Abstract
Purpose
This study aims to look at the integration of gamification in an e-learning model based on the technology acceptance model. The data was collected from respondents residing in India and elements of gamification (achievement, immersion and social) and personal characteristics of learners (self-efficacy, computer anxiety and enjoyment) and their impact on perceived ease of use (PEOU) and perceived usefulness (PU) were tested.
Design/methodology/approach
The data were collected from students and professionals who have ever played games during learning while using an e-learning module. Structural equation modeling using smart partial least square was used to create a model.
Findings
The findings showed that enjoyment affected both PEOU and PU and attitude toward e-learning. Achievement and social elements impacted attitude and the immersion element moderated the relation between enjoyment and PEOU and PU. These finally impact attitude and satisfaction, leading to higher intention to use e-learning platforms.
Research limitations/implications
Because this study is very specific to the Indian context, a broad generalization requires further exploration in other cultural contexts. The absence of this exploration is one of the limitations of this study.
Originality/value
This study tested the GAMEFULQUEST suggested by Högberg et al. (2019) based on self-determination theory and its impact on the overall e-learning experience. The moderation of immersion has come out to be significant and achievement and social elements impacted attitude.
Keywords
Citation
Kashive, N. and Mohite, S. (2023), "Use of gamification to enhance e-learning experience", Interactive Technology and Smart Education, Vol. 20 No. 4, pp. 554-575. https://doi.org/10.1108/ITSE-05-2022-0058
Publisher
:Emerald Publishing Limited
Copyright © 2022, Emerald Publishing Limited
1. Introduction
The current COVID-19 situation is making us sit, work and learn from home. Many students learn from e-learning platforms and schools have completely transferred their teaching online. E-learning is catching up globally in higher education institutions and students have a positive attitude toward e-learning (Johnson et al., 2021). Mohammed (2022) studied the impact of COVID-19 on online or remote learning for students around the world by collecting web-based data from 20 countries and found an increased amount of time spent online for learning, research and obtaining more information regarding new tools and techniques used in online learning. E-learning opportunities are enormous, as they provide various benefits, such as overcoming the differences in time and physical space of the educational system (Bates, 2005). Although e-learning provides various advantages, it cannot keep the learner motivated and motivation is a critical part of any type of learning, including online learning (Bekele, 2010; Jones and Issroff, 2007). Information and communication technologies play an important role in enhancing students’ learning processes, because their effectiveness is determined by the level of acceptance and degree of usage within the student population (Johnson et al., 2021; Teo, 2014; Jia et al., 2020). Katsaris and Vidakis (2021) reviewed 42 papers published from 2015 to 2020 regarding learning and adaptation modules to understand the theoretical and technical knowledge of adaptive learning and the use of learning style in adaptive e-learning to enhance user experience.
Acceptance and usage are determined by the user’s perception of the technology and the knowledge and skills of computers (Tarhini et al., 2015). The technology acceptance model (TAM) (Davis, 1989) is a widely used model to explain technology acceptance. Various studies have explained the TAM framework and its application in e-learning contexts (Alshare et al., 2011; Hu and Hui, 2012; Sharma et al., 2014; Tarhini et al., 2013). TAM (Davis, 1989) is based on the theory of reasoned action (Ajzen and Fishbein, 1975). According to this theory, the behavioral intention to use technology depends on an individual’s perceived usefulness (PU) and ease of use (PEOU) (Venkatesh and Davis, 2000).
Playing games has become very popular and all age group enjoy the same. Hence, gamification is not only used in gaming context but also in non-gaming context including learning. When games can be applied to nongaming context it provides “gameful experience” (Deterding et al., 2011). Landers (2014) proposed a theory of gamified e-learning, wherein the study suggested that gamification impacts behavior/attitudes toward learning. These learning-related behaviors/attitudes moderate the relationship between instructional or content quality and outcomes and learning directly in the form of mediation. The current research is focusing on self-determination theory (SDT) as given by Ryan and Deci (2000) as foundation to understand the relationship of elements of gamification to learning through an empirical study. The work of Högberg et al. (2019) GAMEFULQUEST framework having three important elements of gamification namely achievement, immersion and social related based on Aparicio et al. (2012) gamification aspects: autonomy, competence and social relatedness, which can be extended to e-learning. As many studies have explored achievement elements, very few have investigated immersion elements, particularly in the context of e-learning.
The study showed that immersion elements of gamification affected both PU and PEOU. The study implication is much folded as it emphasizes enhancing the use of gamification to improve PEOU and PU. The objective of the current research is to conceptualize and test gamified e-learning models. This will help us to test its universal appeal as a generalization of theory testing and model building. To date, no study has tested the integration of GAMEFULQUEST into e-learning models empirically. This study raised the following research questions and sought answers:
Which elements of gamification (immersion, achievement and social) affect user perceptions of PEOU and PU) in the e-learning model?
Do gamification elements (immersion, achievement and social) moderate the relationship between personal characteristics and user perceptions (PEOU and PU) in e-learning?
