IJRISS Logo

A Technology Acceptance Perspective on Chatbot Adoption: Perceived Human-Likeness and Emotional Response

Authors

Mariem Sboui

Mariem Sboui, University of Tunis, Tunisia, ARBRE laboratory (Tunisia)

Article Information

DOI: 10.47772/IJRISS.2025.91200088

Subject Category: Artificial Intelligence

Volume/Issue: 9/12 | Page No: 1228-1238

Publication Timeline

Submitted: 2025-12-16

Accepted: 2025-12-24

Published: 2026-01-01

Abstract

Considering recent AI-driven advancements, conversational agents (chatbots) have become fundamental to digital interactions, requiring an assessment of determinants impacting their adoption. This study investigates the effect of anthropomorphism and technology anxiety on chatbot adoption. This study investigates the effects of anthropomorphism and technology anxiety on perceived usefulness (PU), perceived ease of use (PEU), and usage intention in the context of chatbot services. Data were collected via an online survey administered to 380 Tunisian higher education students. Using structural equation modeling with Smart PLS4, the findings confirm the crucial role of (PU) and (PEOU) in predicting usage intention. In contrast, anthropomorphism and anxiety do not significantly influence adoption. These results contribute to the literature by demonstrating that, despite heightened interest in human-likeness and psychological reactions to AI chatbots, traditional cognitive factors remain the primary drivers of chatbot service adoption.

Keywords

Chatbots, Anthropomorphism, Technology Anxiety, Technology Acceptance Model (TAM)

Downloads

PDF JATS XML

References

1. Adamopoulou, E., &Moussiades, L. (2020). Chatbots: History, technology, and applications. Computers in Human Behavior, 107 [Google Scholar] [Crossref]

2. Afzal, A., Khan, S., Daud, S., Ahmad, Z. and Butt, A. (2023), “Addressing the digital divide: access and use of technology in education”, Journal of Social Sciences Review, Vol. 3 No. 2, [Google Scholar] [Crossref]

3. Al-Adwan, A. S., Li, N., Al-Adwan, A., Abbasi, G. A., Albelbisi, N. A., & Habibi, A. (2023). Extending the technology acceptance model (TAM) to Predict University Students’ intentions to use metaverse-based learning platforms. Education and Information Technologies, 28(11), 15381-15413. [Google Scholar] [Crossref]

4. Al-Adwan, A. S., Li, N., Al-Adwan, A., Abbasi, G. A., Albelbisi, N. A., & Habibi, A. (2023). Extending the technology acceptance model (TAM) to predict university students’ intentions to use metaverse-based learning platforms. Education and Information Technologies, 28(11), 15381–15413. https://doi.org/10.1007/s10639-02311805-1 [Google Scholar] [Crossref]

5. Almusharraf, A., & Bailey, D. (2025). Predicting attitude, use, and future intentions with translation websites through the TAM framework: a multicultural study among Saudi and South Korean language learners. Computer Assisted Language Learning, 38(5-6), 1249-1276. [Google Scholar] [Crossref]

6. Alotaibi, M., & Hidayat-ur-Rehman, I. (2025). An empirical analysis of user intention to use chatbots for airline tickets consultation. Journal of Science and Technology Policy Management, 16(1), 204 228. https://doi.org/10.1108/JSTPM-03-2024-0087 [Google Scholar] [Crossref]

7. An, J., Zhu, X., Wan, K., Xiang, Z., Shi, Z., An, J., & Huang, W. (2024). Older adults’ self-perception, technology anxiety, and intention to use digital public services. BMC Public Health, 24(1), 3533. [Google Scholar] [Crossref]

8. Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473. https://doi.org/10.1016/j.tele.2020.101473 [Google Scholar] [Crossref]

9. Ayanwale, M. A., & Molefi, R. R. (2024). Exploring the intention of undergraduate students to embrace chatbots: Evidence from Lesotho. International Journal of Educational Technology in Higher Education, 21(1), Article 20. https://doi.org/10.1186/s41239-024-00458-0 [Google Scholar] [Crossref]

10. Chen, S., Qiu, S., Li, H., Zhang, J., Wu, X., Zeng, W., & Huang, F. (2023). An integrated model for predicting pupils’ acceptance of artificially intelligent robots as teachers. Education and Information Technologies, 28(9), 1163111654. [Google Scholar] [Crossref]

