How chatbot e-services motivate communication credibility and lead to customer satisfaction: The perspective of Thai consumers in the apparel retailing context
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DOIhttp://dx.doi.org/10.21511/im.18(3).2022.02
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Article InfoVolume 18 2022, Issue #3, pp. 15-27
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A Correction to this article was published on 13 July 2023
Many apparel retailing brands use e-service marketing tools such as a chatbot (a system that is available 24 hours a day, 7 days a week) to increase their competitive advantage in today’s world of digitalization. During the COVID-19 pandemic, chatbots gained more power to serve as a communication tool that provides information and maintains customer experience. Therefore, this study is conducted to investigate the influence of chatbot e-service agents’ marketing efforts (involving interaction, entertainment, trendiness, and problem-solving) on Thai customers’ perceived communication credibility and satisfaction in apparel retailing, as research in this area is limited. In order to test the hypotheses, the paper employed structural equation modeling using Amos. In addition, an online survey of 400 Thai consumers who had previously used chatbots in the apparel retailing industry was conducted. The results showed that chatbot e-service marketing efforts, including interaction, trendiness, and problem-solving, affected customer satisfaction without entertainment elements. Beyond this, a chatbot, viewing interaction and entertainment, was found to have an insignificant effect on communication credibility. Thus, the coefficient value proved that information regarding communication credibility is more dominant in customer satisfaction. Therefore, the chatbot e-service marketing effort is essential in motivating communication credibility in customer satisfaction. These findings delivered managerial implications for understanding consumers in the field of digitalization.
- Keywords
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JEL Classification (Paper profile tab)L81, M15, M31
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References49
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Tables5
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Figures1
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- Figure 1. Conceptual framework
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- Table 1. Demographic characteristics
- Table 2. Measuring sampling adequacy and Bartlett’s test of sphericity
- Table 3. Reliability and validity result
- Table 4. Structural model and hypothesis testing
- Table A1. Factor analysis results
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- Adjei, M. T., Noble, S. M., & Noble, C. H. (2010). The influence of C2C communications in online brand communities on customer purchase behavior. Journal of the Academy of Marketing Science, 38(5), 634-653.
- Aleedy, M., Shaiba, H., & Bezbradica, M. (2019). Generating and analyzing chatbot responses using natural language processing. International Journal of Advanced Computer Science and Applications, 10(9).
- Bailey, J., & McCollough, M. (2000). Emotional labor and the difficult customer: Coping strategies of service agents and organizational consequences. Journal of Professional Services Marketing, 20(2), 51-72.
- Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual differences, 42(5), 815-824.
- Ben Mimoun, M. S., Poncin, I., & Garnier, M. (2017). Animated conversational agents and e-consumer productivity: The roles of agents and individual characteristics. Information & Management, 54(5), 545-559.
- Bilgin, N., Kuzey, C., Torlak, G., & Uyar, A. (2015). An investigation of antecedents of organizational citizenship behavior in the Turkish hospitality industry: A structural equation approach. International Journal of Culture, Tourism and Hospitality Research, 9(2), 200-222.
- Bollen, K. A. (1989). A new incremental fit index for general structural equation models. Sociological methods & research, 17(3), 303-316.
- 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 & Distribution Management, 49(11), 1512-1531.
- Cheng, Y., & Jiang, H. (2022). Customer–brand relationship in the era of Artificial Intelligence: Understanding the role of chatbot marketing efforts. Journal of Product & Brand Management, 31(2), 252-264.
- Chung, M., Ko, E., Joung, H., & Kim, S. J. (2020). Chatbot E-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587-595.
- Dabholkar, P. A., Thorpe, D. I., & Rentz, J. O. (1996). A measure of service quality for retail stores: Scale development and validation. Journal of the Academy of Marketing Science, 24(1), 3-16.
- Dawson, S., Bloch, P. H., & Ridgway, N. (1990). Shopping motives, emotional states, and. Journal of retailing, 66(4), 408-427.
- Edwards, C., Edwards, A., Spence, P. R., & Shelton, A. K. (2014). Is that a bot running the social media feed? Testing the differences in perceptions of communication quality for a human agent and a bot agent on Twitter. Computers in Human Behavior, 33, 372-376.
- Emmers-Sommer, T. M. (2004). The effect of communication quality and quantity indicators on intimacy and relational satisfaction. Journal of Social and Personal Relationships, 21(3), 399-411.
- Erdem, T., & Swait, J. (2004). Brand credibility, brand consideration, and choice. Journal of Consumer Research, 31(1), 191-198.
- Esch, P., Cui, Y. G., & Jain, S. P. (2021). Self-efficacy and callousness in consumer judgments of AI-enabled checkouts. Psychology & Marketing, 38(7), 1081-1100.
- Godey, B., Manthiou, A., Pederzoli, D., Rokka, J., Aiello, G., Donvito, R., & Singh, R. (2016). Social media marketing efforts of luxury brands: Influence on brand equity and consumer behavior. Journal of Business Research, 69(12), 5833-5841.
- Gruber, M., de Leon, N., George, G., & Thompson, P. (2015). Managing by design. Academy of Management Journal, 58(1), 1-7.
- Hair, E., Halle, T., Terry-Humen, E., Lavelle, B., & Calkins, J. (2006). Children’s school readiness in the ECLS-K: Predictions to academic, health, and social outcomes in first grade. Early Childhood Research Quarterly, 21(4), 431-454.
