AN ENHANCED INNOVATION RESISTANCE THEORY TO MEASURE THE BARRIERS OF AI-BASED CHATBOTS USAGE AMONG TEACHER TRAINEES

Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4642 AN ENHANCED INNOVATION RESISTANCE THEORY TO MEASURE THE BARRIERS OF AI-BASED CHATBOTS USAGE AMONG TEACHER TRAINEES LIU YONGGANG1 , HAPINI AWANG2 ,NUR SUHAILI MANSOR3 1Institute for Advanced and Smart Digital Opportunities, School of Computing Universiti Utara Malaysia, Malaysia 2Institute for Advanced and Smart Digital Opportunities, School of Computing Universiti Utara Malaysia, Malaysia 3Institute for Advanced and Smart Digital Opportunities, School of Computing Universiti Utara Malaysia, Malaysia E-mail: [email protected], [email protected], [email protected] ABSTRACT In recent years, Information and Communication Technology (ICT) has experienced tremendous progress (especially the advancement of AI-based Chatbots), profoundly affecting the global economic structure, social transformation, business innovation, education models, soft skills acquisition, human lifestyles, and so on. The main objective of this study is to develop and validate an enhanced Innovation Resistance Theory (IRT) model to measure the barriers of AI-based Chatbots usage among teacher trainees. This study mainly uses the quantitative research method and PLS-SEM for data analysis. This study finds that Value Barrier (VB), Risk Barrier (RB), Image Barrier (IB), Information Quality Barrier (IQB), and Job Relevance Barrier (JRB) have a significant and direct influence on teacher trainees’ resistance to AI-based Chatbots (RTAC). However, the effects of Usage Barrier (UB) and Tradition Barrier (TB) on teacher trainees’ RTAC are less significant. VB plays a mediating role in the relationship between Technology Anxiety (TA) and RTAC. RB mediates the relationship between the Electronic Word-of-Mouth Barrier (E-WOMB) and RTAC. JRB can also play a mediating role. This study not only proposes a new theoretical model, which is based on the traditional IRT model and combines new constructs (e.g., IQB and E-WOMB) and new paths (e.g., the mediating role of JRB), but also contributes to the cultivation of future technological talents and the spread and development of AI-based Chatbots in the future. Keywords: AI, AI-based Chatbots, Information and Communication Technology (ICT), Barrier, Education. 1. INTRODUCTION In recent decades, Information and Communication Technology (ICT) has become one of the most inventive technological domains and a crucial facilitator of innovation across different industries (1). ICT has experienced tremendous progress, profoundly affecting the global economic structure, social transformation, business innovation, education models, soft skills acquisition, and human lifestyles (1–4). Subjects associated with ICT have experienced some of the most accelerated growth in patent publications; their proportion of total patent publications increased greatly (Figure 1) (1). In 2020, the seven largest investors in research and development (R&D) were all ICT companies: Alphabet, Amazon, Apple, Huawei, Meta, Microsoft, and Samsung (1). Therefore, ICT has gained a significant share and occupies a nonnegligible position in the global landscape, and it is likely to continue to have a vital influence on future economic dynamics, societal evolution, technological upgrading, educational situation, and others. As an important component of ICT, Artificial Intelligence (AI) incorporates multiple technologies such as Natural Language Processing (NLP), Machine Learning (ML), Deep Learning (DL), and has extremely strong perception, learning, Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4643 inferencing, and problem-solving capabilities (4–6). Figure 1: Global ICT-related Patent Publications from 1980-2020 AI has outperformed human performance on various criteria, such as picture classification, visual reasoning, and English comprehension (Figure 2) (7). According to the International Monetary Fund’s staff forecasts, nearly 40 % of global employment is affected by AI, and about 60 % of jobs in developed economies are likely to be affected by AI (8). As a result, a number of technologies embedded in AI have not only demonstrated the ability to outperform humans in several aspects but also have the potential to have a widespread and profound impact on both the economy and the job market worldwide. Figure 2: Select Al Index Technical Performance Benchmarks VS. Human Performance With the iteration of AI technology, especially the flourishing of AI-based Chatbots represented by ChatGPT and Sora, the capability boundaries, visual scope, NLP capabilities, DL capabilities, simulation capabilities, and so on of AI technologies have been greatly improved. AI-based Chatbots are software programs that can communicate with users verbally or through text (6,9). Currently, many AI-based Chatbots have emerged globally, including but not limited to: ChatGPT, Google Bard, New Bing, Kimi, Ernie Bot, and Tongyi Qianwen. AI-based Chatbots perform well in programming, continuous dialogue, writing, text analysis, logical deduction, memory consolidation, and others. Numerous industries are also gradually being affected to varying degrees by AI-based Chatbots, for example: computing (10– 12), smart driving (13,14), unmanned aerial vehicles (UAVs) (15–17), video production (18,19), data science (20,21), healthcare (22,23), education (10,24). In the field of education, despite the huge possibilities of AI-based Chatbots for lesson planning (25–27), teaching efficiency (28,29), content improvement (30,31), educational assessments (27), personalized instruction (32), stimulating motivation (33), and numerous other benefits, it is still being resisted by lots of teacher trainees. The integration between AI-based Chatbots and education is still very insufficient, and many teacher trainees are resistant (e.g., postponing, delaying, or rejecting) to AI-based Chatbots (34– 40). However, previous studies mainly focused on the relationship between AI-based Chatbots and other populations (5,20,30,40–42), while the study targeting the specific group of teacher trainees are still very limited. Simultaneously, a number of prior researchers have attempted to utilize Technology Acceptance Model (TAM) (43,44), Unified Theory of Acceptance and Use of Technology (UTAUT) (45,46), Diffusion of Innovation (DOI) theory (47) in the field of information systems (IS) to explore different factors influencing users’ technology acceptance, while neglecting the function of Innovation Resistance Theory (IRT) in teacher trainees’ AI-based Chatbots resistance behaviors. Regrettably, TAM, UTAUT, DOI, and other theoretical models are primarily applicable to the analysis of technology acceptance behaviors, and it is difficult to capture the psychological barriers, physical barriers, negative behavior characteristics, and other factors of users in terms of technology resistance behaviors. Since this study mainly focuses on the technology resistance behaviors of teacher trainees, these theoretical models are not suitable for this study. IRT provides a comprehensive framework to analyze why users are resistant to adopting new products or technologies, and explains in-depth the resistance behaviors of consumers when they are confronted with new technologies or products (48–52), so it is suitable to be used as the basic theoretical framework for this study. Additionally, historical research on IRT has primarily focused on its five foundational variables (Usage Barrier (UB), Value Barrier (VB), Risk Barrier (RB), Tradition Barrier (TB) and Image Barrier (IB)) (53–58), but ignoring empirical Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4644 examinations of Information Quality Barrier (IQB) and Job Relevance Barrier (JRB). Noticeably, with the development of information technology (IT), the quality of information generated by AI-based Chatbots is becoming more and more important. With respect to teacher trainees, if the content generated by AI-based Chatbots has serious IQB (e.g., fraudulent information, misleading teaching guidelines, or non-verified statistics), it is quite likely to lead teacher trainees to resist AI-based Chatbots. Unfortunately, a lot of previous studies have paid attention to the relationship between information quality and the adoption of other technologies (45,59,60), while neglecting to deeply analyze the association between IQB and the resistance behaviors of AI-based Chatbots, particularly among the peculiar group of teacher trainees. Besides, the JRB signifies teacher trainees’ perception of barriers referring to the degree to which the AI-based Chatbots are applicable to his or her job. If teacher trainees perceived that AIbased Chatbots are irrelevant to their present job and future work contents, they may lack enough motivation to accept these innovative technologies. Nevertheless, past studies have largely focused on the positive impact of job relevance on acceptance behaviors (29,61–63), while ignoring the influence of JRB among teacher trainees. Previous work has also noted the significant effect of Technology Anxiety (TA) on adoption behaviors or resistance behaviors (64–69), but very few studies have explored the indirect effects of TA on resistance to AI-based Chatbots (RTAC) behaviors via VB and JRB. The role of Electronic Word-of-Mouth Barrier (E-WOMB) in consumer decision-making behaviors should also not be ignored (65,70–72), while the relationship between E-WOMB and RTAC has not been fully explored, especially when RB, JRB are used as mediating variables. Therefore, the main objective of this study is to develop and validate an enhanced IRT model to measure the barriers of AI-based Chatbots usage among teacher trainees. Within this enhanced IRT model, this study also empirically examines the relationship between IQB, JRB, and teacher trainees’ RTAC. Teacher trainees are both users and future promoters of AI-based Chatbots techniques. The persistence of barriers may result in technological lags, economic losses, and weakened competitive advantages. Although innovation has been one of the focuses of scholars, however, previous researchers have devoted more attention to the logical relationship between AI-based Chatbots and positive attributes and less attention to the logical relationship between AI-based Chatbots and negative attributes (73,74). One of core concerns of this study focuses on the barriers of AI-based Chatbots usage among teacher trainees, and its choice is also based on the considerations: (1) Theoretical gap: the existing theoretical models for measuring teacher trainees’ RTAC are still very rare; (2) Realistic demand: studying this topic will not only contribute to the spread of AI-based Chatbots among teacher trainees, but also contribute to the cultivation of future technological talents and the spread and development of AI-based Chatbots in the future society; (3) Method innovation: the test of the negative factors and new scale in this paper can capture the essence of barriers more accurately and make up for the limitations of traditional methods. This study not only helps to extend the theoretical boundaries, suitable scope, and applicable groups of the IRT model, but also provides referenceable data and practical guidance for overcoming the barriers in the process of AI-based Chatbots diffusion. 