The impact of AI chatbot adoption on customer experience in e-retailing

Main Article Content

Jing Shuan Siow
Bak Aun Teoh
Chui Zi Ong
Kai Xin Chee

Abstract

Due to the outbreak of the COVID-19 pandemic, the changes in shopping norms from offline to online and rapid development in the field of artificial intelligence (AI) have redefined customer experience. This change has brought lucrative opportunities for organisations to provide better customer service by interacting with customers using chatbots. Thus, this research was conducted to examine the attributes of AI chatbots that affect online customer experience in the e-retailing market. This paper applied the Technology Acceptance Model (TAM) to design a research model to investigate the relationship between chatbot usability, responsiveness, and online customer experience. A quantitative method was employed to test the research model, and data were collected from an online survey. A total of 101 usable responses were received and examined using SPSS software. The results show a positive relationship between chatbot usability and online customer experience, while no significant relationship is observed between chatbot responsiveness and online customer experience. The findings of this study offer insights for academics, industry practitioners, and policymakers aiming to utilise the potential of AI chatbots to enhance online customer experience and elevate overall customer satisfaction in the e-retail sector.

Article Details

How to Cite
Siow, J. S., Teoh, B. A., Ong, C. Z., & Chee , K. X. (2024). The impact of AI chatbot adoption on customer experience in e-retailing. Issues and Perspectives in Business and Social Sciences, Advance online publication. Retrieved from https://journals.mmupress.com/index.php/ipbss/article/view/1024
Section
Research papers

References

Agnihotri, A. & Bhattacharya, S. (2024). Chatbots’ effectiveness in service recovery. International Journal of Information Management, 76, 102679.

Bhandari, P. (2020). How to find the range of a data set | formula & examples. Available at: https://www.scribbr.com/statistics/range/

Chang, S., Chih, W., Liou, D., & Yang, Y. (2016). The mediation of cognitive attitude for online shopping. Information Technology & People, 29(3), 618–6 46.

Chen J.S., Le, T.T.Y., & Florence D. (2021). Usability and responsiveness of artificial intelligence chatbot on online customer experience in e-retailing. International Journal of Retail and Distribution Management, 49(11), 1512–1531.

Chen, Y., Rorissa, A., & Germain, C.A. (2015). Usability definitions in a dynamically changing information environment, Libraries and the Academy, 15(4), 601–621.

Cheng, X., Bao, Y., Zarifis, A., Gong, W., & Mou, J. (2022). Exploring consumers’ response to text-based chatbots in e-commerce: the moderating role of task complexity and chatbot disclosure. Internet Research, 32(2), 496–517.

Chopra, K. (2019). Indian shopper motivation to use artificial intelligence, International Journal of Retail and Distribution Management, 47(3), 331–347.

Chung, M., Ko, E., Joung, H., & Kim, S.J. (2020). Chatbot e-service and customer satisfaction regarding luxury brand. Journal of Business Research, 117, 587–595.

Cicco, R.D., Silva, S.C., & Alparone, F.R. (2020). Millennials’ attitude toward chatbots: an experimental study in a social relationship perspective. International Journal of Retail & Distribution Management, 48(11), 1213–1233.

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing, Journal of the Academy of Marketing Science, 48(1), 24–42.

DeLone, W.H., & McLean, E.R. (2004). Measuring e-commerce success: applying the DeLone and McLean information systems success model. International Journal of Electronic Commerce, 9(1), 31–47.

Flavian, C., Guinaliu, M. & Gurrea, R. (2006). The role played by perceived usability, satisfaction and consumer trust on website loyalty, Information & Management, 43(1), 1–14.

Go, E., & Sundar, S.S. (2019). Humanizing chatbots: the effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behaviour, 97, 304–316.

Hasal, M., Nowakova, J., Saghair, K.A., Abdulla, H., Snasel, V., & Ogiela, L. (2021). Chatbots: security, privacy, data protection, and social aspects. Concurrency and Computation: Practice and Experience, 33(19), 1–13.

Iriani A., (2021). AI: Fusing AI and agents to create a more personal customer experience. Available at: https://www.theedgemarkets.com/article/ai-fusing-ai-and-agents-create-more-personal-customer-experience

Jain, R., Aagja, J., & Bagdare, S. (2017). Customer experience – a review and research agenda. Journal of Service Theory and Practice, 27(3), 642–662.

Jeyaraj, A. (2020). DeLone & McLean models of information system success: Critical meta-review and research directions. International Journal of Information Management, 54, 102139.

Jovic, D. (2022). The future is now – 37 fascinating chatbot statistics. Available at: https://www.smallbizgenius.net/by-the-numbers/chatbot-statistics/#gref

Kock, J. (2014). The Technology Acceptance Model (TAM). An overview. Available at: https://www.grin.com/document/378123#:~:text=The%20Technology%20Acceptance%20Model%20(TAM)%20is%20an%20information%20systems%20theory,of%20technology%20diffusion%20(Kotr%C3%ADk).

