University of Ghana http://ugspace.ug.edu.gh UNIVERSITY OF GHANA DETERMINANTS OF ADOPTION BY PROFESSIONAL FOOTBALL COACHES IN A DEVELOPING ECONOMY: EVIDENCE FROM GHANA BY DERRICK MENSAH-ANKRAH (10638260) A THESIS SUBMITTED TO THE DEPARTMENT OF OPERATIONS AND MANAGEMENT INFORMATION SYSTEMS, UNIVERSITY OF GHANA BUSINESS SCHOOL, UNIVERSITY OF GHANA, LEGON, IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER OF PHILOSOPHY (MPHIL) IN MANAGEMENT INFORMATION SYSTEMS (MIS) AUGUST, 2019 University of Ghana http://ugspace.ug.edu.gh DECLARATION I hereby declare that this thesis is my own research and has not been submitted by anyone for the award of any academic degree in this or any other University. All references used in the work have been fully acknowledged. ………………………………… …………………………………. DERRICK MENSAH-ANKRAH DATE (10638260) i University of Ghana http://ugspace.ug.edu.gh CERTIFICATION I certify that this thesis was done under my supervision and in accordance with the guidelines on supervision of thesis laid down by the University of Ghana. ……………………………………………. ………………………………… DR. ERIC AFFUL-DADZIE DATE (SUPERVISOR) ……………………………………………. ……………………………..…… PROF. RICHARD BOATENG DATE (CO-SUPERVISOR) ii University of Ghana http://ugspace.ug.edu.gh DEDICATION I dedicate this work to my family The Mensah-Ankrah’s, who have always believed in me. iii University of Ghana http://ugspace.ug.edu.gh ACKNOWLEDGEMENT My outmost appreciation goes to the Almighty God, who has given me the grace and strength to successfully complete this hard task. I acknowledge my unfathomable appreciation to my supervisor Dr. Eric Afful-Dadzie, for putting me in check when I seemed to be heading far off especially read my first draft word- to-word to ensure I presented my best. I reverence your comments and advice during our interactions for the completion of this thesis. To Prof. Richard Boateng (Head of Department – Operations and Management Information Systems), I appreciate the trust you had in me to have granted me the opportunity to pursue this program, after the third time of applying for this program. God richly bless you. I would like to express my deepest gratitude to my case firm; the Ghana Football Association, particularly the Coaches and the Public Relations Officer who gave me his call card on our first day of meeting and three months later we spoke on phone like we had been friends forever. I am particularly grateful to my siblings, Jocelyn, Joanna Israel, and Chris Mensah- Ankrah, who supported me with prayers, inspired and gave me the encouragement to reach this height. I am very grateful to all my course mates and friends, especially, Dorcas Boateng, Sulemana Abubakar and Isaac Yeboah, for proof reading; for their encouragement and insightful comments in completing this study. God richly bless you all. Your efforts in this research have not been in vain. iv University of Ghana http://ugspace.ug.edu.gh TABLE OF CONTENT DECLARATION ..................................................................................................................................... i CERTIFICATION .................................................................................................................................. ii DEDICATION ....................................................................................................................................... iii ACKNOWLEDGEMENT ..................................................................................................................... iv TABLE OF CONTENT .......................................................................................................................... v LIST OF TABLES ................................................................................................................................. ix LIST OF FIGURES ................................................................................................................................ x LIST OF ACRONYMS AND ABBREVIATIONS ............................................................................... xi ABSTRACT ......................................................................................................................................... xiii CHAPTER ONE ..................................................................................................................................... 1 INTRODUCTION .................................................................................................................................. 1 1.1 Background ....................................................................................................................................... 1 1.2 Research Problem ............................................................................................................................. 3 1.3 Research Purpose .............................................................................................................................. 5 1.4 Research Objectives .......................................................................................................................... 5 1.5 Research Questions ........................................................................................................................... 5 1.6 Research Significance ....................................................................................................................... 6 1.7 Synopsis of the Chapters ................................................................................................................... 6 CHAPTER TWO .................................................................................................................................... 8 LITERATURE REVIEW ....................................................................................................................... 8 2.1 Chapter Overview ............................................................................................................................. 8 2.2 Overview of Analytics ...................................................................................................................... 8 2.3 Types of Analytics .......................................................................................................................... 11 2.3.1 Descriptive analytics .................................................................................................................... 11 2.3.2 Diagnostic analytics ..................................................................................................................... 12 2.3.3 Predictive analytics ...................................................................................................................... 12 2.3.4 Prescriptive analytics ................................................................................................................... 13 2.4 Sport Analytics ................................................................................................................................ 14 2.5 Analytics Apps in the Football Fraternity ....................................................................................... 16 2.6 Functional Categorisation of Sports Analytics Apps ...................................................................... 22 2.7 Decision in Sports Analytics ........................................................................................................... 24 2.8 Challenges of Analytics .................................................................................................................. 27 2.9 Theories Used in the Area of Sports Analytics ............................................................................... 29 2.9.1 The Theory of Reasoned Action (TRA) ...................................................................................... 29 v University of Ghana http://ugspace.ug.edu.gh 2.9.2 The Theory of Planned Behaviour (TPB) .................................................................................... 29 2.9.3 The Technology Acceptance Model (TAM) ................................................................................ 31 2.9.4 Unified Theory of Acceptance and Use of Technology (UTAUT) ............................................. 32 2.10 Why Sports Analytics Apps Adoption Research Gaps ................................................................. 33 2.11 Summary ....................................................................................................................................... 34 CHAPTER THREE .............................................................................................................................. 35 RESEARCH FRAMEWORK ............................................................................................................... 35 3.1 Chapter Overview ........................................................................................................................... 35 3.2 Overview of Unified Theory of Acceptance and use of Technology (UTAUT) ............................ 35 3.3 Proposition Development ................................................................................................................ 39 3.3.1 Performance Expectancy.............................................................................................................. 39 3.3.2 Effort expectancy ......................................................................................................................... 40 3.3.3 Social Influence ........................................................................................................................... 40 3.3.4 Facilitating Conditions ................................................................................................................. 41 3.3.5 Behavioural Intention ................................................................................................................... 42 3.4 Effect of Moderators ....................................................................................................................... 42 3.4.1 Gender .......................................................................................................................................... 43 3.4.2 Experience.................................................................................................................................... 44 3.4.3 Age ............................................................................................................................................... 45 3.5 Summary ......................................................................................................................................... 46 CHAPTER FOUR ................................................................................................................................. 47 RESEARCH DESIGN .......................................................................................................................... 47 4.1 Chapter Overview ........................................................................................................................... 47 4.2 Research Paradigm .......................................................................................................................... 47 4.3 Research Design and Methods ........................................................................................................ 49 4.4 Conducting the Case ....................................................................................................................... 50 4.4.1 Selection of Sample for the Case ................................................................................................. 50 4.4.2 Data Collection Method ............................................................................................................... 52 4.4.2.1 Interview ................................................................................................................................... 52 4.4.2.2 Ethics for interview ................................................................................................................... 53 4.4.2.3 Direct Observation .................................................................................................................... 53 4.4.3 Data Collection ............................................................................................................................ 54 4.5 Thematic Analysis .......................................................................................................................... 54 4.6 Chapter Summary ........................................................................................................................... 54 CHAPTER FIVE .................................................................................................................................. 56 RESEARCH FINDINGS AND ANALYSIS ........................................................................................ 56 5.1 Chapter Overview ........................................................................................................................... 56 vi University of Ghana http://ugspace.ug.edu.gh 5.2 Brief Profile of the Professional Football Coaches in a Developing Country ................................ 56 5.2.1 Case A: A Ghana premiership Football Coach ............................................................................ 56 5.2.2 Case B: GN Division One League Football coach....................................................................... 57 5.2.3 Case C: GN Division One League Football Coach ...................................................................... 58 5.2.4 Case D: National Team Football Coach ...................................................................................... 59 5.2.5 Case F: Ghana premiership Football Coach ............................................................................... 60 5.2.6 Case G: National Team Football Coach ...................................................................................... 61 5.2.7 Case H: Division Two League Football Coach............................................................................ 61 5.3 Demographic Representation of Respondents. ............................................................................... 62 5.4 Findings from the Cases of Football Coaches ................................................................................ 66 5.4.1 Types and Functional categorisations of Sports Analytics Applications used in the Ghanaian football fraternity. ................................................................................................................................. 66 5.4.2 Influence of socio-demographic factors on Professional football coaches’ adoption of sports analytics Apps ....................................................................................................................................... 68 5.4.2.1 Influence of Gender on Professional football coaches’ adoption of sports analytics Apps ...... 69 5.4.2.2 Influence of Age on Professional football coaches’ adoption of sports analytics Apps ........... 72 5.4.2.3 Influence of Experience on Professional football coaches’ adoption of sports analytics Apps. .............................................................................................................................................................. 74 5.4.3 Influential factors on Sport Analytics Application adoption by Professional football coaches ... 76 5.4.3.1 Influence of Effort Expectancy on the adoption of Sports Analytics Apps. ............................. 76 5.4.3.2 Influence of Social Influence on the adoption of Sports Analytics Apps. ................................ 79 5.9.3.3 Influence of Facilitating Conditions on the adoption of Sports Analytics Apps....................... 80 5.4.3.4 Influence of Performance Expectancy on the adoption of Sports Analytics Apps. .................. 83 5.9.3.5 Influence of Behavioural Intention on adoption of Sports Analytics Apps. ............................. 85 5. 5 Chapter Summary .......................................................................................................................... 87 CHAPTER SIX ..................................................................................................................................... 91 DISCUSSIONS OF RESULTS ............................................................................................................ 91 6.1 Chapter Overview ........................................................................................................................... 91 6.2 Addressing the Research Questions ................................................................................................ 91 6.2.1 Describe the types and functional categorizations of applications used in the Ghanaian football fraternity ...................................................................................................................................... 91 6.2.2 Influence of socio-demographic factors on Professional football coaches’ adoption of Sports Analytics Apps ...................................................................................................................................... 96 6.2.3 Influential factors on Sport Analytics Application adoption by Professional football. ............... 99 6.2.3.1 Influence of Effort Expectancy on adoption of sports analytic Apps ....................................... 99 6.2.3.2 Influence of Social Influence on adoption of Sports Analytics Apps ..................................... 101 6.2.3.3 Influence of Facilitating Conditions on adoption of Sports Analytics Apps .......................... 103 6.2.3.4 Influence of Behavioural intention to use on Professional football coaches’ adoption of Sports Analytics Apps as a supporting tool by Behavioural Intention ........................................................... 104 vii University of Ghana http://ugspace.ug.edu.gh 6.3 Chapter Summary ......................................................................................................................... 108 CHAPTER SEVEN ............................................................................................................................ 109 SUMMARY, CONCLUSION AND RECOMMENDATIONS ......................................................... 109 7.1 Chapter Overview ......................................................................................................................... 109 7.2 Summary ....................................................................................................................................... 109 7.3 Implications to Research, Policy and Practice .............................................................................. 118 7.3.1 Implication to Research ............................................................................................................. 118 7.3.2 Implication to Practice ............................................................................................................... 119 7.3.3 Implication to Policy .................................................................................................................. 120 7.4 Research Limitations .................................................................................................................... 120 REFERENCES ................................................................................................................................... 122 APPENDIX ......................................................................................................................................... 141 viii University of Ghana http://ugspace.ug.edu.gh LIST OF TABLES Table 2. 1: Some Definitions of the Analytics ........................................................................... 9 Table 2. 3 Functional Categorisation of Sports Analytics Apps .............................................. 23 Table 5. 2 Summary of Lessons drawn from Professional football coaches’ findings........... 88 Table 6. 1: Functional categorisations of sports Analytics Apps in Ghana ............................. 93 Table 6. 2 Influence of Effort Expectancy on adoption of Sports Analytics Apps ................. 99 Table 6. 3 Influence of Social Influence on adoption of Sports Analytics Apps as a supporting tool ......................................................................................................................................... 101 Table 7. 1 Thesis matrix........................................................................................................ 112 ix University of Ghana http://ugspace.ug.edu.gh LIST OF FIGURES Figure 2. 1: Scope of the Analytics .......................................................................................... 11 Figure 3. 1 Research Model ..................................................................................................... 38 Figure 3. 2: Conceptual framework model .............................................................................. 46 Table 5.1 Demographic Representation of Respondents. ....................................................... 63 Figure 7. 1 Sports Analytics Application Adoption Conceptual Framework ....................... 117 x University of Ghana http://ugspace.ug.edu.gh LIST OF ACRONYMS AND ABBREVIATIONS APP Application AU Actual Use BI Behavioural Intention CAF Confederation of African Football EE Effort Expectancy FC Facilitating Condition FIFA The Federation Internationale de Football GCA Ghana Coaches Association GFA Ghana Football Association HR Human Relations iOS iPhone Operation System IT Information Technology OS Operating System PE Performance Expectancy SA Sports Analytics SAA Sports Analytics Application SAS Sports Analytics Software SI Social Influence TAM Technology Acceptance Model TPB The Theory of Planned Behaviour TRA The Theory of Reason Action UEFA The Union of European Football Association xi University of Ghana http://ugspace.ug.edu.gh UTAUT Unified Theory of Acceptance and User of Technology VLW Visible Language Workshop xii University of Ghana http://ugspace.ug.edu.gh ABSTRACT The study investigates the Influence of performance expectancy, effort expectancy, social influence, facilitating conditions and behavioural intentions on professional football coaches’ adoption of Sports Analytics Apps in developing countries. Studies on adoption of Sports Analytics Apps have so far focused more on issues relating to usage behaviour, deployment and diffusion. However, there still remains striking factors of Sports Analytics Apps adoption such as social influence, facilitating conditions and the relationship between behavioural intention and usage behaviour that have not been explored by extant researchers. To address these gaps in knowledge, this study investigated professional football coaches in Ghana using the Unified Theory of Acceptance and Use of Technology (UTAUT) as the theoretical lens and the qualitative method approach as the research methodology. The study employs the use of many sampling techniques: convenience sampling, purposive sampling, snowballing sampling and structural sampling in arriving at the final respondents for the thesis. In total, seven professional football coaches were sampled consisting of two females and five males. The study utilized the thematic analysis and further grouped the interview data collected from the respondents, transcribed and carefully read over and over in order to take notes of key views expressed by respondent and how they reflect on the key themes in the research questions. The result of the study shows that there was no support for the influence of performance expectancy, effort expectancy, social influence, and facilitating condition on behavioural intention. However, behavioural intention seemed to have influenced usage behaviour. The study also reports on non-moderating effects of gender, age, and experience on the various constructs of the model adopted for the study. In view of this, the study recommends that the Ghana Football Association (GFA) should adequately invest in organizing free trial services for potential adopters and help develop xiii University of Ghana http://ugspace.ug.edu.gh measures that will help educate professional football Coaches on the benefits that can be derived from the usage of Sports Analytics Apps. This research contributes to, arguably, the limited literature in the area of Sports Analytics adoption through a multi-faceted and multi- dimensional perspective from a developing country. It is hoped that it would serve as a stepping stone for subsequent studies in the field. It further responds to the research gaps considering the fact that arguably, not many empirical studies have been done from a developing economy context. Finally, the study calls for future researchers to use the UTAUT model in studying the adoption of Sports Analytics Apps as a learning tool by football supporters. Likewise, the investigation suggests that future research should analyse variables that can potentially Influence the appropriation of pool of cloud based Sports Analytics Apps by proficient football coaches. xiv University of Ghana http://ugspace.ug.edu.gh CHAPTER ONE INTRODUCTION 1.1 Background Sports is a discipline that has influenced the world. Its influence has brought peace and friendship amongst countries (Vine, 2016). Sports participation improves pro-social behaviour and reduces crime and anti-social behaviour, particularly among young men. In recent years, technology has had an Influence on almost every aspect of our society. The world has changed. People obtain information more quickly, communicate more directly and effectively, as well as grow ideas (Bryzek, 2017). Technology has Influenceed every facet of society and every industry from healthcare, to government, education, business, as well as sports (Gulek & Demirtas, 2015). The value of sports today has increased partly due to the Influence of information technology. For instance, the global sports industry is worth between €350 billion and €450 billion, according to a recent Kearney (2011) study of sports teams, leagues and federations. These include infrastructure construction, sporting goods, licensed products, live sports events and extensive media coverage of sporting events worldwide. The role of technology in sports has mainly contributed to the development of sports events. Additionally, technology contribution to sports is seen in the communication potential of events where technology helps to extend viewing coverage to spectators and viewers. Increase in revenues through various types of sponsorships and advertisements are some of the other innovative contributions of technology in sports. In recent times, analysis of sports data and events has become extremely important to the industry. Sports Analytics has become the discipline behind the attempt to extract intelligence from the huge volumes of data generated in sports. Thus, sports analytics has become an 1 University of Ghana http://ugspace.ug.edu.gh integral part of everything sports (Petrović, Milovanović, & Desbordes, 2015). Basic descriptive statistics, such as the score of the game or the number of receptions or hits, provide the basis for athletic competition (Valovich et al., 2012). Recently however, the importance of statistics and analytics in sports has increased, with emphasis on measures that improve the likelihood of winning, predicting performance of utility players, and understanding patterns in behavioural characteristics of players. Other interestingness measures of analytics include sport aptitude (eg, strikeouts in baseball, free-throw percentage in basketball), physical location (eg, pitch location in baseball, distance run in basketball), economic value, and, most relevant to medicine, injury incidence and metrics of physical performance. For instance, sports analytics is increasing the recognition and understanding of injury and its occurrence. Specifically, identifying factors that can prevent injury to a team can provide advantage on the field or court via implementation of data-driven injury prevention strategies (National Athletic Trainers’ Association, 2017). Additionally, recent advances in technology have led to an improved understanding of physical ability, functional movement, training load, and fatigue. Understanding an athlete’s workload can lead to improved training that maximizes athletic performance and minimizes fatigue and injury (National Athletic Trainers’ Association, 2017). This situation has made user perception and acceptance an increasingly serious issue. The end user is vital for the effective use of the information technologies(Cheney & Dickson, 1982). Though, user acceptance has received equitably extensive attention in earlier researches in education, health, agriculture (Verkasalo 2006; Bownman et al., 2011;Parganas, Liasko, & Anagnostopoulos, 2017) sports has little attention in this part of the global village. Due to this, it does not come as a surprise that the scientific world has begun to address the phenomenon of the sports event through Sports Analytics Apps adoption (Petrović et al., 2015). 