바로가기메뉴

본문 바로가기 주메뉴 바로가기
 

logo

  • P-ISSN1738-3110
  • E-ISSN2093-7717
  • SCOPUS, ESCI

The Effect of ChatGPT Factors & Innovativeness on Switching Intention : Using Theory of Reasoned Action (TRA)

The Journal of Distribution Science / The Journal of Distribution Science, (P)1738-3110; (E)2093-7717
2023, v.21 no.8, pp.83-96
https://doi.org/10.15722/jds.21.08.202308.83
Cho Hee Young
Yang, Hoe Chang
Hwang Byoung Jo

Abstract

Purpose: This study examined the relationship between the factors (Credibility, Usability) and user Innovativeness of the ChatGPT on TRA (Theory of Reasoned Action; Subjective Norm, Attitude) and Switching Intention. TRA and Innovation Diffusion Theory (IDT) were used. Research design, data and methodology: From April 26 to 27, 2023, an online panel survey agency was commissioned to conduct a survey of GhatGPT users in their 20s and 40s in Korea, and a total of 210 people were used for the final analysis. Verification of the research model was performed using SPSS and AMOS. Results: First, ChatGPT factors (Credibility, Usability) were found to have positive effects on TRA (Subjective Norm, Attitude). Second, ChatGPT user Innovativeness was found to have a positive effect on TRA (Subjective Norm, Attitude). Third, ChatGPT users' TRA (Subjective Norm, Attitude) were found to have positive effects on Switching Intention. Conclusions: These results mean that the superior Usability and Credibility of ChatGPT and the Innovativeness of users have a significant effect on the Switching Intention from existing Portal Service (Naver, Google, Daum, etc.) to ChatGPT. Generative AI such as ChatGPT should strive to develop various services such as improving the convenience of functions so that innovative users can use them easily and conveniently in order to provide services that meet expectations.

keywords
Retail Industry, Online Platform, ChatGPT, Innovativeness, Switching Intention JEL Classification

Reference

1.

Abran, A., Khelifi, A., Suryn, W., & Seffah, A. (2003). Usability meanings and interpretations in ISO standards. Software quality journal, 11(4), 325-338. https://doi.org/10.1023/A: 1025869312943

2.

Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information systems research, 9(2), 204-215. https://doi.org/10.1287/isre.9.2.204

3.

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T

4.

Ajzen, I., & Fishbein, M. (1980). Attitude understanding and predicting social behavior. Prentice-Hall.

5.

Ajzen, I., & Madden, T. J. (1986). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of experimental social psychology, 22(5), 453- 474. https://doi.org/10.1016/0022-1031(86)90045-4

6.

Albert, B., & Tullis, T. (2022). Measuring the User Experience: Collecting, Analyzing, and Presenting UX Metrics. Morgan Kaufmann.

7.

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94. https://doi.org/10.1007/BF02723327

8.

Bansal, H. S., Taylor, S. F., & St. James, Y. (2005). “Migrating” to new service providers: Toward a unifying framework of consumers’ switching behaviors. Journal of the Academy of Marketing Science, 33(1), 96-115. https://doi.org/10.1177/ 0092070304267928

9.

Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological bulletin, 88(3), 588-606. https://doi.org/10.1037 /0033-2909.88.3.588

10.

Bonsu, E. M., & Baffour-Koduah, D. (2023). From the consumers’ side: Determining students’ perception and intention to use ChatGPT in Ghanaian higher education. Journal of Education, Society & Multiculturalism, 4(1), 1-29. https://doi.org/10.2478 /jesm-2023-0001

11.

Borji, A. (2023). A categorical archive of chatgpt failures. arXiv preprint arXiv:2302.03494. https://doi.org/10.48550/arXiv. 2302.03494

12.

Brandtzaeg, P. B., & Følstad, A. (2018). Chatbots: changing user needs and motivations. Interactions, 25(5), 38-43. https://doi.org/10.1145/3236669

13.

Choi, W., & Stvilia, B. (2015). Web credibility assessment: Conceptualization, operationalization, variability, and models. Journal of the Association for Information Science and Technology, 66(12), 2399-2414. https://doi.org/10.1002/ asi.23543

14.

Cutler, K. (2023). ChatGPT and search engine optimisation: The future is here. Applied Marketing Analytics, 9(1), 8-22.

