Analyzing the Impact of Effort Expectancy and Cognitive Attitudes on The Willingness to Accept ChatGPT
Article Sidebar
Main Article Content
Abstract
This study aims to analyze the impact of Effort Expectancy (EE) adapted from the Unified Theory of Acceptance and Use of Technology (UTAUT) and Cognitive Attitude (CA) from the Theory of Reasined Action (TRA) model on Willingness to Accept (WA) adapted from TAM on ChatGPT. By understanding the relationship between these factors, we can identify effective strategies to increase user acceptance of ChatGPT technology. The research method used is quantitative with multiple linear regression calculations in SPSS. This study obtained 50 respondents with a total of 10 variables but there were 3 main variables. With the final result, Effort Expectancy has no significant effect on Willingness to Accept while Cognitive Attitude has a significant effect on Willingness to Accept. This suggests that users’ perceptions of how easy or difficult it is to use ChatGPT do not influence their decision to accept and use the technology. In this context, users may feel that ease of use is not a major factor influencing their acceptance of ChatGPT. This means that users’ cognitive attitudes—including their beliefs, perceptions, and understanding of the technology—play an important role in their decision to accept and use ChatGPT.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
S. J. Russell and P. Norvig, Artificial intelligence : a modern approach Fourth Edition. 2021.
G. Yenduri et al., “Gpt (generative pre-trained transformer)—A comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions,” IEEE access, vol. 12, pp. 54608–54649, 2024.
R. Santhosh, M. Abinaya, V. Anusuya, and D. Gowthami, “ChatGPT: Opportunities, features and future prospects,” in 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI), IEEE, 2023, pp. 1614–1622.
J. Hasselqvist Haglund, “Students Acceptance and Use of ChatGPT in Academic Settings,” Uppsala Universitet, 2023.
Y. Shaengchart, “AConceptual Review of TAM and ChatGPT Usage Intentions Among Higher Education Students,” Advance Knowledge for Executives, vol. 2(3), pp. 1–7, Sep. 2023, [Online]. Available: https://ssrn.com/abstract=4581231
M. Javaid, A. Haleem, R. P. Singh, S. Khan, and I. H. Khan, “Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system,” BenchCouncil Transactions on Benchmarks, Standards and Evaluations, vol. 3, no. 2, p. 100115, 2023.
S. Cuentas-Hernandez, X. Li, M. J. King, and O. Oviedo-Trespalacios, “The impact of road traffic context on secondary task engagement while driving,” Front Psychol, vol. 14, p. 1139373, 2023.
S. Rahi, M. M. Othman Mansour, M. Alghizzawi, and F. M. Alnaser, “Integration of UTAUT model in internet banking adoption context: The mediating role of performance expectancy and effort expectancy,” Journal of Research in Interactive Marketing, vol. 13, no. 3, pp. 411–435, 2019.
J. E. Raffaghelli, M. E. Rodríguez, A.-E. Guerrero-Roldán, and D. Baneres, “Applying the UTAUT model to explain the students’ acceptance of an early warning system in Higher Education,” Comput Educ, vol. 182, p. 104468, 2022.
S. A. Sair and R. Q. Danish, “Effect of performance expectancy and effort expectancy on the mobile commerce adoption intention through personal innovativeness among Pakistani consumers,” Pakistan Journal of Commerce and social sciences (PJCSS), vol. 12, no. 2, pp. 501–520, 2018.
M. M. M. Abbad, “Using the UTAUT model to understand students’ usage of e-learning systems in developing countries,” Educ Inf Technol (Dordr), vol. 26, no. 6, pp. 7205–7224, 2021.
J. Paul, A. Modi, and J. Patel, “Predicting green product consumption using theory of planned behavior and reasoned action,” Journal of retailing and consumer services, vol. 29, pp. 123–134, 2016.
R. J. Tobias-Mamina and E. T. Maziriri, “Millennial street food consumption: an integrated theory of reasoned action approach,” International Review of Management and Marketing, vol. 13, no. 1, pp. 11–18, 2023.
R. H. A. Shiekh and E. F. El-Hashash, “A comparison of the pearson, spearman rank and kendall tau correlation coefficients using quantitative variables,” Asian Journal of Probability and Statistics, pp. 36–48, 2022.
P. Y. Soh et al., “RETRACTED ARTICLE: perception, acceptance and willingness of older adults in Malaysia towards online shopping: a study using the UTAUT and IRT models,” J Ambient Intell Humaniz Comput, vol. 15, no. Suppl 1, p. 101, 2024.
M. B. Morrissey and G. D. Ruxton, “Multiple regression is not multiple regressions: the meaning of multiple regression and the non-problem of collinearity,” 2018.
J. Hair and A. Alamer, “Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example,” Research Methods in Applied Linguistics, vol. 1, no. 3, p. 100027, 2022.
R. Yusuf, “Comparing different supervised machine learning accuracy on analyzing COVID-19 data using ANOVA test,” in 2020 6th International Conference on Interactive Digital Media (ICIDM), IEEE, 2020, pp. 1–6.
A. Melda, Y. E. Wahyuningsih, and S. R. Sani, “The Effect of Extreme Economic Assistance on Community Income in Kaway XVI District, West Aceh Regency (Case Study of 6 Villages in Kaway XVI District),” Jurnal Akuntansi, Manajemen dan Ilmu Ekonomi (Jasmien), vol. 4, no. 04, pp. 145–155, 2024.
H. Tannady and C. S. Dewi, “Exploring role of technology performance expectancy, application effort expectancy, perceived risk and perceived cost on digital behavioral intention of GoFood users,” Jurnal Informasi Dan Teknologi, vol. 6, no. 1, pp. 80–85, 2024.