Research Article

General chatbot acceptance, enjoyment, perceived risk, and value (G-CAVS): Scale development and validation

Seyat Polat 1 * , Günter Renner 1
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1 Friedrich-Alexander-Universität Erlangen-Nürnberg, Nürnberg, GERMANY* Corresponding Author
Contemporary Educational Technology, 18(1), January 2026, ep627, https://doi.org/10.30935/cedtech/17878
Published: 06 February 2026
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ABSTRACT

This study was conducted within the context of the KI meets vhb project funded by the Virtuelle Hochschule Bayern, which addresses the use of artificial intelligence applications in university-based teacher education. Despite the increasing use of chatbots in teacher education programs, there is a lack of comprehensive and psychometrically validated instruments to assess pre-service teachers’ perceptions of different types of education chatbots. To address this gap, the present study reports the development and validation of a scale designed to measure pre-service teachers’ perceptions of different types of chatbots used in educational contexts. The technology acceptance model (TAM, TAM 3) and the value-based adoption model (VAM) served as the theoretical foundation in the development of the scale items. Data were collected from 224 German pre-service teachers enrolled in university-based teacher education programs. Exploratory and confirmatory factor analyses supported a four-factor structure, with strong model fit indices. Criterion-related validity provided initial support for the scale, as significant associations with chatbot usage frequency were observed for all dimensions except perceived risk. The four-factor structure of the scale was further confirmed in an independent sample of 263 in-service teachers in Türkiye, demonstrating the robustness of the model across different teacher populations. Overall, the G-CAVS scale emerged as a valid and reliable instrument for assessing perceptions of chatbots in teacher education contexts, with implications for broader pre-service teachers’ populations beyond the present samples.
Keywords: chatbot scale, scale development, technology acceptance, pre-service teachers, artificial intelligence in education

CITATION (APA)

Polat, S., & Renner, G. (2026). General chatbot acceptance, enjoyment, perceived risk, and value (G-CAVS): Scale development and validation. Contemporary Educational Technology, 18(1), ep627. https://doi.org/10.30935/cedtech/17878