2. LITERATURE REVIEW 2.1 Information and Communication Technology (ICT) In recent decades, the significant advancement of ICT has resulted in many economic and noneconomic transformations, social revolutions, lifestyle modifications, and education changes across the world (3,4,32,75). ICT applications include but are not limited to NLP, internet of Things (IoT), virtual reality (VR), augmented reality (AR), mixed reality (MR), automatic speech recognition (ASR), online learning platforms, intelligent tutoring systems, metaverse, AI-based Chatbots (1,32,76,77). From 2005 to 2019, the global ICT services exports virtually increased fourfold, which was mostly due to IT services, and the proportion of ICT services in overall services exports increased consistently from 7% to 11% (see Figure 3) (1). In 2022, IT services, the fundamental component of ICT services exports, increased by 43% relative to 2019 (1). According to the Trade in Value-Added (TiVA) data set, the value-added development rate of IT services is around double that of the global economy, outperforming all other industries during the previous two decades (1). Nonetheless, a series of difficulties and challenges have arisen, such as ethical issues (32), regional imbalance issues (1,4), and outdated infrastructure Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4645 (75). Therefore, reviewing the great opportunities and myriad challenges that have arisen during the evolution of ICT also provides a valuable reference for the continuation of this study. Figure 3: The Global ICT Services Exports 2.2 Artificial Intelligence As one of the crucial branches of ICT, the role of AI in enhancing international competitiveness, allocating resources, knowledge management, digital transformation, and improving decisionmaking efficiency cannot be underestimated, and it has become one of the core driving forces of the fourth industrial revolution (78–82). According to Statista data, the market size of AI is projected to exhibit a compound annual growth rate (CAGR 2025-2030) of 27.67%, culminating in a market volume of US$826.73 billion by 2030 (83). Simultaneously, the AI market’s impact on GDP might reach 50% to 70% by 2030 (Figure 4) (83). PwC’s Global Artificial Intelligence Study indicates that global GDP may increase by as much as 14% by 2030 due to AI, representing an additional $15.7 trillion, hence being the most significant commercial potential in the current rapidly evolving economy (84). Numerous countries have also paid high attention to the evolution of AI and have taken a series of measures in many aspects such as financial investment, policy preference, talent cultivation, and technological upgradation, for example: the United States, Canada, United Kingdom, Australia, Singapore, China (4,85–88). In terms of industries, AI has also had a widespread and profound impact on different industries, including healthcare, finance, industrial robotics, knowledge management, marketing, journalism, movies, short videos, art, unmanned vehicles, UAVs, education, and so on. For example, in the healthcare industry, AI is playing a role in empowering medical professionals to diagnose patients with a wide range of diseases, reshaping healthcare business models, innovating system performance, improving the patient experience, and others (89–91). In the financial domain, AI has tremendous potential in stock price prediction, asset allocation, investment consulting, risk control, algorithmic transactions, fraud detection, credit scoring, and other directions (92–95). In the educational sector, AI presents both a lot of opportunities, such as: a smart tutoring system (96), supplementary teaching and learning resources(97,98), programming self-efficacy (99), tailoring the learning experience (100), and humancomputer interaction (101); and various challenges, such as: inappropriate utilization of AI technologies (98), misinformation (101), algorithmic biases (24), ethical issues (96,102), or privacy concerns (24). As a result, the current growing tendency of AI is hard to stop, and how to make full use of the advantages brought by AI to raise international competitiveness, optimize the economic and social structure, and promote the development of different industries while circumventing the incidental negative effects and so on are all problems worthy of further indepth study. Figure 4: The Impact of the Artificial Intelligence Market on GDP 2.3 Artificial Intelligence-Based Chatbots and Teacher Trainees In recent years, AI-based Chatbots, which are supported by large-scale language models, for instance ChatGPT, Bard, Grok, New Bing, Kimi, Ernie Bot, Tongyi Qianwen, DeepSeek, have significantly improved the ability of AI in various dimensions, such as language understanding, information generation, human-computer interaction, and content analysis (6,98,103,104). AIbased Chatbots fully leverage NLP, ML, DL, Deep Neural Network (DNN), Sentiment Analysis (SA), Context-Awareness, Conversation Management, and other technologies to enable computers to engage in human-like verbal interactions that lead to conversations, question answering, and task completion (9,10,33,98,103). According to Deloitte forecasts, with the rapid growth in demand for generative artificial intelligence (GAI) training and inferencing, global data center electricity use might double to about 1,065 TWh by 2030 (Figure 5) (105). Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4646 Figure 5: Global Data Center Electricity Use Expected (Note: P represents the predicted value) GAI has the potential to contribute approximately $4.4 trillion annually to the world’s economy, transforming industries and worldwide commerce (106). AI-based Chatbots have begun to influence a variety of different industries, including but not limited to e-commerce, customer service, short video making, healthcare, and education. For instance, in the domain of customer service, AIbased Chatbots are playing a role in enhancing customer engagement (107), improving customer satisfaction (108), smart searching (109), and maintaining brand reputation (110). In the field of education, AI-based Chatbots are becoming an important tool to support teaching and learning. AIbased Chatbots offer a number of benefits for teaching and instruction in different aspects, like teaching methods (29,31,111), teaching resources (31,101,111), individualized feedback (27,30,112), customized tutoring (31,32,76,111), students’ selfpaced learning (24,32,99), classroom management techniques (5,61,63,102,111), and many others. Despite AI-based Chatbots having so many advantages and benefits, however, many teacher trainees still show different levels of resistance (e.g. rejection, postponement, procrastination, or even a tendency of opposition) to AI-based Chatbots (34,36,39,112–114). Some teacher trainees’ resistance to AI-based Chatbots may derive from technological incompatibility, perceived value barriers, risk worries, traditional perceptions, or unfavourable images (34,39,43,50,99,111–113,115– 117). Notably, teacher trainees, as future educators, are both important consumers and influential promoters of AI-based Chatbots, while their negative attitudes or resistance behaviours towards AI-based Chatbots will directly affect the valid promotion and application of AI-based Chatbots in future education. However, so far, there is still a lack of suitable and valid theoretical models to measure the relationship between the main barriers and teacher trainees’ resistance to AI-based Chatbots. 