Digital Technologies Research and Applications

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Personality Traits and the Technology Acceptance of ChatGPT: Mediating Effects of Perceived Usefulness and Ease of Use

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https://doi.org/10.54963/dtra.v5i2.1780
Hwang, H. S., & Kim, S. (2026). Personality Traits and the Technology Acceptance of ChatGPT: Mediating Effects of Perceived Usefulness and Ease of Use. Digital Technologies Research and Applications, 5(2), 15–29. https://doi.org/10.54963/dtra.v5i2.1780
Volume 5 Issue 2: June 2026 (in progress)
Copyright (c) 2026 Ha Sung Hwang, Sunmi Kim
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Authors

  • Ha Sung Hwang

    Department of Media Communication, Dongguk University, 30, Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
  • Sunmi Kim

    School of Media and Communication, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea

Received: 29 October 2025; Revised: 21 January 2026; Accepted: 13 March 2026; Published: 9 April 2026

This study investigates how three personality traits from the Big Five framework—extraversion, neuroticism, and conscientiousness—influence individuals’ continuance intention to use ChatGPT, with a particular focus on the mediating roles of Perceived Ease of Use (PEOU) and Perceived Usefulness (PU). Drawing on the Technology Acceptance Model (TAM), this research aims to integrate personality-based differences into a well-established framework for understanding technology adoption and sustained usage. Data were collected through an online survey targeting active ChatGPT users and analyzed using structural equation modeling to examine both direct and indirect relationships among variables. The findings indicate that conscientiousness has a strong and positive impact on both PEOU and PU, and indirectly enhances continuance intention through these cognitive evaluations. Extraversion shows a limited but positive effect primarily through perceived ease of use, suggesting that socially oriented individuals may engage with the system when it is easy to navigate. In contrast, neuroticism does not demonstrate any statistically significant relationship with the key variables in the model. Consistent with TAM, PEOU significantly influences PU and continuance intention, with PU emerging as the most influential predictor of sustained usage. Overall, this study highlights the critical role of conscientiousness in fostering long-term engagement with generative AI systems and underscores the importance of cognitive perceptions in mediating personality effects. By integrating personality psychology with technology acceptance theory, the research provides theoretical and practical implications for designing personalized and adaptive AI interfaces.

Keywords:

Personality Traits Technology Acceptance Model Perceived Usefulness Perceived Ease of Use Continuance Intention Generative AI ChatGPT Adoption Structural Equation Modeling

