Next Article in Journal
The Effects of Social and Spatial Presence on Learning Engagement in Sustainable E-Learning
Previous Article in Journal
Field Evaluation of Rice Husk Biochar and Pine Tree Woodchips for Removal of Tire Wear Particles from Urban Stormwater Runoff in Oxford, Mississippi (USA)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 17
Issue 9
10.3390/su17094081
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Exploring the Potential Barrier Factors of AI Chatbot Usage Among Teacher Trainees: From the Perspective of Innovation Resistance Theory

by
Yonggang Liu
*,
Hapini Awang
and
Nur Suhaili Mansor
Institute for Advanced and Smart Digital Opportunities, School of Computing, Universiti Utara Malaysia, Sintok 06010, Malaysia
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(9), 4081; https://doi.org/10.3390/su17094081
Submission received: 31 March 2025 / Revised: 27 April 2025 / Accepted: 28 April 2025 / Published: 30 April 2025
Browse Figures
Versions Notes

Abstract

With the development of Artificial Intelligence (AI) technology, more and more AI chatbots (e.g., ChatGPT and DeepSeek) are beginning to affect work and lifestyles. Although AI chatbots have brought many opportunities to education and teacher trainees, they have also caused many problems and resistance among some teacher trainees. However, previous studies have focused more on the influence of positive acceptance factors induced by AI chatbots and less on the negative barrier model induced by AI chatbots. Therefore, this study starts from the negative barrier factors induced by AI chatbots and builds an influencing barrier model of AI chatbot resistance guided by Innovation Resistance Theory (IRT) and appropriately draws on Cultural Dimension Theory (CDT), Unified Theory of Acceptance and Use of Technology (UTAUT), and practical characteristics. The questionnaires mainly adopt convenience sampling and snowball sampling methods, and the data are empirically analyzed. The results show that Uncertainty Avoidance, the Social Influence Barrier, and Technology Anxiety have a significant and direct influence on teacher trainees’ resistance to AI chatbots. Meanwhile, Uncertainty Avoidance, the Social Influence Barrier, and Technology Anxiety play significant mediating roles in the impact of the Usage Barrier (UB), Image Barrier (IB), Value Barrier (VB), Risk Barrier (RB), and Tradition Barrier (TB) on resistance behaviors, revealing the complex path through which cognition-emotion-society factors jointly shape technology resistance behaviors. Therefore, this study not only contributes to enriching the theoretical results of combining Innovation Resistance Theory with AI chatbots and adding new research paths (e.g., the mediating role of Uncertainty Avoidance) but also provides a practical guide for the dissemination of AI chatbots among teacher trainees and future technological talents in a sustainable future.

1. Introduction

Information and Communication Technology (ICT) refers to a combination of information technology (IT) and communication technology to manage, process, and transfer information, and it covers hardware, software, networks, related services, and others [1,2,3]. Since the end of the 20th century, the rapid development of ICT has revolutionized the way human society operates, including but not limited to business, healthcare, marketing, public services, and education [1,2,3]. ICT plays an undeniable part in the process of information processing, digital transformation, and technological revolution, which not only profoundly affects the operation mode of key areas such as business, healthcare, and education, but also tangibly or intangibly shapes the lifestyle and behaviors of individuals. Among numerous ICT–related technologies, Artificial Intelligence (AI)’s performance is particularly outstanding. AI integrates multiple advanced technologies such as natural language processing (NLP), machine learning (ML), and deep learning (DL), and has relatively strong reasoning, judgment, perception, learning, and imitation capabilities [2,3,4,5]. AI offers numerous potential benefits regarding the enhancement of work efficiency, intelligent decision-making systems, personalized feedback, disease screening, pharmaceutical research and development, teaching design, preparation of instructional materials, and talent-based teaching. However, AI also brings challenges such as data privacy leakage, the digital divide, and technological dependency, which require research and improvement at multiple levels, like theory, practice, or technology. The AI industry is expected to develop at a compound annual growth rate (CAGR 2025–2030) of 27.67%, reaching an estimated value of US$826.73 billion by 2030 [6]. After the emergence of ChatGPT, AI chatbots have ushered in a new wave of development since their powerful NLP ability, human–computer interaction ability, deep thinking ability, and wide range of application scenarios have quickly attracted global attention [3,4,7,8]. Globally, various advanced AI chatbots (such as ChatGPT, Google Bard, Grok, DeepSeek, Kimi, Ernie Bot, and Tongyi Qianwen) have emerged and provided tremendous application potential in various fields like computer programming, intelligent customer service, art and design, smart healthcare, marketing promotion, educational transformation, and many others. In the area of education, AI chatbots provide a lot of opportunities and benefits, including offering individualized feedback, generating teaching materials, searching related literature, recommending interesting advice, and so on [2,4,9,10,11,12,13]. Nevertheless, numerous teacher trainees still have different levels of resistance to AI chatbots (RTAC), probably due to risk concerns, incompatibility, knowledge shortage, or other reasons [14,15,16,17,18,19]. For example, teacher trainees may perceive that technology anxiety, ethical risks, a sense of panic that their own value may be weakened, and worries about an uncertain future caused by AI chatbots may lead to teacher trainees’ resistance behaviors toward AI chatbots. Noticeably, teacher trainees are clients and important promoters of AI chatbots, and their acceptance or resistance behaviors toward AI chatbots have a significant influence on the promotion of AI chatbots in a sustainable future. This phenomenon may also have a negative impact on the integration of AI chatbots in education scenarios as it may not only reduce the willingness of teacher trainees to accept AI chatbots in real-world teaching but also extend to the student level, affecting the acceptance level of AI–assisted learning, and further widening the “technology acceptance gap”. Nonetheless, appropriate theoretical models to test the primary barriers to teacher trainees’ RTAC are still limited. Therefore, the primary purpose of this study is to develop a theoretical model to test significant factors influencing teacher trainees’ resistance to AI chatbots from the perspective of Innovation Resistance Theory (IRT). This research is not only beneficial for measuring the main barriers of teacher trainees’ RTAC but also has important guiding significance for the promotion and application of AI chatbots in the future.