11. Chen, S., Qiu, S., Li, H., Zhang, J., Wu, X., Zeng, W., & Huang, F. (2023). An integrated model for predicting pupils’ acceptance of artificially intelligent robots as teachers. Education and Information Technologies, 28(9), 11631– 11654. https://doi.org/10.1007/s10639-023-11605-7 [Google Scholar] [Crossref]

12. Chi, O. H., Chi, C. G., Gursoy, D., & Nunkoo, R. (2023). Customers’ acceptance of artificially intelligent service robots: The influence of trust and culture. International Journal of Information Management, 70, Article 102623. https://doi.org/10.1016/j.ijinfomgt.2022.102623 [Google Scholar] [Crossref]

13. Cho, K. A., & Seo, Y. H. (2024). Dual mediating effects of anxiety to use and acceptance attitude of artificial intelligence technology on the relationship between nursing students’ perception of and intention to use them: A descriptive study. BMC Nursing, 23(1), 1–8. https://doi.org/10.1186/s12912-024-01887-z [Google Scholar] [Crossref]

14. Ciechanowski, L., Przegalinska, A., Magnuski, M., & Gloor, P. (2019). In the shades of the uncanny valley: An experimental study of human–chatbot interaction. Future Generation Computer Systems, 92, 539-548. https://doi.org/10.1016/j.future.2018.01.055 [Google Scholar] [Crossref]

15. Clarke, R. (2019). Why the world wants controls over Artificial Intelligence. Computer Law & Security Report, 35(4), 423–433. https://doi.org/10.1016/j.clsr.2019.04.006 communication research: Media equation and media evocation. Human-Machine Communication, 6, [Google Scholar] [Crossref]

16. Dakduk, S., Van der Woude, D. and Nieto, C.A. (2023), “Technological adoption in emerging economies: insights from Latin America and the Caribbean with a focus on low-income consumers”, IntechOpen. [Google Scholar] [Crossref]

17. Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human : A three-factor theory of anthropomorphism. Psychological review, 114(4), 864. [Google Scholar] [Crossref]

18. European Journal of Marketing, Vol. 56 No. 6, pp. 1748-1771, doi: 10.1108/ejm-02-2020-0084. [Google Scholar] [Crossref]

19. Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: Attentional control theory. Emotion, 7(2), 336–353. https://doi.org/ 10.1037/1528-3542.7.2.336 [Google Scholar] [Crossref]

20. Fincham, F. D., Hokoda, A., & Sanders, R. (1989). Learned helplessness, test anxiety, and academic achievement: A longitudinal analysis. Child Development, 60(1), 138–145. https://doi.org/10.2307/1131079 [Google Scholar] [Crossref]

21. Gopinath, K., & Kasilingam, D. (2023). Antecedents of intention to use chatbots in service encounters : A meta‐analytic review. International Journal of Consumer Studies, 47(6), 2367-2395. https://doi.org/10.1111/ijcs.12933 [Google Scholar] [Crossref]

22. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. [Google Scholar] [Crossref]

23. Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. [Google Scholar] [Crossref]

24. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variancebased structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8 [Google Scholar] [Crossref]

25. Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23, 587–605. in User–AI assistant interactions. Computers in Human Behavior, 118, 106680. https://doi.org/10.1016/ Ismatullaev, U. V. [Google Scholar] [Crossref]

26. U., & Kim, S.-H. (2024). Review of the Factors Affecting Acceptance of AI-Infused Systems. Human Factors:The Journal of the Human Factors and Ergonomics Society, 66(1), 126-144. https://doi.org/10.1177/00187208211064707 j.chb.2021.106680 [Google Scholar] [Crossref]

27. Kumar, A., & Rose, C. (2019). The role of chatbots in customer service. Journal of Business Research, 113, p 51-56 [Google Scholar] [Crossref]

28. Lee, S., Lee, N.,&Sah, Y. J. (2020). Perceiving a mind in a chatbot: Effect of mind perception and social cues [Google Scholar] [Crossref]

29. Li, J., & Huang, J. S. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear. [Google Scholar] [Crossref]

30. Li, W., Zhang, X., Li, J., Yang, X., Li, D., & Liu, Y. (2024). An explanatory study of factors influencing engagement in AI education at the K-12 Level: an extension of the classic TAM model. Scientific Reports, 14(1), 13922. [Google Scholar] [Crossref]