2. Literature review
2.1 Technology acceptance model
The TAM was first introduced by Davis (1989) and is the most widely used framework for understanding technology use and acceptance (Veiga et al., 2001). The two important variables used in the model are PU and PEOU. PU captures the instrumental dimension of the use of technology, whereas PEOU is described by hedonic experience (Tarhini et al., 2017). Other researchers have added new constructs to TAM (Warkentin et al., 2002) to investigate the adoption of e-government by citizens across different countries by taking trust, perceived risk, culture and perceived behavior control. TAM has been applied to e-learning by many researchers, and PEOU and PU have been linked to user intentions (Zhang et al., 2008; Liu et al., 2009). The greater the learners’ PEOU and PU, the higher their chances of being satisfied and having a positive attitude toward e-learning and allowing them to use it (Arbaugh, 2000; Arbaugh and Duray, 2002).
2.2 Perceived ease of use and perceived usefulness
PU is defined as the degree of belief that the use of a system improves performance (Davis, 1989). As e-learning improves the flexibility of time and space and learning at their own pace, it increases PU among users. E-learning also helps in collaborating and sharing knowledge as it connects the learner to other similar groups (Su-Houn Liu, 2009). Similarly, PEOU influences students’ intention to learn through e-learning, as it also affects PU and perceived enjoyment (Lee et al., 2005). According to Gong et al. (2004), PEOU is directly related to students’ attitudes and PU. Many studies have explored the application of TAM to explain students’ acceptance of e-learning tools (Tarhini et al., 2014; Hwang et al., 2012). Sharma et al. (2014) showed that PEOU directly affects the intention to use a system and other researchers have supported this claim (Chang and Tung, 2008; Liu et al., 2010; Tarhini et al., 2013).
2.3 Attitude and satisfaction with e-learning
Attitude is an important aspect of e-learning and understanding what influences attitudes toward e-learning is crucial. Therefore, it is important to use a multidisciplinary approach to understand attitudes toward e-learning (Liaw, 2007). There is a need to build an instrument that measures attitudes by examining different aspects of the user (Wang, 2003). Satisfaction has been studied by many researchers looking at system success (Esterhuyse et al., 2016; Liaw and Huang, 2013). According to Chen (2010), e-learning is considered a system. As e-learning is a user-oriented system, the satisfaction level of users determines their success (Shee and Wang, 2008). System implementation is governed by the pleasure decided by the students, teachers, technology, environment and system design (Teo, 2014). Hence it can be said that the higher the satisfaction level of the user more is the more chances for them to use it (Liaw and Huang, 2013).
2.4 Gamification in e-learning
Huizinga (2000) suggested that games can be defined as “non-serious but strongly engaging voluntary activities structured by rules and secretive social boundaries”. Salen and Zimmerman (2004) define games as “a system in which players engage in an artificial conflict, defined by rules, that results in a quantifiable outcome” (p. 80). Gamification is defined as “the use of game design elements in non-game context” (Deterding et al., 2011. p. 9) and proposed that gamification uses elements of “gamefulness, gameful interaction, and gameful design” with a specific intention in mind (Deterding et al., 2011. p. 10).
Many studies have investigated gamified learning systems in both classroom and online contexts (Landers and Armstrong, 2017; Seaborn and Fels, 2015). Some have used gamification plug-in-like Moodle or Blackboard (de-Marcos et al., 2016; Hew et al., 2016; Huang and Hew, 2015) others have integrated customized applications for gamification (Barata et al., 2017;Kuo and Chuang, 2016; Van Roy and Zaman, 2018). The most studied elements of gamification are leadership boards (de-Marcos et al., 2016; Hanus and Fox, 2015; Hew et al., 2016; Huang and Hew, 2015; Kuo and Chuang, 2016) and bagdes (Barata et al., 2017; de-Marcos et al., 2016; Kuo and Chuang, 2016; Kyewski and Krämer, 2018).
The studies have mixed finding and some find negative impacts (de-Marcos et al., 2016; Domínguez et al., 2013; Hanus and Fox, 2015) while some found that gamification may add knowledge and skill(de-Marcos et al., 2016; Domínguez et al., 2013). Gamification is also seen to impact motivation and engagement levels but also emphasizes choosing the right element of gamification (van Roy and Zaman, 2018; Kuo and Chuang, 2016). Gamification can lead to more positive experiences and cognitive processes that affect player behavior (Huotari and Hamari, 2017). According to previous literature, gamification is defined either by experience aspects, such as gameful experience for satisfying intrinsic motivation Högberg et al. (2019) or by design elements of games (Deterding et al., 2011). Many studies have explored common variables, such as enjoyment and PEOU (Yang et al. (2017), challenges faced during playing and level of interaction (Berger et al., 2018). Others have explored the utilitarian and hedonic aspects of gamified services (Hsu and Chen, 2018).
Bernik et al. (2017) have shown that although many previous studies emphasize the role of gamification in the pedagogy and psychology of learners, hardly any studies have examined the impact of gamification on subjects such as programming. The gamified e-learning module for programming subjects was found to have a more positive effect. Strmecki et al. (2015), through an experimental study, showed that gamified online courses on informatics had greater success in learning, and many gamification elements such as points badges, leadership boards, level, challenges, quest and freedom to fail are applicable in e-learning modules.