2.4 Innovation Resistance Theory Previous studies have attempted to study AIbased Chatbots with different theoretical models, such as TAM (118–120), UTAUT 1 or 2 (121,122), Diffusion Theory of Innovation (123), Theory of Social Support (124), Protection Motivation Theory (PMT) (125), Elaboration Likelihood Model (ELM) (126), Expectation-Confirmation Model (ECM) (122), Use and Gratification Model (127), Status Quo Bias (SQB) Theory (128), and so forth, but they have mainly focused on testing the acceptance willingness or adoption behaviours of different groups of people, and have neglected to measure the resistance behaviours of teacher trainees to AIbased Chatbots from the negative perspective. In exploring the phenomenon of teacher trainees resisting AI-based Chatbots, choosing the appropriate theoretical framework is critical. Although models of technology acceptance categories commonly employed in past research have delivered essential theoretical support for understanding technology adoption behaviour, these models primarily concentrate on users’ positive acceptance paths to technology, emphasizing positive drivers such as perceived usefulness, ease of use or hedonic motivation, while these theories are inadequate for explaining why teacher trainees resist emerging technologies. In contrast, Innovation Resistance Theory (IRT) (Ram & Sheth, 1989) provides a more comprehensive perspective for understanding technological resistance behaviours by systematically analysing the Usage Barrier (UB), Value Barrier (VB), Risk Barrier (RB), Tradition Barrier (TB) and Image Barrier (IB) that users encounter in accepting innovative technologies. Inside the classical constructs of the IRT model, the UB primarily refers to the incompatibility between innovative products and consumers’ existing workflows, practices, or habits (50,129). Prior investigations in information systems have shown that the correlation between UB and the acceptance of different merchandise has garnered significant scholarly focus (130–135). VB is mostly associated with a weaker performance-to-price value, particularly in comparison to alternatives (50,136). In the past, the role of VB has also been tested in different scenarios, which include MOOC (134), eco-friendly cosmetics (135), hotel booking apps (132), mobile payment (137), and so on. Ram and Sheth (1989) posited that consumers were likely to postpone or reject the adoption of new commodities Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4647 after recognizing the relative RBs, which contained economic risk, social risk, physical risk, and functional risk. The correlation between RB and the acceptance or resistance to innovation has been acknowledged as significant by numerous prior surveys (51,54,64,130,137–139). For example, teacher trainees may be resistant to AI-based Chatbots because of worries about the potential RB, including ethical risks, privacy leakage, misinformation dissemination, and more. Some teacher trainees may prefer to follow the traditional mode (one of RB) of teaching because it is more familiar and comfortable for them, whereas AIbased Chatbots are unable to offer timely communication or emotional support during the teaching process, leading to some teacher trainees’ RTAC. Ram and Sheth (1989) thought that TB for personal customers might arise when their behaviours deviate from social norms or familial values. There was a number of published literature explaining the effects of TB (53,133–135,137,139– 141). IB is mainly related to the customer’s unfavourable image of a product, which may stem from any unfavourable association, such as the category to which the product belongs, the industry to which the product belongs, or the country in which the product is manufactured (50). The impact of IB has attracted heightened attention from several researchers (134–137,142). Synchronously, the IRT has been validated and applied to a number of areas of research, for example: mobile payments (52,143), service robots (SRs) (144), metaverse (145), facial recognition payment (146), driver assistance systems (147), smart hotels (74,148), non-fungible tokens (NFTs) (56,149), green IT (150), shopping platforms (151), over-the-top services (OTTs) (152), autonomous delivery vehicles (ADVs) (153), online-learning (154), healthcare (155), fitness apps (156), online dating apps (ODAs) (157), algorithm aversion (158), online-to-offline (O2O) platforms (159), virtual streamers (160), electric vehicle (161,162), travel applications (163,164), and so on. Therefore, the IRT is a suitable foundational model for this study, which not only contributes to deepening the understanding of the phenomenon of teacher trainees’ RTAC at the theoretical level and identifying some of the major barriers behind this phenomenon, but also helps to propose valuable references for the direction of technological research and development of AI-based Chatbots and their applications at the practical level. However, with the shifting economic situation, social restructuring, and technological advances, especially the rapid changes in generative AI such as ChatGPT, the limitations of the traditional IRT model have been gradually exposed, for example: insufficient consideration of the quality of the information generated, and insufficient comprehensiveness in capturing the technical features. Prior researchers have experimented with adding some variables such as mobile innovativeness (165), embarrassment (146), inertia (159), expertise barriers (166), technology vulnerability barriers (152), surveillance (143), information overload (167), or moderating variables such as attitude (157), gender (153), environmental concern (168) and discoverability (159) to increase the explanatory power of the model or to increase the applicability of the scenarios, however, empirical validation of the relationship between the IQB, JRB, and RTAC is still very inadequate, particularly among teacher trainees. What’s more, the progression of AI-based Chatbots technologies, coupled with modifications in economic and social situations, has rendered the constraints of conventional IRT influencing factors increasingly conspicuous (134,137,169,170), whereas the factors that include Information Quality Barrier (IQB) and Job Relevance Barrier (JRB) may become significant constructs that impact teacher trainees’ resistance behaviours. For instance, in the education background, if AI-based Chatbots are unable to generate accurate, valid, latest, or personalized teaching information, it is likely to lead directly to teacher trainees’ resistance to AIbased Chatbots. Unfortunately, most previous studies have paid attention to the association between information quality and the adoption of other technologies (59,119), while having neglected in-depth analysis of the connection between IQB and the resistance behaviours of AI-based Chatbots, particularly among teacher trainees. Besides, if teacher trainees perceive JRB, which means that AI-based Chatbots have little relevance to their present tasks and future jobs, hence lack a strong incentive to apply to these innovative techniques. Nevertheless, past research mainly focused on the positive effect of job relevance on adopter behaviours (29,61–63,171), while few empirical studies have measured the relationship between JRB and resistance to AI-based Chatbots. In this study, Technology Anxiety (TA) mainly relates to the degree of anxiety and emotional reactions that are caused by using AI-based Chatbots or considering the possibility of new Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4648 technology utilization (68,69,172–177). Cham et al. (2022) found that TA was one of the key psychological barriers affecting mobile payment services. The relationship between anxiety and other factors affecting technology adoption (67). In the case of T-Express, there are some researchers noticed the influence of TA on VB (65). In spite of many researchers have pointed out the important role of TA in consumer technology acceptance or resistance behaviours (64–69), little research has systematically explored the indirect effects of TA on resistance to AI-based Chatbots through VB and JRB, and this study will fill this gap. Electronic Word-of-Mouth Barrier (E-WOMB) is primarily associated with the perception of negative comments made by potential, actual, or former netizens about AI-based Chatbots, which are made available to numerous individuals or institutions through the internet (178–180). E-WOMB also played an important role in people’s decisionmaking behaviours (65,70–72), but the connection between E-WOMB and resistance to AI-based Chatbots among teacher trainees has not been thoroughly studied, especially when RB and JRB are treated as mediating variables. As teacher trainees will be pivotal in the future of education, their perceptions of AI-based Chatbots may significantly influence the future implementation of such technology in educational settings. All in all, as ICT’s global influence increases and AI technologies advance rapidly, they are becoming more penetrative and powerful in the education space. In the past, the related studies on theoretical models for measuring teacher trainees’ RTAC were still very limited, while this study proposes a new theoretical model that is based on the traditional IRT and combines new constructs and new paths. In recent years, with the emergence of AI-based Chatbots such as ChatGPT, related research has once again become one of the hot directions. However, there is still a paucity of research examining teacher trainees’ resistance to AI-based Chatbots behaviours from the innovation resistance theory, so there is a need to develop a model and conduct an empirical study. 3. CONCEPTUAL MODEL Based on the analysis of the aforementioned literature review and the core framework of IRT, this study proposes a conceptual model for teacher trainees’ RTAC. Inheriting the five barrier dimensions in the Innovation Resistance Theory - UB, VB, RB, TB, IBthis model further combines the situational characteristics of the field of educational AI-based Chatbots with the uniqueness of the teacher trainee population, so as to construct a theoretical model that is more in line with practical application contexts. This new conceptual model introduces two new independent variables (IQB and JRB) and attempts to test the indirect effects of TA and E-WOMB on RTAC (Figure 6). Unlike the previous TAM and UTAUT models that focus on positive acceptance behaviors, the current conceptual model in this study emphasizes the complex psychological dynamics and behaviors of teacher trainees when facing new technologies from the perspective of “resistance,” providing a new research path for the theory and practice of the application of AI-based Chatbots in the field of information technologies. Figure 6: Overview of Conceptual Model and Research Hypotheses 4. HYPOTHESES The hypothesis is a statement, or a set of statements presented as a provisional causal explanation for an observable phenomenon, and it is critically significant in the scientific process (181– 183). In the present study, to identify the main barriers of teacher trainees’ RTAC, the following hypotheses are proposed (Figure 6). 