15.

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

16.

Deng, J., & Lin, Y. (2023). The benefits and challenges of ChatGPT: An overview. Frontiers in Computing and Intelligent Systems, 2(2), 81–83. https://doi.org/10.54097/fcis.v2i2.4465

17.

Dowling, M., & Lucey, B. (2023). ChatGPT for (finance) research: The Bananarama conjecture. Finance Research Letters, 53, 103662. https://doi.org/10.1016/j.frl.2023.103662

18.

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). “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, 102642. https://doi.org/10.1016 /j.ijinfomgt.2023.102642

19.

Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Harcourt brace Jovanovich college publishers.

20.

Firat, M. (2023). How chat GPT can transform autodidactic experiences and open education. Department of Distance Education, Open Education Faculty, Anadolu Unive. doi:10.31219/osf.io/9ge8m

21.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: an introduction to theory and research. Addison Wesley, 5, 177-189.

22.

Flanagin, A. J., & Metzger, M. J. (2007). The role of site features, user attributes, and information verification behaviors on the perceived credibility of web-based information. New media & society, 9(2), 319-342. https://doi.org/10.1177/146144480 7075015

23.

Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30, 681-694. https://doi.org/10.1007/s11023-020-09548-1

24.

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

25.

Frederick F., Reichheld, & Teal, T. (2001). The Loyalty Effect: The Hidden Force Behind Growth, Profits, and Lasting Value. Harvard Business School Press.

26.

Gawer, A. (2021). Digital platforms’ boundaries: The interplay of firm scope, platform sides, and digital interfaces. Long Range Planning, 54(5), 102045. https://doi.org/10.1016/j.lrp.2020. 102045

27.

Gawer, A. R., & Srnicek, N. (2021). Online platforms: Economic and societal effects. Panel for the Future of Science and Technology (STOA) European Parliament. doi:10.2861/84460 2

28.

Gesing, B., Peterson, S. J., & Michelsen, D. (2018). Artificial intelligence in logistics. DHL Customer Solutions & Innovation, 3.

29.

Green, D. C. (2003). Search Engine Marketing: Why it benefits us all. Business Information Review, 20(4), 195-202. https://doi.org/10.1177/0266382103204005

30.

Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate Data Analysis: A Global Perspective, vol. 7 Pearson Education. Upper Saddle River, NJ.

31.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis. United Kingdom: Cengage Learning.

32.

Hamed, A. A., & Wu, X. (2023). Improving Detection of ChatGPTGenerated Fake Science Using Real Publication Text: Introducing xFakeBibs a Supervised-Learning Network Algorithm. https://doi.org/10.21203/rs.3.rs-2851222/v1

33.

Haque, M. U., Dharmadasa, I., Sworna, Z. T., Rajapakse, R. N., & Ahmad, H. (2022). "I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data. arXiv preprint arXiv:2212.05856. https://doi.org/10.48550/arXiv.2212.05856

34.

Haupt, C. E., & Marks, M. (2023). AI-generated medical advice— GPT and beyond. Jama, 329(16), 1349-1350. doi:10.1001/ jama.2023.5321

35.

Heaven, W. D. (2022). Language Models Like GPT-3 Could Herald a New Type of Search Engine. Ethics of Data and Analytics: Concepts and Cases, 57-59. https://doi.org/10.1201 /9781003278290-9

36.

Hill-Yardin, E. L., Hutchinson, M. R., Laycock, R., & Spencer, S. J. (2023). A Chat (GPT) about the future of scientific publishing. Brain Behav Immun, 110, 152-154. doi:10.1016/ j.bbi.2023.02.022

37.

Hirschman, E. C. (1980). Innovativeness, novelty seeking, and consumer creativity. Journal of consumer research, 7(3), 283- 295. https://doi.org/10.1086/208816

38.

Hoppner, T., & Streatfeild, L. (2023). Chatgpt, bard & co.: An introduction to ai for competition and regulatory lawyers. An Introduction to AI for Competition and Regulatory Lawyers (February 23, 2023), 1-9. http://dx.doi.org/10.2139/ssrn. 4371681

39.