Subhash and Cudney (2018) conducted a systematic literature review for gamified learning in higher education and found less research work done in the area of gamified learning in the engineering field. They identified numerous benefits of gamified learning, such as higher levels of engagement, motivation and attitude, leading to better performance. Their study also demonstrated the relevance of points, badges, leadership boards, levels, feedback and graphics in higher education. Saleem et al. (2021) conducted a qualitative study to show that gamification is a widely used tool to create an engaging environment for learning and common gamification elements used are points, badges, levels and leadership boards.
Research on gamification has many theoretical foundations. SDT has been used by many researchers to understand the role of gamification in learning (Barata et al., 2017; Hew et al., 2016; Kyewski and Kramer, 2018). Some researchers examined games from a design perspective (Blythe et al., 2015) and persuasive technology (Fogg, 2002). Other studies have investigated the lens of design principles (Liu et al., 2017) and rewards (Rapp, 2017).
Aparicio et al. (2012) used Ryan and Deci’s (2000) SDT to understand gamification and used the concepts of autonomy, competence and social relatedness. Nicholson (2012) provided a user-centred approach to gamification and emphasized internal or intrinsic motivation rather than extrinsic motivation, as extrinsic motivation can lead to negative outcomes. Sakamoto et al. (2012) suggested gamification based on values which looks into intrinsic motivation and have five values as information and empathetic values based on social engagement, persuasive values that provide a future outlook, economic values considering collection and ownership and ideological values leading to beliefs generated by stories and messages.
Gamification creates a gameful experience by providing gameful services [Huotari and Humari, 2017; Seaborn and Fels, 2015). Landers et al. (2019) said Gameful experience have three important elements as setting right goals, pursuing these goals and self-motivating to achieve it. Many framework and scales are developed for gamification and prominent ones are immersion questionnaire(IQ) having five elements namely real world dissociation, challenge, immersion, control, cognitive and emotional involvement as suggested by Jennett et al. (2008). Brockmyer et al.(2009) proposed game engagement questionnaire(GEQ) having four levels of engagement like immersion, absorption, flow and presence. IJsselsteijn et al. (n.d.) proposed game experience questionnaire(GExpQ) including seven factors namely challenge, tension, competence, flow, sensory and imaginative immersion and negative and positive effect. Park et al. (2019) used Malone’s theory of intrinsic motivation and identified three drivers of motivation: challenge, curiosity and fantasy. They developed GAMESIT and showed that those who used it had better learning outcomes, such as task performance, comprehension and engagement, similar to cognitive and objective efforts].
Eppmann et al. (2018) provided framework for gameful experience called GAMEX having six dimensions like enjoyment, absorption, creative thinking, activation, the absence of negative effects and dominance. More recently GAMEFULQUEST was proposed by Högberg et al. (2019) having three elements related to games: immersion, achievement and social related based on three dimensions: autonomy, competence and social relatedness as suggested by Ryan and Deci (2000) using SDT as a foundation. Many other studies have used GEMEFULQUEST framework and is well conceptualized (Xi and Hamari, 2020; Wolf et al., 2020).
Gamification is also used in other context like recruitment and employer branding. Buil et al. (2020) have shown that attitude of gamification recruitment can be enhanced by using motivation aspect of game elements like competence, autonomy and autonomous motivation along with technology acceptance factors like PEOU and PU. Kashive et al. (2022), through their qualitative study, integrated gamification with employers and mapped the three aspects of SDT theory: competence, autonomy and relatedness of a game element with employee reviews. We propose three elements of gamification: achievement, immersion and social-related affect PU, PEOU and learner attitude.
Hypothesis 1:
Achievement elements of gamification affect attitude toward e-learning among learners.
Achievement elements of gamification affect perceived usefulness (PU) among learners.
Achievement elements of gamification affect perceived ease of use (PEOU) among learners.
Hypothesis 2:
Immersion elements of gamification affect attitude toward e-learning among learners.
immersion elements of gamification affect perceived usefulness (PU) among learners.
immersion elements of gamification affect perceived ease of use (PEOU) among learners.
Hypothesis 3:
Social elements of gamification affect attitude toward e-learning among learners.
social elements of gamification affect perceived usefulness (PU) among learners.
Social elements of gamification affect learners’ use [use (PEOU)].
Immersion moderates the relationship between enjoyment and perceived usefulness (PU) among learners.
Immersion moderates the relation between enjoyment and perceived ease of use (PEU) among learners.
2.5 Personal characteristics and e-learning
Computer anxiety deals with a lack of motivation or intention to use a system because of anxiety because of the use of a computer, which hinders the completion of tasks using a computer (Igbaria and Parasuraman, 1989). Computer anxiety refers to subjective responses or feelings generated while using computers (Sievert et al., 1988). These feelings can include uneasiness, fear and apprehensiveness for any computer use in the present or near future. Satisfaction with e-learning and intention to use e-learning in the future will be impacted by computer anxiety.