Kraus, D., Reibenspiess, V., & Eckhardt, A. (2019). How voice can change customer satisfaction: a comparative analysis between e-commerce and voice commerce. Internationale Tagung Wirtschaftsinformatik, Siegen.

Lars, M.W., Pavone, G., Poocharoentou, T., Prayatsup, P., Ratinaud, M., Tison, A., & Torne, S. (2020). How service quality influences customer acceptance and usage of chatbots? Journal of Service Management Research, 4(1), 35–51.

Leah (2021). What do your customers actually think about chatbots? Available at: https://www.userlike.com/en/blog/consumer-chatbot-perceptions

Li, L., Lee, K.Y., Emokpae, E., & Yang, S. (2021). What makes you continuously use chatbot services? Evidence from chinese online travel agencies. Electronic Markets, 31, 575–599.

Meerschman, H. & Verkeyn, J. (2019) Towards a better understanding of service quality attributes of a chatbot. Master’s Dissertation. Faculteit Economie en Bedrijfskunde. Available at: https://libstore.ugent.be/fulltxt/RUG01/002/784/388/RUG01-002784388_2019_0001_AC.pdf

Mogaji, E., Balakrishnan, J., Nwoba, A.C., & Nguyen, N.P. (2021). Emerging-market consumers’ interactions with banking chatbots. Telematics and Informatics, 65, 1–16.

Moon, Y. & Armstrong, D.J. (2020). Service quality factors affecting customer attitudes in online-to-office commerce. Information Systems and e-Business Management, 18, 1–34.

Moran, M. (2022). 25 top chatbot statistics for 2022: usage, demographics, trends. Available at: https://startupbonsai.com/chatbot-statistics/#:~:text=15%25%20customer%20service%20interactions%20are,may%20very%20well%20be%20right

Pappas, I.O., Pateli, A.G., Giannakos, M.N., & Chrissikopoulos, V. (2014). Moderating effects of online shopping experience on customer satisfaction and repurchase intentions. International Journal of Retail & Distribution Management, 42(3), 187–204.

Prentice, C., Han, X.Y., Hua, L., & Hu, L. (2019). The influence of identity-driven customer engagement on purchase intention. Journal of Retailing and Consumer Services, 47, 339–347.

Rajaobeline, L., Tep, S.P, Arcand, M., & Ricard, L. (2021). Creepiness: Its antecedents and impact on loyalty when interacting with a chatbot. Physchol Mark, 38, 2339–2356.

Rajnerowicz, K. (2022). The future of chatbots: 80+ chatbot statistics for 2022. Available at: https://www.tidio.com/blog/chatbot-statistics/

Ren, R., Zapata, M., Castro, J., Dieste, O., & Acuna, S.T. (2022). Experimentation for chatbot usability evaluation: a secondary study. Digital Object Identifier, 10, 12430–12464.

Roggeveen, A.L. & Sethuraman, R. (2020). How the COVID-19 pandemic may change the world of retailing. Journal of Retailing, 96(2), 169–171.

Rose, S., Hair, N., & Clark, M. (2011). Online customer experience: a review of the business-to-consumer online purchase context. International Journal of Management Reviews, 13(1), 24–39.

Sabanoglu, T. (2021). Coronavirus: impact on the retail industry worldwide - statistics & facts. Available at: https://www.statista.com/topics/6239/coronavirus-impact-on-the-retail-industry-worldwide/#dossierKeyfigures.

Schmidt, J. & Osebold, R. (2017). Environmental management systems as a driver for sustainability: State of implementation, benefits and barriers in German construction companies. Journal of Civil Engineering and Management, 23(1), 50–162.

Sekaran, U. & Bougie, R. (2016). Research methods for business: a skill-building approach. 7th edn. Wiley, UK.

Stephanie (2016). Kaiser-Meyer-Olkin (KMO) Test for sampling adequacy. Available at: https://www.statisticshowto.com/kaiser-meyer-olkin/

Taber, K.S. (2018). The use of cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48, 1273–1296.

Tighe, D. (2021). Since the coronavirus (COVID-19) pandemic began, have you tried any of the following shopping behaviors? Available at: https://www.statista.com/statistics/1240912/share-of-us-consumers-that-have-tried-a-new-shopping-behavior-during-covid-19/

Wei, W., Torres, E. & Hua, N. (2016). Improving consumer commitment through the integration of self-service technologies: A transcendent consumer experience perspective. International Journal of Hospitality Management, 59, 105–115.

Zhang, M., Hu, M., Guo, L., & Liu, W. (2017) Understanding relationships among customer experience, engagement, and word-of-mouth intention on online brand communities. Internet Research, 27(4), 839–857.

Most read articles by the same author(s)