Hou, A. C., Chern, C. C., Chen, H. G., & Chen, Y. C. (2011). ‘Migrating to a new virtual world’: Exploring MMORPG switching through human migration theory. Computers in Human Behavior, 27(5), 1892-1903. https://doi.org/10.1016/ j.chb.2011.04.013

40.

Huang, Y. S., & Kao, W. K. (2021). Chatbot service usage during a pandemic: fear and social distancing. The Service Industries Journal, 41(13-14), 964-984. https://doi.org/10.1080/02642 069.2021.1957845

41.

Johnson, T. J., & Kaye, B. K. (2004). For whom the Web toils: How Internet experience predicts Web reliance and credibility. Atlantic Journal of Communication, 12(1), 19-45. https://doi.org/10.1207/s15456889ajc1201_3

42.

Johnson, T. J., & Kaye, B. K. (2009). In blog we trust? Deciphering credibility of components of the internet among politically interested internet users. Computers in Human Behavior, 25(1), 175-182. https://doi.org/10.1016/j.chb.2008.08.004

43.

Jones, M. A., Mothersbaugh, D. L., & Beatty, S. E. (2002). Why customers stay: measuring the underlying dimensions of services switching costs and managing their differential strategic outcomes. Journal of business research, 55(6), 441- 450. https://doi.org/10.1016/S0148-2963(00)00168-5

44.

Kasilingam, D. L. (2020). Understanding the attitude and intention to use smartphone chatbots for shopping. Technology in Society, 62, 101280. https://doi.org/10.1016/j.techsoc.2020. 101280

45.

Keaveney, S. M. (1995). Customer switching behavior in service industries: An exploratory study. Journal of marketing, 59(2), 71-82. https://doi.org/10.1177/002224299505900206

46.

Kim, Y. S., & Jeong, B. G. (2016). A study on facts of the user experience in online stores-Focusing on companies that participated in K-sale day. Korean Journal of Design Culture, 22(1), 53-62.

47.

Kosinski, M. (2023). Theory of mind may have spontaneously emerged in large language models. arXiv preprint arXiv:2302.02083. https://doi.org/10.48550/arXiv.2302.02083

48.

Kovalenko, B., Kolyshkin, A., & Kovalenko, E. (2020). Platforms as the Terms of Organizational Leadership in the Digital Economy. In 6th International Conference on Social, economic, and academic leadership (ICSEAL-6-2019), 441, 415-421. Atlantis Press. https://doi.org/10.2991/assehr.k.200526.060

49.

Kwon, H. J. (2022). A New Trend of Tourism in the Post-COVID19 Era: Big Data Analysis of Online Tours in Korea. Social Sciences, 11(12), 574. https://doi.org/10.3390/socsci11120574

50.

Lattin, J. M., & McAlister, L. (1985). Using a variety-seeking model to identify substitute and complementary relationships among competing products. Journal of marketing research, 22(3), 330-339. https://doi.org/10.1177/002224378502200308

51.

Lee, K. J., & Kim, E. Y. (2020). The role and effect of artificial intelligence (ai) on the platform service innovation: The case study of Kakao in Korea. Knowledge Management Research, 21(1), 175-195.

52.

Lee, S., Lee, S. Y., & Ryu, M. H. (2019). How much are sellers willing to pay for the features offered by their e-commerce platform?. Telecommunications Policy, 43(10), 101832. https://doi.org/10.1016/j.telpol.2019.101832

53.

Lee, Y., & Shin, D. (2020). A study on the online assessment using artificial intelligence for distance education. Journal of Learner-Centered Curriculum and Instruction, 20(14), 389- 407.

54.

Liu, Z., Yu, X., Zhang, L., Wu, Z., Cao, C., Dai, H., ... & Li, X. (2023). Deid-gpt: Zero-shot medical text de-identification by gpt-4. arXiv preprint arXiv:2303.11032. https://doi.org/10. 48550/arXiv.2303.11032

55.

Mero, J. (2018). The effects of two-way communication and chat service usage on consumer attitudes in the e-commerce retailing sector. Electronic Markets, 28, 205-217. https://doi.org/10.1007/s12525-017-0281-2

56.

Miao, H., & Ahn, H. (2023). Impact of ChatGPT on interdisciplinary nursing education and research. Asian/Pacific Island Nursing Journal, 7(1), e48136. doi:10.2196/48136

57.

Milmo, D. (2023). ChatGPT reaches 100 million users two months after launch. The Guardian, 3.

58.

Mun, S. R., & Cho, Y. B. (2017). The effects of dining out choice attributes on behavioral intention according to foodservice consumption. J Foodserv Manage, 20(5), 51-72.

59.

Murugesan, S., & Cherukuri, A. K. (2023). The Rise of Generative Artificial Intelligence and Its Impact on Education: The Promises and Perils. Computer, 56(5), 116-121. doi:10.1109/MC.2023.3253292

60.

Nunnally, J. C., & Berstein, I. H. (1994). Psychometric Theory. 3. Edit. McCraw-Hill. Inc. New York.

61.

Patel, S. B., & Lam, K. (2023). ChatGPT: the future of discharge summaries?. The Lancet Digital Health, 5(3), e107-e108. https://doi.org/10.1016/S2589-7500(23)00021-3

62.

Rachini, M. (2022). ChatGPT a'landmark event'for AI, but what does it mean for the future of human labour and disinformation?. CBC, December, 15.

63.

Raman, R., Mandal, S., Das, P., Kaur, T., Sanjanasri, J. P., & Nedungadi, P. (2023). University students as early adopters of ChatGPT: Innovation Diffusion Study. https://doi.org/10.21203/rs.3.rs-2734142/v1

64.

Paul, J., Ueno, A., & Dennis, C. (2023). ChatGPT and consumers: Benefits, pitfalls and future research agenda. International Journal of Consumer Studies, 47(4), 1213-1225. https://doi.org/10.1111/ijcs.12928

65.

Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and CyberPhysical Systems, 3, 121-154. https://doi.org/10.1016/j.iotcps. 2023.04.003

66.

Rogers, E. M. (1995). Diffusion of Innovations 4th ed. The Free Press, New York.

67.

Rogers, E. M. (2003). Diffusion of Innovations 5th ed. The Free Press, New York.

68.

Rospigliosi, P. A. (2023). Artificial intelligence in teaching and learning: what questions should we ask of ChatGPT?. Interactive Learning Environments, 31(1), 1-3. https://doi.org/10.1080/10494820.2023.2180191

69.

Segars, A. H., & Grover, V. (1993). Re-examining perceived ease of use and usefulness: A confirmatory factor analysis. MIS quarterly, 17(4). 517-525. https://doi.org/10.2307/249590

70.

Seo, K. K. (2013). Factor analysis of the cloud service adoption intension of Korean firms: applying the TAM and VAM. Journal of Digital Convergence, 11(12), 155-160.

71.

Shahsavar, Y., & Choudhury, A. (2023). The Role of AI Chatbots in Healthcare: A Study on User Intentions to Utilize ChatGPT for Self-Diagnosis. JMIR Preprints. https://doi.org/10.2196/ preprints.47564

72.

Shin, D. (2022). How do people judge the credibility of algorithmic sources?. Ai & Society, 37(1), 81-96. https://doi.org/10.1007/ s00146-021-01158-4

73.

Shin, K. Y., Lee, J. K., Kang, K. H., Hong, W. G., & Han, C. H. (2019). The current applications and future directions of artificial intelligence for military logistics. Journal of Digital Contents Society, 20(12), 2433-2444. doi:10.9728/dcs.2019. 20.12.2433

74.

Singh, H., & Singh, A. (2023). ChatGPT: Systematic Review, Applications, and Agenda for Multidisciplinary Research. Journal of Chinese Economic and Business Studies, 21(2), 193-212. https://doi.org/10.1080/14765284.2023.2210482

75.

Sitar-Taut, D. A., & Mican, D. (2021). Mobile learning acceptance and use in higher education during social distancing circumstances: An expansion and customization of UTAUT2. Online Information Review, 45(5), 1000-1019. https://doi.org/ 10.1108/OIR-01-2021-0017

76.

Smith, B., & Linden, G. (2017). Two decades of recommender systems at Amazon. com. Ieee internet computing, 21(3), 12- 18. doi: 10.1109/MIC.2017.72

77.

Strzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 1-14. https://doi.org/10.1080/10494820.2023.2209881

78.

