Education and Information Technologies
https://doi.org/10.1007/s10639-019-10057-7
Are we ready for Gamification? An exploratory
analysis in a developing country
Kingsley Ofosu-Ampong1 & Richard Boateng1 & Thomas Anning-Dorson1,2 &
Emmanuel A. Kolog1
Received: 28 August 2019 /Accepted: 8 November 2019/
# Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract
Integrating gamification in the learning process has become a significant factor in the
success of teaching, learning, and research in higher education. Education can leverage
gamification by enhancing learning management systems to make learning enjoyable
and engaging for students. We, however, lack the underpinnings into factors affecting
the acceptance of gamification in education. To help solve this, we tested and extended
previous acceptance models. Overall, we explored the users’ perception and acceptance
of adding gamification to learning among students in higher education. The results
show that Image is an insignificant factor in students’ behavioral intention to use
gamification. The current paper contributes to the perceptual process for gamification
research in learning; deriving implications for gamification application and pioneering
research on gamification acceptance in developing countries. We conclude with op-
portunities, practical and theoretical implications for researchers and practitioners to
extend our knowledge of gamification research.
Keywords Gamificationsupportedlearning.Acceptanceofgamification.Userperception
. Game elements
* Richard Boateng
richboateng@ug.edu.gh
Kingsley Ofosu-Ampong
kingofosu11@gmail.com
Thomas Anning-Dorson
thomas.dorson.anning@wits.ac.za
Emmanuel A. Kolog
eakolog@ug.edu.gh
1 University of Ghana Business School, P O Box LG78, Legon, Accra, Ghana
2 Wits Business School, 2 St Davids Place, 2193 Johannesburg, Parktown, South Africa
Education and Information Technologies
1 Introduction
In recent times, the implementation of learning management systems (LMS) and video
learning systems have become a relevant part of a national effort to promote teaching
and learning in higher education, especially in some parts of the developing countries.
The challenge facing most developing countries is the inability to implement a suitable
learning environment (Boateng et al. 2016) with emphasis on gamifying educational
systems to motivate learners (Leaning 2015). Gamification is “the application of game
design elements in non-game contexts” (Deterding et al. 2011). As a virtual learning
platform that engages and provides feedback for learners (self-regulation), gamification
presents a fascinating area of information systems (IS) research due to the central role
game-based technology plays in affecting target behavioral change. Integrating the
characteristics of computer games to teaching and learning helps in engaging learners
and positively influencing learning outcomes (Nousiainen et al. 2018). Given the
exciting nature of integrating game elements in LMS, the study describes gamification
as “the incorporation of game design elements into a target system while retaining the
target system’s instrumental functions” (Liu et al. 2017). Unlike ‘serious games’ which
seeks to build an entirely new game, gamification refers to adding ‘gamefulness’ to
existing applications.
Many instructors and researchers have promoted gamification to facilitate teaching
and learning in schools. The facilitation is accomplished by incorporating game
elements into existing learning management systems to motivate, engage, and improve
learning outcomes. Unfortunately, universities in Ghana is yet to exploit the advantages
and opportunities of gamification, and even though, in the last decade, these universi-
ties have experienced considerable progress in Information and Communications
Technology infrastructure. There is, therefore, a gap in the improvement of ICT
infrastructures, such as the presence of LMS and the integration of gamification in
learning (Ofosu-Ampong and Boateng 2018). The nascent research efforts of
gamification, which began in 2008, have expanded across several disciplines; however,
researchers in these disciplines scarcely focused on developing a theoretical foundation
for game design elements research (Treiblmaier et al. 2018). To this extent, the
theoretical and conceptual infancy and division in gamification among researchers
“poses an opportunity for the exploration of gamification as an object of study, an
approach to design, and a computer-mediated phenomenon” (Seaborn and Fels 2015).
Current empirical studies emphasize the importance of gamification adoption in learn-
ing (Deterding et al. 2011; Nousiainen et al. 2018). However, existing studies in
developing countries to attest to this practice are limited in scope. Data, through
gamification integration in LMS, could be automatically analyzed to follow the
learning process and behavioral style of students. Given this, Kolog (2018) has
demonstrated, through their system, how student’s data could be analyzed
automatically.
In this article, we present students’ intentions to use gamification in LMS. Our goals
were threefold: 1) to explore gamification integration in LMS 2) to provide a theoretical
basis and a perceptual process for gamification research in LMS 3) to test students’
intentions to adopt gamification and extend the previous model. We begin by present-
ing an overview of gamification adoption theories, gamification in education, and the
evolving nature of gamification from a digital game (Alexiou and Schippers 2018) or
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non-digital play. In the second part, we present the theoretical basis and research model
for gamification adoption and use, particularly the perceptual process of games and
constructs from the UTAUT model. In the third part, we provide a survey of
gamification perception based on students’ use of LMS, by testing constructs for
acceptance and readiness of gamification. We end the paper with practical and theo-
retical implications and trajectories for future research.
1.1 Background
In this section, we discuss gamification and its impact on learning by considering the
institution of higher education in developing country context. Related works from
different contexts are discussed in this section as well.
1.1.1 Gamification in developing countries
Game elements have occupied an increasingly prominent space in our lives. Regarding
the socio-economic inequalities among most African countries, attitude towards digital
games are of less importance, and a minority practice (Walton and Pallitt 2012) while
non-digital games are prevalent. Given the domain of game design elements in
education, there has been less previous evidence from Ghana.