4.1 Main Hypotheses 4.1.1 Usage Barrier Based on Ram and Sheth’s (1989) opinions, one of the important reasons for customers’ resistance to innovative products was the incompatibility between the new and traditional things. Some researchers argued that UB had a non-significant impact on algorithm aversion (158). Nonetheless, a majority of studies discovered that UB had an impact on the acceptance or rejection (52,56,153,157). For instance, Siddiqui et al. proved that UB had a remarkable negative impact on the acceptance of online dating apps (ODAs) (157). For teacher trainees, if they perceived that AI-based Chatbots were incompatible with their current Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4649 habits, working style, or lifestyle, they might not want to utilize them again in the future. Therefore, the present examination proposes the subsequent hypothesis: H1： Usage Barrier has a significant influence on Resistance to AI-based Chatbots among teacher trainees. 4.1.2 Value Barrier One study found that VB was not statistically significant evidence contributing to small merchants’ hesitance toward O2O platforms (159). But a lot of previous studies have proved the relationship between VB and users’ behaviors in various contexts, for instance: green IT (150), overthe-top services (OTTs) (152), autonomous delivery vehicles (ADVs) (153), smart hotels (148), AI algorithms (158), NFTs (56), mobile social commerce (142). According to these findings, it is probable that VB will similarly influence users’ behaviors in the realm of AI-based Chatbots, either positively or negatively. The present study posits that VB is likely a significant factor influencing teacher trainees’ RTAC, hence proposing the following hypothesis: H2： Value Barrier has a significant influence on Resistance to AI-based Chatbots among teacher trainees. 4.1.3 Risk Barrier In the past decade, the majority of research in IS has concentrated on the impact of RB on the acceptance of innovation (51,56,138,150,157). Conversely, Ma and Lee (2019) contended that RB was inconsequential to the utilization of MOOCs in a developing nation. Regrettably, these studies have primarily examined the correlation between RB and the adoption of commodities. Only a limited number of studies have investigated resistance to innovation from the perspective of RB, such as by Cham et al. (2022), Leong et al. (2020), Uddin et al., (2024). The prior inconsistencies and disputes about content and outcomes have prompted the present research to assert that an additional comprehensive examination of the relationship between RB and RTAC is urgently required. Thus, this study proposes the subsequent hypothesis: H3： Risk Barrier has a significant influence on Resistance to AI-based Chatbots among teacher trainees. 4.1.4 Tradition Barrier In the background of psychological resistance, Ram and Sheth (1989) also illustrated that entrenched traditions significantly impact individual behaviors. A considerable number of scholars have already looked into the correlation between TB and technology acceptance or use intention in various dimensions: digital payment systems (139), ecofriendly cosmetics (135), ODAs (157), green IT (150), virtual streamers (160), e-learning (154). Nevertheless, such studies have predominantly been approached from the perspective of TB and customers’ acceptance behaviors. Empirical research carried out by Uddin et al. (2024) that measured the WhatsApp payment system (WPS) revealed the relationship between TB and resistance to innovation. Consequently, based on the preceding considerations, this study posits that teacher trainees’ RTAC may be influenced by TB, and presents the following hypothesis: H4 ： Tradition Barrier has a significant influence on Resistance to AI-based Chatbots among teacher trainees. 4.1.5 Image Barrier Ram and Sheth (1989) thought that innovations acquired a distinct character from their birth, and the IB emerged out of stereotyped concepts and made innovations difficult. Numerous studies have focused on the correlation between IB and usage intention or adoption across many domains: mobile payments service (137), O2O platforms (Chawla et al., 2024), stereotype for MOOCs (134), green IT (150), and so on. Only very few researchers (e.g., 151) studied IB from a resistance viewing angle. In light of the preceding arguments concerning IB, the following hypothesis is proposed in this study: H5： Image Barrier has a significant influence on Resistance to AI-based Chatbots among teacher trainees. 4.1.6 Information Quality Barrier Based on Eom’s (184) findings, the utilization of e-learning management systems (e-LMS) did not exhibit a positive relationship with information quality. Nevertheless, a lot of prior surveys have demonstrated the relevance between information quality and the adoption of innovations in dissimilar domains, such as big data analytics (BDA) (185), blockchain (186), and cash on delivery (COD) payment system in Shopee (187). What’s more, Michel-Villarreal et al. (98) elucidated that the deficiency in accuracy and dependability of the information produced by the GenAI system would lead to issues. For teacher trainees, if the information quality created by AI-based Chatbots was low mass and produced IQB, it might cause teacher trainees’ resistance and the failure of innovative technologies. The preceding information Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4650 and arguments led to this study to propose the hypothesis: H6 ： Information Quality Barrier has a significant influence on Resistance to AI-based Chatbots among teacher trainees. 4.1.7 Job Relevance Barrier Job relevance is an influential element of TAM2 for assessing intention to use or use behaviors (63,188). The majority of previous studies examined the positive correlation between job relevance and the acceptance of innovative products in miscellaneous subjects, for example, LMS (61,63) and AI-based conversational agents (42). Nevertheless, a great deal of prior research has concentrated on job relevance rather than JRB, particularly with the implementation of AI-based Chatbots. On the opposite side, if teacher trainees perceived that the results generated by AI-based Chatbots were irrelevant to their present work or future job (e.g., incorrect teaching information, incompatible pedagogical styles, or misleading instructional guidelines), they may reject the utilization of AI-based Chatbots. Consolidating the above-mentioned deliberations and arguments, the subsequent hypothesis is formally propounded: H7： Job Relevance Barrier has a significant influence on Resistance to AI-based Chatbots among teacher trainees. 4.2 Mediating Hypotheses 4.2.1 The Mediating Effect of Value Barrier between Technology Anxiety and Resistance to AI-based Chatbots Previous studies have realized the negative impact of TA on perceived VB in different situations (65). For example, TA had an impact on the perceived value of an individual’s use of the mobile ticketing application T-express, thereby reducing the willingness to adopt (65). There are also several studies that have found that TA directly or indirectly contributes to people’s acceptance of technology (189,190). Whereas VB is closely related to the final behavioral decision of the users (56,150,154,191). Dogra et al. (192) argued that pricing value is the most important component and essential for visitors’ intention to acquire online travel products. For teacher trainees, if they feel VB due to TA, they are likely to resist AI-based Chatbots. VB may play a mediating role between TA and teacher trainees’ RTAC, which means that teacher trainees with higher technology anxiety are more likely to develop value skepticism towards AI-based Chatbots, which enhances their tendency to resist. Synthesizing the above deliberations, the next hypothesis is formulated for this study: H8： Value Barrier mediates the relationship between Technology Anxiety and Resistance to AIbased Chatbots among teacher trainees. 4.2.2 The Mediating Effect of Risk Barrier between Electronic Word-of-Mouth Barrier and Resistance to AI-based Chatbots Web-based technologies have provided multiple chances for E-WOM transmission (193). Ashtiani and Iranmanesh (194) found that positive word of mouth (P-WOM) had a positive influence on the acceptance of electronic banking, whereas negatively influenced the perceived risk of electronic banking services. In another empirical investigation, Tang and Chen (195) revealed that negative E-WOM had a positive effect on the seller’s resistance to the digital device recycling platform (DDRP). And for users, RB could have a strong implication on their decision to adopt or resist an innovation (52,64,133,137,138). For teacher trainees, electronic word-of-mouth barrier (E-WOMB) (e.g., generating misleading information, erroneous theoretical underpinnings, or various unfavorable comments) may exacerbate their perceived RB of AI-based Chatbots, which in turn may enhance their resistance behaviors. Considering the preceding discussions, the following hypothesis is subsequently proposed: H9 ： Risk Barrier mediates the relationship between Electronic Word-of-Mouth Barrier and Resistance to AI-based Chatbots among teacher trainees. 