Intention for enhancing metaverse-based learning using gamification among university students: a study using Delphi and structural equation modelling approaches

Das, Tarinmoy et al. Article Intention for enhancing metaverse-based learning using gamification among university students: a study using Delphi and structural equation modelling approaches Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Das, Tarinmoy et al. (2024) : Intention for enhancing metaverse-based learning using gamification among university students: a study using Delphi and structural equation modelling approaches, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-23, https://doi.org/10.1080/23311975.2024.2380016 This Version is available at: https://hdl.handle.net/10419/326441 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Business & Management ISSN: 2331-1975 (Online) Journal homepage: www.tandfonline.com/journals/oabm20 Intention for enhancing metaverse-based learning using gamification among university students: a study using Delphi and structural equation modelling approaches Tarinmoy Das, Sivaji Ganesh Kondamudi, Mohammad Dawood Babakerkhell, Debajyoti Pal, Rita Roy & Suree Funilkul To cite this article: Tarinmoy Das, Sivaji Ganesh Kondamudi, Mohammad Dawood Babakerkhell, Debajyoti Pal, Rita Roy & Suree Funilkul (2024) Intention for enhancing metaverse-based learning using gamification among university students: a study using Delphi and structural equation modelling approaches, Cogent Business & Management, 11:1, 2380016, DOI: 10.1080/23311975.2024.2380016 To link to this article: https://doi.org/10.1080/23311975.2024.2380016 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 26 Jul 2024. Submit your article to this journal Article views: 1644 View related articles View Crossmark data Citing articles: 3 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 InformatIon & technology management | research artIcle Cogent Business & ManageMent 2024, VoL. 11, no. 1, 2380016 Intention for enhancing metaverse-based learning using gamification among university students: a study using Delphi and structural equation modelling approaches tarinmoy Dasa , sivaji ganesh Kondamudia , mohammad Dawood Babakerkhellb, Debajyoti Palc, rita royd and suree funilkulc agitaM school of Business, gitaM (Deemed to be university), Visakhapatnam, andhra Pradesh, india; bDepartment of information technology, shaikh Zayed university Khost, Khost, afghanistan; cschool of information technology, King Mongkut’s university of technology thonburi, Bangkok, thailand; dDepartment of Computer science and engineering, gitaM institute of technology, gitaM (Deemed to be university), Visakhapatnam, andhra Pradesh, india ABSTRACT the study investigates the influence of gamification on the education sector, focusing on increasing metaverse-based learning among university students. Using technology for student engagement, retention, and acquisition could give the Indian education system a competitive edge, especially in a growing country like India. the education system may also have an effect if it provides high-quality services. this study proposed 12 hypotheses. first, the Delphi method was performed with the help of academic and industrial experts. subsequently, a survey of university students was conducted. twelve hypotheses were tested, out of which six were accepted. no significant relationships existed between trust, attitude and perceived ease of use. neither discomfort nor insecurity had any significant association with perceived usefulness and ease of use. thus, six hypotheses were rejected. gamification can boost metaverse-based learning. University students benefited from this research. simultaneously, educators can motivate students to learn by using novel approaches. the present investigation suggests exploring all the potential applications of gamification that were not covered in previous studies. this study focused solely on the educational sector. this expands the potential of performing additional research in other unexplored sectors. this research was carried out only in a developing nation, India. so, research can also be conducted in other developing nations. Demographic details like age, location, and occupation could be incorporated into future studies. 1. Introduction the impact of emerging technologies on technological growth has changed several sectors in this digitally driven era. Virtual reality (Vr) is widely regarded as a link to the metaverse (rospigliosi, 2022). high levels of immersion, adaptability, and fitness are features of the metaverse (andembubtob et al., 2023; Kaplan et al., 2021; martín-gutiérrez et al., 2017). the term metaverse designates a generated environment where individuals can live according to the creator’s laws. Working in groups on a project, having discussions on various topics, and learning from successes or mistakes can help individuals interact with each other on metaverse platforms (hwang & chien, 2022). research on gamification (gam) has grown over the past few years. It began with the evolution of the internet. User experience is improved by including joy, excitement, inspiration, and delight. to increase user engagement, the gam adds game-like elements to non-game contexts. gamification has been employed in finance, education, healthcare, wellness, banking, retail, marketing, and employee training (Bitrián et al., 2023). most of the sectors have witnessed growth in the © 2024 the author(s). Published by informa uK Limited, trading as taylor & Francis group CONTACT Mohammad Dawood Babakerkhell dawood[email protected] Department of information technology, shaikh Zayed university Khost, Khost 2558, afghanistan. https://doi.org/10.1080/23311975.2024.2380016 this is an open access article distributed under the terms of the Creative Commons attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent. ARTICLE HISTORY received 5 march 2024 revised 26 may 2024 accepted 2 July 2024 SUBJECTS Business, management and accounting; management of It; technology KEYWORDS gamification; metaverse-based learning; university students; Delphi method; developing nations REVIEWING EDITOR hui shan loh, singapore University of social sciences, singapore 2 t. Das etal. implementation of emerging technologies like gamification. research in the education sector is at a nascent stage. thus, this study is being conducted with two aspects of the implementation of gamification in the education sector. first, we want to study the influence or impact of gamification upon implementation in the education sector. second, if gamification and metaverse are combined to enhance the learning process, then the extent to which the quality of learning can improve. this research is important because metaverse-based learning, along with gamification, has the prohibition of offering many advantages, including experimental learning, activity-based learning, interaction-based learning, real-life scenario-based learning, and studying anytime, anywhere, at the learner’s convenience. learners use digital identities, such as avatars, for their learning process. metaverse-based education can adapt if there is any change in the learning process. educators can provide engaging activities to help the students learn and acquire valuable information. metaverse-based learning also helps the students improve their skills and learn new skillsets. according to Kasirye and Wok (2023) research, students favour web-based video learning platforms to acquire knowledge. Q. Wang et al. (2023) discovered that not all types of smartphone use were connected to technology-induced stress. Because of the game element, emotional involvement, and entertainment, yoon and mecca (2022) web documentaries have been identified as alternatives to online education approaches. said (2023) found that mBl has specific challenges, which include universal access, immersive design, security, and control. (camacho-sánchez et al., 2023) identified game-based learning and gam as major learning strategies because they motivated students and helped them perform academically. rohan et al. (2023) identified four essential psychological requirements–autonomy, achievement, affiliation, and dominance that can influence metaverse usage in education. a massive open online course (mooc) platform’s certificate-issuing feature, which intends to provide online education, is a gamification strategy that can help increase completion rates and user engagement (De notaris et al., 2021). the dynamism of gamification makes it highly recommended for use in the educational process (yildirim, 2017). the metaverse’s social sustainability was significantly predicted by personality traits (arpaci et al., 2022). abu rbeian et al. (2022) proposed that self-efficacy, curiosity, and pleasure all favour perceived usefulness (PU). Based on the research gaps identified in the literature, two research questions were framed as mentioned below. RQ1. how much will gamification improve the quality of metaverse-based learning? RQ2. how will gamification help in using metaverse in the education sector? the authors believe that the gam literature requires theoretical knowledge. Besides education, the metaverse has applications in numerous areas, including healthcare, marketing, gaming, and socialization (Park & Kim, 2022; rohan etal., 2020, 2023). If metaverse features are used in teaching, students’ engagement can be improved (Park & Kim, 2022; rohan et al., 2021, 2023). the emphasis on education has shifted to online learning due to coVID-19, resulting in the growth of the metaverse. hence, this study examined gam’s mediating effect on