Determinants of Customers' Continuance Intention to Use Chatbots in e-Commerce
DOI:
https://doi.org/10.14738/abr.121.16362Keywords:
Chatbot, Continuance Intention, Perceived Usefulness, Perceived EnjoymentAbstract
Artificial intelligence (AI) has revolutionized the e-commerce experience and opened up new ways for businesses to communicate with customers through chatbots. This study aims to investigate how factors of chatbots influence continuance intention in e-commerce. A quantitative approach was adopted to explore the impact of interactivity, compatibility, information quality, service quality on perceived usefulness, perceived enjoyment of chatbots. It also aims to explore the determinants of continuance intention in chatbot use. Empirical data is collected from 476 participants through an online survey using Google Form and analyzed using regression analysis by IBM SPSS software. The results of the study show that compatibility, information quality, service quality positively affect perceived usefulness and perceived enjoyment of chatbot. Perceived usefulness and perceived enjoyment are found to have significant influence on continuance intention. The study contributes to the literature of consumer behavior regarding technology adoption. It also contributes to the refinement of the TAM and presents practical insights to managers, chatbot designers, and customer service units in improving chatbots and enhancing continuance intention.
References
K. Sidaoui, M. Jaakkola, and J. Burton, “AI feel you: customer experience assessment via chatbot interviews,” Journal of Service Management, vol. 31, no. 4, pp. 745–766, 2020.
J. Wirtz et al., “Brave new world: service robots in the frontline,” Journal of Service Management, vol. 29, Quang, H. T. (2024). Determinants of Customers' Continuance Intention to Use Chatbots in e-Commerce. Archives of Business Research, 12(1). 78-92.
K. Rajan and A. Saffiotti, “Towards a science of integrated AI and Robotics,” Artificial Intelligence, vol. 247. Elsevier, pp. 1–9, 2017.
Quintino and Ana Rita Parcelas, “The impact of chatbot technology attributes on customer experience: an example in telecom,” 2019.
M. Chung, E. Ko, H. Joung, and S. J. Kim, “Chatbot e-service and customer satisfaction regarding luxury brands,” J Bus Res, vol. 117, pp. 587–595, 2020.
S. Youn and S. V. Jin, “In AI we trust?” The effects of parasocial interaction and technopian versus luddite ideological views on chatbot-based customer relationship management in the emerging “feeling economy,” Comput Human Behav, vol. 119, p. 106721, 2021.
E. Stoeckli, C. Dremel, F. Uebernickel, and W. Brenner, “How affordances of chatbots cross the chasm between social and traditional enterprise systems,” Electronic Markets, vol. 30, pp. 369–403, 2020.
T. Hu et al., “Touch your heart: A tone-aware chatbot for customer care on social media,” in Proceedings of the 2018 CHI conference on human factors in computing systems, 2018, pp. 1–12.
A. Froehlich, “Pros and cons of chatbots in the IT helpdesk,” Informationweek. com. Accessed October, vol. 18, p. 2018, 2018.
V. Venkatesh and H. Bala, “Technology acceptance model 3 and a research agenda on interventions,” Decision sciences, vol. 39, no. 2, pp. 273–315, 2008.
N. Asokan, “Fairness in electronic commerce,” 1998.
G. Kumar et al., “Decentralized accessibility of e-commerce products through blockchain technology,” Sustain Cities Soc, vol. 62, p. 102361, 2020.
M. Lind and N. Salomonson, “The role of virtual servants in e-interaction,” in Pragmatic web–Proceedings of the first international conference on the pragmatic web, 2006, Gesellschaft für Informatik eV, 2006.
K. Letheren and C. Glavas, “Embracing the bots: How direct to consumer advertising is about to change forever,” The conversation, 2017.
P. R. Daugherty, H. J. Wilson, and P. Michelman, “Revisiting the jobs artificial intelligence will create,” MIT Sloan Manag Rev, vol. 60, no. 4, pp. 0_1-0_8, 2019.
X. Luo, S. Tong, Z. Fang, and Z. Qu, “Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases,” Marketing Science, vol. 38, no. 6, pp. 937–947, 2019.
L. C. Wang, J. Baker, J. A. Wagner, and K. Wakefield, “Can a retail web site be social?,” J Mark, vol. 71, no. 3, pp. 143–157, 2007.
M. Scardamalia and C. Bereiter, “Smart technology for self-organizing processes,” Smart Learning Environments, vol. 1, pp. 1–13, 2014.
X. Fan, N. Ning, and N. Deng, “The impact of the quality of intelligent experience on smart retail engagement,” Marketing Intelligence & Planning, vol. 38, no. 7, pp. 877–891, 2020.
T. Lee, “The impact of perceptions of interactivity on customer trust and transaction intentions in mobile commerce,” Journal of Electronic Commerce Research, vol. 6, no. 3, p. 165, 2005.
A. M. Fiore, J. Kim, and H.-H. Lee, “Effect of image interactivity technology on consumer responses toward the online retailer,” Journal of interactive marketing, vol. 19, no. 3, pp. 38–53, 2005.
H. Baek, S. Kim, and S. Lee, “Effects of interactivity and usage mode on user experience in chatbot interface,” Journal of the HCI Society of Korea, vol. 14, no. 1, pp. 35–43, 2019.
Everett M. Rogers, Diffusion of Innovations, 5th ed. 2003.
R. B. Mostafa and T. Kasamani, “Antecedents and consequences of chatbot initial trust,” Eur J Mark, vol. 56, no. 6, pp. 1748–1771, 2022.
P. Setia, P. Setia, V. Venkatesh, and S. Joglekar, “Leveraging digital technologies: How information quality leads to localized capabilities and customer service performance,” MIS quarterly, pp. 565–590, 2013.
J. J. Cronin Jr and S. A. Taylor, “Measuring service quality: a reexamination and extension,” J Mark, vol. 56,
H. M. Gray, K. Gray, and D. M. Wegner, “Dimensions of mind perception,” Science (1979), vol. 315, no. 5812, p. 619, 2007.
F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, “User acceptance of computer technology: A comparison of two theoretical models,” Manage Sci, vol. 35, no. 8, pp. 982–1003, 1989.
E. Karahanna, D. W. Straub, and N. L. Chervany, “Information technology adoption across time: A crosssectional comparison of pre-adoption and post-adoption beliefs,” MIS quarterly, pp. 183–213, 1999.
A. Bhattacherjee, “An empirical analysis of the antecedents of electronic commerce service continuance,” Decis Support Syst, vol. 32, no. 2, pp. 201–214, 2001.
F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, “Extrinsic and intrinsic motivation to use computers in the workplace 1,” J Appl Soc Psychol, vol. 22, no. 14, pp. 1111–1132, 1992.
J. Hamari, “Why do people buy virtual goods? Attitude toward virtual good purchases versus game enjoyment,” Int J Inf Manage, vol. 35, no. 3, pp. 299–308, 2015.
G. McLean, K. Osei-Frimpong, K. Al-Nabhani, and H. Marriott, “Examining consumer attitudes towards retailers’m-commerce mobile applications–An initial adoption vs. continuous use perspective,” J Bus Res, vol. 106, pp. 139–157, 2020.
S. F. Jahanmir, G. M. Silva, P. J. Gomes, and H. M. Gonçalves, “Determinants of users’ continuance intention toward digital innovations: are late adopters different?,” J Bus Res, vol. 115, pp. 225–233, 2020.
C. Wang, T. S. H. Teo, and L. Liu, “Perceived value and continuance intention in mobile government service in China,” Telematics and Informatics, vol. 48, p. 101348, 2020.
H. Taherdoost, “Determining sample size; how to calculate survey sample size,” International Journal of Economics and Management Systems, vol. 2, 2017.
H. E. A. Tinsley and D. J. Tinsley, “Uses of factor analysis in counseling psychology research.,” J Couns Psychol, vol. 34, no. 4, p. 414, 1987.
H. Hari, R. Iyer, and B. Sampat, “Customer brand engagement through chatbots on bank websites–examining the antecedents and consequences,” Int J Hum Comput Interact, vol. 38, no. 13, pp. 1212–1227, 2022. Quang, H. T. (2024). Determinants of Customers' Continuance Intention to Use Chatbots in e-Commerce. Archives of Business Research, 12(1). 78-92.
M. Ashfaq, J. Yun, S. Yu, and S. M. C. Loureiro, “I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents,” Telematics and Informatics, vol. 54, p. 101473, 2020.
S. S. Saidi and N. M. Siew, “Investigating the Validity and Reliability of Survey Attitude towards Statistics Instrument among Rural Secondary School Students.,” Int J Educ Method, vol. 5, no. 4, pp. 651–661, 2019.
D. George and P. Mallery, “Frequencies,” in IBM SPSS statistics 23 step by step, Routledge, 2016, pp. 115–
N. W. N. Setya et al., “Development of student leadership variables instruments: Validity and reliability analysis,” in 1st International Conference on Information Technology and Education (ICITE 2020), Atlantis Press, 2020, pp. 541–545.
C. F. Zeng and Y.-K. Seock, “Chinese consumers’ perceptions toward social media platform for shopping and eWOM intention: a study of WeChat,” International Journal of Fashion Design, Technology and Education, 2019.
D. Abdullah et al., “Perceived website interactivity, perceived usefulness and online hotel booking intention: a structural model,” Malaysian Journal of Consumer and Family Economics, vol. 21, no. 1, pp. 45–57, 2019.
Y. Gu, X. Cheng, and J. Shen, “Design shopping as an experience: Exploring the effect of the live-streaming shopping characteristics on consumers’ participation intention and memorable experience,” Information & Management, vol. 60, no. 5, p. 103810, 2023.
J.-H. Kim, M. Kim, M. Park, and J. Yoo, “How interactivity and vividness influence consumer virtual reality shopping experience: the mediating role of telepresence,” Journal of Research in Interactive Marketing,
K. Lee and S. Kim, “Structural Relationship between Technological Characteristics, Perceived Usefulness, Trust, and Continuous Intention to Use Mobile Payment Services: Sports Consumers in MZ Generations.,” Sport Mont, vol. 20, no. 1, 2022.
E. T. Lwoga and N. B. Lwoga, “User acceptance of mobile payment: The effects of user‐centric security, system characteristics and gender,” The Electronic Journal of Information Systems in Developing Countries, vol. 81, no. 1, pp. 1–24, 2017.
D. Rad et al., “On the technology acceptance behavior of Romanian preschool teachers,” Behavioral Sciences, vol. 13, no. 2, p. 133, 2023.
H. Lin, “The role of online and offline features in sustaining virtual communities: an empirical study,” Internet Research, vol. 17, no. 2, pp. 119–138, 2007.
C.-C. Chen and J.-L. Tsai, “Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: An empirical study by integrating TAM with ISSM,” Future Generation Computer Systems, vol. 96, pp. 628–638, 2019.
K. Y. Peterkin, “The impact of perceived enjoyment on employee continuance intention in computer-based training in the hotel industry,” 2022.
D. Pratiwi, R. Rahmiati, and A. Abror, “THE INFLUENCE OF EASE OF USE, E-SERVICE QUALITY AND PERCEIVED USEFULNESS AS AN INTERVENING VARIABLE AN INTENTION TO REUSE ZALORA ONLINE FASHION APPLICATION,” Banking and Management Review, vol. 11, no. 1, pp. 1553–1568, 2022.
M. Shahid Iqbal, M. Ul Hassan, and U. Habibah, “Impact of self-service technology (SST) service quality on customer loyalty and behavioral intention: The mediating role of customer satisfaction,” Cogent Business & Management, vol. 5, no. 1, p. 1, 2018.
Y. Lee and O. Kwon, “Intimacy, familiarity and continuance intention: An extended expectation–confirmation model in web-based services,” Electron Commer Res Appl, vol. 10, no. 3, pp. 342–357, 2011.
W.-L. Shiau and M. M. Luo, “Continuance intention of blog users: the impact of perceived enjoyment, habit, user involvement and blogging time,” Behaviour & Information Technology, vol. 32, no. 6, pp. 570–583, 2013.
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