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

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.

Keywords:

• Gamification
• metaverse-based learning
• university students
• Delphi method
• developing nations

Reviewing Editor:

• Hui Shan Loh, Singapore University of Social Sciences, Singapore

SUBJECTS:

• Business, Management and Accounting
• Management of IT
• Technology

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 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 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 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 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.

Figure 1. Conceptual 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).

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.

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 of AI-based robots is influenced by the attitude of students and teachers (Roy et al., 2022). A substantial body of research shows that PBC positively correlates with entrepreneurial ambition and activity (Lim et al., 2021). PBC is the most significant factor influencing students’ ambitions to start a business (A. T. Nguyen et al., 2019; P. M. Nguyen et al., 2020).

3.9. Gamification (GAM)

This enhances user engagement by integrating aspects of game design with a non-game context (Groh, 2012). Through entertainment, prizes, competition, storytelling, and other features, GAM makes user experiences more enjoyable. Employing gamification principles in education actively involves students and facilitates their learning by motivating them to strive for specific objectives. Most gamification involves simulations, enabling students to play and learn simultaneously. Students derive pleasure from immersive gaming experiences, catalyzing their further pursuit of knowledge. Furthermore, the educational experience prioritizes the development of learning opportunities accessible at all times and from any location. This encompasses incorporating materials and technology, the availability of educational resources, interactions among learners and between learners and teachers, hands-on learning, mutual support and assistance, and acknowledging the advantages of these learning encounters.

The idea that people enjoy fun served as an inspiration for GAM. Thus, according to Rodrigues et al. (2016), GAM might improve the educational process and expand the educational industry. Gamification techniques are increasingly applied in online learning environments to increase student engagement and effectiveness. Learner motivation and engagement are increased by gamification. Gamification makes real-time, instantaneous feedback on one’s performance possible (Gejandran & Abdullah, 2024). A significant indicator of intention to continue using gamification is the attitude towards it (Kusumawardani & Soegihono, 2024).

3.10. Intention for adopting metaverse-based learning (MBL)

According to Venkatesh et al. (2003), users’ behaviour when utilizing technology reveals their intention to use it for educational reasons. PEoU, TRU, ATT, and PU substantially impact technological use (De Leon, 2019). MBL research can yield positive results. Students can acquire knowledge and engage with educators and peers, regardless of their location or time of day. It is a means of transmitting information, such as photographs, videos, and other multimedia, and promoting discussions through electronic devices, platforms, and advanced technology. Students can use various types of educational materials. Metaverse is a realm that facilitates collective engagement and participation in virtual and non-virtual activities across multiple domains, encompassing politics, economy, society, and culture (Srisawat & Piriyasurawong, 2022). A Network Intrusion Detection System (NIDS) detects cyberattacks on metaverse learning platforms (Said, 2023). The process by which NIDS identified the cyberattacks affecting IoT networks can be found in the study of Nkoro et al. (2024).

H5: ATT will have a relationship with the GAM for adopting MBL.

H6: DIS will have a relationship with the user’s PU.

H7: DIS will have a relationship with the user’s PEoU.

H8: INS will have a relationship with the user’s PU.

H9: INS will positively be associated with the PEoU of the user.

H10: SN will positively be associated with adopting MBL.

H11: PBC will positively be associated with adopting MBL.

H12: GAM will positively impact the intention to adopt MBL.

4. Research methodology

4.1. Delphi technique

The most suitable variables/factors for a research study are ascertained and confirmed by the Delphi technique. It is a standard way of acquiring expert opinions on a specific research area (Ngowtanasawan, 2017). A group of researchers who collectively believe in direct screening and conduct discussions on a specific topic are formed by experts. The researchers’ discussions finally help confirm the variables that should be considered for the research study, making it appropriate for SEM testing (Parvan, 2012). The Delphi technique was performed by industry experts and academicians related to this study. The group included five academicians with more than ten years of experience in university-level teaching and a strong research background. In addition, the group included six industry experts working in the IT sector or e-learning platforms with ten years or more of experience. While selecting the experts, their experience was given more consideration. The questionnaire was given to the experts to obtain their responses. The researchers scheduled individual interviews with the experts to get their responses to the questionnaires. The questions were asked after taking their verbal consent.