2. Literature Review

2.1. Information and Communication Technology (ICT)

Information and Communication Technology (ICT), as one of the core driving forces behind economic development and social evolution, has sparked widespread research interest in the world. Early ICT research mainly focused on the quality of software and hardware, business performance, and so on [20,21]. With the continuous evolution of technology, research focus is gradually shifting towards the application of ICT in education, healthcare, business, and other fields, as well as its impact on organizations and individuals [22,23,24]. However, despite the many advantages of ICT, its practical application still faces many challenges, such as the digital divide [25], privacy and security issues [26], and user acceptance [14,23]. The successful experience and existing problems of ICT may be reflected in its segmented fields like AI.

2.2. Artificial Intelligence (AI)

AI has become one of the most revolutionary technologies of the 21st century and has sparked a widespread wave of research and applications. Early AI–related research mainly involved definition, characteristics, essence, and technological development, such as the differences between AI and traditional technologies, code, and algorithms [27,28]. With the improvement of computing power and algorithm optimization, the application fields of AI are rapidly expanding, covering multiple industries like healthcare, finance, film, transportation, education, and many others [4,10,23,29]. In the field of education, AI technologies have shown great potential in intelligent evaluation, human–computer dialogue, smart tutoring systems, improving teaching and learning efficiency, and so forth [2,4,7,10,11,23,30,31]. Nonetheless, the rapid development of AI has also raised a series of ethical and social issues, including but not limited to privacy and security concerns, algorithmic biases, and employment pressures [5,8,14,18]. Even though there is great potential in AI, further exploration is still needed to deal with the emergence of various new problems. Compared to AI chatbots, AI has a relatively long history of growth. Simultaneously, the opportunities and difficulties that arise in the evolution of AI are likely to be reflected in AI chatbots. Hence, reviewing the related literature on AI contributes to this study’s more precise capture of the evolutionary lineage of AI, thereby providing a multi-dimensional perspective and an essential practical reference for the study of AI chatbots.

2.3. AI Chatbots

Since the release of ChatGPT by OpenAI, the generation capabilities and types of AI chatbots have ushered in a new round of development. AI chatbots integrate various technologies such as NLP, ML, DL, and deep neural networks (DNN), and have made significant progress in industrial robots, intelligent healthcare, smart driving, customer service, education, and other fields in recent years [4,7,13,15,16,23]. Early research on AI chatbots remained at the stage of limited scenario exploration and functional imagination [32,33,34]. With the upgrade of algorithms and breakthroughs in deep learning technologies, especially the development of generative pre-training models (such as the GPT series), the language generation ability and interaction experience of AI chatbots have been qualitatively improved [4,7,9,13,16,35]. In the field of education, AI chatbots have great potential in improving teaching efficiency, personalized learning support, automated Q and A, learning behavior analysis, and others [9,15,18,19,23,36,37]. However, the application of AI chatbots also faces many challenges, such as data privacy, accuracy of generated content, and copyright issues [10,13,15,37]. Unfortunately, due to the reasons mentioned above or incompatibility, knowledge limitations, etc., many teacher trainees still have varying degrees of resistance to AI chatbots [14,15,16,17,18,19]. Studying the main barriers for teacher trainees to resist AI chatbots can not only promote the spread of AI chatbots among teacher trainees but also lay a good foundation for their future cultivation of technological and innovative talents.

2.4. Innovation Resistance Theory (IRT)

Innovation Resistance Theory (IRT) [17] is a suitable theoretical model for this study to analyze technological resistance behaviors since it not only includes users’ functional barriers (e.g., Usage Barrier (UB), Value Barrier (VB), and Risk Barrier (RB)) but also captures users’ psychological barriers (e.g., Tradition Barrier (TB) and Image Barrier (IB)). Although previous researchers have attempted to add variables such as technology vulnerability barriers [38], information overload [39], and inertia [40] to IRT, they have overlooked the direct and mediating effects of technology anxiety (TA), uncertainty avoidance (UA), and the Social Influence Barrier (SIB). Initially, the IRT model was mainly applied to traditional marketing and traditional business fields, and then gradually expanded to e-commerce, mobile payment, renewable energy, innovative technology, and many other fields. Not only that, the past IRT model focused more on the effects of classical factors (e.g., UB, RB, and TB), but with the progress of IT (especially AI), there is a strong demand for the IRT model to keep up with the times and introduce new constructs (e.g., UA, SIB, and TA). Therefore, this study will enrich the theoretical connotation and practical guidance of the IRT model.

2.5. Theoretical Framework

On the basis of the above literature review and the basic constructs of IRT, the present study proposes a new framework (Figure 1) for teacher trainees’ RTAC. This framework keeps the five basic constructs of IRT and adds some new constructs (e.g., TA and UA) to enhance the explanatory power and adaptability of the original model.