As a form of literacy, Prinsloo and Snyder (2007) documented non-digital play to
present and acknowledge the current reality of young learners in Africa. In an eco-
nomically disadvantaged setting, Prinsloo examined and analyzed playing as an evolv-
ing literacy practice in Africa. With the available resources, children were able to
develop the abilities to mediate and model semiotic practices during gameplay. During
the gameplay, meaning-making was created in the course of interaction, and the
children experimented in the given contextual situation. Instructors’ unrestricted access
to technology in delivering content to learners makes incorporating non-digital games
in learning a preferred approach in developing countries. There is also the social
interaction skills and learning developed through the traditional way of the face-to-
face method of teaching compared to conventional practice with technology. Notwith-
standing, non-digital play can be time-consuming with substantial paper-based activi-
ties that may be expensive for educational institutions (Ssekakubo et al. 2011).
Digital game medium is the “meaningful use of digital technology tools to facilitate
actual games or a collection of digital gaming elements in an educational environment
to enhance student learning through increasing student motivation and engagement”
(Kapp 2012). Constructive views, in general, have been associated with game-based
learning and digital games by providing players with emotional and cognitive experi-
ence in a sandbox environment through experiments (Rooney 2012; Alexiou and
Schippers 2018). For instance, prior research indicates a positive association between
attitude and emotions, such as math anxiety towards learning mathematics. In their
study, Verkijika and De Wet (2015) focused on the “serious crisis of mathematics
education in South Africa” as a way to effectively reduce anxiety among mathematics
students. The study employed brain-computer interface mathematical games to make a
decision. The students were in two training sessions, and the results proved that BCI
could effectively reduce anxiety among math students. Thus, designing digital game-
like encourages problem-solving in a systems-thinking manner among learners in a
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diverse and deeply enjoyable learning experience. Emphasis on real-life experience
(simulation) when instructors are designing gamification is crucial to enable learners to
relish the social interaction skills in learning.
1.1.2 Gamification and education
In the formal and informal learning settings, education application of gamification is the use
of game design elements for academic advancement – closing the gap between work, play,
and learning. Given the nature of gamification in education in developing countries, we
found UNICEF Ghana (2017), and Ofosu-Ampong and Boateng (2018) articles to be of
importance to the study context. The first (UNICEF Ghana 2017) project commenced in
2016 and the empirical study followed in 2017. UNICEF Ghana, in collaboration with the
Right to Play Ghana and Ghana Education Service, launched the Handwash with “Ananse”
termed in English as Spider. The program situated in a non-digital format was to reduce the
rate of pneumonia and cholera cases in Ghana. The Handwash with Ananse (HWWA) is a
game-based curriculum for pupils at stage 3. Themodular taught them how, when, andwhy
to wash hands with soap and water. In an era of scarce attention, conventional narratives
(storytelling) motivated the activities, where the pupils compete to outwit Ananse. In
conclusion, the handwash with Ananse program prompted a behavioral change and
encouraged learning in a non-digital and realistic environment (UNICEF Ghana 2017).
Ofosu-Ampong and Boateng (2018) explored the views of students in higher education
concerning game elements in learning. The researchers found that students were receptive
to the idea of adding game elements to knowledge acquisition. Also, the study identified
points and leaderboards as the most preferred game elements for students’ learning. With
the low rate at which students access LMS, the students recommended that the addition of
game elements would serve as an incentive for their frequent access to LMS.
However, in a generalized review of gamification in higher education, Strmečki
et al. (2015) found cultural differences as the main contribution to students’ attitudes
and expectations towards game design elements in learning to vary in different states.
The cultural differences indicate the regions’ readiness and acceptance of the evolving
nature of learning tools and forms of teaching. The scholars found enhanced student
engagement, attitude, confidence, and performance as the essential use of game design
elements for learning. Using game design elements in an undergraduate computer-
graphic e-learning program, Strmečki et al. (2015) showed the efficacy of gamification
in improving the performance of students. Additionally, the scholars found badges,
levels, points, customization, challenges, feedback, and leaderboards as the most
suitable game design elements used in higher education to promote learning.
2 Gamification adoption theories and related works
In the context of games, the application of gamification is diverse, and several domains
such as health, tourism, and sustainable energy attest to its use. We identified several
approaches to information systems that have been extensively used by researchers to
examine the acceptance of gamification. In IS research, for instance, Davis (1989) and
Venkatesh et al. (2003) proposed the Technology Acceptance Model and the Unified
Theory of Acceptance of Use of Technology, respectively; these theories are the most
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widely and contemporary innovation adoption models. We briefly present these theo-
ries with some related works in this section.
Several theoretical perspectives have influenced survey instruments used in
assessing the acceptance of new technology. There are different perspectives to under-
stand innovation, even though each theory has a connection with innovation adoption.
For instance, Rogers (2003) identified trialability, relative advantage, complexity,
observability, and compatibility as vital to the diffusion of innovation while
Robertson et al. (2008) acknowledged the critical role of agencies of technology
innovation in the spread of innovation. Katz et al. (1996) define diffusion “as the
acceptability over some time, of a specific idea or process by individuals or organiza-
tions associated with some communication mechanism with a social entity, with a set of
values.” Based on Roger’s theories, Moore and Benbasat (1991) proposed a contem-
porary technology acceptance instrument to include voluntariness which refers to the
extent of free will to the use of innovation (voluntary) and image represent the extent to
which one’s status or image is perceived to be enhanced by the use of innovation.
