.66. The highest mean was recorded by Perceived Usefulness M=3.66; Standard deviation-SD=.80; followed by Compatibility M=3.61, SD=.80; Actual Use M(Mean)=3.6 SD =1.57 and Perceived Ease of Use M=3.53, SD= .80. The lowest mean was recorded by Image M=2.37, SD=.84 followed by Trialability, M=2.57, SD=1.02. With the exception Actual Use, the above means were measured on a likert scale of 5-1 where 5 means strongly agree to the lowest, 1, strongly disagree. To check the normality of distribution, kurtosis and skewness were examined. The study maintain that skewness is considered as extreme skewness when its absolute score is greater than 3.0. All the scores in the table 2.4 above satisfied the standard values for the skewness: Compatibility-0.49, Trialability 0.29, Image -0.03, Subjective Norm-0.10, Perceived Usefulness-0.84, Perceived Ease of Use-0.77, -0.59, Actual Use -0.26. Also, kurtosis is seen as extreme kurtosis when its absolute value is between 8.0 and 20.0. As shown in table 4.4, the 104 University of Ghana http://ugspace.ug.edu.gh assumption of multivariate normality was also met with the following kurtosis: Compatibility 0.10, Trialability- 0.64, Image -0.50, Subjective Norm -0.60, Perceived Usefulness 0.71, Perceived Ease of Use 0.85, Facilitating Condition 0.28, Actual Use -0.91. 105 University of Ghana http://ugspace.ug.edu.gh Table 4.5. Correlations among study variables Variable 1 2 3 4 5 6 7 8 9 10 11 1 Age - 2 Sex –.04 - 3 Rank .19* –.02 - 4 Years of teaching .45** .05 .36** - 5 Compatibility –.02 .04 –.01 –.13* - 6 Trialability –.06 –.03 –.07 –.06 .17* - 7 Image –.09 –.05 –.03 –.09 .22** .24** - 8 Subjective norms –.05 –.08 .00 –.07 .19** .11 .46** - 9 Perceived usefulness .08 –.04 .06 .00 .62** .09 .24** .30** - 10 Perceived ease of use –.12* .01 –.08 –.17** .46** .24** .21** .16** .45** - 11 Facilitating conditions –.03 .04 –.03 –.04 .26** .11* .14** .20** .24** .41** - 12 Actual use –.06 –.04 .00 –.01 .30** .18* .18** .20** .35** .43** .21** *p < .05; **p <.005 106 University of Ghana http://ugspace.ug.edu.gh 4.1.4 Inter-correlation matrix The inter-correlation matrix for the variables in the study are presented in Table 4.5. Age (r = –.12, p < .05) and years of teaching (r = –.17, p < .05) had significant, albeit weak negative correlations with Perceived Ease of Use. There was also positive and significant correlation between Compatibility and Perceived Usefulness r= (.62, p<.05).; positive but correlation between Compatibility and Perceived Ease of Use, (r=.40, p <.05). Positive but weak correlation between Compatibility and Actual Use (r=.30, p <.05). The correlation between Image and Subjective Norm, Image and Perceived Usefulness, Image and Perceived Ease of Use were all positive but weak correlations: (r=.46, p<.05, r=.24, p <.05.; r=.21, p<.05); and (r=.18, p<.05) respectively. There was also positive but weak correlation between Subjective Norm and Perceived Usefulness (r=.30, p <.001).; Subjective Norm and Perceived Ease of Use (r=.16, p<.05).; Subjective Norm and Actual use (r=20, p<.05) respectively. There was positive and fairly weak correlation between Perceived Usefulness and Perceived Ease of Use (r=.45, p<.05). Perceived Usefulness and Actual Use also registered positive but weak correlation=(.35, p<.05). Perceived Ease of Use and Facilitating Condition also recorded (r=.41, p<.05). Perceived Ease of Use also positively and weakly correlates with Actual Use, (r=.43, p<.05). 4.1.5 Structural Equation Modelling Analysis The study’s hypotheses were tested by conducting latent variable structural equation modelling analysis using SEM with maximum likelihood estimation in IBM AMOS 21.0. The critical ratios for the standardized regression weights of each path were examined to ascertain which paths, if any, were not significant. The results of the SEM analysis are presented in Table 4.3 above. The 107 University of Ghana http://ugspace.ug.edu.gh chi-square goodness-of-fit test was significant (x2 (253) = 360.39, p < .05), which suggest a poor- fitting model. However, given the limitations of the chi-square statistic, alternative indices of model fit were also examined. The alternative fit indices, however, indicated that the hypothesized model fitted the data well (SRMR = .048; CFI = .971; TLI = .964; RMSEA = .040) as per Awang (2012) recommendation. The modification indices for the model also did not suggest any significant model misspecification. Thus, the hypothesized model was retained to test the study’s hypotheses. The hypothesized indirect effects (mediated relationships) were tested through recommended procedures in SEM. The traditional method of testing for mediation or indirect relationships has been to follow Baron & Kenny’s three-step procedure. However, the Baron and Kenny procedure is limited in that it only allows for the computation of one indirect relationship at a time. As such, a number of researchers have recommended the use of SEM in analysis of indirect relationships (Hayes, 2009). In the present study, the indirect effects were examined using the SEM test of significance of indirect effects in AMOS 21.0. The estimated indirect effects were cross-validated using bootstrap to compute the confidence intervals associated with each indirect effect. Following procedures suggested by Shrout & Bolger (2002), 2000 bootstrap samples were created to estimate the indirect effects of Compatibility, Subjective Norms, Image, Trialbility, and Facilitating Conditions on Actual Use with the bias-corrected percentile method. See the table 4.7 below for the bootstrapped estimates and Confidence interval for indirect effect . 108 University of Ghana http://ugspace.ug.edu.gh Table 4.6. SEM Analysis (Test of Hypotheses) Unstandardized Standard Critical Standardized Path p estimate Error Ratio Estimate Perceived Usefulness <--- Compatibility .69 .07 10.03 .62 <.001 Perceived Usefulness <--- Trialability -.07 .06 -1.33 -.07 .184 Perceived Usefulness <--- Image .03 .06 .44 .03 .661 Perceived Usefulness <--- Subjective Norms .15 .06 2.74 .17 .006 Perceived Usefulness <--- Facilitating Conditions .14 .10 1.46 .09 .144 Perceived Ease of Use <--- Compatibility .51 .07 6.87 .43 < .001 Perceived Ease of Use <--- Trialability .13 .06 1.99 .12 .047 Perceived Ease of Use <--- Image .05 .07 .77 .05 .443 Perceived Ease of Use <--- Subjective Norms -.03 .06 -.51 -.03 .608 Perceived Ease of Use <--- Facilitating Conditions .61 .14 4.41 .36 < .001 Actual Use <--- Perceived Usefulness .26 .15 1.74 .18 .082 Actual Use <--- Perceived Ease of Use .61 .14 4.25 .45 < .001 Actual Use <--- Compatibility -.04 .18 -.24 -.03 .81 Actual Use <--- Trialability .11 .11 1.01 .08 .31 Actual Use <--- Subjective Norms .13 .11 1.19 .10 .233 Actual Use <--- Facilitating Conditions .02 .22 .07 .01 .941 Actual Use <--- Image .01 .12 .06 .01 .951 109 University of Ghana http://ugspace.ug.edu.gh Figure 4.1 Observed Theoretical Model Notes: Only standardized path coefficients are presented in the model; Straight lines represent significant paths; broken lines represent non-significant paths 110 University of Ghana http://ugspace.ug.edu.gh Table 4.7. Bootstrapped estimates and Confidence interval for indirect effect Effects on actual use Direct Indirect 95% CI for indirect effect Total Compatibility –.03 .30* .16 to .53 .27 Subjective norms .10 .02 –.07 to .10 .11 Trialability .08 .04 –.03 to .12 .12 Image .01 .03 –.04 to .12 .04 Facilitating conditions .01 .18* .08 to .31 .18 Note. * p < .05 4.1.6 Tests of Hypotheses HI: Perceived Usefulness and Perceived Ease of Use would have positive relationships with Actual Use of Sakai/MOODLE LMS. In line with this prediction, results from the SEM analysis (Table 4.6) indicated that Perceived Ease of Use had a significant positive relationship with Actual Use (β = .45, p < .05). However, the relationship between Perceived Usefulness and Actual use was not significant (β = .18, p> .05). These results, therefore, provide partial support for Hypothesis 1. 111 University of Ghana http://ugspace.ug.edu.gh H2a: Compatibility has a positive relationship with Perceived Usefulness, Perceived Ease of Use and Actual Use of Sakai/MOODLE LMS. As shown in Table 4.6 above, Compatibility had a significant positive relationship with perceived usefulness (β = .62, p < .05). Compatibility was also positively and significantly related to Perceived Ease of Use of Sakai/ MOODLE LMS (β = .43, p < .05). However, the path from Compatibility to Actual Use was not significant (β = –.03, p > .05), indicating that Compatibility was not significantly related to actual use of Sakai/MOODLE LMS. Together, these results provided partial support for Hypothesis 2a. H2b: Compatibility would be indirectly related to Actual Use via Perceived Usefulness and Perceived Ease of Use. As shown in Table 4.7 above, the bootstrapped estimates for the indirect effect of Compatibility on Actual Use was significant (β = .30, p < .05). The bootstrapped confidence for the indirect effect of Compatibility (CI = .16 to .52) did not contain zero, indicating a significant indirect effect. Since the relationship between Perceived Usefulness and Actual use was not significant, as reported earlier, these results indicate that only Perceived Ease of Use mediated the relationship between Compatibility and Actual use of Sakai/MOODLE LMS. Therefore, the hypothesis that Perceived Usefulness and Perceived Ease of Use would mediate the relationship between Compatibility and Actual use of Sakai/MOODLE LMS was partially supported. H3a: Trialability has a positive relationship with Perceived Usefulness, Perceived Ease of Use and Actual Use of Sakai/MOODLE LMS. The results showed that the paths from Trialability to Perceived Usefulness (β = –.07, p > .05), and actual use (β = .08, p > .05) were not significant. However from Trialability to Perceived 112 University of Ghana http://ugspace.ug.edu.gh Ease of Use (β = .12, p < .05) was significant. Thus, Trialability was not significantly related to perceived usefulness and actual use but significantly related to perceived ease of use of Sakai/MOODLE LMS. Therefore, Hypothesis 3a had a limited support. H3b: Perceived Ease of Use and Perceived Usefulness will mediate Trialability and Actual Use Contrary to expectation, Trialability did not have an indirect relationship with Actual Use. As shown in Table 4.7 above, the bootstrapped estimate for the indirect effect of Trialability on Actual Use was not significant (β = .04, p < .05). The bootstrapped confidence interval for the indirect effect also contained zero (CI = –.03 to .12), a further indication of a non-significant effect. Therefore, Hypothesis 3b, which stated that Perceived Usefulness and Perceived Ease of use would mediate the relationship between Trialability and Actual Use of Sakai/MOODLE LMS (Hypothesis 3b), was not supported. H4a: Image would be positively related to Perceived Usefulness, Perceived Ease of Use and Actual Use of Sakai/MOODLE LMS. As shown in Table 4.6 above, Image was not significantly related to Perceived Ease of Use (β =.05, p > .05), Perceived Usefulness (β = .03, p > .05), and Actual Use of Sakai/MOODLE LMS (β = .01, p > .05). Overall, these results suggest that Hypothesis 4a was not supported. H4B: Perceived Usefulness and Perceived Ease of Use will mediate Image and Actual Use of Sakai/MOODLE LMS Table 4.7 shows that the bootstrapped estimate for the indirect effect of Image on Actual Use was not significant (β = .03, p > .05). The bootstrapped confidence interval associated with the indirect effect of Image ranged from –.04 to .12. Since the bootstrapped confidence interval contains zero, the result suggests a non-significant indirect effect. Therefore, Hypothesis 4b, which 113 University of Ghana http://ugspace.ug.edu.gh stated that Perceived Usefulness and Perceived Ease of Use would mediate the relationship between Image and Actual use of Sakai/MOODLE LMS, was not supported. H5a: Subjective Norms would be positively related to Perceived Usefulness, Perceived Ease of Use and Actual Use of Sakai/MOODLE LMS. In line with this hypothesis, Subjective Norm was found to be positively related to Perceived Usefulness (β = .17, p < .05). However, the paths from Subjective Norm to Perceived Ease of use (β = –.03, p > .05) and Actual Use (β = .10, p > .05) were not significant. This means that Subjective Norm was not significantly related to Perceived Ease of Use or Actual use of Sakai/MOODLE LMS. Therefore, Hypothesis 5a received limited support. H5b: Subjective Norms would be indirectly related to actual use of Sakai/MOODLE LMS via perceived usefulness and perceived ease of use. The results from the analysis of indirect effects (table 2.7 above) failed to support this hypothesis. The bootstrapped estimate (β = .02, p > .05) for the indirect effect of Image and Actual Use and its associated confidence interval (CL = –.07 to .10) were indicative of a non-significant indirect effect. Thus, Perceived Usefulness and Perceived Ease of Use did not mediate the relationship between Image and Actual Use of Sakai/MOODLE LMS. Hypothesis 5b was not supported. H6a: Facilitating Condition has a positive relationship with Perceived Usefulness, Perceived Ease of Use and Actual Use of Sakai/MOODLE LMS. As expected, the path from Facilitating Condition to Perceived Ease of Use was positive and significant (β = .36, p = .05). Thus, Facilitating Condition was positively related to Perceived Ease of Use of Sakai/MOODLE LMS. However, the paths from Facilitating Condition to 114 University of Ghana http://ugspace.ug.edu.gh Perceived Usefulness (β = .09, p > .05) and Actual Use (β = .01, p > .05) were not significant. Thus, Facilitating Condition was not significantly related to Perceived Usefulness and Actual Use of Sakai/MOODLE LMS. Together, these results provided limited support to Hypothesis 6a. H6b: Facilitating Conditions has indirect relationship with Actual Use via Perceived Ease of Use and Perceived Usefulness. As shown in Table 4.7 above, the bootstrapped estimate for the indirect effect of Facilitating Conditions and Actual use was significant (β = .18, p < .05). The bootstrapped confidence interval associated with this indirect effect ranged from .08 to .31. Since this confidence interval did not contain zero, the result is indicative of a significant indirect effect. As stated previously, the path from Perceived Usefulness to Actual Use was not significant. As such, the results indicate that the link between Facilitating Conditions and Actual Use was mediated by Perceived Ease of Use. Therefore, the hypothesis that Perceived Usefulness and Perceived Ease of Use would mediate the relationship between Facilitating Conditions and Actual Use of Sakai/MOODLE LMS (Hypothesis 6b) was partially supported. Key findings of the quantitative study 1. The fit indices suggest that there is a good fit between the measurement model and the data: (χ2 = 383.23, df = 271, p < .05; SRMR = .046; TLI = .965; CFI = .971; RMSEA = .038. 2. Convergent validity and discriminant validity were attained. 3. The SEM results showed that the hypothesized model fitted the data well: (χ2 = 360.39, df = 253, p < .05; SRMR = .048; CFI = .971; TLI = .964; RMSEA = .040. 115 University of Ghana http://ugspace.ug.edu.gh 4. The test of hypotheses revealed that Perceived Ease of Use had a positive and significant relationship with Actual Use (β = .45, p < .05); 5. Compatibility had a significant positive relationship with Perceived Usefulness (β = .62, p < .05) as well as Perceived Ease of Use (β = .43, p < .05). 6. Compatibility indirectly exerted significant influence on Actual Use through Perceived Ease of Use ( β=.30, p < .05). 7. Trialability was found to be positively and significantly related to Perceived Ease of Use (β = .12, p < .05). 8. Subjective Norm was positively and significantly related to Perceived Usefulness (β = .17, p < .05). 9. Facilitating Conditions exerted significant influence on Perceived Ease of Use (β = .36, p = .05). 10. Facilitating conditions does not necessarily lead to technology acceptance, adoption or use of an Open Source LMS. Technology is available for teaching but its acceptance and usage depends on many factors including knowledge, skill and positive mindset of the lecturer. 11. Quantitative study might not be able to find adequate proof of a statistically significant relationship between variables in a cultural context where educational technology adoption and use is slow. Key findings from the hypotheses H1: Perceived Usefulness and Perceived Ease of Use would have positive relationships with Actual Use of Sakai/MOODLE LMS (Partially Supported) H2a: Compatibility would have a positive relationship with Perceived Usefulness, Perceived Ease of Use and Actual Use of Sakai/MOODLE LMS (Partially Supported) 116 University of Ghana http://ugspace.ug.edu.gh H5a: Subjective Norms would be positively related to Perceived Usefulness, Perceived Ease of Use and Actual Use of Sakai/MOODLE LMS (Limited Support) H6b: Facilitating Conditions would be indirectly related to Actual Use of Sakai/MOODLE LMS via Perceived Usefulness and Perceived Ease of Use (Partially Supported). Trialability will be positively and significantly related to Perceived Usefulness, Perceived Ease of Use and Actual Use of Sakai/MOODLE LMS (Not supported) Image will be positively and significantly related to Perceived Usefulness, Perceived Ease of Use and Actual Use of Sakai/MOODLE LMS (Not supported) 4.1.7 Summary of Quantitative Results The chapter looked at the quantitative results analysis. Confirmatory factor analysis was employed to examine the validity and the reliability of the measurement scale. Convergent and discriminant validity were adequate. The consistency and the stability of the constructs as revealed by the reliability test were favourable. Normality test results were within range. Inter-correlation matrix revealed some variables that were positively correlated although weak. Other variables were positive and significant. SEM was carried out to examine the measurement and hypothesized structural models. The model eventually fitted the data. The test of hypotheses revealed some paths that were significant and others that were not. As a result, some hypotheses were supported and others were not supported The next section of this chapter looks at the qualitative results of the study. The intention was to examine how the qualitative findings corroborates the quantitative results. 117 University of Ghana http://ugspace.ug.edu.gh 4.2 Presentation of Results for Qualitative Study 4.2.1 Overview of Qualitative Results There were seven (7) major themes that emerged from the study. These have been organised under Users of the Open Source LMS as well as Non-users. The following were the themes (and their subthemes): (1) Factors influencing adoption and use (sub-themes: Utility of Use, Simplicity of Use, Prior Knowledge, Self-Efficacy, Institutionalisation and Enablers); (2) Uses of the system (sub-themes: Instructional Strategy, Assessment, Facilitating Academic Ethics); (3) Challenges (sub-themes: Inappropriate Faculty Attitude, Lack of Appropriate Software, Lack of Technical Know-how); (4) Enhancing the use of the system (sub-themes: Advisory, Interactivity, Blended Learning, Training, Communication, Assessment, Technical Support). For Non-users of the LMS- (5) Awareness about the System (sub-themes: Awareness, Teaching and Learning Platform, Research Project. (6), Reasons for Non-use (subthemes: Nature of Course, Infrastructural Challenges, Alternative Platforms, Platform Restrictiveness and Complexity); and (7) Recommendations for effective use (subthemes: Training, Fast Internet Connection, Tying the Use of LMS to Promotion). These themes are represented in a thematic map below (Figure 4.2 followed by an in-depth analysis of each. 118 University of Ghana http://ugspace.ug.edu.gh Figure 4.2. Diagrammatical representation of themes and sub-themes 119 University of Ghana http://ugspace.ug.edu.gh 4.2.1 Factors Influencing Use Utility of use (usefulness) There were several benefits associated with the use of Sakai/MOODLE LMS as indicated by the participants. These include: access to information, cost effectiveness; convenience; effective communication and time saving. In response to the above theme, one interviewee responded: The Sakai LMS is very useful. I can be at home and still teach my students. My students do not have to see me face-to-face before we can discuss academic issues. Additionally, less time is spent when using the Sakai LMS. Besides, one has access to a lot of information using the platform (Female, Participant 1). The superlative description of the usefulness of the Sakai is deeply emphasised in the above voice. The participant lists practical usefulness of the Sakai LMS including, research purpose, communication and convenience. An informant was explicit on the notion of usefulness of the LMS thus: The LMS has a lot of uses which make teaching and learning very convenient. For example, I can upload my teaching materials online. Therefore, coming to the classroom to dictate notes is avoided. I can also organise test and quizzes on the platform and the student could respond through the same platform. Thus the burden, cost and the risk of driving to campus to look for a classroom to arrange the place for the exams are eliminated. The financial cost incurred by students in making photocopies of lecture notes is also avoided (Female, participant 2). Three other critical usefulness of the LMS raised by the above participant are cost savings, for teaching, conducting quizzes and risk control. These benefits/usefulness of the LMS are drivers for adopting and using the LMS. 120 University of Ghana http://ugspace.ug.edu.gh A participant also commented on several usefulness of the LMS including stimulating intelligence, enhancing creative and critical thinking and access to wide audience (learners). This could be seen from the narrative below: The use of the MOODLE LMS has a lot of benefits. When the discussion forum is used appropriately, it builds critical thinking of the learner. When the lecturer poses a good question, it could solicit very good responses from the learners. We see students arguing strongly and commenting intelligently on others’ work on the platform. This makes students think outside the box and they are able to bring out their endowed ingenuity and originality of thought to respond to practical issues. Rote memory and passivity which are features of the traditional face-to-face classroom-based teaching and learning is eliminated. Besides, there is no distance barrier and enrolment is boosted (Female, participant 8) The pedagogical strategy that informs how to teach online taps deeper into the learners’ skills and knowledge potential. Besides, the LMS allows the institution to economize, to have more enrolment without considering space or physical classroom infrastructure. Therefore, if a learner is connected appropriately to the Internet, he/she can still learn, work and do other things irrespective of the learner’s geographical location. Students do not need to commute to campus as if it is with campus-based classroom teaching and learning. Based on the pedagogical strategy that inform teaching and learning, it helps the lecturer to understand the learners well; it also helps the lecturer to learn and see the ingenuity of the learners and it creates room for managing learning and working. In all, the LMS enhances