Open research questions in Technology Adoption and User Behaviour

What the literature leaves open

• in Management SMEs A. A. M. Alrifae, “The role of technological enablers and government support in e-commerce adoption among doi: Jordan,” 10.21511/ppm.23(2).2025.56.

  Enhancing SME Competitiveness through E-Commerce Adoption: A Framework for Malang City · 2026 · DOI

• The findings contribute to technology acceptance research by integrating AI-specific constructs within an extended UTAUT framework and providing evidence from the underexplored context of vocational education.

  Extending UTAUT for Generative AI Adoption among Vocational Teachers: The Roles of Trust, AI Literacy, and Risk Awareness · 2026 · DOI

• However, factors such as privacy concerns, data security perceptions, health consciousness, brand trust, hedonic motivation, and lifestyle compatibility were not investigated. The results of the study may not be representative of the general population of Bangladesh, due to purposive sampling and the fact that the sample respondents were young, educated, and technology-oriented.

  Understanding Smartwatch Adoption in a Developing Economy: An Extended Technology Acceptance Model (TAM) Perspective · 2026 · DOI

• , 2021; Damerji & Salimi, 2021), Limited research has examined how AI can complement traditional teaching approaches and enhance existing pedagogies.

  Artificial intelligence adoption in education: A systematic review of trends, theoretical models, and influencing factors (2021-2025) · 2026 · DOI

• To optimize e-commerce platform performance and consumer safety, the study recommends a collaborative approach: platforms must enhance transaction security, transparency, and reliability, while online sellers should reduce consumer hesitation by providing accurate product details, authentic photos, and responsive support. Additionally, the study suggests leveraging social influence strategies like referral programs and influencer partnerships to boost engagement, while advising consumers to practice safe shopping by verifying reviews and using secure payment methods. Finally, policymakers and educational institutions should actively promote digital literacy and fraud prevention, and future researchers are encouraged to expand these findings by investigating how trust, risk, and buying behavior evolve across different generations.

  Consumer trust and risk in electronic commerce platform: its relationship to buying behavior · 2026 · DOI

• Based on the findings of this study, the following recommendations were made: 1. Tertiary institutions in Oyo State should organize regular seminars, workshops, and orientation programmes to increase accounting students’ awareness and understanding of Artificial Intelligence technologies and their relevance to accounting education and practice. 2. Institutions should integrate AI-related courses such as data analytics, machine learning, accounting software applications, and intelligent financial systems into accounting curricula to improve students’ perceived usefulness of AI technologies. 3. School management should provide user-friendly AI learning platforms and practical training opportunities that will enhance students’ perceived ease of use of AI technologies in learning activities. 4. Lecturers and instructors should encourage positive attitudes toward AI technologies by incorporating AI-driven teaching methods, virtual simulations, automated assessment tools, and intelligent tutoring systems into classroom instruction. @ mauijef.com.ng (MAUIJEF), Mau International Journal of Educational Foundations Page 64 MAU INTERNATIONAL JOURNAL OF EDUCATIONAL FOUNDATIONS E-ISSN: 3121-8377 Volume 2, Issue 2, 2026 10.64290/mauijef.v2i2.61 5. Government and institutional authorities should improve digital infrastructure such as stable internet connectivity, electricity supply, computer laboratories, and access to educational technologies to facilitate students’ effective use of AI tools. 6. Tertiary institutions should provide continuous digital literacy training for both lecturers and students to improve competence and confidence in the use of AI technologies for accounting education. 7. Educational policymakers and curriculum planners should formulate policies that support the ethical and responsible use of AI technologies in teaching, learning, assessment, and research activities within tertiary institutions. 8. Institutions should establish technical support units that can assist students and lecturers in resolving challenges associated with the use of AI technologies. 9. Future researchers should extend similar studies to other disciplines and geopolitical zones in Nigeria using larger samples and additional technology acceptance variables to improve generalizability of findings. 10. Accounting departments should collaborate with professional accounting bodies and technology companies such as Microsoft, Google, and OpenAI to expose students to modern AI applications used in the accounting profession.

  ASSESSMENT OF ACCOUNTING STUDENTS’ ACCEPTANCE AND USE OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN OYO STATE TERTIARY INSTITUTIONS · 2026 · DOI

• These findings indicate that students’ adoption of DeepSeek is driven more by perceived academic value and enabling conditions than by social pressure or habitual use in an underexplored secondary-school context.