How individuals perceive technology usefulness and ease of use drives behaviors
through their belief systems. IS scholars suggest that the intent of a user to use technology
is rooted in the ease of use and the potential (ability) performance of the job or task
(Robertson et al. 2008). Several studies in gamification have used TAM. For example,
Yang et al. (2017) used TAM to predict the effect of gamification on customers’ attitudes
towards a brand. The researchers explored the perceptions of customers towards
gamification. The study illustrates no significant relationship between the individual
intention to engage, attitude towards the brand and the perceived ease of use of
gamification. Perceived social influence, however, was significantly related to brand
attitude but showed no relationship with the intention of individual engagement. The
scholars found that individual engagement with the game elements positively influences
perceived usefulness. Furthermore, perceived enjoyment, usability, and social influence
positively influenced brand attitude and the intention of user engagement with the game.
Social influence, satisfaction (Setterstrom and Pearson 2019), attitude toward IS (Hamari
2013) and flow experience (FE) (Kapp 2012; Hsu et al. 2013) have been considered in
gamification research in predicting individual’s intention to use gamification system. The
concept of FE has been extensively accepted in the IS literature by researchers since Davis
et al. (1992) emphasized the significance of users’ hedonic involvement in information
systems. FE describes a thoroughly engaging process (cognitively absorbed) of creating
something newwith heightened enjoyment (Santhanam et al. 2016). The perceptual process
of intending to play a game leads to an increased continuance intention (sense ofmerging) in
an actual play (Harwood and Garry 2015). Previous experiments illustrating FE, have
proven that users are in a deeply engaged and involved state of FE within a system.
Additionally, these experiments indicate that FE is crucial for intention to use (Kim et al.
2013), recurrent use (Ghani andDeshpande 1994), and continual intention to use (Mattingly
and Lewandowski 2013; Deng et al. 2010).
3 Theoretical basis and research model
Advances in technology bring about changes in society, which, in effect, dictates
societal trends. Through the acquisition of information systems and services, an
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individual learns about object properties or materiality in his social or organizational
settings (Robey, Anderson and Raymond 2013). In observing individuals and objects in
separate attributable properties, objects are perceived (Peart et al. 2017) and assume
two forms of beliefs or perception; thus, belief in objects and belief about objects. The
former relates to the existence or nonexistence of an object as perceived by the
individual, while belief about objects is the relationship existing between objects (the
given objects and others - concepts and values) (Jasperson et al. 2005). The evaluation
of the object is related to how the individuals feel about the objects in the belief or
perceptual process. Attitudes are formed as a result of the evaluated beliefs of personal
feelings (favorable or unfavorable) of the individual towards a given object. The
formation of attitudes resulting in behavioral change towards an object ideally aligns
with the individual’s attitudes (Bokhari 2005; Ngampornchai and Adams 2016).
However, there are extraneous cases of attitudes which are inconsistent with observed
behavior due to constraints mediating between the actual user behavior and the
individual’s predisposition. These constraints predominate the effect of attitude. De-
signers of gamification in such a context cannot assume that users have reached a
certain level of technological competence or present a homogenous group or attitude
but relatively must appreciate the diversity that gamification offers. For example,
Holmes and Gee (2016) reviewed game elements in a higher education context and
found potential constraints in gamification implementation. The authors, therefore,
developed a framework to mitigate these changes in education and learning and
concluded that it is important to implement game design elements in a context manner
to demonstrate that gamification is an effective and legitimate method for teaching and
learning in higher education.
When the extended model is positioned in the context of gamification, perception,
and usage, the model assumes a favorable attitude toward the use of LMS and digital
games, which is important in the utilization of gamification systems in learning (Ofosu-
Ampong and Boateng 2018). Attitude formation is thereby influenced by the user’s
belief (Barnes and Kennewell 2017) of the quality of the current system information,
trust, availability, and ease of use of a system before integrating gamification. It must be
noted that beliefs about gamification contribute to the user’s attitude toward
gamification use. Gamification training, the familiarity of technology, performance
and effort expectancy, image and departments such as Computer Science and an
institution’s IT directorate are fundamental to gamification usage. For example, Fisher
et al. (2013), before implementing gamification in higher education, evaluated the
knowledge, attitude, and experience of faculty members who are important to the
success of game elements in teaching and learning of business education programs.
The study found an increased knowledge of gamification among the faculty members
as a tool to enhance engagement and interaction with students and a motivation tool for
increasing students learning. The study, therefore, set the premise for harnessing
gamification in the business education program.
Depending on user context (place), situational constraints like the high cost of data,
LMS unavailability, poor internet connection, computer facilities, and inadequate or
unavailable user/management support may intervene between attitude and usage be-
havior of gamification. These contributing factors sum up the individual belief system
in adopting a gamified system and helps inform designers of user preference and
expectation. Technology affordance and its relationship with gamified systems should
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be considered in research for more exceptional learners’ engagement. Thus, under-
standing the learners and the environment is regarded as a vital recommendation for
educational institutions planning on adopting gamification. For instance, De-Marcos
et al. (2017) focused on the social aspect of gamification tools to satisfy students’
situational motivational needs. The gamified application served as social gamification
that provided students with PBL and virtual shop on successful completion of the
assignment. By considering the student’s (environment) need for competence, autono-
my, and relatedness, the social gamification tool which provided an interactive learning
environment addressed their need and sense of progression. This study, therefore,
focused on user perception or attitudes of gamification in learning in Ghana.
Three sets of variables can be deduced from the model for this study: 1) the
perception of gamification, 2) external variables outside gamification that may affect
attitudes toward the gamified system 3) attitudes towards gamification and use of IS.
How individuals perceive information system determines its use (Moore and
Benbasat 1991) and dimensions (features of game elements). The dimensions of the
system were constructed through literature review and interviews with users and system
designers. The aspects reflect these four criteria: 1) the perceived usefulness to the
gamified user, 2) element of game flow – excitement in system use 3) common to all
game-based information systems 4) significant to the theory of the study.