communication between students, and between students and lecturers. It is convenient since lecturers can be at any place, anytime and engage in teaching, learning and other activities. For example, a lecturer could organise test and quizzes online and 121 University of Ghana http://ugspace.ug.edu.gh students could also respond online; teaching materials could be put online; the cost incurred for making photocopies of lecture notes in the case of face-to-face mode and the risk of driving to campus are all avoided. Using the forum platform to teach could stimulate intelligence, creative and critical thinking in learners. Lecturers can access a wide audience when online and institutions can benefit as the use of LMS ensures increased enrolment. The benefits raised above influence the adoption and use of a LMS. Simplicity of use (Ease of use) Some participants maintained that a factor which influenced their choice for the LMS was its simplicity (ease of use). In response to the above theme, a respondent commented: I easily understand the features of the LMS. Adopting and using the LMS was not difficult for me at all. I feel comfortable using it due to its ease of use. I see myself as an early adopter and user of Sakai and I will continue to use it to teach my students (Female, Participant 4). The participant whose narrative is found above has a good understanding of the Open Source LMS thus making the adoption and use very simple. This may result from personal learning or training in ICT skills provided by the University and attitudes and beliefs regarding technology adoption and use. The simplicity of use has induced some interviewees to smoothly migrate to the adoption and use of the Sakai LMS. The narration below illustrates this: I have adopted the use of the Sakai LMS because it makes my teaching work simple. It was easy for me to use the Sakai LMS because I had already acquired the skills in computers by personal learning for at least sixteen years. During my 122 University of Ghana http://ugspace.ug.edu.gh master’s programme, I used a computer to do all my academic activities. Additionally, I find it easy to use the LMS because I have used similar LMS several years earlier (Male, participant 5). Prior knowledge Some participants hold the view that prior knowledge and skills in the use of educational technology has influenced their adoption and use of the LMS. The narration below shows it: I have acquired knowledge in ICT, such as Excel, Microsoft word, Power Point presentation etc. I send students copy of the slides via their e-mail or give them the hard copy for them to make copies. This takes away the burden of dictating notes to the students. I use the internet for research, e-mails and Microsoft word to save my documents. Transiting to the use of the Sakai LMS was then simple for me. I have also noticed that lack of prior experience in ICT use makes it difficult for many faculty members to easily adopt and use the Sakai platform (Male, Participant 6). Knowledge in the use of ICT in general has direct and positive effect on the adoption and use of LMS for teaching. Besides, ICT use in education is quite a new development in Africa so people find it difficult to adjust to its use. Patience is then needed to roll them in. A respondent also shared a similar perspective: I had my education in the US where I was introduced to the use of the Blackboard LMS which was mandatory for use by every lecturer who was on the online education programme. No lecturer was permitted to go to the classroom with any handout or lecture notes or to make announcement in the class room. Everyone of these activities must be done on the LMS platform. Owing to this, I made effort to learn it well and found it to be very helpful and 123 University of Ghana http://ugspace.ug.edu.gh useful. As a result, shifting to the use of Sakai LMS was very simple to me (Male, Participants 12). On the same theme, a participant also stated: I am a professional ICT person with a good knowledge in Microsoft word, Internet orientation and corporate governance. I have been teaching Emerging Technology in Education- wiki, podcast, blog, book man – web 2 & 3 sensors since 1997. I have also had knowledge in KEWL LMS which was once used by the University of Ghana. As a result, transiting to use the Sakai LMS was smooth for me (Male, Participant 6). The above narration indicates that the respondent has good prior knowledge of an Open Source LMS so shifting to another type of LMS like Sakai/MOODLE was very smooth. Computer self-efficacy This is explained as the judgment (level of confidence or belief) of one’s capability to use computer technology to perform various tasks. One’s perceived confidence and competence to use ICT influenced the adoption and use of Open Source LMS. The remarks below show a response to the above theme. I believe ICT deployment in education has come to stay so I must also use it. Therefore, I have decided to prepare myself by using educational technology. My interest and ability to continue to use the platform is also very high (Male, Participant 8). The quotation above shows that technological advancement and globalization have created the necessity for technological adoption and use. What that means is that all must use it in order to cope with the demands of the digital trends. 124 University of Ghana http://ugspace.ug.edu.gh Another participant shares a similar position in details but points out that the adoption and use must be gradual. The narration below demonstrates it: Whether we like it or not, irrespective of one’s perception, technology has come to stay and must be used. In my view, when technology (LMS), with its multimedia features, for example, video and audio features, is adopted and used well, a lot could be done. Traditional classroom- based education cannot compare with the ICT-mediated teaching and learning at all, although the traditional education has its face-to-face elements which has a good psychological impact on the learner (Male, Participant 9). The voice above demonstrates that the key factor for the use of the technology is confidence/conviction. However, it is appropriate that we go hybrid, although it should be a gradual approach in order to enable people to develop the interest. Institutionalization Some participants pointed out that they have been made to use Sakai/MOODLE because their school/faculty has made it compulsory for use (Mandatory technology use environment) An interviewee elaborated: It is University Management requirement (Mandatory) that every lecturer uses the LMS. Refusal to use it means one does not want to work anymore with the University. I am using it because it is the University’s requirement (Male, Participants 6). 125 University of Ghana http://ugspace.ug.edu.gh This quotation above implies that one of the ways to persuade everyone to adopt and use educational technology for teaching and student’s learning is to make it mandatory, that is, one’s employment and renewal of contracts must be tied to the use of the LMS. Another participant stated: I have decided to use Sakai LMS because it is mandatory for faculty members to use it for the Distance Education programme. This means, refusal to use the Sakai means one should count him/herself out of work. (Male, Participant 7). This narration confirms the adoption and use of the Open Source LMS due to an institutional mandate. Enablers The participants also indicated that availability of technical support, training, availability of power, fast internet connectivity, promotion etc herein referred to as enablers, influence the adoption and use of the LMS. In relation to the above theme a participant remarked: The technical people are always ready to offer their technical support anytime they are needed. The University also runs several training programs for its faculty members and I always participate in them. I also went through the “help tool” on the platform to guide me learn more about the features of the Sakai platform. This facility makes the use of the LMS enjoyable and motivating (Male, Participant 4) The participant above makes it clear that the availability of institutional training and technical staff support made her easily become conversant with the use of the LMS 126 University of Ghana http://ugspace.ug.edu.gh A participant also stated: There is a provision in the University that when a faculty member uploads a course on the MOODLE platform, it counts towards one’s promotion. This has impacted on my use of the MOODLE LMS (Male, Participant 10). From the narrative above, it seems that one of the easiest ways to persuade lecturers to use the Open Source LMS is to make it a stringent policy. However, it will be expected that all the enabling conditions (e.g. Technical support) are made available by the University. The practical factors that influence lecturers’ adoption and use of Open Source LMS have been used to develop a conceptual framework designated ‘Open Source LMS Adoption and Use Conceptual Framework’ as shown below (figure 4.3 below). Hypotheses could be developed from this model and tested in future studies. 127 University of Ghana http://ugspace.ug.edu.gh Figure 4.3: Open Source LMS Adoption and Use Conceptual model Additionally, since the two of the factors within the model, utility and simplicity could be repositioned and treated as intervening variables, mediating between the independent variables and the effect variable (Actual Use), the model could be reformulated and named Open Source LMS Usage Conceptual Model (see figure 4.4). It is also recommended that hypotheses be developed from it and tested in future quantitative study. 128 University of Ghana http://ugspace.ug.edu.gh Figure 4.4. Open source LMS Usage Conceptual Framework 4.2.2 Uses of the system LMSs have been acquired by universities to enhance teaching and learning. While a number of lecturers have been using them in one form or the other, there are others who do not use them in any form whatsoever. In responding to the interview questions, the themes that came up from 129 University of Ghana http://ugspace.ug.edu.gh participants who use it were: Instructional strategy, Students’ assessment, Facilitating Academic ethics and Technology challenges. Instructional strategy Several participants indicated that they use the Sakai/MOODLE as an instructional delivery tool. A respondent narrated thus: I help students to load their profile online, introduce them to the MOODLE platform, load my course syllabus (both the weekly and semester ones) in the resource tool. Also, I use the resource tools to provide learning materials, assignment tool for assignment, discussion forum for teaching. Mostly, I apply images (pictures) by using videos for the teaching and student learning (Male, Participant, 8). The mere availability of the LMS is not enough unless it is effectively deployed for teaching and students’ learning. The lecturer was able to use several tools on the LMS for teaching thus demonstrating technological and pedagogical competence in his professional field. On the same theme under review: a respondent also remarked: I have been using the MOODLE for first and second year students, but not for the final year students whose lessons are project-based. My course materials are put online. The forum is used where questions are posed and students discuss them. I also use videos too (Female, Participant 9). The forum could enhance critical thinking and collaborative learning. However, if the discussion on the forum is not moderated by the lecturer or the lecturer is not able to control what students 130 University of Ghana http://ugspace.ug.edu.gh say at the forum then several irrelevant issues may be brought on board which can distort the flow of thought. A participant remarked in the affirmative thus: I place my lecture notes in the resource tools online. Videos are embedded for use. Assignments put in the assignment tool are given to them every week. Responses are submitted through the drop box or by email attachment (Female, Participant 1). The participant’s voice above demonstrates a good proficiency in managing pedagogy on technology. The effective blend of the face-to-face teaching and learning and the online mode is commendable. A participant also commented: I use hybrid, asynchronous forum discussion for debate. I put audio / video resources online together with the textual material including links for accessing e-books for the students. The use of e-mails, social media (Youtube, twitter, face book) are also brought on board although from the end user point. Collaborative projects are also used (Male, Participant 10). The system provides a sort of one-stop shop for several technological tools and features which enhance access to academic materials for students’ learning. The underlying essence is convenience and flexibility which is protected by law. We also see a demonstration of online instructional delivery skills by the lecturer. For blended and online learning to be effective in Africa, we expect that lecturers are conversant with how to use the educational technology for teaching and learning. 131 University of Ghana http://ugspace.ug.edu.gh Assessment Assessment is the way by which students are examined in order to report on their performance. A particular tool on the platform namely test and quizzes tool is used to test them on multiple choice or True or False type of questions. On the issue of assessment, participants elaborated: In an online assessment where security is not so strong, I use the self-assessment tool, the quizzes and the discussion forum. The self-assessment is where the student assesses his or her own progress. For formative assessment, it is difficult to track the identities of the students. I teach for instance, 3 topics and give quizzes, and students respond through the MOODLE too; and their answers are also auto graded. For the discussion forum, 2 – 3 times is ideal at the graduate level. For the formative where the blending comes in, I go and administer the questions to the students in a classroom for them to respond using the MOODLE LMS. It is important to state that from the beginning, I set the road map clear for the students concerning how they would be assessed in reference to the university’s policies – ie 60 percent in class exams and 40 percent online exams for a blended learning (Female, Participant 11). The above narrative demonstrates a certain level of technological competence of a lecturer who uses the Open Source LMS. This is expressed in the extensive use of the pronoun ‘I’ for about four times, occurring in tandem with something he/she has to do. It is interesting to deduce her sense of active engagement in the teaching and learning transaction from the narrative. Another lecturer remarked: The grade book, quizzes and forum questions are put online with associated date for the students to be tested. Assessment and evaluation tools were also used (Male, Participant 14). 132 University of Ghana http://ugspace.ug.edu.gh Facilitating academic Ethics Ethics has to do with providing the guidelines and the motivation that promote appropriate behaviour and discourage improper behaviour in the teaching and learning transaction. On the issue of ethics, several relevant issues also came up as shown by a lecturer’s remark below: The course syllabus put in the resource tool (online) has a section that outlines the ethical and moral behaviourial tenets needed to be observed during the teaching and learning activities. Students are informed about this at the announcements section. Deadlines for assignments are provided and put in the announcement tool (online) for students’ awareness. Besides, the deadline is rigidly fixed such that students who do not submit within the deadline are blocked from submitting it late (Male, Participant 3). The participant above set the pace from the onset, the ethical issues that students need to observe, and he also makes the effort to instil discipline and time consciousness in the students in terms of prompt delivery of assignment. A participant also remarked: It is also announced for students’ information that all academic resources that are used for their assignments must be rigidly acknowledged. Therefore, advice is given to students using online material to give credit to the sources. One is held responsible if one breaches this simple but major rule of referencing. As a lecturer, all relevant academic material given to students which are from other sources such as audio and video materials are all acknowledged before putting them online. Documentary on work ethics using video tools on the LMS are played for students to learn more about ethics (Male, Participant 14). 133 University of Ghana http://ugspace.ug.edu.gh The announcement and video tools are used to advise students and to show films on ethical issues in order for students to have knowledge in real life situation and to avoid the consequences of breaching ethical issues. Another lecturer elaborated on ethics: Copyright laws and policies determining what constitutes plagiarism and the requisite sanctions associated with it are put online in the resource tool for the students to access them. The Turnitin software was used to check possible plagiarism; that is, using someone else’s information and presenting it as though you are the original owner. Ethical words were emphasized when contributing to issues discussed at the forum platform (Female, Participant 2). The participant used the resource tool and the Turnitin embedded to the Sakai LMS and forum platform to check and control behaviours relating to academic ethics. Figure 4.5 below is a diagrammatic representation of the uses of Open Source LMS among University lecturers in Ghana. We see from the figure above that the key uses of the LMS is for teaching, conducting students’ assessment and checking ethical issues. For example, under teaching, the forum and the windows movie maker tools could be used to teach the students. Under assessment, the test and quizzes tool Under ethics, the turnitin software embedded to the Sakai plat form could be used Figure 2.5 Open Source LMS Uses 134 Figure 4.5. Open Source Learning Management System Uses University of Ghana http://ugspace.ug.edu.gh We now have a software Turnitin embedded in the Sakai LMS that could globally check students’ assignments and thesis against plagiarism within no much time. Previously, no University had one in Ghana and therefore it was difficult to check the originality of students’ assignments and thesis. The Figure 4.6 labelled ‘Educational Technology Relevance in Academia Conceptual Framework below summarises the uses of the LMS. We see from the diagram that technology is the disc, the centre that spins the design of instructional strategy, conducting assessment and managing academic ethics online. The educational technology links instructional strategy and assessment (A and B); instructional strategy and ethics (A and C) and assessment and ethics (B and C); and finally links instructional strategy, assessment and ethics together. Currently, there is no mode of education that could replace or substitute the use of educational technology for teaching, assessing students and managing academic ethics. There is also no mode of education that could replace the usefulness of applying education technology to teach, conduct assessment and manage academic ethics in the future. Technological advancement will keep growing at an increasing rate while the use of manual effort will keep declining overtime. Therefore, examining the factors that influence lecturers’ use of an Open Source LMS in order to determine a university policy that motivate all lecturers to get on board for the use of educational technology is a very good effort. 135 University of Ghana http://ugspace.ug.edu.gh Figure 4.6: Educational Technology relevance model 4.2.3 Challenges associated with the use of technology Participants also showed clearly that despite the uses associated with LMS, such as for delivering instruction, students’ assessment and facilitating academic ethics, there are also a number of challenges associated with the use the LMS that must be addressed if blended learning will be boosted in Ghana and Africa in general. The challenges could be grouped in two forms: 1. Human dimension: e.g. faculty attitudes and 2) technological deficiency challenge such as lack of appropriate software, power outages, slow Internet connectivity and lack technology know-how. 136 University of Ghana http://ugspace.ug.edu.gh Faculty attitude Faculty attitude is about faculty mind-set that influence their behavioural decision to adopt and use the Open Source LMS or not. A participant elaborated on the above theme: The first observation is about faculty members’ attitude to the use of the LMS. There are still laggards and late adopters of LMS in the University due to apathetic attitude, lack of skills and demotivation from the University. Faculty members are drivers of the technology-based teaching and learning. Therefore, if they do not embrace it, then students cannot enjoy ICT – based learning. (Male, Participant, 12). Although, there are still many laggards, effective training, motivation and provision of enabling environment can facilitate quick adoption and use of the LMS by lecturers. Technological challenge This has to do with issues about technological deficiency. A participant elaborated: Video conference draws a lot of data, therefore if there is Internet connection problem, it poses a challenge. Besides, there is a problem distributing the data due to network inhibition. I also face a problem linking the LMS to the assessment management system (OSIS). To manage this, I capture all the exams scores on Excel format then export from MOODLE to the OSIS. In terms of assessment, if the student opens a quiz on two different browsers at the same time, the student can cheat because the MOODLE instantly gives the correct answers after one submits the answers. The students using different browser then checks the correct answers and uses the second browser to upload the correct answers. Students do this quickly within 30 minutes. To control this, I use a tool embedded on the MOODLE which accepts only the first submission. Thus 137 University of Ghana http://ugspace.ug.edu.gh quizzes are submitted at first attempt to prevent student from cheating. Several lecturers teaching blended learning without this knowledge, may be carried away by smart students (Male, Participant 13). Issues about technology: internet access, technical support, ability to customise the platform are very critical for effective E-learning education. Lack of appropriate software We have many software that aid teaching and learning. Lack of an appropriate type will pose a challenge. A participant reported on the above theme as follow: I teach Introduction to ICT, Social Literacy in Education, Social Communication and Report Writing. The special education students like the virtually impaired, need a software called Jaws but this is not provided by the School. I have 10 students who have a challenge with their sight and they always feel neglected. The hearing impaired students also do not have Interpreter films that should be embedded on the MOODLE LMS to help them understand what is being taught online. Therefore, the students enrolled in online learning are not doing well (Male, Participant 11). The above narrative suggests that there is a clear problem regarding the use of appropriate technology to teach. Lack of technology know-how and other technology problems This is about technological literacy or proficiency that could easily drive technology adoption and use. On the theme above a participant remarked: The challenge is that many of the distance education students are not computer literate, so several of them are not able to submit assignment into the Sakai 138 University of Ghana http://ugspace.ug.edu.gh platform. On one occasion out of 438 students, only 237 of them were able to submit the assignment into the platform. Deadlines are also abused sometimes due to poor Internet connectivity or power problem. Sometimes, Turnitin was not functioning to check plagiarism. There was also intermittent power fluctuation while there is lack of a power generator for replacement. (Male, Participant 7). If the students are not properly trained in the use of the LMS, then it does not matter how conversant lecturers become with the use it, the whole vision of e-learning education using an Open Source LMS will be a failure in Ghana. 