2.6. Main Hypotheses

Technology anxiety (TA) refers to the emotions of tension, anxiousness, fear, etc., that individuals feel when facing new technologies, which may stem from fear of the technology’s complexity or doubt about their technological abilities [41,42]. Research has shown that TA significantly reduces individuals’ acceptance of innovative products [42,43,44]. Specifically, when teacher trainees are worried about the complexity of operating AI chatbots or their technical skills, they are more likely to exhibit a resistance tendency. Based on Innovation Resistance Theory [17] and Cultural Dimension Theory [45], this study speculates that UA may have a significant impact on resisting AI chatbots. As an emerging technology, AI chatbots’ functionality, reliability, and unpredictability may cause uncertainty among teacher trainees, leading to a resistance phenomenon. In this study, the Social Influence Barrier (SIB) mainly refers to the degree to which teacher trainees perceive that it is important that others believe he or she should not use AI chatbots. Proposal of the SIB is primarily based on Technology Acceptance Model 2 [44,46] and a Structural Theory of Social Influence [47]. Research has shown that social influence plays an important role in the process of technology acceptance or resistance, especially in collectivist cultural contexts where individuals are more susceptible [38,44,47]. Based on a review of the literature and the aforementioned discussions, this study proposes the following hypotheses:
H1. 
Technology Anxiety has a significant influence on resistance to AI chatbots among teacher trainees.
H2. 
Uncertainty Avoidance has a significant influence on resistance to AI chatbots among teacher trainees.
H3. 
The Social Influence Barrier has a significant influence on resistance to AI chatbots among teacher trainees.

2.7. Mediating Hypotheses

Vadakkemulanjanal Joseph and Thomas [48] discovered that teachers’ TA and attitudinal barriers occupied an important mediatory influence on their Technology Exposure and Adoption. Leong et al. [49] proved that the UB had a significant effect on innovation resistance. Meanwhile, the IB was the most significant resistance of usage intention, and occupied 100% normalized relative importance among the barriers of social commerce usage [50]. In addition, the IB is also one of the strongest inhibitory forces on consumers’ willingness to purchase eco-friendly cosmetics [51]. Therefore, there may be a connection between TA, UB, IB, and resistance to innovative technologies.
H4. 
Technology Anxiety mediates the relationship between the Usage Barrier and resistance to AI chatbots among teacher trainees.
H5. 
Technology Anxiety mediates the relationship between the Image Barrier and resistance to AI chatbots among teacher trainees.
Laukkanen [52] found that innovation resistance was significantly impacted by UA, and one of the biggest impacts related to the RB. UA played a mediating role in the association between culture and preferences toward the novel versus useful dimensions of creativity [53]. Simultaneously, S. Kumar et al. [54] demonstrated that the VB was a momentous predictor of discontinuance intention. The VB was expressively connected to algorithm aversion [55]. In addition, the direct or indirect impact of the RB on resistance behaviors should not be ignored [49,56,57]. Nie et al. [58] highlighted that the TB and RB significantly negatively affected customer intention to use metaverse commodities. The VB, RB, and TB were all the main barriers impacting consumer resistance [59]. In the background of collective culture, the TB may exacerbate the SIB, leading to the emergence of resistance behaviors among teacher trainees. Consequently, consolidating previous results and the above-mentioned deliberations, the following hypotheses are formally propounded:
H6. 
Uncertainty Avoidance mediates the relationship between the Value Barrier and resistance to AI chatbots among teacher trainees.
H7. 
Uncertainty Avoidance mediates the relationship between the Risk Barrier and resistance to AI chatbots among teacher trainees.
H8. 
The Social Influence Barrier mediates the relationship between the Tradition Barrier and Resistance to AI chatbots among teacher trainees.

3. Research Methodology

This study mainly uses quantitative research methodology since it is more suitable for the purpose of the study and the features of the IRT model. All items were modified from previous scales [46,49,52,54,55,56,59] and adjusted appropriately based on the theme and investigation population characteristics. Because all items are derived from previous mature scales, exploratory factor analysis (EFA) is not necessary for the current research. This study mainly adopts the convenience sampling and snowball sampling methods [60,61,62], and the sampling subjects are mainly teacher trainees who are RTAC. The questionnaires were collected through both online and offline methods, and the partial least squares structural equation modeling (PLS-SEM) technique was used for data analysis. Reliability testing of the questionnaire was mainly conducted in the pilot study, and 35 questionnaires were collected for the pilot study and reliability testing. Cronbach’s Alpha (α) was employed to measure the reliability of the research instrument [63,64], and the final results are shown in Table 1. Although the individual Cronbach’s alpha value is slightly below the traditionally recommended standard of 0.70, overall reliability is still in the acceptable range and does not materially affect subsequent analysis of the data. Most Cronbach’s Alpha values are above 0.7, with only UA slightly below 0.7 (0.697) but still within an acceptable range.

4. Data Analysis and Discussion

After data collection was completed, the process of data analysis mainly relied on IBM SPSS Statistics26 and Smart PLS 4. Calculation of the minimum sample size was carried out using G Power 3.1.9.7 software [65]. After data collection, 231 questionnaires were returned, of which 9 questionnaires were removed due to missing data, quitting midway, or other reasons, and 222 questionnaires were available for further analysis. At the stage of examining suspicious response patterns, this study deleted 27 questionnaires with a sample standard deviation less than 0.5 and retained 195 questionnaires. The examination of outliers was primarily carried out by univariate and multivariate detection methods [64,66,67,68], and 24 questionnaires were removed, leaving 171 questionnaires for further analysis. In terms of data distribution, Skewness and Kurtosis were detected based on advice from Hair et al. (2022) [64]; all of them were between −2 and +2 (see Table 2).

