Causal Relationships among Consumer Behavior, Intercultural Communication, and Customer Satisfaction toward Intention to Use Call Center Platform Services
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Abstract
This study aimed to 1) examine the causal relationships among consumer behavior, intercultural communication, and customer satisfaction influencing the intention to use call center platform services using structural equation modeling (SEM), and 2) investigate the role of customer satisfaction as a mediating variable linking consumer behavior and intercultural communication to the intention to use call center platform services. This research employed a quantitative approach using a structured questionnaire to collect data from 240 users of an omnichannel call center platform at a private company, selected through simple random sampling. Data were analyzed using descriptive statistics, confirmatory factor analysis (CFA), and structural equation modeling.
Results revealed that the model demonstrated an acceptable fit with the empirical data and explained 87.40% of the variance in intention to use (R²=0.874). Overall, customer satisfaction had the strongest direct effect (β=0.932, p<.001) and served as the primary predictor of intention to use. In contrast, consumer behavior had no significant direct effect (β=0.003, p>.05) but exerted an indirect effect through customer satisfaction. Intercultural communication had a significant direct effect on customer satisfaction (β=0.991, p<.001).
These findings confirm that customer satisfaction functions as a key mediating mechanism linking consumer behavior and intercultural communication to the intention to use. This highlights the importance of designing service experiences that emphasize communication quality and user satisfaction to enhance service quality and user experience in digital platform contexts.
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References
Ali, A. H., Melkonyan, A., & Gruchmann, T. (2021). Developing a sustainable logistics service quality scale for logistics service providers in Egypt. Logistics. 5(2), 1-15. https://doi.org/10.3990/logistics5020021
Bigelow, L. M. Jr., Fahey, R.T., Grabosky, J., Hallett, R. A., Henning, J. G., Johnson, M. L., & Roman, L. A. (2024). Predictors of street tree survival in Philadelphia: Tree traits, biophysical environment, and socioeconomic context. Urban Forestry & Urban Greening, 94, 128284. https://doi.org/10.1016/j.ufug.2024.128284
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
Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319-34. https://doi.org/10.2307/249008
Dixon, M., Freeman, K., & Toman, N. (2010). Stop trying to delight your customers. Harvard Business Review, 88(7). Retrieved from https://www.researchgate.net/publication/294462815_Stop_Trying_to_Delight_Your_Customers
Guangqian, L., & Manansala, L. (2023). Factors influencing consumers’ usage intention of live streaming e-commerce. Paradigm Academic Press Frontiers in Management Science, 2(6), 22-29. https://doi.org/10.56397/FMS.2023.12.04
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. (7th ed.). New York: Prentice Hall.
Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). California, CA: Sage Publications.
Hirata, E. (2019). Service characteristics and customer satisfaction in the container liner shipping industry. The Asian Journal of Shipping and Logistics, 35, 24-29. https://doi.org/10.1016/j.ajsl.2019.03.004
Hofstede, G., & Hofstede, G. J. (2005). Culture and organizations software of the mind: Intercultural cooperation and its importance for survival. (2nd ed.). New York: McGraw Hill.
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. https://doi.org/10.1080/10705519909540118
Ihtiyar, A., & Ahmad, F.S. (2015). The impact of intercultural communication competence on service quality and customer satisfaction. Services Marketing Quarterly, 36(2), 136-152. https://doi.org/10.1080/15332969.2015.1014238
Kim, J., & Lee, Ki. H. (2019). Influence of integration on interactivity in social media luxury brand communities. Journal of Business Research, Elsevier, 99(C), 422-429. https://doi.org/10.1016/j.jbusres.2017.10.001
Oliver, R. L. (2014). Whence Consumer Loyalty?. Journal of Marketing, 63, 33-44. https://doi.org/10.2307/1252099
Terblanche, N. S., & Babin, B.J. (2024). The gloomy side value co-creation for service employee. Journal of Service Marketing. 38(10), 44-65. https://doi.org/10.1108/JSM-12-2023-0465
Wang, Y., & Yu, C. (2017). Social interaction-based consumer decision-making model in social commerce: The role of word of mouth and observational learning. International Journal of Information Management, 37(3), 179-189. https://doi.org/10.1016/j.ijinformgt.2015.11.005
Wutthipongpaiboon, N. (2025). Factors influencing the intention to use online streaming businesses in Chiang Rai province. Graduate School Journal Chiang Rai Rajabhat University, 18(1), 66-80. Retrieved from https://so01.tci-thaijo.org/index.php/crrugds_ejournal/article/view/244470
Zhang, K. Z.K., Benyoucef, M., & Zhao, S. J. (2016). Consumer behavior in social commerce: A literature review. Decision Support Systems, 86, 95-108. Retrieved from https://www.scirp.org/reference/referencespapers?referenceid=2742210