4.2.3 The Mediating Effect of Job Relevance Barrier between Technology Anxiety and Resistance to AI-based Chatbots TA encompassed fears of total incapacity to acquire new technologies, inadequate mastery of new technologies, inability to apply learned skills, and job displacement by younger (196). TA may increase users’ worries about JRB. Some research suggested that different forms of anxiety (e.g., Learning Anxiety, AI Configuration Anxiety, and Job Replacement Anxiety) may influence practitioners’ attitudes and psychological responses to AI (197). For teacher trainees, those individuals with higher levels of TA are more likely to perceive AI-based Chatbots as potentially of insufficient practical functionalities in their current professional training and future teaching job, and to develop a perception of JRB that enhances their tendency to resist AI-based Chatbots. After considering the above factors and preceding discussions, this study Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4657 19];12:55682–96. Available from: https://ieeexplore.ieee.org/abstract/docu ment/10500490 [18]. Cho J, Puspitasari FD, Zheng S, Zheng J, Lee LH, Kim TH, et al. Sora as an AGI World Model? A Complete Survey on Text-to-Video Generation [Internet]. arXiv; 2024 [cited 2024 May 26]. Available from: http://arxiv.org/abs/2403.05131 [19]. Mogavi RH, Wang D, Tu J, Hadan H, Sgandurra SA, Hui P, et al. Sora OpenAI’s Prelude: Social Media Perspectives on Sora OpenAI and the Future of AI Video Generation [Internet]. arXiv; 2024 [cited 2024 May 26]. Available from: http://arxiv.org/abs/2403.14665 [20]. Allahham M, Ahmad A. AI-induced anxiety in the assessment of factors influencing the adoption of mobile payment services in supply chain firms: A mental accounting perspective. Int J Data Netw Sci [Internet]. 2024 [cited 2024 Jul 29];8(1):505–14. Available from: https://m.growingscience.com/beta/ijds/6 500-ai-induced-anxiety-in-theassessment-of-factors-influencing-theadoption-of-mobile-payment-services-insupply-chain-firms-a-mental-accountingperspective.html [21]. Hassani H, Silva ES. The Role of ChatGPT in Data Science: How AIAssisted Conversational Interfaces Are Revolutionizing the Field. Big Data Cogn Comput [Internet]. 2023 Mar 27 [cited 2023 Nov 23];7(2):62. Available from: https://www.mdpi.com/2504-2289/7/2/62 [22]. Bilal M, Jamil Y, Rana D, Shah HH. Enhancing Awareness and Self-diagnosis of Obstructive Sleep Apnea Using AIPowered Chatbots: The Role of ChatGPT in Revolutionizing Healthcare. Ann Biomed Eng [Internet]. 2024 Feb;52(2):136–8. Available from: https://link.springer.com/10.1007/s10439 -023-03298-8 [23]. Cascella M, Montomoli J, Bellini V, Bignami E. Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios. J Med Syst [Internet]. 2023 Mar 4 [cited 2024 May 4];47(1):33. Available from: https://doi.org/10.1007/s10916-02301925-4 [24]. Baskara FxR. The Promises and Pitfalls of Using Chat GPT for Self-Determined Learning in Higher Education: An Argumentative Review. Pros Semin Nas Fak Tarb dan Ilmu Kegur IAIM Sinjai [Internet]. 2023 May 22 [cited 2024 May 4];2:95–101. Available from: https://journal.uiad.ac.id/index.php/SEN TIKJAR/article/view/1825 [25]. Bower M, Torrington J, Lai JWM, Petocz P, Alfano M. How should we change teaching and assessment in response to increasingly powerful generative artificial intelligence? Outcomes of the ChatGPT teacher survey. Educ Inf Technol [Internet]. 2024 Aug 1 [cited 2025 Feb 19];29(12):15403–39. Available from: https://doi.org/10.1007/s10639-02312405-0 [26]. ElSayary A. An investigation of teachers’ perceptions of using ChatGPT as a supporting tool for teaching and learning in the digital era. J Comput Assisted Learn [Internet]. 2024 [cited 2025 Feb 19];40(3):931–45. Available from: https://onlinelibrary.wiley.com/doi/abs/1 0.1111/jcal.12926 [27]. Fui-Hoon Nah F, Zheng R, Cai J, Siau K, Chen L. Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. J Inf Technol Case Appl Res [Internet]. 2023 Jul 3 [cited 2023 Nov 23];25(3):277–304. Available from: https://www.tandfonline.com/doi/full/10. 1080/15228053.2023.2233814 [28]. Elbanna S, Armstrong L. Exploring the integration of ChatGPT in education: adapting for the future. Manag Sustain: Arab Rev [Internet]. 2024 Jan 3 [cited 2024 Apr 4];3(1):16–29. Available from: https://www.emerald.com/insight/content /doi/10.1108/MSAR-03-20230016/full/html [29]. Algerafi MAM, Zhou Y, Alfadda H, Wijaya TT. Understanding the Factors Influencing Higher Education Students’ Intention to Adopt Artificial IntelligenceBased Robots. IEEE Access [Internet]. 2023 [cited 2024 Feb 4];11:99752–64. Available from: Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4658 https://ieeexplore.ieee.org/document/102 47529/ [30]. Han J, Li M. Exploring ChatGPTsupported teacher feedback in the EFL context. System [Internet]. 2024 Nov 1 [cited 2025 Feb 19];126:103502. Available from: https://www.sciencedirect.com/science/ar ticle/pii/S0346251X24002847 [31]. Kasneci E, Sessler K, Küchemann S, Bannert M, Dementieva D, Fischer F, et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learn Individ Differ [Internet]. 2023 Apr [cited 2023 Nov 23];103:102274. Available from: https://linkinghub.elsevier.com/retrieve/p ii/S1041608023000195 [32]. Chinonso OE, Theresa AME, Aduke TC. ChatGPT for Teaching, Learning and Research: Prospects and Challenges. Glob Acad J Humanit Soc Sci [Internet]. 2023 Mar 2 [cited 2023 Nov 23];5(02):33–40. Available from: https://www.gajrc.com/media/articles/G AJHSS_52_33-40.pdf [33]. Qu K, Wu X. ChatGPT as a CALL tool in language education: A study of hedonic motivation adoption models in English learning environments. Educ Inf Technol [Internet]. 2024 Mar 20 [cited 2024 Apr 4]; Available from: https://link.springer.com/10.1007/s10639 -024-12598-y [34]. Jiao J, Chen L, Wu W. Educational Issues Triggered by ChatGPT:Possible Impacts and Counter Measures. Chinese Journal of ICT in Education [Internet]. 2023; Available from: https://qikan.cqvip.com/Qikan/Article/De tail?id=7109383640&from=Qikan_Searc h_Index [35]. Li X, Jiang Y. Artificial intelligence in education: opportunities, current status, and prospects. Geogr Res Bull [Internet]. 2024 Aug 14 [cited 2025 Feb 19];3:146– 74. Available from: https://www.jstage.jst.go.jp/article/grb/3/ 0/3_146/_article/-char/ja/ [36]. Lu Y, Zheng Y. AI-TPACK: What It Means, Difficulties, and Possibilities of Cultivating Normal University Students’ Abilities in the Era of Artificial Intelligence. Advances in Psychological Science. 2024; [37]. Ma W, Shi S, Zhu C, Xing B, Wang Y, Shu J. Exploration of AI-empowered professional skill training models for normal school students. In: 2024 4th International Conference on Educational Technology (ICET) [Internet]. 2024 [cited 2025 Feb 19]. p. 31–4. Available from: https://ieeexplore.ieee.org/abstract/docu ment/10868158 [38]. Qureshi R, Hajare S, Verma P. A Review on the Role of Artificial Intelligence in Personalized Learning. In: 2024 Asia Pacific Conference on Innovation in Technology (APCIT) [Internet]. 2024 [cited 2025 Feb 19]. p. 1–5. Available from: https://ieeexplore.ieee.org/abstract/docu ment/10673706 [39]. UNESCO. Ministry of Education’s First Batch of “Artificial Intelligence + Higher Education” Application Scenarios Typical Case Reference. [Internet]. 2024. Available from: https://aiedchair.bnu.edu.cn/%E6%95%9 9%E8%82%B2%E9%83%A8%E9%A6 %96%E6%89%B9%E4%BA%BA%E5 %B7%A5%E6%99%BA%E8%83%BD %E9%AB%98%E7%AD%89%E6%95% 99%E8%82%B2%E5%BA%94%E7%94 %A8%E5%9C%BA%E6%99%AF%E5 %85%B8%E5%9E%8B%E6%A1%88/ [40]. Wu D, Zhang X, Wang K, Wu L, Yang W. A multi-level factors model affecting teachers’ behavioral intention in AIenabled education ecosystem. Educ technol res dev [Internet]. 2024 Sep 11 [cited 2025 Feb 19]; Available from: https://doi.org/10.1007/s11423-02410419-0 [41]. Lian Y, Tang H, Xiang M, Dong X. Public attitudes and sentiments toward ChatGPT in China: a text mining analysis based on social media. Technol Soc [Internet]. 2024 Mar 1 [cited 2025 Feb 19];76:102442. Available from: https://www.sciencedirect.com/science/ar ticle/pii/S0160791X23002476 [42]. Sonntag M, Mehmann J, Teuteberg F. AI-based Conversational Agents for Customer Service – A Study of Customer Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4659 Service Representative’ Perceptions Using TAM 2. 2022; [43]. Ma J, Wang P, Li B, Wang T, Pang XS, Wang D. Exploring user adoption of ChatGPT: a technology acceptance model perspective. Int J Hum–Comput Interact [Internet]. 2025 Jan 17 [cited 2025 Feb 19];41(2):1431–45. Available from: https://doi.org/10.1080/10447318.2024.2 314358 [44]. Saif N, Khan SU, Shaheen I, ALotaibi FA, Alnfiai MM, Arif M. Chat-GPT; validating technology acceptance model (TAM) in education sector via ubiquitous learning mechanism. Comput Hum Behav [Internet]. 2024 May 1 [cited 2025 Feb 19];154:108097. Available from: https://www.sciencedirect.com/science/ar ticle/pii/S074756322300448X [45]. Chen G, Fan J, Azam M. Exploring artificial intelligence (AI) chatbots adoption among research scholars using unified theory of acceptance and use of technology (UTAUT). J Librariansh Inf Sci [Internet]. 2024 Aug 19 [cited 2025 Feb 19];9610006241269189. Available from: https://doi.org/10.1177/09610006241269 189 [46]. Strzelecki A. Students’ acceptance of ChatGPT in higher education: an extended unified theory of acceptance and use of technology. Innov High Educ [Internet]. 2024 Apr 1 [cited 2025 Feb 19];49(2):223–45. Available from: https://doi.org/10.1007/s10755-02309686-1 [47]. Abdalla AA, Bhat MA, Tiwari CK, Khan ST, Wedajo AD. Exploring ChatGPT adoption among business and management students through the lens of diffusion of innovation theory. Comput Educ: Artif Intell [Internet]. 2024 Dec 1 [cited 2025 Feb 19];7:100257. Available from: https://www.sciencedirect.com/science/ar ticle/pii/S2666920X24000602 [48]. Alghamdi S, Alhasawi Y. Exploring the factors influencing the adoption of ChatGPT in educational institutions: insights from innovation resistance theory. J Appl Data Sci [Internet]. 2024 May 18 [cited 2025 Feb 19];5(2):474–90. Available from: https://brightjournal.org/Journal/index.php/JADS/artic le/view/198 [49]. Laukkanen P, Sinkkonen S, Laukkanen T. Consumer resistance to internet banking: postponers, opponents and rejectors. Int J Bank Mark [Internet]. 2008 Sep 5 [cited 2023 Nov 8];26(6):440–55. Available from: https://www.emerald.com/insight/content /doi/10.1108/02652320810902451/full/ht ml [50]. Ram S, Sheth JN. Consumer Resistance to Innovations: The Marketing Problem and its solutions. J Consum Mark [Internet]. 1989 Feb 1 [cited 2023 Nov 1];6(2):5–14. Available from: https://www.emerald.com/insight/content /doi/10.1108/EUM0000000002542/full/h tml [51]. Talwar S, Talwar M, Kaur P, Dhir A. Consumers’ Resistance to Digital Innovations: A Systematic Review and Framework Development. Australas Mark J [Internet]. 2020 Nov [cited 2023 Nov 13];28(4):286–99. Available from: http://journals.sagepub.com/doi/10.1016/ j.ausmj.2020.06.014 [52]. Uddin SMF, Kirmani MD, Bin Sabir L, Faisal MN, Rana NP. Consumer resistance to WhatsApp payment system: Integrating innovation resistance theory and SOR framework. Mark Intell Plan [Internet]. 2024 Oct 29 [cited 2025 Feb 14]; Available from: https://www.webofscience.com/wos/wos cc/full-record/WOS:001347133900001 [53]. Dotzauer K, Haiss F. Barriers towards the adoption of mobile payment services : an empirical investigation of consumer resistance in the context of Germany [Internet]. 2017 [cited 2025 Feb 19]. Available from: https://urn.kb.se/resolve?urn=urn:nbn:se: kau:diva-55360 [54]. Moorthy K, Suet Ling C, Weng Fatt Y, Mun Yee C, Ket Yin EC, Sin Yee K, et al. Barriers of Mobile Commerce Adoption Intention: Perceptions of Generation X in Malaysia. J theor appl electron commer res [Internet]. 2017 Apr [cited 2024 Feb 18];12(2):37–53. Available from: http://www.scielo.cl/scielo.php?script=sc Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4660 i_arttext&pid=S071818762017000200004&lng=en&nrm=iso &tlng=en [55]. Moorthy K, Low YQ, Loh CT. Understanding the resistance towards apple pay among malaysians: DOI: https://doi.org/10.33093/ijomfa.2024.5.2. 6. Int J Manag Finance Account [Internet]. 2024 Aug 30 [cited 2025 Feb 19];5(2):132–63. Available from: https://journals.mmupress.com/index.php /ijomfa/article/view/994 [56]. Rabaai AA, Maati SAA, Muhammad NB, Eljamal EM. Barriers to invest in NFTs: An innovation resistance theory perspective. Uncertain Supply Chain Manag [Internet]. 2024 [cited 2025 Feb 12];12(1):601–14. Available from: http://www.growingscience.com/uscm/V ol12/uscm_2023_149.pdf [57]. Xue Y, Zhang X, Zhang Y, Luo E. Understanding the barriers to consumer purchasing of electric vehicles: the innovation resistance theory. Sustainability [Internet]. 2024 Jan [cited 2025 Feb 19];16(6):2420. Available from: https://www.mdpi.com/20711050/16/6/2420 [58]. Zhang Q, Khan S, Khan SU, Khan IU, Mehmood S. Tourist motivations to adopt sustainable smart hospitality: an innovation resistance theory perspective. Sustainability [Internet]. 2024 Jan [cited 2025 Feb 19];16(13):5598. Available from: https://www.mdpi.com/20711050/16/13/5598 [59]. Behl A, Sampat B, Pereira V, Jayawardena NS, Laker B. Investigating the role of data-driven innovation and information quality on the adoption of blockchain technology on crowdfunding platforms. Ann Oper Res [Internet]. 2024 Feb 1 [cited 2025 Jan 25];333(2):1103– 32. Available from: https://doi.org/10.1007/s10479-02305290-w [60]. Niu B, Mvondo GFN. I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users’ loyalty and ethical usage concerns of ChatGPT. J Retail Consum Serv [Internet]. 2024 Jan [cited 2023 Oct 27];76:103562. Available from: https://linkinghub.elsevier.com/retrieve/p ii/S0969698923003132 [61]. Altawalbeh MA. Adoption of Academic Staff to use the Learning Management System (LMS): Applying Extended Technology Acceptance Model (TAM2) for Jordanian Universities. Int J Stud Educ [Internet]. 2023 Apr 11 [cited 2023 Nov 20];5(3):288–300. Available from: https://ijonse.net/index.php/ijonse/article/ view/124 [62]. Garcia MB. Factors Affecting Adoption Intention of Productivity Software Applications Among Teachers: A Structural Equation Modeling Investigation. Int J Hum–Comput Interact [Internet]. 2024 May 18 [cited 2025 Feb 20];40(10):2546–59. Available from: https://doi.org/10.1080/10447318.2022.2 163565 [63]. Khoa BT, Ha NM, Nguyen TVH, Bich NH. Lecturers’ adoption to use the online Learning Management System (LMS): Empirical evidence from TAM2 model for Vietnam. HCMCOUJS - ECON AND BUS ADM [Internet]. 2020 May 27 [cited 2024 Jan 13];10(1). Available from: http://journalofscience.ou.edu.vn/index.p hp/econ-en/article/view/216 [64]. Cham TH, Cheah JH, Cheng BL, Lim XJ. I Am too old for this! Barriers contributing to the non-adoption of mobile payment. Int J Bank Mark [Internet]. 2022 Jun 8 [cited 2023 Nov 2];40(5):1017–50. Available from: https://www.emerald.com/insight/content /doi/10.1108/IJBM-06-20210283/full/html [65]. Chen CC, Chang CH, Hsiao KL. Exploring the factors of using mobile ticketing applications: Perspectives from innovation resistance theory. J Retail Consum Serv [Internet]. 2022 Jul [cited 2023 Oct 29];67:102974. Available from: https://linkinghub.elsevier.com/retrieve/p ii/S0969698922000674 [66]. Edo OC, Ang D, Etu EE, Tenebe I, Edo S, Diekola OA. Why do healthcare workers adopt digital health technologies - a cross-sectional study integrating the TAM and UTAUT model in a developing economy. Int J Inf Manage Data Insights [Internet]. 2023 Nov 1 [cited 2025 Feb Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4661 27];3(2):100186. Available from: https://www.sciencedirect.com/science/ar ticle/pii/S2667096823000332 [67]. Henderson J, Corry M. Teacher anxiety and technology change: a review of the literature. Technol Pedagogy Educ [Internet]. 2021 Aug 8 [cited 2025 Feb 27];30(4):573–87. Available from: https://doi.org/10.1080/1475939X.2021.1 931426 [68]. Jeng MY, Pai FY, Yeh TM. Antecedents for older adults’ intention to use smart health wearable devices-technology anxiety as a moderator. Behav Sci [Internet]. 2022 Apr [cited 2025 Feb 27];12(4):114. Available from: https://www.mdpi.com/2076328X/12/4/114 [69]. Pillai R, Ghanghorkar Y, Sivathanu B, Algharabat R, Rana NP. Adoption of artificial intelligence (AI) based employee experience (EEX) chatbots. Inf Technol amp; People [Internet]. 2023 Feb 7 [cited 2025 Feb 27];37(1):449–78. Available from: https://www.emerald.com/insight/content /doi/10.1108/itp-04-2022-0287/full/html [70]. Azemi Y, Ozuem W, Howell KE. The effects of online negative word‐of‐mouth on dissatisfied customers: A frustration– aggression perspective. Psychol Market [Internet]. 2020 Apr [cited 2024 Feb 22];37(4):564–77. Available from: https://onlinelibrary.wiley.com/doi/10.10 02/mar.21326 [71]. Donthu Naveen, Kumar Satish, Pandey Neeraj, Pandey Nitesh, Mishra Akanksha. Mapping the electronic word-of-mouth (eWOM) research: A systematic review and bibliometric analysis. J Bus Res [Internet]. 2021;135. Available from: https://kns.cnki.net/kcms2/article/abstract ?v=RyaFSLOYMk4Gm41kPaNbhZady5 MTQoG0evsk4wl0eRCO4cmod9gic6Ey HXN2uXGan6rvWdRjvcAtP1eC94VZT c7GTq9dmsh_MrUTy0GXtoGv9Np2B7U4y-Fb2hiT3KYiEAVFmHNslw3cB3XZzEg==&uniplatform=NZKPT&language =gb [72]. Yaylı A, Bayram M. e-WOM: the effects of online consumer reviews on purchasing decisions. Int J Internet Mark Advert [Internet]. 2012 Jan 17 [cited 2024 Jul 29]; Available from: https://www.inderscienceonline.com/doi/ 10.1504/IJIMA.2012.044958 [73]. Abbas M, Abbasi SG, Dastgeer G, Hanif A, Ashraf M. Factors causing consumer resistance to innovation by applying resistance to innovation theory. Int Trans J Eng Manag Appl Sci Technol [Internet]. 2021 [cited 2025 Feb 13];12(2):12A2U. Available from: https://www.webofscience.com/wos/wos cc/full-record/WOS:000634795300021 [74]. Yang Y, Lu P, Niu Y, Yuan G. Research on unmanned smart hotels resistance from the perspective of innovation resistance theory. Sage OPEN [Internet]. 2024 Jul [cited 2025 Feb 14];14(3):21582440241281570. Available from: https://www.proquest.com/docview/3112 148578?pqorigsite=wos&accountid=42599 [75]. Kallal R, Haddaji A, Ftiti Z. ICT diffusion and economic growth: evidence from the sectorial analysis of a periphery country. Technol Forecasting Social Change [Internet]. 2021 Jan 1 [cited 2025 Feb 20];162:120403. Available from: https://www.sciencedirect.com/science/ar ticle/pii/S0040162520312294 [76]. Lin CC, Huang AYQ, Lu OHT. Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review. Smart Learn Environ [Internet]. 2023 Aug 28 [cited 2025 Feb 21];10(1):41. Available from: https://doi.org/10.1186/s40561-02300260-y [77]. Luckyardi S, Karin J, Rosmaladewi R, Hufad A, Haristiani N. Chatbots as digital language tutors: revolutionizing education through AI. Indones J Sci Technol [Internet]. 2024 [cited 2025 Feb 21];9(3):885–908. Available from: https://ejournal.upi.edu/index.php/ijost/ar ticle/view/79514 [78]. Kim K, Kim B. Decision-making model for reinforcing digital transformation strategies based on artificial intelligence technology. Information [Internet]. 2022 May [cited 2025 Feb 21];13(5):253. Available from: https://www.mdpi.com/20782489/13/5/253 Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4662 [79]. Krakowski S, Luger J, Raisch S. Artificial intelligence and the changing sources of competitive advantage. Strateg Manag J [Internet]. 2023 [cited 2025 Feb 21];44(6):1425–52. Available from: https://onlinelibrary.wiley.com/doi/abs/1 0.1002/smj.3387 [80]. Li C, Xu Y, Zheng H, Wang Z, Han H, Zeng L. Artificial intelligence, resource reallocation, and corporate innovation efficiency: evidence from China’s listed companies. Resour Policy [Internet]. 2023 Mar 1 [cited 2025 Feb 21];81:103324. Available from: https://www.sciencedirect.com/science/ar ticle/pii/S0301420723000326 [81]. Oosthuizen RM. The Fourth Industrial Revolution – Smart Technology, Artificial Intelligence, Robotics and Algorithms: Industrial Psychologists in Future Workplaces. Front Artif Intell [Internet]. 2022 Jul 6 [cited 2023 Oct 23];5:913168. Available from: https://www.frontiersin.org/articles/10.33 89/frai.2022.913168/full [82]. Soori M, Jough FKG, Dastres R, Arezoo B. AI-based decision support systems in industry 4.0, a review. J Econ Technol [Internet]. 2024 Aug 28 [cited 2025 Feb 21]; Available from: https://www.sciencedirect.com/science/ar ticle/pii/S2949948824000374 [83]. Statista. Artificial Intelligence - Worldwide [Internet]. 