4.1. Assessment of the Measurement Model

Indicator Reliability was evaluated through outer loadings (≥0.708), and Internal Consistency Reliability was evaluated through Composite Reliability (CR) (≥0.7) [64,66]. Convergent Validity was assessed by measuring the value of Average Variance Extracted (AVE) (AVE ≥ 0.5), and Discriminant Validity was assessed by observing the Fornell–Larcker criterion [64,66,69,70]. Based on the above-mentioned methods and the specific situation of this study, three items (RTAC1, RTAC2, and VB4) were deleted. All values for constructs exceeded the squared correlations of the other constructs, which suggests that the requirement for discriminant validity is accepted. The results of outer loadings, CR, and AVE (Table 3) all fulfilled the criteria.

4.2. Assessment of the Structural Model

Hair et al. (2022) [64] stressed that Variance Inflation Factor (VIF) values should be under five to ensure that collinearity issues do not influence assessment of a structural model. The analysis results of this study show that all the VIF values are under 5 (Minimum: UA1 = 1.412, Maximum: RTAC5 = 3.736), indicating there is no collinearity problem. The accepted metrics were, for example, like the following: path coefficients varied from −1 to +1; critical t values were, e.g., 1.65, 1.96, and 2.57; and a critical p-value of 0.05 [64,66]. Table 4 exhibits that all the hypotheses are supported. Figure 2 shows path coefficients, p-values, and the model’s moderate explanatory power R 2 = 0.695.

4.3. Discussion

Hypothesis 1 has received statistical support, indicating that TA has a significant impact on teacher trainees’ RTAC. On the basis of previous research [42,44], this study enriches research results on the logical relationship between TA and RTAC. Hypothesis 2 has been confirmed, showing that UA has a significant influence on teacher trainees’ RTAC. This result reinforces Innovation Resistance Theory [17], Cultural Dimension Theory [45], and subsequent research [52,53]. As future educators, teacher trainees may hold a cautious attitude toward the application of AI chatbots, especially when they feel UA or potential risks. Hypothesis 3 is supported by significant empirical research, which finds that the SIB has a significant effect on RTAC. This discovery suggests that when promoting AI chatbots, it is not only necessary to focus on individual-level technology acceptance but also to pay attention to the shaping of the SIB, and reduce resistance behaviors by creating a positive technology usage atmosphere.
This study validates hypothesis 4, which suggests a significant mediating effect of TA between the UB and RTAC among teacher trainees. The UB (e.g., technological complexity or operational difficulties) may directly trigger TA among teacher trainees, thereby reinforcing their resistance to AI chatbots. Hypothesis 5 is supported by data showing that the mediating effect of TA between the IB and RTAC is confirmed. This result expands previous findings on affective mechanisms in technology adoption or resistance [50,51]. This indicates that the IB perceived by teacher trainees not only directly affects resistance but also plays a role through high anxiety about technical abilities. These dual pathways imply that future AI chatbot development strategies should simultaneously address cognitive barriers to technology image, brand image, and company image, as well as emotional responses to their implementation.
Hypothesis 6 is verified, demonstrating that UA has a significant mediating effect on the relationship between VB and RTAC. This discovery not only provides new insights into Innovation Resistance Theory [17] and Cultural Dimension Theory [45] but also provides a structured implementation framework for further exploration of uncertainty-driven resistance in the future. The analysis results support Hypothesis 7, indicating that UA significantly mediates the relationship between the RB and teacher trainees’ RTAC. This achievement expands the previous research path and perspective [49,56,58] on the RB and technological resistance. The results indicate that when teacher trainees perceive the RB that AI chatbots may bring (such as academic integrity risk and copyright risk), their inherent UA tendency significantly strengthens resistance behaviors, revealing the internal psychological mechanism of risk cognition transforming into behavioral decision-making. This study verifies the significant mediating effect of the SIB between TA and RTAC among teacher trainees (Hypothesis 8), which deepens the application of Innovation Resistance Theory and Unified Theory of Acceptance and Use of Technology in research on AI chatbot adoption.
This study not only verifies the theoretical relationships among the main variables but also reveals the practical barriers to the promotion and application of AI chatbots in educational situations. The results indicate that despite the potential of AI chatbots to assist teaching and personalized feedback, teacher trainees’ resistance significantly influenced their willingness to accept them. These findings also imply that technology acceptance or resistance models need to adequately consider the complex mechanisms of cognitive, emotional, psychological, and social factors when they are applied in the field of education.

5. Conclusions, Limitations, and Directions for Future Research

This study is based on Innovation Resistance Theory, and appropriately draws on Cultural Dimension Theory, Unified Theory of Acceptance and Use of Technology, and practical characteristics to systematically explore the multi-level formation mechanism of resistance behaviors of teacher trainees toward AI chatbots. This study develops a new theoretical model to test significant factors influencing teacher trainees’ resistance to AI chatbots from the perspective of Innovation Resistance Theory. The empirical results indicate that Technology Anxiety, Uncertainty Avoidance, and the Social Influence Barrier all have a significant and direct influence on teacher trainees’ resistance to AI chatbots. Contemporaneously, Technology Anxiety, Uncertainty Avoidance, and the Social Influence Barrier play significant mediating roles in the impact of the Usage Barrier, Image Barrier, Value Barrier, Risk Barrier, and Tradition Barrier on resistance behaviors, revealing the complex path through which cognition-emotion-society factors jointly shape technology resistance. In the future process of technology promotion, it is not only important to focus on usability and value of the AI chatbots but also to pay attention to users’ emotional reactions, such as alleviating technology anxiety. These findings not only expand the theoretical perspective of research on the adoption of AI chatbots but also provide preliminary exploratory empirical evidence for identifying the application of AI chatbot technology in different scenarios in a sustainable future.
However, this study has several limitations: (1) firstly, the sample size is small and the non-probability sampling method has certain limitations; (2) secondly, there are limitations in the geographical and population coverage of the samples, which lead to the need to verify the universality of the conclusions; (3) finally, the variables covered by the theoretical model in this study have limitations, and the effects and pathways of some factors have not been fully considered. Future research could consider expanding the sample coverage to cover different regions or cultural diversity, and introducing more independent, mediating, or moderating variables at the individual or organizational level. How to further enhance human-machine co-evolution ability and situational awareness of future robots is also one of the directions that deserves attention [71]. Future research could consider applying Innovation Resistance Theory to different groups or interdisciplinary research domains.