References

  1. OpenAI. GPT-4 Technical Report. arXiv preprint 2023, arXiv:2303.08774. DOI: https://doi.org/10.48550/arXiv.2303.08774
  2. Aslan, S.; Durham, L.M.; Alyuz, N.; et al. Immersive multi-modal pedagogical conversational artificial intelligence for early childhood education: An exploratory case study in the wild. Comput. Educ. Artif. Intell. 2024, 6, 100220. DOI: https://doi.org/10.1016/j.caeai.2024.100220
  3. Jo, H. From Concerns to Benefits: A Comprehensive Study of ChatGPT Usage in Education. Int. J. Educ. Technol. High Educ. 2024, 21, 35. DOI: https://doi.org/10.1186/s41239-024-00471-4
  4. Davis, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989, 13, 319–340. DOI: https://doi.org/10.2307/249008
  5. Venkatesh, V.; Davis, F.D. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Manage. Sci. 2000, 46, 186–204. DOI: https://doi.org/10.1287/mnsc.46.2.186.11926
  6. Bhattacherjee, A. Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Q. 2001, 25, 351–370. DOI: https://doi.org/10.2307/3250921
  7. Alshammari, S.H.; Alshammari, M.H. Factors Affecting the Adoption and Use of ChatGPT in Higher Education. Int. J. Inf. Commun. Technol. Educ. 2024, 20, 1–18. DOI: https://doi.org/10.4018/IJICTE.339557
  8. McCrae, R.R.; Costa, P.T. Adding Liebe Und Arbeit: The Full Five-Factor Model and Well-Being. Pers. Soc. Psychol. Bull. 1991, 17, 227–232. DOI: https://doi.org/10.1177/014616729101700217
  9. John, O.P.; Srivastava, S. The Big-Five Trait Taxonomy: History, Measurement, and Theoretical Perspectives. In Handbook of Personality: Theory and Research, 2nd ed.; Pervin, L.A., John, O.P., Eds.; Guilford Press: New York, NY, USA, 1999; pp. 102–138.
  10. Devaraj, S.; Easley, R.F.; Crant, J.M. How Does Personality Matter? Relating the Five-Factor Model to Technology Acceptance and Use. Inf. Syst. Res. 2008, 19, 93–105. DOI: https://doi.org/10.1287/isre.1070.0153
  11. Maxwell, D.; Oyarzun, B.; Kim, S.; et al. Generative AI in Higher Education: Demographic Differences in Student Perceived Readiness, Benefits, and Challenges. TechTrends 2025, 69, 1248–1259. DOI: https://doi.org/10.1007/s11528-025-01109-6
  12. Venkatesh, V.; Morris, M.G.; Davis, G.B.; et al. User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003, 27, 425–478. DOI: https://doi.org/10.2307/30036540
  13. Marangunić, N.; Granić, A. Technology Acceptance Model: A Literature Review from 1986 to 2013. Univ. Access Inf. Soc. 2015, 14, 81–95. DOI: https://doi.org/10.1007/s10209-014-0348-1
  14. Sindermann, C.; Riedl, R.; Montag, C. Investigating the Relationship between Personality and Technology Acceptance with a Focus on the Smartphone from a Gender Perspective: Results of an Exploratory Survey Study. Future Internet 2020, 12, 110. DOI: https://doi.org/10.3390/fi12070110
  15. Salar, H.C.; Hamutoglu, N.B. The Role of Perceived Ease of Use and Perceived Usefulness on Personality Traits among Adults. Open Praxis 2022, 14, 133–147. DOI: https://doi.org/10.55982/openpraxis.14.2.142
  16. Zhang, N.; Fard, M.; Xu, J.; et al. Influence of non-driving related tasks on driving performance after takeover transition in conditionally automated driving. Transp. Res. Part F Traffic Psychol. Behav. 2023, 96, 248–264. DOI: https://doi.org/10.1016/j.trf.2023.05.009
  17. Ding, N.; Chen, M.; Hu, L. Examining the impact of big five personality traits on generation Z designers’ subscription to paid AI drawing tools using SEM and FsQCA. Sci. Rep. 2025, 15, 17587. DOI: https://doi.org/10.1038/s41598-025-02926-8
  18. Dom, J.; Joy, P. Big-Five Personality Factors and Technology Acceptance among University Students. INSPA J. Appl. Sch. Psychol. 2024, 5, 309–315. Available online: https://ijasp.in/wp-content/uploads/2024/04/34-Dom-Joy-1.pdf
  19. Siu, B.; White, J. When Ease Becomes a Barrier: What Influences Student Intentions to Use Generative AI in Higher Education? Br. Educ. Res. J. 2025, 51, 2893–2919. DOI: https://doi.org/10.1002/berj.4206
  20. Liu, K.; Tao, D. The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart healthcare services. Comput. Hum. Behav. 2022, 127, 107026. DOI: https://doi.org/10.1016/j.chb.2021.107026
  21. Polyportis, A.; Pahos, N. Understanding Students’ Adoption of the ChatGPT Chatbot in Higher Education: The Role of Anthropomorphism, Trust, Design Novelty and Institutional Policy. Behav. Inf. Technol. 2025, 44, 315–336. DOI: https://doi.org/10.1080/0144929X.2024.2317364
