Abstract
Companies lose their online customers due to the competitive business environment. Customer loyalty is one of the important topics in the Electronic Commerce (E-commerce) domain. Gaining new loyal customers requires extensive expenditure of time and money. In addition, loyal customers are an important asset for a company, which brings long-term benefits. In this research, a comprehensive conceptual framework is presented that shows E-loyalty based on E-trust and E-satisfaction. The critical factors which influence E-trust and E-satisfaction are classified in organizational, customer and technological groups. Statistical analysis is applied for validity and reliability of the model. Another important method for estimation of uncertain measures is Artificial Neural Fuzzy Network System (ANFNS). E-trust and E-satisfaction data were used as inputs of the ANFIS and the output utilized E-loyalty. The result demonstrated, there is no difference between the aforementioned and the ANFIS model can be used for estimation of E-loyalty in E-commerce.
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References
Jacoby, J. and R.W. Chestnut, Brand Loyalty. 1978.
Oliver, Whence consumer loyalty? Journal of Marketing, 1999. 63(4): p. 12.
Reichheld, F.F., R.G. Markey, and C. Hopton, The loyalty effect - the relationship between loyalty and profits. European Business Journal, 2000a. 12(3): p. 7.
Deng, W.J. and W. Pei, Fuzzy neural based importance-performance analysis for determining critical service attributes. Expert Systems with Applications, 2006. 36: p. 3774-3784.
Fang, Y.-H., C.-M. Chiu, and E.T.G. Wang, Understanding Customers’ Satisfaction and Repurchase Intentions: An Integration of IS Success Model, Trust, and Justice. Emerald Group Publishing Limited, 2011. 21 (4): p. 479-503.
Chang, H.H. and S.W. Chen, The impact of customer interface quality, satisfaction and switching costs on e-loyalty: Internet experience as a moderator. Computers in Human Behavior, 2008. 24(6): p. 2927-2944.
Palvia, P., The role of trust in e-commerce relational exchange: A unified model. Information & Management, 2009. 46(4): p. 213-220.
Lai, J.Y., Assessment of employees’ perceptions of service quality and satisfaction with e-business. International Journal of Human-Computer Studies, 2006. 64(9): p. 926-938.
Habibpor, K. and R. Safari, SPSS Comprehensive Guide for Research. 2008.
Nunally, J.C., Psychometric Theory. McGraw-Hill, 1978.
Brown, F.G., Principles of Educational and Psychological Testing. 1983.
McCulloch, W.S., A Heterarchy of Values Determined by the Topology of Nervous Nets. Bulletin of Mathematical Biophysics, 1945. 89(93).
Chen, C.-C., C.-S. Wu, and R.C.-F. Wu, e-Service enhancement priority matrix: The case of an IC foundry company. Information & Management, 2006. 43(5): p. 572-586.
Hsiao, S.-W. and H.-C. Tsai, Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design. International Journal of Industrial Ergonomics, 2005. 35(5): p. 411-428.
Althuwaynee, O.F., B. Pradhan, and S. Lee, Application of an evidential belief function model in landslide susceptibility mapping. Computers & Geosciences, 2012. 44(0): p. 120-135.
Chang, H.H. and I.C. Wang, An investigation of user communication behavior in computer mediated environments. Computers in Human Behavior, 2008. 24(5): p. 2336-2356.
Keskin, M.E., D. Taylan, and O. Terzi, Adaptive neural-based fuzzy inference system approach for modelling hydrological time series.. Hydrological Sciences Journal, 2006. 51(4): p. 588-598.
You, H., et al., Development of customer satisfaction models for automotive interior materials. International Journal of Industrial Ergonomics, 2006. 36(4): p. 323-330.
Kwong, C.K., T.C. Wong, and K.Y. Chan, A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach. Expert Systems with Applications, 2009. 36(8): p. 11262-11270.
Jang, S., ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern, 1993. 23(3): p. 665-685.
Hush, D.R. and B.G. Horne, Progress in supervised neural networks. IEEE Signal Processing Magazine Vol. 10. 1993. 32.
A. El-Shafie, O.J.a.S.A.A., Adaptive neuro-fuzzy inference system based model for rainfall forecasting in Klang River, Malaysia. International Journal of the Physical Sciences, 2011. 6(12): p. 2875-2888.
Armstrong, J.S. and F. Collopy, Error measures for generalizing about forecasting methods: Empirical comparisons. International Journal of Forecasting, 1992. 8(1): p. 69-80.
Lehmann, E.L. and G. Casella, Theory of Point Estimation (2nd ed.). New York: Springer, 1998.
Armstrong, S. and F. Collopy, Error Measures For Generalizing About Forecasting Methods: Empirical Comparisons. International Journal of Forecasting, 1992. 8: p. 69-81.
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Safa, N.S., Ismail, M.A. (2014). Customer E-loyalty: From an Estimate in Electronic Commerce with an Artificial Neural Fuzzy Interface System (ANFIS). In: Herawan, T., Deris, M., Abawajy, J. (eds) Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). Lecture Notes in Electrical Engineering, vol 285. Springer, Singapore. https://doi.org/10.1007/978-981-4585-18-7_75
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DOI: https://doi.org/10.1007/978-981-4585-18-7_75
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