Author Contributions

Conceptualization, Y.L.; methodology, Y.L.; validation, Y.L.; formal analysis, Y.L.; investigation, Y.L.; resources, Y.L.; data curation, Y.L.; writing—original draft preparation, Y.L.; writing—review and editing, Y.L., H.A. and N.S.M.; visualization, Y.L.; supervision, H.A. and N.S.M.; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Llorens, A.; Trullols, E.; Perez-Poch, A.; Petrovic, N. Soft skills development in ICT students: An evaluation of teaching methods by university educators. Cogent Educ. 2025, 12, 2437906. [Google Scholar] [CrossRef]
  2. United Nations. Digital Economy Report 2024: Shaping an Environmentally Sustainable and Inclusive Digital Future. 2024. Available online: https://unctad.org/system/files/official-document/der2024_en.pdf (accessed on 20 January 2025).
  3. World Bank. Digital Progress and Trends Report 2023. 2024. Available online: https://openknowledge.worldbank.org/server/api/core/bitstreams/95fe55e9-f110-4ba8-933f-e65572e05395/content (accessed on 20 January 2025).
  4. AI Index Report. Available online: https://aiindex.stanford.edu/report/ (accessed on 20 January 2025).
  5. Russell, S.J.; Norvig, P. Artificial Intelligence: A Modern Approach, 4th ed.; Prentice Hall series in artificial intelligence; Pearson: Boston, MA, USA, 2022. [Google Scholar]
  6. Statista. Artificial Intelligence—Worldwide. 2024. Available online: https://www.statista.com/outlook/tmo/artificial-intelligence/worldwide (accessed on 20 January 2025).
  7. OpenAI. Sora is an AI Model that Can Create Realistic and Imaginative Scenes from Text Instructions. 2024. Available online: https://openai.com/index/sora/ (accessed on 20 January 2025).
  8. PwC. Global Artificial Intelligence Study: Sizing the Prize. 2023. Available online: https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf (accessed on 20 January 2025).
  9. AlAfnan, M.A.; Dishari, S.; Jovic, M.; Lomidze, K. ChatGPT as an Educational Tool: Opportunities, Challenges, and Recommendations for Communication, Business Writing, and Composition Courses. J. Artif. Intell. Technol. 2023, 3, 60–68. [Google Scholar] [CrossRef]
  10. Al-Sharafi, M.A.; Al-Emran, M.; Iranmanesh, M.; Al-Qaysi, N.; Iahad, N.A.; Arpaci, I. Understanding the impact of knowledge management factors on the sustainable use of AI-based chatbots for educational purposes using a hybrid SEM-ANN approach. Interact. Learn. Environ. 2023, 31, 7491–7510. Available online: https://www.tandfonline.com/doi/abs/10.1080/10494820.2022.2075014 (accessed on 28 June 2024). [CrossRef]
  11. Fontao, C.B. ChatGPT’s role in the education system: Insights from the future secondary teachers. Int. J. Inf. Educ. Technol. 2024, 14, 1035–1043. [Google Scholar] [CrossRef]
  12. Ghazali, S.A. Exploring the potential of ChatGPT as a substitute teacher: A case study. Int. J. Inf. Educ. Technol. 2024, 14, 271–278. [Google Scholar] [CrossRef]
  13. Kasneci, E.; Seßler, K.; Küchemann, S.; Bannert, M.; Dementieva, D.; Fischer, F.; Gasser, U.; Groh, G.; Günnemann, S.; Hüllermeier, E.; et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learn. Individ. Differ. 2023, 103, 102274. [Google Scholar] [CrossRef]
  14. 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. 2023, 39, 910–922. [Google Scholar] [CrossRef]
  15. Jiao, J.; Chen, L.; Wu, W. Educational Issues Triggered by ChatGPT: Possible Impacts and Counter Measures. Chin. J. ICT Educ. 2023, 3, 19–32. Available online: https://qikan.cqvip.com/Qikan/Article/Detail?id=7109383640&from=Qikan_Search_Index (accessed on 20 January 2025).
  16. Ma, J.; Wang, P.; Li, B.; Wang, T.; Pang, X.S.; Wang, D. Exploring user adoption of ChatGPT: A technology acceptance model perspective. Int. J. Hum.–Comput. Interact. 2025, 41, 1431–1445. [Google Scholar] [CrossRef]
  17. Ram, S.; Sheth, J.N. Consumer Resistance to Innovations: The Marketing Problem and its solutions. J. Consum. Mark. 1989, 6, 5–14. [Google Scholar] [CrossRef]
  18. Rajest, S.S.; Regin, R.; Ajitha, Y.; Paramasivan, P.; Christabel, G.J.A.; Shynu, T. The Analysis of How Artificial Intelligence Has an Effect on Teachers and the Education System. EAI Endorsed Trans. E-Learn. 2023, 9, 7–8. [Google Scholar] [CrossRef]
  19. UNESCO. Ministry of Education’s First Batch of ‘Artificial Intelligence + Higher Education’ Application Scenarios Typical Case Reference. Available online: https://aiedchair.bnu.edu.cn/%E6%95%99%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/ (accessed on 20 January 2025).