  22. Gefen, D.; Straub, D.W. The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption. J. Assoc. Inf. Syst. 2000, 1, 1–28.
  23. Dwivedi, Y.K.; Kshetri, N.;Hughes, L.; et al. So What If ChatGPT Wrote It? Multidisciplinary Perspectives on Opportunities, Challenges and Implications of Generative Conversational AI for Research, Practice and Policy. Int. J. Inf. Manage. 2023, 71, 102642. DOI: https://doi.org/10.1016/j.ijinfomgt.2023.102642
  24. Al Murshidi, G.; Shulgina, G.; Kapuza, A.; et al. How Understanding the Limitations and Risks of Using ChatGPT Can Contribute to Willingness to Use. Smart Learn. Environ. 2024, 11, 36. DOI: https://doi.org/10.1186/s40561-024-00322-9
  25. Rudolph, J.; Tan, S.; Tan, S. ChatGPT: Bullshit Spewer or the End of Traditional Assessments in Higher Education? J. Appl. Learn. Teach. 2023, 6, 342–363.
  26. Zhai, X. ChatGPT for Next-Generation Science Learning. SSRN Electron. J. 2023, 32, 1–8.
  27. Hair Jr., J.F.; Black, W.C.; Babin, B.J.; et al. Multivariate Data Analysis, 8th ed.; Cengage: Boston, MA, USA, 2019.
  28. Brown, T.A. Confirmatory Factor Analysis for Applied Research, 2nd ed.; Guilford Press: New York, NY, USA, 2015.
  29. Tavakol, M.; Dennick, R. Making Sense of Cronbach’s Alpha. Int. J. Med. Educ. 2011, 2, 53–55.
  30. Cardella, G.M.; Hernández-Sánchez, B.R.; Sánchez-García, J.C. Development and Validation of a Scale to Evaluate Students’ Future Impact Perception Related to the Coronavirus Pandemic (C-19FIPS). PLoS One 2021, 16, e0260248. DOI: https://doi.org/10.1371/journal.pone.0260248
  31. Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39–50. DOI: https://doi.org/10.2307/3151312
  32. Henseler, J.; Ringle, C.M.; Sarstedt, M. A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. DOI: https://doi.org/10.1007/s11747-014-0403-8
  33. Saha, K.; Kumar, R.; Dutta, S.K.; et al. Validating multidimensional entrepreneurial orientation in emerging economies. Eur. Bus. Rev. 2021, 33, 797–817. DOI: https://doi.org/10.1108/EBR-07-2020-0184
  34. Schermelleh-Engel, K.; Moosbrugger, H.; Müller, H. Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods Psychol. Res. 2003, 8, 23–74.
  35. Nitzl, C.; Roldan, J.L.; Cepeda, G. Mediation Analysis in Partial Least Squares Path Modeling: Helping Researchers Discuss More Sophisticated Models. Ind. Manag. Data Syst. 2016, 116, 1849–1864. DOI: https://doi.org/10.1108/IMDS-07-2015-0302
  36. Hooper, D.; Coughlan, J.; Mullen, M.R. Structural Equation Modelling: Guidelines for Determining Model Fit. Electron. J. Bus. Res. Methods 2008, 6, 53–59.
  37. Hu, L.T.; Bentler, P.M. Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives. Struct. Equ. Model. Multidiscip. J. 1999, 6, 1–55. DOI: https://doi.org/10.1080/10705519909540118
  38. Kline, R.B. Principles and Practice of Structural Equation Modeling, 4th ed.; Guilford Press: New York, NY, USA, 2016.
  39. Maxwell, S.E.; Cole, D.A. Bias in Cross-Sectional Analyses of Longitudinal Mediation. Psychol. Methods 2007, 12, 23–44. DOI: https://doi.org/10.1037/1082-989X.12.1.23
  40. Hofstede, G. Culture’s Consequences: Comparing Values, Behaviors, Institutions, and Organizations across Nations. Sage: Thousand Oaks, CA, USA, 2001.
  41. Almogren, A.S.; Al-Rahmi, W.M.; Dahri, N.A. Exploring Factors Influencing the Acceptance of ChatGPT in Higher Education: A Smart Education Perspective. Heliyon 2024, 10, e31887. DOI: https://doi.org/10.1016/j.heliyon.2024.e31887
  42. Kim, Y.B.; Joo, H.C.; Lee, B.G. How to Forecast Behavioral Effects on Mobile Advertising in the Smart Environment Using the Technology Acceptance Model and Web Advertising Effect Model. KSII Trans. Internet Inf. Syst. 2016, 10, 4997–5013.
  43. Sallam, M. ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns. Healthcare 2023, 11, 887. DOI: https://doi.org/10.3390/healthcare11060887
  44. Lakhal, S.; Khechine, H. Relating Personality (Big Five) to the Core Constructs of the Unified Theory of Acceptance and Use of Technology. J. Comput. Educ. 2017, 4, 251–282.
  45. Rammstedt, B.; John, O.P. Measuring Personality in One Minute or Less: A 10-Item Short Version of the Big Five Inventory in English and German. J. Res. Pers. 2007, 41, 203–212. DOI: https://doi.org/10.1016/j.jrp.2006.02.001