2 University of Ghana http://ugspace.ug.edu.gh 1.2 Research Problem Sports Analytics has generated a lot of interest in information systems research and valuable studies have been conducted in this regard. The high adoption rate of Sports Analytics in different sports disciplines (Kim, 2015) has led to a sustained research into Sports Analytics Apps adoption in developing economies (Kim, 2015; Schuckers & Argeris, 2015; Rodenberg & Feustel, 2015; Hassett, Sullivan, & Veuger, 2015 and Deutscher, 2015). However, Hassett, Sullivan, and Veuger (2015) have established that failure to persuade football coaches to switch from traditional means of analysing teams to use sports Analytics has been the major barrier to the adoption. On this basis, studies that have sort to identify the more important factors that influence coaches' Sports Analytics App adoption behaviour have dominated sports Analytics research in developing countries (Wilkerson, & Gupta, 2016). These studies have had their focus on issues such as factors that influence coaches' intention to adopt sports analytics (Davenport, 2014; Halvorsen, Sægrov, Mortensen, Kristensen, Eichhorn, Stenhaug & Johansen, 2013, Alamar, 2013); Sports Analytics usage (Davenport, 2014; Alamar, 2013; Backholm, Bott, & Luna,2015); attitude towards adoption (Armitage, & Conner, 2001; Downs, & Hausenblas, 2005); trust (Davenport, 2014; Anzaldo, 2015; Ramanathan, Philpott, Duan, & Cao, 2017; Cordes, & Olfman, 2016); and security and privacy (Ghosh, & Swaminatha, 2001; Schwartz, 2010; Garba, Armarego, Murray, & Kenworthy, 2015; Peppet, 2014). Nonetheless, an assessment of these studies revealed that although different factors were identified as having an influence on the adoption of Sports Analytics Apps, several other factors such as social influence, facilitating conditions and the considerable relationship between behavioural intention and usage behavior have not been explored by extant researchers. Despite these studies, there is a call for more studies to test earlier findings in different contexts and fields 3 University of Ghana http://ugspace.ug.edu.gh in order to better understanding of the determinants of sports analytics Apps adoption by professional football coaches in developing countries. Secondly, different frameworks have been developed ‒ an effort to identify the factors affecting information system success continued quickly in the nineteenth and twentieth century (Davis et al., 1989; DeLone & Mclean, 1992; Venkatesh et al., 2003). The Technology Acceptance model was developed to verify the low usage of installed information systems and concluded that computer systems cannot improve (Davis et al., 1989). Whiles Davies (1989) proposed that the acceptance of technology is dependent on two main variables: perceived usefulness and perceived ease of use, DeLone and Mclean (1992) proposed six distinct dimensions of information systems success: system quality, information quality, use, user satisfaction, individual Influence, and organizational Influence. Some of these variables are referred to as external influences (Davis et al., 1989). These studies are unique. However, they are quite silent on moderating factors such as age, gender, experience) which the Unified Theory Acceptance and Use of Technology (UTAUT) considers which is the aim of this thesis. Again, they are quite silent on the user perception in the context of Ghana a developing country. Thus, a study that offers such a solution could provide great relieve, better understanding of issues and successful implementation of sports analytics App based on the relationships between performance expectancy, facilitating conditions, effort expectancy, social influence and behavioural intention in the context of a developing country like Ghana to help design interventional programs for professional football coaches. Also, the lack of attention for Sports Analytics Apps and facilitating conditions is a deficiency in most information system research. 4 University of Ghana http://ugspace.ug.edu.gh 1.3 Research Purpose The motivation behind this exploration is to identify determinants of adoption Sports Analytics Applications by professional football coaches. The study describes the type and categorisations of Sports Analytics Applications used in the Ghanaian football fraternity. Again, the study investigates the influence of performance expectancy, effort expectancy, social influence, facilitating conditions and behavioural intention on professional football coaches’ adoption of Sports Analytics Apps as supporting tool in a developing country. Additionally, the study is to find out the influence of socio-demographic factors on a professional Football coaches’ adoption of Sports Analytic Apps. 1.4 Research Objectives 1. To explore: (i) the types of Sport Analytics Applications, and (ii) the functional categorisations of Sports Analytics Applications used in the Ghanaian football 2. To explore the influence of Performance Expectancy, Facilitating Conditions, Effort Expectancy, Social Influence and Behavioural Intention on Professional football coaches’ adoption of sports analytics App supporting tool and 3. To explore the influence of socio-demographic factors on a professional Football coaches’ adoption of Sports Analytic Apps. 1.5 Research Questions 1. What are the types and functional categorisations of sports Analytics Applications used in the Ghanaian football? 2. What is the influence of Performance Expectancy, Facilitating Conditions, Effort Expectancy, Social Influence and Behavioural Intention on Professional football coaches’ adoption of Sports Analytic Apps as a supporting tool? 3. What is the influence of socio-demographic factors on Professional football coaches’ adoption of Sports Analytic Apps? 5 University of Ghana http://ugspace.ug.edu.gh 1.6 Research Significance Firstly, this research contributes to, the limited literature in the area of Sports Analytics adoption through a multi-faceted and multi-dimensional perspective from a developing country. It is hoped that it would serve as a stepping -stone for subsequent studies in this field. Secondly, the findings of this study maps out the Sports Analytics Apps adoption and implementation strategies not only in the Ghana Football Association but also Football Associations in Africa, and other Football Association in developing countries in general. Again, the findings of this study can inform professional football coaches of the best practices to apply in the adoption and implementation of Sports Analytics in their activities. For the systems administrator and designer, the study identifies some of the challenges or weaknesses in the Sports Analytics Apps that require improvement or redesigning. Furthermore, the study can inform football coaches of the best practices in the use of Sports Analytics App to maximize productivity. Finally, the study findings can also inform the policy-making bodies and Football Associations, how best to improve the deployment and management of Sports Analytics Apps at the national and international levels. 1.7 Synopsis of the Chapters The thesis is arranged in seven (7) different chapters, which correspond to the steps taken in the study. Chapter One (1): Introduction; chapter one provides a shorts introduction into the research area. In order to present a clear picture, the research problem is discussed which leads to the 6 University of Ghana http://ugspace.ug.edu.gh research purpose, research objectives, research questions, research significance and, scope and limitation of research. Chapter Two (2): Literature Review; chapter two presents literature relevant to the study. Theories and models that form the foundation of adoption in the Ghana Football Association are identified. The types and challenges of analytics are discussed. Chapter Three (3): Research framework; this chapter explores the research framework used for the study, which guided the research design, data collection methods, instrument, and served as a yardstick for the data analysis and discussions. Chapter Four (4): Methodology; This chapter highlights the research strategy and paradigm, discussion of sampling techniques and size are utilized. The instrument for data collection and the method used as well as data processing and analysis are expounded in this chapter. Chapter Five (5): Research findings; this chapter deals with the data presentation and analysis. The chapter presents a brief of each professional football coaches used in the research. In the concluding parts of the chapter, the findings are presented in line with research questions posed in the chapter one (section 1.5) using the thematic analysis. Chapter Six (6): Discussion of results; this chapter deals with the discussion of the results. The chapter addresses the research questions, discusses and analyses alongside the findings of the study. Chapter Seven (7): Conclusion; the conclusion of the research, implications (and recommendations) to research, practice and policy and the future research directions are discussed. Finally, references and appendices are presented after conclusion. 7 University of Ghana http://ugspace.ug.edu.gh CHAPTER TWO LITERATURE REVIEW 2.1 Chapter Overview As discussed earlier in the previous chapter, the purpose of this study is to explore how factors such as performance expectancy, effort expectancy, social influence, facilitating conditions and behavioural intention influence the adoption of Sports Analytics Apps in Ghana. The current chapter reviews related literature on Sports Analytics Apps adoption in developing countries. This is aimed at facilitating the advancement of knowledge on the phenomenon, unearth new research areas and justify the need for this study. The review process undertaken is divided into four parts. The first part reviews literature in the general mobile application context, followed by the definition and conceptualisation of Sports Analytics Apps. The second part reviews studies on Sports Analytics Apps in order to highlight the research issues and areas. In the third part, conceptual approaches and methodological approaches adopted by extant researchers are reviewed. Finally, the last part identifies the gaps that were highlighted during the review of the issues, frameworks and methods in order to firmly argue the need for the study. 2.2 Overview of Analytics Analytics is focused on understanding the past; what happened? Analytics focuses on why it happened and what will happen next (Shieh, Chang, Fu, Lin, and Chen, 2014)? Data analytics is a multidisciplinary field. There is extensive use of computer skills, mathematics and statistics, the use of descriptive techniques and predictive models to gain valuable knowledge from data (Bose, 2009). The insights from data are used to recommend action or to guide decision - making rooted in business context. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with the entire methodology (Daniel, 2015). There 8 University of Ghana http://ugspace.ug.edu.gh is a pronounced tendency to use the term analytics in business settings. For instance, text analytics vs. the more generic text mining to emphasize this broader perspective (Ganti et al., 2011). There is an increasing use of the term advanced analytics, typically used to describe the technical aspects of analytics, especially in the emerging fields such as the use of machine learning techniques like neural networks, Decision Tree, Logistic Regression, linear to multiple regression analysis, and Classification to do predictive modelling. It also includes; Unsupervised Machine learning techniques like cluster analysis, Principal Component Analysis, segmentation profile analysis and association analysis (Wolcott, Kamal, & Qureshi, 2008). Analytics in sports is unquestionably as old as sports. But how individuals understand what analytics means differs from person to person. So, first let us understand what analytics implies in today’s context. On a general level, analytics can be defined as using information or data to make improved decisions (Agrawal, 2014). The decisions can be made in sports and business contexts, or even private lives. On any given occasion where we take some information or statistics, structured or unstructured, analyse it, and then take a decision based on the analysis, we are by definition engaging in analytics. So, it is no surprise that all of us have been doing analytics in some shape or form. However, here we will focus on using analytics in business, and we will consider a more stringent definition of analytics, so that our scope is limited to sports decisions and also limited to the tools and technology that is available today and being built for tomorrow in the context of modern industry. Table 2. 1: Some Definitions of the Analytics Author(s) Definition Agrawal (2014) Analytics can be defined as using information to make better decisions. Cheng et. al., (2016) Analytics are a collection of relevant, historical, statistics that when properly applied can provide a competitive advantage to a team or individual. 9 University of Ghana http://ugspace.ug.edu.gh Author(s) Definition Long and Siemens Analytics is as “the measurement, collection, analysis and reporting (2011) of data about learners and their contexts, for purpose of understanding and optimising learning and the environments in which it occurs” Jantti and Heath (2016). The proliferation of data sets that contribute to new forms of learning analytics have been in part enabled through the “big data” drawn from virtual learning environments, institutional transaction processing systems and the creation of institutional data warehouses to administer and report on these relational data set Tyagi (2003) Analytics is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. According to the definitions as seen in Table 2.1, analytics could be understood in the following ways. Firstly, using information to make better decisions (Agrawal, 2014). In other words, the analytics influences better decision making in organisations. Secondly, Analytics involves the “collection of relevant, historical, statistics that when properly applied can provide a competitive advantage to a team or individual”. For example, in seeking for competitive advantage over an opposing team, a coach would need the data and historical statistics of the opposing team in other to beat the opposing team. With information available to a player or a team during playing time brings a lot of advantages to that team (Cheng et. al., 2016). Thirdly, analytics is facilitated by the proliferation of data sets that contribute to new forms of learning analytics, which is partly enabled through the “big data” drawn from virtual learning environments, institutional transaction processing systems and the creation of institutional data warehouses to administer and report on this relational data set (Jantti & Heath, 2016). Lastly, analytics examines data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software (Tyagi, 2003). 10 University of Ghana http://ugspace.ug.edu.gh 2.3 Types of Analytics There are 4 types of analytics. Here, we start with the simplest one and go down to the more sophisticated: as it happens, the more complex an analysis is, the more value it brings. Figure 2. 1: Scope of the Analytics Source: Jantti and Heath (2016). 2.3.1 Descriptive analytics Descriptive analytics answers the question of ‒ what happened? For instance, a stadium manager will learn how many supporters were at a specific game and other information about the game. Again, the average weekly sales at the gates, the rate of the tickets returned for a past month, etc. A football coach was able to decide on his players categories based on the analysis of fitness, monthly performance, number of interceptions, player influence, (Raden & Brains, 2012). Descriptive analytics juggles raw data from multiple data sources to give valuable insights into the past. However, these findings simply signal that something is 11 University of Ghana http://ugspace.ug.edu.gh wrong or right, without explaining why. For this reason, highly data-driven companies do not content themselves with descriptive analytics only, and prefer combining it with other types of data analytics (Gandomi & Haider, 2015). 2.3.2 Diagnostic analytics At this stage, historical data can be measured against other data to answer the question of ‒ why something happened? Thanks to diagnostic analytics, there is a possibility to drill down, to find out dependencies and to identify patterns. Companies go for diagnostic analytics, as it gives a deep insight into a particular problem. At the same time, a company should have detailed information at their disposal; otherwise data collection may turn out to be individual for every issue and time-consuming (Siemens& Long, 2011). In the health sector for example, an athlete’s injury data can be shared in order to manage similar injury scenarios in other football clubs. Also, customer segmentation coupled with several filters applied (like diagnoses and prescribed medications) determines the risk of hospitalization (Gandomi & Haider, 2015). 2.3.3 Predictive analytics Predictive analytics tells what is likely to happen. It uses the findings of descriptive and diagnostic analytics to detect tendencies, clusters and exceptions, and to predict future trends, which makes it a valuable tool for forecasting. Despite numerous advantages that predictive analytics brings, it is essential to understand that forecasting is just an estimate, the accuracy of which highly depends on data quality and stability of the situation, so it requires a careful treatment and continuous optimization (Tanwar, Duggal, & Khatri, 2015). Thanks to predictive analytics and the proactive approach, it enables a football coach and his or her 12 University of Ghana http://ugspace.ug.edu.gh medical team, for instance, to identify the football players who are likely to play in other positions if the need comes for such tactical change or Influence. A management team can weigh the risks of investing into player transfer (buying or selling) based on cash flow analysis, forecasting and the injury history of the particular player. In a related case, advanced data analytics allowed a manager of a footballer to predict what they could expect after changing brand positioning (Tanwar, Duggal, & Khatri, 2015). 2.3.4 Prescriptive analytics The purpose of prescriptive analytics is to literally prescribe what action to take to eliminate a future problem or take full advantage of a promising trend. An example of prescriptive analytics from the Barclays English Premier League: Manchester United FC was able to identify opportunities in signing international Footballers from Europe over their African international counterparts based on the fact that these African players leave their clubs in January for African Cup of Nations (AFCON). This leaves the clubs handicap and lacking of key elements on the field of play (Saravanakumar & Nandini, 2017). This state-of-the-art type of data analytics requires not only historical data, but also external information due to the nature of statistical algorithms. Besides, prescriptive analytics uses sophisticated tools and technologies, like machine learning, business rules and algorithms, which make it sophisticated to implement and manage. That is why, before deciding to adopt prescriptive analytics, a company should compare required efforts vs. an expected added value (Gandomi & Haider, 2015). 13 University of Ghana http://ugspace.ug.edu.gh 2.4 Sport Analytics Sports analytics is defined as “the management of structured historical data, the application of predictive analytic models that utilize that data, and the use of information systems to inform decision makers and enable them to help their organizations in gaining a competitive advantage on the field of play” (Long & Siemens, 2011, p 2). The definition is both expansive (in the sense that it includes not only statistical models but also the broader information value chain that surrounds these models) and restrictive (because it excludes traditional analytics applications such as demand forecasting, revenue management and financial modelling, all of which are certainly relevant in the business of professional sports). A framework for sports analytics is presented in Figure 2.2 Figure 2. 2: A framework of sports analytics. Source: Cheng et al. (2016) Data management includes any and all processes associated with acquiring, verifying and storing data in an efficient manner. In a sports organization, data can come from a variety of sources and may be presented in many different forms. 14 University of Ghana http://ugspace.ug.edu.gh As shown in Figure 2.2, the data management function will feed both the predictive analytics function and the information systems that support decision-makers. Given this crucial role, good data management is essential, hence incomplete and/or inaccessible data inherently reduces the value of any other investments in analytics. In many football organizations, data is often stored in isolated silos; as a result getting data is often not a smooth process. Different groups within a football organization may have extensive data on players that other groups either do not have access to or do not even know exist. For example, the personnel group of one English Premier League team had been collecting extensive performance data on various groups of both opposing players and their own players. The coaching staff had no idea that the data existed, but when they did discover it, they had difficulty accessing it. The data resided in spread - sheets on the computers of the personnel group instead of being integrated into a common data archive. This is a common situation within professional sports organizations (Kearney, 2011). Predictive analysis, the next piece of the framework, is the process of applying statistical tools to data to gain insight into what is likely to happen in the future. In sports, this can involve the projection of the pro careers of amateur players, identifying how the strengths and weaknesses of an opponent will play out against your own team’s strengths and weaknesses, or assessing whether a free agent would fill a need on a team at an appropriate cost. Depending on the importance of the problem, the time until an answer is needed and the data available, these analyses can range from simple comparisons to extremely complicated and cutting-edge statistical analysis. The results of these analyses may feed directly into an intelligent information system that provides decision-makers with standardized results. Alternately, such results may be reported directly to a decision-maker for special projects that may be outside of any standard systems. 15 University of Ghana http://ugspace.ug.edu.gh Information systems, the next component in the framework, are increasingly common in the world of sports. When designed and implemented correctly, such information systems typically allow for visualization and interactive analysis of relevant information from multiple sources in one place, organized in a meaningful way to provide insights for decision makers. For example, a cutting-edge sports information system might combine unstructured information from reports, summary reports from multiple data sources and results from predictive models. Such a system not only provides a data-driven decision support SAS and integrates data from multiple sources, but also has the potential to fundamentally alter and enhance the way a decision-maker does his or her job. Decision-makers are the ultimate customers for all components in the sports analytics framework. However, the modern professional sports organization typically has many different decision-makers, including the general manager, coaches, scouts, trainers, salary cap managers and other personnel executives. Decision-makers in different functional areas may utilize different data and models to tackle different types of questions. Conversely, as mentioned above, one key problem today is that decision-makers in one functional area (such as scouts) rarely have easy access to information generated by personnel in other areas (such as assistant coaches or salary cap managers). To summarize, our definition and framework for sports analytics encompasses several different and related aspects associated with turning raw data into information that is valued by – and has an Influence on – decision-makers in the world of sports. 2.5 Analytics Apps in the Football Fraternity There are different versions of sports Analytics Apps used by many coaches around the world. Below is table showing a number of software used by football coaches. 16 University of Ghana http://ugspace.ug.edu.gh Table 2. 2 Some definitions of the analytics Index Application Mobile Company/ Features/ Uses nos Name Operating Cost & Type System (MOS) 1 Four Four Two Android Opta's / •Live match stats; See every pass, shot, tackle, interception, foul, assist and the top Entertainment Free match pass combinations. •Player influence: Compare player v player, team v team including all event dashboard views. •See how each player's influence changes through the game and how a match changes after key events. •Goal Build-ups: Find out more than just who scored with assist information and goal build-up chalkboards •Insights: Use the app to help create the perfect Fantasy 2 Squawka Android None/ •The most in-depth hand-held football companion around. •Real-time stats on leagues Entertainment Free from all over the world including the EPL, La Liga and the UEFA Champions League. •All the stats, photos, news and more on your favourite team or player LIVE. • Ground breaking animations of key in-game events minutes after they happen. Instant stats on your favourite team or player in seconds with our incredible navigation system 3 Dartfish Note Android Dartfish •Describe events in your sport with up to three keywords or phrases. • Assign team Game Analysis TV / Free and player to a button from pick lists. • Easily create panels from the provided templates. •Create templates for others and share them via dartfish.tv. •Notes and panels can be edited … even during the game.