boosting mBl among university students. although extensive gam research has been conducted in the educational sector, the extent to which it might influence the education sector, with an emphasis on improving mBl, requires more extensive research. liu etal. (2014) claimed that PeoU was related to the trust. the technology acceptance model (tam) was modified to incorporate the trust variable to predict PeoU. Kansal (2016) found that incorporating trust into tam aided in predicting PU. trust affects user attitudes. students with high self-efficacy exhibited a positive attitude towards utilizing new technologies (Jayashankar et al., 2018). this implies that when utilizing these emerging technologies, users possess the self-belief to carry out their tasks effectively, referred to as PU. additionally, if these technologies are user-friendly, also known as PeoU, their usefulness can be linked to users’ attitudes (al-adwan et al., 2023; malaquias & silva, 2020; Purohit & arora, 2021). gam improves students’ attitudes towards learning, giving them a sense of control and participation at their convenience (Park etal., 2021). Users experienced less discomfort when using useful technologies (chen, 2022). User-friendliness facilitates the ease of use of technology and lowers user discomfort (li & yu, 2022). a lack of confidence in technology can be defined as insecurity, doubting its effectiveness and efficiency. Users can accept technological services when their Ins are low (Jeong & Kim, 2023). subjective norms significantly affected higher education acceptance and attitudes towards cogent BUsIness & management 3 metaverse technology (salman et al., 2023). roy et al. (2022) stated that perceived behavioural control influences academic help-seeking intentions and behaviours. thus, PBc will assist students in adopting mBl. gam research has grown substantially in the last decade, predominantly in developed countries, but has not been done extensively in developing countries. the enhancement of mBl motivated the authors to understand the need to comprehend and analyse the mediating role of gam in the education sector in emerging nations. existing literature based on metaverse and gam has found that learning opportunities increase, engaging the involvement of teachers and students. the adoption factors for this study were acquired from the literature. the theory of Planned Behavior (tPB), tam, and technology readiness Index (trI) were the three theories used in this study. accordingly, a framework was developed using the related independent, dependent, and mediating variables. there is additional discussion of the study’s theoretical and practical aspects. this research offers new perspectives in addition to the existing literature. the paper is organized as follows. section 2 reviews relevant literature on this topic. section 3 outlines the development of the hypothesis. section 4 summarizes the research methodology. the data analysis in this study is presented in section 5. the discussion and implications of this study are presented in section 6. the conclusions, limitations and future research directions are discussed in section 7. 2. Literature review 2.1. Theoretical underpinning tam, developed by Davis (1985), is an information systems theory. Davis (1985) proposed three variables to explain user motivation: PU, PeoU, and attitude (att) towards using the system. We adapted psychology-based theories of reasonable action (tra) and tPB to the context of user acceptance of an information system. hubona and cheney (1994) contrasted tam with tPB and concluded that tam provides a minor empirical advantage and is a much simpler, easier to apply, and more powerful model for explaining users’ technological acceptance (lee etal., 2003). masrom (2007) studied students’ use of tam in adopting e-learning technology and found that perceived usefulness was the most important variable in determining students’ intent to adopt e-learning technology. In recent years, teaching-learning practices have incorporated emerging technologies that have transformed the education sector (Dwivedi et al., 2022). synchronous learning between instructors and students is made possible by meeting tools like Zoom and Webex, which remove geographical barriers. teachers have utilised these technologies as a suitable substitute for conventional teaching techniques (Iivari et al., 2020). according to Pappas and giannakos (2021), creating virtual learning techniques will help accomplish learning objectives and maintain student engagement using modern technologies, but it will also be challenging. two features that differentiate the virtual world from other environments are the incorporation of avatars and the capacity to work with other avatars simultaneously (Badilla Quintana & meza fernández, 2015). existing research studies found that tPB was primarily used to understand an individual’s behaviour. one of the constructs of tPB is Perceived Behavioural control (PBc), which, according to researchers, is included in the extended tra. thus, sommer (2011) examined and stated in his research that PBc directly influences behaviour. Parker and manstead (1995) finally concluded that PBc, att and subjective norms (sn) are the constructs of tPB. Pappas and giannakos (2021) highlighted the necessity of replicating in-person learning experiences to address the issues facing higher education systems. applying the metaverse to higher education enables more user-environment interaction than possible with previous technologies, enhances emotional experiences, and more closely resembles the traditional class environment. according to yang et al. (2022; Q. yang et al., 2022), integrating the metaverse into higher education will enable successful communication between teachers and students in a virtual environment, replicating the emotional elements of the actual world. the 36-item trI scale assesses a person’s tendency to adopt and employ cutting-edge technologies. global mobile subscriptions increased from 2.3 billion in 2005 to 6.8 billion in 2013. the four dimensions of technology readiness are (a) optimism, (b) innovativeness, (c) discomfort, and (d) insecurity. of the four dimensions, optimism and innovativeness are motivators that enhance trI, whereas discomfort and 4 t. Das etal. Ins are inhibitors that hinder it (Parasuraman & colby, 2015). Innovativeness is related to technological readiness because both entail a person’s willingness to accept a new idea (Victorino et al., 2009). trI represents a set of attitudes about technology but does not predict an individual’s capability to use it (andaleeb etal., 2010). to improve the generalizability and applicability of the trI, lin and hsieh (2012) duplicated, refined, and cross-validated it across contexts and cultures. In numerous educational contexts, the metaverse has grown dramatically as a result of the emergence of immersive technologies like mixed reality (mr), extended reality (Xr), augmented reality (ar), and Virtual reality (Vr) (Zhang et al., 2022). Iot devices make learning more interactive and interesting. aI, big data, and text mining can be included in the metaverse platforms to analyze the learners’ performance (hwang & chien, 2022). as related technologies advance, the metaverse is anticipated to evolve and mature simultaneously. this will eventually lead to a more widespread metaverse application in learning environments. these implementations may shed light on the technology developments needed to replicate the learning process in the metaverse accurately. 2.2. Gamification in the world of metaverse-based learning the gam in education has the potential to improve students’ learning motivation. Using game design features in a learning environment is known as the gamification of education and is a method of inspiring students to learn. a metaverse-simulated environment generates the exchange of information through communication among individuals. Incorporation of the virtual world with that of the physical world using the metaverse platform allows people to be involved with each other because it is a 3D atmosphere where several users can have acquaintance with each other. metaverse-based education might have great potential for future research, the evidence of which has been found in previous studies. metaverse assists in communication among students in real and virtual arenas (said, 2023). education was one of the methods that satisfied students and instructors and was considered an alternative method. a metaverse platform was used to provide this service. majuri et al. (2018) stated that process-oriented elements and performance-based growth using gam in the learning process had been used more frequently. comparatively, the use of immersion-oriented affordances is considerably less common. Positively oriented results have been reported in the existing literature. Previous studies have also provided evidence that research outcomes mainly focus on quantitative methods. 