The experts were asked to examine the contents of the questionnaire clearly and rank them according to their knowledge. Experts’ opinions were included in the questionnaire and model finalizations. This research exercise helped improve the questionnaire and its items. In addition, experts suggested how to proceed with the study, which helped conduct the final survey. Using experts’ opinions, the difficulties were resolved using Delphi’s approach. A closed-ended questionnaire survey allowed us to understand the respondents’ thoughts without further discussion. However, the Delphi technique has the advantage that all research opinions are based on thorough discussion. The Delphi technique, developed by Dalkey and Helmer (1963), is a group knowledge convergence strategy. The primary goal of this approach is to reach an expert group consensus (Giannarou & Zervas, 2014). Multilateral and complicated decision-making processes can be managed using this technique. The opinions and responses of the experts were distributed among the group members multiple times until everyone in the group agreed. Reverse logistics, product development, marketing, forecasting, and other fields have used this technique.

4.2. Scale adoption and instrument used

Three variables constitute the conceptual framework of this study: technology acceptance, behavioural, and technology usage variables. Mediators and dependent variables are also part of the framework. PEoU (Purohit & Arora, 2021), PU (Purohit & Arora, 2021), and TRU (Ali et al., 2023) are technology acceptance variables. ATT(Al-Adwan et al., 2023), SN (İbili et al., 2023), and PBC (Bornschlegl et al., 2021) are behavioural variables. DIS (Li & Yu, 2022) and INS (Jeong & Kim, 2023) are technology usage variables. GAM (Groh, 2012) was used as the mediator in this study, while the adoption of MBL (Said, 2023) is the dependent variable.

4.3. Questionnaire design

A survey-based questionnaire was created using prior scales to determine the influence of the GAM on MBL research in the education sector. This study’s variables were adapted from existing literature. The terms PU and PEOU are obtained from (Purohit & Arora, 2021). The TRU was derived from previous studies (Ali et al., 2023). ATT is adapted from (Al-Adwan et al., 2023). SN is drawn from (İbili et al., 2023). PBC is obtained from (Bornschlegl et al., 2021). The DIS was adapted from (Li & Yu, 2022), and the INS was taken from (Jeong & Kim, 2023). The GAM was obtained from (Groh, 2012), and the MBL was adopted from (Said 2023).

A questionnaire was developed using scales from earlier studies to gather responses. In the questionnaire authors were told that this survey data will be used for this study and after taking their informed consent survey. The survey was divided into three sections: respondents’ optional contact information, demographic information and research study relevant questions. First, a pilot survey was conducted to test and validate the questions before obtaining primary data. After reviewing the collected responses, it was concluded that one of the study-related items from the third portion of the questionnaire needed to be eliminated. A question from the research-related part was removed from the questionnaire after making the appropriate revisions, and the final draft was then made before being sent to respondents for data collection. The questionnaire made it clear that the information provided by respondents will only be used for this research study. Due to the non-availability of an official Institutional Review Board (IRB) of the first author’s university, the requisite permission for data collection has been obtained from the relevant department of the concerned university. This has been attached as an annexture. The nature of the work is purely perceptual, dealing with the perceptions of human participants.

4.4. Data collection

In this study, the descriptive research approach is used because it attempts to fully characterize a scenario, problem, occurrence, service, or programme; provide information on, for example, a community’s living conditions; or explain multiple points of view on an issue (Kumar, 2011). Data will be collected from university students aged 18–30 years. Secondary data sources include previous studies that are available in journals, web portals, and available documents. The questionnaire will be administered to students via social media to collect their responses. This study employed a convenience sampling technique to achieve high validity, provide each member of the population with an equal chance of being chosen, and eliminate bias.