4.2.4 Enhancing use The use of the Sakai/MOODLE LMS in Ghana is about 6 to 7 years old; very recent. The features are still under developmental process. Participants were therefore asked to suggest ways to upgrade the features in order to ensure effective use of the LMS. In response to the theme above, several suggestions came up and have been organised into 5 sub-themes as follow: Advisory, interactivity, blended learning, training, communication and assessment. Advisory Some of the participants hold the view that the Sakai/MOODLE platform could be upgraded to enable them play their course advisory role very well. Regarding the above theme, a participant stated: My suggestion to improve the Sakai platform is based on the year group. For instance, for the first year students, I want to see an online office hour section with a video interface on the Sakai LMS. This will allow multiple uses so that student will not have to visit lecturers’ offices for course advice (Female, Participant 1). 139 University of Ghana http://ugspace.ug.edu.gh This suggestion is laudable due to the huge students’ numbers and the small office space of lecturers. Besides, it will help conserve time and energy since with the use of the video conference, many students will have access to the lecturer visually, advising them in general. Specific and more serious cases will then be addressed on one-on-one in the lecturer’s office. If the University may have to acquire a video conference facility with a wide coverage, it will entail huge cost. Interactivity One of the key functions of a LMS is to facilitate interaction among students, and between students and lecturers. Relating to the above theme, a participant remarked: When you have more students group in one Sakai platform class, it creates problem in interaction since there is no way to distinguish the various groups. There should therefore be a way on the Sakai platform tool to segment these groups for specific lessons, and also be able to access the merged class too if the need arises (Male, Participant 3) Blended learning By blended learning, there is merger of face-to-face classroom-based teaching and learning and online teaching and learning. Relating to the above theme, a participant reported: For graduate students, the need for some face-to-face meeting is still very important. In order to make them become graduate learners, they need to do some PowerPoint presentations. A full use of educational technology will be too rigid for them. They need to learn how to upload materials, do PowerPoint presentations using videos, images or sound. Pure online education does not allow this (Female, Participant 1). 140 University of Ghana http://ugspace.ug.edu.gh The above quote highlights blended learning for both undergraduate and graduate education with emphasis on graduate education. This makes sense because there are advantages associated with meeting face-to-face with students which can never be replaced by the pure online education. Training This has to do with equipping the faculty members with the requisite technology know- how and behaviour required to use technology effectively and efficiently. A participant remarked: Training programme must be organised to benefit all participants irrespective of their levels of knowledge in the LMS. That is, training must be organised based on individual needs; and not to run omnibus training which can cause the slow adopters of the LMS to lag behind or delay those at advanced stage due to involving them in basic issues they know long time ago. Additionally, I expect that faculty members be made aware of the LMS features and how to use them to deliver the course to students without difficulty. For example, how to use notes, use types of video, download or upload resources (Male, Participant 5). The use of educational technology is in its infantry stage in Ghana. If users are suggesting ways to improve the features of the Open Source LMS in order to improve teaching and students’ learning, then that must be given serious attention by the university authorities. What the interviewee is suggesting is an instructional designer who is able to help lecturers to use the technology and courseware to teach the students well. Communication The LMS is meant to facilitate a fast flow of information between students and between students and faculty members. On communication, one interviewee illustrated: 141 University of Ghana http://ugspace.ug.edu.gh All LMS platform is not perfect. Sakai LMS as an Open Source LMS is already going through several changes; new features are being added. University of Ghana is looking at how to adjust its use to meet all its needs. More functions are being considered to be added. SMS could be added to Sakai features so that students are given instant alert on the cell phone concerning any announcement or assignment instead of going on the platform before seeing this announcement as it pertains now. Google apps must be underway to add to Sakai LMS. E-mails, calendar are more effective and detailed on the Sakai platform (Male, Participant 7). SMS alert and Google app have been suggested to upgrade the features of the Sakai platform. This shows clearly that if the lecturers are involved in the management of the Sakai/MOODLE platform, more improvement of its use will be realised. Assessment structure The theme above implies how the students’ exams scores must be structured A participant reported: Online courses must constitute 70% while 30% marks allotted face-to-face” (Male, Participant 10). Technical and technological enablers These are technical facilities that enhance the use of the LMS. There must be regular update of anti-virus software to enhance longevity and quality work of ipads, Tablets and other PCs used by lecturers and students. Technical support must be forthcoming to encourage the use of the LMS. Internet connectivity and power supply must not be a problem (Male, Participant 6). These issues raised above are very important for using LMS for teaching and learning. Therefore, attention must be given to them. 142 University of Ghana http://ugspace.ug.edu.gh The analysis further examined the knowledge, reasons and recommendations for use from those participants who were not using the system. 4.2.5 Awareness about the system Since the Saki/MOODLE platform has been acquired by the respective universities and there is an ongoing training programme for lecturers, it is expected that lecturers will cooperate accordingly. Incidentally, that is not what is happening on the ground. A number of the lecturers are not using the Open Source LMS for teaching. The theme above is meant to find out what lecturers know about Sakai/MOODLE LMS. In response to this theme, several sub-themes emerged as follows: awareness, teaching and learning platform, research project. Awareness and attitude The above theme aims to find out if the participants have been informed about the availability of the LMS. In responding to the above theme, a participant remarked: I am aware that the University is on Sakai LMS. However, I am not eager to learn how to use it because it is difficult to blend online teaching with the face – to-face classroom-based teaching and learning (Female, Participant 18). The participant mentions that he is aware of the system but it is difficult to go hybrid, blend the face- to-face with the online mode. Another participant also elaborated: “I am not aware of MOODLE LMS, although I have worked here in this University for 2 years” (Male, Participant 16). 143 University of Ghana http://ugspace.ug.edu.gh It appears there is low publicity; low awareness of the presence of MOODLE LMS meant for teaching and learning. Teaching and learning platform Regarding the theme above, respondents made the following statement: I know Sakai is an LMS. Although, I am not using it now, I intend to start using it next semester (Male, participant 19). Yes, I know that MOODLE is an LMS. It is used for teaching and learning but I do not use it (Female, Participant 17). Research project Regarding the above theme, a participant commented: I think the Sakai is a particular professor’s research project in its pilot stage. I will be glad if I know that professor to work under him (Male, participant 20). 4.2.6 Reasons for Non-use Several lecturers are not using the MOODLE/Sakai platform for teaching or in any form whatsoever. These lecturers include those who have had training for its use and those who have no training at all. The researcher thought that as one looks at the factors that influence lecturers’ adoption and use of Open source LMS, it is equally important to find out the factors accounting for the reasons why some lecturers are not using the Open Source LMS at all. 6 lecturers from both University of Education and University of Ghana who do not use it at all were interviewed. The factors that emerged indicating the reason why they do not use the LMS include: Nature of course, 144 University of Ghana http://ugspace.ug.edu.gh infrastructural and technical challenges, alternative platform, platform restrictiveness and complexity of use. Nature of course There are some courses that need special software or features on the LMS before they could be taught online. These include: French, Mathematics and some courses for Special Education students. Regarding the theme above, a participant remarked: I teach Mathematics, and it is difficult to use the MOODLE LMS because several symbols used in Mathematics are not captured on the MOODLE platform. As a result of this, assignments, marking and grading are done manually (Male, Participant,15). University authorities must be aware of this to take proactive measures towards that. Another participant narrated thus: I teach Special Education students. Those who are hearing impaired lack a good interpreter when teaching online. As a result of this I do not use the MOODLE to teach (Male, Participant,16). This response is similar to the previous narrative. They express technical challenge hindering their use of LMS to teach. A similar response given by another participant is found below: I teach French language. This cannot be done online. The French letters and punctuation marks are not embedded in the Sakai LMS. Besides, most of the students are not French natives and therefore, teaching them through face-to- face approach is far better than making strides to teach them online (Female, Participant,17). 145 University of Ghana http://ugspace.ug.edu.gh Again, the reason why the MOODLE is not used is because it does not have the symbols of French language embedded in it. If Language lecturers must get on board in the use of the Sakai/MOODLE then their challenge must be sorted out. Infrastructural and technical deficiency The issue of power fluctuation and lack of backup, low bandwidth and inadequate technical support also affected the use of the platform. The narrative below shows it: Power fluctuation is a problem. In the middle of your presentation, power goes off. Generator needed is always broken down. I lost huge data on my pen drive due to power fluctuation. Downloading is also very slow. Technical support is also a problem. Some of the ICT – official need serious training (Female, Participant,18). These are critical comments that must be considered seriously by university authorities if the use of LMS for teaching and students’ learning will be boosted. Another participant elaborated: There are problems with internet connection and power fluctuation especially when away from campus (Male, Participant, 20). These challenges are not motivating to lecturers to adopt and use an LMS. Another comment from a participant: Although the University wants lecturers to use the MOODLE, it has not provided computer laptops or desktops for lecturers, so I do not use it (Female, Participant 17,). 146 University of Ghana http://ugspace.ug.edu.gh This is also another issue on the need for the university institution to provide the enabling conditions for lecturers to use the LMS. This is because if the LMS is not used, students cannot benefit from it. Alternative platform There are several available platforms that could be used to aid teaching and learning apart from the LMS. Some of these platforms are designed by the faculty members to aid their teaching. A participant commented: I do not use Sakai LMS because I have developed a platform similar to Sakai platform where my students and I send and exchange academic materials and communicate. I am comfortable with that (Female, Participant 18). This lecturer feels comfortable with a platform she has created for teaching and students’ learning and so does not see the need to use Sakai. I think that the awareness creation and training needed for lecturers to appreciate the LMS platform for effective uses is not adequate. More work has to be done. Platform restrictiveness Some of the participants believe that the Sakai/MOODLE LMS is too narrowed and a broader media could be used better than the Sakai/MOODLE LMS. I do not use Sakai because it is too restrictive and limited. It does not have features that could reach out to the global audience. I prefer to use new technologies such as Youtube, Facebook, and Twitter which can reach out to the global audiences (Male, Participant,19). 147 University of Ghana http://ugspace.ug.edu.gh The above response also seems interesting and must be encouraged for a holistic use of various technologies for teaching and learning in higher education. Complexity Complexity is how a person perceives the easiness by which a particular educational technology could be used for teaching. Regarding the above theme, a participant illustrated: Loading courses on the MOODLE platform is really a demanding task. Additionally, several students are not abreast of the use of the MOODLE LMS, although I am aware that training and tutorials have been made available to them (Male, Participant 15) These issues raised above are the reasons why the lecturer above is not using the MOODLE. Find below (Figure 4.7) which is a simple conceptual model built from the themes discussed above indicating factors hindering lecturers’ adoption and use of Sakai/MOODLE for teaching. The model is unique for three reasons: First, most of the models or frameworks on technology and education are on factors determining or influencing the adoption or acceptance or use of technology. This one is specifically looking at the reverse side: factors that hinder technology adoption or acceptance or use; thus, adding another perspective to educational technology use or non-use in the education research literature. Second, when a University Management is able to address those conditions causing the non-use of the LMS, it is more likely that the majority in this category will develop the interest to adopt and use the Open Source LMS. Third, it provides the information that lecturers are not just complaining and showing disinterest in the use of LMS but have reasons that need to be heard. 148 University of Ghana http://ugspace.ug.edu.gh Figure 4.7. Non-Use of Open Source LMS 4.2.7 Recommendation for effective use The theme above addresses what non-users of the LMS hope could be put in place to influence their use of the system. The following sub-themes came up: Regular training for lecturers and students, swift internet connectivity and tying the use of the platform to promotion. Regular training for both lecturers and students An introduction of a LMS among other things must go with training of the users such as the lecturers and the students if it will be sustainable. This is reflected in the narratives below: 149 University of Ghana http://ugspace.ug.edu.gh Lecturers and students are pivotal in the LMS environment. There must be regular training for both parties. A shortfall of any or both sides will lead to a failure of the management objective of embracing e-learning (Male, Participant 15). Fast internet connectivity The success of the e-learning programme is largely dependent on the fast connectivity of the internet. A participant maintained: Universities have to increase the bandwidth in order to ensure fast internet connectivity. They also need to ensure that wifi and wireless are functional (Male, Participant 19). Tying LMS usage to promotion It is believed that motivational incentives are moral boosting. Therefore, when LMS use is tied to the promotion of lecturers in their career, many will adopt and use the system. (Male, Participant 15) A participant said, If the University authorities want us to fully use the platform, then it should count towards our promotion. This is because it is difficult to practice blended learning as lecturers (Male, Participant,16). The blended learning used in the above narrative means combining the traditional face-to- face education with online learning. To the participant, it is difficult to practice that. Therefore, some motivation is needed in order to induce that. 150 University of Ghana http://ugspace.ug.edu.gh Key findings of the qualitative study 1. Simplicity of use, the relevance of educational technology to one’s job, institutional policy, mandatory technology adoption policy and enablers such as sound internet connectivity, availability of power, technical support and appropriate software were found to be important factors directly influencing the Actual Use of Open Source LMS. 2. Apart from using the LMS for courseware development and instructional purposes, it could also be used to facilitate academic ethics by embedding Turnitin in the LMS and using it to check possible plagiarism. 3. Technological and technical challenges such as slow Internet connectivity, lack of appropriate software and poor technical support constitute a major setback to the use of LMS in the universities used as study areas for the current study. 4. Faculty attitude, the mindset that blended learning practice is difficult to use, and that the LMS was too narrowed, were found among the factors accounting for Non-use of the LMS. 5. It was also found out that the Sakai and the MOODLE LMS do not have all the features or relevant extensions each was supposed to have in order to enable users to use it to the maximum. For example, University of Education Special education students lack a software called Jaws and Interpreter films which should be embedded in the MOODLE platform for use. Thus, the Special Education students were not performing to expectation. 6. Turnitin embedded in the LMS was not functioning to enable the check of possible plagiarism. Thus determining the originality of work done by students was a problem. 151 University of Ghana http://ugspace.ug.edu.gh 7. Linking the LMS to the Assessment Management System (OSIS) was a challenge in the University of Education, Winneba. 8. Many students were not computer literate, despite the regular training organised for them. Therefore, many of them were not able to submit assignment into the LMS as required. 9. A number of faculty members were laggards and late adopters of LMS in the various universities due to apathetic attitude, lack of skills and demotivation from the University. These situations were hindering the e-learning education process. 10. Some lecturers also did not know that the LMS was acquired by the University for them to use it. Therefore, they were not using it for teaching as expected by the universities. 4.2.8 Summary of Qualitative Results This aspect of chapter four presented the results of the qualitative study that sought to find out factors that influenced lecturers’ adoption and use, and non-use of an Open Source LMS. There were participants who used the system and those who did not use the system. The reasons for the use and the non-use were unfolded. The various ways that the LMS was put to use also came up. The challenges that confronted users of the system and also debarred the non-users from using the system also emerged from the respondents’ narratives. Also, users stated factors that could enhance the use of the system; and the non-users also recommended factors that would attract lecturers to use the system. Several conceptual models were generated from the qualitative data which could be tested in future studies. The next chapter threw light on the discussions of the quantitative and qualitative findings in line with the existing literature, and then moved on to demonstrate the extent to which the qualitative data augmented the quantitative findings 152 University of Ghana http://ugspace.ug.edu.gh CHAPTER 5 DISCUSSION OF RESULTS 5.0 Chapter Overview This chapter discusses the reliability and validity test, normality test, correlation of the study variables and the results of the hypotheses in relation to the existing literature. 