The external variables on use behavior reflect the individual (objects) demographic
rather than the gamification system implemented. Thus, the potential learner is signif-
icant to the use of the gamified system and his educational environment. The diffusion
and acceptance model of technology influenced the variables for the study (literature
search) and interviews with IS personnel at higher education.
The current study explores students’ attitudes and perceptions of gamification in
learning by adopting the TAM and UTAUT model. As extensive used by researchers in
Educational Technology, the use of UTAUT for this study is to motivate the progressive
development of gamification in higher education institutions and schools. Perceived
Usefulness, as captured in the model, is the possibility that using a target system will
improve the potential users’ activities; whereas Perceived Ease of Use denotes the
effortless nature of the target IS as perceived by the potential user (Davis et al. 1989).
There are other factors known as the external variables that may influence the potential
user’s attitude to the system (Fig. 1).
Drawing upon the UTAUT model, we identified age, gender, voluntariness, and
experience as the moderators, and we study four constructs that constitute the mea-
surement items. The constructs include: Performance Expectancy refers to the extent to
Performance Expectancy
Trust
Effort Expectancy
Behavioural 
Facilitating Conditions Intention
Attitude
Image
Social influence
Fig. 1 Research model
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which technology is perceived by an individual to improve the performance of a job.
Effort Expectancy represents the ease of use of technology as perceived by an individ-
ual. Social Influence is “the extent to which another individual impacts an individual’s
decision to use technology.” Facilitating Conditions represents the organizational
support available to potential users for the use of the system. Attitude has been
highlighted as an indirect determinant of UTAUT model and represents the pleasure
linked to the use of technology (Venkatesh et al. 2003). The control variables for this
study are age, trust, and image.
The research model reflects the belief system of individuals as favorable or unfa-
vorable towards gamification acceptance and use. Thus, a set of 5 consequences
(measurement items) of the use of LMS was developed to test gamification integration.
Two reflect direct outcomes of internet services and the capability of IT personnel and
management to support gamification services. The other three consequences focused on
the quality dimensions, system organization and attractiveness, and constant availabil-
ity for use. These evaluative beliefs result in the behavioral process and assessment of
beliefs towards a system. The Ghanaian educational environment necessitated the
construction of these attitude beliefs after an interview with Chief Information Officers
and systems users – students.
In summary, for gamification to be proven effective and successful, it is dependent
on users using the gamified system in learning. The study, therefore, explores the
student’s behavioral intention to use the application. Future studies can investigate the
effectiveness of game design elements after the integration.
Based on this rationale, we hypothesize that behavioral intention to use gamification
in learning is positively associated with performance expectancy (H1), effort expec-
tancy (H2), attitude (H3), facilitating conditions (H4), trust (H6) and social influence
(H7); and is negatively influenced by image (H5).
4 Methods
4.1 Instrument and procedure
We used questionnaires to collect responses from the students. The questionnaire
contained three sections, all administered in the English language. Section one
consisted of demographic questions and questions on computer game playing habits
and preferences. Section two consisted of twenty-three (23) questions with a 5-point
Likert scale. The measurement items were categorized to reflect the ten main constructs
(performance expectancy, effort expectancy, attitude toward gamification, image, facil-
itating conditions, behavioral intention, social influence, behavioral intention, trust, and
quality). Out of the ten (10) constructs, we included seven (7) constructs from the
UTAUT model, and two new constructs (questions) were included by the researchers
based on gamification constraints deduced from literature analysis. The new construct
was to measure the trust level with higher education services and the quality of the
Sakai in hosting gamification. We adopted two items from the construct of Image from
Moore and Benbasat’s (1991) construct of technology adoption. As a matter of validity,
the item constructs were classified and adjusted in discussion with a faculty member,
and an IS expert at the University of Ghana. Insight from Son and Han (2011) aided in
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modifying the last question on the use of technology (Sakai) frequency (from “several
times each day” to “less than once a week”). The study adopted the University of
Ghana Sakai LMS.
A minimalist view of gamification was presented to the participants since
gamification is a new concept in Ghana, and the participants understanding of the term
proved useful in answering the questions. The reluctance of students to used digital-
based encourage the use of a paper-based questionnaire. In controlling socially desir-
able responses, the participants were assured of no wrong answers to questions and as
such honest answers were required (Podsakoff et al. 2003). The data collection covered
14 days between August and September 2018. Descriptive statistics, component
matrix, Cronbach alpha, and correlations were performed to answer the research
questions. The measurements were important in testing the relationship between the
constructs and other variables and obtaining factors influencing students’ acceptance of
gamification.
4.1.1 Reliability
For testing the reliability and internal consistency, our study used the Cronbach (α) values –
a measure of reliability test. Prior research indicates that Cronbach’s alpha value ≥ to 0.7 is
an indication of a strong, reliable instrument, and < 0.7 values indicate weak reliability
(Nunnally 1978). From the obtained results (Table 2), each construct represents its reliability
coefficient. In total, the items from the UTAUT model yielded a reliability coefficient of
0.86. The implication is that the internal consistency of all the items is reliable.
4.2 Participants
The survey participants are graduate and undergraduate students of the University of
Ghana (UG) who have had experience with the University’s Sakai LMS. The Univer-
sity did not have a gamified system or course at the time of data collection. Data were
collected from students who have enrolled for the Digital Literacy Training at the
University of Ghana Computing Systems (UGCS). The UGCS runs IT training for
students at the beginning of the semester as a beginner or intermediate level training.