Sun, Y., Liu, D., Chen, S., Wu, X., Shen, X. L., & Zhang, X. (2017). Understanding users' switching behavior of mobile instant messaging applications: An empirical study from the perspective of push-pull-mooring framework. Computers in Human Behavior, 75, 727-738. https://doi.org/10.1016/j.chb. 2017.06.014

79.

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176. https://doi.org/10.1287/isre. 6.2.144

80.

Thorp, H. H. (2023). ChatGPT is fun, but not an author. Science, 379(6630), 313-313. doi: 10.1126/science.adg7879

81.

Tseng, S., & Fogg, B. J. (1999). Credibility and computing technology. Communications of the ACM, 42(5), 39-44. https://dl.acm.org/doi/fullHtml/10.1145/301353.301402

82.

Twum, K. K., Ofori, D., Keney, G., & Korang-Yeboah, B. (2022). Using the UTAUT, personal innovativeness and perceived financial cost to examine student’s intention to use E-learning. Journal of Science and Technology Policy Management, 13(3), 713-737. https://doi.org/10.1108/JSTPM-12-2020-0168

83.

Van Dis, E. A., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. (2023). ChatGPT: five priorities for research. Nature, 614(7947), 224-226. https://doi.org/10.1038/d41586-023- 00288-7

84.

Velazco, C. (2023, May 19). ChatGPT has an official app now. You can even talk to it. Washington Post, NA. https://link.gale.com/apps/doc/A749836647/AONE?u=anon~ 155e0867&sid=googleScholar&xid=6f709c20

85.

Venkatesh, A. (1996). Computers and other interactive technologies for the home. Communications of the ACM, 39(12), 47-54.

86.

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

87.

Ventayen, R. J. M. (2023). OpenAI ChatGPT generated results: Similarity index of artificial intelligence-based contents. Available at SSRN 4332664. http://dx.doi.org/10.2139/ssrn. 4332664

88.

Wamba, S. F., Bawack, R. E., Guthrie, C., Queiroz, M. M., & Carillo, K. D. A. (2021). Are we preparing for a good AI society? A bibliometric review and research agenda. Technological Forecasting and Social Change, 164, 120482. https://doi.org/10.1016/j.techfore.2020.120482

89.

Wang, F. Y., Miao, Q., Li, X., Wang, X., & Lin, Y. (2023). What does ChatGPT say: The DAO from algorithmic intelligence to linguistic intelligence. IEEE/CAA Journal of Automatica Sinica, 10(3), 575-579. https://doi.org/10.1109/JAS.2023. 123486

90.

Wu, K., Vassileva, J., & Zhao, Y. (2017). Understanding users' intention to switch personal cloud storage services: Evidence from the Chinese market. Computers in Human Behavior, 68, 300-314. https://doi.org/10.1016/j.chb.2016.11.039

91.

Xames, M. D., & Shefa, J. (2023). ChatGPT for research and publication: Opportunities and challenges. Journal of Applied Learning and Teaching, 6(1), 1-6. https://doi.org/10.37074/jalt. 2023.6.1.20

92.

Xia, Y., & Yang, Y. (2019). RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. Behavior research methods, 51, 409-428. https://doi.org/10.3758/s13428-018- 1055-2

93.

Yu, H. (2023). Reflection on whether Chat GPT should be banned by academia from the perspective of education and teaching. Frontiers in Psychology, 14, 1-12. https://doi.org/10.3389/ fpsyg.2023.1181712

94.

Zhang, B. (2023). ChatGPT, an Opportunity to Understand More About Language Models. Medical Reference Services Quarterly, 42(2), 194-201. https://doi.org/10.1080/02763869. 2023.2194149

95.

Zhang, C., Zhang, C., Li, C., Qiao, Y., Zheng, S., Dam, S. K., Zhang, M., Kim, J. U., Kim, Se. T., Choi, J., Park, G. M., Bae, S. H., Lee, L. H., Hui, P., Kweon, I. S., & Hong, C. S. (2023). One small step for generative ai, one giant leap for agi: A complete survey on chatgpt in aigc era. arXiv preprint arXiv:2304.06488. https://doi.org/10.48550/arXiv.2304.06488

96.

Zhu, J. J., Jiang, J., Yang, M., & Ren, Z. J. (2023). ChatGPT and environmental research. Environmental Science & Technology. https://doi.org/10.1021/acs.est.3c01818

The Journal of Distribution Science