5.1.0 Discussion of Quantitative Results 5.1.1 Discussions of preliminary results This section highlights results of reliability and validity test, normality test and correlation matrix. The data sets were subjected to confirmatory factor analysis and reliability test, and the results were favourable. The constructs’ convergent and discriminant validity were favourable. Since the Maximum Shared Variance between the constructs were less than the Average Variance Extracted for the individual variables, it was implied that discriminant validity was attained. The fact that the Average Variance Extracted recorded values greater than 0.5 also indicates convergent validity was adequate. With the exception of Facilitating Conditions which recorded 0.68, Reliability test registered values beyond the standard value of .70. It means that the consistency of the items within the scale of a related construct, its stability and firmness within a particular timeframe was favourable. The data were also subjected to test of normality such as mean, standard deviation, maximum and minimum, skewness and kurtosis as suggested by Pallant (2011). The mean for the variables ranged between 2.37-3.66 on a five likert scale of 1-5 in general. This means the responses were within the scale of ‘average to fairly strongly agree’. The multi-variate normality test yielded acceptable outcomes within the standard range. The work was then ready for SEM analysis, after escaping inflated goodness-of-fit statistics, underestimated standard errors and 153 University of Ghana http://ugspace.ug.edu.gh taking note of the sensitive of Chi square to non-normal data (MacCallum, Roznowski & Necnowitz, 1992). Inter- correlation matrix was applied to measure the degree of association between some study variables. The intention was far from establishing causality (where one variable cause a change in another variable). This means if even if the researcher found that there exists significant relationship between two variables at all, he did not conclude that one variable caused a change in another variable. Additionally, there were two values reported: one for correlation between two variables represented by an ‘r’, and one for statistical probability represented by a ‘p’. The inter- correlation matrix for the variables in the study as presented in table 4.5. shows that some of the correlations were positively significant, positive, although weak, and negatively significant. The strongest and positively significant correlation among the study variable was the correlation between Compatibility and Perceived Usefulness r=(.62, p<.05). In the study context, it was an indication that the Perceived Usefulness of a system was significantly associated with Compatibility. This indicates that one may find the use of the Open Source LMS consistent with his experience, beliefs and needs by factoring in the relevance (the usefulness) of the system to his/her job. 5.1.2 Results of hypothesis Hypotheses of the study were tested using SEM with maximum likelihood estimation in IBM AMOS 21.0. In the path estimates, the critical ratios for the standardized regression weights of each path were estimated to ascertain which paths in the model were significant or non- significant. The positive and significant relationship between independent, mediating and the output variables were tested together with their corresponding indirect effects. The estimated 154 University of Ghana http://ugspace.ug.edu.gh indirect effects were cross-validated using bootstrapping to compute the confidence intervals associated with each indirect effect. The results from the SEM analysis showed that Perceived Ease of Use had a positive and significant relationship with Actual Use. It means that lecturers who Perceived that the Open Source LMS could be used without much effort, adopted and used it. Lecturers’ perception that no much effort was needed to use an Open Source LMS, positively influenced their use. The easier (simpler) it was to use the system or the more flexible it was to interact with the Open Source LMS by the lecturers, the more it influenced their’ Actual Use of the LMS. The reason why lecturers found the Open Source LMS easy to use might be that they had prior knowledge in the use of a similar LMS or they had a positive mind set and attitude to learn and use it. In case the University Management wants to influence massive use of the Open Source LMS, it is important that the system is made simple to learn, adopt and use. This is can be done through counselling, motivation and training offered to the current users and prospective users. Additionally, if a policy makes it mandatory to use the LMS, despite how difficult it is to even use it, University lecturers will still find a way to learn and use it. If a policy ties it to other benefits such as promotion and renewal of contract, lecturers will be compelled to use it or lose the benefit or resign from the university. Considering the high unemployment level and the ban on employment by the government of Ghana, many will be persuaded to use it. The finding that Perceived Ease of Use influenced Actual Use of the LMS is similar to other findings that show that when people think that a particular technology is easy to use, it positively influenced their Intention to Use or Actual Use of that technology (Abdulah, Ward & Ahmed, 2016; Fong & Wong, 2015; Tung, Lee, Chen & Hsu, 2009; Chin & Todd, 1995). 155 University of Ghana http://ugspace.ug.edu.gh Additionally, Šebjan & Tominc (2015) found out that the more the Perceived Ease of Use of Statistical Package for Social Sciences, the higher its Actual Use. Perceived Ease of Use was also found instrumental in explaining the variance in users’ Intentions to Use technology (Tung, Lee, Chen & Hsu, 2009). However, Wu, Wu & Chang (2016) in their study, noted that Perceived Ease of Use was not significant with Actual Use of a technology for work. This means that although prospective users may perceive that an educational technology could be used without effort, that does not necessarily influence their Actual Use. Their Actual Use may be influenced by other factors such as how they perceive the usefulness of the system to their job and whether the environment is enabling enough to use the system. It is expedient that users of the Open Source LMS who find it easy to use, should encourage and help others to adopt and use it rather than highlighting the negative side of the use. This will increase the adoption and the usage rate, thus fulfilling the objective for embracing the LMS. Similarly, the relationship between Perceived Usefulness and Actual Use being positive, although not significant, (see SEM table) in this study means lecturers who participated in the current study, somehow (but not wholeheartedly) used the LMS because of the Perceived Usefulness or the relevance (eg. convenience and comfortability) to their teaching job. The usefulness must have been realised after the practical use of the system. The finding that Perceived Usefulness positively related to Actual Use is consistent with literature (Abdulah, Ward & Ahmed, 2016; Calisir, Gumussoy, Bayraktaroglu & Kara, 2014; Tung, Lee, Chen & Hsu 2009; Venkatesh & Davis 2000; Chin & Todd, 1995) who found in their studies that the Usefulness of technology influences its acceptance or Actual Use. van Raaij & Schepers (2008) in their research also observed that Perceived Usefulness has direct effect on the Use of Virtual Learning Environment. 156 University of Ghana http://ugspace.ug.edu.gh Perceived Usefulness was also found instrumental in explaining the variance in users’ Intentions to Use technology (Tung, Lee, Chen & Hsu, 2009). These observations from the extant literature are interesting in that they show that for a system such as LMS to be used by lecturers, it must be found useful and relevant to their work. What it means is that universities who have acquired the LMS infrastructure and want it to be used by lecturers must create the awareness of the availability and usefulness of the system to users. It also important that they provide counselling services to users in order to appreciate the importance of the system, and also offer them the necessary training to enable them acquire the skills to use it. In addition to that, other facilitative and congenial environment such as power and fast Internet connectivity must be made available to influence the use of the LMS. From the current study, the path from Perceived Usefulness to Actual Use not being significant although positive, means that lecturers have not found the LMS system to be really important to their teaching task although they know it is good if they use it. The reasons may include: No much sensitization is going on for them to appreciate the system very well, relevant teaching software that needs to be embedded in the LMS to help them to be able to teach some courses such as French, Spanish, Greek or Mathematics may be lacking, and there are volatility of power flow and Internet connection distortions. Thus, the lecturers do not see very clearly, the relevance of the system to use it to teach their students. Considering the huge amount of capital injected into organising e-learning education, much effort must be made by University authorities to enhance the use by making the LMS very useful to the lecturers. The non-significant relationship between Compatibility and Actual Use means that Compatibility did not exert influence on lecturers’ use of the system. Lecturers did not find the use 157 University of Ghana http://ugspace.ug.edu.gh of an Open Source LMS to be consistent with their belief, values and needs to prompt them to use it. If some lecturers were using it, then it was not due to the fact that it was compatible to their experience or needs. This is inconsistent with the findings from studies conducted by Carter & Belanger (2005) and Hardgrave et al (2003) who found out that Compatibility influenced Actual Use of a technology for a particular purpose. The positively significant relationship between Compatibility and Perceived Usefulness implies that the more the lecturers perceived the LMS to be compatible with their belief and needs, the more they perceived it to be useful to enhance their job performance. Conversely, the more they saw the LMS to be incompatible to their needs and belief, the more they perceived that it was not useful enough to enhance their job performance. This finding is in tandem with studies by Oha & Yoon (2014), Wu & Wang (2005), and Hardgrave et al, (2003). Additionally, the path from Compatibility to Perceived Ease of Use being positive and significant means lecturers’ perceived compatibility of the LMS was linked to the perceived ability to use it without much effort. This means if the LMS was found Compatible, it was also because it was also found to be Easy to Use. The finding that Compatibility was positively, significantly related to Perceived Ease of Use is similar to other studies such as one conducted by Wu & Wang (2005). Perceived Ease of Use mediates Compatibility and Actual Use means, through the Path of Perceived Ease of Use, Compatibility exerts indirect influence on Actual Use of the LMS. Lecturers’ perceived Compatibility of the LMS, factors in, their ability to use the LMS effortlessly, which also influences their Actual Use. The conclusion is that Compatibility exerts influence on Actual Use of LMS through Perceived Ease of Use. This assertion implies, Management must ensure that the LMS is made Easy to Use by providing training to lecturers to enhance their skills 158 University of Ghana http://ugspace.ug.edu.gh and to provide the environment with the relevant structures, policy, infrastructure and power that will motivate the use. The relationship between Trialability and Perceived Usefulness; and Actual Use being non- significant means a great number of the lecturers did not find the LMS useful or better than the existing mode to let them appreciate the need to try their hands on it; or the lecturers did not have the opportunity to experiment/or put on trial, the LMS at all. Therefore, Trialability could not exert influence on Perceived Usefulness or Actual Use of the LMS. This finding differ from similar study by Lee (2007) and Yang (2007) who found a positive and significant relationship between Trialability and Actual Use or Intention to Use technology for its given purpose. Trialability being significantly related to Perceived Ease of Use means lecturers found the system to be experimentable and practicable to try their hands on them, and they found it easy to use. Nevertheless, looking at the results, Perceived Ease of Use was not enough to induce Trialability variable to directly influence Actual Use of the LMS. This means, other factors were needed to be considered in order for Trialability to influence the Actual Use of the LMS. Additionally, Perceived Usefulness and Perceived Ease of Use could not significantly mediate Trialability and Actual Use. There was no indirect relation from Trialability through either Perceived Usefulness or Perceived Ease of Use to Actual Use. Trialability never indirectly influenced Actual Use of the LMS regardless of the Usefulness or the Ease of Use of the LMS. The core factor indicating the non-significant relationship of Trialability and Perceived Usefulness and Actual use is the fact that Ghana, located in Africa, is within a low technology context. Individuals are not used to using educational technology to teach; trying their hands on the LMS was not even interesting to them thus many of them were not interested to use it. This is opposed to the developed world where technology usage is the practice. 159 University of Ghana http://ugspace.ug.edu.gh The importance of all the discussions above is that lecturers must make themselves available to learn and use the LMS because we are in a digital era. If they do not do that, they cannot appreciate the LMS, let alone gain mastery over it and use it for teaching. They will therefore, be left behind and face the consequences of being unable to fit into the job environment that is demanding the use of technological innovations than the conventional way, which is also fading quickly. For this to hold, mandatory technology adoption policy and stringent policies such as tying promotion and renewal of contracts to the use of the LMS must be considered in addition to offering lecturers with relevant and regular training in the use of the LMS. Image implies the extent to which the use of a system is perceived as enhancing one’s status or reputation among his/her colleagues. The current results show that, Image is not positively significant with Perceived Ease of Use. This means, using a system as a status symbol was not a function of the ease of use of it. The path from Image to Perceived Usefulness being not significant also means, using a system for a prestige was not based on how Useful the system was to the user. Lecturers did not perceive Image associated to the use of the LMS to be influenced by the Usefulness or the relevance of the system to their job performance. Besides, the path from Image to Actual Use not being significant also means lecturers did not Perceive the use of a system to enhance their reputation or boost their image. Therefore, they did not use the system for Image enhancement. The Actual Use of the LMS was not influenced by Image enhancement. This is also an interesting finding in that, several authors have found that Image is a relative advantage that informs the Intention or Actual Use of a technological innovation (Venkatesh & Davis, 2000; Rogers,1995) and for that matter using a technology (system) to work will enhance one’s Image (Yi et al., 2006; Moore & Benbasat, 1991). The results also show that Image has no 160 University of Ghana http://ugspace.ug.edu.gh indirect relationship with Actual Use as depicted by the bootstrapping estimate for the indirect effect of Image on Actual Use. A Subjective Norm refers to a person’s perception that significant others think she or he should or should not perform a particular behaviour (Fishbein & Ajzen, 1975). Subjective Norm was found to be positively and significantly related to Perceived Usefulness; and this confirms other studies including Lee (2006); while its relationship with Actual Use was not significant. This is not consistent with other studies including (Venkatesh & Davies, 2000; Schierz, et al, 2010; Abbad, Moris & de Nahlik, 2009) who found Subjective Norm to be significant with Actual Use or Behavioural Intention to Use a system, especially in mandatory technology adoption setting. Also, the influence of Subjective Norm on Perceived Ease of Use was not significant. Abdulah, Ward & Ahmed (2016) found out that Subjective Norm exerts influence on Perceived Ease of Use of e-portfolio. What it means is that social influence on the use a system becomes real if the system is perceived to be relevant to working effectively and efficiently. If the system is not perceived to be Useful or relevant to their teaching job, peer or social persuasion has no impact on the lecturers. In this very study, Subjective Norm was influenced by Perceived Usefulness. However, the path from Subjective Norm to Actual Use was not significant, meaning the direct influence on Actual Use of the system did not emerge from social influence. Contrary, several studies on technology adoption and use have found positive effect of Subjective Norm on technology adoption and use (Kreijnsa et. al, 2013) and on Behavioural Intention to Use technology (Fong & Wong, 2015; Schierz, et al, 2010 Shanan et al, 2008; Venkatesh et al, 2003). Furthermore, Abdulah, Ahmed (2016) in their study found that students’ Perceived Ease of Use of technology was influenced by Subjective Norm. Additionally, it is 161 University of Ghana http://ugspace.ug.edu.gh important to report that Perceive Usefulness and Perceived Ease of Use together, could not positively, significantly mediate the path between Subjective Norm and Actual Use. It can be inferred from this study that, persuasion from colleagues alone did not influence lecturers to use the LMS. This confirms Schepers & Wetzels (2007) results that could not affirm a direct positive influence of Subjective Norm on the Actual Use of a system, but confirmed an indirect positive influence of Subjective Norm on Actual Use of the system through Perceived Usefulness. Facilitating Condition was positively, significantly related to Perceived Ease of Use of Sakai/MOODLE LMS. What it means is that the availability of the enabling conditions were useful only it was also found that the system could be used effortlessly. Are the features on the platform easy or simple to use, is it understandable? If yes, then it makes sense to say that Facilitating Conditions (for example fast internet connectivity, availability of power) influenced Perceived Ease of Use. This results coincide with numerous studies that found that Facilitating Conditions have a significant effect on Perceived Ease of Use (Teo, 2010; Li & Huang, 2009; Teo et. al, 2008; Bergeron, Rivard & DeSerre, 1990). The finding that Facilitating Condition was not significantly related to Actual Use of Sakai/MOODLE LMS, is in line with Hutinger et al. (1996) studies. They found out that, Facilitating Condition did not necessarily lead to technology acceptance or use. However, Panda & Mishra (2007) found in their studies that poor technology adoption was a result of poor Facilitating Condition. Besides, Perceived Usefulness was not found to mediate the relationship between Facilitating Conditions and Actual Use. As stated previously, the path from Perceived Usefulness to Actual Use was not significant. However, the link between Facilitating Conditions 162 University of Ghana http://ugspace.ug.edu.gh and Actual Use was mediated by Perceived Ease of Use. This means that irrespective of the availability of educational technologies and technical infrastructures in the universities, the LMS will still not be used unless it is made simple to use. In all, it was only Image, one of the external factors that was not significantly related to Perceived Usefulness or Perceived Ease of Use of the LMS. Additionally, Subjective Norm, Trialability and Image were not directly or indirectly positively, significantly related to Actual Use of the LMS. However, Compatibility and Facilitating Conditions were positively and significantly related to Actual Use through the Perceived Ease of Use of the LMS. 5.1.3 Summary of Quantitative Discussion In conclusion, although some of the relationships between the study variables were not significant. It is not conclusive that those individuals’ relationships that were not significant did not exist or none of them could directly influence Actual Use. Rather, it could be inferred that, the quantitative study could not find an adequate proof of a statistically significant relationship between such variables in a cultural context where Open Source LMS’s adoption and use was low. Nevertheless, the results provide reasons for strengthening the use of the Open Source LMS by focusing on some significant variables and also working on those found not significant. Additionally, it gives the reason why we have to embark on further studies, re-examining the relationship in the observed theoretical model. However, the influence of Compatibility and Facilitating Conditions on Actual Use through Perceived Ease of Use were established. Furthermore, the qualitative study as an aspect of the current study also sought to find out what factors influence the Use or Non-Use of an Open Source LMS. The next section discussed the results of the qualitative dimension of the current study. 163 University of Ghana http://ugspace.ug.edu.gh 5.2 Discussion of Qualitative Results- Overview The focus of the qualitative phase of the study was to address two major questions pertaining to: 1) lecturers’ experience about the use of the LMS and 2) the reasons underlying those who are not using the LMS. Thematic analysis of the data showed seven (7) major themes addressing the adoption and use of the system. 5.2.1 Discussions of Qualitative results and findings In terms of the Utility (Usefulness) of Sakai/MOODLE LMS, the respondents indicated that the Sakai/MOODLE LMS was very useful because it provides convenience, flexibility and comfort in teaching a wide and dispersed audience. It also enhances research and communication, it is cost-saving, enhances students’ intelligence, and boosts creative and critical thinking. Any of these factors or all of them influenced their Actual Use of the LMS. This is in tandem with a study that discovered that when a person sees technology to be useful for his or her work, he or she adopts and uses it (D’Ambra & Rice, 2001). Mukoko (2012) also found out in his study that Perceived Utility/Usefulness of a technology plays a crucial role in the adoption and use of computer and the Internet. It is also obvious from the participants’ responses that institutions also benefit from LMS: It enhances enrolment, reduces cost of education and increases quality of education. Since Utility/Usefulness has a direct influence on the Actual Use of the system, it means that for more lecturers to adopt and use the LMS, adequate education on the uses of the system and training to equip the lecturers with the requisite skills to use the system must be taken seriously by the University administrators. Additionally, the technical support and the infrastructural network issues must be free of problems. Regarding Simplicity of Use (Ease of Use), there are some previous studies that found that Simplicity of Use (Ease of Use) of a technology was an important factor in the adoption and use 164 University of Ghana http://ugspace.ug.edu.gh of computer and the Internet (Mukoko, 2012). In the current study, participants found the LMS to be simple to use because they could use it without much effort. With respect to Prior Knowledge in the use of ICT and the use of a LMS, numerous studies attest to the fact that teachers’ Prior Skills and Knowledge in the use of computer, is a key factor for effective use of ICT in teaching; thus lack of Prior Knowledge in ICT can hinder the adoption and use of E-learning (Kreijnsa et. al., 2013; Gautreau, 2011). However, other studies including the one conducted by Hutinger et al. (1996) have found that Prior Knowledge in the use of ICT does not influence ICT embracement and use. Despite this mixed findings, the current study maintains that Prior Knowledge is an influencing factor for the adoption and use of LMS for teaching. Additionally, it makes sense to state that lecturers who are novices in the use of computer need more effort to adopt and use the Sakai/MOODLE for teaching. The situation is worsened if there is no institutional policy that offers them counselling and training for the use of the facility. Furthermore, certain Enablers such as training facility, availability of computers, fast Internet connectivity and availability of technical support, to mention just a few, were found in this study as influencing the adoption and use of Sakai/MOODLE LMS for teaching. These Enabling Conditions are however meant to be provided by the University. Literature abound indicating that Enabling Conditions such as training and availability of technical staff support, pedagogical support, provision of IT equipment, repairs of faculty computers and Internet infrastructure provide teachers/lecturers the confidence needed to use technology for teaching (Buabeng-Andoh,2012; Teo, 2010). This implies that, poor repairs of technology, shortage of computer hardware and software, power outages and poor downloading as well as cumbersome institutional culture inhibit the adoption and use of educational technology (Sarfo & Yidana, 2016; Boateng, 2015). 