REFERENCES

  1. Akdogan, E. (2021). Life and education built on trust. Journal of Research in Social Sciences and Language, 1(2), 128-137. https://doi.org/10.71514/jssal/2021.38
  2. Al-Abdullatif, A. M. (2023). Modeling students’ perceptions of chatbots in learning: Integrating technology acceptance with the value-based adoption model. Education Sciences, 13(11), Article 1151. https://doi.org/10.3390/educsci13111151
  3. APA. (2017). Ethical principles of psychologist and code of conduct. APA. http://www.apa.org/ethics/code/index.aspx
  4. Balcı, Ö. (2024). The role of ChatGPT in English as a foreign language (EFL) learning and teaching: A systematic review. International Journal of Current Educational Studies, 3(1), 66-82. https://doi.org/10.46328/ijces.107
  5. Borsci, S., Malizia, A., Schmettow, M., Van Der Velde, F., Tariverdiyeva, G., Balaji, D., & Chamberlain, A. (2022). The chatbot usability scale: The design and pilot of a usability scale for interaction with AI-based conversational agents. Personal and Ubiquitous Computing, 26(1), 95-119. https://doi.org/10.1007/s00779-021-01582-9
  6. Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). Guilford Press.
  7. Bulut, M. A., Adıgüzel, T., & Kaya, M. H. (2025). Exploring views and experiences of faculty members’ participation in an asynchronous online program: Using a micro-learning format and CoP framework. Innovations in Education and Teaching International. https://doi.org/10.1080/14703297.2025.2532056
  8. Büyüköztürk, Ş. (2021). Sosyal bilimler için veri analizi el kitabı [A handbook of data analysis for the social sciences]. Pegem Publications.
  9. Castells, M., Fernandez-Ardevol, M., Qiu, J. L., & Sey, A. (2009). Mobile communication and society: A global perspective. MIT Press. https://doi.org/10.1111/j.1944-8287.2008.tb00398.x
  10. Chambers, C. (2004). Technological advancement, learning, and the adoption of new technology. European Journal of Operational Research, 152(1), 226-247. https://doi.org/10.1016/S0377-2217(02)00651-3
  11. Chang, C. Y., Hwang, G. J., & Gau, M. L. (2022). Promoting students’ learning achievement and self-efficacy: A mobile chatbot approach for nursing training. British Journal of Educational Technology, 53(1), 171-188. https://doi.org/10.1111/bjet.13158
  12. Chen, Q., Lu, Y., Gong, Y., & Xiong, J. (2023). Can AI chatbots help retain customers? Impact of AI service quality on customer loyalty. Internet Research, 33(6), 2205–2243. https://doi.org/10.1108/INTR-09-2021-0686
  13. Costello, A. B., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10(1). https://doi.org/10.7275/jyj1-4868
  14. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
  15. DeVellis, R. F., & Thorpe, C. T. (2021). Scale development: Theory and applications. SAGE.
  16. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., ... Wright, R. (2023). Opinion paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, Article 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
  17. Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272-299. https://doi.org/10.1037/1082-989X.4.3.272
  18. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
  19. Gable, R. K., & Wolf, M. B. (2012). Instrument development in the affective domain: Measuring attitudes and values in corporate and school settings. Springer.
  20. Gil-Garcia, J. R., Helbig, N., & Ojo, A. (2014). Being smart: Emerging technologies and innovation in the public sector. Government Information Quarterly, 31, I1-I8. https://doi.org/10.1016/j.giq.2014.09.001
  21. Goli, M., Sahu, A. K., Bag, S., & Dhamija, P. (2023). Users’ acceptance of artificial intelligence-based chatbots: An empirical study. International Journal of Technology and Human Interaction, 19(1), 1-18. https://doi.org/10.4018/IJTHI.318481
  22. Güldal, H., & Dinçer, E. O. (2025). Can rule-based educational chatbots be an acceptable alternative for students in higher education? Education and Information Technologies, 30(3), 3979-4012. https://doi.org/10.1007/s10639-024-12977-5
  23. Gumusel, E., Zhou, K. Z., & Sanfilippo, M. R. (2024). User privacy harms and risks in conversational AI: A proposed framework. arXiv. https://doi.org/10.48550/arXiv.2402.09716
  24. Gupta, A. K. (2022). Ethical considerations in the deployment of AI chatbots: Lessons from ChatGPT. Data Science Insights Magazine, 5, 19-22.
  25. Hair, J. F. (2014). Multivariate data analysis: An overview. In M. Lovric (Ed.), International encyclopedia of statistical science (pp. 904-907). Springer. https://doi.org/10.1007/978-3-642-04898-2_395
  26. Hinkin, T. R. (1998). A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods, 1(1), 104-121. https://doi.org/10.1177/109442819800100106
  27. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  28. Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36. https://doi.org/10.1007/BF02291575
  29. Kim, H. W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43(1), 111-126. https://doi.org/10.1016/j.dss.2005.05.009
  30. Kim, Y., Park, Y., & Choi, J. (2017). A study on the adoption of IoT smart home service: Using value-based adoption model. Total Quality Management & Business Excellence, 28(9-10), 1149-1165. https://doi.org/10.1080/14783363.2017.1310708
  31. Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford Publications.
  32. Köhler, C., & Hartig, J. (2024). ChatGPT in higher education: Measurement instruments to assess student knowledge, usage, and attitude. Contemporary Educational Technology, 16(4), Article ep528. https://doi.org/10.30935/cedtech/15144
  33. Labadze, L., Grigolia, M., & Machaidze, L. (2023). Role of AI chatbots in education: Systematic literature review. International Journal of Educational Technology in Higher Education, 20(1), Article 56. https://doi.org/10.1186/s41239-023-00426-1