For this study, students available for training were to choose from three available
sessions per their timetable schedule: 1. Digital literacy 2. Microsoft office specialist
and 3. Statistical Package for Social Sciences. Students applied for spots via google
forms, and each session (12) accommodated forty-seven (47) students. The total
number of students who applied for the course was 564, of which the number changed
as the training progressed. Students who registered for the training were in batches of
twelve. Two hundred and one (201) questionnaires were retrieved. After sorting (non-
response, inconsistent, and incomplete) and data entry, the responses examined were
one hundred and eighty-five (185).
Roldan and Sanchez-Franco (2012) have indicated the importance of addressing the
sample size issues in a parametric test. Even though less restrictive measures exist,
scholars suggest that to reach an acceptable level of statistical power; the sample size
should be increased to 100 (Thompson et al. 1995). Although the criteria have been
used extensively, Roldan and Sanchez-Franco (2012) have proposed that to attain a
more accurate assessment, the sample size of each regression should be specified in line
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with Cohen’s (1994) power table. In determining the sample size, it is essential to
specify the expected effect size (ES), power (β), and significant alpha values (α) in the
study. Generally, a power of 80% and an alpha of .05 are acceptable. Using an average
ES of .15, β of .95, and α of .05 in line with Cohen (1994), a multiple regression was
determined with four predictors to determine the study sample size. Overall, the results
yielded a sample of N = 128 – which implies that our sample of 185 exceeded all
criteria for analyzing the measurement models.
5 Results
In this section, we present two categories of results: descriptive and inferential (corre-
lation). The descriptive results focused on the demographic exploration of the partic-
ipants, while the correlational results established the association among the various
constructs in this study.
5.1 Descriptive statistics
Of the 185 respondents in this study, 46.5% were male and 53.5% female. Sixty-six
(66.5%) percent of the students were between 17 and 23 years old, 16.2% were between
24 and 30 years, and 17.3% were 31 years and above. During the time of data collection,
the first-year students were not enrolled in the IT training, accordingly the level of study
percentage are as follows: year two (55.2%, n = 102), year three (13.5%, n = 25), year four
(8.6%, n = 16) and postgraduates (PG) were 22.7% (n = 42).Most of the respondents were
full time students (76.8%, n = 142) and remaining were distance learning students (23.2%,
n = 43). In exploring the playing habits of the students, 90.8% (n = 168) play games
(female 46.5%; male 44.3%) whilst 9.2% (n = 17) do not play (see Table 1). The preferred
game types were puzzles (35.7%), adventure (22.7%), racing (14.6%), strategy (9.2%),
multi-playing (13.0%), and shooter (4.9%).
Majority of the students (45.9%) have less than 1-year experience with the Sakai
LMS, 31.4% of them have 1–3 years of experience, and students with more than three
Table 1 Demographics of Respondents
Gender Age Level of study
Male Female 17–23 24–30 >30 Year 2 Year 3 Year 4 PG
46.5% 53.5% 66.5% 16.2% 17.3% 55.2% 13.5% 8.6% 22.7%
Preferred game type Enrolment Type
Puzzle Adventure Racing Strategy Multi-Playing Shooter Full-time Distance Learning
35.7% 22.7% 14.6% 9.2% 13.0% 4.9% 76.8% 23.2%
Playing habit
Play games (% of Gender players) Do not play games
90.8% → Male 44.3% 9.2%
Female 46.5%
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years’ experience were 3.8%. The students that do not use the Sakai platform were
18.9%. The students on average logged in to Sakai less than once a week (47%, n = 87)
while (29.7%, n = 55) of them logged into the Sakai platform about once each week
when school is in session. Seventeen (9.2%) of the respondents log in several times
each day, 18 (9.7%) logged in about once each day, and 8 (4.3%) logged in several
times each week. On average, the length of time students spend every time they log on
to Sakai are as follows: more than 60 min represents 1.1%; between 46 and 60 min –
5.9%; between 31 and 45 min – 31.4%; between 15 and 30 min – 25.4%; less than
15 min – 21.1% and students who chose “not applicable” were 15%. The students
stated that they mostly log on to Sakai for three primary purposes: to take quizzes,
download course materials, and check plagiarism.
5.2 Acceptance of gamification: Constructs validation
Each item of the main constructs was measured with a 5-point Likert scale. The
students after the learning session rated a twenty-one (21) statement modeled according
to UTAUT, based on gamification in education. The reliability of the test score was
high, with a Cronbach’s alpha of 0.86. In computing for the mean and standard
deviation, we used SPSS. The mean determines the average students’ responses, while
the standard deviation value shows the level of variation and closeness to the mean, as
shown in Table 1.
When we tested for internal consistency using Cronbach’s alpha, all seven factors
(PE, EE, AT, FC, IM, TR, and BI) obtained very good reliability (Cronbach’s alpha
>.70) as indicated in Table 2. With a mean of 3.66, the student’s acceptance level of
gamification was slightly more than the neutral. To analyze the goodness of fit and
support the mean and SD results, we tested for the chi-square coefficient (X2) and chi-
square degree of freedom (Kline 2011). The results X2 = 603.47; X2 / df = 3.38 indicate
an acceptable measurement model fit.
The Kaiser-Meyer-Olkin Measure of sampling adequacy was .501 (Approx. Chi-
Square 4.99; df = 325; sig = .00).
The correlation coefficient was deducted to establish the relationship between
the key variables. As shown in Table 3, a moderate positive correlation is
identified among the constructs within the acceptance scale (1–10). The strongest
among the level was the relationship between attitude and acceptance of
gamification (r = .821, p > .01), followed by performance expectancy and attitude
(r = .66, p > .01). The weakest among the acceptance scale was the relationship
between user behavior (new construct introduced) and facilitating conditions
(r = .27, p < .01). The following three (3) associations in the acceptance scale were
significant at p < .05 and was the highest at that level; performance expectancy
and image (r = .18, p < .05), attitude and image (r = .18, p < .05), and trust and use
behaviour (r = .18, p < .05).