165 University of Ghana http://ugspace.ug.edu.gh Training in the use of ICT for teaching equips both new and old faculty members with the skill to use it for teaching (Franklin, 2007; Wozney et al., 2006). Teachers/lecturers need ICT experts to guide them to incorporate ICT into teaching (Plair, 2008). For example, providing faculty members/students with tutorials to enable them navigate through the online applications and tools provides them the skill to use it. Lack of such technical support stifle the use and successful implementation of E-learning systems (Marfo & Okine, 2010). Tagoe (2012) in his study found that only a scanty number of lecturers incorporate ICT into teaching and learning activities in Ghana. This observation corroborates other studies by On, Lai, Wang (2004) and Moore & Benbasat (1991). One of the reasons is the low motivation offered them. Low motivation to lecturers to use ICT was what happened in Kwame Nkrumah University of Science and Technology during the initial stages of their adoption of e-leaning system. The same could explain the case of University of Ghana during the time of the use of KEWL (Dakubu, 2009). It was also found that incorporating ICT into teaching design is also influenced by Institutional factors (per the interview data) and this supports existing literature on the subject (Chen, 2008; Lim & Chai, 2008). However, social and institutional contexts are often unsupportive of lecturers' efforts to integrate educational technology into their work, and lecturers have often been provided with inadequate training and other needed support for this task. Computer Self-Efficacy such as one’s belief and confidence that one could use educational technology effectively to achieve great results was found in this study to influence lecturers’ use of Sakai/MOODLE for teaching. Researchers such as Kreijnsa et. al., (2013) maintain that Self- Efficacy towards technology was a significant predictor of teachers’ Intention to Use technology. Lecturers’ beliefs and confidence to use technology influence their adoption and use of the 166 University of Ghana http://ugspace.ug.edu.gh technology (Marcelo, Yot & Mayor, 2015). Eagerness to use technology has also been found to influence technology use (Buabeng-Andoh, 2012; Awidi, 2008). By way of addition, this study also found out that lecturers put the platform to several uses. The resource tool, assignment tool, discussion forum, chatroom, glossary, videos, pdf files, email, grade book, announcement and several other tools were used to support their teaching. This reflects previous studies that LMS has several features that could be used for teaching and students’ learning (Mtebe, 2015; Schoonenboom, 2014). The participants used hybrid, asynchronous forum discussion for debate. Youtube and Twitter tools were added to the Sakai/MOODLE platform for a holistic teaching and learning. The essence of the use of the Open Source LMS was to augment teaching, ensure effective learning output and sustain higher education; and several authors have reported on that in their studies (Halabi, Essop, Carmichael & Steyn, 2014; Chandra & Briskey, 2012; Chandra & Watters, 2012). Lecturers’ skills were integrated in digital technology both in their teaching design process and the development of this design when in contact with their students (Jump, 2011). Getting an understanding of how university lecturers apply LMS in their teaching and how they display their pedagogical awareness in such a teaching context is very important. Besides, the effective use of self-assessment tool (test & quizzes) and the discussion forum has taken teaching and learning into another pedigree which is far beyond the use of face-face-teaching and learning. The use of the LMS as a help tool in classroom assessment has been found in some studies including Khatib (2016). Furthermore, the use of the LMS was found as a tool that facilitates ethics (academic integrity) in higher education. The rules and regulations guiding academic integrity, the use of Turnitin attached to Sakai LMS are all meant to ensure academic ethics. The primary aim of the 167 University of Ghana http://ugspace.ug.edu.gh academic ethic is to ensure fairness in academic practice. This means that academic theft should be avoided and academic integrity should be projected. Sims (1993) said in his research that students who cheat in school normally manifest the same behaviour at work. Therefore, I assert that regular workshops and seminars that conscientize lecturers and students on academic integrity must be organised regularly. Per the findings of this study, lecturers were still struggling to use the LMS due to technological and technical challenges. These critical challenges include regular power fluctuations, slow Internet connectivity, unavailability of technical staff, to mention just a few which have been highlighted in previous studies (Asamoah & Mackin, 2016; Sarfo & Yidana, 2016; Boaten, 2015; Fathema et al, 2015). The acquisition of a LMS is relevant only if the challenges associated with its deployment are addressed and it is put to meaningful uses (Fathema, et al, 2015). Reyes (2015) notes that lecturers’ skills and knowledge acquired clash with new educational technologies. Also, teaching and administrative duties are incompatible with regular pressure to go for training seminars and workshop to acquire IT knowledge and skills. Anderson (2010) observes that making a transition from a traditional face-to-face teaching and learning to a technology-rich learning environment is challenging for both teachers and students because it generally requires a shift in their roles and responsibility for learning. Nevertheless, the onus lies on university authorities to address these challenges in order to ensure that the LMS is used effectively. In suggesting how to upgrade the LMS, respondents said the system design should include course advisory and interactivity functions online; and that has been found in other studies (Twiggs, 2010). This is meant for the lecturer, who is a course advisor, to be able to meet with 168 University of Ghana http://ugspace.ug.edu.gh several students online to address their academic issues. This will prevent the need to meet the students one on one in the small office of the lecturer. The suggestion from the participants of the current study that universities must go hybrid is very laudable. This is because it also allows tapping into the benefits associated with the face- to face teaching and learning such as the psychological dimensions and the human relations aspects. Besides, courses that could not be taught online would be managed by the face-to-face classroom-based teaching and learning. Additionally, there is no way that faculty and students could use the LMS effectively if there is no regular and timely training for them to keep them abreast of the use of the platform. Many of these suggestions are policy issues which must be addressed by the universities. The results also showed that Non-users of the LMS were aware that LMS had been acquired for lecturers’ use and students’ learning but they were not interested to use it. Other lecturers also did not know that the LMS was acquired by the University for them to use it. With regard to Non-users of the system, it was not the lecturers’ intention or desire not to use the Sakai LMS. For example, to lecturers who teach mathematics or French language, the Sakai/MOODLE did not have the features (mathematical/french symbols) to enable them to use it to teach such courses. The discussion above is followed by a thematic map of the themes developed from the interview data of both Users and Non-users of the LMS, and reflects the interconnections, linkages and the relationships among them (Figure 4.8): 169 University of Ghana http://ugspace.ug.edu.gh Figure 4.8 Open Source LMS adoption and use Thematic Mapping Model The above thematic map is an upgrade of the thematic map discussed earlier in the qualitative results section. Several of the themes overlap, they highlight the same issues that need to be addressed by university administrators. Relevant policies are required to facilitate a broader 170 University of Ghana http://ugspace.ug.edu.gh adoption and use of the Open Source LMS that is compatible with the universities’ culture, values and vision. First, the Users of LMS are those who have embraced and integrated the LMS into teaching; and Non-users are those who have not integrated the LMS into teaching. From the qualitative results captured in the thematic map above, we can infer some relationships between the themes in respect of the Users and the Non-users. For instance, while the Users of the LMS found it to be simple to use, the Non-users perceived the system to be complex and thus hindered their use. If the system is perceived to be complex to use by Non-users then University administrators need to make the system simple to use for them. Second, while the Users found the system to have Utility (Usefulness), the Non-users perceived the system as Narrowed and Restrictive and thus preferred to deploy the social media platform such as Youtube to support their teaching or to use their self- designed learning platform instead of the LMS. What the Non-users are not aware of is the fact that the Open Source LMS could be modified, extended or customized to suit local needs and expectations. This means the social media platform could be used alongside the LMS during the teaching and learning transaction without creating any problem. The Non-users need to be sensitized about this. Besides, the LMS has a lot of features embedded in it that is already made for organizing teaching and learning. The Non- users of the LMS must be informed about this. Moreso, while the Users maintained that using the system was an innovation with several benefits, the Non-users still preferred the old system, the face-to-face mode because they found it simple to use. While the Users employed the system for delivering instruction and other things, Non-users did not use it at all, although they were aware that the system was available and could be used for delivering instruction and other activities. 171 University of Ghana http://ugspace.ug.edu.gh While the Users of the system maintained that there were challenges including lack of appropriate software, internet connectivity issues in using the LMS, the Non-users also found these challenges as some of the reasons for not using the LMS, and they recommend that such problems be addressed by the university administrators. What that means is that if they are not addressed, Users of the system today will be discouraged and eventually back out of the use, and the Non- users will remain as they are. It is expedient to mention that the qualitative data and the thematic map (figure 4.8 above) are hinged on two key factors, namely i) determinants of usage and ii) improving use of the Open Source LMS (Sakai/MOODLE). The causal factors of Use and Non-use of the Open Source LMS (as discussed already) are herein referred to us determinants. Users of the system suggest enhancement of use and Non-users recommend factors likely to facilitate usage; together these are referred to as improvement of the system. Thus LMS adoption and use for teaching and learning are influenced by several factors; and improvement of the system will require innovative interventions by University authorities in order to ensure success in the e-learning education in Ghana. The explanatory mixed method design requires that the relevance of the qualitative study to the quantitative study is demonstrated. The discussion below highlights the corroboration alone. It does not factor in the views of other studies because the discussion chapter for both quantitative and the qualitative phase have addressed that. 5.3 Corroboration of the qualitative phase to the quantitative results It is important to mention that the Utility of Use factor as a theme in the qualitative study holds the same meaning has Perceived Usefulness in Technology Acceptance Model (TAM) used in the quantitative study. Utility of the system depicts the benefits of the system such as economy, 172 University of Ghana http://ugspace.ug.edu.gh enhancing interactivity, stimulating creative and critical thinking and enabling the ability to teach a wider audience who are geographically dispersed. Usefulness of the system implies the relevance of the system (ie. the effectiveness and efficiency of the system) to the users. The quantitative study found a positive, though not significant relationship between Perceived Usefulness and Actual use (β = .18, p = .082) (per table 4.6 in the results chapter). The qualitative study also shows a positive influence of Utility (Usefulness) on Actual use of the Open Source LMS (in the qualitative results chapter). Therefore, the qualitative results corroborate the quantitative results. Again, Simplicity of Use has the same meaning as Perceived Ease of Use in TAM. Perceived Ease of use (in TAM) implies the extent to which the system could be used without difficulty. Simplicity of Use also depicts how less effort is needed to use the system as defined as Perceived Ease of Use in TAM. In the quantitative results, Perceived Ease of Use had a positive and significant relationship with Actual Use (β = .45, p < .05) (Table 4.6). Also, participants (qualitative phase) found the system simple to use and this influenced their use of the LMS. Therefore, the qualitative results confirm the quantitative results. Furthermore, Facilitating Conditions (also referred to as enablers in a qualitative phase) are the factors seen as enablers or barriers in an environment that influence a person’s decision to use or not to use a system. For example, the availability of fast Internet connectivity, sound IT infrastructural network and access to computers determine the use of a system. In the quantitative phase, the path from Facilitating Condition to Actual Use was not significant (Table 4.6, quantitative results section) but from the qualitative phase, the users of the system (Participants) maintained that enablers (facilitating conditions) influence their use of the LMS. Although the two results appear inconsistent with participants who use the system, it is consistent with the views of the participants who are Non-users of the system. For example, the Non-users of the system 173 University of Ghana http://ugspace.ug.edu.gh maintained that the University environment was not enabling enough and that was why they did not use the system. This discussion can be broadened further by looking at the path from Facilitating Condition to Perceived Ease of Use. The result was positive and significant (Per table 4.6, quantitative results section). What it means is that the availability of enablers was necessary but not a sufficient condition to influence the adoption and use of the LMS unless the system was to be found easy to use. This means that, in the quantitative phase, Ease of Use mediated Facilitating Condition and Actual Use of the system (table 4.7). The final meaning is that, Facilitating Conditions influenced Actual Use of the system at least, indirectly. It is concluded that the qualitative phase is adding more meaning to the quantitative results. Institutionalization (Management policy) as a theme in the qualitative study, and Facilitating Condition in quantitative study have the same meaning. Institutional policy (facilitating conditions) include tying the use of the system to promotion or renewal of contract or providing technological and technical support. These are found to influence the use of the system (from the qualitative response). Although, the path from Facilitating Condition to Actual use was not significant (per table 4.6 in quantitative results section), we can infer that similar to the immediate discussions, Facilitative Conditions can influence Actual Use through Perceived Ease of Use (when the system is made simple to use). Thus the qualitative phase augments the quantitative results. Other important issues have been discussed in the qualitative phase which augment the quantitative study. These include: the thorough discussions on the Uses of LMS; the reasons why some lecturers were not using the LMS, and the interconnections and relationships of themes from 174 University of Ghana http://ugspace.ug.edu.gh both Users and Non-users, as represented by the thematic mapping model (Figure 5.1, Discussions chapter above). 5.4 Summary of Discussion Chapter In the discussions of both the quantitative and the qualitative results, some of the findings were consistent with the literature and others were not. Those findings that were consistent with the existing literature were confirming what were already established, and those that were not consistent with literature indicated a new piece of information that was adding to knowledge in the field. The qualitative data corroborated and augmented the quantitative findings. The next chapter highlights summary of the thesis, contributions of the thesis to knowledge and implications for policy, limitations of the study and suggestion for future studies, and conclusion. Finally, recommendations based on the findings were made. 175 University of Ghana http://ugspace.ug.edu.gh CHAPTER SIX SUMMARY, CONCLUSION AND RECOMMENDATIONS 6.0 Chapter Overview This is the last chapter and it highlights the summary of the thesis, contributions of the thesis to knowledge in areas such as conceptual /theoretical framework, additions to the existing literature and methodology, and policy implications for higher education institutions and researchers. In addition, the limitations of the study and suggestions for future studies, conclusion and recommendations have been addressed. 6.1 Summary This study was about E-learning education in Ghana. It specifically examined factors influencing lecturers’ adoption and use of an Open Source Learning Management System (LMS) in four universities in Ghana namely, the University of Ghana, Legon; University of Education, Winneba; University for Professional Studies, Accra; and the Ghana Technology University College. The core objective of the study was to analyze the factors that influence lecturers’ adoption and use of MOODLE/Sakai Open Source LMS by the lectures at these institutions of higher learning in order to ensure competitive full online and blended learning programmes. Technology Acceptance Model, Innovation Diffusion Theory, Image, Subjective Norm and Facilitating Conditions were integrated to guide the study. A Mixed Method approach with a focus on Explanatory Sequential Design was used where more priority was given to the quantitative study and less priority was given to the qualitative approach. The investigation was piloted and conducted among 25 lecturers who use Sakai LMS in the University of Ghana. Likert scale questions and interviews were used as strategies to collect 176 University of Ghana http://ugspace.ug.edu.gh data. Three hundred and eight three (283) out of 435 participants (the accessible population made up of lecturers within the purposefully sampled departments of schools/faculties) completed and returned their questionnaire, representing 65% response rate. Twenty (20) participants noted to be information-rich cases were also intentionally sampled for the qualitative study. Confirmatory factor analysis, structural equation modelling (SEM) and thematic analytical tools were used for analysis of quantitative data. The fit indices suggest that there is a good fit between the measurement model and the data: (χ2 = 383.23, df = 271, p < .05; SRMR = .046; TLI = .965; CFI = .971; RMSEA = .038. Convergent validity and discriminant validity were attained. The SEM results showed that the hypothesized model fitted the data well: (χ2 = 360.39, df = 253, p < .05; SRMR = .048; CFI = .971; TLI = .964; RMSEA = .040. Based on the data presented and analyzed, the following are the key findings of the study:  The test of hypotheses revealed that Perceived Ease of Use had a positive and significant relationship with Actual Use (β = .45, p < .05);  Compatibility had a significant positive relationship with Perceived Usefulness (β = .62, p < .05), as well as Perceived Ease of Use (β = .43, p < .05).  Trialability was found to be positively and significantly related to Perceived Ease of Use (β = .12, p < .05).  Subjective Norm positively and significantly related to Perceived Usefulness (β = .17, p < .05).  Facilitating Conditions exerted significant influence on Perceived Ease of Use (β = .36, p = .05).  The bootstrapped estimate for the indirect effect of Compatibility on Actual use was significant (β=.30, p < .05). 177 University of Ghana http://ugspace.ug.edu.gh  Facilitating Conditions also registered significant indirect effect on Actual use of Open source LMS.  Regarding qualitative study, Simplicity of Use, Utility of Use, Institutional Policy, Mandatory Technology Adoption Environment and Enablers (such as, sound internet connectivity, availability of power, technical support and appropriate software) were found to influence the use of Open Source LMS. 6.1 Contributions of the thesis to knowledge The issues examined in this thesis have made meaningful contributions to knowledge. 6.1.1 Conceptual/theoretical contribution The research extended previous research by integrating some constructs in Technology Acceptance Model (TAM), Innovation Diffusion Theory (IDT) and three other parameters, namely Image, Subjective Norm and Facilitating Conditions as a proposed model to examine the factors that influence lecturers’ adoption and use of an Open Source Learning Management System (LMS). After testing the variables in the model, an observed theoretical model has emerged reflecting paths that are significant and those that are not. This model is a theoretical contribution to scholarship. There exist conceptual and theoretical models but there is no practical model that influences lecturers’ adoption and use of an Open Source LMS in the Ghanaian context. Fortunately, in this work, the qualitative data helped to develop a conceptual model designated ‘Open Source LMS adoption and Use model’. This model has also been reformulated capturing two parameters that mediate the independent variables and Actual Use of an Open Source LMS. 178 University of Ghana http://ugspace.ug.edu.gh The researcher has proposed that hypotheses should be developed from them and tested in future studies. Additionally, a model that shows why Sakai/MOODLE is not being used or will not be used by some lecturers has also been formulated from the qualitative data. This is contributing to knowledge in the existing literature on technology adoption/acceptance or use in higher education because while most, if not all, of the theories, examined the adoption/acceptance or the use of the technology for teaching in higher education, this aspect of the study also investigated factors determining the reasons why some lecturers are not using the Open Source LMS for teaching and students’ learning. Furthermore, a figure that illustrates the uses of Open Source LMS in the selected universities and a diagram that shows how technology works as a disc to impact on instructional strategy, students’ assessments and to facilitate research ethics, have also been developed thus indicating a lot of contributions to the existing conceptual and theoretical models in the existing academic data. 6.2 Contributions to Literature The study adds to the literature on the adoption and use of an Open Source LMS as a result, contributing to the regional and global literature on LMS adoption and use by lecturers. Some of the findings of this study support other findings in the existing literature. Such resemblances in findings have been captured and reported in this study. However, other findings have been observed in this study which are not consistent with the existing literature and this is a unique addition to the existing literature on the adoption and use of Open Source LMS by university lecturers in Ghana. 