  Measuring senior high school students’ use of GenAI using structural equation modeling and artificial neural networks · 2026 · DOI

• Future studies are recommended to involve broader samples, use probability sampling, and include individual perceptions and an 356 " Asian Journal of Applied Education (AJAE) " Vol. FURTHER RESEARCH This research still has limitations so further research on this topic is still needed “Students’ use of Internet-Based Payment Applications: An Extension of the Technology Acceptance Model Theory” REFERENCES Agarwal, R.

  Students’ use of Internet-Based Payment Applications: An Extension of the Technology Acceptance Model Theory · 2026 · DOI

• significantly influenced purchase intention, whereas non-personalized recommendations showed no significant effect (Mican and Sitar Taut, 2024).AI produces value mainly by enhancing shopping efficiency and decision-making quality for consumers, leading to higher acceptance of the technology (Bawack et al., 2022). H1: Perceived usefulness positively influences on purchase intention.

  How AI shapes sustainable consumer behaviour. The role of perceived usefulness, perceived anthropomorphism, perceived value, purchase intention · 2026 · DOI

• The study proposes a new framework to capture the entire gamut of psychological processes that individuals undergo while accepting and adopting new technologies. While the current study focuses on educators, the model can be applied to other professions as well. As a prefatory step, this study sifts through literature and available models to come up with a framework that better captures the entire spectrum. However, future research on samples will bolster the conceptual framework proposed in this study. REFERENCES Al-Emran, M., & Granić, A. (2021). Is It Still Valid or Outdated? A Bibliometric Analysis of the Technology Acceptance Model and Its Applications From 2010 to 2020. In M. Al-Emran & K. Shaalan (Eds.), Recent Advances in Technology Acceptance Models and Theories (Vol. 335, pp. 1–12). Springer International Publishing. https://doi.org/10.1007/978-3-030-64987-6_1 Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology Acceptance Model in M-learning context: A systematic review. Computers & Education, 125, 389–412. https://doi.org/10.1016/j.compedu.2018.06.008 AlManei, M., Salonitis, K., & Tsinopoulos, C. (2018). A conceptual lean implementation framework based on change management theory. Procedia Cirp, 72, 1160–1165. https://doi.org/10.1016/j.procir.2018.03.141 Armitage, C. J. (2009). Is there utility in the transtheoretical model? British Journal of Health Psychology, 14(2), 195–210. https://doi.org/10.1348/135910708X368991 Bagga, S. K., Gera, S., & Haque, S. N. (2023). The mediating role of organizational culture: Transformational leadership and change management in virtual teams. Asia Pacific Management Review, 28(2), 120–131. Baumeister, R. F., Vohs, K. D., Nathan DeWall, C., & Liqing Zhang. (2007). How Emotion Shapes Behavior: Feedback, Anticipation, and Reflection, Rather Than Direct Causation. Personality and Social Psychology Review, 11(2), 167–203. https://doi.org/10.1177/1088868307301033 9 Bhattacherjee, A., & Park, S. C. (2014). Why end-users move to the cloud: A migration-theoretic analysis. European Journal of Information Systems, 23(3), 357–372. https://doi.org/10.1057/ejis.2013.1 Boonstra, A. (2024). Change management for digital transformation. In A Research Agenda for Digital Transformation (pp. 227–254). Edward Elgar Publishing. https://www.elgaronline.com/edcollchap/book/9781035306435/bookpart-9781035306435-16.xml Conceição, S. C. O. (2006). Faculty Lived Experiences in the Online Environment. Adult Education Quarterly, 57(1), 26–45. https://doi.org/10.1177/1059601106292247 Corr, C. A. (2021). Should We Incorporate the Work of Elisabeth Kübler- Ross in Our Current Teaching and Practice and, If So, How? OMEGA - Journal of Death and Dying, 83(4), 706–728. https://doi.org/10.1177/0030222819865397 Darban, M., & Polites, G. L. (2016). Do emotions matter in technology training?