• After the game, share Notebooks using dartfish.tv – small files and rapid upload 17 University of Ghana http://ugspace.ug.edu.gh Index Application Mobile Company/ Features/ Uses nos Name Operating Cost & Type System (MOS) 4 Dartfish Android Cost: •With EasyTag, notational analysis comes to iOS. •Start the timer at the beginning of EasyTag Game €2.69 the game then a fully customizable tagging panel is used to time-stamp the key Analysis performance indicators (KPI) of your sport and display instant statistics of their frequencies. •EasyTag creates a .csv file which can be further analysed by spread sheet software or tagged events can be related to a video recording by import into Dartfish video analysis software (TeamPro and Connect+ editions 5 Performa Android Cost: • Create your own tagging system. • Setup and manage your player’s performances. • sports License Save and share your performance data and video online. • Capture your teams and (Game approx. players performances in real-time. • Visualise your performances through graphs and Analysis) €600 per statistical analysis. • Add post-game analysis data to the video of your games. • year Export the playlists as video complications for sharing. • Export your library of data as a CSV file to create. 6 Sportstec game Android Free •Import performance (video) for analysing. *Code the moments that are important to breaker you for seeking improvement. •Review the key moments. •Use the Movie dock for (Game powerful feedback. *Drawing tools to highlight moments of improvement. Moments Analysis) of potential. 7 Pocket coder Android $13.99 • Create notational analysis templates in minutes on your iPhone or iPad. •Lead/Lag (Game times: Create 'one touch' events with pre-defined lead and lag times. •Create colour- 18 University of Ghana http://ugspace.ug.edu.gh Index Application Mobile Company/ Features/ Uses nos Name Operating Cost & Type System (MOS) Analysis) coded events with customised labels. •Record time-based occurrence of events with ‘two touch’ starts and finishes times. •Save and load templates, and share with other users through iTunes File Sharing. • Save coding output, and either share using iTunes File Sharing or E-mail direct to your computer 8 Tag&go Android Free •Data is collected with a button template created by the user or imported from the (Game following Nacsport software: Basic, Basic Plus, and Scout Plus, Pro Plus y Elite. analysis) •Once the event has finished the resulting database can be exported to the before mentioned Nacsport software or as an XML file for other video analysis timeline based products (such as Gamebreaker © or Sportscode ©). •The database will be linked to a video of the corresponding event. 9 icoda 2 Android Free •Icoda 2 allows you to be a part of the event you are coding, instead of being trapped (Game behind the computer. •Capture information, code and add tags to the live event, analysis) saving you valuable time instead of waiting for the events to be logged. •Use live counters to get stats and feedback in the field. •An addition to the Sportstec suite of products; icoda 2 is designed for existing CODA customers. 10 Video tagger Android €2.69 •Video Tagger is a unique and powerful video analysis and assessment tool that pro – video makes it possible to capture and tag sports performance. •Start recording and tap the highlight. customisable buttons to tag performance as it happens. •The app will record a few (Game seconds either side of the tap. •Continue this for the duration of the performance and analysis) when finished two video montages will be compiled with the highlights you identified. 19 University of Ghana http://ugspace.ug.edu.gh Index Application Mobile Company/ Features/ Uses nos Name Operating Cost & Type System (MOS) 11 Tagit (Multi Android €3.59 • Simple & flexible design allows you control your own data & define the events you sport options) want to record. •Create a game & start analysing your team with 2 quick touches. (Game •View general reports; a Summary report, Time-line & Event Type reports. •Save up analysis) to 30 Games and view reports from your archive- Generate Full Match Report in Pdf format with option to purchase and email. 12 Focus x2I Android Free • The simple design enables you to very quickly create your own ‘tagging templates’ (Game and our unique video technology allows you to; - instantly view and ‘tag’ video that analysis) is being directly captured using your iPad camera - carry out your analysis later by using a video that has already been captured and stored in the ‘Photos’ or ‘Videos’ folders on your iPad - analyse the performance at any time using video that you have initially stored on your laptop/desktop and have imported from iTunes 13 Dartfish Android €5.99 • Record videos using your iPhone or ipad’s built-in camera. • Import them from Express (Skills your camera roll or from any app, including email. • Replay the video frame-by- Analysis / frame or play it in slow motion. • Use drawings, labels, animated arrows and angles Video Review) to underline what the video reveals. • Ensure that what is learned is not forgotten - share your opinion using voice or text notes. • Share your analyses online with the people you 14 Coaches Eye Android €4.49 • Instantly review video with slow-motion playback and drawing tools. • Compare (Skills two videos with side-by-side video analysis. • Slow-motion video review is easy Analysis) using our flywheel for precise video scrubbing. • Zoom and pan videos during analysis to see just the details that matter. • Draw on videos using lines, arrows, circles, squares, and freehand tools. • Create analysis videos with audio commentary, annotations, and slow-motion 20 University of Ghana http://ugspace.ug.edu.gh Index Application Mobile Company/ Features/ Uses nos Name Operating Cost & Type System (MOS) 15 Ianalyze Android $4.99 • IAnalyze allows in-depth video analysis of sports video clips, providing precise (Skills scrubbing, frame by frame viewing for 30, 60, or 120 fps video, a pop-out protractor Analysis) that can be rotated for measuring angles, a drawing tool, and a stopwatch feature to time intervals in frame by frame or normal speed. • You can also record from the app and save to your library 16 Ubersense Android Free Ubersense Features; • Playback in multiple slow motion speeds and frame-by-frame. (Skills • Zoom and pan videos for access to every detail. • Use drawing tools to measure or Analysis) highlight form. • Compare two videos either stacked or side-by-side. • Synchronize comparison videos for a more effective evaluation. • Add drawings and audio commentary to voice-overs. • Use Airplay or HDMI/VGA connectors to mirror to a big-screen for group settings. 17 Sportstec Android €44.99 • Work with up to 4 camera angles per event • Build customized views for each of player your players • Put a spotlight on key moments with intelligent drawing tools • Swipe (Game review) up and down to navigate video events • Swipe left and right to switch between camera views • Works with Sportscode Gamebreaker Plus, Sportscode Pro and Sportscode Elite • Easily manage and distribute all of your Player files with the Sportstec Command Centre! Exclusive Functionality for Sportscode Elite • Review Sportscode driven statistics with related video • View your customized output reports with related video 21 University of Ghana http://ugspace.ug.edu.gh Index Application Mobile Company/ Features/ Uses nos Name Operating Cost & Type System (MOS) 18 Ap viewer Android Free • Users can explore a timeline and matrix of their packaged footage to quickly (Game review) review the key clips from their video, or watch it in its entirety. • Each package on the app can support 1, 2, 3 or 4 different video angles so that users can swipe between these or use a hot-corner functionality to zoom into their preferred view. 19 Replay Android Free • Stream video over Wi-Fi and cellular networks - • Your Replay Analysis video analysis library is available to watch over 3G/4G cellular networks and Wi-Fi. - Explore your (Game review) video analysis through an intuitive interface. • We are compatible with your existing analysis products and allow you to explore and instantly view your video analysis. - Powerful discussion tools Using the @symbol you are able to target key moments in the video (e.g. '@24:35' or ‘@now’). • These become clickable links that take you to the specific moment in the video. • Browse and discuss your playlists All your playlists are available to view on the iPad where they are fully integrated with the discussion tools 20 Axis coaching Android License • Complete a short online training package and a practice observation and you’re technology approx. ready to go! • Creates optimal coaching and learning environments • Provides (coaching €800 per objective data about coaching performance • Creates individual and detailed coaching analysis) year reports • Creates coach behaviour profiles and databases • Supports coach education AXIS and professional development with data and supporting video feedback • Supports coach education assessment 21 Tactics Board Android Free • Plan and draw your team winning tactics in 22 different sports using Tactics Playbook Hd Board. • Using Tactics Board • You can explain your sport tactics drawing lines, 22 University of Ghana http://ugspace.ug.edu.gh Index Application Mobile Company/ Features/ Uses nos Name Operating Cost & Type System (MOS) (Coaching adding objects to represent players, balls or explain the plays, as fast as possible. • analysis) You can add notes, save your plays, send them to the TV output – which is perfect for lectures or creating videos–, and share them as pictures or PDFs with colleagues and friends – everything with auto save, so no play is ever lost. 22 JumiOne Android Free • Ultimate control for your iPhone and iPad Transforming your mobile iDevice into a centralized remote control for your entire PC with JumiOne today! • This powerful mobile control hub has the value of six unique remote apps that let you control your desktop’s mouse, camera, as well as Winamp and other software from anywhere! • Featured on numerous high profile media outlets such as CNET, JumiOne is one of the premier remote SASs for controlling PC’s fully tailored and optimized for use via iOS devices . 23 University of Ghana http://ugspace.ug.edu.gh 2.6 Functional Categorisation of Sports Analytics Apps Sports Analytics are a collection of relevant, historical, statistics that when properly applied can provide a competitive advantage to a team or individual. Through the collection and analyse of these data, sports analytics inform players, coaches and other staff in order to facilitate decision making both during and prior to sporting events (Kim, 2015). The term "sports analytics" was popularized in mainstream sports culture following the release of the 2011 film, Money ball, in which Oakland Athletics General Manager Billy Beane (played by Brad Pitt) relies heavily on the use of analytics to build a competitive team on a minimal budget (Tobergte & Curtis, 2013, p.13). There are two key aspects of sports analytics - on-field and off-field analytics. On-field analytics deals with improving the on-field performance of teams and players. It digs deep into aspects such as game tactics and player fitness. Off-field analytics deals with the business side of sports. Off-field analytics focuses on helping a sport organisation or body surface patterns and insights through data that would help increase ticket and merchandise sales, improve fan engagement, etc. Off-field analytics essentially uses data to help rights holders take better decisions that would lead to higher growth and increased profitability (Ryall, 2012, p.4). As technology has advanced over the last number of year’s data collection has become more in-depth and can be conducted with relative ease. Advancements in data collection have allowed for sports analytics to grow as well, leading to the development of advanced statistics as well as sport specific technologies that allow for things like game simulations to be conducted by teams prior to play, improve fan acquisition and marketing strategies, and even understand the Influence of sponsorship on each team as well as its fans (Araújo, de Carlos, & Antonio Fraiz, 2014). 22 University of Ghana http://ugspace.ug.edu.gh Table 2. 3 Functional Categorisation of Sports Analytics Apps Type Function Tools Communicative To share ideas, information, and Semantic search, artificial intelligence, creations social networking, blog (video, audio blogs), Instant Message tools Web- conferencing Collaborative/ Joint Publishing To work with others for a Authoring, Editing tools, Virtual specific purpose in a shared Community of Practice (VCOP), wikis, work area semantics search and 3D graphics Documentative (Content Management) To collect and / or present Blogs, video blogs, open journalism, evidence of experiences distributed network and 3D graphics thinking over time, etc. Generative To create something new that Mashups, ubiquity, widgets, intelligent can be seen and/ or used by personal agents and VCOP other Interactive To exchange information, ideas, Social bookmarking, VCOPS resources materials Sources: Adapted from McGee and Diaz (2007); Richardson (2007) 23 University of Ghana http://ugspace.ug.edu.gh 2.7 Decision in Sports Analytics Growth and expansion are two of the biggest goals when an organization enters a particular industry or market. For example, subscription-based ecommerce organizations always hope to widen their fan base, providing strong service and maintaining fan relationships. It’s important for organizations to always think ahead to find ways to continue their development. Using analytics allows organizations to locate potential opportunities that may be worthwhile in terms of increasing profits and client reach, according to TargetHalvorsen, Sægrov, Mortensen, Kristiansen, Eichhorn, Stenhaug and Johansen, 2013. Organizations focused on subscriptions may realize their warehouse distribution and order management may hold them back with the assistance of certain data. As a result, they can locate a third-party partner that can provide these services to them, at a price that fits the overall budget. To keep expanding over time, organizations can use analytics to pinpoint flaws within their system and discover a quick solution (Chaffey & Patron, 2012). Every organization faces an opponent in one-way or another. Similar competition within the same industry poses a threat, as each organization aims to improve their practices and processes to outshine the other. Certain elements set organizations apart, especially in the eyes of consumers. To gain an edge on comparable businesses in the market, organizations can use analytics (Kim, 2009). The data gained from looking closely at the sales funnel, customer buying habits and more can highlight where other organizations may have a leg up. As a result, organizations can change up how they operate down to nitty-gritty details. Even the smallest alteration could mean an improvement in profits and consumer loyalty. In fact, a new report from the Massachusetts Institute of Technology Sloan Management Review shows 67 per cent of companies report gaining a competitive edge by using analytics (Chaffey & Patron, 2012). 24 University of Ghana http://ugspace.ug.edu.gh Many organizations have certain systems in place that work well enough to fit their needs. Replacing these practices is time-consuming and costly, which causes organizations to look for fixes instead of alternative actions. To locate the problematic areas, many organizations implement data analytics use. These technologies can help enterprises augment the processes that need work, according to SAS Institute magazine. Furthermore, the information gained from these tests can help organizations locate the practices that cost too much money without yielding enough positive results (Grissom, Berry, & Cheng, 2010). Analysing different potential steps in action can showcase the direction organizations need to move in, instead of enabling organizations to continue with time-wasting and expensive processes. Organizations can utilize a number of different methods to improve their efficiencies. Yet, none are as conclusive and important as analytics. These materials can show organizations every imperfection their organization is facing at the current moment, as well as the potential for future problems. With this insight, organizations can make necessary alterations to their practices. The benefits of analytics – including anticipation of business opportunities, a competitive advantage and cost efficiency – are many, so implementing the technology will result in positive advantages for organizations in a number of industries (Grissom, Berry, & Cheng, 2010). Most businesses have a mission statement. Most companies teach new hires and retrain long- term employees on the fundamental values that drive the company to success. What many companies fail to do, however, is quantify these values. With business analytics, companies can measure how these values translate into numbers. By using quantifiable numbers, broad value and mission statements can be quantified too, rather than just left to interpretation. Companies can use data to focus on operating with processes that keep in line with company values. For example, a business might identify what its measures of return look like, both tangible returns 25 University of Ghana http://ugspace.ug.edu.gh (such as profits) and intangible returns (such as giving back to the community). These can then be quantified to clearly define expectations for employees. This should improve processes because everyone will be working toward the same clear goal (Menzies, & Zimmermann, 2013). Analytics can be used to Ingrain Smart Decision-Making into Company Culture with more information at companies' fingertips; it's easier to empower a team to make quick decisions. Fast movement and development is important for a business if it wants to stay ahead of the competition. Equally important is careful consideration of each decision. Nothing can derail a company faster than jetting off in the wrong direction. Making fast decisions is easy, but what is more important is to make smart decisions in a short period of time. With so much data at hand, it’s possible for everyone – not just higher-level employees – to make informed decisions. That's why every department needs access to analytics. It should be ingrained in corporate culture. Fuel your team’s success by offering access to vital data. This way, it's possible to ensure that each new idea, direction or project will build the business, rather than set it back (Menzies, & Zimmermann, 2013). Words and numbers are great when you need to dig into the details, but data visualization can be a faster, better way to distinguish clear trends. Using visual data can make companies more agile, and can help them find revealing insights faster and make decisions without having to take as much time to understand what’s really happening in the market. With charts, graphs and other visual aids, decision-makers make speedier choices. This puts each team in motion faster. By keeping an organization fluid and constantly moving, processes improve (Sullivan, 2013). Today’s world moves faster than ever before. The way people purchase consumer goods is changing. The way businesses communicate is changing. The way companies reach clients is changing. With so much change happening at such a rapid pace, it’s easy for even the largest, 26 University of Ghana http://ugspace.ug.edu.gh smartest companies to get left behind. Business analytics can help companies avoid falling into that trap. Using analytics allows businesses to create rolling forecasts of the business and of the market (Sullivan, 2013). These offer valuable insight into what’s happening internally and externally. Staying abreast of the latest new forecasts can spark innovative ideas, bringing more depth to a company's brand. Improving processes paves the way to releasing innovative new products, services and information. And that can help a company charge ahead of its competition. 2.8 Challenges of Analytics In the industry of commercial analytics software, an emphasis has emerged on solving the challenges of analysing massive, complex data sets, often when such data is in a constant state of change. Such data sets are commonly referred to as big data. Whereas once the problems posed by big data were only found in the scientific community, today big data is a problem for many businesses that operate transactional systems online and, as a result, amass large volumes of data quickly (Keim & Zhang, 2011). The analysis of unstructured data types is another challenge getting attention in the industry. Unstructured data differs from structured data in that its format varies widely and cannot be stored in traditional relational databases without significant effort at data transformation (Keim, Andrienko, Fekete, Görg, Kohlhammer, & Melançon, 2008). Sources of unstructured data, such as email, the contents of word processor documents, PDFs, geospatial data, etc., are rapidly becoming a relevant source of business intelligence for businesses, governments and universities (Ferguson, 2012). For example, in Britain the discovery that one company was illegally selling fraudulent doctor's notes in order to assist people in defrauding employers and insurance companies, is an opportunity for insurance firms to increase the 27 University of Ghana http://ugspace.ug.edu.gh vigilance of their unstructured data analysis. The McKinsey Global Institute estimates that big data analytics could save the American health care system of $300 billion per year and the European public sector of €250 billion (Sullivan, 2013). These challenges are the current inspiration for much of the innovation in modern analytics information systems, giving birth to relatively new machine analysis concepts such as complex event processing, full text search and analysis, and even new ideas in presentation. One such innovation is the introduction of grid-like architecture in machine analysis, allowing increases in the speed of massively parallel processing by distributing the workload to many computers all with equal access to the complete data set. Analytics is increasingly used in education, particularly at the district and government office levels. However, the complexity of student performance measures presents challenges when educators try to understand and use analytics to discern patterns in student performance predict graduation likelihood, improve chances of student success, etc. For example, in a study involving districts known for strong data use, 48% of teachers had difficulty posing questions prompted by data, 36% did not comprehend given data, and 52% incorrectly interpreted data (Keim & Zhang, 2011). To combat this, some analytics tools for educators adhere to an over-the-counter data format (embedding labels, supplemental documentation, and a help system, and making key package/display and content decisions) to improve educators’ understanding and use of the analytics being displayed (Keim, & Zhang, 2012). One more emerging challenge is dynamic regulatory needs. For example, in the banking industry, Basel III and future capital adequacy needs are likely to make even smaller banks adopt internal risk models. In such cases, cloud computing and open source programming language R can help smaller banks to adopt risk analytics and support branch level monitoring by applying predictive analytics (Ferguson, 2012). 28 University of Ghana http://ugspace.ug.edu.gh 2.9 Theories Used in the Area of Sports Analytics 2.9.1 The Theory of Reasoned Action (TRA) The Theory of Reasoned Action (TRA) posits that the behaviour of an individual is usually driven by behavioural intention, which is often a function of an individual’s attitude toward the behavioural and subjective norms surrounding the performance of the behaviour. This theory generally posits that the performance of a person’s specific behaviour is determined by his or her behavioural intention to perform the behaviour. Therefore, this theory is guided by the principle of compatibility and behavioural intention. In regards to Sports Analytics, the more positive the attitude towards Sports Analytics Apps adoption and the greater the perception of social pressure towards the use of Sports Analytics Apps, the stronger the intention to adopt or continue using Sports Analytics. However, Ajzen (1985) has argued that the theory was limited by correspondence. In essence, for the theory to predict specific behaviour there should be an agreement of attitude and intention on an action, target, context, time frame and specificity (Sheppard, Hartwick, & Warshaw, 1988). In view of this, a major criticism of TRA is that it ignores the situational factors that may influence the attitude–intention–behaviour relationship and is thus ill equipped to predict situations in which individuals have low levels of volitional control (Yousafzai, Foxall, & Pallister, 2010). 2.9.2 The Theory of Planned Behaviour (TPB) The theory of planned behaviour (TPB) builds on the limitations of the theory of reasoned action (TRA) by expanding the boundary conditions of the theory of reasoned action to deal with the behaviours over which individuals by introduction have incomplete volition control. The theory posit that the behaviour of an individual is driven by behavioural intentions where behavioural 29 University of Ghana http://ugspace.ug.edu.gh intentions are a function of an individual's attitude towards the behaviour, the subjective norms surrounding the performance of the behaviour, and the individual's perception of the ease with which the behaviour can be performed (behavioural control). Ajzen (1985) opined that an additional determinant of intentions and behaviour is the perceived behavioural construct. Therefore, this construct is said to be the resource and opportunities available to an individual that influence the adoption of a particular behaviour. For instance, in the context of Sports Analytics Apps, if an individual realises that technology is available and other resources are available to him and that he is able to use it, there is the possibility of adoption and continued usage of Sport Analytics Apps. The theory of planned behaviour has also short- comings with some writer criticising it for ignoring important factors that may influence intention behaviour relationships (Yousafzai et al., 2010). For instance, Eagly and Chaiken (1993) has argued that habit, perceived moral obligation and self-identity are variables that could predict intention in the TRA that TPB failed to address. Yee-Loong Chong, Ooi, Lin, and Tan (2010) and Taylor and Todd (1995) have criticised the theory by stating that, since the theory requires individuals to be motivated to perform a certain behaviour, this assumption may be problematic when studying consumer adoption in addition to an identical belief structure among respondents when it comes to performing a behaviour. The use of the theory of planned behaviour has been successfully applied to predict Sports Analytics Apps behaviour and has been seen as a better alternative to the theory of reason action. Accordingly, (Yee-Loong Chong, Ooi, Lin, & Tan, 2010) and (Tan & Teo, 2000) have used this theory to study the factors that influence the adoption of Sports Analytics Apps. 30 University of Ghana http://ugspace.ug.edu.gh 2.9.3 The Technology Acceptance Model (TAM) The technology acceptance model (TAM) was developed in 1989 by Davis (1989) to test the acceptance and use of technology. This theory is focused on studying users’ adoption behaviour based on internal and external variables. The theory then turns to be an answer to the criticism of other adoption theories like the Theory of Planned Behaviour (TPB) and The Theory of Reason Action (TRA) (Fishbein & Ajzen, 1975). The Technology Acceptance Model (TAM) is a simplification of the Theory of Reason Action (TRA) and the Theory of Planned Behaviour (TPB). The two theories have been criticised for not acknowledging the influence of external factors in the adoption of technology and being limited in measuring users’ attitude towards behaviour, subjective norm and perceived behavioural intentions. This has therefore made it difficult for these two theories to assess the time gap between the assessment of behaviour and the actual behaviour, leading to the formation of the Technology Acceptance Model (TAM). TAM has two main constructs that influence behavioural intention to use a system and finally actual usage. These two factors are; the perceived usefulness, and perceived ease of use. Perceived usefulness is seen as having a direct influential Influence on perceived ease of use. However, there have been attempts to extend TAM, which has led to the development of TAM2 and TAM3. This has been done through the introduction of factors from other related models; the introduction of additional or alternative belief factors; and the examination of the antecedent and moderators of perceived usefulness and perceived ease of use (Wixom & Todd, 2005). However, most of the studies that have used TAM to study Sports Analytics App adoption have either used it in its original form or extended form by adding on certain constructs. TAM is sometimes combined with the Theory of Planned Behaviour to form a theoretical basis. In spite of this, TAM has been criticised for relying on respondents’ self- 31 University of Ghana http://ugspace.ug.edu.gh reporting and assuming that self-reported usage reflects actual usage. Taylor and Todd (1995b) criticised the model for providing only a limited guidance of how design and implementation can be used to anticipate technology usage. 