179 University of Ghana http://ugspace.ug.edu.gh 6.3 Methodological additions The measurement scale was adopted and fine-tuned. With the exception of one item, all the factors were loaded significantly to their respective constructs after subjecting them to confirmatory factor analysis. The Reliability Test also registered very high values. The items in the scale manifested adequate validity and reliability in a different culture and geographical location like Ghana. The triangulation effect of integrating quantitative (positivist) and qualitative (constructivist) paradigms for studying factors influencing lecturers’ use of Open Source LMS is clearer than if it were not blended. This is because the numerical and statistical test of the constructs based on and driven by the integrated theoretical model was found to be workable in a new culture where such integrated theories have not been tested before. We can conduct research on LMS in education in Ghana that is both theory driven and explorative with theory building. Several researchers who studied predictors of technology adoption and use employed correlation or regression which highlighted linear relationships among the study variables without examining the interactions and causal relationship between and among the studied variables. This thesis used Confirmatory Factor Analysis and Structural Equation Modelling (SEM). Therefore, variables that have positive and significant relationship as well as an indirect relationship with the output variable using bootstrapping estimates and confidence interval have been established. 6.4 Policy implications for higher education studies and researchers The findings give information that will help university administrators to gain insighst into the determinants of adoption and use and also non-use of LMS in Ghana. Thus, the results have policy and decision making implications for educational institutions in Ghana and Africa. A good appreciation and understanding of the antecedent factors affecting lecturers’ adoption and use of LMS could lead to effective discourses on institutional interventions and an increased and more 180 University of Ghana http://ugspace.ug.edu.gh effective adoption, use and a deeper learning experience for students. LMS designers and university policy- makers could focus more efforts working on significant variables regarding the use of a LMS to make them more appealing and acceptable to the faculty members. It means the study will help plan for wider LMS adoption and use or implement LMS in a more effective way considering common challenges that could limit the adoption and use of an Open Source LMS. The findings of the current study offer practical implications for scholars in Ghana to place a high priority on enhancing E-learning environments through research, publication and advocacy. For researchers, the findings provide a platform on which further investigations could be made by strengthening determinants for lecturer’s adoption and use of Sakai/MOODLE for teaching and learning. The applicability of the proposed model could be tested in other universities with similar contextual conditions to ascertain its replicability or otherwise. Generally, the findings contribute knowledge regarding the role of educational technology in education in a developing country like Ghana. The major lies in the way the study creates awareness among academics of the unstoppable incursion of educational technologies into the higher education environment and the fact that lecturers should get on board of the technological wagon or be left with terrible consequences including being redundant. It also creates awareness among University authorities to create the enabling conditions for smooth adoption and use of the Open Source LMS among lectures in Ghana. 6.5 Limitations and future directions Although a Mixed Methods approach was used, the quantitative aspect employed a cross- sectional survey design. A longitudinal study would provide a more comprehensive picture over time. Another limitation is that, even though the study’s sample size and response rate were reasonable, the participants were selected from four universities only, thus necessitating a wider 181 University of Ghana http://ugspace.ug.edu.gh coverage in future studies. The study was limited to those who were trained in the use of a LMS as well as using Sakai/MOODLE LMS, therefore, future studies should cover the perspectives of those who are not using Sakai/MOODLE LMS. In addition, the study was based on just two types of LMS (Sakai /MOODLE) from an Open Source System. Future studies could investigate both Open Source and the Proprietary System. There were no sampling frames available to enable the researcher to use a systematic or simple random sampling method. To address this deficiency, future investigations should be conducted to test the proposed model using a simple random sampling approach involving a huge data set and multiples of sites. Also, the sample size of participants for the qualitative study could be increased in future studies. 6.6 Conclusion It can be concluded from the current study that Compatibility and Facilitating Conditions indirectly, positively and significantly influenced Actual Use of LMS. Perceived Ease of Use had a significant positive relationship with Actual Use. Trialability was significantly related to Perceived Ease of Use. Additionally, with the exception of Compatibility and Facilitating Conditions, Ease of Use could not mediate the other external factors such as Trialability, Subjective Norm, Image and Actual Use. Providing congenial and facilitative conditions without users’ knowledge on the usefulness and a feeling of ability to use the LMS was meaningless, since it does not impact on Actual Use.The responsibility of University authorities to provide ICT infrastructure and regular training for faculty to appreciate the E-learning environment is at the core of successful embracement and use of LMS and other emerging technologies for teaching and learning. 182 University of Ghana http://ugspace.ug.edu.gh The qualitative study corroborated the quantitative study which found out that factors that influence the use of the Open Source Learning Management Systems (LMS) include Simplicity of Use, Utility, Institutional Policy and Enablers. Technology challenges such as Internet connectivity and power fluctuations that pose a challenge to the adoption and use of the LMS must be addressed by the university administrators. Training offered to all lecturers at different levels of technological skills but boxed in a room without considering their levels, caused boredom for those who were above what was being taught, and difficulty for those who needed the basic training but were sitting under advanced level training. Sakai/MOODLE LMS do not have all the features they are supposed to have in order to enable users use the systems to the maximum. The study re-emphasized the importance of educational technology in higher education in the developing country like Ghana where the conventional way of teaching and learning have dominated the scene for a very long time and it seems to be the norm. 6.7 Recommendations 1. It would be expedient that university authorities and deliverers of learning solutions seek a deeper insight into those factors that influence lecturers’ adoption of an Open Source LMS such as Simplicity of Use, Utility of use and Enablers and capitalize on them. Additionally, the factors inhibiting the use of the Open Source LMS such as lack of appropriate software and slow internet connectivity must be addressed. 2. University authorities need to educate lecturers more on the benefits associated with the use of the Open Source LMS in order that they will adopt and use it. The reasons include the following: First, the relationship between Perceived Usefulness and Actual Use was not found to 183 University of Ghana http://ugspace.ug.edu.gh be significant although positive. Second, Perceived Usefulness of the system influences the perception that the new technology is better than the existing one due to its benefits. Third, if the use of a system is not relevant or does not enhance job performance, users (lecturers) demonstrate less receptiveness to learning and using it although not so significantly. Fourth, the transition from Facilitating Conditions to Actual Use was also not found to be significant, meaning that although provision of enabling conditions may be appreciable, that was not enough to influence lecturers’ Actual Use of the system directly. Lecturers may want to better understand, the usefulness of the Open Source LMS, then they may appreciate the use of the enabling conditions that have been made available. Having acquired an LMS, it would be expedient to inform the faculty members about its features, benefits, and technical relevance to job performance issues so that they can appreciate the LMS and boldly use it. 3. University managements must work effectively to ensure that the use of the LMS is made simple since the findings shows a positively significant influence of Perceive Ease of Use on the Actual Use of the LMS. What it means is that lecturers must be trained well and the enabling conditions must also be made available to ease the use of the Open Source LMS. 4. A mandatory policy for technology adoption and use must be enforced. This is because it has also been found that attitudes and beliefs in using face-to-face classroom based teaching and learning are key barriers to deploying ICT for teaching and learning. 5. It is largely observed that lecturers’ knowledge, beliefs, attitudes and persuasion influence their planning, instructional decisions, ICT adoption and behaviours. Therefore, a detailed analysis of lecturers’ perceptions of the use ICT in education can provide insights into the prerequisites for their successful preparation. If top managements ignore this fact, then their move to engage in blended learning to meet the demand of the times will be a failure. 184 University of Ghana http://ugspace.ug.edu.gh 6. University management must also investigate and work on the lecturers’ belief, values, philosophies and adherence to past practices that that stifle managements’ LMS embracement policy. This could be achieved through training and counselling. This is because Compatibility had a positive relationship with Perceived Usefulness. In other words, the more lecturers perceive a system to be compatible with their belief and needs, the more they perceive the system to be useful to enhance their job performance. Conversely, if their belief is unfavourable, they will not perceive the system to be good for teaching and learning. Also, that the link between Compatibility and Perceived Ease of Use, being significant, suggest that the systems must be simple to use to enhance compatibility. 7. The design of faculty training for digital literacy and right attitude development must be seriously tailored to the needs of non-users since they have a specific attitudinal problem that requires attention. Students must also be properly trained in the use of the Sakai/MOODLE, otherwise no matter how conversant lecturers become with the use of the Sakai/MOODLE, the intention will still be defeated if students lack skill in using same. 8. Training given to faculty on LMS must focus on the individual’s values, belief, past experiences, attitude and prior knowledge in educational technology since they affect Actual Use of the LMS. Besides, educational technology is in its infantry stage in Africa, and as such its adoption will be slow and will also need motivation. Management must factor this in its staff training policy and motivation packages. 9. Adequate motivation must be given to lecturers to integrate educational technology into the face-to-face teaching and learning in order to boost e-learning education. This includes regular training and tying the use of e-learning to promotion and other means of recognition. 185 University of Ghana http://ugspace.ug.edu.gh 10. Lecturers work load must be balanced if they can effectively blend classroom-based and online learning, publish papers in very good journals and also participate in academic administration. The issues above must be factored in when designing training programmes and promotion policy for lecturers to use the Open Source LMS for teaching. 11. Furthermore, regular awareness creation to the effect that there exists Sakai/MOODLE LMS which has been acquired by the educational institutions and must be adopted and used by the lecturers for teaching and students’ learning will help in its smooth adoption and use. Universities should plan awareness campaigns especially for minimizing the wrong perceptions of the lecturers that it is difficult to use Open Source LMS and rather boost confidence in its use by communicating its benefits. Access to the Open Source LMS must be communicated and be made available to fresh lecturers and existing lecturers who are not using it so that they try their hands on the use. Early adopters also have to create the time to help those who are ‘laggards’ to get on board. 12. Social and institutional contexts must be supportive of lecturers' efforts to integrate educational technology into teaching. Power availability, technical support, swift Internet connectivity, as well as regular training of lecturers and students need to be accessible to faculty for successful implementation of E-learning systems. Universities need to engage instructional designers and other technology experts to help lecturers and students to use the Open Source LMS effectively in relation to pedagogy, course design and assessment of students and ethics. Sound design of pedagogy or social interaction depends to a large extent on the availability of technological support. Without sufficient support of technology, undoubtedly many pedagogical and social design activities such as asynchronous online discussions would be difficult to implement. 186 University of Ghana http://ugspace.ug.edu.gh 13. The universities authorities must also be up and doing to ensure that the blended learning environment is made enabling and comfortable for Open Source LMS’ adoption and use. This means that the necessary technical and infrastructural facilities, suitable IT network and IT training must be made available. The interface, features and tools, the functioning of the system, the richness of the contents, the speed of navigation and the interactive ability of the LMS should be regularly monitored, evaluated and upgraded according to the needs and expectations of faculty members and students. 14. Faculty members should be informed of the LMS features such as notes, videos, images or sound and be educated on how to use them. They should also learn how to download and upload resources when delivering instructions to students without difficulty. 15. Google apps and SMS alert must be linked to the LMS features so that students are given instant alert on their cell phone concerning any announcement or assignment. E-mails and calendar are more effective and detailed on the LMS platform and must be made functional. 16. A regular update of anti-virus software to enhance quality and longevity of iPad, Tablets and PCs should be carried out by the university authorities. 17. Budgetary allocations must be regularly made towards the acquisition maintenance of new educational technological tools and devices in order to enhance the sustainability of the E- learning programme. 18. The findings suggest that University authorities, system developers, designers and institutional purchasers of Open Source LMS carefully consider the expectations of lecturers and ensure that selected systems have the features which effectively meet their needs. 187 University of Ghana http://ugspace.ug.edu.gh 19. An evaluation form must be designed and periodically given to faculty members and students to complete. This should be aimed at collecting information about their experiences with the use of the LMS regarding the challenges they encounter and their suggestions to improve the system. The top management of the universities should then critically examine the data and strategize to improve the management of the LMS in order to facilitate adoption and use widely. 20. The proposed conceptual model built from the qualitative data must be hypothesized and tested. 188 University of Ghana http://ugspace.ug.edu.gh REFERENCES Abbad, M. M., Moris, D. & de Nahlik, C. (2009). Looking under the Bonnet: Factors Affecting Student Adoption of E-learning System in Jordan. International Review of Research in Open and Distance Learning, 10(2), Abdallah, A., El, Arif, T., & Elgazzar, A. (2009). The effect e-learning approach on students’ achievement in Biomedical instrumentation course at Palestine polytechnique University. Communication of IBIMA, Vol. 9. ISSN:1943-7765 Abdulah, F., Ward, R. & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behaviour, 63, 75–90. Adedoja, G. & Abimbade, O. (2016). Influence of Mobile Learning Training on Pre-Service Social Studies Teachers' Technology and Mobile Phone Self-Efficacies. Journal of Education and Practice, 7(2), 74-79. Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30, 361-391. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50: 179–211. [CrossRef], [Web of Science ®] Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social behavior, Englewood Cliffs, NJ: Prentice Hall Allen, I. E., & Seaman, J. (2010). Learning on demand: Online education in the United States 2009. Retrieved June 1, 2010 from, http://www.sloan-c.org/publications/survey/pdf/ learningondemand 189 University of Ghana http://ugspace.ug.edu.gh Alghamdi, S. R. & Bayaga, J. (2016). Use and attitude towards Learning Management System(LMS) in Soudi Arabian universities. Eurasi Journal of Mathematics, Science & Technology Education, 12(9),2309-2330. Doi:12973/Eurasia.2016.1281a. Alias, N. A. & Zainuddin, A. M. (2005). Innovation for Better Teaching and Learning: Adopting the Learning Management System. Malaysian Online Journal of Instructional Technology Vol.2, No.2, pp 27-40. ISSN: 1823-1144. Alsabawy, A.Y. & Cater-Steel,S.J.(2016). Determinants of perceived usefulness of e-learning systems. Computers in Human Behaviour. Vol.64.pp.843-858. Anderson, J. (2010). ICT transforming education: A regional guide. Bangkok: UNESCO. Asabere, N.Y. & Enguah, S. E .(2012). Use of information communication technology in tertiary Education in Ghana: A case study of electronic learning. International Journal of communication Technology research. Vol.2(1). Asabere, N. A & Mends-Brew.E.(2012). Distance Learning and Electronic Learning (E-Learning): Are They the Same? An Overview of Some Tertiary Institutions in Ghana. International Journal of Information and Communication Technology Research. Volume 2 No. 9 Asamoah, M. K & Mackin,E. E. (2016). PhD year 1 students’ experience with the Educational Technology and Innovation Course. Africa Education Review,Volume 13, Issue 2, 31-47. Asiedu-Asante, S. K. & Temeng, V. A. (2010). “ICT Development at University of Mines and Technology (UMaT)”, Ghana Mining Journal, Vol. 12, pp. 58 - 62. Asiri, M. J. S., Mahmud, R., Baker, K. A., & Ayub, A.F.B.M. (2012). Factors influencing the use of learning management system in Soudi Arabia higher education: A theoretic framework. Higher education studies. vol.2 (2). 190 University of Ghana http://ugspace.ug.edu.gh Aviram, R. & Tami, D. (2004). The impact of ICT on education: The three opposed paradigms, the lacking discourse. Unpublished manuscript, Beer-Sheva University, Israel Awang, Z. (2012). Structural equation modelling using AMOS graphic. Shah Alam: UiTM Press Awidi,T.I.(2008). Developing an E-learning strategy for public universities in Ghana. EDUCAUSE QUARTERLY.Vol. 31, Number 2. Retrieved from http://www,edcause.edu/EDUCAUSE+EDUCAUSE QuarterlyMagazineVolum/DevelopingElearningStrategyf/162878 Bagozzi, R. P., Davis, F. D., Warshaw, P. R. (1992). Development and test of a theory of technological learning and usage. Human Relations, 45 (7), pp. 660–686 Barker, K. (2004). Diffusion of Innovations: A World Tour. Journal of Health Communication: International Perspectives Volume 9, Supplement 1, pages 131-137. DOI: 10.1080/10810730490271584 Barker, C., Pistrang, N., & Elliot, R., (2002). Research methods in clinical psychology (2nd ed.). Chicester: Wiley.10.1002/0470013435 [CrossRef] Bergeron, F., Rivard, S. and DeSerre, L. (1990). Investigating the support role of the information center. MIS Quarterly, 14: 247–260. [CrossRef]). Berthorn, P.,Wwing, M. & Hah, L. L.(2005). Captivating Company: Dimensions of attractiveness in employer branding. International Journal of advertising,24(2).Pp.151-172 Bertrand, M. & Bouchard, S. (2008). Applying the technology acceptance model to Virtual Reality with people who are favorable to its use. Journal of Cyber Therapy & Rehabilitation, 1: 200–210. 191 University of Ghana http://ugspace.ug.edu.gh Bhattacherjee, A. (2001): Understanding Information Systems Continuance: and Expectation- Confirmation Model,” MIS Quarterly 25(3), 351--370 Boateng, B.A. (2007). Technology in Education: A Critical Social Examination of a Rural Secondary School in Ghana. Unpublished Doctoral Thesis, Ohio University. Boateng, J. K. (2015).Adults pursuing e-learning in Ghana-opportunities, challenges and expectations. Journal of Education and e-learning Research.Vol.2 no.4,64-71. Bollen, K. A. (1989). Structural equations with latent variables, New York, NY: Wiley. Bollen, K. A & Noble, M. D. (2011). Structural equation models and the quantification of behavior. Proceeding of the National Academy of Sciences, 108 (3).Pp. 15639–15646 Borokhovski, E., Tamim, R., Bernard, R. M., Abrami, P. C., & Sokolovskaya, A. (2012). Are contextual and designed student–student interaction treatments equally effective in d istance education? Distance Education, 33, 311–329. doi:10.1080/01587919.2012.723162 [Taylor & Francis Online], [Web of Science] Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. doi: 10.1191/1478088706qp063oa Buabeng-Andoh, C. (2012). Factors influencing teachers’ adoption and integration of information and communication technology into teaching: A review of the literature. International Journal of Education and Development using Information and Communication Technology (IJEDICT), Vol. 8, Issue 1, pp. 136-155 Budu, K.W.A. & Ackah, O. (2016). What challenges affect the implementation of E-learning in Ghana tertiary institutions? International journal of innovation and scientific research. Vol 23(1). pp.214-225 192 University of Ghana http://ugspace.ug.edu.gh Bryne, B. M (2010). Structural equation modelling with AMOS, basic concepts and programming,2nd edition. Rouledge: New York. Calisir, F., Gumussoy, C. A., Bayraktaroglu, A. E. & Kara, D. (2014).Predicting the Intention to Use a Web-Based Learning System: Perceived Content Quality, Anxiety, Perceived System Quality, Image, and the Technology Acceptance Model. Humn Factors and Ergonomics in Manufacturing and Service industries. Volume 24, Issue 5. Pages 515–531 DOI: 10.1002/hfm.20548. Carlsson, C., Carlsson, J., Hyvönen, K. K., Puhakainen, J. & Walden, P. (2006). Adoption of mobile devices/services – Searching for answers with the UTAUT. 