2.3. Research gaps Web-based games have been seen to be readily available to enhance users’ digital literacy about library services. the researchers have little influence over the long-term viability of selected games because they are online, web-based, and independent platforms (t r & gala, 2023). Both PeoU and PU, as well as attitudes towards e-learning, were influenced by enjoyment. social factors and accomplishments impacted attitude. these ultimately affect attitude and satisfaction, which increases the intention to use e-learning systems (Kashive & mohite, 2023). several research difficulties in developing gamification tactics for online learning environments have been made possible by Industry 4.0 (oliveira et al., 2023). research has shown that gamified systems can effectively encourage students in education, but it has also revealed that different students are motivated differently (chapman etal., 2023). hence, it can be concluded that if the students’ motivation varies, the enhancement of the learning process on the metaverse platform using gamification will also vary. the benefits of gamification and learning motivation are mediated by students’ psychological requirements, as demonstrated in previous studies utilising the self-determination theory. students’ intrinsic motivation is influenced by several factors, including social, achievement, and immersion qualities (luarn et al., 2023). students’ perceptions of gamification, interactions with instructors, and other learners in moocs had a favourable impact on their perceived gamification, instructional presence, and social presence (cheng, 2023). Different gamified learning situations should be used to explore a broad spectrum of emotions. this will facilitate comprehension of the feelings that students experience before, during, and after their gamified learning process (an, 2023). It can be inferred that social factors are one of the most important factors which motivate students in the learning process. however, the emotional aspects cogent BUsIness & management 5 of the students should also be assessed to find out whether their learning process on metaverse platforms using gamification has increased or not. 3. Hypothesis development Prior studies on gamification in various sectors have used the tam, self-Determination theory (sDt), tPB, UtaUt, UtaUt2, and trI. the trI, tam, and tPB models were proposed for this research. this study uses only DIs and Ins, also called inhibitors, among the four elements of technology readiness. tam defines PU as the user’s belief that technology usage will improve their performance and PeoU as the user’s belief that utilizing technology will not need extra effort. these are the two key elements influencing users’ attitudes towards various technologies. PBc and behavioural intent were depicted as behavioural functions in the tPB. therefore, att, sn, and PBc are the three determinants of behavioural intention. the variables considered for this study were DIs, Ins, PU, PeoU, trust (trU), att, sn, and PBc, with gam mediating the intention to adopt mBl. the conceptual framework is illustrated in figure 1. the research hypothesis was developed based on this framework. 3.1. Perceived ease of use (PEoU) malaquias and silva (2020) examined and stated that if technology is user-friendly, then the usefulness of enhancement of learning through metaverse can be related. hence, the PeoU construct plays an important role in technology adoption. the ease of use of technology was measured by PeoU (Purohit & arora, 2021). their research study examined the potential for a considerable impact of PeoU on customers’ usage intentions of a technology. Instructors use the gamified moodle platform to provide training, but it will only be effective if it is simple (Vanduhe et al., 2020). certain game features, such as challenges and storyline, made employees happier; nevertheless, feedback and explicit goals had no discernible effects. additionally, they attested that workers’ satisfaction with the gamified e-training raised their assessments of the programs’ usability and convenience, improving their attitudes towards gamified e-training programs (Bitrián et al., 2023). Pal and Patra (2021) found that students were compelled to utilize digital tools for educational purposes. these findings indicate that video-based learning aligns favourably with students’ perspectives and practical system utilization. enjoyment had an impact on PeoU towards e-learning. the intention to use e-learning systems is increased as they eventually impact satisfaction (Kashive & mohite, 2023). Figure 1. Conceptual framework. 6 t. Das etal. 3.2. Perceived usefulness (PU) Purohit and arora (2021) stated that PU is one’s belief that employing technology will assist them in better executing their work. as predicted by the tam, PU anticipates people’s attitudes towards a particular technology (Davis, 1989). according to Davis (1989), in conjunction with the PeoU, the PU determines a user’s attitude towards and intention to use a given information system. although explicit objectives and feedback had no discernible effect, certain game features, such as obstacles and stories, boosted employees’ satisfaction. they also verified that employees’ positive attitudes towards gamified e-training systems were improved by their enjoyment of e-training, which raised their opinions on the training’s usability and convenience (Bitrián et al., 2023). for an educational innovation to be ultimately accepted, it is imperative to comprehend the elements that go into a gamified learning tool’s perceived usefulness (luo, 2023). 3.3. Trust (TRU) li and yu (2022) reported in their study that the metaverse should not be widely used in education (Qin, 2022; younas mughal et al., 2022; yue, 2022) because today’s technology lacks understanding and trU. User faith increases through blockchain technology and explainable aI (ali et al., 2023). the use of the metaverse idea in education was assessed in light of the technological environment. trust is crucial in adopting technology because consumers must have confidence in their usage, which can only be achieved through sufficient awareness and knowledge. the banking sector can build deep relationships with consumers at every point of the customer journey. regulating compliance, protecting data privacy, and security are just a few of the risks that come with these advantages (ooi etal., 2023). students from various university majors who trust technology were more likely to intend to continue their education on digital platforms (mou et al., 2023). Perceived risk and performance expectancy completely mediate between consumer trust in technology and behavioural intention (X. Wei et al., 2024). 3.4. Attitude (ATT) PU and PeoU affect people’s attitudes. attitude can be described as the extent to which an individual holds a positive or negative opinion towards a particular technology. attitude encompasses both excellent and negative sentiments or beliefs towards a particular technology. attitude is perceived as a mental inclination that depends on the specific technology. User acceptance of technology refers to an individual’s attitude towards their effective reaction, encompassing delight, joy, and pleasure when employing innovation. students who possess a high level of self-confidence are significantly more likely to embrace novel approaches to learning and have a favourable outlook towards utilising cutting-edge technologies, including mBl platforms (al-adwan et al., 2023). Various quantitative and qualitative approaches have primarily been used to analyze user attitudes towards It development (said, 2023). gam improves the player’s attitude towards learning, allowing them to participate and control the situation at their convenience (Park et al., 2021). In metaverse-based classrooms, students have a higher level of immersion, better attitudes, and enjoyment of learning than traditional learning approaches (lee & Jo, 2023). social integration benefits positively impact people’s opinions about gamification services (hamari & Koivisto, 2015). tourists’ attitudes, PU and PeoU, improve when they visit a tourist destination (h. liu & Park, 2024). nguyen and nguyen (2024) stated the robustness of the regression model relating individual attitude to the desire for digital entrepreneurship. anjum et al. (2023) have proposed that an entrepreneurial temperament and a perceived creative propensity positively influence entrepreneurial intention. hence, the below-mentioned four hypotheses are propositioned: H1: PeoU will have a positive impact on the user’s trU. H2: PU will positively be associated with the user’s trU. H3: PeoU will positively be associated with the user’s att. H4: PU will positively be associated with the user’s att. cogent BUsIness & management 7 3.5. Discomfort (DIS) a certain amount of digital literacy is necessary to keep students and teachers engaged in their education and prepare them for the challenges of metaverse-based learning (li & yu, 2022). edu-metaverse challenges include issues with engagement, content production, game addiction, privacy, and ethics (chen, 2022). the classroom experience was not replicated by the video-conferencing software platforms used in education. a virtual physical blended classroom has been suggested to connect students and teachers (Wang et al., 2022). mukherjee et al. (2021) found that employees were uncomfortable using service robots in hotels that offered contactless services. this is an excellent alternative because it prevents germs from spreading from person to person. students are concerned that metaverse-based learning may result in a decline in their traditional offline study practices. Integrating metaverse-based learning in education is swiftly advancing and providing students with the advantages of executing repeated activities with exceptional accuracy, adaptability, and intense human-robot interaction. these gadgets provide diverse qualities, providing students with captivating activities and genuine experiences and creating stimulating and attractive learning environments. students harbouring a pessimistic disposition towards innovation tend to cultivate scepticism and contrary perspectives. one of trI 2.0’s four components, discomfort, is viewed as negative (Parasuraman & colby, 2015; rafdinal & senalasari, 2021). trI has been used in prior studies to assess the adoption of new technologies in mobile payment apps (humbani & Wiese, 2019; rafdinal & senalasari, 2021), educational apps (Warden etal., 2022), travel apps (Jarrar et al., 2020), and virtual tourism (c. yang et al., 2022). according to rafdinal and senalasari (2021), it is reasonable to infer that technological discomfort stems from an overabundance of sophistication and is not for everyone. 