  Mapping the technology acceptance curve · 2026 · DOI

• In conclusion, media experience, Based on the UTAUT2 model, this study investigated digitally disadvantaged students’ behavioral intention toward educational television programming and the factors that underlie it. Our path analysis revealed a distinct polarization among the predictors: enjoyment-driven impulses (HM), routines (Habit), and structural support (FC) emerged as prominent positive catalysts for students’ behavioral intention. In contrast, core utility and social dimensionsnamely PE, EE, SI, and Price Value-exhibited statistically negligible predictive power. routine engagement and practical support conditions proved to be stronger determinants of students’ intention than instrumental evaluations, social norms and price-related considerations. This investigation extends the application of UTAUT2 to the domain of educational television and to offers further evidence for understanding the acceptance of alternative learning media in specific educational settings. Educational television is a relatively unique learning medium compared to mainstream digital learning tools such as smartphones, tablets and computers. The constellation of factors that shape students’ behavioral intention toward it may differ accordingly. The findings suggest that, in contexts of limited access to digital learning resources, educational television should be understood not only as a continuation of a traditional medium but also as a potentially accessible form of learning support. Several limitations of the current study should be kept in view. First, the sample was drawn from Zhejiang Province in China and consisted mostly of respondents were between 12 and 16 years of age. Thus, the findings should be generalized to other age groups, regions and educational contexts with caution. Second, the study centered on behavioral intention rather than actual use, and not all respondents had direct experience with the referred educational television service context.

  Exploring factors influencing digitally disadvantaged students' behavioral intention to use educational television programs · 2026 · DOI

• 9% of the variance in BI remains unknown in our framework; this may be more explicable in future studies. Therefore, further investigation into incorporating FC into our proposed conceptual paradigm would be interesting.

  Exploring the Factors Influencing Artificial Intelligence Adoption among Gen Z University Students: the Role of Self-Efficacy and Perceived Trust · 2026 · DOI

• Given the lack of significant findings in both the present and previous studies, future research should investigate how to operationalize the UTAUT constructs specifically within the context of GenAI.

  Understanding and Modeling Technology Adoption in a New Era: A Cross-Sectional Study on Higher Education Teachers’ Adoption and Use of Generative Artificial Intelligence · 2026 · DOI

• for Researchers Future studies may expand TAM by including constructs such as trust, per- ceived risk, and institutional policy support, explore discipline-specific adoption patterns, and examine long-term impacts on teaching and learning. Impact on Society Generative AI has the potential to enhance academic productivity while raising ethical and integrity concerns. Balanced and responsible implementation can maintain the educational and social mission of universities.

  Extending TAM for Generative AI - How Technophobia and Institutional Context Shape AI Adoption Among Egyptian Academics: A Mixed-Methods Lens · 2026 · DOI

• for Practitioners Universities should establish clear policies for the use of generative AI in teach- ing, assessment, and research, and provide regular training and awareness pro- grams to support responsible adoption. Institutions should encourage critical and purposeful use rather than dependence on generative AI.

  Extending TAM for Generative AI - How Technophobia and Institutional Context Shape AI Adoption Among Egyptian Academics: A Mixed-Methods Lens · 2026 · DOI

• Further research should involve a wider range of institutions, consider modera- tors such as digital literacy and organizational readiness, and develop ethical and pedagogical frameworks for the constructive use of AI in higher education.

  Extending TAM for Generative AI - How Technophobia and Institutional Context Shape AI Adoption Among Egyptian Academics: A Mixed-Methods Lens · 2026 · DOI