39th Hawaii international conference on system sciences, 6 (2006), pp. 1–10 (IEEE Computer Society. Carter, L., & Be’langer, F. (.2005). The utilization of e-government services:Citizens trust, innovation and acceptance factors. Information System Journal, 15(1) 5-25. Carvus, N. & Zabadi, T. (2014). A Comparison of Open Source Learning Management Systems. Procedia-Social and Behavioural Sciences, 143. 521-526. Chai, J.X. & Fan, K.K. (2016). Mobile Inverted Constructivism: Education of Interaction Technology in Social Media. Eurasia Journal of Mathematics, Science & Technology Education, 2016, 12(5), 1425-1442doi: 10.12973/eurasia.2016.1522a Chang, S.& Tung, F. (2007). An empirical investigation of students' behavioural intentions to use the online learning course websites. British Journal of Educational Technology. Volume 39, Issue 1. Pages 71–83 DOI: 10.1111/j.1467-8535.2007.00742.x 193 University of Ghana http://ugspace.ug.edu.gh Chandra, V., & Briskey, J. (2012). ICT driven pedagogies and its impact on learning outcomes in high school mathematics. International Journal of Pedagogies and Learning, 7, 73–83. [Taylor & Francis Online] Chandra, V., & Watters, J. J. (2012). Re-thinking physics teaching with web-based learning. Computers & Education, 58(1):631–640. Chau, Y. K., & Hu, J. H. (2001). Information Technology Acceptance by Individual Professionals: A Model Comparison Approach. Decision Sciences, 32(4), 669-719. Chau, P. K. and Hu, P. H. (2002). Investigating healthcare professionals' decisions to accept telemedicine technology: An empirical test of competing theories. Information & Management, 39: 297–311. Chen, C. H. (2008). Why do teachers not practice what they believe regarding technology integration? The Journal of Educational Research, vol. 102, no.1, pp. 65-75. Chin, W. C., & Todd, P. A. (1995). On the use, usefulness and ease of use of structural equation modelling in MIS research: a note of caution. MIS Quarterly, 19(2), 237–246. Chiu, T.K.F. & Churchill, D. (2016) Adoption of mobile devices in teaching: changes in teacher beliefs, attitudes and anxiety. Interactive Learning Environments. Volume 24, Issue 2. Pages 317-327.http://dx.doi.org/10.1080/10494820.2015.1113709 Chris, S. (2016). Giving up technology and social media: why university lecturers stop using technology in teaching. Technology, Pedagogy and Education. Pages 1-19. http://dx.doi.org/10.1080/1475939X.2016.1217269 Christensen R. & Knezek G. (2008). Self-report measures and findings for information technology attitudes and competencies. In International Handbook of Information Technology in 194 University of Ghana http://ugspace.ug.edu.gh Primary and Secondary Education (eds J. Voogt & G. Knezek), pp. 397–417. Springer, New York, NY. Cigdem, H.& Topcu, A. (2015). Predictors of instructors’ behavioural intention to use learning management system: A Turkish Vocational College example. Computers in Human Behaviour.Vol.52. pp22-28. Coates, H., James, R. Baldwin, G. (2005). Learning Management system on University teaching and learning. Tertiary Education and Management 11:19–36 Conradie, P. W. (2014). Supporting self-directed learning by connectivism and personal learn-ing environments. International Journal of Information and Education Technology, 4,254– 259. Conole, G., & Oliver, M. (2007). Contemporary Perspectives in E-learning Research. London: Routledge. coreDNA (2009). Everything You Need to Know About Digital Marketing. http://www.coreDNA.com retrieved 30th October, 2010 Cocks, K., & Torgerson, D. J. (2013). Sample size calculations for pilot randomized trials: A confidence interval approach. Journal of Clinical Epidemiology, 66. Pp.197–201. doi:10.1016/j.jclinepi.2012.09.002 [CrossRef], [PubMed], [Web of Science ®] Colvin, A.D. & Bullock, A. N. (2014). Technology Acceptance in Social Work Education: Implications for the Field Practicum. Journal of Teaching in Social Work. Volume 34, Issue 5, pages 496-513. DOI:10.1080/08841233.2014.952869 Cowan, P. & Earls, J. (2016). Using the Technology Acceptance Model to determine Teachers’ Attitudes towards the introduction of iPads in the Classroom. In Proceedings of EdMedia: 195 University of Ghana http://ugspace.ug.edu.gh World Conference on Educational Media and Technology 2016 (pp. 921-926). Association for the Advancement of Computing in Education (AACE). Retrieved September 20, 2016 from https://www.learntechlib.org/p/173059. Creswell, J.W, & Plano Clark,V. L. (2011). Designing and Conducting Mixed Methods Research (Second ed.). Sage Publications, Los Angeles, CA Dadzi, P. S. (2009). E-learning and E-library Services at the University of Ghana: Prospects and Challenges. Information Development. Vol. 25(3).207- 217.Doi:10.1177/0266666909340791 Dahlstrom, E., Brooks, D. C. & Bichsel, J. (2014). The Current Ecosystem of Learning Management Systems in Education: Student, Faculty, and IT Perspectives. Research report. Louisville, CO: ECAR. http://www.educause.edu/ecar Daniel,J., Kanwar, A. & Uvalić-Trumbić, S. (2009). Breaking Higher Education's Iron Triangle: Access, Cost, and Quality, Change: The Magazine of Higher Learning, 41:2, 30-35, DOI: 10.3200/CHNG.41.2.30-35 D’Ambra, J., & Rice, R. E. (2001). Emerging factors in user evaluation of the World Wide Web. Information and Management, 38 (6), 373-384 Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Quarterly, 13(3), 319-340. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 475–487. [CrossRef] DiCicco-Bloom, B., & Crabtree, B. F. (2006). The qualitative research interview. MedicalEducation, 40, 314–321. doi:10.1111/j.1365-2929.2006.02418.x Dobre,I. (2015). Learning management systems for higher education - An overview of available options for higher education organizations. The 6th international conference edu world 196 University of Ghana http://ugspace.ug.edu.gh 2014 “education facing contemporary world issues, 180, Procedia - Social and Behavioral Sciences, pp. 313–320. Falvo, D.A. & Johnson, B .A. (2007). The Use of Learning Management Systems in the United States. TechTrends .Volume 51, Number 2 Fathema, N. Shannon, D.& Ross, M. (2015). Expanding The Technology Acceptance Model (TAM) to Examine Faculty Use of Learning Management Systems (LMSs) In Higher Education Institutions. MERLOT Journal of Online Learning and Teaching .Vol. 11, No. 2. Fathema, N., Sutton, K. (2013). Factors influencing faculty members’ Learning Management Systems adoption behavior: An analysis using the Technology Acceptance Model. International Journal of Trends in Economics Management & Technology, Vol. II(vi), pg20-28. Fill, C. (2005). Marketing Communications: Engagement, Strategies and Practice. 4th edition. Prentice-Hall: Essex Fishbein, M. & Ajzen I. (1975). Belief, attitude, intention and behavior: an introduction to theory and research, Reading MA: Addison-Wesley. Fornell, C. & Lacker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement. Journal of Marketing, 18: 39–50. [CrossRef], [Web of Science ®] Fong,K.K. & Wong, S. K. S.(2015). Factors Influencing the Behavior Intention of Mobile Commerce Service Users: An Exploratory Study in Hong Kong. International Journal of Business and Management; Vol. 10, No. 7. doi:10.5539/ijbm.v10n7p39. 197 University of Ghana http://ugspace.ug.edu.gh Franklin, C. (2007). Factors that influence elementary teachers use of computers. Journal ofTechnology and Teacher Education, vol. 15, no. 2.Pp. 267–293. Fraser, K. (2014). Position paper: Defeating the paradigm wars in accounting: A mixed-methods approach is needed in the education of PhD scholars. International Journal of Multiple Research Approaches, 8:1. Pp.49-62, DOI: 10.5172/mra.2014.8.1.49. Gautreau ,C. (2011).Motivational factors affecting the integration of a learning management system by faculty. The Journal of Educators Online, 8 (1) , pp. 1–25 Giovanis ,A.N., Binioris, S. & Polychronopoulos, G. (2012) "An extension of TAM model with IDT and security/privacy risk in the adoption of internet banking services in Greece", EuroMed Journal of Business, Vol. 7 Issue: 1, pp.24-53, https://doi.org/10.1108/14502191211225365 , N. (2003). Understanding Reliability and Validity in Qualitative Research. The Qualitative Report, 8 (4). 597-607 Grgurović, M. (2014). An application of the Diffusion of Innovations theory to the investigation of blended language learning. Innovation in Language Learning and Teaching. Volume 8, Issue 2. pages 155-170. DOI:10.1080/17501229.2013.789031 Habib, L .& Johannesen, M. (2014). Perspectives on academic staff involvement in the acquisition and implementation of educational technologies. Teaching in Higher Education, Volume 19, Issue 5. Pp. 484-496. Halabi, A.K., Essop, A. Carmichael, T. and Steyn, B. (2014). Preliminary evidence of a relationship between the use of online learning and academic performance in a South African first-year university accounting course. Africa Education Review Volume 11, pages 164-182. Taylor and Frances Online. 198 University of Ghana http://ugspace.ug.edu.gh Hall, D. and Mansfield, R. (1975). Relationship of age and seniority with career variables of engineers and scientists. Journal of Applied Psychology, 16: 250–279. Hair, J. F.,Black, W. C., Babib, B .J., Anderson, R.E.,& Tatham, R .L. (2007). Multivariate analysis, Pearson, New Jersey. Hair, J. F.,Black,W. C.,Babbin, B. J. & Anderson, R. E.(2010). Multivariate Analysis .7th ed. New Jersey: Prince Hal, Upper Saddle River. Hardgrave, B., Davis, F., & Riemenschneider, C. (2003). Investigating Determinants of Software Developers’ Intentions to Follow Methodologies. Journal of Management Information Systems, 20(1), 123-151. Heiman, G.W. (2002). Research Method in Psychlogy(3ed). Boston,MA: Houghhton Mifflin Co Hellström, T. (2008). Transferability and naturalistic generalization: New generalizability concept for social science or old wine in new bottles? Quality and Quantity, 42.Pp. 321–337. [CrossRef], [Web of Science ®] Henriksen, D. Mishra, P.,Greenhow,G., Cain,W. & Cary Roseth, C.(2014). Michigan State University. A tale of two courses: Innovation in the Hybrid/ Program at Michigan State University TechTrends.Volume 58, Number 4. Henriksen, D. Mishra,P. & Fisser,P.(2016). Infusing Creativity and Technology in 21st Century Education: A Systemic View for Change. Journal of Educational Technology & Society. Vol. 19, No. 3. pp. 27-37. Stable URL: http://www.jstor.org/stable/jeductechsoci.19.3.27 Hong,S. T., Shin, J.C.,Kang, M. S. (2008). Study of factors affecting adoption intention toward home use robot services. Asian Market. J., 9 (4), pp. 271–303. Howie, S. J. (2010). ICT-supported pedagogical policies and practices in South Africa and Chile: emerging economies and realities. Journal of Computer Assisted Learning, 26(6), 507-522 199 University of Ghana http://ugspace.ug.edu.gh Hu, L., Bentler, P. M. and Kano, Y. (1992). Can test statistics in covariance structure analysis be trusted?. Psychological Bulletin, 112: 351–362. [CrossRef], [PubMed], [Web of Science ®], [CSA] Huang, L. Y. (2004). A study about the key factors affecting users to accept Chunghwa Telecom's Multimedia on Demand. Unpublished Master Thesis, National Sun Yat-Sen University. . Hutinger, P., Johanson, J. and Stoneburner, R. (1996). Educational programs of children with multiple disabilities: A case study report on the state of the practice. Journal of Special Education Technology, 8: 16–35. Ingram, N. R. (2014). Time past: impacts of ICT on the pedagogic discourse in the Interactive project. Technology, Pedagogy and Education. DOI:10.1080/1475939X.2014.972440 Jaschik, S. & Lederman, D. (2014). The 2014 Inside Higher Ed Survey of faculty Attitudes on Technology: A Study by Gallup and Inside Higher Ed. Washington, DC: https://www.insidehighered.com/news/survey/online-ed-skepticism-and-self-sufficiency- survey-faculty-views-technology. Jaffer, S., Ng’ambi, D. & Czerniewicz, L. (2007). The role of ICTs in higher education James, PTJ. (2008). Academic staff perceptions of ICT and e-learning: A Thai HE case study. The Turkish Online Journal of Educational Technology, 7(4), 36-45. Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigmwhose time has come. Educational Researcher, 33(7).Pp.14–26. doi:10.3102/0013189X033007014. 200 University of Ghana http://ugspace.ug.edu.gh Johnson, R. B., Onwuegbuzie, A. J., & Turner, L. A. (2007). Toward a definition of mixed methods research. Journal of Mixed Methods Research,1 (2). Pp.112-133. Jöreskog, K. G. and Sörbom,D. (1997). LISREL 8: A Guide to the Program and Applications. Chicago, IL: SPSS. Jump, L. (2011). Why University Lecturers Enhance their Teaching through the Use of Technology: A Systematic Review. Learning, Media and Technology, 36 (1), 55-68. DOI: http://dx.doi.org/10. -1080/17439884.2010.521509. Kasim, N. N. M. & Khalid, F. (2016). Choosing the Right Learning Management System (LMS) for the Higher Education Institution Context: A Systematic Review. iJET ‒ Volume 11, Issue 6. http://dx.doi.org/10.3991/ijet.v11i06.5644. Karahanna, E., Straub, D.W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23, 183-213. Khatib, N. M. (2016)The Adoption of Technology-Enhanced Instruction to Support Education for All. Gifted and Talented International. Volume 29,Issue 1-2, Pages 93-98 http://dx.doi.org/10.1080/15332276.2014.11678432 . Kearsley, G. (2002). Is online learning for everybody? Educational Technolo-gy, 42(1), 41–44 Kilmon, C.& Hagan, M.H. (2007). Course Management Software adoption: A diffusion of innovations perspectives.Campus-wide information System, Vol. 24, Issue 2. Pp.134-144 Knezek, G. & Christensen, R. (2002). Impact of New Information Technologies on Teachers and Students. Education and Information Technologies, vol. 7, no. 4. Pp. 369–376 Koça,T., Turana,A. H., Okursoyb, A. (2016). Acceptance and usage of a mobile information system in higher education: An empirical study with structural equation modeling.The 201 University of Ghana http://ugspace.ug.edu.gh International Journal of Management Education. Volume 14, Issue 3. Pages 286–300. http://dx.doi.org/10.1016/j.ijme.2016.06.001 Kwofie,B. & Henten, A. (2011).The advantages and challenges of e-learning implementation: The story of a developing nation. Paper presented at World Conference on Education sciences;Bahcesehir University, istabul,Turkey. Kreijnsa, K, Ackerc,F.V., Vermeulend,M., Buurenc, H. V. (2013).What stimulates teachers to integrate ICT in their pedagogical practices? The use of digital learning materials in education. Computers in Human Behavior. Volume 29, Issue 1, Pages 217–225. http://dx.doi.org/10.1016/j.chb.2012.08.008 Lau, C.,Palvia, P. & Chen, J. (2009). Information technology adoption begaviour life cycle: Towards a technology continuance theory(TCT). International journal of information management, 29(4),309-320. Lee,Y. H.,Hsieh, Y. C.,& Hsu, C. N. (2011).Adding innovation diffusion theory to the technology acceptance model: supporting employees’ intentions to use E-learning systems. Education technology & society,14(4),124-137. Lee, S., Yoon, J.& Lee, I. (2009). Learners Acceptance of e-learning in South Korea: Theories and results. Computers and Education, Vol.53. 1320-1329. Lee,S.Y. & Zhu, H.(2002). Maximum likelihood estimation of nonlinear structural equation models, Psychometrika 67. 189–210. Lee, Y.-H., Hsieh, Y.-C., & Hsu, C.-N. (2011). Adding Innovation Diffusion Theory to the Technology Acceptance Model: Supporting Employees' Intentions to use E-Learning Systems. Educational Technology & Society, 14 (4), 124–137 Lee, Y. H. (2007). Exploring key factors that affect consumers to adopt e-reading services. Unpublished Master Thesis, Huafan University 202 University of Ghana http://ugspace.ug.edu.gh Legris, P., Ingham, J. and Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information and Management, 40(3): 1–14. [CrossRef] Legris, P.,Ingham,J. & Collerette,P. (2003).Why do people use information technology? A critical review of the technology acceptance model. Information &Management, 40. pp. 191–204 Li, Y. H. & Huang, J. W. (2009). Applying theory of perceived risk and technology acceptance model in the online shopping channel. World Academy of Science. Engineering and Technology, 53: 919–925. Lim, C. P., & Chai, C. S. (2008). Teachers’ pedagogical beliefs and their planning and conduct of computer-mediated classroom lessons. British Journal of Educational Technology, vol. 39, no. 5, pp. 807–828. López-Nicolása, C., Molina-Castilloa,F. L. & Bouwmanb, H. (2008).An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information & Management. Volume 45, Issue 6., 359–364. Long, H. (2017).Validity in mixed methods research in education: the application of Habermas’critical theory. International Journal of Research and Methods in Education. VOL. 40, No. 2, 201–213 http://dx.doi.org/10.1080/1743727X.2015.1088518 Lu, Y., Deng, Z. & Wang, B. (2010). Exploring factors affecting Chinese consumers’ usage of short message service for personal communication. Information Systems Journal, 20 (2) pp. 183–208 203 University of Ghana http://ugspace.ug.edu.gh Lustig M. W. & Koester J. (2006). Intercultural Competence: Inter personal Communication Across Cultures. 3rd edition. Pearson Education: Boston, MA South Africa: One strategy for addressing teaching and learning challenges. International Journal of Education and Development using Information and Communication,3 (4), 131-142. Marcelo,C.Yot, C.Mayor, C. (2015). University Teaching with Digital Technologies. Comunicar Vol. 23, Issue 45, p117-124. 8p. 1 MacCallum,J., Cumming‐Potvin,W., Durrant,C., Kissane,B. & Erica‐Miller,E. (2008).Teachers’ journeys towards critical use of ICT. Learning, Media and Technology. Volume 33, Issue 4, 313-327. MacCallum, R. C., Roznowski, M. & Necnowitz, L. (1992). Model modifications in covariance structure analysis: the problem of capitalization on chance. Psychological Bulletin, 111: 490–504. [CrossRef], [PubMed], [Web of Science ®], [CSA] Majhi,S. & Mahrana, B. (2010). Innovative web 2.0 technologies for integrating e-learning process. XI MANLIBNET:Trends and challenges in management and corporate libraries in Digital era, no.8. Makura, A. H. (2014). Students’ Perceptions of the Use of ICT in a Higher Education Teaching and Learning Context: The Case of a South African University. Mediterranean Journal of Social Sciences. Vol 5, No 11. DOI: 10.5901/mjss.2014.v5n11p43. Marfo, J.S. & Okine, R. K. (2010). Implementation of e-Learning in Ghanaian Tertiary Institutions. (A Case Study of KNUST) retrieved 1st October, 2016. 204 University of Ghana http://ugspace.ug.edu.gh Marra, R.M., Moore, J. L. & Klimczak, A.K. (2004). Content analysis of online discussion forums: A comparative analysis of protocols. Educational Technology: Research and Development, 52(2): 23–40. Moore, G. C. & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222. Morris, M. G. & Venkatesh, V. ( 2000). Age differences in technology adoption decisions: Implications for a changing workforce. Personnel Psychology, 53: 375–403. [CrossRef], [Web of Science ®]) Mullamaa, K. (2010). ‘ICT in language learning-benefits and methodological implications’, International Education Studies, 3(1), 38-44 Mukoko, B. (2012). Determinants of Computer and Internet Adoption and use in Cameroon. African Review of Economics and Finance. 3(2),96-128. Mtebe, J. S (2015). Learning Management System success: Increasing Learning Management System usage in higher education in sub-Saharan Africa. International Journal of Education and Development using Information and Communication Technology (IJEDICT)., Vol. 11, Issue 2, pp.51-64 Mykhnenko , V. (2016): Cui bono? On the relative merits of technology-enhanced learning and teaching in higher education, Journal of Geography in Higher Education, DOI:10.1080/03098265.2016.1217832. Ngololo, E.N., Howie. S. J. & Plomp, T. (2012) An evaluation of the implementation of the National ICT Policy for Education in Namibian rural science classrooms, African Journal of Research in Mathematics, Science and Technology Education,16:1, 4-17. 205 University of Ghana http://ugspace.ug.edu.gh Norris, D. M. & Lefrere. (2011). Transformation through expeditionary change using online learning and competence building technologies. Research in Learning Technologies, Vol. 19(1).pp61-72. Norris, D.M. & Dollence, M.D. (1995). Transforming higher education, a vision for learning in the 21st Century. Ann Arbor,MI. Society for College and university planning. Nunnally, J. C. (1978). Psychometric theory, 2nd. Ed. New York, NY: McGraw-Hill. O’Bannon, B. and Judge, S. 2004/2005. Implementing partnerships across the curriculum with technology. Journal of Research on Technology in Education, 37(2): 197–217. [Taylor & Francis Online] INSPEC Accession Number: 9442923. DOI: 10.1109/FIE.2006.322498 Oh,J. & S Yoon,S.(2014).Validation of Haptic Enabling Technology Acceptance Model (HE- TAM): Integration of IDT and TAM. Telematics and Informatics. Volume 31, Issue 4, Pages 585-596. https://doi.org/10.1016/j.tele.2014.01.002 Oha,J., Yoonb,S. (2014)Validation of Haptic Enabling Technology Acceptance Model (HE- TAM): Integration of IDT and TAM. Telematics and Informatics. Volume 31, Issue 4, 585–596. http://dx.doi.org/10.1016/j.tele.2014.01.002 Onwuegbuzie, A. J., Dickson, W. B., Leech, N. L., Zoran, A. G. (2009). Qualitative framework for collecting and analyzing data in Focus Group Research. International Institute for Qualitative Methodology. Oruça, O.E. & Tatarb, C. (2016). An investigation of factors that affect internet banking usage based on structural equation modeling. Computers in Human Behavior. Volume 66.Pp. 232–235. http://dx.doi.org/10.1016/j.chb.2016.09.059 206 University of Ghana http://ugspace.ug.edu.gh Pallant,J .(2011). SPSS Survival Manual: A step by step guide to data analysis using SPSS. Australia Panda, S., & Mishra, S. (2007). E-Learning in a Mega Open University: Faculty attitude, barriers and motivators. Educational Media International, 44(4), 323-338. doi: 10.1080/0952398070168085 Pelgrum, W. J. (2001). Obstacles to the integration of ICT in education: Results from a worldwide educational assessment. Computers & Education, 37: 163–178. [CrossRef], [Web of Science Picciano, A. G. (2009). “Blending with Purpose: The Multimodal Model.” Journal of Asynchronous Learning Networks 13 (1): 7–18. http://sloanconsortium.org/jaln/v13n1/blending-purpose-multimodal-model Plair, S. (2008). Revamping professional development for technology integration and fluency. The clearing house, vol. 82, no .2. Pp. 70-74. Pretorius, HW., Steyn, A.A & Johnson, RD. (2012). Pair teaching of ICT in higher education: a multi-perspective reflection, Research in Higher Education Journal, 17, 1- 12.Powers, S. and Mitchell, J. . Student perceptions and performance in a virtual classroom environment. Paper presented at the annual meeting of the American Educational Research Association. Chicago. Rangaswamy, A. and S. Gupta. (2000). Innovation adoption and diffusion in the digital environment: some research opportunities. Rieflier, P.,Diamantopoulos, A.