31. Li, X., & Sung, Y. (2021). Anthropomorphism brings us closer: The mediating role of psychological distance [Google Scholar] [Crossref]

32. Liu, G., & Ma, C. (2024). Measuring EFL learners’ use of ChatGPT in informal digital learning of English based on the technology acceptance model. Innovation in Language Learning and Teaching, 18(2), 125–138. https://doi.org/10.1080/17501229.2023.2240316 [Google Scholar] [Crossref]

33. Lubbe, I., & Ngoma, N. (2021). Useful chatbot experience provides technological satisfaction : An emerging market perspective. South African Journal of Information Management, 23(1), 1 8. https://doi.org/10.4102/sajim.v23i1.1299 [Google Scholar] [Crossref]

34. Maier, S. F., & Seligman, M. E. (1976). Learned helplessness: Theory and evidence. Journal of Experimental Psychology: General, 105(1), 3–46. https://doi.org/10.1037/ 0096-3445.105.1.3 [Google Scholar] [Crossref]

35. Mostafa, R.B. and Kasamani, T. (2022), “Antecedents and consequences of chatbot initial trust”, European Journal of Marketing, Vol. 56 No. 6, pp. 1748-1771, doi: 10.1108/ejm-02-2020-0084. [Google Scholar] [Crossref]

36. Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199 3226. [Google Scholar] [Crossref]

37. Pizzi, G., Vannucci, V., Mazzoli, V., & Donvito, R. (2023). I, chatbot! The impact of anthropomorphism and gaze direction on willingness to disclose personal information and behavioral intentions. Psychology & Marketing, 40(7), 1372-1387. https://doi.org/10.1002/mar.21813 pp. 883-895, doi: 10.54183/jssr.v3i2.326. [Google Scholar] [Crossref]

38. Qatawneh, N., Aljaafreh, A., & Aladaileh, R. (2024). Critical success factors influencing the behavioral intention to adopt smart home technologies. IEEE Access, 12, 132996-133007. [Google Scholar] [Crossref]

39. Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education, 145, 103732. [Google Scholar] [Crossref]

40. Rapp, A., Curti, L., & Boldi, A. (2021). The human side of human-chatbot interaction : A systematic literature review of ten years of research on text-based chatbots. International Journal of Human-Computer Studies, 151, 102630. [Google Scholar] [Crossref]

41. Sboui, M., Baati, O., & Sfar, N. (2024). Influencing factors and consequences of chatbot initial trust in AI telecommunication services: a study on Generation Z. The TQM Journal. [Google Scholar] [Crossref]

42. Sfar, N., Sboui, M., & Baati, O. (2025). The impact of chatbot anthropomorphism on customer experience and chatbot usage intention: a technology acceptance approach. International Journal of Quality and Service Sciences, 17(2), 168-194. [Google Scholar] [Crossref]

43. Topsakal, Y. (2024). How Familiarity, Ease of Use, Usefulness, and Trust Influence the Acceptance of Generative Artificial Intelligence (AI)-Assisted Travel Planning. International Journal of Human–Computer Interaction, 1 14. https://doi.org/10.1080/10447318.2024.2426044 [Google Scholar] [Crossref]

44. Van Der Goot, M., & Etzrod, K. (2023). Disentangling two fundamental paradigms in human–machine Van Raaij, E. M., & Schepers, J. J. (2008). The acceptance and use of a virtual learning environment in China. Computers & education, 50(3), 838-852. [Google Scholar] [Crossref]

45. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365. https://doi.org/10.1287/isre.11.4.342.11872 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 [Google Scholar] [Crossref]

46. Wang, C., Ahmad, S. F., Ayassrah, A. Y. B. A., Awwad, E. M., Irshad, M., Ali, Y. A., ... & Han, H. (2023). An empirical evaluation of technology acceptance model for Artificial Intelligence in E-commerce. Heliyon, 9(8). [Google Scholar] [Crossref]

47. Wang, Y.-Y., & Wang, Y.-S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619–634. https://doi.org/ 10.1080/10494820.2019.1674887 [Google Scholar] [Crossref]

48. Yao, X., & Xi, Y. (2025). From Assistants to Digital Beings: Exploring Anthropomorphism, Humanness Perception, and AI Anxiety in Large-Language-Model Chatbots. Social Science Computer Review, 08944393251354976. [Google Scholar] [Crossref]

49. Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: a multigroup analysis. International Journal of Educational Technology in Higher Education, 20(1), 49. [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles