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A Study on the Generative AI users' WOM : Focusing on the Mediation Effect of Continuous Use Intention

  • Byoung Jo HWANG (Business Administration, Kyonggi University) ;
  • Yoon Hwang JU (Dept. of Online Shopping, Jangan University) ;
  • Hoe-Chang YANG (Dept. Of Distribution Management, Jangan University)
  • Received : 2024.09.23
  • Accepted : 2024.10.30
  • Published : 2024.10.30

Abstract

Purpose: This study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) to explore the impact of ChatGPT users' technology acceptance (performance expectancy, effort expectancy, and social influence) on WOM. Research design, data, and methodology: A survey was conducted targeting ChatGPT users in their 20s or older in Korea and used for analysis. Testing of research hypotheses is performed using SPPS and AMOS. Results: First, ChatGPT users' technology acceptance (performance expectancy, effort expectancy, social influence) was found to have a positive effect on continuous use intention. Second, ChatGPT users' continuous use intention was found to have a positive effect on WOM. Third, ChatGPT users' continuous use intention ChatGPT was found to have a full or partial mediation effect on the relationship between technology acceptance and WOM. Conclusions: These results mean that ChatGPT's outstanding functional utility, convenience of use, and recommendations from people around them have a significant impact on the continuous use intention ChatGPT and WOM. As Generative AI becomes routine, disruptive innovation through Retailtech is expected to promote changes in distribution. This study confirmed the relationship between continuance use/WOM and technology acceptance. Distribution companies need to improve efficiency/convenience using Generative AI and implement various WOM marketing.

Keywords

References

  1. Ajith T, N., & P, S. (2023). Play, pause or praise?-a dual factor theory exploration of continuance, discontinuance and recommendation intentions in OTT platforms. World Leisure Journal, 1-25.
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
  3. Al-Hattami, H. M. (2023). Understanding perceptions of academics toward technology acceptance in accounting education. Heliyon, 9(1).
  4. Al Amin, M. (2022). The influence of psychological, situational and the interactive technological feedback-related variables on customers' technology adoption to use online shopping applications. Journal of Global Marketing, 35(5), 384-407.
  5. An, S., Eck, T., & Yim, H. (2023). Understanding consumers' acceptance intention to use mobile food delivery applications through an extended technology acceptance model. Sustainability, 15(1), 832.
  6. Arfi, W. B., Nasr, I. B., Kondrateva, G., & Hikkerova, L. (2021). The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context. Technological Forecasting and Social Change, 167, 120688.
  7. Atlas, S. (2023). ChatGPT for higher education and professional development: A guide to conversational AI.
  8. Aydin, O., & Karaarslan, E. (2023). Is ChatGPT leading generative AI? What is beyond expectations?. Academic Platform Journal of Engineering and Smart Systems, 11(3), 118-134.
  9. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1).
  10. Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62.
  11. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 351-370.
  12. Bhattarai, A. (2023). Exploring Customer Engagement through Generative AI Innovative Strategies in Digital Marketing Campaigns. Quarterly Journal of Emerging Technologies and Innovations, 8(12), 1-9.
  13. Blumel, J. H., Zaki, M., & Bohne, T. (2023). Personal touch in digital customer service: a conceptual framework of relational personalization for conversational AI. Journal of Service Theory and Practice, 34(1), 33-65.
  14. Bokhari, S. A. A., & Myeong, S. (2023). An Analysis of Artificial Intelligence Adoption Behavior Applying Extended UTAUT Framework in Urban Cities: The Context of Collectivistic Culture. Engineering Proceedings, 56(1), 289.
  15. Camilleri, M. A. (2024). Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework. Technological Forecasting and Social Change, 201, 123247.
  16. Chakraborty, D., Siddiqui, M., Siddiqui, A., Paul, J., Dash, G., & Dal Mas, F. (2023). Watching is valuable: Consumer views-Content consumption on OTT platforms. Journal of Retailing and Consumer Services, 70, 103148.
  17. Charm, T., Dhar, R., Haas, S., Liu, J., Novemsky, N., & Teichner, W. (2020). Understanding and shaping consumer behavior in the next normal. McKinsey & Company, 24.
  18. Charm, T., Dhar, R., Haas, S., Liu, J., Novemsky, N., & Teichner, W. (2020). Understanding and shaping consumer behavior in the next normal. McKinsey & Company, 24.
  19. Chatterjee, J., & Dethlefs, N. (2023). This new conversational AI model can be your friend, philosopher, and guide... and even your worst enemy. Patterns, 4(1).
  20. Cheng, L. K., Huang, H. L., & Lai, C. C. (2022). Continuance intention in running apps: the moderating effect of relationship norms. International Journal of Sports Marketing and Sponsorship, 23(1), 132-154.
  21. Chitturi, R., Raghunathan, R., & Mahajan, V. (2008). Delight by design: The role of hedonic versus utilitarian benefits. Journal of marketing, 72(3), 48-63.
  22. Cho, H. Y., Yang H. C., & Hwang, B. J. (2023). The Effect of ChatGPT Factors & Innovativeness on Switching Intention: Using Theory of Reasoned Action (TRA). Journal of Distribution Science, 21(8), 83-96.
  23. Cobos, L. (2017). Determinants of continuance intention and word of mouth for hotel branded mobile app users.
  24. Cui, Y. G., van Esch, P., & Phelan, S. (2024). How to build a competitive advantage for your brand using generative AI. Business Horizons.
  25. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
  26. Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data-evolution, challenges and research agenda. International journal of information management, 48, 63-71.
  27. 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.
  28. Farzin, M., Sadeghi, M., Yahyayi Kharkeshi, F., Ruholahpur, H., & Fattahi, M. (2021). Extending UTAUT2 in M-banking adoption and actual use behavior: does WOM communication matter?. Asian Journal of Economics and Banking, 5(2), 136-157.
  29. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research.
  30. Flavian, C., & Casalo, L. V. (2021). Artificial intelligence in services: current trends, benefits and challenges. The Service Industries Journal, 41(13-14), 853-859.
  31. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
  32. Goyette, I., Ricard, L., Bergeron, J., & Marticotte, F. (2010). e-WOM Scale: word-of-mouth measurement scale for e-services context. Canadian Journal of Administrative Sciences/Revue Canadienne des Sciences de l'Administration, 27(1), 5-23.
  33. Gu, D., Yang, X., Li, X., Jain, H. K., & Liang, C. (2018). Understanding the role of mobile internet-based health services on patient satisfaction and word-of-mouth. International journal of environmental research and public health, 15(9), 1972.
  34. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., and Tatham, R. L. (2010). Multivariate Data Analysis. Prentice Hall Upper Saddle River.
  35. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., Black, W. C., & Anderson, R. E. (2019). Multivariate data analysis (Eighth Edi). Hampshire, United Kingdom: Cengage Learning EMEA. https://doi.org/10.1002/9781119409137.ch4.
  36. Hameed, I., Akram, U., Khan, Y., Khan, N. R., & Hameed, I. (2024). Exploring consumer mobile payment innovations: An investigation into the relationship between coping theory factors, individual motivations, social influence and word of mouth. Journal of Retailing and Consumer Services, 77, 103687.
  37. Hermann, E. (2023). Artificial intelligence in marketing: friend or foe of sustainable consumption?. AI & SOCIETY, 38(5), 1975-1976.
  38. Hill-Yardin, E. L., Hutchinson, M. R., Laycock, R., & Spencer, S. J. (2023). A Chat (GPT) about the future of scientific publishing. Brain, behavior, and immunity, 110, 152-154.
  39. Hu, K. (2023). ChatGPT sets record for fastest-growing user base-analyst note. reuters, 12, 2023.
  40. 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.
  41. Huang, M. H., Rust, R., & Maksimovic, V. (2019). The feeling economy: Managing in the next generation of artificial intelligence (AI). California Management Review, 61 (4), 43-65.
  42. Huete-Alcocer, N. (2017). A literature review of word of mouth and electronic word of mouth: Implications for consumer behavior. Frontiers in psychology, 8, 1256.
  43. Hwang, B. J., & Cho, H. Y. (2023). Effect of Perceived Value of OTT Platform on Consumer's Technology Acceptance, Continuous Use Intention, and WOM. The Journal of Economics, Marketing and Management, 11(5), 29-44.
  44. Hwang, J. S., & Lee, H. J. (2017). A Study on Unified Theory of Acceptance and Use of Technology (UTAUT) Improvement using Meta-Analysis: Focused on Analysis of Korea Citation Index (KCI)-Listed Researches. The Journal of Bigdata, 2(2), 47-56.
  45. Iranmanesh, M., Senali, M. G., Ghobakhloo, M., Foroughi, B., Yadegaridehkordi, E., & Annamalai, N. (2024). Determinants of intention to use ChatGPT for obtaining shopping information. Journal of Marketing Theory and Practice, 1-18.