• The findings of the study offer valuable recommendations to support the responsible and effective implementation of AI-based proctoring systems. The purpose of these recommendations is to make professors, students, and parents responsive similarly, to ensure technology is shared fairly, and to support using digital assessments. Initially, it is essential to prioritize user-centric design principles to ensure accessibility and ease of use for wide range of users (Luo, 2024). AI-proctoring systems should incorporate intuitive interfaces, simplified and streamlined navigation, and clear, concise instructions to cater to both technologically adept users and those with limited digital proficiency (Somavarapu et al., 2024). Educational institutions should organize frequently orientation sessions, open Q&A (Questions and Answers) forums, and accessible documentation can develop user confidence and mitigate resistance. Moreover, the AI-based proctoring platforms should implement robust data privacy and protection protocols. To ensure ethical implementation, it is necessary to address fairness and algorithmic bias. This includes regular audits of AI algorithms, involvement of independent reviewers, and the establishment of redressal systems for users who perceive injustice in monitoring outcomes. Addressing these concerns is vital to maintain the credibility and integrity of assessment processes. At the policy level, adoption of AI-based proctoring systems should align with India’s Digital Personal Data Protection (DPDP) Act, 2023, ensuring lawful data processing, informed consent, purpose limitation, and adequate security safeguards. Institutions should establish internal AI governance frameworks consistent with national data protection regulations to enhance accountability, transparency, and stakeholder trust. To address the parameter on monitoring effectiveness, it is important to increase parental support (Moran et al., 2004). Providing evidence-based outcomes, such as reduced academic dishonesty and enhanced exam integrity. Furthermore, educational policymakers must embed ethical guidelines and accountability measures into the regulatory framework governing AI applications in education. These should cover transparency, data governance, fairness, and user consent, thereby creating a foundation for responsible innovation. Institutions should also establish mechanisms for ongoing feedback and iterative system improvement. This participatory approach, involving all stakeholders in system refinement, can enhance the responsiveness and effectiveness of AI-based solutions. By implementing the above recommendations, educational stakeholders can support a more equitable, trustworthy, and pedagogically aligned integration of AI-proctoring technologies. Kepping students in mind, since privacy concerns significantly influenced behavioural intention of students, institutions should implement transparent data-handling policies, provide clear consent mechanisms, and communicate how AI-based proctoring data are stored, processed, and deleted. For parents, as awareness and perceived usefulness are key factors, institutions should conduct orientation sessions and provide educational materials explaining system accuracy, fairness, and data protection safeguards. Finally, for faculty members training programs should be introduced to enhance trust in system reliability and ethical usage practices. Developers should incorporate clear explainability features into AI systems. For example, proctoring tools such as Proctorio and Respondus should provide instructors with accessible dashboards that clarify: what data are collected, how behavioral flags are generated, the probability thresholds for detecting “suspicious” activity and known limitations or bias risks. Providing interpretable AI outputs would reduce perceived ethical risk and increase trust among faculty.

  Adoption of AI-based proctoring platforms: A multi-stakeholder perspective from the education sector stakeholders · 2026 · DOI

• Previously, studies yielded inconsistent results regarding the direct relationship between PE and CI. Further, previous research was inconclusive about the influence of PU on CI.

  An Empirical Analysis of School Students’ Continuance Intention to Use Mobile Learning · 2026 · DOI

• https://doi.org/10.1080/10494820.2020.1734028 framework. Azam, M., Kingdon, G., & Wu, K. B. (2016). Impact of private secondary schooling on cognitive 465-480.

  An Empirical Analysis of School Students’ Continuance Intention to Use Mobile Learning · 2026 · DOI

• Future research could explore how prompt engineering strat- egies and individual differences jointly shape the usage out- comes and decision-making processes of AIGC tools. Future research should consider conducting comparative studies across different cultural contexts to reveal the potential impact of cultural factors on user behavior.

  Exploring the impact of AIGC Tools’ technical features: the moderating power of emotional and functional value on users’ continuance intention · 2026 · DOI

• Future research could examine the factors shaping the AI adoption process in greater depth. In this regard, the role of variables such as trust in AI, perceived risk and organizational support could be explored in detail. It is clear that investing solely in technical infrastructure is insufficient for the widespread adoption of AI applications in public institutions.

  Technological Readiness as a Driver of Artificial Intelligence Adoption in Public Administration and Auditing: Serial Mediation by Perceived Ease of Use and Perceived Usefulness · 2026 · DOI

• Finally, future research should investigate the psychological and contextual conditions that trigger transitions from conscious algorithmic engagement to anticipatory behavioural conformity. Limitations and Future Research Directions First, the primary limitation of this study lies in its national scope. This demographic exhibits higher algorithmic fluency and platform exposure, which may not be generalizable to older, less digitally immersed populations.

  Algorithmic Awareness and Digital Responsibility: The Role of Platform Trust and Digital Literacy · 2026 · DOI

• Policymakers and HEI leaders should consider a strategic pivot as conducive conditions predict intention and behavior, suggesting infrastructure alone is insufficient.