When discussing enjoyment in information systems, enjoyment refers to the level of pleasure a person perceives when completing a task using technology, without bothering the outcome of performance (Davis et al., 1992). According to Venkatesh and Speier (2000), enjoyment can be considered intrinsic motivation. Intrinsic motivation is an activity that is motivated by positive feelings and genuine interest in doing so (Deci et al., 1999).
Self-efficacy is determined by the belief that a person has capabilities to use motivation and cognitive resources and decide the course of action to manage a given situation (Wood and Bandura, 1989). According to Bandura (1986), self-efficacy is not related to the skills required to complete a task but to the judgment and belief that one possesses the right skills to accomplish a task. Self-efficacy is the amount of effort, degree of perseverance and path taken to achieve success in a difficult task (Bandura, 1986). Self-efficacy becomes very relevant in system usage to drive intention to use (Hwang and Yi, 2003; Venkatesh and Davis, 2000). In their research Venkatesh (2000) suggested that self-efficacy and enjoyment can be determined by ease of use. Hwang and Yi (2003) have demonstrated that self-efficacy metrics can have a significant effect on enjoyment.
We propose that all three components of personal characteristics: computer anxiety, enjoyment and self-efficacy affects PU and PEOU.
Hypothesis 4:
Computer anxiety of learners affects perceived usefulness (PU) among learners.
Computer anxiety affects perceived ease of use (PEOU) among learners.
Hypothesis 5:
Enjoyment of learner affects attitude toward e-learning among learners.
Enjoyment of learner affects perceived usefulness (PU) among learners.
Enjoyment of learner affects satisfaction toward e-learning among learners.
Enjoyment of learner affects perceived ease of use (PEOU) among learners.
Hypothesis 6:
Learners’ self-efficacy affects their perceived usefulness (PU).
Self-efficacy of learners affects perceived ease of use (PEOU) among learners.
2.6 Intention for using e-learning
Finally, the success of any e-learning module depends on its usage (Esterhuyse et al., 2016; Mohammadi, 2015). Research has investigated factors that improve the experience of using any particular system for future use (Chu and Chen, 2016; Cheung and Vogel, 2013). Many factors, such as the usefulness of technology (Davis, 1989; Jacques et al., 2009), openness to experience, subjective norms (Schepers and Wetzels, 2007) and perception of enjoyment (Wang et al., 2012) are related to behavioral intentions for technology use. Behavioral intention in TAM is a particularly important factor to be considered, as it determines the usage of technology and individual readiness to perform a certain task. Both PEOU and PU indirectly impact technology usage and affect user behavior. Many studies on e-learning have confirmed behavioral intentions and usage relationships have been confirmed by many studies on e-learning (Chang and Tung, 2008; Liu et al., 2010; Park, 2009; Tarhini et al., 2015). Hence, PEOU) and PU) impact attitude and satisfaction, leading to an overall intention to use e-learning.
Hypothesis 7:
Perceived ease of use (PEOU) is positively related to attitude toward e-learning among learners.
Perceived ease of use (PEOU) is positively related to satisfaction with e-learning.
Hypothesis 8:
Perceived usefulness (PU) is positively related to attitudes toward e-learning among learners.
Perceived usefulness (PU) is positively related to satisfaction with e-learning.
Perceived usefulness (PU) is positively related to the intention to use e-learning among learners.
Hypothesis 9:
Satisfaction toward e-learning is positively related to learners’ intention to use e-learning.
Hypothesis 10:
Attitude toward e-learning is positively related to the intention to use e-learning among learners.
3. Research model
The above hypothesis led us to build a conceptual model (Figure 1) based on the TAM, wherein the moderating role of elements of gamification (immersion, achievement and social) can be seen in the relationship between personal characteristics (self-efficacy, enjoyment and computer anxiety) and PEU and PU. These all-effects of PEOU and PU lead to more positive user attitudes and overall satisfaction. This led to a higher intention to use e-learning.
4. Methodology
4.1 Sample and procedure
Data were collected from respondents who had the opportunity to learn from the e-learning modules. The perceptions regarding the use of e-learning concerning aspects of TAM such as PEOU and PU, attitude, satisfaction and intention, along with gamification elements as moderators and personal characteristics and demography as independent variables, were captured through a structured questionnaire.
From India, 150 responses were taken which had 59%students and 41% working professionals. There were 52% female and 48% male patients. The most prominent age group observed was between 21 and 34 years, with 56% were from the junior level, 32% from the middle level and 12% from the senior level.
As shown in Figure 2 structural equation modeling (SEM) using smart partial least squares (PLS) was used for model building, and model paths and hypotheses were tested to examine the causal relationships between the variables (Urbach and Ahlemann, 2010). This approach is variance-based and does not require normalization, as in the case of the covariance-based approach, and is good for a small sample, where theory building is an attempt by research (Hair et al., 2014). It is suggested that “In situations where theory is less developed, the researcher should consider using [partial least squares equation modeling] PLS-SEM) (Hair et al., 2017; p. 40). As the conceptual framework created is complex and completely new variables related to gamification aspects are introduced, PLS-SEM was chosen as the method for analysis. Previous studies have also emphasized the use of PLS-SEM for models with mediation and moderation effects (Henseler and Fassot, 2010).