3.6. Insecurity (INS) a lack of trust in technology might be characterized as insecurity because it raises questions about how well it will work and foresees possible undesirable results. With a high level of Ins, users are reluctant to accept technological services (Jeong & Kim, 2023). Ins can develop in young adults because they spend much time on social media and playing 3D immersive games (Usmani et al., 2022). conditional privacy is vital for metaverse-based services to regulate harmful users and avoid fraud and Ins (far & rad, 2022). aI technologies are increasingly used in education. this allows teachers to swiftly address student questions, remove laborious, repetitive work, and improve personalized and adaptable learning (roy et al., 2022). according to lai and lee (2020 Parasuraman, 2000), insecurity is a barrier to technology readiness and typically signifies a reluctance to accept the technology. Insecurity will have different effects based on the time of day and the technology employed. 3.7. Subjective norms (SN) Individual intentions are influenced by sn, which in turn influences an individual’s actual behaviour when adopting new technology (İbili et al., 2023). the adoption of and attitude toward metaverse technology in higher education are significantly impacted by sn (salman et al., 2023). adding the sn construct to tam makes it easier for researchers to get an idea of the metaverse explaining the behaviour of individuals (Jo, 2023). the purpose of any situation is to inspire a person to perform a desired task, after which he expects some value from the outcomes of that task (Z. Wei et al., 2023). sn can also ascertain entrepreneurial requirements (Bouarir etal., 2023). sn influences adopting emerging technologies, as proposed by the latest research studies (al Deir et al., 2023). 3.8. Perceived behavioural control (PBC) PBc is the difficulty or ease with which a behaviour occurs. PBc varies from case to case since it is based on predetermined assumptions (cordano & frieze, 2000). the association between entrepreneurial intention and women’s self-efficacy is mediated by PBc (Khurshid & Khan, 2017). In higher education, the use 14 t. Das etal. Hypothesis 6 is rejected, indicating that consumers’ PU is not associated with their DIs. this leads to the conclusion that consumers will be uncomfortable if technology-based learning does not have benefits. Previous research by li and yu (2022) contradicts our results, which state that metaverse platforms provide a self-directed learning experience. PeoU has no association with DIs; hence, Hypothesis 7 was rejected. as a result, users are not motivated to use mBl with gam. Hypothesis 8 was rejected, stating that Ins is not associated with PU. hence, the results indicate that consumers might lack trust in technology-boosting mBl. therefore, if a high Ins is involved in any technology, users hesitate to adopt Figure 2. structural equation model. Table 5. Hypothesis results. Hypothesis Parameters  estimate se CR P Hypotheses H 1 tRu <--- Peou −0.014 0.066 −0.206 0.837 not supported H 2 tRu <--- Pu 0.38 0.073 5.234 *** supported H 3 att <--- Peou −0.023 0.056 −0.422 0.673 not supported H 4 att <--- Pu 0.541 0.065 8.345 *** supported H 5 gaM <--- att 0.161 0.056 2.884 0.004 supported H 6 Pu <--- Dis 0.047 0.063 0.744 0.457 not supported H 7 Peou <--- Dis 0.056 0.068 0.825 0.409 not supported H 8 Pu <--- ins −0.057 0.073 −0.779 0.436 not supported H 9 Peou <--- ins −0.027 0.079 −0.342 0.732 not supported H 10 MBL <--- sn −0.153 0.05 −3.073 0.002 supported H 11 MBL <--- PBC 0.161 0.047 3.414 *** supported H 12 MBL <--- gaM 0.247 0.047 5.231 *** supported cogent BUsIness & management 15 it (Jeong & Kim, 2023). Hypothesis 9 was rejected, meaning there was no significant association between Ins and PeoU. as a result, the study’s findings suggest that due to less trust in the technology, growth in mBl may not be possible. Jeong and Kim (2023) research outcomes align with our study, as technology insecurity leads to users not adopting this technology. Hypothesis 10 was supported, implying a significant association between sn and the intention to adopt mBl. thus, it can be inferred that adopting mBl to influence user behaviour is possible. the research of İbili et al. (2023) has also shown that sn strongly impacts users’ beliefs. Hypothesis 11 was supported, implying a significant association between PBc and the intention to adopt mBl. hence, increased PBc leads to increased help-seeking, resulting in mBl adoption. this result is supported by the work of Bornschlegl et al. (2021). Hypothesis 12 was supported, implying a significant association between gam and the intention to adopt mBl. gamification is the addition of game design elements to non-game circumstances, creating a fun factor for users. as a result, the students were motivated to adopt mBl. groh (2012) reported similar outcomes in his research study. 6.1. Theoretical implications this study examines how gam affects the Indian educational system, focusing on improving mBl. this research enhances both theoretical and practical perspectives. Potential contributions to the literature on gam adoption behaviour in the context of enhancing mBl could be made by the research study. the mBl has been enhanced in developed countries through the application of gam. this research was conducted among university students because it found that the existing literature was insufficient and that new research was required (hussain etal., 2019; ostrom etal., 2015; Purohit & arora, 2021). the benefits of mBl for university students are enhanced by this study’s analysis of research gaps in a developing market scenario. the mediating effect of gam in improving mBl was examined in this study using tam, tPB, and trI models. att, sn, and PBc are tPB variables; DIs and Ins are trI variables; and PeoU, trU, and PU are the three components of tam. given that eduverse makes learning tasks and activities more accessible to complete and enhances learning performance, gen Z students are likely to be eager to adopt it (al-adwan & al-Debei, 2023). according to earlier studies (faqih & Jaradat, 2021; hu etal., 2020; teng et al., 2022; f. yang etal., 2022; Q. yang et al., 2022), this conclusion is consistent with how performance expectancy affects behavioural intention towards different educational technologies. metaverse platforms built by universities for the learning process should be student-centric. students can access the learning content available on the metaverse platforms provided by universities. att, sn, and PBc constructs aid the researchers in assessing the intent of university students to use gamification to enhance their metaverse-based learning. Prior research studies provide evidence of the relationship between knowledge of PU and PeoU, suggesting that a basic understanding of technology would ensure that a person considers it simple (Purohit & arora, 2021). the importance of performance expectancy in predicting intention to adopt a particular technology helps to explain why university students will be eager to use metaverse platforms if they realise that it makes it easier to complete their academic tasks and activities more efficiently (al-adwan & al-Debei, 2023). When creating a technology-driven curriculum, teachers must think that using technology is pleasant and engaging and that they take student motivation into account (spector & seung, 2018). our research examines how gamification can enhance the learning process through a metaverse platform, as seen from utilizing the trI, tam, and tPB models. By incorporating tam, trI, and tPB into our study methodology, we were able to understand better students’ aspirations to use gamification to increase their engagement with the curriculum while taking social and psychological aspects into account. the working environment and the users significantly impact the results of gamification. according to the study, addressing people’s prior awareness of gamification, the metaverse, and its applications is important. according to prior research, the tam components appear to be largely consistent across subgroups related to age, gender, and It proficiency (lai & li, 2005). When a fully functional metaverse is widely adopted, it can change behavioural aspects of human interaction, culture, and society, which could benefit large segments of the populace (Dwivedi et al., 2022). 16 t. Das etal. 6.2. Practical implications the results suggest that the gam can improve university students’ mBl. the study’s conclusions indicate that a lack of technological knowledge is a major barrier to recognizing the advantages of gam. this report provides practical recommendations for educational organizations working to develop mBl. educators might offer college students incentives to encourage their participation in mBl must be identified. our findings imply that, to fully comprehend the potential of gam to enhance mBl, the education system should consider the combination of PU, PeoU, att, trU, PBc, and sn. Videos posted on social media platforms may educate users about the benefits of gam. this research has significant consequences for higher education institution executives and policymakers in addition to its theoretical significance. to improve understanding of metaverse-based learning and, consequently, the uptake of gamification in educational domains, the study’s conclusions can be of great practical use to educational institutions, educators, students, marketers, managers, policymakers, practitioners, and systems developers. the study’s research outputs will involve a wider range of educational stakeholders, including students, faculty and university officials, policymakers, and researchers, to collect firsthand information on the metaverse-based learning environment in higher education from numerous angles. Policymakers should aim to modify the teaching-learning process. Institutions should improve learning regarding collaboration, communication, transparency, and productivity. Policymakers should ensure that teachers have adequate training to utilize gamification to enhance university students’ learning experiences by utilizing the metaverse. marketers and system developers should pay more attention to how to offer learning material creation platforms that make gamification more impactful, useful, and convenient. since technology enhances learning and helps learners retain information more readily, learners would be more inclined to accept it. Practical implementations in real-world classrooms are required to fully comprehend the usage of gamification technology to enrich the educational environment. the creation of real-world assignments may also be considered by instructors, educators, and policymakers, who might investigate the benefits of gamification in enhancing university students’ classes. to adopt gamification and improve metaverse-based learning, practitioners must grasp the essential concepts and values in the suggested conceptual framework, including adding gaming elements into non-game environments. researchers and practitioners should devote more emphasis to measuring users’ technological preparedness. the modified 16-item scale simplifies and improves the utility of trI in various contexts and cultures, benefiting practitioners and researchers. the trI has been redesigned to be less complex and simpler to use in surveys, which will be extremely beneficial in determining consumers’ technological readiness levels. further research aimed at reducing the amount of components will make the trI even more useful to researchers and practitioners. 7. Conclusions, limitations, and future research this study assessed the variables due to gam. tam, tPB, and trI were employed in this study. students from various universities in a developing nation participated in this study. this study provides a conceptual framework and identifies variables from the three models. these factors were used to create the structured questionnaire. a survey was conducted at an Indian university using the questionnaire. the sample size was three hundred and eighteen. the acceptable hypotheses showed a significant relationship between gam, sn, PBc and the adoption of mBl. att was also positively associated with the gam for adopting mBl. PU has a significant association with trU and att, respectively. PU and PeoU were not significantly correlated with Ins or DIs. PeoU also didn’t have a significant relationship with trU and att. educational institutions will benefit from this research’s understanding of the advantages and constraints of gam in enhancing mBl. most respondents were not familiar with the notion of gam. regardless of their educational degree, conducting this study required participants to understand the gam. 7.1. Limitations and future research for this study, both online and offline surveys were conducted. a random selection of respondents was used to collect data for this study. therefore, the collected data must be examined for bias and reliability cogent BUsIness & management 17 to eliminate irrelevant responses. consequently, not all the potential applications of this technology were examined in this study. this study was restricted to the education sector. this increases the possibility of conducting further studies. this study was limited to one country. further research could integrate demographic characteristics such as age, region, and employment. future research should investigate how different user characteristics influence the psychological effects of gam element use. more research should be conducted to understand the links between service characteristics and psychological responses. future research on the DIs and Ins should be undertaken. a comparative analysis of developed and emerging nations may provide further insights. Author contributions tarinmoy Das: conceptualization, methodology, formal analysis, Validation, Writing – original Draft, Writing – review & editing. sivaji ganesh Kondamudi: conceptualization, Writing – review and editing, supervision. mohammad Dawood Babakerkhell: conceptualization, Writing – review and editing, Investigation. Debajyoti Pal: conceptualization, Writing – review and editing, methodology, Validation. rita roy: Writing – review & editing, Investigation, Data curation. suree funilkul: conceptualization, Writing – review & editing. Consent It was obtained from the respondents verbally (Delphi study) and informed (survey). Disclosure statement no potential conflict of interest was reported by the author(s) Research involving human participants and animals this study involves humans for the survey and the nature of the work is purely perceptual, dealing with the perceptions of human participants. Funding the authors received no funding for this study. Data availability Data available on request from the author (name: tarinmoy Das; email: [email protected]) About the authors Tarinmoy Das is pursuing his PhD from gItam school of Business gItam (Deemed to be University), Visakhapatnam, andhra Pradesh, India. he has done an mBa in marketing from KIIt University and an m. tech in metallurgy & materials engineering from IIt madras. also B. tech in metallurgy from nIt raipur. he has work experience in various industries like software, banking, etc. his areas of interest are gamification, consumer behaviour, etc. Dr Sivaji Ganesh Kondamudi is working as an assistant Professor in gItam school of Business gItam (Deemed to be University), Visakhapatnam, andhra Pradesh, India. his areas of interest are healthcare marketing, consumer behaviour, etc. Mohammad Dawood Babakerkhell was born in afghanistan. he has received Bcs degree in computer science from shaikh Zayed University (Khost) afghanistan in 2009. he completed msc in network technology and management from amity Institute of Information technology, amity University Uttar Pradesh, India in 2019. he has been working as a lecturer at Information technology Department, computer science faculty, shaikh Zayed University (Khost) since 2011. he is currently working as a vice chancellor of student’s affairs in shaikh Zayed university. his area of interest is Information technology management, computer network and security, cloud computing, and Internet of things. 18 t. Das etal. Rita Roy is working as an assistant Professor at gItam Institute of technology, gItam (Deemed to be University), Visakhapatnam. she has eight years of teaching and research experience. her areas of interest include artificial intelligence, machine learning, computer networks, and gamification. Debajyoti Pal received the B.e degree in electrical engineering from nagpur University, maharashtra, India, in 2005 and the m.tech. degree in Information technology from Indian Institute of engineering science and technology, shibpur, Kolkata, India in 2007. he completed his Ph.D. in Information technology from the school of It, KmUtt, Bangkok, thailand, and is presently employed as a lecturer with the same university. his research interests are in multimedia systems, Iot, and human-computer Interaction. Suree Funilkul received her B.sc. in mathematics and m.sc. in Information technology from mahidol University, thailand, and the King mongkut’s University of technology thonburi, respectively. she obtained her Ph.D. in Information technology from the King mongkut’s University of technology thonburi in 2008. her research interests include information systems, database programming, etc. ORCID tarinmoy Das http://orcid.org/0000-0002-6641-0876 sivaji ganesh Kondamudi http://orcid.org/0000-0001-8614-4627 References abu rbeian, a. m., owda, a. y., & owda, m. (2022). a technology acceptance model survey of the metaverse prospects. AI, 3(2), 285–302. https://doi.org/10.3390/ai3020018[mismatch] al Deir, c., al Khasawneh, m., abuhashesh, m., masa’deh, r., & ahmad, a. m. (2023). a development of a newly constructed model related to the impact of entrepreneurial motivation on entrepreneurial intention. Studies in Computational Intelligence, 1056, 1559–1584. https://doi.org/10.1007/978-3-031-12382-5_86/coVer al-adwan, a. s., & al-Debei, m. m. (2023). the determinants of gen Z’s metaverse adoption decisions in higher education: Integrating UtaUt2 with personal innovativeness in It. Education and Information Technologies, 29(6), 7413– 7445. https://doi.org/10.1007/s10639-023-12080-1 al-adwan, a. s., li, n., al., adwan, g. a., abbasi, n. a., albelbis, , & habibi, a. (2023). extending the technology acceptance model (tam) to predict university students’ intentions to use metaverse-based learning platforms. Education and Information Technologies, 28(11), 1–33. https://doi.org/10.1007/s10639-023-11816-3 ali, sikandar, armand, tagne Poupi