Data were collected online and offline, which enhanced the response rate. Respondents took individual approaches on university campuses during the offline data collection. The responses were randomly chosen from college and university students. The aim of the study was explained to respondents when they were approached. The researcher simplified the GAM technology so that respondents could understand it. The respondents were also shown a brief film to help them grasp better how this technology is used in the education sector. When confirmed that the respondents clearly understood the study’s objectives and, more significantly, the GAM technology, they were asked to complete the questionnaire. A total of 521 respondents from various universities were contacted. The responses of 145 students were collected offline, and 376 were collected online. Ninety valid responses were obtained offline, and 228 were obtained online. The data analysis, therefore, used 318 replies.

A pilot survey was conducted at a university in Visakhapatnam, Andhra Pradesh, with 110 respondents to determine the accuracy of the responses (Mukherjee & Chittipaka, 2022). All questions had valid Cronbach’s alpha (CR) values of more than 0.07, except for one question in the third section of the study (Henseler et al., 2009). The survey response rate, which accounts for offline and online data collection techniques, was 71.20%. The survey was conducted between July 2023 and September 2023. Respondents’ demographic profiles are presented in Table 1.

Table 1. Respondents’ demographic profile.

Demographic	Number	Percentage
Gender
Male	254	50.60
Female	248	49.40
Age
18 to 21 years	127	25.30
22 to 25 years	203	40.44
Above 25 years	172	34.26
Educational Qualifications
B.A.	30	5.97
B. Arch	20	3.98
B. Com	36	7.17
B. Sc	44	8.76
B. Tech	47	9.36
BBA	78	15.53
MA.	37	7.37
M.Com	40	7.96
M.Sc	31	6.17
M.Tech	60	11.95
MBA	79	15.73
Gamification and its application awareness
Yes	206	41.03
No	296	58.97
Knowledge about Metaverse
Yes	236	47.01
No	266	52.99
Metaverse applications awareness
Yes	241	48.01
No	261	51.99

5. Data analysis

This section discusses the common method bias for determining whether the data obtained were biased. Using CR, we determined the validity and reliability. Next, the structural and measurement models were computed.

5.1. Common method bias (CMB)

Common method bias (CMB) occurs when respondents’ true predispositions bring about the instrument’s response discrepancies that the instrument seeks to elucidate. We used Harman’s single-factor test after data collection. The results of the exploratory factor analysis show that the first component explains the majority of the variance, or covariance, at a maximum of 10.321% under the advised level of 50% (Podsakoff et al., 2003).

5.2. Reliability and validity (Cronbach’s alpha)

The dependability of the data was measured using Cronbach’s alpha (Hair et al., 2019). The Cronbach’s alpha values for the items in Table 2 were equal to or greater than 0.70.

Table 2. Values of Cronbach alpha and factor loadings.