For self-reporting surveys, common bias methods need to be handled judiciously, and this was done by loading all the indicators on their latent variables and then loading the indicators on the common method latent variable. Herman single-factor analysis showed no item load on a single factor and the model did not converge (Podsakoff et al., 2003). The correlation coefficient was modest to high (r < 0.90 (Wamba et al., 2017). The collinearity test showed that all the VIFs values were less than 3.00, and hence, no collinearity effect was observed (Kock, 2015).
4.2 Measures
PEOU, PU and attitude toward behavioral intention were adapted from the TAM model (Davis, 1989). The PEOU, PU and attitude scales were adapted. User satisfaction and behavioral intention to use have also been derived (Esterhuyse et al., 2016). Personal characteristics such as self-efficacy, enjoyment and computer anxiety were adapted from Esterhuyse et al. (2016). Högberg et al. (2019) measured the gamification aspect of e-learning using GAMEFULQUEST within three important frameworks: immersion-related, achievement-related and social-related.
5. Results and findings
Cronbach’s alpha values above 0.75 are considered acceptable, and any value greater than 0.8 is very good. However, values higher than 0.95 are not necessarily good, which may indicate redundancy (Hulin, Netemeyer, and Cudeck, 2001). Model assessment was performed by testing reliability and validity and values above 0.80 were accepted. Table 1 shows that composite reliability values were greater than 0.8, and the average variance extracted was higher than 0.5 (Hair et al., 2014).
Discriminant validity showed that the square root of AVE values was higher than the inner construct correlations, and all indicator loadings were higher than their respective cross loadings, as shown in Table 2.
After applying nonparametric bootstrapping, the path coefficients can be seen through structural model assessments, as shown in Tables 3 and 4.
Achievement, immersion and social related gamification elements did not impact PEOU and PU, as the p-values were all greater than 0.05 and 0.10 at 95% and 90% sig level(H1b, H1c, H2a, H2b, H2c, H3b and H3c not supported). But it was seen that achievement affected attitude at 95% sig level(p-value 0.027 < 0.05), social elements affected attitude at 90% sig level(p-value 0.097 < 0.10), and H1a and H3a were supported.
When looking at personal characteristics, self-efficacy and computer anxiety were not found to impact PEOU and PU, as the p-values were all greater than 0.05(H4a, H4b, H6a and H6b not supported). Only enjoyment impacted PEOU and PU as p-values were 0.00 and 0.001 < 0.05 at 95% sig level and attitude as p-value was 0.06 < 0.10 at 90% sig level(H5a, H5b and H5d were supported).
Immersion moderated the relationship between enjoyment and PEOU and PU, with p-values of 0.001 and 0.004 < 0.05 at 95% sig level (H3d and H3e are supported). Figures 3 and 4 show the moderating effect of immersion at low, moderate and high values on enjoyment and PEOU and PU.
It was observed (Table 4) that PEOU did not significantly affect both attitude and satisfaction, with p-values of 0.158 and 0.257 > 0.05, at a 95% significance level (H7a and H7b not supported). But PU significantly affected attitude, satisfaction as well and intention with p-values are 0.00, 0.014 and 0.00 < 0.05 at 95% significance level (H8a, H8b and H8c all were supported).
Finally, it was observed that only satisfaction impacted behavioral intention to use e-learning, as p-values were both 0.00 < 0.05, at a 95% significance level (H9 supported), but p-values for the impact of user attitude on behavioral intention for e-learning were 0.333 > 0.05, at the 95% significance level. Consequently, H10 is not supported. Further, the number of hours spent negatively impacted PU as p-values were both 0.003 < 0.05, at the 95% significance level (H11b not supported). The education level of the respondent also impacted PU as p-values were both 0.09 < 0.10 at a 90% significance level (H12b supported).
A mediation analysis was conducted using bootstrapping. It was PU mediated between enjoyment and satisfaction, as well as enjoyment and attitude, as the p-values were 0.006 and 0.003 < 0.05at 95% sig level. PU was also mediated by the number of an hours spent and satisfaction as well as several hours spent and attitude, as the p-values were 0.004 and 0.008 < 0.05 at a 95% sig level.
It was also seen that PU mediated between the immersion and enjoyment combined effect and both satisfaction and attitude as the p-values were 0.009 and 0.012 < 0.05at 95% sig level. As zero does not fall within the bias-corrected upper- and lower-level bootstrapped confidence intervals, the indirect effect is shown in Table 5.