theodore, athar, ali, hussain, ali, ali, maisam, yaseen, muhammad, Joo, moon-Il, Kim, hee-cheol, abdullah, (2023). metaverse in healthcare integrated with explainable aI and blockchain: enabling immersiveness, ensuring trust, and providing patient data security. Sensors (Basel, Switzerland) 23(2), 1–17. https:// doi.org/10.3390/s23020565 an, y. (2023). the impact of gamification on doctoral students’ perceptions, emotions, and learning in an online environment. TechTrends, 67(4), 706–717. https://doi.org/10.1007/s11528-022-00833-7 andaleeb, a. a., Idrus, r. m., Ismail, I., & mokaram, a. K. (2010). technology readiness index (trI) among Usm distance education students according to age. World Academy of Science, Engineering and Technology, 39, 1039–1042. andembubtob, D. r., Keikhosrokiani, P., & abdullah, n. l. (2023). a concise review on the concept of metaverse: types, history, features, and technological perspectives. In Handbook of research on consumer behavioral analytics in metaverse and the adoption of a virtual world (pp. 40–67). https://doi.org/10.4018/978-1-6684-7029-9.ch003 anjum, t., amoozegar, a., farrukh, m., & heidler, P. (2023). entrepreneurial intentions among business students: the mediating role of attitude and the moderating role of university support. Education + Training, 65(4), 587–606. https://doi.org/10.1108/et-01-2021-0020 arpaci, I., Karatas, K., Kusci, I., & al-emran, m. (2022). Understanding the social sustainability of the metaverse by integrating UtaUt2 and big five personality traits: a hybrid sem-ann approach. Technology in Society, 71, 102120. https://doi.org/10.1016/j.techsoc.2022.102120 Badilla Quintana, m. g., & meza fernández, s. (2015). a pedagogical model to develop teaching skills. the collaborative learning experience in the Immersive Virtual World tymmI. Computers in Human Behavior, 51, 594–603. https:// doi.org/10.1016/j.chb.2015.03.016 Bitrián, P., Buil, I., catalán, s., & hatfield, s. (2023). the use of gamification strategies to enhance employees’ attitudes towards e-training systems. The International Journal of Management Education, 21(3), 100892. https://doi. org/10.1016/j.ijme.2023.100892 Bornschlegl, m., townshend, K., & caltabiano, n. J. (2021). application of the theory of planned behavior to identify variables related to academic help seeking in higher education. Frontiers in Education, 6, 738790. https://doi. org/10.3389/feduc.2021.738790 Bouarir, h., Diani, a., Boubker, o., & rharzouz, J. (2023). Key determinants of women’s entrepreneurial intention and behavior: the role of business opportunity recognition and need for achievement. Administrative Sciences, 13(2), 33. https://doi.org/10.3390/admsci13020033 cogent BUsIness & management 19 camacho-sánchez, r., manzano-león, a., rodríguez-ferrer, J. m., serna, J., & lavega-Burgués, P. (2023). game-based learning and gamification in physical education: a systematic review. Education Sciences, 13(2), 183. https://doi. org/10.3390/educsci13020183 Çera, g., Pagria, I., Khan, K. a., & muaremi, l. (2020). mobile banking usage and gamification: the moderating effect of generational cohorts. Journal of Systems and Information Technology, 22(3), 243–263. https://doi.org/10.1108/ JsIt-01-2020-0005 chapman, J. r., Kohler, t. B., & gedeborg, s. (2023). so, why do students perform better in gamified courses? Understanding motivational styles in educational gamification. Journal of Educational Computing Research, 61(5), 927–950. https://doi.org/10.1177/07356331221127635 chen, Z. (2022). exploring the application scenarios and issues facing metaverse technology in education. Interactive Learning Environments, 32(7), 1–13. https://doi.org/10.1080/10494820.2022.2133148 cheng, y. m. (2023). how gamification and social interaction stimulate moocs continuance intention via cognitive presence, teaching presence and social presence? Library Hi Tech, 41(6), 1781–1801. https://doi.org/10.1108/ lht-03-2022-0160/fUll/Xml cordano, m., & frieze, I. h. (2000). Pollution reduction preferences of Us environmental managers: applying ajzen’s theory of planned behavior. Academy of Management Journal, 43(4), 627–641. Dalkey, n., & helmer, o. (1963). an experimental application of the DelPhI method to the use of experts. Management Science, 9(3), 458–467. https://doi.org/10.1287/mnsc.9.3.458 Davis, f. (1985a). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Unpublished Doctoral Dissertation, mIt sloan school of management, cambridge, ma]. https://doi. org/10.1126/science.146.3652.1648 Davis, f. (1985b). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Unpublished Doctoral Dissertation, mIt sloan school of management, cambridge, ma]. https://doi. org/10.1126/science.146.3652.1648 Davis, f. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.5962/bhl.title.33621 De leon, m. V. (2019). factors influencing behavioural intention to use mobile banking among retail banking clients. Jurnal Studi Komunikasi (Indonesian Journal of Communications Studies), 3(2), 118. https://doi.org/10.25139/jsk.v3i2.1469 De notaris, D., canazza, s., mariconda, c., & Paulon, c. (2021). how to play a mooc: Practices and simulation. Entertainment Computing, 37, 100395. https://doi.org/10.1016/j.entcom.2020.100395 DeVellis, r. f., lewis, m. a., & sterba, K. r. (2003). Interpersonal emotional processes in adjustment to chronic illness. In Social psychological foundations of health and illness. Wiley online library. Dwivedi, y. K., hughes, l., Baabdullah, a. m., ribeiro-navarrete, s., giannakis, m., al-Debei, m. m., Dennehy, D., metri, B., Buhalis, D., cheung, c. m. K., conboy, K., Doyle, r., Dubey, r., Dutot, V., felix, r., goyal, D. P., gustafsson, a., hinsch, c., Jebabli, I., … Wamba, s. f. (2022). metaverse beyond the hype: multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 66, 102542. https://doi.org/10.1016/j.ijinfomgt.2022.102542 faqih, K. m. s., & Jaradat, m. I. r. m. (2021). Integrating ttf and UtaUt2 theories to investigate the adoption of augmented reality technology in education: Perspective from a developing country. Technology in Society, 67, 101787. https://doi.org/10.1016/j.techsoc.2021.101787 far, s. B., & rad, a. I. (2022). applying digital twins in metaverse: User interface, security and privacy challenges. Journal of Metaverse, 2(1), 8–15. gejandran, P., & abdullah, n. (2024). gamification in e-learning: a systematic review of benefits, challenges, and future possibilities. Journal of Logistics, Informatics and Service Science, 11(2), 84–104. https://doi.org/10.33168/ JlIss.2024.0206 giannarou, l., & Zervas, e. (2014). Using Delphi technique to build consensus in practice. International Journal of Business Science & Applied Management (IJBSAM), 9(2), 65–82. http://hdl.handle.net/10419/190657https:// creativecommons.org/licenses/by/2.0/uk/ groh, f. (2012). Gamification: State of the art definition and utilization. Institute of media Informatics Ulm University. 31. hair, J. f., risher, J. J., sarstedt, m., & ringle, c. m. (2019). When to use and how to report the results of Pls-sem. European Business Review, 31(1), 2–24. https://doi.org/10.1108/eBr-11-2018-0203 hamari, J., & Koivisto, J. (2015). “Working out for likes”: an empirical study on social influence in exercise gamification. Computers in Human Behavior, 50, 333–347. https://doi.org/10.1016/j.chb.2015.04.018 henseler, J., ringle, c. m., & sinkovics, r. r. (2009). the use of partial least squares path modeling in international marketing. New Challenges to International Marketing (Advances in International Marketing), 20, 277–319. https://doi. org/10.1007/978-3-319-53469-5_12 hu, s., laxman, K., & lee, K. (2020). exploring factors affecting academics’ adoption of emerging mobile technologies-an extended UtaUt perspective. Education and Information Technologies, 25(5), 4615–4635. https://doi.org/10.1007/ s10639-020-10171-x hubona, g. s., & cheney, P. h. (1994). system effectiveness of knowledge-based technology: the relationship of user performance and attitudinal measures. Proceedings of the hawaii International conference on system sciences, 4, 532—541. https://doi.org/10.1109/hicss.1994.323465 20 t. Das etal. humbani, m., & Wiese, m. (2019). an integrated framework for the adoption and continuance intention to use mobile payment apps. International Journal of Bank Marketing, 37(2), 646–664. https://doi.org/10.1108/IJBm-03-2018-0072 hussain, m., mollik, a. t., Johns, r., & rahman, m. s. (2019). m-payment adoption for bottom of pyramid segment: an empirical investigation. International Journal of Bank Marketing, 37(1), 362–381. https://doi.org/10.1108/ IJBm-01-2018-0013 hwang, g. J., & chien, s. y. (2022). Definition, roles, and potential research issues of the metaverse in education: an artificial intelligence perspective. Computers and Education: Artificial Intelligence, 3(april), 100082. https://doi. org/10.1016/j.caeai.2022.100082 İbili, e., Ölmez, m., cihan, a., Bilal, f., İbili, a. B., okumus, n., & Billinghurst, m. (2023). Investigation of learners’ behavioral intentions to use metaverse learning environment in higher education: a virtual computer laboratory. Interactive Learning Environments, 1–26. https://doi.org/10.1080/10494820.2023.2240860 Iivari, n., sharma, s., & Ventä-olkkonen, l. (2020). Digital transformation of everyday life – how coVID-19 pandemic transformed the basic education of the young generation and why information management research should care? International Journal of Information Management, 55, 102183. https://doi.org/10.1016/j.ijinfomgt.2020.102183 Jaiswal, D., mohan, a., & Deshmukh, a. K. (2023). cash rich to cashless market: segmentation and profiling of fintech-led-mobile payment users. Technological