2.9.4 Unified Theory of Acceptance and Use of Technology (UTAUT) While explaining user adoption of new innovation is a full-grown exploration train in contemporary information systems research, the plethora of theoretical models and experimental examinations that began a decade-and-a-half earlier was reviewed and synthesised as a unified theory of acceptance and use of technology (UTAUT) by Venkatesh, Morris, Davis, and Davis (2003). A more comprehensive set of factors is obtained from Venkatesh et al. (2003) UTAUT as a unified view of user adoption. By combining eight competing theoretical models, the authors derived an overarching set of four constructs that have an immediate influence on acceptance and usage behaviour. Performance Expectancy that is the first among the other constructs comprises of constructs like; perceived usefulness (Davis, 1989), and extrinsic motivation (Davis et al., 1989) in TAM and Relative advantage in Diffusion Theory (Moore & Benbasat, 1991). Effort expectancy is also made up of constructs such as perceived ease of use (Davis, 1989) in TAM and complexity in Diffusion Theory (Moore & Benbasat, 1991). Whiles social influence is made up of subjective norm (Davis, 1989) in TAM and image in Diffusion Theory (Moore & Benbasat, 1991). Also, facilitating conditions comprises of constructs such as behavioural control (Davis, 1989) in TAM and compatibility (Moore & Benbasat, 1991) in Diffusion theory. These factors are deemed as having a direct effect on Sports Analytics App adoption and are likewise used as fundamental antecedents to unravelling Sports Analytics App adoption in the developing economies. 32 University of Ghana http://ugspace.ug.edu.gh Although UTAUT is still a relatively new model and has not been as widely used as TAM, it has gradually drawn researchers’ attention and has been recently applied to exploring the users’ acceptance of Sports Analytics App (Wixom & Todd, 2005; Yee-Loong Chong, Ooi, Lin, & Tan, 2010). 2.10 Why Sports Analytics Apps Adoption Research Gaps The previous section attempted a review and discussion on Sports Analytics Apps adoption in developing economies. This section intends to study the influence of performance expectancy, effort expectancy, social influence, facilitating conditions and behavioural intention on consumers' adoption of Sports Analytics Apps in Ghana based on the evidence presented and the future directions suggested. Since the success of Sports Analytics Apps is dependent on football coaches’ acceptance of the system, this study is motivated to understand the factors that influence the usage of the system in order to identify why Sports Analytics Apps adoption in Ghana is relatively slow. The decision to study the gap is informed by: i. The need for research to explore the influence of the social influence on behavioural intention among Sports Analytics Apps users in developing economies. ii. The need for research to identify the influence of the facilitating conditions variable on usage behaviour among users of Sports Analytics Apps in developing economies. iii. The need for research to examine the relationship between behavioural intention and usage behaviour from a developing country perspective. iv. The need to establish the moderating influence of age, gender and experience on football coaches’ adoption of Sports Analytics Apps. 33 University of Ghana http://ugspace.ug.edu.gh 2.11 Summary The main purpose of this chapter is to explore the areas in Sports Analytics Apps research and finding the most researched issues, and the most used theoretical approaches in the area and suggest the need for further studies. As a result of this, the various evidence presented and the subsequent discussions produced, indicated the need for more studies to focus on other factors that influence the adoption of Sports Analytics Apps in developing countries; and the need to use a stronger framework that would incorporate most of the factors that influence the adoption of Sports Analytics Apps in developing countries. In view of this, the gaps coincide with the purpose of the study that seeks to explore the influence of performance expectancy, effort expectancy, social influence, facilitating conditions and behavioural intention on football coaches’ adoption of Sports Analytics Apps and how these factors are moderated by demographics like age, gender and education. 34 University of Ghana http://ugspace.ug.edu.gh CHAPTER THREE RESEARCH FRAMEWORK 3.1 Chapter Overview This chapter seeks to probe further into the research framework adopted for this study. In order to effectively delve into the issue of the influence of performance expectancy, effort expectancy, social influence and facilitating conditions on professional football coaches’ adoption of sports analytics Apps in developing countries. This study sought to use the Unified Theory of Acceptance and Use of Technology (UTAUT) as its theoretical lens. The reason behind the adoption of this theory is its ability to help researchers reach a unified view of users’ acceptance of technology. Therefore, for the purpose of finding answers to the research questions stated earlier, this section discusses literature relating to the chosen theoretical framework in order to build a solid research structure (Proposition) on the influence of performance expectancy, effort expectancy, and social influence and facilitating conditions of professional football coaches’ adoption of Sports Analytics Apps in Ghana. This section will therefore provide an overview of the UTAUT, followed by an explanation of the construct of the theory and the development of Proposition base on the review of literature. 3.2 Overview of Unified Theory of Acceptance and Use of Technology (UTAUT) The unified theory of acceptance and use of technology (UTAUT) is a technology acceptance model formulated by Venkatesh and others in "User acceptance of information technology: Toward a unified view". The UTAUT aims to explain user intentions to use an information system and subsequent usage behaviour. The theory postulates four key constructs: Performance expectancy, effort expectancy, social influence, and facilitating conditions. 35 University of Ghana http://ugspace.ug.edu.gh The first three are direct determinants of usage intention and behaviour, and the fourth is a direct determinant of user behaviour. Gender, age, experience, and voluntariness of use are posited to moderate the Influence of the four key constructs on usage intention and behaviour. The theory was developed through a review and consolidation of the constructs of eight models that earlier research had employed to explain information systems usage behaviour (Theory of Reasoned Action, Technology Acceptance Model, Motivational Model, Theory of Planned Behaviour, a combined Theory of Planned Behaviour or Technology Acceptance Model, Model of Personal Computer Use, Diffusion of Innovations Theory, and Social Cognitive Theory). Subsequent validation by Venkatesh et al. (2003) of UTAUT in a longitudinal study found it to account for 70% of the variance in Behavioural Intention to Use (BI) and about 50% in actual use. The problem with information systems researchers having to choose a suitable model among multitudes of models, and the issue of being bound to choose a construct across models or a favoured model which may result in ignoring the contribution from other alternative ones, led to the need for a combination of eight different adoption models in order to reach a unified view of the acceptance of technology by users (Venkatesh, Morris, Davis, & Davis, 2003). Venkatesh et al (2003) reviewed and compared eight dominant models that have been used by information systems researchers to explain technology acceptance behaviour. However, upon the review, the authors identified five major limitations of the use of these dominant theories as explained below. i. In TAM, the technology studied was simple and individualistic compared to the complex and sophisticated organizational technology theories. 36 University of Ghana http://ugspace.ug.edu.gh ii. In most of the dominant theories, there is a contention between applying the study on the original theory as opposed to the application of its extended versions and exactly when to use which. iii. In UTAUT, general cross-sectional form of measurement was used. iv. In UTAUT, It becomes rather difficult to generalized results to mandatory settings since most studies were conducted in voluntary usage context. In order to overcome these limitations, Venkatesh et al. (2003) empirically compared the eight models in a longitudinal field study and divided the data into mandatory and voluntary settings. Moderating variables that had been reported in literature as having an influence on information systems adoption and usage decisions were also considered. However, it was realized that, with the exception of motivation model and social cognitive theory, there was an increase in the predictive validity of the models after the inclusion of the moderators. Venkatesh et al. (2003) also investigated the commonality among these models and found seven constructs to be significant direct determinants of intention or usage in one or more of the individual models. Therefore, they propositioned that five of these constructs play a significant role as direct determinants of user acceptance and usage Behaviour. These include performance expectancy; effort expectancy; social influence; facilitating conditions; and behavioural intention. i. Performance Expectancy: This refers to the degree to which an individual perceives that the use of a system will help him/her to attain gains in job performance. ii. Effort Expectancy: This refers to the perception of ease associated with the usage of a new technological innovation or system. 37 University of Ghana http://ugspace.ug.edu.gh iii. Social Influence: This refers to the degree to which an individual perceives that important others believe he/she should use a new technological innovation or system. iv. Facilitating Conditions: This refers to the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of a new technological innovation or system. v. Behavioural Intention: This refers to the degree to which an individual intends to use a system. Figure 3. 1 Research Model Source: (Venkatesh et al., 2003) 38 University of Ghana http://ugspace.ug.edu.gh 3.3 Proposition Development 3.3.1 Performance Expectancy This represents the degree to which individuals using Sports Analytic Apps believes that the use of the system will help in the attainment of gains in the job performance. This particular construct is made up of constructs of other models that are deemed as having a relation with performance expectancy. These constructs include: perceived usefulness (TAM, and combined TAM-TPB); extrinsic motivation (MM); job-fit (MPCU); relative advantage (DOI); and outcome expectancy (SCT). However, in relation to Sports Analytics Apps adoption AL Awadhi and Morris (2008), has defined performance expectancy as the terms of utilities extracted by using Sports Analytics Apps which is productive relative to the traditional encounter. Therefore, it is motivating to add that other related constructs such as perceived influence and relative advantage have been widely captured as fundamental determinants of behavioural intention towards Sports Analytics Apps adoption (Im, Hong & Kang, 2011; Kijsanayotin, Pannarunothai, & Speedie, 2009). Based on this, Caya and Bourdon (2016) has empirically demonstrated that the greater the perceived relative advantage, the more likely Sports Analytics Apps would be adopted. Similarly, other studies have also indicated that a more critical factor to the adoption of Sports Analytics Apps is the perceived usefulness construct (Im, Hong & Kang, 2011; Kijsanayotin, Pannarunothai, & Speedie, 2009; Caya & Bourdon 2016). Hence, the following proposition is proposed: Proposition 1: Performance Expectancy will influence football coaches’ intention to adopt Sports Analytic App in a developing country. 39 University of Ghana http://ugspace.ug.edu.gh 3.3.2 Effort expectancy Effort expectancy on the other hand represents the degree of ease associated with the use of a system. Other constructs in different models also capture this same concept. They include: perceive ease of use (TAM); and complexity (DOI and MPCU). However, the relationship between effort expectancy and behavioural intentions is often debated due to the effect of performance expectancy on behavioural intention. Even though the effort expectancy construct was aggregated in the UTAUT from the perceived ease of use and complexity construct, research conducted using the TAM model has provided contradictory outcomes when reviewing the perceived ease of use and studies using TAM, IDT and MPCU in examining complexity (Davis, Bagozzi, & Warshaw, 1989; Moore & Benbasat, 1991; Thompson, Higgins, & Howell, 1991). Proposition 2: Effort Expectancy will influence coaches’ intention to adopt Sports Analytics Apps in Ghana. 3.3.3 Social Influence Social influence can also be defined as the degree to which an individual perceives how important others believe he/she should use a new system. This particular construct is represented differently in existing models such as subjective norms (TRA, TAM2, TPB/DTPB and combined TAM-TPB), social factors (MPCU), and image (DOI). Al-Qeisi (2009) has posited that a comparison between models established that the behaviour of these construct in relation to the adoption of new systems is similar. Hence, making Datta (2011) posit that for adopters without enough experience, the perception of referent becomes an important issue for behavioural intention. Moreover, although social influence has been modelled on different models, the result in regards to its importance in predicting behavioural intentions has been debatable. Kijsanayotin et al. (2009) have therefore stated that social influence is expected to positively influence 40 University of Ghana http://ugspace.ug.edu.gh behavioural intention in relation to Sports Analytics Apps adoption. Based on the review of literature, social influence can be deemed as having a positive influence on behavioural intention of consumers to adopt Sports Analytics Apps in Ghana. Hence, the Proposition: Proposition 3: Social Influence will influence coaches’ intention to adopt Sports Analytics Apps in Ghana. 3.3.4 Facilitating Conditions Facilitating conditions refer to the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of a system (Venkatesh et al., 2003). It is important to note that the usage of a system requires a particular skill, resources and technical infrastructure (Riffai et al., 2012; Yeow, Yuen, Tong, & Lim, 2008); and these facilities such as the Internet and computers are usually not free from the coach’s context (Venkatesh et al., 2012). Therefore, Caya and Bourdon (2016) have postulated that, the more convenient the access of respondents to the Internet and computers, the more proficient their use of the computer and Internet, which may result in a higher adoption rate of respondents using the Internet. Hence, facilitating conditions has a major role to play in enhancing or hindering coaches’ intention and adoption of Sports Analytics Apps as well as facilitating the utilities that are extracted from using Sports Analytics Apps (Caya & Bourdon, 2016; Riffai et al., 2012). Thus, the proposition: Proposition 4: Facilitating conditions will influence coaches’ adoption of Sports Analytics Apps in Ghana. 41 University of Ghana http://ugspace.ug.edu.gh 3.3.5 Behavioural Intention In support with all the different model from psychological theories, which argue that individual behaviour is predicted and influenced by individual intention, the UTAUT model contended and proved behavioural intention to have influence on technology usage (Venkatesh et al., 2003; Venkatesh & Zhang, 2010). Therefore, for the purpose of maintaining consistency with the underlying theory for all the intention models, behavioural intention is expected to have a positive influence on the usage of a new system (Venkatesh et al., 2003). Considering that the ultimate goal of every football club is to attract coaches to adopt a system rather than their intention to adopt, it is necessary for authors to examine the relationship between behavioural intention and actual usage. Extant studies have posited that behavioural intention positively influences Sports Analytics Apps usage (Barney, 1996), thus, it can be proposition that: Proposition 5: Behavioural Intention (BI) will have an influence on Sports Analytics Apps Usage Behaviour in Ghana. 3.4 Effect of Moderators The study also explores the effect of some socio-demographic moderators in relation with the constructs of the UTAUT model. Information systems researchers studying Sports Analytics Apps adoption have reported investigation of some demographic moderators such as age, education, experience and gender. This is aimed at establishing the extent to which these demographics tend to influence the adoption of Sports Analytics Apps for the purpose of understanding and making informed decisions on Sports Analytics Apps usage, deployment, design and implementation (Im, Hong & Kang, 2011; Kijsanayotin, Pannarunothai, & Speedie, 2009). Correspondingly, based on the reported influence of some demographics on Sports Analytics Apps adoption intention, this study seeks to explore gender, age and experience to 42 University of Ghana http://ugspace.ug.edu.gh replicate the model in order to establish coaches’ Sports Analytics Apps adoption intentions and usage as well. 3.4.1 Gender Extant research has highlighted that with respect to technology acceptance studies, decision- making processes by males and females are different (Morris, Venkatesh, & Ackerman, 2005; Venkatesh, 2006). For instance, Karjaluoto, Cruz, Barretto Filgueiras Neto, Muñoz-Gallego, and Laukkanen (2010) posited that, with regard to internet banking adoption, men appear to be more task-oriented as compared to women and internet banking is typically motivated by goal achievement, therefore, the likelihood of males accepting technology is higher than that of females (Wan, Luk, & Chow, 2005). On the other hand, Garbarino and Strahilevitz (2004) have also stated that the influence of social norm on intention to adopt a technology is stronger among women than men. Based on this, (Martins et al., 2014) posited that men are more likely to adopt Sports Analytics Apps services than women. In respect of the UTAUT model, gender is reported as a moderator in the relationships between performance expectancy (which is deemed as stronger for men), effort expectancy (which is also regarded as stronger for women) and social influence (regarded as stronger for women under mandatory use) (Venkatesh et al., 2003). Therefore, based on the finding pertaining to gender in relation to the UTAUT model, it is propositioning that: Proposition 6: Gender will moderate PE, EE, SI (Performance Expectancy, Effort Expectancy, and Social influence) and BI (behavioural intention) in UTAUT. Proposition 6.1: Performance Expectancy effect on Sports Analytics Apps usage is stronger for males than females. 43 University of Ghana http://ugspace.ug.edu.gh Proposition 6.2: Effort Expectancy effect on Sports Analytics Apps usage is stronger for females than males. Proposition 6.3: Social Influence effect on Sports Analytics Apps usage is stronger for females than males. 3.4.2 Experience It is apparent that the moderating factor experience in football can be viewed from different perspectives. It can be viewed as working years, achievements, awards and certifications. Extant authors have stated that the level of experience has a positive Influence on perceived ease of use rather than perceived usefulness (Agarwal & Prasad, 1999). This assertion is made based on the fact that experience leads to a positive association with perceived usefulness; and greater education increases perceived ease of use by reducing anxiety and improving attitude (Morris, Venkatesh, & Ackerman, 2005; Venkatesh, 2006). Hence, it is propositioned that: Proposition 7: Experience will moderate Performance Expectancy and Social Influence in Sports Analytics App adoption in the UTAUT model. Proposition 7.1: Effort Expectancy effect on usage is stronger for those with higher levels of experience than those with lower levels of experience. Proposition 7.2: Social Influence effect on Sports Analytics Apps usage is stronger for those with lower levels of experience than those with higher levels of experience. 44 University of Ghana http://ugspace.ug.edu.gh 3.4.3 Age Although young people have been cited as favourites for the adoption of new technologies by the UTAUT model, other researchers have stated otherwise. For instance, some authors have established that elderly people tend to adopt the system more, since they are believed to have experience and need to use Sports Analytics Apps for their tactical needs (AbuShanab et al., 2010). Contrary to this assertion other studies have said that older people are more risk aversive; hence prefer a more personal relation with the players, management and fans (Gan, Clemes, Limsombunchai, & Weng, 2006). Other studies have also affirmed the assertion of the model by stating that the younger population are adopting and using Sports Analytics Apps more than the older generation (Njuguna et al., 2012). Therefore, age can be said to have a positive influence on Sports Analytics Apps adoption. Hence, it is propositioned that: Proposition 8: Age will moderate Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Condition in UTAUT. Proposition 8.1: Performance Expectancy effect on Sports Analytics App usage is stronger for young individuals than older individuals. Proposition 8.2: Effort Expectancy effect on Behavioural Intention is stronger for young individuals than older individuals. Proposition 8.3: Social Influence effect on Behavioural Intention is stronger for older individuals than younger individuals. Proposition 8.4: Facilitating Conditions effect on Behavioural Intention is stronger for older individuals than younger individuals 45 University of Ghana http://ugspace.ug.edu.gh Figure 3. 2: Sports Analytics Performance Expectancy Conceptual framework model Source: Author’s own constructs 3.5 Summary In summary, the chapter comprised of the theoretical foundation used for the study, that is, the UTAUT. The chapter discussed the UTAUT model and provided an overview, detailed description and explanation of the theory as posited by Venkatesh and other relevant researchers that are directly related to the studied area. The chapter also showed evidence of the extent to which the UTAUT framework has been used in prominent studies. The chapter then proposed specific proposition under each construct of the framework with the view of satisfying the first and second research questions set out at the beginning of the study. 46 University of Ghana http://ugspace.ug.edu.gh CHAPTER FOUR RESEARCH DESIGN 4.1 Chapter Overview The previous chapter discussed the frameworks needed to undertake this study. It further presented the conceptual framework of the UTAUT to guide empirical testing of the concepts in the framework. This chapter is aimed at discussing all the philosophical and methodological issues that are related to this study. The qualitative research method is also discussed in this chapter with justification being made in line with the research approach and philosophy underpinning the study for choosing the qualitative method. The data collection method as well as the instruments used in data collection, analysis and quality criteria is discussed. Finally, the chapter discusses sampling issues as well as explaining the construct measure used for the study. The focus of this chapter is therefore on the research paradigm, research method, and data collection method, sampling and data analysis technique used in the study. 4.2 Research Paradigm Kuhn (1970) defines research paradigm as a “set of beliefs, values and techniques which are shared by members of a scientific community, and which acts as a guide or map, dictating the kinds of problems scientists should address and the types of explanations that are acceptable to them” (p. 175). Thus, paradigms can be said to aim at developing research within a particular set of acceptable ideological or philosophical thinking (Johnson & Clark, 2006). In view of this, authors like Myers and Avison (2002) have stated that, in addition to a given definite definition of research (i.e. ‘valid research’), the most appropriate and applicable methods to use for a study 47 University of Ghana http://ugspace.ug.edu.gh is provided by a paradigm. Creswell (2009) refers to this as worldview; the general orientation about the world and the nature of research that a researcher holds. Although these beliefs usually remain implicit in most research, they affect the practice of the research. This is because it enables the researcher to better defend the stance chosen in relation to other possible alternatives, and does not dwell on the researcher’s philosophical know-how or the ability to reflect on a specific philosophical choice. There are three dimensions of a research paradigm namely; ontology, epistemology and methodology. The epistemology dimension provides a philosophical background for deciding what kinds of knowledge are legitimate and adequate and it tries to understand what it means to know (Grey, 2014). Epistemology poses the following questions: What is the relationship between the knower and what is known? How do we know what we know? What counts as knowledge? Epistemology is intimately related to ontology and methodology; as ontology involves the philosophy of reality and the view of how one perceives reality (Krauss, 2005; Wahyuni, 2012) whether it is external or a construct of our mind (Jonker & Pennink, 2010). Methodology meanwhile identifies the particular practices used to attain knowledge of the reality (Krauss, 2005; Wahyuni, 2012) that is, qualitative, quantitative or mixed methods. In information systems, there exist three main paradigms. Myers and Avison (2002) discussed these as: the positivist, interpretive, and critical. Interpretive research seeks people’s accounts of how they make sense of the world and the structures and processes within it (Fisher, 2010). Positivist studies generally attempt to test theory in an attempt to increase the predictive understanding of phenomena (Myers & Avision, 2002). However, critical realism according to Mingers et al. (2013) offers a robust framework for the use of a variety of methods in order to 48 University of Ghana http://ugspace.ug.edu.gh gain a better understanding of the meaning and significance of information systems in the contemporary world. Having looked at the above, this study therefore employed the critical realist stance to help achieve the purpose for which this research was conducted; to describe the type of applications used in the Ghanaian football fraternity and the factors that influence the use. Again, it investigates the Influences of performance expectancy, effort expectancy, social influence, facilitating conditions and behavioural intention on professional football coaches’ adoption of sports analytics App as supporting tool in a developing country (Section 1.3). The justification for selecting critical realist was based on the fact that it enables an information system’s researcher to “get beneath the surface to understand and explain why things are as they are, to propositionalize the structures and mechanisms that shape observable events” (Mingers, 2004). 