6. Discussion
Gamification can be an important aspect of e-learning, and the current study explored the elements of gamification, such as achievement, immersion and social. The empirical research tested an e-learning model that was integrated with gamification elements. Furthermore, the moderating roles of gamification elements between personal characteristics and PEOU and PU were explored. The study showed that no elements of gamification (achievement, immersion and social) directly impacted the PEOU and PU aspect of TAM. Achievement and social elements affect attitudes toward e-learning. This confirmed Lander’s (2014) study, which also suggested that the gamification aspect, along with instruction content, affects attitudes toward learning for e-learning.
Personal characteristics play an important role in deciding attitude and satisfaction by impacting PEOU and PU as per Esterhuyse et al., (2016). The current study validated the work and found that only enjoyment affected PEOU and PU, while self-efficacy and computer anxiety did not.
As the three elements of gamification did not affect PEOU and PU, the research investigated the moderating effect of each element of gamification between personal characteristics and PEOU and PU. Immersion moderated between enjoyment, PEOU and PU. Furthermore, PU affects attitudes, satisfaction and intention.
6.1 Theoretical implications
The implications of this study are twofold. First, it explores the elements of gamification based on Högberg et al.’s (2019) GAMEFULQUEST, which uses SDT and takes three dimensions of intrinsic motivation (autonomy, competence and social relatedness) as suggested by Ryan and Deci (2000). GAMEFULQUEST is integrated with TAM and empirically tested for moderation and mediation effect.
The achievement aspect talks about the leaderboard and badge/s medals/trophies embedded in games to engage the learner, while social elements talk about the learner’s social support and messages, blogs, chat and connection to social networks connecting with other learners. It was observed that both achievement and social elements of games impact attitudes toward e-learning. Attitude plays an important role in any learning and can be built into e-learning by continuously motivating the learner by setting goals and achieving them and positively reinforcing the behavior by rewarding it. These findings are in line with the previous research of Strmecki et al. (2015), who also suggested that all elements of gamification, that is, points, badges, leadership boards, customization, levels, challenges and feedback, improve the success of e-learning courses. Saleem et al. (2022) suggested that common gamification elements used are points, badges, levels and leadership boards. Gamification elements such as achievement, immersion and social elements have been explored in many studies, but hardly any studies have examined the moderating effect of these elements of gamification on personal characteristics and users’ perceptions regarding ease of use and usefulness.
While immersion did not affect PEOU and PU but did moderate the relation between enjoyment and PEOU and PU. This shows that the immersion aspect of game elements can be used to increase enjoyment, leading to higher ease of use and usefulness of an e-learning module. The immersion aspect is how much you create customization/personalization with avatar/virtual identity/profile and use narrative/story in games during learning. A higher level of immersion in the game will lead to better PEOU and PU of the e-learning module. PU also affects all three components of the TAM: attitude, satisfaction and intention to learn through e-learning. Previous studies have given more focus on achievement element of gamification i.e. leadership boards (de-Marcos et al., 2016; Hanus and Fox, 2015; Hew et al., 2016; Huang and Hew, 2015; Saleem et al. (2022) and badges (Barata et al., 2017; Kuo and Chuang, 2016; Kyewski and Krämer, 2018; Saleem et al., 2022), but downplayed the role of immersion which can be improved by providing customization and personalization through game elements.
6.2 Managerial implication
The current research provides academicians and practitioners with a platform to understand how elements of games can be integrated into the e-learning module based on the TAM. All three elements of gamification are critical for a higher perception of the usefulness of e-learning platforms and for creating the correct attitudes and satisfaction levels.
The correct element of immersion can be used to create better user perceptions, especially with e-learning modules. Immersion may vary across different groups of learners and understanding the level of customization and personalization by understanding users’ personalities and personal characteristics. Kashive et al. (2020) in their study used an artificial intelligence-enabled e-learning model and found that the two aspects of personal learning environment and personalized learning profile (PLP) were relevant. This emphasizes the importance of creating an enjoyable environment and personalizing e-learning platforms. In the current study, the immersion aspect of gamification is taking care of a PLP to enhance the e-learning experience.
The academic institute that is designing their content and planning to deliver through the online mode can look deeper into the immersion element of gamification and designers of games must integrate all aspects of gamification and see how they can enhance the user experience by innovating the game element. Bernik et al. (2017) found that gamified e-learning modules for programming subjects have more positive effects than non-gamified modules. Subhash and Cudney (2018) pointed out the lack of research in the gamified engineering field. Gamified e-learning courses can be very relevant in courses such as programming, mathematics and engineering, where the level of immersion is very much needed to keep the learner engaged in learning, especially in the remote mode, where self-motivation plays an important role.
In the age of COVID-19, there is a need for both academicians and practitioners to put resources to improve the overall experience of learners and to keep learners motivated to be continuously engaged in online learning. Gamification elements can be applied to blended learning environments that provide the advantages of both online and offline learning. This would be only one step forward in the journey of integrating games into a non-game environment. Gamified e-learning can be applied to diverse courses and pedagogy can be enhanced to improve the learning experience.
7. Limitation and future scope
As the study is conducted with a small sample to test the newly created hypothesis and theory building using Smart PLS, more empirical studies can be undertaken with larger samples. This study focused on the three elements of gamification in general, but future studies can examine the sub-dimension of each element, namely immersion, which works better in an e-learning environment. Finally, there may be some aspects of these elements that can be generalized across all cultures, but some may be specific to one culture. Hence, future studies should examine the culture-specific factors for each gamification element.