The new construct introduced (Trust) had a moderate level of acceptance
(Cronbach’s alpha >.70). On average, the students indicated a moderate level of trust
with the university’s staff support (M = 3.26, SD = .15). In particular, the average
student purported to disagree that the UG internet is trustworthy for supporting
gamification in Sakai (M = 3.02, SD = .12). Additionally, the results revealed a negative
correlation between the trustworthiness of Sakai hosting gamification services,
Education and Information Technologies
Table 2 Testing the mean and standard deviation of the construct items
Factors and items Mean Std.
Deviation
Performance Expectancy (α = 0.849) (Adapted from Davis et al. 1989) 3.94 .901
PE1: Gamification would improve my academic performance 3.97 .881
PE2: Gamification “would allow me to do more work in less time” 3.95 .877
PE3: Gamification would make it easier to do my school work 3.89 .859
PE4: Gamification would encourage interactive learning with my colleagues 3.91 .940
PE5: Gamification would motivate and encourage learning 3.99 .918
Effort Expectancy (α = 0.842) (Adapted from Bourgonjon et al. 2010) 3.91 .903
EE1: Learning to use a gamified system would be easy for me 4.00 .730
EE2: Using a gamified system will be easy and without much help 3.76 1.011
EE3: It would be easy for me to become skillful at using gamification 3.93 .950
EE4: I would find gamification easy to use because of my game skills and use of 3.95 .919
Sakai
Attitude (α = 0.841) (Adapted from Ajzen 1991) 4.29 .793
AT1: I think gamification is a good idea for students 4.22 .807
AT2: I think gamification is a good idea for the university 4.22 .845
AT3: I am interested in using computer game in learning 4.43 .727
Facilitating Conditions (α= 0.864) (Adapted from Bourgonjon et al. 2010) 3.60 .911
FC1: My familiarity with Sakai and playing games would equip me in using other 3.54 .827
added features as gamification
FC2: There is a specific person or unit available for assistance with any technical 3.65 .994
problem I may encounter
Image (α = 0.863) (Adapted from Moore and Benbasat 1991) 3.355 .881
IM1: “I think that people who use gamification in learning are getting a better 3.41 .868
education”
IM2: I think that people who use gamification have bragging right and social capital 3.30 .894
as they achieve a high score
Trust (α= 0.869) Author’s construct 3.265 1.015
TR1: The UG internet is trustworthy for gamification services in Sakai 3.02 1.120
TR2: The UGCS can be trusted to carry out gamification in Sakai 3.51 .910
Behavioural Intention (α = 0.866) (Adapted from Venkatesh et al. 2003) 4.57 .607
BI1: I intend to use gamification in the future 4.54 .651
BI2: I plan to use gamification in the future 4.64 .554
BI3: I intend to recommend gamification to friends in the future 4.52 .618
α – Cronbach alpha
precisely the UG internet stability, and age (r = −.40, p < .01). In summary, the trust
scale had a significant relationship with only two of the ten variables, namely facilitat-
ing conditions and use behavior of technology. The quality of the LMS (Sakai
attractiveness, organization, and availability) had a negative correlation with effort
expectancy (r = −.22, p < .01), but had a small positive relationship with attitude
(r = .30, p < .05) and facilitating condition (r = .30, p < .05).
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Table 3 Correlations between Key Variables
Constructs 1 2 3 4 5 6 7 8 9 10 11
1.Performance 1
expectancy
2.Effort expectancy .52** 1
3. Attitude .66** .58** 1
4. Image .18* .16* .18* 1
5. Facilitating .08 .34** .38** .43** 1
condition
6. Use behaviour −.23** −.16* .14 .03 .27** 1
7. Trust −.04 .10 .05 −.12 .17* .18* 1
8. Quality −.13 −.22** .30** .05 .30** .35** .30** 1
9. Age .18* .18* .18* .35** .23** .08 −.40** −.09 1
10. Social influence −.08 −.07 −.08 .31** .12 −.07 −.08 .10 .13 1
11.Acceptance of .56** .58** .821** .38** .61** .41** .34** .43** .18** .06* 1
gamification
**. Significant at the 0.01 level (2-tailed)
*. Significant at the 0.05 level (2-tailed)
5.3 Computation of predictive factors
We conducted Multiple Linear Regression to explain the relationship between the
variables. Our results for all the constructs showed a strong internal consistency of
0.86. The MLR was used to predict the students’ behavioral intention towards
gamification adoption in Ghana. The dependent variable identifier of the model is
Behavioural Intention (BI) while the independent variable was Performance Expectan-
cy (PE), Effort Expectancy (EE), Attitude (AT), Image (IM), Trust (TR), Facilitating
Conditions (FC) and Social Influence (SI). The following shows the model summary,
which was computed using SPSS 23 (Table 4, 5 and 6).
5.4 Reliability of the constructs
Overall, the internal consistency of the model is 0.862 representing 86% reliability of
the constructs. This indicates a strong internal consistency of the model. Table 2 also
Table 4 Model summary
Model R R Square Adjusted Std. Error Change Statistics
R Square of the Estimate
R Square F df1 df2 Sig. F
Change Change Change
1 .847a .717 .706 .098011 .717 64.092 7 177 .000
a. Predictors (Constant), SI, TR, AT, IM, FC, EE, PE.