2024. Available from: https://www.statista.com/outlook/tmo/arti ficial-intelligence/worldwide [84]. PwC. Global Artificial Intelligence Study: Sizing the Prize [Internet]. 2023. Available from: https://www.pwc.com/gx/en/issues/analyt ics/assets/pwc-ai-analysis-sizing-theprize-report.pdf [85]. Horwitch M. The AI challenge for national technology strategy. In: 2024 Portland International Conference on Management of Engineering and Technology (PICMET) [Internet]. 2024 [cited 2025 Feb 21]. p. 1–11. Available from: https://ieeexplore.ieee.org/abstract/docu ment/10653430 [86]. Montasari R. National Artificial Intelligence Strategies: A Comparison of the UK, EU and US Approaches with those Adopted by State Adversaries. In: Montasari R, editor. Countering Cyberterrorism: The Confluence of Artificial Intelligence, Cyber Forensics and Digital Policing in US and UK National Cybersecurity [Internet]. Cham: Springer International Publishing; 2023 [cited 2025 Feb 21]. p. 139–64. Available from: https://doi.org/10.1007/978-3-03121920-7_7 [87]. Vu HT, Lim J. Effects of country and individual factors on public acceptance of artificial intelligence and robotics technologies: a multilevel SEM analysis of 28-country survey data. Behav Inf Technol [Internet]. 2022 May 19 [cited 2025 Feb 21];41(7):1515–28. Available from: https://doi.org/10.1080/0144929X.2021.1 884288 [88]. Xu J, Lee T, Goggin G. AI governance in Asia: policies, praxis and approaches. Commun Res Pract [Internet]. 2024 Jul 2 [cited 2025 Feb 21];10(3):275–87. Available from: https://doi.org/10.1080/22041451.2024.2 391204 [89]. Apell P, Eriksson H. Artificial intelligence (AI) healthcare technology innovations: the current state and challenges from a life science industry perspective. Technol Anal Strateg Manag [Internet]. 2023 Feb 1 [cited 2025 Feb 21];35(2):179–93. Available from: https://doi.org/10.1080/09537325.2021.1 971188 [90]. Kulkov I. Next-generation business models for artificial intelligence start-ups in the healthcare industry. Int J Entrep Behav amp; Res [Internet]. 2021 Oct 15 [cited 2025 Feb 21];29(4):860–85. Available from: https://www.emerald.com/insight/content /doi/10.1108/ijebr-04-20210304/full/html [91]. Zahlan A, Ranjan RP, Hayes D. Artificial intelligence innovation in healthcare: literature review, exploratory analysis, and future research. Technol Soc [Internet]. 2023 Aug 1 [cited 2025 Feb 21];74:102321. Available from: Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4663 https://www.sciencedirect.com/science/ar ticle/pii/S0160791X23001264 [92]. Ashta A, Herrmann H. Artificial intelligence and fintech: an overview of opportunities and risks for banking, investments, and microfinance. Strateg Change [Internet]. 2021 [cited 2025 Feb 21];30(3):211–22. Available from: https://onlinelibrary.wiley.com/doi/abs/1 0.1002/jsc.2404 [93]. Manta EM, Davidescu AA, Geambasu CM. A bibliometric analysis of artificial intelligence in the financial journals. In: Kour M, Taneja S, Özen E, Sood K, Grima S, editors. Financial Landscape Transformation: Technological Disruptions [Internet]. Emerald Publishing Limited; 2025 [cited 2025 Feb 21]. p. 17–35. Available from: https://doi.org/10.1108/978-1-83753750-120251002 [94]. Milana C, Ashta A. Artificial intelligence techniques in finance and financial markets: a survey of the literature. Strateg Change [Internet]. 2021 [cited 2025 Feb 21];30(3):189–209. Available from: https://onlinelibrary.wiley.com/doi/abs/1 0.1002/jsc.2403 [95]. Weber P, Carl KV, Hinz O. Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literature. Manag Rev Q [Internet]. 2024 Jun 1 [cited 2025 Feb 21];74(2):867–907. Available from: https://doi.org/10.1007/s11301-02300320-0 [96]. Kamalov F, Santandreu Calonge D, Gurrib I. New era of artificial intelligence in education: towards a sustainable multifaceted revolution. Sustainability [Internet]. 2023 Jan [cited 2025 Feb 21];15(16):12451. Available from: https://www.mdpi.com/20711050/15/16/12451 [97]. Chen Y, Jensen S, Albert LJ, Gupta S, Lee T. Artificial intelligence (AI) student assistants in the classroom: designing chatbots to support student success. Inf Syst Front [Internet]. 2023 Feb 1 [cited 2025 Feb 21];25(1):161–82. Available from: https://doi.org/10.1007/s10796022-10291-4 [98]. Michel-Villarreal R, Vilalta-Perdomo E, Salinas-Navarro DE, Thierry-Aguilera R, Gerardou FS. Challenges and Opportunities of Generative AI for Higher Education as Explained by ChatGPT. Educ Sci [Internet]. 2023 Aug 23 [cited 2023 Oct 27];13(9):856. Available from: https://www.mdpi.com/22277102/13/9/856 [99]. Yilmaz R, Karaoglan Yilmaz FG. The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence [Internet]. 2023 Jan 1 [cited 2024 Aug 11];4:100147. Available from: https://www.sciencedirect.com/science/ar ticle/pii/S2666920X23000267 [100]. Pratama MP, Sampelolo R, Lura H. Revolutionizing education: harnessing the power of artificial intelligence for personalized learning. Klasikal : J Educ Lang Teach Sci [Internet]. 2023 Aug 10 [cited 2025 Feb 21];5(2):350–7. Available from: http://www.journalfkipuniversitasbosowa .org/index.php/klasikal/article/view/877 [101]. Baidoo-Anu D, Owusu Ansah L. Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. SSRN Electron J [Internet]. 2023 [cited 2023 Nov 23]; Available from: https://www.ssrn.com/abstract=4337484 [102]. Voulgari I, Stouraitis E, Camilleri V, Karpouzis K. Artificial intelligence and machine learning education and literacy: teacher training for primary and secondary education teachers. In: Handbook of Research on Integrating ICTs in STEAM Education [Internet]. IGI Global Scientific Publishing; 2022 [cited 2025 Feb 21]. p. 1–21. Available from: https://www.igiglobal.com/chapter/artificialintelligenceand-machine-learningeducation-and-literacy/www.igiglobal.com/chapter/artificialintelligenceand-machine-learningeducation-and-literacy/304839 Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4664 [103]. Niu B, Mvondo GFN. I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users’ loyalty and ethical usage concerns of ChatGPT. J Retail Consum Serv [Internet]. 2024 Jan [cited 2023 Oct 27];76:103562. Available from: https://linkinghub.elsevier.com/retrieve/p ii/S0969698923003132 [104]. Wangsa K, Karim S, Gide E, Elkhodr M. A systematic review and comprehensive analysis of pioneering AI chatbot models from education to healthcare: ChatGPT, bard, llama, ernie and grok. Future Internet [Internet]. 2024 Jul [cited 2025 Feb 22];16(7):219. Available from: https://www.mdpi.com/19995903/16/7/219 [105]. Deloitte Insights. 2025 Technology, Media and Telecommunications Industry Forecast [Internet]. 2025. Available from: https://www2.deloitte.com/content/dam/ Deloitte/cn/Documents/technologymedia-telecommunications/deloitte-cntmt-predictions-2025-zh-250217.pdf [106]. World Economic Forum. World Economic Forum. 2024 [cited 2025 Feb 22]. ChatWTO: an analysis of generative artificial intelligence and international trade 2024. Available from: https://www.weforum.org/publications/c hatwto-an-analysis-of-generativeartificial-intelligence-and-internationaltrade/ [107]. Ryan, Lay WB, Chia JJ, Gui A. Transforming E-Commerce: AI Chatbots for Supercharged Customer Experiences. In: 2024 International Conference on Information Technology Research and Innovation (ICITRI) [Internet]. 2024 [cited 2025 Feb 22]. p. 299–304. Available from: https://ieeexplore.ieee.org/abstract/docu ment/10698874 [108]. Kediya S, Mohanty V, Gurjar A, Chouhan N, Sharma R, Golar P. Chatbots in customer service: a comparative analysis of performance and customer satisfaction. In: 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI) [Internet]. 2024 [cited 2025 Feb 22]. p. 1–6. Available from: https://ieeexplore.ieee.org/abstract/docu ment/10842848 [109]. Kummar RG, Shetty SJ, Vishwas SN, Vismith Upadhya PJ, Munavalli JR. Edubot: an AI based smart chatbot for knowledge management system. In: 2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) [Internet]. 2021 [cited 2025 Feb 22]. p. 1–6. Available from: https://ieeexplore.ieee.org/abstract/docu ment/9683011 [110]. Christopher Y, Sundjaja AM, Mulvono. The role of AI chatbots on E-commerce platforms: understanding its influence on customer trust and dependability. In: 2024 9th International Conference on Information Technology and Digital Applications (ICITDA) [Internet]. 2024 [cited 2025 Feb 22]. p. 1–8. Available from: https://ieeexplore.ieee.org/abstract/docu ment/10809919 [111]. Suman Rajest S, Regin R, Y A, Paramasivan P, Christabel GJA, T S. The Analysis of How Artificial Intelligence Has an Effect on Teachers and The Education System. EAI Endorsed Trans e-Learn [Internet]. 2023 Oct 10 [cited 2023 Oct 23];9. Available from: https://publications.eai.eu/index.php/el/ar ticle/view/3494 [112]. Kizilcec RF. To advance AI use in education, focus on understanding educators. Int J Artif Intell Educ [Internet]. 2024 Mar 1 [cited 2025 Feb 22];34(1):12–9. Available from: https://doi.org/10.1007/s40593-02300351-4 [113]. Choi S, Jang Y, Kim H. Influence of pedagogical beliefs and perceived trust on teachers’ acceptance of educational artificial intelligence tools. Int J Hum– Comput Interact [Internet]. 2023 Feb 25 [cited 2025 Feb 22];39(4):910–22. Available from: https://doi.org/10.1080/10447318.2022.2 049145 [114]. Zhang C, Khan I, Dagar V, Saeed A, Zafar MW. Environmental impact of information and communication technology: unveiling the role of education in developing countries. Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4665 Technol Forecasting Social Change [Internet]. 2022 May 1 [cited 2025 Feb 21];178:121570. Available from: https://www.sciencedirect.com/science/ar ticle/pii/S0040162522001020 [115]. Pokrivcakova S. Teacher trainees´ attitudes towards integrating chatbots into foreign language classes. INTED2022 Proc [Internet]. 