8. Conclusion
This study provides insights into how gamification can be integrated into e-learning modules to improve learners’ perception of PU and PEOU. It also observes the impact of the gamification aspect on the attitude and satisfaction of a learner, which ultimately impacts the intention to use e-learning. The study used the previous framework of GAMEFULQUEST, as suggested by Högberg et al. (2019), which categorized gamification into achievement-related, immersion-related and social-related based on Aparicio et al.’s (2012) framework using SDT. The role of gamification in e-learning is enormous and needs to be explored by practitioners and academics to enhance the overall experience of e-learning.
Figures
Figure 1.
Conceptual model for gamification of e-learning
Figure 2.
PLS-SEM diagram
Figure 3.
Moderating effect of immersion on enjoyment and perceived ease of use (PEOU)
Figure 4.
Moderating effect of immersion on enjoyment and perceived usefulness (PU)
Reliability values for the model
| Construct | Cronbach’s alpha | rho_A | Composite reliability | Average variance extracted (AVE) |
|---|---|---|---|---|
| Achievement-related | 0.89 | 0.9 | 0.916 | 0.648 |
| Attitude | 0.755 | 0.759 | 0.86 | 0.672 |
| Computer-anxiety | 0.835 | 0.882 | 0.886 | 0.662 |
| Enjoyment | 0.718 | 0.827 | 0.833 | 0.627 |
| Immersion-related | 0.874 | 0.9 | 0.913 | 0.725 |
| Intention | 0.834 | 0.862 | 0.899 | 0.75 |
| Perceived usefulness | 0.85 | 0.855 | 0.899 | 0.691 |
| Satisfaction | 0.83 | 0.845 | 0.899 | 0.748 |
| Self-Efficacy | 0.756 | 0.763 | 0.844 | 0.576 |
| Social -related | 0.76 | 0.808 | 0.857 | 0.669 |
| perceived ease of use | 0.861 | 0.868 | 0.905 | 0.705 |
Discriminate validity
| Construct | Achievement- related |
Attitude | Computer- anxiety |
Enjoyment | Immersion- related |
Intention | Perceived usefulness |
Satisfaction | Self-efficacy | Social- related |
perceived ease of use |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Achievement-related | 0.805 | ||||||||||
| Attitude | 0.168 | 0.82 | |||||||||
| Computer-anxiety | 0.055 | 0.113 | 0.814 | ||||||||
| Enjoyment | 0.47 | 0.441 | 0.082 | 0.792 | |||||||
| Immersion-related | 0.841 | 0.197 | 0.088 | 0.438 | 0.852 | ||||||
| Intention | 0.158 | 0.752 | 0.018 | 0.435 | 0.22 | 0.866 | |||||
| Perceived usefulness | 0.289 | 0.791 | −0.038 | 0.439 | 0.279 | 0.731 | 0.831 | ||||
| Satisfaction | 0.121 | 0.782 | 0.097 | 0.33 | 0.165 | 0.801 | 0.692 | 0.865 | |||
| Self-Efficacy | 0.324 | 0.422 | 0.04 | 0.663 | 0.389 | 0.285 | 0.318 | 0.349 | 0.759 | ||
| Social-related | 0.775 | 0.198 | 0.08 | 0.332 | 0.666 | 0.126 | 0.23 | 0.105 | 0.319 | 0.818 | |
| Perceived ease of use | 0.378 | 0.64 | 0.176 | 0.594 | 0.371 | 0.514 | 0.671 | 0.549 | 0.508 | 0.284 | 0.84 |
Path coefficient direct
| Relationships | Original sample (O) | T statistics (|O/STDEV|) | p-values | Hypothesis |
|---|---|---|---|---|
| H1a. Achievement-related → Attitude | −0.363 | 2.209 | 0.027 | Supported |
| H1b. Achievement-related → Perceived usefulness | 0.164 | 0.711 | 0.477 | Not supported |
| H1c. Achievement-related → perceived ease of use | 0.177 | 0.832 | 0.406 | Not supported |
| H2a. Immersion-related → Attitude | 0.061 | 0.452 | 0.652 | Not supported |
| H2b. Immersion-related → Perceived usefulness | −0.025 | 0.117 | 0.907 | Not supported |
| H2c. Immersion-related → perceived ease of use | −0.017 | 0.088 | 0.93 | Not supported |
| H3a. Social-related → Attitude | 0.184 | 1.662 | 0.097 | Supported |
| H3b. Social-related → Perceived usefulness | −0.025 | 0.17 | 0.865 | Not supported |
| H3c. Social-related → perceived ease of use | −0.05 | 0.348 | 0.728 | Not supported |
| H3d. imm_Enj → Perceived usefulness | 0.318 | 2.924 | 0.004 | Supported |
| H3e. immer_Enj → perceived ease of use | 0.276 | 3.357 | 0.001 | Supported |
| H4a. Computer anxiety → Perceived usefulness | −0.092 | 0.876 | 0.381 | Not supported |
| H4b. Computer anxiety → perceived ease of use | 0.077 | 0.928 | 0.354 | Not supported |
| H5a. Enjoyment → Attitude | 0.143 | 1.88 | 0.06 | Supported |