  Factors Affecting Behavior, Perceived Impact of AI on Work Engagement, and AI Application · 2026 · DOI

• This study, while providing valuable insights, has certain limitations to consider for future research. First, future studies should include participants from diverse regions and cultures to produce more generalisable results and enable meaningful comparisons. The second limitation concerns the evaluation of behavioural intention in our model as a proxy for actual use. There is ongoing debate in theories and models, such as TAM and TPB, about whether behavioural intention accurately predicts actual use. Therefore, we recom- mend further research into ChatGPT usage experiences. Third, anthropomorphic factors such as coolness, warmth, and cuteness may influence the use of ChatGPT for travel (Pham et al., 2024). More comprehensive models that integrate Behavioural Reasoning Theory (BRT), including reasons for and against adoption fac- tors (Al-Qaysi et al., 2025), could provide deeper insights into ChatGPT usage and intentions. Finally, we believe that conducting interactive studies with ChatGPT will offer valuable understanding of actual usage behaviour in tourism.

  Adoption of ChatGPT for Travel · 2026 · DOI

• Future studies could investigate additional variables, including perceived advantages and cultural influences on gamification adoption, while addressing the limitations of this study, including its geographical breadth and cross-sectional design. However, the minimal influence of hedonic behaviour on perceived usefulness reveals a complex aspect of user acceptance and adoption within the expanded TAM model, warranting further study.

  Game on · 2026 · DOI

Most-cited papers in Technology Adoption and User Behaviour

• Extrinsic and Intrinsic Motivation to Use Computers in the Workplace¹ · Journal of Applied Social Psychology · 1992 · 4,285 citations
• Choosing among Alternative Service Delivery Modes: An Investigation of Customer Trial of Self-Service Technologies · Journal of Marketing · 2005 · 1,133 citations
• Understanding consumers’ acceptance of automated technologies in service encounters: Drivers of digital voice assistants adoption · Journal of Business Research · 2020 · 537 citations
• “Extending the Technology Acceptance Model (TAM) to Predict University Students’ Intentions to Use Metaverse-Based Learning Platforms” · Education and Information Technologies · 2023 · 495 citations
• Users as Innovators: A Review, Critique, and Future Research Directions · Journal of Management · 2010 · 461 citations
• Developing a general extended UTAUT model for M-payment adoption · Technology in Society · 2020 · 409 citations
• Information System Success: Individual and Organizational Determinants · Management Science · 2006 · 404 citations
• Measuring EFL learners’ use of ChatGPT in informal digital learning of English based on the technology acceptance model · Innovation in Language Learning and Teaching · 2023 · 381 citations
• The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart healthcare services · Computers in Human Behavior · 2021 · 351 citations
• What drives FinTech adoption? A multi-method evaluation using an adapted technology acceptance model · Management Decision · 2020 · 337 citations

Most recent work

• Factors influencing generative artificial intelligence such as ChatGPT adoption and actual use in higher education: an extended UTAUT2 perspective · Journal of Further and Higher Education · 2026
• Factors influencing university students’ adoption of online education platforms in Bangladesh: an extended TAM-based analysis · The Educational and Developmental Psychologist · 2026
• From information evaluation to technology use: Generative AI adoption in Chinese business schools · The International Journal of Management Education · 2026
• Propelling MSMEs towards automation adoption and sustainable development · International Journal of Productivity and Performance Management · 2026
• Exploring the Factors Influencing Artificial Intelligence Adoption among Gen Z University Students: the Role of Self-Efficacy and Perceived Trust · Journal for STEM Education Research · 2026
• Unveiling Student Adoption of Generative AI in Higher Education: The Roles of Perceived Humanness, Personal Innovativeness, and Trust · Beijing international review of education · 2026
• Determinants of Mobile Banking Usage in Malaysia: Health, Social, and Technology Factors · International Journal of Business and Social Science · 2026
• Second-hand goes digital: a systematic literature review of consumer motivations and barriers in online resale platforms · The International Review of Retail Distribution and Consumer Research · 2026
• Impact of Quick Commerce Applications on Consumers Buying Behaviour and Brand Perception · International Journal of Emerging Research in Science Engineering and Management · 2026
• Exploring the adoption of smart facilities management in Ghana: understanding the key factors and challenges through the Diffusion of Innovation Theory · Facilities · 2026

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