  20. Brynjolfsson, E.; Hitt, L.M. Beyond computation: Information technology, organizational transformation and business performance. J. Econ. Perspect. 2000, 14, 23–48. Available online: http://search.ebscohost.com/login.aspx?direct=trueu0026db=buhu0026AN=3836822u0026site=ehost-live (accessed on 17 March 2025). [CrossRef]
  21. Grainger, R.; Tolhurst, D. Organisational Factors Affecting Teachers’ Use and Perception of Information & Communications Technology; Australian Computer Society, Inc.: Sydney, Australia, 2005; Available online: http://www.semanticscholar.org/paper/c078dcb24eb383f6581d76b2d1ca2759f5fe3917 (accessed on 17 March 2025).
  22. Buabeng-Andoh, C. Factors influencing teachers’ adoption and integration of information and communication technology into teaching: A review of the literature. Int. J. Educ. Dev. Using Inf. 2012, 8, 733–738. [Google Scholar] [CrossRef]
  23. Esiyok, E.; Gokcearslan, S.; Kucukergin, K.G. Acceptance of educational use of AI chatbots in the context of self-directed learning with technology and ICT self-efficacy of undergraduate students. Int. J. Hum.–Comput. Interact. 2025, 41, 641–650. [Google Scholar] [CrossRef]
  24. Joudeh, J.M.M. Exploring the impact of technology attributes on usefulness and acceptance of technology: A study on e-university learning systems. Int. J. Inf. Educ. Technol. 2025, 15, 408–418. [Google Scholar] [CrossRef]
  25. Barra, C.; Grimaldi, M.; Muazzam, A.; Troisi, O.; Visvizi, A. Digital divide, gender gap, and entrepreneurial orientation: How to foster technology adoption among pakistani higher education students. Socio-Econ. Plan. Sci. 2024, 93, 101904. [Google Scholar] [CrossRef]
  26. 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. 2023, 36, 261–292. [Google Scholar] [CrossRef]
  27. Benderskaya, E.N.; Zhukova, S.V. Multidisciplinary trends in modern artificial intelligence: Turing’s way. In Artificial Intelligence, Evolutionary Computing and Metaheuristics: In the Footsteps of Alan Turing; Yang, X.-S., Ed.; Springer: Berlin/Heidelberg, German, 2013; pp. 319–343. [Google Scholar] [CrossRef]
  28. Fetzer, J.H. (Ed.) What is artificial intelligence? In Artificial Intelligence: Its Scope and Limits; Springer: Dordrecht, The Netherlands, 1990; pp. 3–27. [Google Scholar] [CrossRef]
  29. Fakhar, H. Artificial intelligence from teachers’ perspectives and understanding: Moroccan study. Int. J. Inf. Educ. Technol. 2024, 14, 856–864. [Google Scholar] [CrossRef]
  30. Bawaneh, A.K. AI shaping the future of education: Science and math teachers’ satisfaction level and motivating factors towards integrating artificial intelligence in teaching and learning. Int. J. Inf. Educ. Technol. 2025, 15, 496–509. [Google Scholar] [CrossRef]
  31. Xu, X. Game-based adaptive learning in probability education. Int. J. Inf. Educ. Technol. 2025, 15, 1–7. [Google Scholar] [CrossRef]
  32. Pincus, M.S.; Pincus, K.; Golden, J.B. Artificial intelligence systems. In The CD ROM Handbook; McGraw-Hill, Inc.: New York, NY, USA, 1988; Volume 12, pp. 329–340. [Google Scholar]
  33. Shawar, B.A.; Atwell, E.S. Using corpora in machine-learning chatbot systems. Int. J. Corpus Linguist. 2005, 10, 489–516. [Google Scholar] [CrossRef]
  34. Slater, S.; Burden, D. Emotionally responsive robotic avatars as characters in virtual worlds. In Proceedings of the Conference in Games & Virtual Worlds for Serious Applications, Coventry, UK, 23–24 March 2009. [Google Scholar] [CrossRef]
  35. Kingchang, T.; Chatwattana, P.; Wannapiroon, P. Artificial intelligence chatbot platform: AI chatbot platform for educational recommendations in higher education. Int. J. Inf. Educ. Technol. 2024, 14, 34–41. [Google Scholar] [CrossRef]
  36. Benjamín, M.-Q. Evaluation of the impact of ChatGPT on the development of research skills in secondary education students: An experimental approach. Int. J. Inf. Educ. Technol. 2025, 15, 59–69. [Google Scholar] [CrossRef]
  37. Ugli, A.M.B. Understanding uzbekistan university EFL teachers’ perceptions of ChatGPT: From benefits to ethical challenges. Int. J. Inf. Educ. Technol. 2025, 15, 246–254. [Google Scholar] [CrossRef]
  38. Polisetty, A.; Sowmya, G.; Pahari, S. Streaming towards innovation: Understanding consumer adoption of OTT services through IRT and TAM. Cogent Bus. Manag. 2023, 10, 2283917. [Google Scholar] [CrossRef]
  39. Kumar, A.; Shankar, A.; Tiwari, A.K.; Hong, H.-J. Understanding dark side of online community engagement: An innovation resistance theory perspective. Inf. Syst. e-Bus. Manag. 2023, 1–27. [Google Scholar] [CrossRef]
  40. Chawla, U.; Verma, B.; Mittal, A. Resistance to O2O technology platform adoption among small retailers: The influence of visibility and discoverability. Technol. Soc. 2024, 76, 102482. [Google Scholar] [CrossRef]