Forecasting and Social Change, 193(June 2022), 122627. https://doi. org/10.1016/j.techfore.2023.122627 Jarrar, y., awobamise, a. o., & sellos, P. s. (2020). technological readiness index (trI) and the intention to use smartphone apps for tourism: a focus on in Dubai mobile tourism app. International Journal of Data and Network Science, 4(3), 297–304. https://doi.org/10.5267/j.ijdns.2020.6.003 Jayashankar, P., nilakanta, s., Johnston, W. J., gill, P., & Burres, r. (2018). Iot adoption in agriculture: the role of trust, perceived value and risk. Journal of Business & Industrial Marketing, 33(6), 804–821. https://doi.org/10.1108/ JBIm-01-2018-0023/fUll/PDf Jeong, s. h., & Kim, h. K. (2023). effect of trust in metaverse on usage intention through technology readiness and technology acceptance model. Tehnicki Vjesnik, 30(3), 837–845. https://doi.org/10.17559/tV-20221111061245 Jo, h. (2023). tourism in the digital frontier: a study on user continuance intention in the metaverse. Information Technology & Tourism, 25(3), 307–330. https://doi.org/10.1007/s40558-023-00257-w Kansal, P. (2016). factors affecting adoption of mobile banking at the bottom of the pyramid in India. International Journal of Marketing & Business Communication, 5(3), 8–19. Kaplan, a. D., cruit, J., endsley, m., Beers, s. m., sawyer, B. D., & hancock, P. a. (2021). the effects of virtual reality, augmented reality, and mixed reality as training enhancement methods: a meta-analysis. Human Factors, 63(4), 706–726. https://doi.org/10.1177/0018720820904229 Kashive, n., & mohite, s. (2023). Use of gamification to enhance e-learning experience. Interactive Technology and Smart Education, 20(4), 554–575. https://doi.org/10.1108/Itse-05-2022-0058/fUll/Xml Kasirye, f., & Wok, s. (2023). factors influencing the usage of web-based video conferencing platforms in knowledge acquisition among students. International Social Science Journal, 73(248), 261–277. https://doi.org/10.1111/issj.12392 Khurshid, J., & Khan, m. I. (2017). Impact of self-efficacy on women entrepreneurial intention: mediating role of perceived behavior control and moderating role of openness to experience. Journal of Managerial Sciences, 9(3), 275–292. Kumar, r. (2011). research methodology. Kusumawardani, K. a., & soegihono, l. f. (2024). Does gamification on an e-commerce application lead intention to use the application and spread word of mouth? International Journal of Business and Systems Research, 18(1), 65– 84. https://doi.org/10.1504/IJBsr.2024.135779 lai, V. s., & li, h. (2005). technology acceptance model for internet banking: an invariance analysis. Information & Management, 42(2), 373–386. https://doi.org/10.1016/j.im.2004.01.007 lai, y. l., & lee, J. (2020). Integration of technology readiness index (trI) into the technology acceptance model (tam) for explaining behavior in adoption of BIm. Asian Education Studies, 5(2), 10. https://doi.org/10.20849/aes.v5i2.816 lee, n., & Jo, m. (2023). exploring problem-based learning curricula in the metaverse: the hospitality students’ perspective. Journal of Hospitality, Leisure, Sport and Tourism Education, 32, 100427. https://doi.org/10.1016/j.jhlste.2023.100427 lee, y., Kozar, K. a., & larsen, K. r. t. (2003). the technology acceptance model: past, present, and future. Communications of the Association for Information Systems, 12(1), 50. https://doi.org/10.17705/1caIs.01250 li, m., & yu, Z. (2022). a systematic review on the metaverse-based blended english learning. Frontiers in Psychology, 13, 1087508. https://doi.org/10.3389/fpsyg.2022.1087508 lim, J. y., Kim, g. m., & Kim, e. J. (2021). Predictors of entrepreneurial intention of nursing students based on theory of planned behavior. Journal of Multidisciplinary Healthcare, 14, 533–543. https://doi.org/10.2147/JmDh.s288532 lin, J. s. c., & hsieh, P. l. (2012). refinement of the technology readiness index scale: a replication and cross-validation in the self-service technology context. Journal of Service Management, 23(1), 34–53. (https://doi. org/10.1108/09564231211208961 liu, h., & Park, K. s. (2024). exploring the impact of metaverse tourism experiences on actual visit intentions: an integrated model of presence, the technology acceptance model, and the theory of planned behavior. International Journal of Tourism Research, 26(1), e2616. https://doi.org/10.1002/jtr.2616 cogent BUsIness & management 21 liu, y., li, h., Kostakos, V., goncalves, J., hosio, s., & hu, f. (2014). an empirical investigation of mobile government adoption in rural china: a case study in Zhejiang province. Government Information Quarterly, 31(3), 432–442. https://doi.org/10.1016/j.giq.2014.02.008 luarn, P., chen, c. c., & chiu, y. P. (2023). enhancing intrinsic learning motivation through gamification: a self-determination theory perspective. The International Journal of Information and Learning Technology, 40(5), 413– 424. https://doi.org/10.1108/IJIlt-07-2022-0145/fUll/Xml luo, Z. (2023). Determinants of the perceived usefulness (PU) in the context of using gamification for classroom-based esl teaching: a scale development study. Education and Information Technologies, 28(4), 4741–4768. https://doi. org/10.1007/s10639-022-11409-6 majuri, J., Koivisto, J., & hamari, J. (2018). gamification of education and learning: a review of empirical literature. CEUR Workshop Proceedings, gamifIn (pp. 11–19). malaquias, r. f., & silva, a. f. (2020). Understanding the use of mobile banking in rural areas of Brazil. Technology in Society, 62, 101260. https://doi.org/10.1016/j.techsoc.2020.101260 martín-gutiérrez, J., mora, c. e., añorbe-Díaz, B., & gonzález-marrero, a. (2017). Virtual technologies trends in education. EURASIA Journal of Mathematics, Science and Technology Education, 13(2), 469–486. https://doi.org/10.12973/ eurasia.2017.00626a masrom, m. (2007). technology acceptance model and e-learning. Technology, 21(24), 81. mou, t. y., Kao, c. P., lin, K. y., & osborne, m. (2023). exploring the mediator in science service learning: analysis of university students’ behavioural intention to use digital platforms. The Asia-Pacific Education Researcher, 32(6), 841– 854. https://doi.org/10.1007/s40299-022-00700-2 mukherjee, s., Baral, m. m., Venkataiah, c., Pal, s. K., & nagariya, r. (2021). service robots are an option for contactless services due to the coVID-19 pandemic in the hotels. DECISION, 48(4), 445–460. https://doi.org/10.1007/ s40622-021-00300-x mukherjee, s., & chittipaka, V. (2022). analysing the adoption of intelligent agent technology in food supply chain management: an empirical evidence. FIIB Business Review, 11(4), 438–454. https://doi. org/10.1177/23197145211059243 ngowtanasawan, g. (2017). a causal model of BIm adoption in the thai architectural and engineering design industry. Procedia Engineering, 180, 793–803. https://doi.org/10.1016/j.proeng.2017.04.240 nguyen, a. t., Do, t. h. h., Vu, t. B. t., Dang, K. a., & nguyen, h. l. (2019). factors affecting entrepreneurial intentions among youths in Vietnam. Children and Youth Services Review, 99, 186–193. https://doi.org/10.1016/j. childyouth.2019.01.039 nguyen, P. m., Dinh, V. t., luu, t. m. n., & choo, y. (2020). sociological and theory of planned behaviour approach to understanding entrepreneurship: comparison of Vietnam and south Korea. Cogent Business & Management, 7(1), 1815288. https://doi.org/10.1080/23311975.2020.1815288 nguyen, P. n. D., & nguyen, h. h. (2024). Unveiling the link between digital entrepreneurship education and intention among university students in an emerging economy. Technological Forecasting and Social Change, 203, 123330. https://doi.org/10.1016/j.techfore.2024.123330 nkoro, e. c., nwakanma, c. I., lee, J. m., & Kim, D. s. (2024). Detecting cyberthreats in metaverse learning platforms using an explainable Dnn. Internet of Things, 25, 101046. https://doi.org/10.1016/j.iot.2023.101046 oliveira, r. P., santos, I. l., de souza, c. g., reis, a. d c., & de souza, W. m. (2023). a study on the relation between industry 4.0 technologies and gamification in e-learning. Interactive Technology and Smart Education, 20(4), 449– 474. https://doi.org/10.1108/Itse-02-2022-0020/fUll/Xml ooi, K. B., tan, g. W. h., aw, e. c. X., cham, t. h., Dwivedi, y. K., Dwivedi, r., hughes, l., Kar, a. K., loh, X. m., mogaji, e., Phau, I., & sharma, a. (2023). Banking in the metaverse: a new frontier for financial institutions. International Journal of Bank Marketing, 41(7), 1829–1846. https://doi.org/10.1108/IJBm-03-2023-0168 ostrom, a. l., Parasuraman, a., Bowen, D. e., Patrício, l., & Voss, c. a. (2015). service research priorities in a rapidly changing context. Journal of Service Research, 18(2), 127–159. https://doi.org/10.1177/1094670515576315 Pal, D., & Patra, s. (2021). University students’ perception of video-based learning in times of coVID-19: a tam/ttf perspective. International Journal of Human–Computer Interaction, 37(10), 903–921. https://doi.org/10.1080/1044731 8.2020.1848164 Pappas, I. o., & giannakos, m. n. (2021). rethinking learning design in It education during a pandemic. Frontiers in Education, 6, 652856. https://doi.org/10.3389/feduc.2021.652856 Parasuraman, a. (2000). technology readiness Index (trI) a multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001 Parasuraman, a., & colby, c. l. (2015). an updated and streamlined technology readiness index: trI 2.0. Journal of Service Research, 18(1), 59–74. https://doi.org/10.1177/1094670514539730 Park, s. m., & Kim, y. g. (2022). a metaverse: taxonomy, components, applications, and open challenges. IEEE Access, 10, 4209–4251. https://doi.org/10.1109/access.2021.3140175 Park, s., min, K., & Kim, s. (2021). Differences in learning motivation among Bartle’s player types and measures for the delivery of sustainable gameful experiences. Sustainability, 13(16), 9121. https://doi.org/10.3390/su13169121 Parker, D., & manstead, a. s. r. (1995). evaluating and extending the theory of planned behaviour. European Review of Social Psychology, 6(1), 69–95. https://doi.org/10.1080/14792779443000012 22 t. Das etal. Parvan, K. (2012). estimating the impact of building information modeling (BIm) utilization on building project performance. University of maryland, college Park. Podsakoff, P. m., macKenzie, s. B., lee, J.