Attribute Reduction of Service Quality Based on Factor Analysis and Neighborhood Model

Abstract:

How to choose the attributes of service quality have become the foundation and the key for researches of service quality. In connection with the current situation and characteristics of the existing e-commerce service quality evaluation, we analyze the advantages and shortcomings of the widely used way, which is combining item-to-total correlation and factor analysis to reduce our service attribute scale. Then a method of neighborhood granulation and rough approximation for numerical attribute reduction is proposed. With the comparison of the two methods through specific examples of empirical research data, the validity and superiority of the application of neighborhood model for numerical attribute reduction are verified.

• Periodical: Advanced Engineering Forum (Volume 4)
• Pages: 201-207
• DOI: https://doi.org/10.4028/www.scientific.net/AEF.4.201
• Online since: June 2012
• Authors: Lin Chen, Xian Hu
• Keywords: Attribute Reduction, Evaluation of Service Quality, Factor Analysis (FA), Neighborhood Granulation, Rough Approximation

References

[1] Gronroos, Christian, Strategic Management and Marketing in the Service Sector. Research Reports No. 8, Swedish School of Economics and Business Administration, Helsinki. (1982).

[2] Parasuraman, A., Zeithaml, V.A. and Berry, L.L., A Conceptual Model of Service Quality and Its Implications for Future Research, Journal of Marketing, Vol. 49 (Fall), pp.41-50. (1985).

DOI: 10.1177/002224298504900403

[3] Parasuraman, A., Zeithaml, V.A. and Berry, L.L., SERVQUAL, A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality. Journal of Retailing, Vol. 64, 1 (Spring), pp.12-40. (1988).

[4] Cronin, J.J. Jr., &Taylor, S.A., Measuring service quality: a reexamination extension. Journal of Marketing, 56(3), 55-68. (1992).

DOI: 10.1177/002224299205600304

[5] Parasuraman, A., Valarie A. Zeithaml&Malhotra, E-S-QUAL: A Multiple-Item Scale for Assessing Electronic Service Quality[J]. 7(3), 213-233. (2005).

DOI: 10.1177/1094670504271156

[6] Loiacono, E. T., Watson, R. T, &Goodhue, D .L., WebQual: a measure of Web site quality. Marketing Theory and Applications. (2002).

[7] Yoo, B., &Donthu, N., Developing a Scale to Measure the Perceived Quality of an Internet Shopping Site (SITEQUAL). Quarterly Journal of Electronic Commerce, pp.31-46. (2001).

[8] Santos, E-service quality: a model of virtual Managing Service Quality, 13(3), 233-246. (2003).

DOI: 10.1108/09604520310476490

[9] Collier, J.E. and Bienstock, C.C., Measuring service quality in e-retailing, Journal of Service Research, Vol. 8 No. 3, pp.260-75. (2006).

[10] Pawlak Z., Rough Sets-Theoretical Aspects of Reasoning about Data . Kluwer Academic, Dordrecht. (1991).

[11] Qinghua Hu, Daren Yu, Zongxia Xie. Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern recognition letters . 27 (5): 414-423. (2006).

DOI: 10.1016/j.patrec.2005.09.004

[12] Lixin Cui, Service-Quality Evaluation Model. Economic Daily Press, Beijing. (2003).

[13] Kassim, N. and N. Abdullah, The effect of perceived service quality dimensions on customer satisfaction, trust, and loyalty in e-commerce settings. Asia Pacific Journal of Marketing and Logistics, 22(3): 351-371. (2010).

DOI: 10.1108/13555851011062269

[14] Wenying, W. and Q. Sun, Analysis the Factors Affecting Consumers to Use Internet Banking Services. Proceeding of International Conference on Information Management and Engineering (ICIME): 180-184. (2010).

DOI: 10.1109/icime.2010.5477778