2022 [cited 2025 Feb 22];8294–302. Available from: https://library.iated.org/view/POKRIVC AKOVA2022TEA [116]. Rigley E, Bentley C, Krook J, Ramchurn SD. Evaluating international AI skills policy: a systematic review of AI skills policy in seven countries. Glob Policy [Internet]. 2024 [cited 2025 Feb 21];15(1):204–17. Available from: https://onlinelibrary.wiley.com/doi/abs/1 0.1111/1758-5899.13299 [117]. Sperling K, Stenberg CJ, McGrath C, Åkerfeldt A, Heintz F, Stenliden L. In search of artificial intelligence (AI) literacy in teacher education: a scoping review. Comput Educ Open [Internet]. 2024 Jun 1 [cited 2025 Feb 22];6:100169. Available from: https://www.sciencedirect.com/science/ar ticle/pii/S2666557324000107 [118]. Esiyok E, Gokcearslan S, Kucukergin KG. Acceptance of educational use of AI chatbots in the context of self-directed learning with technology and ICT selfefficacy of undergraduate students. Int J Hum–Comput Interact [Internet]. 2025 Jan 2 [cited 2025 Feb 23];41(1):641–50. Available from: https://doi.org/10.1080/10447318.2024.2 303557 [119]. Niu B, Mvondo GFN. I Am ChatGPT, the ultimate AI chatbot! Investigating the determinants of users’ loyalty and ethical usage concerns of ChatGPT. J Retail Consum Serv [Internet]. 2024 Jan 1 [cited 2025 Feb 23];76:103562. Available from: https://www.sciencedirect.com/science/ar ticle/pii/S0969698923003132 [120]. Saihi A, Ben-Daya M, Hariga M, As’ad R. A structural equation modeling analysis of generative AI chatbots adoption among students and educators in higher education. Comput Educ: Artif Intell [Internet]. 2024 Dec 1 [cited 2025 Feb 23];7:100274. Available from: https://www.sciencedirect.com/science/ar ticle/pii/S2666920X24000778 [121]. Mohd Rahim NI, A. Iahad N, Yusof AF, A. Al-Sharafi M. AI-Based Chatbots Adoption Model for Higher-Education Institutions: A Hybrid PLS-SEM-Neural Network Modelling Approach. Sustainability [Internet]. 2022 Jan [cited 2025 Feb 23];14(19):12726. Available from: https://www.mdpi.com/20711050/14/19/12726 [122]. Tian W, Ge J, Zhao Y, Zheng X. AI chatbots in chinese higher education: adoption, perception, and influence among graduate students—an integrated analysis utilizing UTAUT and ECM models. Front Psychol [Internet]. 2024 Feb 7 [cited 2025 Feb 23];15. Available from: https://www.frontiersin.org/journals/psyc hology/articles/10.3389/fpsyg.2024.1268 549/full [123]. Ayanwale MA, Ndlovu M. Investigating factors of students’ behavioral intentions to adopt chatbot technologies in higher education: perspective from expanded diffusion theory of innovation. Comput Hum Behav Rep [Internet]. 2024 May 1 [cited 2025 Feb 23];14:100396. Available from: https://www.sciencedirect.com/science/ar ticle/pii/S2451958824000290 [124]. Lee CT, Pan LY, Hsieh SH. Artificial intelligent chatbots as brand promoters: a two-stage structural equation modelingartificial neural network approach. Internet Res [Internet]. 2021 Dec 24 [cited 2025 Feb 23];32(4):1329–56. Available from: https://www.emerald.com/insight/content /doi/10.1108/intr-01-2021-0030/full/html [125]. Arpaci I. A Multianalytical SEM-ANN Approach to Investigate the Social Sustainability of AI Chatbots Based on Cybersecurity and Protection Motivation Theory. IEEE Trans Eng Manag [Internet]. 2024 [cited 2025 Feb 23];71:1714–25. Available from: https://ieeexplore.ieee.org/abstract/docu ment/10342742 [126]. Chen Q, Yin C, Gong Y. Would an AI chatbot persuade you: an empirical answer from the elaboration likelihood Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4666 model. Inf Technol amp; People [Internet]. 2023 Dec 8 [cited 2025 Feb 23];ahead-of-print(ahead-of-print). Available from: https://www.emerald.com/insight/content /doi/10.1108/itp-10-2021-0764/full/html [127]. Marjerison RK, Zhang Y, Zheng H. AI in E-Commerce: Application of the Use and Gratification Model to The Acceptance of Chatbots. Sustainability [Internet]. 2022 Jan [cited 2025 Feb 23];14(21):14270. Available from: https://www.mdpi.com/20711050/14/21/14270 [128]. Chakraborty D, Kumar Kar A, Patre S, Gupta S. Enhancing trust in online grocery shopping through generative AI chatbots. J Bus Res [Internet]. 2024 Jul 1 [cited 2025 Feb 23];180:114737. Available from: https://www.sciencedirect.com/science/ar ticle/pii/S0148296324002418 [129]. Laukkanen T, Sinkkonen S, Kivijärvi M, Laukkanen P. Innovation resistance among mature consumers. J Consum Mark [Internet]. 2007 Nov 6 [cited 2023 Nov 8];24(7):419–27. Available from: https://www.emerald.com/insight/content /doi/10.1108/07363760710834834/full/ht ml [130]. Chakraborty D, Singu HB, Patre S. Fitness Apps’s purchase behaviour: Amalgamation of Stimulus-OrganismBehaviour-Consequence framework (S– O–B–C) and the innovation resistance theory (IRT). J Retail Consum Serv [Internet]. 2022 Jul [cited 2023 Oct 29];67:103033. Available from: https://linkinghub.elsevier.com/retrieve/p ii/S0969698922001266 [131]. Chen CC. Exploring the factors of using mobile ticketing applications: Perspectives from innovation resistance theory. 2022; [132]. Kumar S, Dhiman N, Kanojia H, Joshi R. What resists millennials to adopt hotel booking apps? An empirical analysis based on extended innovation resistance theory. foresight [Internet]. 2023 Aug 22 [cited 2023 Oct 29]; Available from: https://www.emerald.com/insight/content /doi/10.1108/FS-10-2021-0209/full/html [133]. Leong LY, Hew TS, Ooi KB, Wei J. Predicting mobile wallet resistance: A two-staged structural equation modelingartificial neural network approach. Int J Inf Manage [Internet]. 2020 Apr [cited 2023 Nov 18];51:102047. Available from: https://linkinghub.elsevier.com/retrieve/p ii/S0268401219306012 [134]. Ma L, Lee CS. Understanding the Barriers to the Use of MOOCs in a Developing Country: An Innovation Resistance Perspective. J Educ Comput Res [Internet]. 2019 Jun [cited 2023 Oct 25];57(3):571–90. Available from: http://journals.sagepub.com/doi/10.1177/ 0735633118757732 [135]. Sadiq M, Adil M, Paul J. An innovation resistance theory perspective on purchase of eco-friendly cosmetics. J Retail Consum Serv [Internet]. 2021 Mar [cited 2023 Oct 25];59:102369. Available from: https://linkinghub.elsevier.com/retrieve/p ii/S0969698920313771 [136]. Laukkanen T. Consumer adoption versus rejection decisions in seemingly similar service innovations: The case of the Internet and mobile banking. J Bus Res [Internet]. 2016 Jul [cited 2023 Nov 8];69(7):2432–9. Available from: https://linkinghub.elsevier.com/retrieve/p ii/S0148296316000266 [137]. Migliore G, Wagner R, Cechella FS, Liébana-Cabanillas F. Antecedents to the Adoption of Mobile Payment in China and Italy: an Integration of UTAUT2 and Innovation Resistance Theory. Inform Syst Front [Internet]. 2022 Dec [cited 2023 Oct 29];24(6):2099–122. Available from: https://link.springer.com/10.1007/s10796 -021-10237-2 [138]. Mou Y, Meng X. Alexa, it is creeping over me – exploring the impact of privacy concerns on consumer resistance to intelligent voice assistants. Asia Pac J Mark Logist [Internet]. 2023 Jul 26 [cited 2025 Feb 12];36(2):261–92. Available from: https://www.emerald.com/insight/content /doi/10.1108/apjml-10-20220869/full/html [139]. Sivathanu B. Adoption of digital payment systems in the era of demonetization in India: An empirical study. J Sci Technol Policy Manag [Internet]. 2019 Mar 4 [cited 2024 Feb Journal of Theoretical and Applied Information Technology 15th June 2025. Vol.103. No.11 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4673 2008 [cited 2025 Jan 9];31(2):180–91. Available from: https://onlinelibrary.wiley.com/doi/abs/1 0.1002/nur.20247 [220]. Hunt SD, Sparkman RD, Wilcox JB. The Pretest in Survey Research: Issues and Preliminary Findings. J Marketing Res [Internet]. 1982 May [cited 2024 Apr 27];19(2):269–73. Available from: https://doi.org/10.1177/00222437820190 0211 [221]. Hair JF, Hult T, Ringle C, Sarstedt M. A primer on partial least squares structural equation modeling (PLS-SEM). SAGE Publications; 2014. [222]. Hair JF, Hult T, Ringle C, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLSSEM). Third Edition. SAGE Publications; 2022. [223]. Henseler J. Partial least squares path modeling. In: Leeflang PSH, Wieringa JE, Bijmolt THA, Pauwels KH, editors. Advanced Methods for Modeling Markets [Internet]. Cham: Springer International Publishing; 2017 [cited 2024 Aug 22]. p. 361–81. (International Series in Quantitative Marketing). Available from: http://link.springer.com/10.1007/978-3319-53469-5_12 [224]. Sarstedt M, Mooi E. A concise guide to market research: the process, data, and methods using IBM SPSS statistics [Internet]. Berlin, Heidelberg: Springer Berlin Heidelberg; 2019 [cited 2025 Feb 25]. (Springer Texts in Business and Economics). Available from: http://link.springer.com/10.1007/978-3662-56707-4 [225]. Shmueli G, Ray S, Velasquez Estrada JM, Chatla SB. The elephant in the room: Predictive performance of PLS models. J Bus Res [Internet]. 2016 Oct 1 [cited 2024 Dec 22];69(10):4552–64. Available from: https://www.sciencedirect.com/science/ar ticle/pii/S0148296316301217 [226]. Hair JF, editor. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Los Angeles: SAGE; 2014. 307 p. [227]. Tabachnick BG, Fidell LS. Using Multivariate Statistics. 5th ed. Boston: Pearson/Allyn & Bacon; 2007. 980 p. [228]. Garson GD. Testing statistical assumptions. 2012; [229]. Esposito Vinzi V, Chin WW, Henseler J, Wang H, editors. Handbook of partial least squares: Concepts, methods and applications [Internet]. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010 [cited 2024 Aug 25]. Available from: https://link.springer.com/10.1007/978-3540-32827-8 [230]. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Marketing Res [Internet]. 1981 [cited 2024 Aug 26];18(1):39–50. Available from: https://www.jstor.org/stable/3151312 [231]. Talwar M, Corazza L, Bodhi R, Malibari A. Why do consumers resist digital innovations? An innovation resistance theory perspective. Int J Emerg Mark [Internet]. 2024 Nov 26 [cited 2025 Feb 15];19(11):4327–42. Available from: https://www.webofscience.com/wos/wos cc/full-record/WOS:000959657500001