4.3 Research Design and Methods A realist researcher who needs to investigate a phenomenon finds himself thinking about issues such as why it is necessary to study the phenomenon; the kind of knowledge it stands to develop; what the best way to gain knowledge is, and who will derive the benefits from the study (Harnesk, 2004). For the realist, exploratory research is one of the valuable mediums to delve into ISDRP, seek new insights and to assess phenomena in a new light (Robson, 1993). Saunders and Thornhill (2000) add that exploratory studies are a particularly useful approach when a researcher wishes to improve a problem’s understanding. Moreover, because the objective of this study is to describe the type of applications used in the Ghanaian football fraternity and the 49 University of Ghana http://ugspace.ug.edu.gh factors that influence the use. Again, it investigates the influences of Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions and Behavioural Intention on professional football coaches’ adoption of Sports Analytics Apps as supporting tool in a developing country. The realist researcher achieves this by gathering information on the multiple perceptions of research participants (football coaches). This provides the realist with the actual perceptions football coaches have with regards to the football analytic app. Within a realism framework, both qualitative and quantitative methodologies are seen as appropriate (Healy & Perry, 2000) for researching the underlying mechanisms that drive actions and events. The researcher therefore adopted the qualitative methods for this research. The researcher collected data using a qualitative instrument interviews and direct observation. This encouraged respondent to put forth their needs, ideas, experiences and attitudes vital to the research involved (Wright, 2006). 4.4 Conducting the Case 4.4.1 Selection of Sample for the Case According to Castillo (2009), samples are drawn because it will be impractical to investigate all members of a target population. Sampling is a process of selecting research participants (Creswell, 2009). Four different sampling techniques are used in the study to sample the professional football coaches. There are purposive sampling, convenience sampling, snowball sampling and structural sampling. i. Purposive sampling is a non-random technique that does not need underlying theories or a set number of participants. Simply put, the researcher decides what needs to be known and sets out to find people who can and are willing to provide the information by virtue 50 University of Ghana http://ugspace.ug.edu.gh of knowledge or experience Battaglia (2008): All seven coaches were purposively sampled on the bases of being professional football coaches and active in the administration of football and related activities and a registered and recognised member of a football coaches association. In addition has being operating actively in the last five years. ii. Convenience sampling (also known as Haphazard Sampling or Accidental Sampling) is a type of nonprobability or non-random sampling where members of the target population that meet certain practical criteria, such as easy accessibility, geographical proximity, availability at a given time, or the willingness to participate are included for the purpose of the study Creswell (2011): the professional football coaches conveniently sampled on the bases of being registered professional football coaches and willing to involve themselves in the study. iii. Snowball sampling is where research participants recruit other participants for a test or study. It is used where potential participants are hard to find. It’s called snowball sampling because (in theory) once you have the ball rolling, it picks up more “snow” along the way and becomes larger and larger Creswell (2011): the coaches were identified based on recommendation from other football coaches in the football fraternity who knew they were actively involved in the use of Sports Analytics Applications. iv. Structural sampling refers to the use of a combination of sampling and model-based methods Creswell (2011): the structural sampling for selection was based on the following; Professional football coaches with either UEFA license or CAF license and using sports Analytics Apps. 51 University of Ghana http://ugspace.ug.edu.gh 4.4.2 Data Collection Method In a case study, there are various ways of collecting data from the respondents; Interview, Participant- observation, Physical artefacts, Archival records and documents (Yin, 2009). “Benbasat et al. (1987) are also of the view that, in conducting a case study, evidence must be collected from two or more sources in order to support the research findings. Further, critical realism encourages the use of multiple data collection methods to enhance triangulation of perspectives of respondents and also unearths mechanisms and structures which underpin events which are observable”. In this study the interviews and direct observation are used (Benbasat et al., 1987). 4.4.2.1 Interview One of the very famous ways of collecting data is through the use of data (Yin, 2003). By interviewing the interviewee the interviewer is able clarify issues related to the study at hand. In this study majority of the questions where obtained from the constructs of the UTAUT theory; Performance Expectancy, Effort expectancy, Social Influence, Facilitating condition and Behavioural Intention which forms the theological lens of the thesis. Questions such as the following where used in the study; i. How do you find your understanding of using SAA for your task as a football coach? ii. How do you find learning to use the SAA? iii. How easy is it for you to use SAA? The researchers visited some of the respondents at their homes and others at their various club secretariats and conducted the interviews using a radio recorder and a note book. The recordings 52 University of Ghana http://ugspace.ug.edu.gh were later transcribed and used for the study. The full interview guide is found in the appendix. In total, seven professional football coaches where interviewed. 4.4.2.2 Ethics for interview The following research ethics were observed during the conduct of the study. Firstly, the researcher in an attempt to seek audience from respondents got a letter introducing him as a student from the Departments of Operations and Management Information Systems (OMIS) of the University of Ghana Business School. Secondly, the interviews were held at the convenience of the respondents and in all cases face to face interview was conducted. The duration for the interviews was done between 40 minutes to 1 hour. Thirdly, prior to conducting the interviews, respondents were fully be made aware of the use of a voice recorder to capture their responses and their consent sought alongside writing notes in a small book. The purpose of taking the notes will serve as reference in order to make follow up questions if need be. Finally, respondents were assured of the confidentially of the information they have provided. 4.4.2.3 Direct Observation The researcher also used direct observation in all the cases due to the fact they had similar and different Sport Analytics Application they used. These direct observations were done when the researcher was granted access by the coaches. Direct observation was thus useful in understanding some of the activities in the Sports Analytics Application used by the football coaches. 53 University of Ghana http://ugspace.ug.edu.gh 4.4.3 Data Collection Data was collected among professional football coaches in two stages. The first stage which th th took place from 10 September, 2018 to 15 September, 2018 involved football coaches active as national team coach and the premier division of the Ghanaian Premier League. The st th second stage that took place from 21 September 2018 to 27 September 2018 involved the other coaches in the other league divisions. The interviews and direct observations were conducted across all the Ghanaian football leagues. 4.5 Thematic Analysis The Data gathered from the interviews of the study was analysed using thematic analysis collected for this study was analysed using thematic analysis. Thematic analysis is used to categorize data and present it in the form of patterns that relate the data (Alhojailan, 2012). In thematic analysis, the researcher makes notes and sorts it into various categories (Hinson et al., 2009). In using thematic analysis, the researcher is able to provide an analysis of the data from a broad reading of the data towards discovering patterns and developing themes. The output of the interview were transcribed and read over very well in order to identify key views from the interviews and then related to the key themes I the research questions. This highlighted the similarities and differences in the responses provided. 4.6 Chapter Summary This chapter discussed all the philosophical and methodological issues that are related to this study. The qualitative research method is also discussed in this chapter with justification being 54 University of Ghana http://ugspace.ug.edu.gh made in line with the research approach and philosophy underpinning the study for choosing a qualitative method. The data collection method as well as the instruments used in data collection, analysis and quality criteria is discussed. Finally, the chapter discussed sampling issues as well as explaining the construct measure used for the study. The focus of this chapter was therefore on the research paradigm, research method, and data collection method, sampling and data analysis technique used in the study. 55 University of Ghana http://ugspace.ug.edu.gh CHAPTER FIVE RESEARCH FINDINGS AND ANALYSIS 5.1 Chapter Overview The previous chapter discussed the methodology that was used for the study. This section presents the findings and analysis of the findings, and sets a pivotal stage for confronting the theoretical chapters with empirical evidence. This chapter begins by providing an overview of an overview of the professional football coaches. It further presents the finding of the study from the professional football coaches. The concluding section of the chapter is a summary on the findings. 5.2 Brief Profile of the Professional Football Coaches in a Developing Country The purpose of this part of the study is to provide brief case description of the participants of the research who are professional football coaches. These participants include ex- professional football players now coaches and others from different fields of life. 5.2.1 Case A: A Ghana premiership Football Coach Case A (hereafter referred to as FF) was born on 1969 in Hamilton; Scotland is a Scottish professional football manager. FF was an apprentice at Celtic in his playing days 1987. When his footballing career ended, he decided to study physical education at Cardiff Metropolitan University and went to Loughborough University to get a master’s degree in sports science. At age 24, he coached his first team under the Welsh Football Association managing their Under-14 56 University of Ghana http://ugspace.ug.edu.gh and Under-15 sides. He took over West Bromwich Albion F.C. dressing room as a fitness coach in 1998. He tried to make a comeback to active football but that was not fruitful. As a result he decided to pursue a more propitious managing career by getting an 'A' License and Pro License. St Mirren F.C. in 2003 and Ranger F.C in 2004 as a fitness coach appointed him respectively. Later, he was assistant manager of Al-Nasr Dubai SC but he was unable to settle in his new job and his family could not come so he left after a period of time. He eventually joined Gor Mahia, champions of the Kenyan Premier League; it would be the first time he would serve as a head coach. Seemingly, he became the highest-paid coach in the Kenyan Premier League as he was paid over 600,000 Kenyan shillings a month. In Kenya, he won a lot of silverwares; which include; i. Under an unbeaten league title in 2015, an achievement never done before in the competition since 1976 ii. the Scot guided K’Ogalo to KPL Top 8 Cup and Kenyan Super Cup titles in 2015 besides winning the league in 2014 and 2015 and iii. As a result of his efforts, the former Rangers fitness coach was named 2015 SportPesa Coach of the Year. Following the end of his contract as assistant coach of Zamalek, FF joined Ghanaian outfit Accra Hearts of Oak F.C. in 2017. 5.2.2 Case B: GN Division One League Football coach Case B (hereafter referred to as DD) was born on 1974 in Accra is a former Ghanaian international footballer. He has played as a midfielder, mostly in Germany. Currently he works 57 University of Ghana http://ugspace.ug.edu.gh as the coach. He first went to Germany in 1992. He played for Fortuna Köln in the second tier league for six years. Due to his unique style of play in 1998 VfL Wolfsburg signed him. In Wolfsburg, he quickly developed as a resourceful player and one of the teams' most important elements and, in 2001–02, was given team captaincy. However, he was injured and operated on the knee that prompted a January 2004 move to SpVgg Unterhaching, in a second-level return. In 2005 DD moved to Denmark to play with AC Horsens. In 2009, he was playing for Germany based club SC Langenhagen he announced the end of his career. A holder of 41 international caps; DD was named captain of the Black Stars following retirement of the then captain. However, he was never recalled again after applying for and receiving German citizenship. In the year of his retirement, DD signed a contract as head coach for Sekondi Wise Fighters where he worked with his former national teammate as his assistant. He later became the director of sport of the Sekondi Wise Fighters. In 2012, DD was named as head coach of the Glo Premier League club Hearts of Oak. He is the current Coach of Dreams Football Club, a Ghanaian professional football club based in the Greater Accra Region, Ghana. 5.2.3 Case C: GN Division One League Football Coach Case C (hereafter referred to as Yale; born in 1964), Yale is a Ghanaian former footballer who played as an attacking midfielder and who served as captain of the Ghana national team. He is regarded as one of the greatest African footballers of all-time. He played for several European clubs and found his fame in the French Ligue 1 with Lille and Marseille, the latter where he won the UEFA Champions League in 1993, among other titles. Three of his sons, Ibrahim, André and Jordan, have also become internationals for Ghana. Ibrahim and André were selected for the 2010 FIFA World Cup, while André and Jordan played at the 2014 World Cup. André is the 58 University of Ghana http://ugspace.ug.edu.gh current captain of the senior men’s team the Black Stars. In 2001, the UEFA–CAF Meridian Cup All-Star Match format was changed slightly for the second All-Star Match to bring together great players aged between 35 and 45 who now revel in their 'veteran' status and play the game purely for pleasure. The squad sparked off memories of great footballing moments at club and international level. In the same year, he was nominated by the present government of Ghana to serve as the next Chairman of the FA, an opportunity he later gave up for a more experienced former coach of Ghana for which in his own words said that this was to be an opportunity to learn from his superiors. At present he owns a first division club, called Nania, with the future hopes of nurturing the young talent to augment the fledging league of the country. He has also been involved with various charity works across the African continent. 5.2.4 Case D: National Team Football Coach Case D (hereafter referred to Pep); was born June 1978 is a former professional football goalkeeper and currently goalkeeper coach for the Ghana national football team. After leaving home country, he played for several clubs in Turkey, for Hammarby in Sweden, and in England for Birmingham City, Wigan Athletic and Blackpool, who released him at the end of the 2010– 11 seasons. Pep was the vice-captain for the Ghana national football team. He holds both a Ghanaian passport and a Turkish passport. Pep was the starting goalkeeper of the Ghana national football team, and was called up for the 2006 FIFA World Cup, the 2008 Africa Cup of Nations and the 2010 FIFA World Cup. 59 University of Ghana http://ugspace.ug.edu.gh 5.2.5 Case F: Ghana premiership Football Coach Case F (hereafter referred to as Zola; 1969 in Japan) is a Japanese football manager currently managing Inter Allies FC. Arriving at New York City in 1988, he began coaching youth soccer teams: the Elmont Ravens girls' team, the Brooklyn Patriots boys' team, and the Gotham Girls Chargers team. Next, Zola became coach of the BMCC men's soccer team for nine years In 2012, Zola moved to Kyrgyzstan where he served as the Kyrgyzstan Football Federation technical director and Kyrgyzstan U16 coach. He has guided them during U16 AFC Qualifications. Announced as coach of Accra Hearts of Oak in November 2015, he became the first Asian to coach a Ghanaian team in the process. Almost immediately, he was criticized on account of his inexperience coaching professional teams. Eventually, he was fired. He has expressed desire to return to coaching in Ghana and has hinted at a move to Asante Kotoko. Living in New York City for 23 years, he sees it as his home even though he is ethnically Japanese. Licenses and certifications i. USSF National Youth License ii. USSF "A" License iii. NSCAA Goalkeeping Diploma iv. NSCAA Coach of the Year Award v. FIFA Goalkeeping License vi. CONCACAF International Coaching License vii. AFC Pro License 60 University of Ghana http://ugspace.ug.edu.gh 5.2.6 Case G: National Team Football Coach Case G hereafter referred to as Tower; born in Ghana defied the odds as she led the Black Queens team to win the maiden edition of the WAFU Women’s Cup of Nations tournament in Cote d’ Ivoire. Having worked as the deputy Coach for the side, the former Amidaus Professional assistant trainer was handed the opportunity to lead the side at the competition. Tower (Middle) was one of the centre referees at the 2007 FIFA Women’s World Cup. The Coach however made a big case for herself by supervising the team to win their second major silverware. Having succeeded in her first major test, the former Ghana international female star is likely to be named the substantive head Coach for the Black Queens. She had previously made history as the first Ghanaian female referee to officiate at the FIFA Women’s World Cup in 2007. She was also part of the history making Black Queens squad that qualified for their first ever FIFA World Cup in 1999. 5.2.7 Case H: Division Two League Football Coach Case H hereafter referred to as Rock. The 26-year-old first joined DC United as assistant coach last year and in 2017 was given the job as head coach of the Sunyani-based club in the Brong Ahafo Region. Despite having studied coaching as part of her BSc degree in Physical Education, which she completed 2016 at the University of Education, Winneba, and possessing a CAF coaching licence C, Rock says it was not easy to land the job. She worked with some great coaches, especially Mas-Ud Dramani, who is the former Asante Kotoko head coach and is currently in charge of the Ghana women’s team, the Black Queens. 61 University of Ghana http://ugspace.ug.edu.gh 5.3 Demographic Representation of Respondents. As asserted by Etikan, Musa and Alkassim, (2016), it is not possible to include every subject when undertaking a research because the population is almost infinite. Hence, the presentation of Table 5.1 is not in any way to justify a statistical selection but to give an overview of the final respondents used in the study based on the characteristics or criteria for selecting the respondents 62 University of Ghana http://ugspace.ug.edu.gh Table 5.1 Profile of Respondents and Achievements. Identification & Number of teams Achievement Number of Gender Age Years’ Coach Rank Managed interviews Experience Frank (hereafter 11 5 silverware 3 Male 50 Above 10 referred to as FF) 'A' License and Pro License Kwablan (hereafter 7 2 silverware 2 Male 45 Above 10 referred to DD) UEFA B Licence Abedi (hereafter 1 5 silverware 3 Male 55 5 referred to Yale ‘A' License and Pro License Franck (hereafter 1 2 silverware 2 Male 41 5 referred to Pep 63 University of Ghana http://ugspace.ug.edu.gh Identification & Number of teams Achievement Number of Gender Age Years’ Coach Rank Managed interviews Experience Keni hereafter 13 1 4 Male 50 5 referred to Zola SCAA Coach of the Year Award  AFC Pro License  USSF "A" License  USSF National Youth License  CONCACAF International Coaching License  FIFA Goalkeeping License  NSCAA Goalkeeping Diploma 64 University of Ghana http://ugspace.ug.edu.gh Identification & Number of teams Achievement Number of Gender Age Years’ Coach Rank Managed interviews Experience Mavis hereafter 2 0 4 Female 30 9 years referred to Rock CAF Coaching Licence C Mercy hereafter 2 1 silverware 4 Female 30 6 years referred to Tower CAF Coaching Licence B Source: Author’s Construct 65 University of Ghana http://ugspace.ug.edu.gh 5.4 Findings from the Cases of Football Coaches The motivation behind this exploration is to identify determinants of adoption by professional football coaches. The study describes the type and categorisations of Sports Analytics Applications used in the Ghanaian football fraternity. Again, it investigates the Influence of performance expectancy, effort expectancy, social influence, facilitating conditions and behavioural intention on professional football coaches’ adoption of Sports Analytics Apps as a supporting tool in a developing country. Also, it tends to find out the influence of socio- demographic factors on a professional Football coaches’ adoption of Sports Analytics Apps. The study further looks at what motivates the adoption of the Sports Analytics App and its outcomes from professional football coaches. 5.4.1 Types and Functional categorisations of Sports Analytics Applications used in the Ghanaian football fraternity. All applications used by Professional football coaches in the Ghanaian football fraternity can generally be classified into five (5) groups, namely; communicative, documentative (Content Management), generative and interactive. From the findings, some coaches identified the case (Sports Analytics Applications (S.A.A)) to be communicative. The SAA are used to share ideas, information, and creations. One coach said: “Facebook, Myspace, vidipedia and Sports blogs provide me with information on the game of football in Ghana and around the globe. I get to read about some current news on transfers, injuries and performance ratings on a particular player, team and on a fellow coach to inform my decision. Again, using my YouTube and video tagger Pro apps gives access too many videos 66 University of Ghana http://ugspace.ug.edu.gh on players, teams and coaches. In addition, tutorials on the use of some of the Sports Analytics Application are easy to learn on YouTube. Twitter and whatsapp help me with my daily communication with colleagues and work-related groups and it helps me with the description of events”. Another coach said: “I use it together with my team ‒ to be specific my technical team ‒ to perform activities like authoring, editing and virtual communication. Hence, Facebook, blogger, YouTube, video tagger Pro, Myspace, Twitter, vidipedia, Whatsapp, Four four two, and Performa sports come in handy. This helps me to measure against other data to answer the question of ‒ why something happened? This action is known as diagnostic analytics.” Yale said: “Sports Analytics Applications are helpful in my documentation of data retrieved from the team, individual players, and other teams. The SAA collects, and presents evidences of experiences, as a result of that, I am able to predict games. This influences my line-up or team selection, which is player to field? y and at what position.?”. Another coach said: “My creativity has improved drastically. Sport Analytics Applications helps improve my creativity in terms of my formation, tactics and strategies. Applications like Tagit, FocusX21, ianalyze and YouTube are very unique mobiles applications to use. With this creativity I am able to eliminate a future problem or take full advantage of a promising trend”. 67 University of Ghana http://ugspace.ug.edu.gh Lastly Zola said: “Facebook, MySpace, Performa Sport, Dartfish AP Weiver, Replay analysis and JumiOne help with the exchange of information, ideas, resources and materials with my team”. Hence, the Sports Analytics Applications was known to professional football coaches as Communicative, Collaborative or Joint Publishing, Documentative (Content Management), Generative and Interactive. In summary, the findings identified the categorization of sports Analytics Apps to be Communicative, Collaborative or Joint Publishing, Documentative (Content Management), Generative and Interactive. Also, with regards to the type of Sports Analytics Applications, the findings identified; descriptive analytics apps, Diagnostic analytics apps, Predictive analytics apps and Prescriptive analytics apps. 5.4.2 Influence of socio-demographic factors on Professional football coaches’ adoption of sports analytics Apps These sections explore the influence of socio-demographic factors on a professional Football coaches’ adoption of sports analytics Apps. Each factor is examined under a subsection. 