& Siguaw, J.A. (2012). Cosmopolitan consumers as a target group for segmentation. Journal of international Business Studies,43.285-305. 207 University of Ghana http://ugspace.ug.edu.gh Risjord, M., Moloney, M., & Dunbar, S. (2001). Methodological triangulation in nursing research. Philosophy of the Social Sciences, 31(1), 40–59. Rogers, E. M. (2003). Diffusion of innovations. New York: Free Press Rogers, E. M. (1995). Diffusion of Innovations(3rd ed.). New York: MacMillan. Rogers, E. (1995).. Diffusion of innovations, Vol. 4, New York, NY: The Free Press Rogers E. M. (1983). The Diffusion of Innovations. 3rd edition.Free Press: New York, NY. Reyes, V.C. (2015). How do school leaders navigate ICT educational reform? Policy learning narratives from a Singapore context. International Journal of Leadership in Education: Theory and Practice. Volume 18, Issue 3, pages 365- 385DOI:10.1080/13603124.2014.982200 Rytkønen, M. & Larsen,S. E-learning Appraisal Report University of Ghana College of Health Sciences Ghana.http://itlc.science.ku.dk/projekter/building_stronger_universities/Appraisal_Report _BSU_UG_FINAL.pdf. Retrieved 3rd October,2016 Saade, R. G., Nebebe, F. and Tan, W. (2007). Viability of the “technology acceptance model” in multimedia learning environments: A comparative study. Learning, 3: 175–184). Sale, J. E. M., Lohfeld, L. H. & Brazil, K. (2002). Revisiting the quantitative-qualitative debate: Implications for mixed-method research. Quality and Quantity, 36.Pp.43–53. [CrossRef], [Web of Science ®] Sandelowski, M. (1995). Sample size in qualitative research. Research in Nursing & Health, 18. Pp.179–183. [CrossRef], [PubMed], [Web of Science ®] 208 University of Ghana http://ugspace.ug.edu.gh Sánchez-Santamaría,J., Ramos,F.J. Sánchez-Antolín,P. (2015). The Student’s Perspective: Teaching Usages of Moodle at University. Proceedings of ICERI2012 Conference, 19th- 21st November 2012, Madrid, Spain (pp. 2968-2973). Sarfo,F. K.,& Yidana,I. (2016). University lecturers’ experience in the design and use of moodle and blended learning environment. The online journal of Horizons in Education.Vol.6(2).pp.143- Schneider,J. B., Nora,A.,Stage, F. K., Balow, E.A., & King,J. (2006). Reporting structural equation modelling and confirmatory factor analysis results: A review. The journal of Educational research ,99(6).Pp. 323-338. Selim,H.M. (2007).Critical success factors for e-learning acceptance: Confirmatory factor models. Computer and Education. Shanan G., Michael, L. Harris & Susan M. Colaric (2008). Technology Acceptance in an Academic Context: Faculty Acceptance of Online Education. Journal of Education for Business, 83:6, 355-359, DOI: 10.3200/JOEB.83.6.355-359 Sekaran,V. (2000). Research Methods for Business: A skill Binding Approach.2nd ed. John Wiley & Sons Inc. New York. Pp.93. Šebjan,U & Tominc, P.(2015). Impact of support of teacher and compatibility with needs of study on usefulness of SPSS by students. Computers in Human Behavior. Volume 53. Pages 354– 365. Schierz.P., Schilke,O. Wirtz, B.(2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, 9 (3), pp. 209–216 209 University of Ghana http://ugspace.ug.edu.gh Schoonenboom, J. (2014). Using an adapted, task-level technology acceptance model to explain why instructors in higher education intend to use some learning management system tools more than others. Computer and Education, Vol. 71. PP.247-256 Schepers, J .L.& Wetzels, M.G.M.(2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects: Information & Management Volume 44, Issue 1. Pages 90-103. https://doi.org/10.1016/j.im.2006.10.007 Schierz ,P., Schilke,O. & Wirtz,B. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, 9 (3), pp. 209–216. Sims, R. (2008). Rethinking (e)learning: A manifesto for connected generations.Distance Education, 29, 153–164. [Taylor & Francis Online], [Web of Science ®] Sherry, L., & Gibson, D. (2002). The path to teacher leadership in educational technology. Contemporary issues in technology and teacher education, vol. 2, no. 2, pp. 178-203. Shih, H. P. (2004). Extended technology acceptance model of Internet utilization behavior. Information & Management, 41: 719–729. [CrossRef], [Web of Science) Singh. G. and Hardeiker, B. (2012). Barriers and enablers to adoption and diffusion of eLearning- A systematic review of the literature in a need for an integrated approach. Journal of Education and Training. 56(2/3):105-121. Smith, K. (2012). Lessons learnt from literature on the diffusion of innovative learning and teaching practices in higher education. Innovations in Education and Teaching International, Volume 49, Issue 2, pages 173-182. DOI:10.1080/14703297.2012.677599. Smith, J. K. (1983). Quantitative versus qualitative research: An attempt to clarify the issue. Educational Researcher, 12. Pp.6–13. [CrossRef] 210 University of Ghana http://ugspace.ug.edu.gh Smith, J., Flowers, P., & Larkin, M. (2009). Interpretative phenomenological analysis: Theory, method and research. London: Sage. Song,X, Y. & Lee, S. Y. (2002). Analysis of structural equation model with ignorable missing continuous and polytomous data, Psychometrika 67. Pp.261–288. Song, X. Y., Lu, Z., Hser,Y. I., Lee, S. Y. (2011). A Bayesian approach for analyzing longitudinal structural equation models. Struct. Equ. Model. 18.(183–194). Sunkwa, S. (2008). Online learning in higher education in sub-Saharan Africa: Ghanaian universities students’ experiences and perceptions. International Review of Research in Open Distance learning.Vol.9(3). ISSN 1492-3831 Suh,S.H. (2004). Application of TAM and IDT regarding adoption of supply chain model. J. Ind. Econ. Stud., 17 (4), pp. 1443–1466 Sun, Y. H. S. (2011). Online language teaching: The pedagogical challenges. Knowledge Management & E-Learning: An International Journal, 3, 428–447. Retrieved from http://www.kmel-journal.org/ojs/index.php/online-publication/index. Strodel, E. J., Thomson,T. L & MacDonald, C. J. (2006). Learners’ perceptions on what is missing from e-learning: Interpretations through the community of inquiry framework. International Review of Research in Open and Distance- learning, 7(3):1–24. Taylor, S. & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2) pp. 144-176. 211 University of Ghana http://ugspace.ug.edu.gh Tagoe, M. (2012). Students’ perceptions on incorporating e-learning into teaching and learning at the University of Ghana. International Journal of Education and Development using information communication technology, vol.8(1). pp.91-103). Tagoe, M.A. (2013). Incorporating e-learning into teaching and learning at the University of Ghana: Perception of Faculty. Ghana Social Science journal, volume 10, Nos 1and 2, 53- 78. Tashakkori, A and Teddlie C (Eds) (2003). Handbook of Mixed-methods in Social & Behavioral Research Thousand Oaks CA, Sage Tashakkori, A., Teddlie.C. (2003). “The Past and Future of Mixed Methods Research: From Data Triangulation to Mixed Model Designs.”In Handbook of Mixed Methods in Social and Behavioral Research, edited by A. Tashakkori and C.Teddlie, 671701. Thousand Oaks, CA: Sage Tashakkori, A., & Creswell , J.W. (2007.“The New Era of Mixed Methods.” Journal of Mixed Methods Research 1(1): 3–7. doi:10.1177/2345678906293042 Teo, T. (2012). Examining the intention to use technology among pre-service teachers: an integration of the Technology Acceptance Model and Theory of Planned Behavior. Interactive Learning Environments. Volume 20, Issue 1, 3-18. DOI:10.1080/10494821003714632 Teo, T. T., Lee, C. B., & Chai, C. S. (2008). Understanding pre-service teachers' computer attitudes: applying and extending the technology acceptance model. Journal Of Computer Assisted Learning, 24(2), 128-143.doi:10.1111/j.1365-2729.2007.00247.x Teo, T. (2009).Modelling technology acceptance in education: A study of pre-service teachers. 212 University of Ghana http://ugspace.ug.edu.gh Computers & Education, 52 (2), pp. 302–312. Teo, T. (2010). Examining the influence of subjective norm and facilitating conditions on the intention to use technology among pre-service teachers: A structural equation modeling of an extended Technology Acceptance Model. Asia Pacific Education Review, 11(2), 253- 262 Thorleif, L. (2012). Combining Qualitative and Quantitative Approaches: Some Arguments for Mixed Methods Research. Scandinavian Journal of Educational Research, 56:2. Pp.155-165, DOI: 10.1080/00313831.2011.568674 Tok, BR & Sora M. (2013). Perspective of emerging integrating technology (ICT) in learning and teaching. International Journal of Information and Education Technology, 3 (2) pp.282-285. Tondeur, J., Valcke, M., & van Braak, J. (2008). A multidimensional approach to determinants of computer use in primary education: Teacher and school characteristics. Journal of Computer Assisted Learning, vol. 24, pp. 494–506. Tshabalala, M.I, Ndeya-Ndereya, C. & van der Merwe, T.(2014). Implementing Blended Learning at a Developing University: Obstacles in the Way. Electronic Journal of e-Learning, v12 n1, p101-110 2014 Tung,F., Chang,S., Chou,C. (2008). An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. I. J. Medical Informatics 77 (5). Pp. 324–335. Underwood, J., & Dillon, G. (2011). Chasing dreams and recognising realities: Teachers’ responses to ICT. Technology, Pedagogy and Education, 20.pp. 317–330. [Taylor & Francis Online], [Web of Science ®] 213 University of Ghana http://ugspace.ug.edu.gh . Tung,F.; Chang,S.; Chou, C. (2007). An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. International journal of medical informatics 77, 324–335 Tung, F., Lee, M. S.,Chen, C.,Hsu, Y. (2009). An Extension of Financial cost and TAM Model With IDT For Exploring Users’’ Behavioral Intention to use. The CRM Information System. Social Behaviour and Personality. 37(5.pp., 621-626. DOI 10.2224/sbp.2009.37.5.621 University of Education (2012). Using MOODLE for Teaching and Learning at University of Education. Final Report- A Research Project Sponsored by the Partnership for Higher Education in Africa Educational Technology Initiative (PHEAETI) University of Education (2014). Partnership for Higher Education in Africa. Johannesburg: Published by the South African Institute for Distance Education (Saide). Creative Commons BY 3.0 License (CC BY 3.0).ISBN: 978-0-620-60354-6 University of Ghana (2009). ICT Policy University of Ghana (2014). Report of the Committee for the deployment of ICT in University of Ghana Academic Process. Unwin,T., Kleessen, B., Hollow, D., Williams, J. B., Oloo, L .M.John Alwala, Mutimucuio, A.I., Eduardo, F. & Muianga, X. (2010). Digital learning management systems in Africa: myths and realities. Open Learning: The Journal of Open, Distance and e-Learning Vol. 25 , Iss. 1. Pp.5-23. van Raaij,E.M. &Schepers, J. (2008). The acceptance and use of a virtual learning environment in China. Computers and education, vol.50. pp.838-852. 214 University of Ghana http://ugspace.ug.edu.gh Venkatesh, V & Davis,F. (2000).A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46 (2) (2000), pp. 186–204. Venkatesh, V., Morris M. G., Davis, G. B. & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3): 425-478. Venkatesh,V.& Morris,M.G.(2000). Why don’t men stop to ask for directions? Gender,social influence and their role in technology acceptance usage. MIS Quarterly Vol.24.No 1 pp.115-139.Doi: 10.2307/3250981. Venkatesh, V.; , S.; and Bala, H. (2013). "Bridging the Qualitative–Quantitative Divide: Guidelines for Conducting Mixed Methods Research in Information Systems," MIS Quarterly, 37 (1) pp.21-54. Vilhelmina, V.(2012). Dynamics of student performance and attitude to the use of ICT in higher Education. Vocational Education: Research & Reality. Issue 23.pp160-169. Wang, Q. (2008). A generic model for guiding the integration of ICT into teaching and learning. Innovations in Education and Teaching International Volume 45, Issue 4.Pp. 411-419. Wang, Y., & Chen, N. S. (2013). Engendering interaction, collaboration, and reflection in the design of online learning assessment in language learning: A reflection from the course designers. In B. Zou, M. Xing, C. Xiang, Y. Wang, & M. Sun (Eds.), Computer-assisted foreign language teaching and learning: Technological advances (pp. 16–38). Hershey, PA: IGI Global. Wang, W. & Li,H. (2012). Factors influencing mobile services adoption: A brand-equity perspective. Internet Research, 22 (2), pp. 142–179. Wang, W. & Wang,C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53 (3), 761–774. 215 University of Ghana http://ugspace.ug.edu.gh Willig, C. (2001). Introducing qualitative research in psychology. Adventures in theory and method. Philadelphia: Open University Press. Winter, V.R., MSW (2013). Diffusion of Innovations Theory: A Unifying Framework for HIV Peer Education. America journal of Sexuality Education.Volume 8 (4), 228-245. Wozney, L., Venkatesh, V., & Abrami, P.C. (2006). Implementing computer technologies: Teachers' perceptions and practices. Journal of Technology and teacher education, 14(1), 173-207. Woods, R. Baker, J.D.& Hopper, D.(2004). Hybrid structures: faculty use and perception of web- based courseware as a supplement to face-to-face instruction. The Internet and Higher Education, 7, 281–297 http://dx.doi.org.ez.sun.ac.za/10.1016/j.iheduc.2004.09.002. Wu,J. H. & Wang, S.(2005).What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & Management.Volume 42(5), 719– 729. Wu, J. H., Wang,S. C. & Lin, L.M (2007). Mobile computing acceptance factors in the healthcare industry: a structural equation model, Int. J. Med. Inform. 76. 66–77. Wu,L. Wu,L.,Chang, S. (2016). Exploring consumers’ intention to accept smartwatch. Computers in Human Behavior. Volume 64, 383–392. Wu,I.L.& Wu,K.W. (2005). A hybrid technology acceptance approach for exploring e- CRMadoption in organizations. Behaviour & Information Technology, Vol. 24(4) 303 – 316. Xu, Z.,& Yuan, Y.(2009). Principle based disputes resolution for consumer protection. Journal of Knowledge-based system,22(1), 18-27. 216 University of Ghana http://ugspace.ug.edu.gh Yang, H. (2012). ICT in English schools: transforming education? Technology, Pedagogy and Education Volume 21, Issue 1. Pp. 101-118.DOI:10.1080/1475939X.2012.65988 Yang. K.(2010). Determinants of US consumer mobile shopping services adoption: Implications for designing mobile shopping services. Journal of Consumer Marketing, 27 (3), 262–270. Yang, M. M. (2007). An exploratory study on consumers’ behavioral intention of usage of third generation mobile value-added services. Unpublished Master Thesis, National Cheng Kung University. Yi,M.Y.,Jackson,J. D., Pack,J. S.& Probst, J.C.(2006). Understanding information technology acceptance by individual professionals: Toward an integrated view. Information and Management 4,.350-363. Yuen, H.K., & Ma, W.W.K (2008). Exploring teachers’ acceptance of e-learning technology. Asia Pacific Journal of teacher Education, Vol.36(3), pp.229-243. Zhang, D., Zhao, J., Zhou, L., & Numamaker, J. (2004). Can e-learning replace classroom learning? Communication of the ACM, 47(5),75 78. Zhang, N., Guo X.,Chen G. (2008). IDT-TAM Integrated Model for IT Adoption. Tsinghua Science and Technology, 13(3) 306-311. Zhang,Y.; Tian,G., Tang,N. (2016).Latent variables election in structural equation models. Journal of Multivariate Analysis 152, 190–205 Zhao, Y. (2007). Social studies teachers’ perspective of technology integration. Journal of Technology and Teacher Education, 15(3), 311–333. Zhou,T.(2011). The effect of initial trust on user adoption of mobile payment. Information Development, 27 (4), 290–300. 217 University of Ghana http://ugspace.ug.edu.gh APPENDICES PH.D. DATA COLLECTION QUESTIONS Thank you so much for your consent to participate in this survey research. I am Moses K. Asamoah (Rev.), a Ph.D. candidate at University of Ghana, Legon. This questionnaire is designed to solicit data from lecturers who have the opportunity to adopt and use Sakai or MOODLE Learning Management System (LMS) for teaching and students’ learning due to the fact that their University has acquired the Sakai/MOODLE LMS. The questionnaire has three sections. Section ‘A’ constitutes the respondents’ bio-data. Section ‘B’ focuses on the respondents’(lecturers’) Sakai or MOODLE LMS adoption behaviour. Section ‘C’ is on the actual use of Sakai/MOODLE LMS. Participation in this survey is voluntary and your responses will be kept anonymous. Kindly read each question carefully and select your true answers by clearly circling or ticking the appropriate response category at the specific place provided. Please feel free to make any other comments. If you have any questions, please contact me on 0244635226 or 12345moseskumi@gmail.com SECTION A BIO-DATA In response to each question, please select the answer within the response set that applies to you. 1. Your age: □ Less than 30 years □ 30-39 years □ 40-49 years □50 and above years. 2. Sex: □Male □Female 3. The University you (the respondent) are teaching: □ University of Education □ University of Ghana. □University of Professional Studies Accra 4. Your Faculty/College: □ Arts and Humanities □Education □Health Sciences □Others (specify)________________________ 5. Educational level: □MA/MSC/MBA □MPhil □Ph.D. 218 University of Ghana http://ugspace.ug.edu.gh 6. Rank: □ Lecturer/ Res. Fellow □Snr Lecturer/ Snr Res. Fellow □Ass, Pro./Professor 7. The number of years you have been teaching in the University: □ less than 10 years □11-20 years □21-30 years □31 years and over 8. Religious affiliation: □Christianity □Islam □Buddhism □Other (specify)_________ 9. Marital Status: □Married □Unmarried 10. Nationality: □ Ghanaian □ Non-Ghanaian 11. I am aware of the available of Sakai/MOODLE LMS in my University: a. Yes b. No 12. I have been trained to use MOODLE/Sakai LMS for teaching. a. Yes b. No SECTION B Please answer each question based on the ratings below: 1 =Strongly Disagree; 2=Disagree; 3-Neutral; 4=Agree; 5=Strongly Agree Strongly Disagree Neutral Agree Strongly Disagree Agree CPL:1 Learning the Sakai/MOODLE LMS was 1 2 3 4 5 easy for me. CPL:2 I think the Sakai/MOODLE LMS is clear 1 2 3 4 5 and understandable. CPL:3 I find the Sakai/MOODLE LMS easy to 1 2 3 4 5 use. ADV:1 Using the Sakai/MOODLE LMS enables 1 2 3 4 5 me to accomplish tasks more quickly. ADV:2 Using the Sakai/MOODLE LMS improves 1 2 3 4 5 the quality of the work I do. ADV:3 Using the Sakai/MOODLE LMS makes it 1 2 3 4 5 easier to do my job. ADV:4 Using the Sakai/MOODLE LMS enhances 1 2 3 4 5 my effectiveness on the job. 219 University of Ghana http://ugspace.ug.edu.gh ADV:5 Using the Sakai/MOODLE LMS increases 1 2 3 4 5 my productivity. CPA:1 I think that using the Sakai/MOODLE 1 2 3 4 5 LMS fits well with the way I like to teach. CPA:2 Using the Sakai/MOODLE LMS fits into 1 2 3 4 5 my teaching work style. CPA:3 Using Sakai/MOODLE LMS is appropriate 1 2 3 4 5 for my teaching style. CPA:4 Using Sakai/MOODLE LMS is suitable to 1 2 3 4 5 most of my teaching approaches. OBS:1 Before deciding on whether or not to use the Sakai/MOODLE LMS, I do see people 1 2 3 4 5 using it on some computers around. OBS:2 Before deciding on whether or not to use Sakai/MOODLE LMS, I had seen many 1 2 3 4 5 people using the Sakai/MOODLE LMS Successfully. Strongly Disagree Neutral Agree Strongly Disagree Agree TRI:1 Before deciding on whether or not to use Sakai/MOODLE LMS for teaching, I 1 2 3 4 5 have/had the opportunity to use it long enough to see what it can do. TRI:2 Before deciding on whether or not to use Sakai/MOODLE LMS for teaching, I 1 2 3 4 5 have/had access to try its various uses. TRI:3 Before deciding on whether or not to use the Sakai/MOODLE LMS, I know/knew a 1 2 3 4 5 place where I could try it out. IMG:1 Lecturers in my University who use the Sakai/MOODLE LMS have more prestige 1 2 3 4 5 than those who do not use it. IMG:2 Lecturers in my University who use Sakai/MOODLE LMS have high 1 2 3 4 5 profile/recognition. IMG:1 Using the Sakai/MOODLE platform is a 1 2 3 4 5 status symbol. 220 University of Ghana http://ugspace.ug.edu.gh SUBN:1 Colleague lecturers who influence my behaviour think that I should use 1 2 3 4 5 Sakai/MOODLE LMS. SUBN:2 People who are important to me think I 1 2 3 4 5 should use Sakai/MOODLE LMS. SUBN:3 Colleagues in my department suggest that 1 2 3 4 5 I use the Sakai/MOODLE. PU:1 Using the Sakai/MOODLE LMS improves 1 2 3 4 5 my teaching performance. PU:2 Using the Sakai/MOODLE LMS makes it 1 2 3 4 5 easier to teach the course content. PU:3 Using the Sakai/MOODLE makes me 1 2 3 4 5 effective and efficient. PEU:1 I find Sakai/MOODLE LMS features 1 2 3 4 5 simple to use. PEU:2 I find SAKAI/MOODLE LMS flexible to 1 2 3 4 5 interact with. PEU:3 I clearly understand how to use 1 2 3 4 5 Sakai/MOODLE LMS. PEU:4 I do not spend too much time when working with Sakai/MOODLE LMS because it is 1 2 3 4 5 simple to use BIU:1 I intend to use Sakai/MOODLE LMS to 1 2 3 4 5 assist my teaching. Strongly Disagree Neutral Agree Strongly Disagree Agree BIU:2 I intend to use Sakai/MOODLE LMS every 1 2 3 4 5 semester BIU:3 I intend to use Sakai/MOODLE as an 1 2 3 4 5 autonomous teaching tool. BIU:4 I intend to use Sakai/MOODLE LMS tools to assist my teaching. 1 2 3 4 5 FC:1 There is easy access to computers to enable 1 2 3 4 5 you use the Sakai/MOODLE platform FC:2 Internet connectivity is fast (on the 1 2 3 4 5 University campus) 221 University of Ghana http://ugspace.ug.edu.gh FC:3 There is periodic training to upgrade skills 1 2 3 4 5 required to use the Sakai MOODL platform FC:4 There is technical staff support to help you 1 2 3 4 5 use the platform Section C Please circle the response category indicating how you use Sakai/MOODLE. 1. How frequently do you believe you use Sakai/MOODLE LMS? □Extremely frequent □Quite frequent □Neither frequent nor infrequent □Slightly infrequent □Quite infrequent □Not at all 2. How many times do you believe you use Sakai/MOODLE LMS during a week? □Several times each day □ Once a day □About once a week □ Several times a week □2-3 times a week □Less than once a week □Not at all 3.How many hours do you believe you use Sakai/MOODLE LMS every week? □ More than 25 hours □Between 20-25 hours □Between 15-20 hours □Between 10-15 hours □Between 5-10 hours □Between 1-5 hours □Not at ll Thank you for taking time to complete this survey. INTERVIEW GUIDE FOR LECTURERS WHO USE SAKAI/MOODLE FOR TEACHING 1. What factors influence your decision to adopt and use Sakai/MOODLE LMS? 2. How do you use Sakai/MOODLE in the teaching profession? 3. Describe how you think Sakai/MOODLE LMS could be upgraded to broaden its uses. 222 University of Ghana http://ugspace.ug.edu.gh INTERVIEW GUIDE FOR LECTURERS WHO DO NOT USE SAKAI/MOODLE FOR TEACHING a. What do you know about Sakai/MOODLE LMS? b. Why are you not using Sakai/MOODLE LMS? c. What conditions may motivate you to use the Sakai/MOODLE LMS? 223 University of Ghana http://ugspace.ug.edu.gh 224