-y., & Podsakoff, n. P. (2003). common method biases in behavioral research: a critical review of the literature and recommended remedies. The Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879 Purohit, s., & arora, r. (2021). adoption of mobile banking at the bottom of the pyramid: an emerging market perspective. International Journal of Emerging Markets, 18(1), 200–222. https://doi.org/10.1108/IJoem-07-2020-0821 Qin, y. (2022 Investment Potential Analysis on Chinese Stock Market in MetaverseTake VR Industry as a Sample [Paper presentation]. Proceedings of the 2022 7th International conference on financial Innovation and economic Development (IcfIeD 2022), 648(IcfIeD), 1001–1007. https://doi.org/10.2991/aebmr.k.220307.165 rafdinal, W., & senalasari, W. (2021). Predicting the adoption of mobile payment applications during the coVID-19 pandemic. International Journal of Bank Marketing, 39(6), 984–1002. https://doi.org/10.1108/IJBm-10-2020-0532 rodrigues, l. f., oliveira, a., & costa, c. J. (2016). Playing seriously - how gamification and social cues influence bank customers to use gamified e-business applications. Computers in Human Behavior, 63, 392–407. https://doi. org/10.1016/j.chb.2016.05.063 rohan, r., Pal, D., & funilkul, s. (2020). Gamifying MOOC’s a step in the right direction?: A systematic literature review [Paper presentation]. Proceedings of the 11th International conference on advances in Information technology, 1–10. https://doi.org/10.1145/3406601.3406607 rohan, r., Pal, D., funilkul, s., chutimaskul, W., & eamsinvattana, W. (2021). how gamification leads to continued usage of moocs? a theoretical perspective. IEEE Access, 9, 108144–108161. https://doi.org/10.1109/ access.2021.3102293 rohan, r., roy, P., Vanijja, V., funilkul, s., mukherjee, s., & Pal, D. (2023). What affects the adoption of metaverse in education ? A SEM-based approach [Paper presentation]. 2023 20th International Joint conference on computer science and software engineering (Jcsse), 448–453. https://doi.org/10.1109/Jcsse58229.2023.10202090 rohan, r., roy, P., Vanijja, V., funilkul, s., mukherjee, s., & Pal, D. (2023). What affects the adoption of metaverse in education? A SEM-based approach [Paper presentation].2023 20th International Joint conference on computer science and software engineering (Jcsse), 448–453. https://doi.org/10.1109/Jcsse58229.2023.10202090 rospigliosi, P. (2022). metaverse or simulacra? roblox, minecraft, meta and the turn to virtual reality for education, socialisation and work. Interactive Learning Environments, 30(1), 1–3. https://doi.org/10.1080/10494820.2022.2022899 roy, r., Babakerkhell, m. D., mukherjee, s., Pal, D., & funilkul, s. (2022). evaluating the intention for the adoption of artificial intelligence-based robots in the university to educate the students. IEEE Access, 10, 125666–125678. https://doi.org/10.1109/access.2022.3225555 said, g. r. e. (2023). metaverse-based learning opportunities and challenges: a phenomenological metaverse human–computer interaction study. Electronics, 12(6), 1379. https://doi.org/10.3390/electronics12061379 said, g. r. e. (2023). metaverse-based learning opportunities and challenges: a phenomenological metaverse human–computer interaction study. Electronics (Switzerland), 12(6), 1379. https://doi.org/10.3390/electronics12061379 salman, h., almohsen, e., henari, t., shatnawi, s., Buzaboon, a., fardan, m., & albinali, K. (2023 Using machine learning and SEM to analyze attitudes towards adopting metaverse in higher education [Paper presentation]. 2023 International conference on smart applications, communications and networking, smartnets, 2023, 1–6. https:// doi.org/10.1109/smartnets58706.2023.10215936 sommer, l. (2011). the theory of planned behaviour and the impact of past behaviour. International Business & Economics Research Journal (IBER), 10(1), 91–110. https://doi.org/10.19030/iber.v10i1.930 spector, J. m., & seung, W. P. (2018). Motivation, learning, and technology: Embodied educational motivation. routledge. srisawat, s., & Piriyasurawong, P. (2022). metaverse virtual learning management based on gamification techniques model to enhance total experience. International Education Studies, 15(5), 153. https://doi.org/10.5539/ies.v15n5p153 t r, m., & gala, B. (2023). gamification for digital humanities in libraries. DESIDOC Journal of Library & Information Technology, 43(04), 241–248. https://doi.org/10.14429/djlit.43.04.19259 teng, Z., cai, y., gao, y., Zhang, X., & li, X. (2022). factors affecting learners’ adoption of an educational metaverse platform: an empirical study based on an extended UtaUt model. Mobile Information Systems, 2022, 1–15. https:// doi.org/10.1155/2022/5479215 Usmani, s. s., sharath, m., & mehendale, m. (2022). future of mental health in the metaverse. General Psychiatry, 35(4), e100825. https://doi.org/10.1136/gpsych-2022-100825 Vanduhe, V. Z., nat, m., & hasan, h. f. (2020). continuance intentions to use gamification for training in higher education: Integrating the technology acceptance model (tam), social motivation, and task technology fit (ttf). IEEE Access, 8, 21473–21484. https://doi.org/10.1109/access.2020.2966179 Venkatesh, V., morris, m. g., Davis, g. B., & Davis, f. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540 Victorino, l., Karniouchina, e., & Verma, r. (2009). exploring the use of the abbreviated technology readiness index for hotel customer segmentation. Cornell Hospitality Quarterly, 50(3), 342–359. https://doi.org/10.1177/1938965509336809 Wang, Q., Zhong, y., Zhao, g., song, r., & Zeng, c. (2023). relationship among content type of smartphone use, technostress, and sleep difficulty: a study of University students in china. Education and Information Technologies, 28(2), 1697–1714. https://doi.org/10.1007/s10639-022-11222-1 cogent BUsIness & management 23 Wang, y., lee, l. h., Braud, t., & hui, P. (2022). Re-shaping Post-COVID-19 teaching and learning: a blueprint of virtual-physical blended classrooms in the metaverse era [Paper presentation]. Proceedings – 2022 Ieee 42nd International conference on Distributed computing systems Workshops, IcDcsW 2022, 241–247. https://doi. org/10.1109/IcDcsW56584.2022.00053 Warden, c. a., yi-shun, W., stanworth, J. o., & chen, J. f. (2022). millennials’ technology readiness and self-efficacy in online classes. Innovations in Education and Teaching International, 59(2), 226–236. https://doi.org/10.1080/1470329 7.2020.1798269 Wei, X., Peng, X., & Prybutok, V. (2024). consumer behavioral intention of adopting emerging healthcare technology. IEEE Transactions on Engineering Management, 71, 888–898. https://doi.org/10.1109/tem.2022.3140952 Wei, Z., Zhang, J., huang, X., & Qiu, h. (2023). can gamification improve the virtual reality tourism experience? analyzing the mediating role of tourism fatigue. Tourism Management, 96(December 2022), 104715. https://doi. org/10.1016/j.tourman.2022.104715 yang, c., yan, s., Wang, J., & Xue, y. (2022). flow experiences and virtual tourism: the role of technological acceptance and technological readiness. Sustainability, 14(9), 5361. https://doi.org/10.3390/su14095361 yang, f., ren, l., & gu, c. (2022). a study of college students’ intention to use metaverse technology for basketball learning based on UtaUt2. Heliyon, 8(9), e10562. https://doi.org/10.1016/j.heliyon.2022.e10562 yang, Q., Zhao, y., huang, h., Xiong, Z., Kang, J., & Zheng, Z. (2022). fusing blockchain and aI with metaverse: a survey. IEEE Open Journal of the Computer Society, 3, 122–136. https://doi.org/10.1109/oJcs.2022.3188249 yang, X., yang, J., hou, y., li, s., & sun, s. (2023). gamification of mobile wallet as an unconventional innovation for promoting fintech: an fsQca approach. Journal of Business Research, 155, 113406. https://doi.org/10.1016/j.jbusres.2022.113406 yildirim, I. (2017). the effects of gamification-based teaching practices on student achievement and students’ attitudes toward lessons. The Internet and Higher Education, 33, 86–92. https://doi.org/10.1016/j.iheduc.2017.02.002 yoon, h., & mecca, m. (2022). fieldwork from desktop: Webdoc for teaching in the time of pandemic. Journal of Geography in Higher Education, 47(3), 349–368. https://doi.org/10.1080/03098265.2022.2045575 younas mughal, m., andleeb, n., farooq ahmad Khurram, a., ali, y., aslam, s., & saleem, n. (2022). Perceptions of teaching-learning force about metaverse for education: a qualitative study. Journal of Positive School Psychology, 6(9), 1738–1745. yue, K. (2022 Breaking down the Barrier between Teachers and Students by Using Metaverse Technology in Education: Based on A Survey and Analysis of Shenzhen City, China [Paper presentation]. acm International conference Proceeding series, 40–44. https://doi.org/10.1145/3514262.3514345 Zhang, X., chen, y., hu, l., & Wang, y. (2022). the metaverse in education: Definition, framework, features, potential applications, challenges, and future research topics. Frontiers in Psychology, 13, 1016300. https://doi.org/10.3389/ fpsyg.2022.1016300