| H5b. Enjoyment → Perceived usefulness | 0.402 | 3.261 | 0.001 | Supported |
| H5c. Enjoyment → Satisfaction | −0.01 | 0.12 | 0.904 | Not supported |
| H5d. Enjoyment → perceived ease of use | 0.451 | 3.725 | 0 | Supported |
| H6a. Self-Efficacy → Perceived usefulness | −0.044 | 0.364 | 0.716 | Not supported |
| H6b. Self-Efficacy → perceived ease of use | 0.147 | 1.162 | 0.245 | Not supported |
Path coefficient direct
| Relationships | Original sample (O) | T statistics (|O/STDEV|) | p-values | Hypothesis |
|---|---|---|---|---|
| H7a. perceived ease of use → Attitude | 0.155 | 1.413 | 0.158 | Not supported |
| H7b. perceived ease of use → Satisfaction | 0.156 | 1.134 | 0.257 | Not supported |
| H8a. Perceived usefulness → Attitude | 0.7 | 7.547 | 0 | Supported |
| H8b. Perceived usefulness → Satisfaction | 0.593 | 5.346 | 0 | Supported |
| H8c. Perceived usefulness → Intention | 0.272 | 2.459 | 0.014 | Supported |
| H9. Satisfaction → Intention | 0.502 | 3.821 | 0 | Supported |
| H10. Attitude → Intention | 0.142 | 0.968 | 0.333 | Not supported |
| H11a. No. of hours spent → Attitude | 0.096 | 1.414 | 0.158 | Not supported |
| H11b. No. of hours spent → Perceived usefulness | −0.272 | 3.007 | 0.003 | Not supported |
| H12a. No. of hours spent → Satisfaction | 0.008 | 0.089 | 0.929 | Not supported |
| H12b. Education → Perceived usefulness | 0.164 | 1.699 | 0.09 | Supported |
Path coefficient indirect effect
| Mediation effect | Original Sample (O) | T Statistics (|O/STDEV|) | p-values | Bias | 2.50% | 97.50% | Mediation |
|---|---|---|---|---|---|---|---|
| Enjoyment → Perceived usefulness → Attitude | 0.281 | 2.979 | 0.003 | −0.014 | 0.116 | 0.495 | Significant |
| Enjoyment → Perceived usefulness → Satisfaction | 0.238 | 2.775 | 0.006 | −0.011 | 0.097 | 0.433 | Significant |
| Enjoyment → Perceived usefulness → Satisfaction → Intention | 0.12 | 2.038 | 0.042 | −0.004 | 0.035 | 0.277 | Significant |
| imm_Enj → Perceived usefulness → Attitude | 0.223 | 2.627 | 0.009 | 0.005 | 0.038 | 0.363 | Significant |
| imm_Enj → Perceived usefulness → Satisfaction | 0.189 | 2.503 | 0.012 | 0.004 | 0.048 | 0.33 | Significant |
| Perceived usefulness → Satisfaction → Intention | 0.298 | 2.673 | 0.008 | 0.002 | 0.109 | 0.549 | Significant |
| no of hrs spent → Perceived usefulness → Attitude | −0.19 | 2.877 | 0.004 | 0.01 | −0.338 | −0.072 | Significant |
| no of hrs spent → Perceived usefulness → Satisfaction | −0.161 | 2.648 | 0.008 | 0.007 | −0.3 | −0.056 | Significant |
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Further reading
Azjen, I. (1980), Understanding Attitudes and Predicting Social Behaviour, Englewood Cliffs.
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Davis, F.D. (1989), “Perceived usefulness, perceived ease of use, and user acceptance of information technology”, MIS Quarterly, Vol. 13 No. 3, pp. 319-340, doi: 10.2307/249008.
Deci, E.L. and Ryan, R.M. (2000), “The ‘what’ and ‘why’ of goal puSuits human needs and the self-detemination of behavior”, Psychological Inquiry, Vol. 11 No. 4, pp. 227-268, doi: 10.1207/S15327965pli1104_01.
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Corresponding author
About the authors
Neerja Kashive is currently working as Associate Dean-Research at VES’s Business School. She has completed her PhD from Mumbai University in area of employer branding with the title, “Impact of Employer and Employee branding on Organizational attractiveness and Firm performance in IT Sector in Mumbai.” Her area of research focusses on employer branding, internal branding, brand loyalty, brand citizenship behavior, artificial intelligence, gamification and technology in HR. She is a certified HR Analytics professional and conducts workshop and seminar.
Sayali Mohite is a student of PGDM-HR in VES Business School. She has her interest area in gamification, e-learning, HR technology and artificial intelligence in HR. She has presented many research papers in this topic in different research conferences.