  46. Islam, T., Miron, A., Nandy, M., Choudrie, J., Liu, X., & Li, Y. (2024). Transforming Digital Marketing with Generative AI. Computers, 13(7), 168.
  47. Ismagilova, E., Slade, E., Rana, N. P., & Dwivedi, Y. K. (2020). The effect of characteristics of source credibility on consumer behaviour: A meta-analysis. Journal of Retailing and Consumer Services, 53, 101736.
  48. Jan, A., Khan, M., Ajmal, M. M., & Patwary, A. K. (2023). From traditional advertising to digital marketing: Exploring electronic word of mouth through a theoretical lens in the hospitality and tourism industry. Global Knowledge, Memory and Communication.
  49. Jeong, E., & Jo, H. (2024). Omnichannel word-of-mouth genesis: the confluence of online-offline experiences, social influence and skepticism. Asia Pacific Journal of Marketing and Logistics.
  50. Jeyaraj, A., Dwivedi, Y. K., & Venkatesh, V. (2023). Intention in information systems adoption and use: Current state and research directions. International Journal of Information Management, 73, 102680.
  51. Jo, H. (2023). Understanding AI tool engagement: A study of ChatGPT usage and word-of-mouth among university students and office workers. Telematics and Informatics, 85, 102067.
  52. Jo, H., & Park, D. H. (2024). Effects of ChatGPT's AI capabilities and human-like traits on spreading information in work environments. Scientific Reports, 14(1), 7806.
  53. Khan, M., Ajmal, M. M., Jan, A., Rahman, H. U., & Zahid, M. (2024). Identification of novel antecedents towards generating positive electronic word of mouth: evidence from the hospitality and tourism industry. Global Knowledge, Memory and Communication.
  54. Kietzmann, J., Paschen, J., & Treen, E. (2018). Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58(3), 263-267.
  55. Kim, J. H., & Kang, E. (2023). An empirical research: Incorporation of user innovativeness into TAM and UTAUT in adopting a golf app. Sustainability, 15(10), 8309.
  56. Kim, J. W., Jo, H. I., & Lee, B. G. (2019). The study on the factors influencing on the behavioral intention of chatbot service for the financial sector: Focusing on the UTAUT model. Journal of Digital Contents Society, 20(1), 41-50.
  57. Kim, J., Kim, J. H., Kim, C., & Park, J. (2023). Decisions with ChatGPT: Reexamining choice overload in ChatGPT recommendations. Journal of Retailing and Consumer Services, 75, 103494.
  58. Konya-Baumbach, E., Biller, M., & von Janda, S. (2023). Someone out there? A study on the social presence of anthropomorphized chatbots. Computers in Human Behavior, 139, 107513.
  59. Larsen, T. J., Sorebo, A. M., & Sorebo, O. (2009). The role of task-technology fit as users' motivation to continue information system use. Computers in Human behavior, 25(3), 778-784.
  60. Lee, H., & Kim, J. H. (2023). Effects of UTAUT on the Digital Literacy and Acceptance Intention of ChatGPT Users. The Society of Convergence Knowledge Transactions, 11(2), 33-43.
  61. 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.
  62. Li, H., & Liu, Y. (2014). Understanding post-adoption behaviors of e-service users in the context of online travel services. Information & Management, 51(8), 1043-1052.
  63. Li, H., Chen, Q., Zhong, Z., Gong, R., & Han, G. (2022). E-word of mouth sentiment analysis for user behavior studies. Information Processing & Management, 59(1), 102784.
  64. Li, H., Chen, Q., Zhong, Z., Gong, R., & Han, G. (2022). E-word of mouth sentiment analysis for user behavior studies. Information Processing & Management, 59(1), 102784.
  65. Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarok or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 100790.
  66. Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarok or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 100790.
  67. Loureiro, S. M., Cavallero, L., & Miranda, F. J. (2018). Fashion brands on retail websites: Customer performance expectancy and e-word-of-mouth. Journal of Retailing and Consumer Services, 41, 131-141.
  68. Ma, L., & Sun, B. (2020). Machine learning and AI in marketing-Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481-504.
  69. Mattas, P. S. (2023). ChatGPT: A study of AI language processing and its implications. Journal homepage: www. ijrpr. com ISSN, 2582(7421), 7-8.
  70. Mishra, A., Shukla, A., Rana, N. P., Currie, W. L., & Dwivedi, Y. K. (2023). Re-examining post-acceptance model of information systems continuance: A revised theoretical model using MASEM approach. International Journal of Information Management, 68, 102571.
  71. Mithas, S., Murugesan, S., & Seetharaman, P. (2020). What is your artificial intelligence strategy?. IT Professional, 22(2), 4-9.
  72. Morgan, P. J., Cleave-Hogg, D., DeSousa, S., & Tarshis, J. (2004). High-fidelity patient simulation: validation of performance checklists. British Journal of Anaesthesia, 92(3), 388-392.
  73. Ooi, K. B., Tan, G. W. H., Al-Emran, M., Al-Sharafi, M. A., Capatina, A., Chakraborty, A., ... & Wong, L. W. (2023). The potential of generative artificial intelligence across disciplines: Perspectives and future directions. Journal of Computer Information Systems, 1-32.
  74. 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.
  75. Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior research methods, instruments, & computers, 36, 717-731.
  76. Quinones, M., Romero, J., Schmitz, A., & Diaz-Martin, A. M. (2024). What factors determine the intention to use and recommend public autonomous shuttles in a real-life setting?. European Journal of Management and Business Economics.
  77. Ramadhan, A., Hidayanto, A. N., Evik, C. S., Rizkiandini, N., Rahimullah, N. A., Muthiah, R. H., ... & Phusavat, K. (2022). Factors affecting the continuation to use and e-WOM intention of online library resources by university students: A study in Indonesia. The Journal of Academic Librarianship, 48(6), 102592.
  78. Shi, D., & Maydeu-Olivares, A. (2020). The effect of estimation methods on SEM fit indices. Educational and psychological measurement, 80(3), 421-445.
  79. Shi, J. (2023). Digital Technology and Value Chain Agglomeration: Evidence from East Asia. Emerging Markets Finance and Trade, 1-16.
  80. Siagian, H., Tarigan, Z. J. H., Basana, S. R., & Basuki, R. (2022). The effect of perceived security, perceived ease of use, and perceived usefulness on consumer behavioral intention through trust in digital payment platform (Doctoral dissertation, Petra Christian University).
  81. Slade, E. L., Dwivedi, Y. K., Piercy, N. C., & Williams, M. D. (2015). Modeling consumers' adoption intentions of remote mobile payments in the United Kingdom: extending UTAUT with innovativeness, risk, and trust. Psychology & marketing, 32(8), 860-873.
  82. Stahl, B. C. (2021). Artificial intelligence for a better future: an ecosystem perspective on the ethics of AI and emerging digital technologies (p. 124). Springer Nature.
  83. 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.
  84. Tassiello, V., Amatulli, C., Tillotson, J. S., & Laker, B. (2024). aiWOM: Artificial Intelligence Word-of-Mouth. Conceptualizing Consumer-to-AI Communication. International Journal of Human-Computer Interaction, 1-13.
  85. Terwiesch, C. (2023). Would Chat GPT3 get a Wharton MBA? A prediction based on its performance in the operations management course. Mack Institute for Innovation Management at the Wharton School, University of Pennsylvania, 45.
  86. Tu, W., & He, J. (2023). Can digital transformation facilitate firms' M&A: Empirical discovery based on machine learning. Emerging Markets Finance and Trade, 59(1), 113-128.
  87. 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.
  88. Venkatesh, V. (2022). Adoption and use of AI tools: a research agenda grounded in UTAUT. Annals of Operations Research, 308(1), 641-652.
  89. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
  90. Venkatesh, V., Maruping, L. M., & Brown, S. A. (2006). Role of time in self-prediction of behavior. Organizational Behavior and Human Decision Processes, 100(2), 160-176.
  91. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
  92. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.
  93. Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the association for Information Systems, 17(5), 328-376.
  94. Wolf, V., & Maier, C. (2024). ChatGPT usage in everyday life: A motivation-theoretic mixed-methods study. International Journal of Information Management, 79, 102821.
  95. 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.
  96. Xu, D., Guo, Y., & Huang, M. (2021). Can artificial intelligence improve firms' competitiveness during the COVID-19 pandemic: international evidence. Emerging Markets Finance and Trade, 57(10), 2812-2825.
  97. Xu, M., Li, B., Scott, O. K., & Wang, J. J. (2023). New platform and new excitement? Exploring young educated sport customers' perceptions of watching live sports on OTT services. International Journal of Sports Marketing and Sponsorship, 24(4), 682-699.