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Table 5 ANOVAa
Model Sum of Squares Df Mean Square F Sig.
1 Regression 430.977 7 61.568 64.092 .000b
Residual 170.029 177 .961
Total 601.005 184
a. Dependent Variable: BI.
b. Predictors: (Constant), SI, TR, AT, IM, FC, EE, PE.
shows the individual reliabilities of the constructs (PE – 0.85, EE – 0.84, AT – 0.84, FC
– 0.86, TR – .87, IM – .86 and BI – 0.87).
From Table 4, we confirm the predictor variables as significant because the p value
for the model is 0.000. The implication is that the model is statistically significant at
F = 64.092, df = 177, 7, sig. = 0.000. The overall Adjusted R Square showing the
relationship between the dependent and independent variables is 0.706, while the
Multiple R is 0.717. The value indicates the acceptance of the overall model. Therefore,
71.7% of the overall model explains the variance in student’s behavioral intention to
adopt gamification in learning in Ghana. The MLR model with the seven constructs
yielded R2 = .717, F (7, 177) = 64.092 with ≤ .05 significance level. As shown in
Table 7, PE, EE, AT, TR, and SI are significant predictors of Behavioural Intention.
Therefore, H1, H2, H3, H5, and H7 are accepted. The remaining, IM (p value = .101) and
FC (p value = .428) are rejected.
6 Discussion
The study explored students’ level of acceptance of gamification integration into
existing LMS - Sakai. The acceptance of gamification was more than the neutral as
indicated by the students of the University of Ghana. Previous studies (Filippou et al.
2018) interestingly found similar findings of the acceptance of gamification.
Table 6 Regression coefficients
Unstandardized Coefficients Standardized Coefficients
Model B Std. Error Beta t Sig.
1 (Constant) 1.350 .724 1.864 .064
PE .421 .055 .362 7.588 .000
EE −.077 .030 −.132 −2.561 .000
AT .509 .052 .604 9.821 .000
IM .052 .032 .096 1.648 .101
FC .060 .076 .041 .795 .428
TR .306 .042 .302 7.265 .000
SI .393 .091 .182 4.313 .000
Significance is p < .005; Dependent Variable: BI.
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Table 7 Summary of hypothesis test
Relationship P value Support
→H1: Performance expectancy behavioral intention .000 Accepted
→H2: Effort expectancy behavioral intention .000 Accepted
→H3: Attitude behavioral intention .000 Accepted
→H4: Image behavioral intention .101 Rejected
→H5: Trust behavioral intention .000 Accepted
→H6: Facilitating condition behavioral intention .428 Rejected
→H7: Social influence behavioral intention .000 Accepted
Rooted in the UTAUT model and prior research on technology adoption, our results
found a direct effect of PE, EE, AT, IM, and TR onBI- which implies a strong indication of
technology acceptance. This confirms users’ views on 1) how gamification can improve
studies and 2) how the skills developed using LMS and playing computer games equips
them to use gamification. Measurement item 4 of effort expectancy reveals that 87.6% of
the users will find gamification easy to use based on their computer game skills and use of
LMS. It is of interest to know that the students (56.2%) declined the indication that those
using gamification are getting an improved education and social capital than those not
using. The perspective does not confirmMoore and Benbasat’s (1991) view on the image
(social status) as one of the key indicators when adopting an innovation. Our results
suggest an unfavorable outcome with the average ratings of predictors of the image
construct. Another interesting finding was that social influence (my colleagues will like
it if I choose to learn with games) had no relationship with eight of the variables but with
image. However, facilitating conditions had a significant association with effort expec-
tancy, attitude, and image, wherein facilitating conditions were operationalized as a
specific unit in place to assist students with technological problems they may encounter.
Students who reported that their familiarity with games would equip them in using other
added features as game elements also have a positive attitude and perception toward
gamification. However, there was a negative relationship (r = −.2.3, p < .01) between
students’ use of LMS (use behavior) and how adding gamification would motivate
learning and encourage the use of the gamified system (performance expectancy). Thus,
student’s familiarity with a particular technology and appreciation of innovation does not
necessarily confirm the acceptance of the use of the innovation or added features to the
system. Previous studies showed that perception, demographic variables, and associated
variables had an impact on the development, application, and use of gamification factors.
This study, similar to Featherstone and Habgood (2019), showed that experience with
games contributes to players’ interest to use game design elements in learning and can
improve students’ grades if the experience of the elements is more compulsive.
Furthermore, previous research has revealed the dominance of males in playing games.
A recent empirical research (Seabon and Fels 2015) supported this claim and found that
females are less interested in games than males. In contrast, we found female students
interest in games just, if not more, in intending to engage in gamification learning asmales.
Extant research in gamification (Filippou et al. 2018, Ofosu-Ampong andBoateng 2018)
in developing economies found that users’ experiencewith computer games and technology
Education and Information Technologies
in learning had a direct impact on the acceptance of gamification. Our research shows a
mixed relationship between use behavior (Sakai) and acceptance of gamification. Whereas,
there was no relationship between familiarity and attitude and image; there was a negative
relationshipwith performance expectancy and effort expectancy as a construct for accepting
gamification. However, there was a strong positive relationship with facilitating conditions.
Therefore, we conclude that student’s familiarity (facilitating condition) with technology
does not guarantee the acceptance of adopting gamification in learning. Further studies may
seek to understand an individual’s familiarity with a system before adding a new feature to
decide the adoption of gamification within the Ghanaian educational system.
