Abstract
In this chapter, the relationship between customer satisfaction and factors related to I-CRM, namely: perceived value, interactivity, customer loyalty, and customer acquisition, are analysed. As previously suggested, customer satisfaction plays a significant role in I-CRM. In order to verify the relationship between our proposed variables based on the conceptual framework, we utilise linear modelling to formulate the relationship between customer satisfaction with its antecedents and consequences. Also, utilizing a formula, we test and prove the relationships.
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References
Kwong, C., Wong, T., & Chan, K. (2009). A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach. Expert Systems with Applications, 36, 11262–11270.
Faed, A., Hussain, O. K., Faed, M., & Saberi, Z. (2012). Linear modelling and optimization to evaluate customer satisfaction and loyalty. Presented at the The 9th IEEE International Conference on e-Business Engineering.
A. Faed, Hussain, O. K., & Chang, E. (2012). Linear and fuzzy approaches for customer satisfaction analysis. Service Oriented Computing and Applications, Springer, Under review.
Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7, 1–13.
Tsukamoto, Y. (1979). An approach to fuzzy reasoning method. In M. M. Gupta, R. K. Ragade, & R. R. Yager (Eds.), Advances in fuzzy set theory and application. Amsterdam: Holland.
Takagi, T., & Sugeno, M. (1985). Fuzzy identification of system and its applications to modelling and control. Transactions on Systems, Man, and Cybernetics, 1, 5.
Li, J. A., Liu, K., Leung, S. C. H., & Lai, K. K. (2004). Empty container management in a port with long-run average criterion*. Mathematical and Computer Modelling, 40, 85–100.
Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics, IEEE Transactions on, 23, 665–685.
Jang, S. R., Sun, T., & Mizutani, E. (1997). Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. Upper Saddle River, NJ: Prentice Hall.
Bao, Y., Bao, Y., & Sheng, S. (2011). Motivating purchase of private brands: Effects of store image, product signatureness, and quality variation. Journal of Business Research, 64, 220–226.
Yager, R., & Filev, D. (1994). Generation of fuzzy rules by mountain clustering. Journal of Intelligent & Fuzzy Systems, 2(3), 209–219.
Faed, A., & Chang, E. (2012). Adaptive neuro-fuzzy inference system based approach to examine customer complaint issues. Presented at the Second World Conference on Soft Computing, Baku, Azerbaijan.
Kwong, C. K., Wong, T. C., & Chan, K. Y. (2009). A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach. Expert Systems with Applications, 36, 11262–11270.
Coussement, K., & Van den Poel, D. (2008). Improving customer complaint management by automatic email classification using linguistic style features as predictors. Decision Support Systems, 44, 870–882.
Namkung, Y., Jang, S., & Choi, S. K. (2011). Customer complaints in restaurants: Do they differ by service stages and loyalty levels? International Journal of Hospitality Management, 30, 495–502.
Ro, H., & Wong, J. (2012). Customer opportunistic complaints management: A critical incident approach. International Journal of Hospitality Management, 31, 419–427.
Galitsky, B. A., González, M. P., & Chesñevar, C. I. (2009). A novel approach for classifying customer complaints through graphs similarities in argumentative dialogues. Decision Support Systems, 46, 717–729.
Faed, A. R., Ashouri, A., & Wu, C. (2011). Maximizing productivity using CRM within the context of M-commerce.
Elmuti, D., Jia, H., & Gray, D. (2009). Customer relationship management strategic application and organizational effectiveness: An empirical investigation. Journal of Strategic Marketing, 17, 75–96.
Krasnikov, A., Jayachandran, S., & Kumar, V. (2009). The impact of customer relationship management implementation on cost and profit efficiencies: Evidence from the US commercial banking industry. Journal of Marketing, 73, 61–76.
Richards, K. A., & Jones, E. (2008). Customer relationship management: Finding value drivers. Industrial Marketing Management, 37, 120–130.
Chang, H. H., Wang, Y. H., & Yang, W. Y. (2009). The impact of e-service quality, customer satisfaction and loyalty on e-marketing: Moderating effect of perceived value. Total Quality Management, 20, 423–443.
Keh, H. T., & Xie, Y. (2009). Corporate reputation and customer behavioral intentions: The roles of trust, identification and commitment. Industrial Marketing Management, 38, 732–742.
Walsh, G., Mitchell, V. W., Jackson, P. R., & Beatty, S. E. (2009). Examining the antecedents and consequences of corporate reputation: A customer perspective. British Journal of Management, 20, 187–203.
News, A. (2012, May 2012). Containers catch fire at Port of Fremantle. Available: http://www.abc.net.au/news/2012-04-08/containers-ablaze-at-port-of-fremantle/3938518
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Faed, A. (2013). Linear and Non-linear Analytics and Opportunity Development in I-CRM. In: An Intelligent Customer Complaint Management System with Application to the Transport and Logistics Industry. Springer Theses. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00324-5_8
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DOI: https://doi.org/10.1007/978-3-319-00324-5_8
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