Latest variables	Indicators	Cronbach’s alpha	Factor loadings	Tolerance	VIF
Perceived Ease of Use (PEOU)	PEOU 1: Learning based on Metaverse using gamification is advantageous	0.857	.826	0.852	1.174
	PEOU 2: Gamification can be considered one of the best technologies for Metaverse-based learning		.874
	PEOU 3: Understanding of Metaverse and Gamification is a pre-requisite for this type of learning		.887
	PEOU 4: Usage of gamification is user-friendly for Metaverse-based learning		.693
Perceived Usefulness (PU)	PU 1: Using gamification, my Metaverse-based learning will improve	0.92	.865	0.65	1.47
	PU 2: Using apps makes things easier for me		.856
	PU 3: My productivity will be enhanced once I am acquainted with Metaverse-based learning		.854
	PU 4: I can adapt my Metaverse-based learning enhancement due to gamification		.827
Trust (TRU)	TRU 1: Use of gamification to boost Metaverse-based learning is reliable	0.872	.838	0.776	1.286
	TRU 2: Use of gamification is secured for Metaverse-based learning		.867
	TRU 3: I am assured of the gamification technology in developing Metaverse-based learning		.838
Attitude (ATT)	ATT 1: I believe that employing gamification to improve Metaverse-based learning is beneficial	0.874	.812	0.74	1.352
	ATT 2: Technological user-friendliness will affect the growth of Metaverse-based learning		.851
	ATT 3: It helps in creating awareness about metaverse and gamification		.856
	ATT 4: I feel assured that the technological benefits of gamification are adequate for improving Metaverse-based learning		.740
Discomfort (DIS)	DIS 1: I try to avoid using technology if I am uncomfortable with it	0.85	.845	0.837	1.194
	DIS 2: My confidence goes down if I am having high discomfort		.893
	DIS 3: I believe technology is less useful for me if my discomfort is high		.819
Insecurity (INS)	INS 1: I am concerned about technology’s productivity if I can’t believe in it	0.901	.840	0.745	1.342
	INS 2: Providing personal information is unsafe and can be misused over the internet		.891
	INS 3: Dependency on technology too much can be harmful sometimes		.881
Subjective Norms (SN)	SN1: I was encouraged to improve my Metaverse-based learning using gamification by my friends, classmates, seniors, and juniors	0.935	.896	0.661	1.513
	SN2: People who influence me in improving Metaverse-based learning prefer to use gamification		.880
	SN3: Metaverse-based learning enhancement through gamification is my status symbol		.850
Perceived Behavioural Control (PBC)	PBC1: I am confident that gamification will boost my Metaverse-based learning	0.923	.838	0.644	1.553
	PBC2: I can understand that my Metaverse-based learning is enhanced due to gamification		.840
	PBC3: I have control over performing the defined behaviour		.854
Gamification (GAM)	GAM1: Metaverse-based learning became better due to gamification	0.951	.867	0.655	1.528
	GAM2: Knowledge about gamification increased because of the development of Metaverse-based learning		.872
	GAM3: I would most likely use the technology again if fun and entertainment were added to the learning		.862
	GAM4: I would prefer using this technology more if it gave me points, incentives, etc.		.878
Adoption of Metaverse-based learning (MBL)	MBL1: My Metaverse-based learning is boosted due to the implementation of gamification	0.871	.800
	MBL2: I can understand how gamification influenced the improvement of my Metaverse-based learning		.839
	MBL3: Now, I know that many factors are required to enhance my Metaverse-based learning along with gamification technology		.875
	MBL4: I plan to grow my Metaverse-based learning in future		.807

5.3. Exploratory factor analysis

The loading values of all factors were found to be greater than or equal to 0.5, which was the acceptance level. Table 2 presents the factor loadings, VIF (Multi-collinearity Test).

5.4. Measurement model

Composite reliability scores greater than 0.70 indicate the constructs’ good indication reliability (DeVellis et al., 2003). Additionally, all AVE values were higher than the recommended threshold of 0.50, meeting the convergent validity requirement (Jaiswal et al., 2023). The construct pairs attained discriminant validity upon evaluation. Table 3 summarizes the results as the calculated correlation between the indicators is less than the square root. The Heterotrait-Monotrait (HTMT) test addresses some limitations of the Fornell-Larcker criterion and provides a more robust assessment of the distinctiveness between constructs. Table 4 shows that the values of HTMT between the pairs of constructs are less than 0.9. So, discriminant validity among the construct is exhibited. Convergent and discriminant validity refers to the reliable and valid measurement model.

Table 3. Divergent validity matrix.

	CR	AVE	MSV	MaxR(H)	TRU	MBL	GAM	PU	PEoU	SN	ATT	INS	DIS	PBC
TRU	0.907	0.71	0.362	0.921	0.842
MBL	0.862	0.612	0.235	0.884	0.462***	0.782
GAM	0.872	0.631	0.369	0.883	0.522***	0.400***	0.794
PU	0.888	0.667	0.362	0.903	0.601***	0.472***	0.547***	0.816
PEoU	0.855	0.597	0.286	0.857	0.478***	0.457***	0.473***	0.534***	0.773
SN	0.829	0.618	0.163	0.833	0.241***	0.263***	0.288***	0.292***	0.292***	0.786
ATT	0.849	0.652	0.369	0.86	0.514***	0.380***	0.607***	0.561***	0.394***	0.166**	0.808
INS	0.819	0.603	0.188	0.834	0.351***	0.296***	0.344***	0.432***	0.433***	0.404***	0.345***	0.777
DIS	0.787	0.555	0.169	0.809	0.226***	0.263***	0.227***	0.331***	0.279***	0.235***	0.199**	0.412***	0.745
PBC	0.799	0.57	0.262	0.807	0.512***	0.485***	0.367***	0.458***	0.502***	0.319***	0.444***	0.379***	0.242***	0.755

Table 4. HTMT analysis.

	GAM	PU	MBL	ATT	PEOU	SN	INS	PBC	TRU	DIS
GAM
PU	0.194
MBL	0.354	0.206
ATT	0.143	0.515	0.123
PEOU	0.195	0.292	0.112	0.074
SN	0.381	0.008	0.033	0.059	0.207
INS	0.422	0.039	0.101	0.011	0.018	0.454
PBC	0.543	0.237	0.278	0.116	0.015	0.489	0.361
TRU	0.001	0.302	0.218	0.359	0.034	0.153	0.141	0.124
DIS	0.089	0.036	0	0.216	0.034	0.181	0.121	0.189	0.432

5.5. Structural model

The structural model for the subsequent hypothesis testing is presented in Figure 2. Figure 2 depicts how the researchers tested the hypothesis using structural equation modelling with AMOS 22.0. Table 5 shows that the six hypotheses were not supported, while the rest were supported.

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

6. Discussion and implications

In this study, we examined the function of gamification in the education sector to assess its impact on MBL among university students. The key goal of GAM technology adoption is to promote user engagement, retention, and acquisition. GAM fosters fun and aids user retention, engagement, and acquisition. PEoU is not significantly associated with the TRU of the user, which suggests that Hypothesis 1 is not supported. This suggests that metaverse-based learning might not be enhanced if gamification is implemented. As a result, PEoU might not help with metaverse-based learning. The research work by Purohit and Arora (2021) contradicts our research study findings. Their research indicated that less-educated users prefer to adopt user-friendly technologies. Hypothesis 2 is supported, implying that consumers’ PU is associated with the TRU of the user. As a result, it is possible to establish that PU positively influences consumers’ opinions of the role of gamification. These findings contradict prior research, suggesting that technological knowledge does not improve MBL (Purohit & Arora, 2021). Hypothesis 3 was not supported, stating that PEoU doesn’t influence user ATT. Thus, PEoU didn’t indicate consumers’ attitudes towards MBL. The study of Çera et al. (2020; Yang et al., 2023) doesn’t support our results. Hypothesis 4 was supported, indicating that PU and ATT have significant associations. Customers are motivated to adopt new technologies because they believe using a particular technology will boost their performance. Al-Adwan et al. (2023) support our results, stating that PU can help researchers understand a person’s trust in a certain technology. Hypothesis 5 is supported, implying a significant relationship between ATT and GAM in adopting MBL. The research of Al-Adwan et al. (2023; Groh, 2012) indicated that GAM positively impacts students’ ATT, which supports our findings.

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 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).

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 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).

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.

Disclosure statement

No potential conflict of interest was reported by the author(s)

Data availability

Data available on request from the author (Name: Tarinmoy Das; Email: [email protected])

Additional information

Funding

The authors received no funding for this study.

Notes on contributors

Tarinmoy Das

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.

Sivaji Ganesh Kondamudi

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

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.

Debajyoti Pal

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.

Rita Roy

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.

Suree Funilkul

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.