  34. Lemke, C., Kirchner, K., Anandarajah, L., & Herfurth, F. N. (2023, October). Exploring the student perspective: Assessing technology readiness and acceptance for adopting large language models in higher education. In Proceedings of the 22nd European Conference on e-Learning: ECEL 2023. Academic Conferences and Publishing Limited. https://doi.org/10.34190/ecel.22.1.1828
  35. Liao, Y. K., Wu, W. Y., Le, T. Q., & Phung, T. T. T. (2022). The integration of the technology acceptance model and value-based adoption model to study the adoption of e-learning: The moderating role of e-WOM. Sustainability, 14(2), Article 815. https://doi.org/10.3390/su14020815
  36. Lima, M. L., Barnett, J., & Vala, J. (2005). Risk perception and technological development at a societal level. Risk Analysis: An International Journal, 25(5), 1229-1239. https://doi.org/10.1111/j.1539-6924.2005.00664.x
  37. Mathavan, B., Vafaei-Zadeh, A., Hanifah, H., Ramayah, T., & Kurnia, S. (2024). Understanding the purchase intention of fitness wearables: Using value-based adoption model. Asia-Pacific Journal of Business Administration, 16(1), 101-126. https://doi.org/10.1108/APJBA-04-2022-0166
  38. Mijwel, M. M. (2015). History of artificial intelligence. Computer Science, 1(1), 1-6. https://doi.org/10.13140/RG.2.2.16418.15046
  39. Mokoena, O. P., & Seeletse, S. M. (2025). AI in rural classrooms: Challenges and perspectives from South African educators. International Journal of Current Educational Studies, 4(2), 30-52. https://doi.org/10.46328/ijces.199
  40. Nemt-Allah, M., Khalifa, W., Badawy, M., Elbably, Y., & Ibrahim, A. (2024). Validating the ChatGPT usage scale: Psychometric properties and factor structures among postgraduate students. BMC Psychology, 12(1), Article 497. https://doi.org/10.1186/s40359-024-01983-4
  41. Özcan, A., & Polat, S. (2023). Artificial intelligence and chat bots in academic research. Journal of Research in Social Sciences and Language, 3(2), 81-90. https://doi.org/10.71514/jssal/2023.111
  42. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce—Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7, 69-103. https://doi.org/10.1080/10864415.2003.11044275
  43. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), Article 879. https://doi.org/10.1037/0021-9010.88.5.879
  44. Raiche, A. P., Dauphinais, L., Duval, M., De Luca, G., Rivest-Hénault, D., Vaughan, T., Proulx, C., & Guay, J. P. (2023). Factors influencing acceptance and trust of chatbots in juvenile offenders’ risk assessment training. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1184016
  45. Şanlı, C. (2025). Artificial intelligence in geography teaching: Potentialities, applications, and challenges. International Journal of Current Educational Studies, 4(1), 47-76. https://doi.org/10.46328/ijces.170
  46. Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90-103. https://doi.org/10.1016/j.im.2006.10.007
  47. Şeker, H. ve Gençdoğan, B. (2006). Psikolojide ve eğitimde ölçme aracı geliştirme [Developing measurement tools in psychology and education]. Nobel Yayıncılık.
  48. Simon, H. A. (1995). Artificial intelligence: An empirical science. Artificial Intelligence, 77(1), 95-127. https://doi.org/10.1016/0004-3702(95)00039-H
  49. Sohn, K., & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telematics and Informatics, 47, Article 101324. https://doi.org/10.1016/j.tele.2019.101324
  50. Srinivasan, R. (2008). Sources, characteristics and effects of emerging technologies: Research opportunities in innovation. Industrial Marketing Management, 37(6), 633-640. https://doi.org/10.1016/j.indmarman.2007.12.003
  51. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. Pearson.
  52. Taktak, M. & Bafrali, G. (2025). ChatGPT usage scale in education: Validity and reliability study. International Journal of Technology in Education, 8(1), 193-207. https://doi.org/10.46328/ijte.1024
  53. Tarhini, A., Masa’deh, R. E., Al-Busaidi, K. A., Mohammed, A. B., & Maqableh, M. (2017). Factors influencing students’ adoption of e-learning: A structural equation modeling approach. Journal of International Education in Business, 10(2), 164-182. https://doi.org/10.1108/JIEB-09-2016-0032
  54. Ursavaş, Ö., Şahin, S., & Mcılroy, D. (2014). Technology acceptance measure for teachers: T-TAM/Öğretmenler için teknoloji kabul ölçeği: Ö-TKÖ. Eğitimde Kuram ve Uygulama, 10(4), 885-917. https://dergipark.org.tr/tr/pub/eku/article/74152
  55. Valdez, G., McNabb, M., Foertsch, M., Anderson, M., Hawkes, M., & Raack, L. (1999). Computer-based technology and learning: Evolving uses and expectations. NCREL.
  56. Vashishth, T. K., Sharma, V., Sharma, K. K., Kumar, B., Kumar, A., & Panwar, R. (2024). Artificial intelligence (AI)-powered chatbots: Providing instant support and personalized recommendations to guests 24/7. In P. Thaichon, P. K. Dutta, P. R. Chelliah, & S. Gupta (Eds.), Technology and luxury hospitality (pp. 211-236). Routledge. https://doi.org/10.4324/9781003488248-15
  57. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
  58. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
  59. Verhoef, P. C., Stephen, A. T., Kannan, P. K., Luo, X., Abhishek, V., Andrews, M., Bart, Y., Datta, H., Fong, N., Hoffman, D. L., Hu, M. M., Novak, T., Rand, W., & Zhang, Y. (2017). Consumer connectivity in a complex, technology-enabled, and mobile-oriented world with smart products. Journal of Interactive Marketing, 40(1), 1-8. https://doi.org/10.1016/j.intmar.2017.06.001
  60. Yang, J., Chen, Y. L., Por, L. Y., & Ku, C. S. (2023). A systematic literature review of information security in chatbots. Applied Sciences, 13(11), Article 6355. https://doi.org/10.3390/app13116355
  61. Yurt, E. (2025). The self-regulation for AI-based learning scale: Psychometric properties and validation. International Journal of Current Educational Studies, 4(1), 95-118. https://doi.org/10.46328/ijces.176