In exploring gamification integration in existing LMSs, we included system quality as a
new construct to ascertain the acceptability of gamification in Sakai. Interestingly, there was
a negative relationship between system quality defined as attractiveness, organization, and
availability of Sakai to students’ performance expectancy and effort expectancy. Addition-
ally, there was no relationship with performance expectancy. However, there was a strong
relationship with attitude, facilitating conditions, and familiarity with technology to suggest
the tendency of gamification adoption by designing an organized, attractive system and
making it readily available. Based on this finding, we conclude there is uncertainty with the
student’s decision to adopt gamification as contingent upon the system quality. Future
research may explore the negative ratings to understand student’s system quality experience
with the design, organization, and availability of LMSs in adopting gamification.
Moreover, our findings indicate that the majority of the students play puzzle and
adventure games, with only 4.9% of the students playing shooter games. Interestingly,
90.8% of the students play computer games. These statistics support our earlier
assertion that students enrolled in IT professional courses are likely to play computer
games. The results show a high level of access to technology devices available to the
students. With regard to the Sakai LMS usage, the majority of the students, on average,
login less than once a week while 29.7% of students login about once each week when
school is in session. The majority of the students have less than one year or between 1
and 3 years of experience (77.3%) in using Sakai. Also, the majority of the students on
average spend between 31 and 45 min on Sakai. These statistics support our assertion
that student’s prior knowledge of technology is a boost to encourage innovation since
the use of Sakai requires basic IT skills (web browsing, watching online videos).
Recent studies indicate that game design elements are transformative and serve as a
motivational tool in teaching and learning in higher education (Nousiainen et al. 2018;
Hanus and Fox 2015). This current study, however, indicates low knowledge, use, and
uptake of gamification in learning by students. A restraining factor for student’s
acceptance and use of gamification is due to the limited access to these innovations
at the university. Higher institutions, therefore, need to effectively integrate various
learning (formal or informal) strategies to enable students to identify their learning
habits. Through this, the different learning habits of students can be known via an
experiment when adopting future learning strategies for educational institutions.
6.1 Practical and theoretical implications
The study found motivational support for gamification in learning in higher education.
Specifically, we found out that attitude played an important role in students’ intention to
use gamification. Thus, attitude had a direct effect on behavioral intention – which
Education and Information Technologies
implies that higher education institutions may need (find it useful) to shape the attitudes
of students for influencing behaviors. Context wise, we found trust and social influence
to have a direct effect on behavioral intention to use gamification. This implies that
students will associate prior experience with institutions services (IT) or preparedness
in championing the use of new technology. Educational institutions should, therefore,
provide training and high-quality infrastructures to boost learning and in effect, pre-
dispose students to new technologies such as game design elements in education.
Accordingly, designers should incentivize social elements to include social learning
and comparison or competition to improve the potency and use of game design
elements.
Moreover, the significant effect of performance expectancy and effort expectancy on
user intention shows the importance of the learner attributes to gamification acceptance.
Hence, higher education should prioritize the usefulness of the gamified system and its
corresponding ease of use for effective user engagement.
Theoretically, our study findings proved that the UTAUT model is still important in
understanding student’s acceptance and readiness of integrating gamification in existing
systems. However, with the introduction of a new construct (i.e., trust), new insight into
the original model for user intention to use technology was uncovered. Our findings
showed a direct effect of trust on students’ intention to use gamification – this implies
that the trust imposed on the institution’s IT personnel, infrastructure, and management
of technology by students is vital in explaining the acceptance of innovation.
6.2 Conclusion and future research
The study explored the extent to which gamification can be integrated and accepted
into learning, and thus, an extended model was used to support the study. Our model
shows that students’ acceptance of gamification depends on performance expectancy,
effort expectancy, attitude, social influence, and trust. Image and Facilitating Condi-
tions of the MLR analysis proved insignificant to the student’s acceptance (intention to
use) of gamification in learning. Facilitating conditions such as prior exposure to
computer games, accessibility, and skills positively impacted student’s intention to
use gamification. The significance of the study findings is to support the development
of gamified systems for future use. Therefore, the study should inform designers of user
perception and attributes of integrating gamification into learning systems.
The study faced a number of institutional and students’ based challenges to
gamification in learning. These included a lack of basic IT training for students and
exposure to digital technologies, instructors’ willingness to gamify courses and em-
brace gamification, attractiveness, and redesign of LMS to accommodate gamification
and availability of technical support. Other challenges were student’s low level of trust
with the institution’s internet bandwidth for gamification services to be effectively
implemented.
The study had some limitations, and future research can improve the constructs used
in the acceptance of gamification. We found out that some items in the factor analysis
did not load well with the UTAUT original constructs. The results of the study are
therefore not directly generalizable because the research was exploratory with a limited
sample of 185 in a localized environment (Ghana) – thus, the complexity of the
conceptual model for testing was also limited by the available sample size.
Education and Information Technologies
Future research: empirical testing of the Sakai system is necessary to find out the
effectiveness of the intervention. Thus, further studies should consider conducting a
pre-test using the gamified system and a post-test using the gamified system and
compare the students’ learning outcomes in a cross-sectional survey, e.g. assessment
scores on the concepts learned to find out whether the Sakai intervention is effective. In
using the theoretical and reliable scale of UTAUT, we hope this study serves as one of
the pioneering research on gamification acceptance in Ghana and providing a basis to
support gamification research for learners of interactive systems.
Authors’ contributions KOA and RB formulated the study idea and developed the conceptual framework.
KOA, RB, TAD and EAK designed the data collection instrument and KOA collected the data. KOA, TAD,
and EAK analyzed and interpreted the research data. All authors wrote, read, and approved the final
manuscript.
Compliance with ethical standards
Competing interests The authors declare that they have no competing interests.
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