- Hair, J. F. J., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate Data Analysis (5th ed.). Upper Saddle River, NJ, USA: Prentice Hall.
- Holzwarth, M., Janiszewski, C., & Neumann, M. M. (2006). The influence of avatars on online consumer shopping behavior. Journal of Marketing, 70(4), 19-36.
- Hu, L., & 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.
- Izard, C. E. (1977). Differential emotions theory. In Human emotions (pp. 43-66). Boston, MA: Springer.
- Jansom, A., & Pongsakornrungsilp, S. (2021). How Instagram influencers affect the value perception of Thai millennial followers and purchasing intention of luxury fashion for sustainable marketing. Sustainability, 13(15), 8572.
- Jian, G., Shi, X., & Dalisay, F. (2014). Leader–member conversational quality. Management Communication Quarterly, 28(3), 375-403.
- Joosten, H., Bloemer, J., & Hillebrand, B. (2016). Is more customer control of services always better? Journal of Service Management, 27(2), 218-246.
- Kang, J.-W., & Namkung, Y. (2019). The information quality and source credibility matter in customers’ evaluation toward food O2O commerce. International Journal of Hospitality Management, 78, 189-198.
- Kim, A. J., & Ko, E. (2012). Do social media marketing activities enhance customer equity? An empirical study of luxury fashion brand. Journal of Business Research, 65(10), 1480-1486.
- Kim, S., Park, G., Lee, Y., & Choi, S. (2016). Customer emotions and their triggers in luxury retail: Understanding the effects of customer emotions before and after entering a luxury shop. Journal of Business Research, 69(12), 5809-5818.
- Kumar, V., Dixit, A., Javalgi, R. G., & Dass, M. (2016). Research framework, strategies, and applications of Intelligent Agent Technologies (IATS) in marketing. Journal of the Academy of Marketing Science, 44(1), 24-45.
- Ladhari, R., Souiden, N., & Dufour, B. (2017). The role of emotions in utilitarian service settings: The effects of emotional satisfaction on product perception and behavioral intentions. Journal of Retailing and Consumer Services, 34, 10-18.
- Lee, S. Y., & Choi, J. (2017). Enhancing user experience with conversational agent for movie recommendation: Effects of self-disclosure and reciprocity. International Journal of Human-Computer Studies, 103, 95-105.
- Lester, R. K., & Piore, M. J. (2004). Innovation - the missing dimension. Harvard University Press.
- Maltz, E. (2000). Is all communication created equal?: An investigation into the effects of communication mode on perceived information quality. Journal of Product Innovation Management: An International Publication Of The Product Development & Management Association, 17(2), 110-127.
- Mohr, J. J., & Sohi, R. S. (1995). Communication flows in distribution channels: Impact on assessments of communication quality and satisfaction. Journal of Retailing, 71(4), 393-415.
- Morra, M. C., Gelosa, V., Ceruti, F., & Mazzucchelli, A. (2018). Original or counterfeit luxury fashion brands? The effect of social media on purchase intention. Journal of Global Fashion Marketing, 9(1), 24-39.
- Muntinga, D. G., Moorman, M., & Smit, E. G. (2011). Introducing COBRAs. International Journal of Advertising, 30(1), 13-46.
- Naaman, M., Becker, H., & Gravano, L. (2011). Hip and trendy: Characterizing emerging trends on Twitter. Journal of the American Society for Information Science and Technology, 62(5), 902-918.
- Nunnally, J. C., & Bernstein, I. H. (1994). The Assessment of Reliability. Psychometric Theory, 3, 248-292.
- Park, N., Kee, K. F., & Valenzuela, S. (2009). Being immersed in social networking environment: Facebook groups, uses and gratifications, and social outcomes. CyberPsychology & Behavior, 12(6), 729-733.
- Redman, T., & Mathews, B. P. (2002). Managing services: Should we be having fun? The Service Industries Journal, 22(3), 51-62.
- Rese, A., Ganster, L., & Baier, D. (2020). Chatbots in retailers’ customer communication: How to measure their acceptance? Journal of Retailing and Consumer Services, 56, 102176.
- Taylor, R. (2000). Marketing strategies: Gaining a competitive advantage through the use of emotion. Competitiveness Review, 10(2), 146-152.
- Teo, T. S., Srivastava, S. C., & Jiang, L. (2008). Trust and electronic government success: An empirical study. Journal of Management Information Systems, 25(3), 99-132.
- Tran, A. D., Pallant, J. I., & Johnson, L. W. (2021). Exploring the impact of chatbots on consumer sentiment and expectations in retail. Journal of Retailing and Consumer Services, 63, 102718.
- Wu, P. C. S., & Wang, Y. C. (2011). The influences of electronic word-of-mouth message appeal and message source credibility on brand attitude. Asia Pacific Journal of Marketing and Logistics, 23(4), 448-472.
- Xu, Y., Shieh, C.-H., van Esch, P., & Ling, I.-L. (2020). AI customer service: Task complexity, problem-solving ability, and usage intention. Australasian Marketing Journal, 28(4), 189-199.
- Yen, C., & Chiang, M.-C. (2021). Trust me, if you can: A study on the factors that influence consumers’ purchase intention triggered by chatbots based on brain image evidence and self-reported assessments. Behaviour & Information Technology, 40(11), 1177-1194.
- Zolkepli, I. A., & Kamarulzaman, Y. (2015). Social media adoption: The role of media needs and innovation characteristics. Computers in Human Behavior, 43, 189-209.