  41. Kwarteng, M.A.; Ntsiful, A.; Osakwe, C.N.; Jibril, A.B.; Chovancova, M. Cultural influences on future transportation technology usage: The role of personal innovativeness, technology anxiety and desire. Int. J. Consum. Stud. 2023, 47, 629–651. [Google Scholar] [CrossRef]
  42. Meuter, M.L.; Ostrom, A.L.; Bitner, M.J.; Roundtree, R. The influence of technology anxiety on consumer use and experiences with self-service technologies. J. Bus. Res. 2003, 56, 899–906. [Google Scholar] [CrossRef]
  43. Chen, C.-C.; Chang, C.-H.; Hsiao, K.-L. Exploring the factors of using mobile ticketing applications: Perspectives from innovation resistance theory. J. Retail. Consum. Serv. 2022, 67, 102974. [Google Scholar] [CrossRef]
  44. Tsai, T.-H.; Lin, W.-Y.; Chang, Y.-S.; Chang, P.-C.; Lee, M.-Y. Technology anxiety and resistance to change behavioral study of a wearable cardiac warming system using an extended TAM for older adults. PLoS ONE 2020, 15, e0227270. [Google Scholar] [CrossRef] [PubMed]
  45. Hofstede, G.; Hofstede, G.J.; Minkov, M. Cultures and Organizations: Software of the Mind: Intercultural Cooperation and Its Importance for Survival, 3rd ed.; McGraw-Hill: New York, NY, USA, 2010. [Google Scholar]
  46. Venkatesh, V.; Davis, F.D. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Manag. Sci. 2000, 46, 186–204. [Google Scholar] [CrossRef]
  47. Friedkin, N.E. A Structural Theory of Social Influence; Cambridge University Press: Cambridge, UK, 1998. [Google Scholar] [CrossRef]
  48. Vadakkemulanjanal Joseph, G.; Thomas, M.A. The mediation effect of technology anxiety and barriers on technology exposure to teachers’ technology adoption. In Proceedings of the 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), Kannur, India, 11–12 August 2022; pp. 1737–1741. [Google Scholar] [CrossRef]
  49. Leong, L.-Y.; Hew, T.-S.; Ooi, K.-B.; Wei, J. Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach. Int. J. Inf. Manag. 2020, 51, 102047. [Google Scholar] [CrossRef]
  50. Hew, J.-J.; Leong, L.-Y.; Tan, G.W.-H.; Ooi, K.-B.; Lee, V.-H. The age of mobile social commerce: An Artificial Neural Network analysis on its resistances. Technol. Forecast. Soc. Change 2019, 144, 311–324. [Google Scholar] [CrossRef]
  51. Sadiq, M.; Adil, M.; Paul, J. An innovation resistance theory perspective on purchase of eco-friendly cosmetics. J. Retail. Consum. Serv. 2021, 59, 102369. [Google Scholar] [CrossRef]
  52. Laukkanen, T. How Uncertainty Avoidance Affects Innovation Resistance in Mobile Banking: The Moderating Role of Age and Gender. In Proceedings of the 2015 48th Hawaii International Conference on System Sciences, Kauai, HI, USA, 5–8 January 2015; pp. 3601–3610. [Google Scholar] [CrossRef]
  53. Adair, W.L.; Xiong, T.X. How Chinese and caucasian canadians conceptualize creativity: The mediating role of uncertainty avoidance. J. Cross-Cult. Psychol. 2018, 49, 223–238. [Google Scholar] [CrossRef]
  54. 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 2024, 26, 98–113. [Google Scholar] [CrossRef]
  55. Mahmud, H.; Islam, A.K.M.N.; Mitra, R.K. What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness. Technol. Forecast. Soc. Change 2023, 193, 122641. [Google Scholar] [CrossRef]
  56. Cham, T.-H.; Cheah, J.-H.; Cheng, B.-L.; Lim, X.-J. I Am too old for this! Barriers contributing to the non-adoption of mobile payment. Int. J. Bank Mark. 2022, 40, 1017–1050. [Google Scholar] [CrossRef]
  57. Yang, Y.; Lu, P.; Niu, Y.; Yuan, G. Research on unmanned smart hotels resistance from the perspective of innovation resistance theory. Sage OPEN 2024, 14, 21582440241281570. [Google Scholar] [CrossRef]
  58. Nie, Q.; Ma, L.; Li, Z. Why are people reluctant to use metaverse products? Investigating barriers based on innovation resistance theory. Online Inf. Rev. 2025, ahead-of-print. [Google Scholar] [CrossRef]
  59. Talwar, M.; Corazza, L.; Bodhi, R.; Malibari, A. Why do consumers resist digital innovations? An innovation resistance theory perspective. Int. J. Emerg. Mark. 2024, 19, 4327–4342. [Google Scholar] [CrossRef]
  60. Bellhouse, D.R. Systematic sampling methods. In Encyclopedia of Biostatistics; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2005. [Google Scholar] [CrossRef]
  61. Emerson, R.W. Convenience Sampling, Random Sampling, and Snowball Sampling: How Does Sampling Affect the Validity of Research? J. Vis. Impair. Blind. 2015, 109, 164–168. [Google Scholar] [CrossRef]
  62. Etikan, I. Comparison of Convenience Sampling and Purposive Sampling. Am. J. Theor. Appl. Stat. 2016, 5, 1. [Google Scholar] [CrossRef]
  63. Gliner, J.A.; Morgan, G.A. Measurement reliability. J. Am. Acad. Child Adolesc. Psychiatry 2001, 40, 486–488. [Google Scholar] [CrossRef]