68 University of Ghana http://ugspace.ug.edu.gh 5.4.2.1 Influence of Gender on Professional football coaches’ adoption of sports analytics Apps Regarding the moderating variable Gender, it can be viewed from the Task oriented perspective. Karjaluoto, Cruz, Barretto Filgueiras Neto, Muñoz-Gallego, and Laukkanen posited that, with regard to SAA adoption, men appear to be more task-oriented as compared to women and Sports Analytics Apps is typically motivated by goal achievement, therefore, the likelihood of males accepting Sports Analytics Apps is higher than that of females. From the task-oriented perspective, the football manager or coach focuses on the tasks that needs to be performed in order to meet certain goals, achieve certain performance standards. They often define certain principles to guide the activities being performed in order to achieve the tagged aim. These are; i. Emphasis on work facilitation ii. Focus on structure, roles and tasks iii. Desired results in a priority iv. Emphasis on goal- setting and a clear plan to achieve. Below are some respondent’s accounts on the moderating variable Gender: Another coach replied: “As a football coach, Sports Analytics Apps provides me so much information on my players, management and the football fraternity in the broader scene. I am able to perform the following activities with the click of the bottom;  Setup and manage my player’s performances.  Teams’ and players’ performances in real-time.  Visualize their performances through graphs and statistical analysis 69 University of Ghana http://ugspace.ug.edu.gh  Add post-game analysis data to the video of the games.  Player influence: See how each player's influence changes through the game and how a match changes after key events. These tasks involve so much time and dedication to be able to sail through. One must cut off most social association. It does not seriously depend on gender” Another coach also said: “As a football mentor, Sports Analytics Apps gives me such a great amount of data on my players, administration and the football brotherhood in the more extensive scene. I am ready to play out the accompanying exercises with the snap of the base;  Insights: Use the app to help create the perfect Fantasy  All the stats, photos, news and more on your favourite team or player live.  Ground-breaking animations of key in-game events minutes after they happen.  Instant stats on your favourite team or player in seconds with our incredible navigation system  Mark favourite events for later review; create your own tagging system.  Setup and manage your player’s performances.  Add post-game analysis data to the video of your games.  Live match stats: See every pass, shot, tackle, interception, foul, assist and the top match pass combinations.  Analysis: compare player v player, team v team including all event dashboards.  Player influence: See how each player's influence changes through the game and how a match changes after key events. 70 University of Ghana http://ugspace.ug.edu.gh With all this information at my disposal, I need to be disciplined and dedicated”. Another coach replied: “As a football coach, Sports analytics Apps provides me so much information on my players, management and the football fraternity in the broader scene. I am able to perform the following activities with the click of the bottom;  Player influence: See how each player's influence changes through the game and how a match changes after key events.  Playback in multiple slow-motion speeds and frame-by-frame.  Zoom and pan videos for access to every detail.  Use drawing tools to measure or highlight form.  Compare two videos either stacked or side-by-side.  Synchronize comparison videos for a more effective evaluation.  Add drawings and audio commentary to voice-overs.  Use Airplay or HDMI/VGA connectors to mirror to a big-screen for group settings”. It is indeed time consuming but me being a male or a female does not any Influence on my task performance.” Hence, though SAA adoption can be viewed from the Task oriented perspective where it is posited that, men appear to be more task-oriented as compared to women, thus have the tendency of likely adopting Sports Analytics Apps than their female counterparts; the study however, identifies goal achievement as a motivating factor in Sports Analytics Apps adoption. 71 University of Ghana http://ugspace.ug.edu.gh Nevertheless, the findings also revealed that, gender has no Influence on Professional football coaches’ adoption of sports analytics Apps. 5.4.2.2 Influence of Age on Professional football coaches’ adoption of sports analytics Apps This section explores the Influence of Age on Professional football coaches’ adoption of Sports Analytics Apps. Some authors have established that elderly people tend to adopt the system more, since they are believed to have experience and need to use Sports Analytics Apps for their tactical needs. Contrary to this assertion, other studies have said that older people are more risk aversive; hence prefer a more personal relation with the players, management and fans. Other studies have also affirmed the assertion of the model by stating that the younger population are adopting. Below are some respondent’s accounts on the moderating variable Age: One coach said: “I would say I am a late explorer due my age of 50 but I am not a computer expert”. I am currently very active on social media which includes facebook, twitter, whatsapp and Instagram which are some of the sport Analytics Applications I use in my everyday task at my work place as a coach. DD replied: “Born in the early 70’s does not have any Influence on my productivity with regards to my usage of my internet and sports analytics Applications. At Wolfsburg, as player we were taught basic IT skills; typing, sending of electronic mails and some skills using the 72 University of Ghana http://ugspace.ug.edu.gh computers. Here in Accra, Ghana, I enrolled in a computer school and learnt basic IT skills: typing, website development, database creating and power point presentation. Tower replied: “I'm very comfortable using computers and confident in my ability to learn any new programs quickly regardless of my age. For example, in my last job as a video analyst, I mastered a new content management system very quickly: within two weeks, I was teaching our interns about the system. Yale replied: “I have a certificate in Microsoft package in the year 2007. I am able to create presentations using my power point. As a result of these experiences, using these Sport Analytics applications is not difficult regardless of my age. In summary, the age of the participants was not a deterrent in their usage of the Sports Analytics Apps in the performance of their jobs as football coaches in their respective clubs. Although the majority of the participants lay between the ages 21 to 50, arguably within the younger age group categories, the few that were found to be in the older age group categories showed they could equally use Sports Analytics Apps. 73 University of Ghana http://ugspace.ug.edu.gh 5.4.2.3 Influence of Experience on Professional football coaches’ adoption of sports analytics Apps. It is apparent that the moderating factor experience in football can be viewed from different perspectives. It can be viewed as working years, achievements, awards and certifications. From the findings, professional football coaches identified experience to be working years, achievements, awards and certifications. One coach said: “At age 24, I started pursuing my career by undertaking coaching education at the Welsh FA and working with their Under-14 and Under-15 sides. In 1998, I became a fitness coach of West Bromwich Albion F.C.; t i never envisaged a career as a long-term fitness coach since I wanted to make a return to playing football. However, I could not return as a player so i decided to pursue a more propitious managing career by getting an 'A' License and Pro License. FF started his career in St Mirren F.C. as a fitness coach in the year 2003 to 2004.Later on, Rangers F.C. appointed me as a fitness coach in 2004. I was part of their backroom staff when they won the league in 2004-05. Later, I became assistant manager of Al-Nasr Dubai SC but I was unable to settle in my new job because my family could not come so I left after a period of time. Soon after, I did some work for Middleborough and joined their backroom staff as fitness coach in 2008. Some years later, I worked in China as a coach for the national team and clubs there. Looking for a club in 2014, I eventually joined Gor Mahia, champions of the Kenyan Premier League; that was it the first time I served as a head coach. While there, I became the highest-paid coach in the Kenyan Premier League as 1 was paid over 600,000 Kenyan shillings a month. Under my stewardship, I led the K'Ogalo to an unbeaten league title in 2015, an 74 University of Ghana http://ugspace.ug.edu.gh achievement never done before in the competition since 1976. Adding to this, I guided them to KPL Top 8 Cup and Kenyan Super Cup titles in 2015 besides winning the league in 2014 and 2015. As a result of my efforts, I was named 2015 SportPesa Coach of the Year. Following the end of my contract as assistant coach of Zamalek, I joined Ghanaian outfit Accra Hearts of Oak F.C. in 2017” In summary, the findings identified the following: i. Apparently, the moderating factor experience in football can be viewed from different perspectives. It can be viewed as working years, achievements, awards and certifications. From the findings, professional football coaches identified experience to be working years, achievements, awards and certifications. ii. Some authors have established that elderly people tend to adopt the system more, since they are believed to have experience and need to use Sports Analytics Apps for their tactical needs. Contrary to this assertion other studies have said that older people are more risk aversive; hence prefer a more personal relation with the players, management and fans. iii. Regarding the moderating variable Gender, it can be viewed from the Task oriented perspective. Some authors posited that, with regard to SAA adoption, men appear to be more task-oriented as compared to women and Sports Analytics Apps is typically motivated by goal achievement; therefore, the likelihood of males accepting Sports Analytics Apps is higher than that of females. 75 University of Ghana http://ugspace.ug.edu.gh Hence, the influence of socio-demographic factors on a professional Football coaches’ adoption of sports analytics Apps are not influencing factor in their adoption. 5.4.3 Influential factors on Sport Analytics Application adoption by Professional football coaches These sections examine the Influences of Effort Expectancy, Social Influence, Facilitating conditions, Performance Expectancy and Behavioural Intention on Professional football coaches’ adoption of sports analytics App supporting tool. Each factor is examined under a subsection. 5.4.3.1 Influence of Effort Expectancy on the adoption of Sports Analytics Apps. It is apparent that the ease with which most coaches found the use of the Sports Analytics Applications (SAA) was because they understood how it works. Again, most of them asserted that learning to use SAS came to them naturally and so they found the use of the SAS to be very easy to use. Some respondents are even able to teach others to use the SAS. Furthermore, understanding the SAA could be attributed to the understanding of how the features of the application work. In a response to the question regarding effort expectancy, one respondent said: “Moving from the traditional analytics to the Sports Analytics App was a totally new experience. At once I knew I could compete with the other coaches around the globe. It was a bold step I took and I don’t regret it one bit. In the initial stages, it was complex and difficult but with the help of the IT technical support from the club’s tech guys I am operating swiftly with these applications. Currently, you can register for extra lesson on the use of some Sports Analytics Apps in general. I think it’s easy to understand. First of all, when I heard the concept of how SAA works, right away I understood what is does and how it will work even though the technicality behind it was quite intriguing. But 76 University of Ghana http://ugspace.ug.edu.gh generally, I understand how to use Sports Analytics Application. Learning to use the Apps is easy. There are even times when I’ve had to teach people to use the Apps and they have been able to use it afterwards” (PeP). Another coach also in a response said: “There are some applications that are very user friendly like the live scores App which gives the end-user a vivid description of the event. This is so because of my IT background and so I can confidently say I understand how the SAS works. On the other hand, I always have my technical team around to help me with the challenges and difficulties that come along with the use of such applications. The learning process is very easy” (Zola). Another coach also said: “From a user perspective, I know how the app works so it’s very easy to use. As a matter of facts, using the applications came relatively easier due to the following reasons;  As a footballer and captain in a developed country we were taught to use and interpret applications to be able to direct my colleagues on the field of play,  As a professional coach, I was taught on how to use applications in general and  Once in a while, I need assistance from my technical team to assist.” (FF). 77 University of Ghana http://ugspace.ug.edu.gh Another respondent, a female said: “I understand how features on the Apps work. It was very easy for me. In the beginning it was difficult understanding the graphs and tables. Using the applications came relatively easier due to the following reasons;  Most of these applications come along with user manual that vividly explains the steps in the application’s use.  As a professional footballer, we were taught to use and interpret applications to able to direct my colleague on the field of play,  As a professional coach, I was taught on how to use applications in general but once in a while, I seek assistance from my technical team.  With video from YouTube I am able to easily understand the purpose of most of the applications I use. In most cases, I even find the easier ways of running these applications”. (Tower). Hence the statements above indicate that the ease with which Football coaches find the use of the SAS is mediated through their understanding of the SAS as well as and ease with which they learn to use it. In summary, the findings reveal that Football coaches’ effort expectancy was as a result of their understanding of the SAS, the ease in learning to use the SAS as well as the ease of use of the SAS. 78 University of Ghana http://ugspace.ug.edu.gh 5.4.3.2 Influence of Social Influence on the adoption of Sports Analytics Apps. On the aspect of Social Influence, majority of Professional Football Coaches are of the view that their decision to use the SAA is in one way or the other influenced by the organisations or football Club they work for or with individuals they engage with. For most Professional Football Coaches, the organisations they work for perceive SAA to provide more accountability in terms of reporting on activities of the coach, players and the team as a whole. For others, the influence comes from their association or engagement with other people, which is usually due to the image they want to portray to such people. In response to the question on this subject matter one coach said: “I happen to have worked with a couple of football organization and are some of the tool we use in the training schools. I would say it started from my training institution in Cardiff and Celtic. Some applications were also introduced to me by some of the football clubs I have worked with”. In most cases, the assistant coaches in the clubs which includes goalkeepers coach, physician, video analytics and the manager would introduce a coach to an existing application.” (Rock). Another coach also said: “The management of the club is vital in my choosing of the sports analytics Apps. My club has a strong tradition of winning trophies and that the organisation provides a number of Sports Analytics Apps to aid any manager or coach to achieve his aims and objectives. Again, my fellow coaches in the football fraternity really affect decision in using my Analytics Apps”. (Zola). Some respondents also attribute their influence to use the SAA to come from the people with who they associate themselves or engage with. 79 University of Ghana http://ugspace.ug.edu.gh Another coach asserted that: “In my case, the management team of the clubs I have managed introduce me to these applications, The coaching schools also did their part and other coaching friends also introduce me to such Sports Analytics Apps” (Tower). One respondent for example said: “In choosing my sports Analytics Applications, my colleagues were an influence in the selection of sports Analytics Application. Most of the coaches in the Ghanaian fraternity in the Ghana Premier league use some form of applications. Surely, I can’t be left alone. Secondly, modern trends in the use of technology has also forced me to adopt the use of a sports analytic App. The management of my clubs I have coached introduce me to these applications. Also, the coaching schools also did their parts” (FF). Hence the statements above indicate that, football coaches are generally socially influenced by their organisations, the people with whom they engage or associate themselves with. In summary, football coaches were socially influenced by their organisations. Also the people they engage or associate themselves with. 5.9.3.3 Influence of Facilitating Conditions on the adoption of Sports Analytics Apps. In the use of every information system, the assurance of a reliable technical support and even the resources and tools to use the SAA are important to its successful adoption by users. Limited resources and the lack of needed support in times of challenges will lead to frustration and the lack satisfaction is using the system. In this case of the SAA under study, management of the 80 University of Ghana http://ugspace.ug.edu.gh respective clubs provide the needed resources and support for these coaches. Additionally, the SAS has built - in support systems where users can readily send their complaints and grievances in order to have them addressed. This happens especially when Football coaches face challenges with the analyses of Data collected in the application as well as any related matters such as injuries, transfers etc. Apart from the support systems that are provided through the Application there is a physical location (support centre) where football coaches can go to in order to lodge their complaints. Additionally, the knowledge in using the system was something that coaches had which made it possible for them to use the SAA. According to one coach: “As for resources you need a phone, laptop or tablet which supports the use of internet” (Yale). Another coach also in response said: “You need a smartphone that has internet access and internal memory of 128 GB and 4 GB RAM or more. A device that comes with a large screen and electronic pen, which enables me, take note and draw formation as and when I need it. Currently, I am also infected by the ‘globe’s social media disease’. I am able to follow news on football, footballers, coaches, football fans, and current tactics in the world of soccer” and I have all of these” (FF). Another coach said: “Basically, you need a smartphone or a laptop that can support or connect to a projector for larger display” (DD). 81 University of Ghana http://ugspace.ug.edu.gh On the aspect of knowledge needed to use the SAA and support with regards to the provision of assistance in time of challenges respondents indicated that; “There are technical guys ready to assist. Even the next person around may be able to assist you because most coaching staffs are taught to use these software” (Tower). Another coach said: “Yes, I have the knowledge to use the Apps. You need to know how to use a phone or any device you are using. The app has in it some features that you can use to report a problem. For example, there is a help button which helps report faults and receiving feedback solutions in a form of user manuals”(Pep). Another coach said: “There is even a section on the app called FAQs where you can refer to find answers to some issues that have already happened and have been answered”(FF). Hence from the responses given above, it is indicative that having the resources and tools such as; an internet equipped smart phone, Laptop, or tablet to install the application and possession of the requisite knowledge to use the SAA contributes immensely to its adoption. Again, the availability of applications’ support features as a means to ask for help and support through the application also contributes to Football coaches’ use of the SAS. In summary, facilitating conditions for football coaches in their adoption of the SAA was largely based on availability of resources and tools such as a smart phone, tablet or Laptop to install the application, internet access and the knowledge to use the SAA. Additionally, the availability of in-built support features on the application as a means of seeking for help in times of challenges was also key. 82 University of Ghana http://ugspace.ug.edu.gh 5.4.3.4 Influence of Performance Expectancy on the adoption of Sports Analytics Apps. It is apparent that individuals using Sports Analytic Apps found out that the SAA helps in the achievement of targets in the job performance. Again, most of them asserted that the use of SAS has resulted in significant change in performance of their jobs. Some respondents have been able to teach others to use the SAS and it has also changed their job performance. In a response to the question regarding Performance Expectancy, one coach said: “As a football coach, Sports analytics Apps provides me so much information on my players, management and the football fraternity in the broader scene. I am able to perform the following activities with the click of the bottom;  Setup and manage your player’s performances.  Teams and players performances in real-time.  Visualize your performances through graphs and statistical analysis  Add post-game analysis data to the video of games.  Player influence: See how each player's influence changes through the game and how a match changes after key events. Currently, I teach and I am also assisting the U12, U15 and U 16 coaches. This is as a result of the use of the SAA” (Pep). 83 University of Ghana http://ugspace.ug.edu.gh Another coach also in a response said: “As a football mentor, Sports Analytics Apps gives me such a great amount of data on my players, administration and the football brotherhood in the more extensive scene. I am ready to play out the accompanying exercises with the snap of the base;  Insights: Use the app to help create the perfect fantasy  All the stats, photos, news and more on my favourite team or player live.  Ground-breaking animations of key in-game events minutes after they happen.  Instant stats on my favourite team or player in seconds with an incredible navigation system  Mark favourite events for later review; create my own tagging system.  Setup and manage players’ performances.  Add post-game analysis data to the video of games.  Live match stats: See every pass, shot, tackle, interception, foul, assist and the top match pass combinations.  Analysis: compare player v player, team v team including all event dashboard.  Player influence: See how each player's influence changes through the game and how a match changes after key events. With the SAS my performance can be compared to class A coaches in the continent and far” (Zola). Hence the statements above indicate that SAA helps in the attainment of gains in the job performance. Again, SAS has resulted in significant change in performance of their jobs. Some 84 University of Ghana http://ugspace.ug.edu.gh respondents have been able to teach others to use the SAS and it has also changed their job performance. In summary, the findings reveal that Football coaches Performance expectancy was as a result of the functionalities they receive from the applications that helps increase their job performance. 5.9.3.5 Influence of Behavioural Intention on adoption of Sports Analytics Apps. The behaviour of the football coaches in their intention to use the application were found to be aligned to the usefulness with which they found the Sports Analytics Applications (SAA). For most Football coaches, they had no predictive time with which they see themselves using the SAA but will continue to use the application as long as it continuous to remain useful to them. Even for those who had plans of still using the traditional methods admitted they would complement it with the use of SAA alongside if need be, as far as it remains useful. According to a respondent; “I’ll use it for as long as the Apps remain useful to me. I can’t predict but as long as the app remains useful to me. I can’t tell maybe for 12 or 15 years more, nevertheless, would use the traditional methods alongside” (Pep). Another said: “Yes, I don’t have the intention to stop any time soon. This is because my job has no retirement time frame. As long as football is still a sport, I would continue to use it” (FF). 85 University of Ghana http://ugspace.ug.edu.gh Another coach said: “I can’t tell how long I’ll use it but even if I stop using it, I’m sure I’ll still have it on my phone” (Tower). However, there were other football Coaches whose behaviour towards the use of the application was on the emergence of a competitor who could provide them with a better service delivery. A respondent for example stated that: “It depends; until some better competitor (another form of technology) comes up I think I’ll continue to use it. I can’t predict how long I’d use it but it depends on how soon the competitor comes” (Pep). Another coach said: “I cannot tell, maybe when there is a lot more competition in the business with a different technology. In other words when a better competitor arrives that can challenge them”. Hence, football coaches’ behavioural intention to use the SAA depended on how useful the SAA remains to them and the availability of a competitor that would offer a better service delivery than the SAS under study. For now, coaches find the SAA to be useful and very competitive in the mist of similar application. In summary, coach’s behavioural intention to use the SAA was found to be the continuous usefulness of the SAA as well as competitiveness of the SAA. 86 University of Ghana http://ugspace.ug.edu.gh 5. 5 Chapter Summary This chapter discussed the findings of the study. The chapter focused on issues relating to the demographic characteristics of the respondents. The chapter also focused on analysing the factors that influence Sports Analytics Apps adoption using the UTAUT model for testing the proposed hypotheses outlined in Chapter 3. Finally, the chapter analysed the influence of moderators such as gender, age and experience on the proposed model used for the study, in order to explore the effect of these moderators on the adoption of Sports Analytics Apps in Ghana. 87 University of Ghana http://ugspace.ug.edu.gh Table 5. 2 Summary of Lessons drawn from Professional football coaches’ findings. Framework Construct Factors Lessons Understanding of the Sport Professional football coaches generally Analytics understood how their Sports Analytics Application works because they know about its features and functions. Effort Expectancy Professional football coaches generally found their Sports Analytics Apps to be easy to use. Ease with which Ease of Use Professional football coaches found the use of their Sports Analytics Applications were because they understood how it works Ease of Learning Professional football coaches generally found the use of their Sports Analytics Applications to be easy to use. For some of the Professional football coaches, they used it for Communicative, Collaborative/ Joint Publishing, Documentative, (Content Management), Generative and Interactive purposes. A ssociation with other Professional football coaches are Social Influence individuals or groups generally socially influenced by their family members, football players, fellow coaches and their friend to adopt the Sports Analytics Applications. 88 University of Ghana http://ugspace.ug.edu.gh Framework Construct Factors Lessons Knowledge is using the sports Knowing how to use the Sports Analytics Analytics Applications facilitates its adoption and Professional football coaches had the knowledge that facilitated their use of the Sports Analytics Applications. Facilitating Conditions Availability of needed resources Having the resources and tools such as a smart phone, tablet and laptops to install the application on backed by internet access contributes to the adoption of the Sports Analytics Applications and all coaches had the needed resources. Availability of help and support The availability of support features on systems the application contributes to its adoption. Coaches means of seeking help and support in times of challenges Sports Analytics Applications was mostly from the IT support unit Behavioural Intention Social Influences Behavioural intention to use the SAS also comes from the influence of those who had an influence in their decision to use the SAS mostly of whom were family and friends. 89 University of Ghana http://ugspace.ug.edu.gh Framework Construct Factors Lessons Performance Expectancy Achievement in the Professional football coaches generally performance Job use Sports Analytics Apps to perform their duties and helps in the achievement of targets in the job performance 90 University of Ghana http://ugspace.ug.edu.gh CHAPTER SIX DISCUSSIONS OF RESULTS 6.1 Chapter Overview The previous chapter sought to give an analysis of the empirical findings in relation to the objective of the research. In this chapter, the discussion is based on the Proposition results and findings in respect to the UTAUT model. On the basis of this, the current chapter is divided into three different parts: the first focuses on exploring the types of applications used in the Ghanaian football fraternity; the second explores the influence of socio-demographic factors on a professional Football coaches’ adoption of Sports Analytic Apps; and the third exploring the influence of performance expectancy, effort expectancy, social influence on behavioural intention and the influence of facilitating conditions and behavioural intention on Professional football coaches’ adoption of sports analytic Apps as a supporting tool. This chapter, then, concludes by addressing the fulfilment of the research objectives. 6.2 Addressing the Research Questions In order to meet its objectives, the study posed a number of research questions. These are discussed and analysed alongside the finding of the study in the subsections below; 6.2.1 Exploring the types and functional categorizations of applications used in the Ghanaian football fraternity In this sub section, below the researcher seeks to find answers to the research question posed in (Section 1.5); 91 University of Ghana http://ugspace.ug.edu.gh a. What are the types and functional categorizations of Sports Analytics Applications (technologies) used by Professional football coaches’ in Sports Analytics? Table 6.1 outlines the functional categorisations of sports Analytics Apps in Ghana as recognized from the findings of the study. The categorisations of Sports Analytics Apps (SAA) in Ghana by professional football coaches were based on their functionality of the Sports Analytics Apps used by the participants of the research. From the findings, SAA performed five (5) basic functions; a. To share ideas, information, and creations, b. To work with others for a specific purpose in a shared work area, c. To collect and / or present evidence of experiences over time, etc., d. To create something new that can be seen and/ or used by others and e. To exchange information, ideas, and resources materials 92 University of Ghana http://ugspace.ug.edu.gh Table 6. 1: Functional categorisations of sports Analytics Apps in Ghana Type Function Tools Mobile Applications Communicative To share ideas, Semantic search, artificial Face, blogger, YouTube, information, and creations intelligence, social networking, video tagger Pro, Myspace, blog (video, audio blogs), Instant Twitter, vidipedia, whatsapp Message tools, Web- Four four two and Performa conferencing sports. Collaborative/ Joint Publishing To work with others for a Authoring, Editing tools, Virtual Tactics board playbook Hd, specific purpose in a Community Of Practice (VCOP), Wikipedia, WordPress, four shared work area wikis, semantics search and 3D four two, Squawkwa, ianalyze, graphics icoda 2, coaches’ eye. Documentative (Content To collect and / or present Blogs, video blogs, open See N Report, Joomla, Management) evidence of experiences journalism, distributed network Vodacom Stats, Dartfish Note thinking over time, etc. and 3D graphics Replay, Performa Sports Analysis, Tag & go Generative To create something new Mashups, ubiquity, widgets, YouTube, ianalyze, WhatApp, that can be seen and/ or intelligent personal agents and pocket coder, Tagit and used by other VCOP focusX21 93 University of Ghana http://ugspace.ug.edu.gh Interactive To exchange information, Social bookmarking, VCOPS, Facebook, MySpace, Performa ideas, resources materials VLW’s Sport, Dartfish, Ap viewer, Replay analysis and Jumione McGee and Diaz (2007) Source: Author’s Constructs based on findings 94 University of Ghana http://ugspace.ug.edu.gh This indicates that participants use the Sports Analytics Apps for one of the above functionalities or more in their everyday work as football coaches. Additionally, some of the SAA support internet connectivity, which facilitate the process of sending, receiving and sharing information via the internet. These findings are consistent with assertions by Rahmati, Tossell, Shepard, Kortum, and Zhong, (2012) as well as Fuss, Subic, and Mehta, (2008) that “Sports Analytics” jobs are acquired via mobile applications but are executed through traditional working activities such as sports training and other forms of jobs training. This consistency is also found in Stokes et al. (2014) and Benkler (2004) where they assert that the internet has fashioned an opportunity for people to connect with one another and to coordinate their activities and as such creates the means for SAAs to rent, sell or share things with others without the involvement of other agencies. With respect to the categorisations of sports Analytics Apps used by participants in the football fraternity the findings revealed that most of coaches use the SAS for; a. Sharing ideas, information, and creations, b. Working with others for a specific purpose in a shared work area, c. Collecting and / or presenting evidence of experiences thinking over time, etc., d. Creating something new that can be seen and/ or used by other and e. Exchanging information, ideas, and resource materials For that reason, the categorizations are based on the functionality of the SAA to the professional football coaches. Additionally, even though football coaches were using the same SAA for different functionalities, it did not alter the categorizations. This is assertive of the view of Richardson, (2007) and McGee and Diaz (2007) that categorization of Sports Analytics Applications is made possible based on the uses or functionalities of the Analytics applications but not based on whether or not it is compatible with the internet. In the findings, some of the football coaches had other forms of grouping based on whether or not these 95 University of Ghana http://ugspace.ug.edu.gh Analytics Applications are internet based or not. These participants termed it as; online, and offline based applications. Nevertheless, this was not enough for the categorization. 6.2.2 Influence of socio-demographic factors on Professional football coaches’ adoption of Sports Analytics Apps The present area centres mainly on the discussions of the Influence of moderators on the connections in the model. The moderators utilized for the study refers to the socioeconomics of the respondents, for example, gender, age, and experience. In perspective, the exchange based on the Influence of every demographic variable on the UTAUT model utilized for the investigation. 6.2.2.1 Influence of gender on Professional football coaches’ adoption of Sports Analytics Apps Extant research has highlighted that with respect to technology acceptance studies, decision- making processes by males and females are different (Morris, Venkatesh, & Ackerman, 2005; Venkatesh, 2006). On the other hand, Garbarino and Strahilevitz (2004) have also stated that the influence of social norm on intention to adopt a technology is stronger among women than men. On the contrary, this study proves that gender has no moderating influence. Most of the coaches did not consider gender as a hindrance for use of these Sport Analytics Applications. Majority of the coaches interviewed have the view that, once you are complacent with the use of any computing gadget then you would relate easily with the Sports Analytics Application. In view of the fact that, findings from extant studies and the respondents used for this current study are actual users of Sports Analytics Applications with prior computer and Internet knowledge, gender has no moderating influence on the 96 University of Ghana http://ugspace.ug.edu.gh professional football coaches’ adoption of Sports Analytics Application as a supporting tool. The result of this study confirms other findings reported in similar conditions (AbuShanab & Pearson, 2007; Al-Qeisi, 2009; Martins et al., 2014). Therefore, based on the finding pertaining to gender in the study: it is concluded that: Gender has no significant moderate effect on the relationship between PE, EE, SI (performance expectancy, effort expectancy, and social influence) and BI (behavioural intention) in study. 6.2.2.2 Influence of age on professional football coaches’ adoption of Sports Analytics Apps The results obtained can be explained based on the composition of the age ranges of the two groups. The inability to use a wider range for age groups due to the demographic characteristic of the respondents in respect of the age distribution makes the comparison less effective. In light of this, the results can therefore be attributed to the closeness of the age ranges of the respondent as can be seen in studies such as Martins et al. (2014). Moreover, existing studies Morris et al. (2005) have examined the effect of age on the adoption of Sports Analytics Applications using wider age range categories for young age, middle age and older age such as ages 39 and below as younger age group and ages 40 and above as older age group based on the research that suggest that ages above 40 years are considered the older or maturing workforce. However, in all this studies age had no moderating influence on them likewise on this study. Most of the coaches interviewed have the view that, age has no moderating influence but rather the individual should be ready and willing to use software. Others have the view that, most people mistake laziness for old age and that gender has o moderating influence on the study. 97 University of Ghana http://ugspace.ug.edu.gh Therefore, based on the finding pertaining to age in the study: it is concluded that: Age has no significant moderating influence on the relationship between PE, EE, SI and FC (performance expectancy, effort expectancy, social Influence and facilitating condition) in the study. 6.2.2.2 Influence of experience on Professional football coaches’ adoption of Sports Analytics Apps It is apparent that the moderating factor experience in football can be viewed from different perspectives. It can be viewed as working years, achievements, awards and certifications. In view of the fact that findings from extant studies, and the respondents used for this current study are actual users of Sports Analytics Applications with working years, achievements, awards and certifications. The result of this study confirms other findings reported in similar conditions (AbuShanab & Pearson, 2007; Al-Qeisi, 2009; Martins et al., 2014). However, the study has proven that no matter the working years, achievements, awards and certifications earned by a professional football Coach have no Influence on him or her adoption of Sports Analytics Application. Some coaches have the view that, experience does help in surviving in the league, keeping your job as a coach, winning game but not adoption of Sports Analytics Applications as a supporting tool. Therefore, based on the finding pertaining to experience in the study: it is concluded that: Experience has no significant moderating influence on the relationships between (performance expectancy and social influence) and Sports Analytics App adoption intention in the study. 98 University of Ghana http://ugspace.ug.edu.gh 6.2.3. Influential factors on Sport Analytics Application adoption by Professional football This sub-section seeks to answer one research question: a. What is the Influence of effort expectancy, social influence, facilitating conditions, behavioural intention and performance expectancy on Professional football coaches’ adoption of Sports Analytics Apps as a supporting tool? 6.2.3.1 Influence of Effort Expectancy on adoption of sports analytic Apps Table 6. 2 Influence of Effort Expectancy on adoption of Sports Analytics Apps Factors Football Supporting Reference Coaches Understanding of  Zhou et al. (2010) Sports Analytics Apps Ease of Use  AbuShanab et al.(2010); Amin (2007); Daniel and Jonathan ( 2013); Davis et al. (1989); Guriting and Ndubisi (2006); Mohan et al.( 2013); Venkatesh (2000); Venkatesh and Davis ( 2000) Ease of Learning  Agarwal and Prasad (1999), Luo et al. (2010) and Yu’s (2012) Source: Author’s Own Constructs based on findings Effort Expectancy (EE) is the degree of ease associated with coaches’ use of technology (Venkatesh et al., 2003). Technologies that are simpler to understand are easier to use and learn and faster to adopt than those requiring the adopter to develop new skills and understanding. From the case findings, it is revealing that users had an understanding of the 99 University of Ghana http://ugspace.ug.edu.gh Sports Analytics Apps that makes it easy for them to use the SAA. This confirms the stance of AbuShanab et al. (2010) in their study in relation to user adoption of internet banking where they posited that, if users found the technology services in internet banking easy to use and do not require much effort then they are more likely to adopt it. A similar confirmation is made by Amin, (2007) and Daniel and Jonathan, (2013) that Effort Expectancy has a significant positive influence on the behavioural intention of users to use an innovation like Internet banking. Existing studies have supported this relationship based on the association between the ease of use of a system and the intentions to use the system (AbuShanab et al., 2010; Daniel & Jonathan, 2013; Guriting & Ndubisi, 2006; Mohan et al., 2013; Venkatesh, 2000). Additionally, the learning process in using SAA which is particularly evident in responses of coaches shows that training organized by the management of the clubs on how to use the SAA is instrumental to coaches’ understanding of the SAA and its ease of use. This is consistent with the studies by Luo et al. (2010) and Agarwal and Prasad, (1999) that self- efficacy could be increased by providing step-by-step guidance and training in using technology and further confirms Yu’s (2012) study that football coaches get more and more familiar with using electronic devices and applications when they undergo training. Hence, the ease with which Professional Football Coaches found the use of the SAA was an influencing factor in their adoption of SAA. From the above discussion, there is evidence that is suggestive of the first proposition that: Effort Expectancy (EE), through Perceived Ease of Use (PEOU), may have a positive effect on the intention of Professional Football coaches to adopt Sports Analytic App as a supporting tool. 100 University of Ghana http://ugspace.ug.edu.gh 6.2.3.2 Influence of Social Influence on adoption of Sports Analytics Apps Table 6. 3 Influence of Social Influence on adoption of Sports Analytics Apps as a supporting tool Factors Football Supporting Reference Coaches Organisational  Taiwo, Mahmood, and Downe (2012); Influence Bankole, Bankole and Brown (2011) Yu (2012); Venkatesh et al., (2003); Association with  Venkatesh and Zhang, (2010); Taiwo, other individuals Mahmood, and Downe (2012), Burton-Jones and Hubona (2006), Kelman (1958) Source: Author’s Own Constructs based on findings Social influence refers to the extent to which an individual allows the opinions of others they consider important to them to influence their decisions to use the system (Venkatesh et al., 2003). Hence in this study, the perception of football coaches are explored in order to identify which persons’ opinions influence their decision to use the Sports Analytics Applications. From the case findings, it is indicative that the social influence can be categorised into two groups of people, which are; people of their respective organizations they work with; and also the people they associate themselves with such as family and friends. Regarding organizational influence, football coaches indicated that staffs of organisations play a huge role in their choosing of SAA. In most cases, these influences come from football players, technical team, and administrative staff of the organisation. Again, influence comes from family and friends as well as other persons who they had engagement with. This finding of social influence in this study are coherent with the research conducted by Bankole, Bankole and Brown (2011) and Yu (2012) on mobile banking in developing 101 University of Ghana http://ugspace.ug.edu.gh countries that identifies social factors as strong influencers on customers’ decision to adopt mobile banking services. It is also coherent with Burton-Jones and Hubona (2006) assertion in the acceptance of technology in a study conducted in Kenya on internet banking that the average Kenya is ready to adopt and accept certain behaviours just in order to impress the group he or she belongs to. Similarly, much of the empirical research in information system found social influence to be an important antecedent to users’ behavioural intention to adopt a technology (Venkatesh et al., 2003; Venkatesh & Zhang, 2010). Further, Taiwo, Mahmood, and Downe (2012), have posited that football coaches might not be obliged to use an information system until they are motivated by important others that can influence their attitude and behaviour. In light of this, the current study reported that social influence contributes towards the behavioural intention to use the SAA. It is therefore indicative that the significant influence of the social influence on behavioural intention is a clear indication that the users (Football Coaches) of the SAA are concerned about factors such as the opinion of family and friends as well as the people they work for. Hence, the ease with which coaches found the use of the SAA was an influencing factor in their adoption of the SAA. From the above discussion, there is evidence that is suggestive of the proposition that: Social Influence (SE) will Influence users’ intention to use the SAA. 102 University of Ghana http://ugspace.ug.edu.gh 6.2.3.3 Influence of Facilitating Conditions on adoption of Sports Analytics Apps Table 6. 4 Influence of Facilitating Conditions on adoption of Sports Analytics Apps. Factors Football coaches Knowledge is using  Alalwan et al.( 2014); Martins et al. the (2014); Zhou et al.(2010); Venkatesh et al. (2003). Availability of needed  Alalwan et al. (2014); Martins et al. resources (2014); Zhou et al. (2010); Venkatesh et al. (2003). Availability of help  Alalwan et al. (2014); Martins et al. and support systems (2014); Zhou et al. (2010); Venkatesh et al. (2003). Source: Author’s Own Construct based on findings According to Venkatesh, (2003) users’ intentions to use an information system are facilitated by some conditions. This Facilitating Conditions (FC) is the degree of ease associated with use of technology (Venkatesh et al., 2003). The availability of technical support systems as well as resources and tools to use an information system SAA are important to its successful adoption by users. Limited resources and the lack of needed support in times of challenges will lead to frustration and the lack satisfaction is using the system. In this study this theme explores the conditions that facilitate SAA participants’ (football coach) use of the SAA. The Facilitating Conditions explore the availability of knowledge, resources and tools, as well as support structures in using the SAA. On the part of football coaches, they readily had the resources to access the SAA. Additionally, the SAA has in built support systems where users can readily send their complaints and grievances in order to have them addressed. This happens especially when football coaches face challenges like malfunctioning Apps. Apart from the support systems 103 University of Ghana http://ugspace.ug.edu.gh that are provided through the SAA there is a physical location where Coaches can go to in order to lodge their complaints. Football coaches on their part had the knowledge in using the SAA. They indicated that one needed basically to know how to use a smartphone or Laptop and its basics feature such as using an application and turning on its feature such as the report generation. These findings are consistent with the study by Alalwan et al. (2014) in their study on healthcare where they noted that facilitating conditions in healthcare acceptance technology is very important. They argue further that availability of resources that include technical knowledge and adequate knowledge of computer are some of the facilitating conditions that promote the use of clinical informatics. Additionally, there is no doubt that using IB services requires a particular kind of skill, resources and technical infrastructure and these facilities are not usually free at customer context (Zhou et al., 2010). Hence, the ease with which football coaches found the use of SAA was an influencing factor in their adoption of the SAA. From the above discussion, there is evidence that is suggestive of the first proposition that:  Facilitating Conditions (FC) will have an Influence on users’ use of SAA. 6.2.3.4 Influence of Behavioural intention to use on Professional football coaches’ adoption of Sports Analytics Apps as a supporting tool by Behavioural Intention Table 6. 5 Influence of Behavioural intention to use on adoption of sports analytic Apps Factors Football Supporting Reference Coach Continues Usefulness of the  Martins et al. (2014). SAA Competitiveness of the SAA  (AbuShanab et al., 2010). 104 University of Ghana http://ugspace.ug.edu.gh Factors Football Supporting Reference Coach Social Influences  Venkatesh et al. (2003); Venkatesh and Zhang (2010); Taiwo, Mahmood, and Downe (2012) Source: Author’s Own Construct based on findings This is in reference to a user’s subjective possibility that he or she will perform the behaviour in question (Venkatesh et al., 2003). Behavioural intention to use the application for football coaches’ was found to be aligned to the usefulness with which they found SAA. For most football coaches, they had no predictive time with which they see themselves using the SAA but will continue to use the application as long as it remains useful to them. In a like manner, the behaviour of football coaches in their intention to use the application was directed towards the benefit they enjoyed from using the SAA. For most football coaches, they had no predictive time with which they see themselves using the SAA but will continue to use the application as long as it remains useful to them, it provided them with the opportunity to earn a living or extra income through their analytics skills. Additionally, football coaches intended to use the SAA as long as it ensures competitiveness. This is coherent with Martins et al. (2014) assertion that the quality of services and provision of quality services of information systems will influence their intention to adopt the SAA. For football coaches, one additional factor of Social Influence was also identified as influencing their decision to use the SAA. Football coaches’ use of the SAA was influenced by opinion of family and friends. This is confirmed by existing studies done by Venkatesh et al. (2003); Venkatesh and Zhang, (2010) and Taiwo, Mahmood, and Downe (2012) who posited that football coaches might not be obliged to use an information system until they are motivated by important others that can influence their attitude and behaviour. 105 University of Ghana http://ugspace.ug.edu.gh To conclude, as recorded in previous studies, the relationship between behavioural intention and actual use behaviour was supported. This result is in support with Venkatesh et al., 2003’s work and other replicated works (AbuShanab et al., 2010). There is considerable evidence of the significant influence of Behavioural Intention on Use Behaviour in information technology acceptance studies (Venkatesh et al., 2003, 2012; Venkatesh and Zhang, 2010). Hence, the ease with which football coaches found the use of the SAA was influencing factor in their adoption of the SAA. From the above discussion, there is evidence that is suggestive of the proposition that: Behavioural Intention to Use the SAA influences users Use Behaviour of the SAA. 6.2.3.5 Influence of Performance Expectancy on Professional football coaches’ adoption of Sports Analytic Apps as a supporting tool Table 6. 5 Performance Expectancy on Professional football coaches’ adoption of Sports Analytics Apps as a supporting tool Factors Football Supporting Reference Coaches Achievement in the  AbuShanab and Pearson (2007); Al- performance Job Somali et al. (2009); Alalwan et al. (2014); Daniel and Jonathan (2013); Riffai et al. (2012); Tan and Teo (2000) Source: Author’s Own Constructs based on findings This represents the degree to which individuals using Sports Analytics Apps believe that the use of the system will help in the attainment of gains in the job performance. Technologies that help in the attainment of gains in the job performance are likely to be adopted faster than technologies, which do not help achieve this objective. In this study 106 University of Ghana http://ugspace.ug.edu.gh performance expectancy refers to the level to which the users of the SAA perceive the SAA helps in achievement of job performance. From the case findings, it is revealing that users had the perception that once they understand and use the SAA effectively, they would attain job performance. In view of this, Alalwan et al. (2014) have stated that performance expectancy can be defined as the terms of utilities extracted by using Internet banking which is productive relative to the traditional encounter. In this study, the performance expectancy construct positively contributed to explaining the variance in behavioural intention. This implies that football coaches who have high performance expectancy are likely to have the intention to use SAA. The result of this study is therefore in support with some works in the UTAUT model Venkatesh et al.(2003) and in TAM Davis et al. (1989) and other replication of those models (AbuShanab & Pearson, 2007; Al-Somali et al., 2009; Alalwan et al., 2014; Daniel & Jonathan, 2013; Riffai et al., 2012; Tan & Teo, 2000). Hence, the ease with which Professional Football Coaches found the use of the SAA was an influencing factor in their adoption of SAA. From the above discussion, there is evidence that is suggestive of the first proposition that: Performance Expectancy (PE) has a positive effect on the intention of Professional Football coaches to adopt Sports Analytics Apps as a supporting tool. 107 University of Ghana http://ugspace.ug.edu.gh 6.3 Chapter Summary This chapter set out to discuss the case findings identified in chapter 5 in relation to the research questions and identified themes taking into consideration the determinants, moderators and the types and functional categorisation of Sports Analytics Application with the aid of the conceptualized model developed from the UTAUT framework. The chapter further discussed the analysis of the findings and specifically addressed the research questions in collocation with the literature reviewed in chapter 2, the research framework in chapter 3 and the findings in chapter 5 as well as the analysis of the findings thereby suggesting 6 propositions. The chapter also presented the findings of the study by way of presentation an empirically tested and a revised research framework in chapter. 108 University of Ghana http://ugspace.ug.edu.gh CHAPTER SEVEN SUMMARY, CONCLUSION AND RECOMMENDATIONS 7.1 Chapter Overview The focus of the previous chapter was to discuss and analyse the findings of the study particularly in relation to the literature presented in chapters 2 and 3. This chapter however has its focus on presenting a summary of the study, discussing what the implications are for future research, policy, and practice as well as a presentation on what the research limitations are and conclusion of the research. 7.2 Summary Chapter one provides a short introduction into the research area. In order to present a clear picture, the problem is discussed which leads to the research purpose, research objectives, research questions, research significance and, scope and limitation of research. The study began in chapter one by providing an understanding of sports discipline and its contribution to society, in terms of improvement of pro-social behaviour and reduction of crime and anti-social behaviour, particularly for young men. Having done this, the researcher set out to explore the following research questions that will lead to the meeting of the objectives set out for this study: Out of the objectives and the research purpose, the following research questions were asked; 1. What are the types and functional categorizations of Sports Analytics Applications used in the Ghanaian football fraternity? 2. What is the Influence of performance expectancy, effort expectancy, social influence, facilitating conditions and behavioural intention on Professional football coaches’ adoption of sports analytic App as a supporting tool? 109 University of Ghana http://ugspace.ug.edu.gh 3. What is the influence of socio-demographic factors on Professional football coaches’ adoption of sports analytic Apps? In order to realize the objectives of the research and to find answers to the research questions, the researcher identified 7 professional football coaches then went ahead to hold in-depth interviews in order to understand: 1. To explore: (i) the types of Sport Analytics Applications, and (ii) the functional categorisations of Sports Analytics Applications used in the Ghanaian football 2. To explore the Influences of Performance Expectancy, Facilitating Conditions, Effort Expectancy, Social Influence and Behavioural Intention on Professional football coaches’ adoption of sports analytics App supporting tool and 3. To explore the influence of socio-demographic factors on a professional Football coaches’ adoption of sports analytic App. Chapter two begins by providing an overview of the relevant literature pertaining to the concept of the analytics; overview, definitions, scope, types, benefits and challenges. It also further provided an in-depth review of literature regarding the Sports Analytics Applications by divulging into the current knowledge and gaps in the area. The chapter finally ends by summarizing and presenting gaps for future research. In chapter three, the study focuses on discussing the research framework that is considered appropriate to help meet the objectives of the study. In view of this, chapter 3 discusses relevant literature that directly or indirectly relate to the selected research framework. The framework considered fit for meeting the objectives of this study is the Unified Theory of Acceptance and Use of Technology (UTAUT framework). The chapter further conceptualizes a model from the UTAUT model rather than coming up with a full-blown theory on Sports Analytics Application adoption. Also touched on in this chapter are the advantages, some use 110 University of Ghana http://ugspace.ug.edu.gh of the theory in exiting research, justification of the choice of the adopted research framework, limitations and an explanation of the constructs used in the study. The chapter concludes with a summary of what has been discussed in the chapter. Having looked at the theoretical lens and conceptualized a model for this study, the researcher went ahead in chapter 4 to discuss the research methodology employed for this study. The study further discusses the research paradigm followed by the research design and method and how data was collected and analysed. A summary on the chapter is provided as the final section for the chapter. Chapter five presents the findings of the data collected from responses received from the participants of the study. The chapter based on the UTAUT framework narrated how each case participant is influenced by Efforts Expectancy, Social Influence, Facilitating Conditions and Performance Expectancy that influences their behavioural intention and actual use of the Sports Analytics Application. Chapter six analysed the findings identified in chapter 5 in relation to the research questions and identified themes taking into consideration the factors with the aid of the conceptualized model developed from the UTAUT framework. The chapter further discussed the analysis of the findings and specifically addressed the research questions in connection with the literature reviewed in chapter two, the research framework in chapter three and the findings in chapter five (Table 7.1). The findings also made way for a presentation of an empirically tested and revised research framework (Figure 7.1). 111 University of Ghana http://ugspace.ug.edu.gh Table 7. 1 Thesis matrix Research Objective Research Findings Extant Literature Contributions, Implications and Recommendations 1. To explore: (i) the types of Sport This research contributes to, arguably, the Analytics Applications, and (ii) the The type of Sports Analytics This consistency is also found in Stokes, et al., limited literature in the area of Sports functional categorizations of Sports Applications, the findings (2014) and Benkler (2004) where they assert that Analytics adoption through a multi-faceted Analytics Applications used in the identified; descriptive analytics the internet has fashioned an opportunity for and multi-dimensional perspective from a Ghanaian football fraternity? apps, Diagnostic analytics apps, people to connect with one another and to developing country. It is hoped that it would Predictive analytics apps and coordinate their activities and as such creates the serve as a stepping stone for subsequent Prescriptive analytics apps means for SAAs to rent, sell or share things with studies in this field others without the involvement of other agencies. Additionally, the UTAUT model that was used in the study happens to be the first The Sports Analytics Applications This is assertive of the view of Richardson, (2007) model. was known to professional football and McGee and Diaz (2007) that categorization of Finally, time was also another constraint, as coaches as Communicative, Sports Analytics Applications is made possible the researcher would have explored more Collaborative or Joint Publishing, into other factors that determine the adoption based on the uses or functionalities of the Documentative (Content of Sports Analytics Applications. Management), Generative and Analytics applications but not based on whether or In addition, appointments for the researcher Interactive not it is compatible with the internet. to interview respondents especially some top coaches suffered some delays and were sometimes brief due to their busy schedules. Again, the undercover investigation of the GFA conducted by renowned Ghanaian Journalist Anas Aremeyaw limited the interviews with these coaches Systems administrator and designer, the study recognizes a portion of the challenges or shortcomings in the Sports Analytics App that require improvement or redesigning The study can inform football coaches of the best practices in the use of Sports Analytics App to maximize productivity 112 University of Ghana http://ugspace.ug.edu.gh Research Objective Research Findings Extant Literature Contributions, Implications and Recommendations The thesis can also inform the policy making bodies and Football Associations, how best to develop the deployment and management of Sports Analytics App at the National and international levels. 2.To explore the Influences of Effort The ease with which Professional AbuShanab et al., (2010) in their investigation in The propositions made in this study also Expectancy, Facilitating Conditions Football Coaches found the use of relation to user adoption of Sports Analytic Apps provides guidelines in strategically guiding Social Influence, Behavioural the SAA was an influencing factor where they posited that, if users found the stakeholders in Sports Analytics including Intention And Performance in their adoption of SAA technology services in Sports Analytics Apps football coaches, managers, football players, Expectancy on Professional football easy to use and do not require much effort then football administrations and regulatory coaches’ adoption of sports analytics The availability of resources that they are more likely to adopt it agencies in Ghana and in other developing App supporting tool include technical knowledge and economies to understand and use to their adequate knowledge of computer Amin, (2007) and Daniel & Jonathan, (2013) that advantages . are some of the facilitating Effort Expectancy has an influence on the conditions that promote the use of behavioural intention of users to use an innovation Again, the findings of this study can inform SAS. like Sports Analytics Apps. professional football coaches of the best practices to apply in the adoption and implementation of Sports Analytics in their It is therefore indicative that the It is important to note that the usage of a system activities significant effect of the social requires a particular skill, resources and technical influence on behavioural intention infrastructure (Riffai et al., 2012; Yeow, Yuen, is a clear indication that the users Tong, & Lim, 2008); and these facilities such as The study findings can also inform the policy (Football Coaches) of the SAA are the Internet and computers are usually not free making bodies and Football Associations, concerned about factors such as the from the coach’s context (Venkatesh et al., 2012). how best to improve the deployment and opinion of family and friends as management of Sports Analytics App at the well as the people they work for. Caya and Bourdon (2016) have postulated that, National and international levels. . the more convenient the access of respondents to the Internet and computers, the more proficient Having identified Sports Analytics as an area their use of the computer and Internet, which may with a vibrant future, this research also 113 University of Ghana http://ugspace.ug.edu.gh Research Objective Research Findings Extant Literature Contributions, Implications and Recommendations Performance expectancy refers to result in a higher adoption rate of respondents provides a good foundation of reference for the level to which the users of the using the Internet. students and researchers. SAA perceive the SAA helps in achievement of job performance. Social Influence has been modelled on different This contributes to researchers who would models; the result in regards to its importance in want to conduct research in the area in not predicting behavioural intentions has been only Ghana but also test the propositions of debatable. Kijsanayotin et al. (2009) have this research in other parts of the world most therefore stated that social influence is expected to especially in other developing economies. influence behavioural intention in relation to Sports Analytics Apps adoption. In relation to Sports Analytics Apps adoption AL Awadhi and Morris (2008), has defined performance expectancy as the terms of utilities extracted by using Sports Analytics Apps that is productive relative to the traditional encounter. Therefore, it is motivating to add that other related constructs such as perceived influence and relative advantage have been widely captured as fundamental determinants of behavioural intention towards Sports Analytics Apps adoption (Im, Hong & Kang, 2011; Kijsanayotin, Pannarunothai, & Speedie, 2009). Based on this, Caya and Bourdon (2016) has empirically demonstrated that the greater the perceived relative advantage, the more likely Sports Analytics Apps would be adopted. 3. To explore the Influence of Gender has moderate effect on Karjaluoto, Cruz, Barretto Filgueiras Neto, The propositions made in this study also provides socio-demographic factors on the relationship between PE, EE, Muñoz-Gallego, and Laukkanen (2010) guidelines in strategically guiding stakeholders in Professional football coaches’ SI (performance expectancy, posited that, with regard to internet banking Sports Analytics including football coaches, adoption of Sports Analytics effort expectancy, and social adoption, men appear to be more task-oriented managers, football players, football Apps influence) and BI (behavioural as compared to women and internet banking is administrations and regulatory agencies in Ghana intention) in study. typically motivated by goal achievement, and in other developing economies to understand Majority of the coaches therefore, the likelihood of males accepting and use to their advantages . 114 University of Ghana http://ugspace.ug.edu.gh Research Objective Research Findings Extant Literature Contributions, Implications and Recommendations interviewed have the view that, technology is higher than that of females once you are complacent with the (Wan, Luk, & Chow, 2005). use of any computing gadget then you would relate easily with the Sports Analytics Application. Age has no significant Garbarino and Strahilevitz (2004) have also Again, the findings of this study can inform moderating influence on the stated that the influence of social norm on professional football coaches of the best practices relationship between PE, EE, SI intention to adopt a technology is stronger to apply in the adoption and implementation of and FC (performance expectancy, among women than men. Sports Analytics in their activities effort expectancy, social Influence and facilitating The study findings can also inform the policy condition) in the study. Some authors have established that elderly making bodies and Football Associations, how Most of the coaches interviewed people tend to adopt the system more, since best to improve the deployment and management have the view that, age has no they are believed to have experience and need of Sports Analytics App at the National and moderating influence but rather to use Sports Analytics Apps for their tactical international levels. the individual should be ready needs (AbuShanab et al., 2010). and willing to use software Contrary to this assertion other studies have Having identified Sports Analytics as an area . said that older people are more risk aversive; with a vibrant future, this research also provides Others have the view that, most hence prefer a more personal relation with the a good foundation of reference for students and people mistake laziness for old players, management and fans (Gan, Clemes, researchers. age and that gender has o Limsombunchai, & Weng, 2006). moderating influence on the This contributes to researchers who would want study. Other studies have also affirmed the assertion to conduct research in the area in not only Ghana The study has proven that no of the model by stating that the younger but also test the propositions of this research in matter the working years, population are adopting and using Sports other parts of the world most especially in other achievements, awards and Analytics Apps more than the older developing economies. certifications earned by a generation (Njuguna et al., 2012). professional football Coach have no Influence on him or her Some authors have established that elderly adoption of Sports Analytics people tend to adopt the system more, since Application. they are believed to have experience and need to use Sports Analytics Apps for their tactical Some coaches have the view that, needs (AbuShanab et al., 2010). experience does help in surviving in the league, keeping your job as Contrary to this assertion other studies have a coach, winning game but not said that older people are more risk aversive; 115 University of Ghana http://ugspace.ug.edu.gh Research Objective Research Findings Extant Literature Contributions, Implications and Recommendations adoption of Sports Analytics hence prefer a more personal relation with the Application as a supporting tool players, management and fans (Gan, Clemes, Limsombunchai, & Weng, 2006). 116 University of Ghana http://ugspace.ug.edu.gh Figure 7. 1 Sports Analytics Application Adoption Conceptual Framework Performance Expectancy Actual Use - Improve performance - Carry out task -Increase my productivity -Setup and manage your player’s - understand the benefits of SAA performance Behavioral Intention - Continues Usefulness of the Platform - Competitiveness of the platform -Teams and players performances in real- Effort Expectancy - Social Influences - Understanding of Sports time, Analytics Application - Ease of Use - Ease of Learning -Visualize your performances through graphs and statistical analysis, Social Influence -Add post-game analysis data to the video - Organizational Influence - Association with other individuals of your games and Facilitating Conditions - Knowledge is using the Sports Analytics Application - Availability of needed Resources - Availability of help and Support systems 117 University of Ghana http://ugspace.ug.edu.gh 7.3 Implications to Research, Policy and Practice The significance of the study can be explored along three strands: implications to research, to practice, and to policy. 7.3.1 Implication to Research This research contributes to, arguably, the limited literature in the area of Sports Analytics adoption through a multi-faceted and multi-dimensional perspective from a developing country. It is hoped that it would serve as a stepping -stone for subsequent studies in this field. It further responds to the research gaps considering the fact that arguably, many studies have not been done academically or intellectually from a developing economy context. Having identified Sports Analytics as an area with a vibrant future, this research also provides a good foundation of reference for students and researchers. This contributes to researchers who would want to conduct research in the area in not only Ghana but also test the propositions of this research in other parts of the world most especially in other developing economies. Additionally, concerning the contribution of this research to knowledge, this research conceptualized a framework based on the UTAUT framework that has been widely used quantitatively in other studies. However, in this study the UTAUT framework has been used in a qualitative method to identify; 1. To explore the types of applications and functional categorizations of Sports Analytics Applications used in the Ghanaian football fraternity, 2. To explore the Influence of: (i) performance expectancy, (ii) facilitating conditions, (iii) effort expectancy, (iv) social influence and (v) behavioural intention on Professional football coaches’ adoption of sports analytic App supporting tool; and 118 University of Ghana http://ugspace.ug.edu.gh 3. To explore the influence of socio-demographic factors on a professional Football coaches’ adoption of sports analytic App. This research therefore provides as foundation for researchers to also explore the UTAUT framework qualitatively. Furthermore, this research establishes factors for consideration for creating an enabling Sports Analytics ecosystem from a developing economy context. In this regard, future researchers can also therefore look into this to test their generalizability quantitatively. 7.3.2 Implication to Practice Concerning the implications of the research to practice, this study reveals the key determinants and moderators of Sports Analytics Application adoption by professional football coaches not only from literature but also providing some form of evidence from the field. The propositions made in this study also provides guidelines in strategically guiding stakeholders in Sports Analytics including football coaches, managers, football players, football administrations and regulatory agencies in Ghana and in other developing economies to understand and use to their advantages . Again, the findings of this study can inform professional football coaches of the best practices to apply in the adoption and implementation of Sports Analytics in their activities. For the systems administrator and designer, the study identifies some of the challenges or weaknesses in the Sports Analytics App that require improvement or redesigning. Furthermore, the study can inform football coaches of the best practices in the use of Sports Analytics App to maximize productivity 119 University of Ghana http://ugspace.ug.edu.gh 7.3.3 Implication to Policy Finally, the study findings can also inform the policy making bodies and Football Associations, how best to improve the deployment and management of Sports Analytics App at the National and international levels. 7.4 Research Limitations A number of limitations were identified during the conduct of this study. One of such limitations is that this study focused on only on football coaches in active service in the last five years. Additionally, the UTAUT model that was used in the study happens to be the first model. Finally, time was also another constraint, as the researcher would have explored more into other factors that determine the adoption of Sports Analytics Applications. In addition, appointments for the researcher to interview respondents especially some top coaches suffered some delays and were sometimes brief due to their busy schedules. Again, the undercover investigation of the GFA conducted by renowned Ghanaian Journalist Anas Aremeyaw limited the interviews with these coaches. 7.5 Future Research Directions The findings of this research have major implications and points to a number of research avenues. It is however important to note that outlining every possible area that is worth exploring for further study is practically impossible. In view of this, the following highlighted areas have been found to be relevant and significant for exploration by way of future research. 120 University of Ghana http://ugspace.ug.edu.gh First, this study was explored using the qualitative to study the determinants and moderators of Sports Analytics Applications. Moreover, undertaking this study quantitatively and qualitatively may produce different results if tested among coaches’ different sports disciplines. The researcher therefore recommends that this study be explored using a quantitative and mixed methods in order to provide some generalization in these respects. Secondly, this research was conducted from the point of view of the football coaches without looking at the sports fans and other administrative workers, which limits the scope of this study. It will therefore be imperative to explore a study that encompasses the sports fans and other administrative workers. 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The study seeks to determine the influence of determines like performance expectancy effort expectancy social influence and behavioural intention on Professional football coaches’ adoption of Sports Analytics Application as a supporting tool in order to better understand the relationship between user perception, behavioural intention and usage Behaviour among users of the system. This research seeks to meet the following objectives; 1. To explore the type of applications and categorisations of Sports Analytics Applications used in the Ghanaian football fraternity; 2. To explore the Influence of: (i) performance expectancy, (ii) facilitating conditions, (iii) effort expectancy, (iv) social influence and (v) behavioural intention on Professional football coaches’ adoption of sports analytic App supporting tool; and 141 University of Ghana http://ugspace.ug.edu.gh 3. To explore the influence of socio-demographic factors on a professional Football coaches’ adoption of sports analytic App. Many thanks for taking a few minutes to answer this questionnaire. Please note that all information provided will be strictly confidential and will be used for academic purposes only. By completing the survey, you indicate that you voluntarily wish to participate in this research. Your participation is vital to the success of this research. Is rest assured that the information you’ll provide is intended solely for academic purposes. Interview questions Types and Categorisation of Application Background of Respondent: 1. Please tell me about yourself and what you do? Technology Related Questions: 2. How would you describe your general knowledge about computers? 3. How long have you been using computers? 4. How often do you use the internet? 5. How often do you user Sport Analytics Application? 6. How easy do you find using the Sports Analytics? 7. Are there any special features provided by the Sport Analytics Application to ensure you offer the services well? 8. What value do you get from using this Sports Analytics Application? Effort Expectancy related questions: 142 University of Ghana http://ugspace.ug.edu.gh 9. How do you find your understanding of using SAA for your task as a football coach? 10. How do you find learning to use the SAA? 11. How easy is it for you to use SAA? Social Influence 12. In what way do other people/organisations you consider an influence or important in your life think you should use the SAA? Facilitating Conditions 13. Please tell me about the resources needed to use the SAA. In other words, what are the things that you’d require in order to use the app? 14. So do you think you have these resources? 15. Please tell me about the knowledge needed to use the SAA? 16. Do you think you have the knowledge needed? 17. Given the resources and knowledge it takes to use the SAA do you think it would be easy for you to use the platform? 18. Is there a specific person (or a group) available to provide assistance with the SAA in times of difficulties? Behavioural Intention 19. How long have you been using the platform? 20. Do you intend to use the SAA for long? Or how long do you intend to use the SAA? 21. How long do you predict you will use SAA? Performance Expectance 22. Do you think Sports Analytics App is useful to carry out your task? 23. Do believe that using Sports Analytics Apps enables you to conduct task more efficiently? 24. Do believe using Sports Analytics Apps increases you productivity as a football coach? 25. Does Sports Analytics Apps improve your performance? 143 University of Ghana http://ugspace.ug.edu.gh Use Behaviour (USE) 26. How do you use the Sports Analytics Application in your everyday task? 144