  64. Hair, J.F.; Hult, T.; Ringle, C.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 3rd ed.; SAGE Publications: Los Angeles, CA, USA, 2022. [Google Scholar]
  65. Faul, F.; Erdfelder, E.; Lang, A.-G.; Buchner, A. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 2007, 39, 175–191. [Google Scholar] [CrossRef]
  66. Hair, J.F. (Ed.) A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); SAGE: Los Angeles, CA, USA, 2014. [Google Scholar]
  67. Sarstedt, M.; Mooi, E. A Concise Guide to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics; Springer Texts in Business and Economics; Springer: Berlin/Heidelberg, Germany, 2019. [Google Scholar] [CrossRef]
  68. Tabachnick, B.G.; Fidell, L.S. Using Multivariate Statistics, 5th ed.; Pearson/Allyn & Bacon: Boston, MA, USA, 2007. [Google Scholar]
  69. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  70. Henseler, J. Partial least squares path modeling. In Advanced Methods for Modeling Markets; Leeflang, P.S.H., Wieringa, J.E., Bijmolt, T.H.A., Pauwels, K.H., Eds.; International Series in Quantitative Marketing; Springer International Publishing: Cham, Switzerland, 2017; pp. 361–381. [Google Scholar] [CrossRef]
  71. Pollini, A.; Giacobone, G.A.; Zannoni, M.; Pucci, D.; Vignali, V.; Falegnami, A.; Tomassi, A.; Romano, E. Human-machine interaction design in adaptive automation. Procedia Comput. Sci. 2025, 253, 1034–1044. [Google Scholar] [CrossRef]
Sustainability 17 04081 g001
Figure 1. An enhanced IRT model with the introduction of new constructs.
Figure 1. An enhanced IRT model with the introduction of new constructs.
Sustainability 17 04081 g001
Sustainability 17 04081 g002
Figure 2. Results of the enhanced IRT model analysis: path coefficients, p-values, and the model’s explanatory power.
Figure 2. Results of the enhanced IRT model analysis: path coefficients, p-values, and the model’s explanatory power.
Sustainability 17 04081 g002
Table 1. Results of reliability analysis.
Table 1. Results of reliability analysis.
UBVBRBTBIBTAUASIBRTAC
Cronbach’s Alpha0.8090.7460.7070.8770.7530.8740.6970.9290.803
Table 2. Data distribution test.
Table 2. Data distribution test.
VariablesSkewnessKurtosis
UB0.061−0.379
VB−0.129−0.653
RB−0.223−0.570
TB−0.005−0.531
IB0.067−0.781
TA−0.088−0.793
UA−0.204−0.011
SIB−0.046−0.680
RTAC−0.040−0.542
Table 3. The outer loadings, CR, and AVE.
Table 3. The outer loadings, CR, and AVE.
ConstructsItemsOuter LoadingsCRAVE
UBUB10.7870.9120.731
UB20.893
UB30.876
UB40.845
UB50.870
VBVB10.8360.8280.735
VB20.867
VB30.869
RBRB10.8240.8540.627
RB20.852
RB30.800
RB40.751
RB50.726
TBTB10.8450.8690.718
TB20.858
TB30.854
TB40.833
IBIB10.8770.8990.762
IB20.888
IB30.859
IB40.867
TATA10.8760.9150.746
TA20.850
TA30.855
TA40.870
TA50.867
UAUA10.8080.7840.575
UA20.748
UA30.758
UA40.715
SIBSIB10.8610.9230.762
SIB20.889
SIB30.877
SIB40.883
SIB50.853
RTACRTAC30.8910.9090.785
RTAC40.852
RTAC50.921
RTAC60.880
Table 4. Assessment of the IRT structural model.
Table 4. Assessment of the IRT structural model.
HypothesesRelationshipsβT PResults
H1TA → RTAC0.4734.1820.000Supported
H2UA→ RTAC0.2022.7750.006Supported
H3SIB → RTAC0.2432.2950.022Supported
H4UB → TA → RTAC0.1392.5840.010Supported
UB → RTAC0.1392.5840.010
H5IB → TA → RTAC0.253.5180.000Supported
IB → RTAC0.253.5180.000
H6VB → UA → RTAC0.0792.2510.024Supported
VB → RTAC0.0792.2510.024
H7RB → UA → RTAC0.0622.070.038Supported
RB → RTAC0.0622.070.038
H8TB → SIB → RTAC0.1672.2160.027Supported
TB → RTAC0.1672.2160.027
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Liu, Y.; Awang, H.; Mansor, N.S. Exploring the Potential Barrier Factors of AI Chatbot Usage Among Teacher Trainees: From the Perspective of Innovation Resistance Theory. Sustainability 2025, 17, 4081. https://doi.org/10.3390/su17094081

AMA Style

Liu Y, Awang H, Mansor NS. Exploring the Potential Barrier Factors of AI Chatbot Usage Among Teacher Trainees: From the Perspective of Innovation Resistance Theory. Sustainability. 2025; 17(9):4081. https://doi.org/10.3390/su17094081

Chicago/Turabian Style

Liu, Yonggang, Hapini Awang, and Nur Suhaili Mansor. 2025. "Exploring the Potential Barrier Factors of AI Chatbot Usage Among Teacher Trainees: From the Perspective of Innovation Resistance Theory" Sustainability 17, no. 9: 4081. https://doi.org/10.3390/su17094081

APA Style

Liu, Y., Awang, H., & Mansor, N. S. (2025). Exploring the Potential Barrier Factors of AI Chatbot Usage Among Teacher Trainees: From the Perspective of Innovation Resistance Theory. Sustainability, 17(9), 4081. https://doi.org/10.3390/su17094081

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop