A rule-based method for identifying the factor structure in customer satisfaction

Information Sciences

Volumn 198, Issue , 2012, Pages 118-129

  Ahmad, Amir ^a   Dey, Lipika ^b   Halawani, Sami M ^a  

^a KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

^b TATA CONSULTANCY SERVICES   (India)

Author keywords

Categorical features; Customer satisfaction; Feature value importance; Market research; Three factor theory

Indexed keywords

CATEGORICAL FEATURES; COMPUTATIONALLY EFFICIENT; DATA FEATURE; DATA SETS; DOMAIN KNOWLEDGE; FACTOR STRUCTURE; FEATURE SELECTION METHODS; FEATURE VALUE IMPORTANCE; FEATURE VALUES; MARKET RESEARCHES; NON-LINEARITY; PRODUCT FEATURE; RULE-BASED METHOD; THREE-FACTOR THEORY;

DATA PROCESSING; EXPERIMENTS; FACTOR ANALYSIS; FEATURE EXTRACTION;

CUSTOMER SATISFACTION;

EID: 84859583011     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2012.02.056     Document Type: Article

References (50)

1. A. Ahmad, and L. Dey A feature selection technique for classificatory analysis Pattern Recognition Letters 26 1 2005 43 56
2. R. Agrawal, R. Srikant, Mining sequential patterns, in: 11th International Conference on Data Engineering (ICDE'95), 1995, pp. 3-14.
3. R. Agrawal, R. Srikant, Fast algorithms for mining association rules, in: Proceedings of the 20th international conference on very large databases, Santiago, Chili, 1994, pp. 487-499.
4. E.W. Anderson, and V. Mittal Strengthening the satisfaction-profit chain Journal of Service Research 3 2 2000 107 120
5. L.L. Berry, Relationship marketing, in: L.L. Berry, G. Shostack, G. Upah, (Eds.), Proceedings of Emerging Perspectives in Service Marketing, Chicago, 1983, pp. 25-28.
6. M.J.A. Berry, and G.S. Linoff Data Mining Techniques: For Marketing, Sales, and Customer Support 1997 John Wiley and Sons
7. M. Beynon Reducts within the variable precision rough sets model: a further investigation European Journal of Operational Research 134 2001 592 605 (Pubitemid 32768265)
8. L.A. Blum, and P. Langley Selection of relevant features and examples in machine learning Artificial Intelligence 97 1997 245 271 (Pubitemid 127401106)
9. H.H. Bock The Classical Data Situation, Analysis of Symbolic Data 2002 Springer pp. 139-152
10. R.N. Bolton, and H.D. James A multi-stage model of customer's assessments of service quality and value Journal of Consumer Research 17 March 1991 375 384
11. A.C. Burns, and R.F. Bush Marketing Research 2005 Prentice Hall
12. P. Cheshire, and S. Sheppard Introduction to feature: the price of access to better neighbourhoods The Economic Journal 114 November 2004 F391 F396
13. U.M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth The KDD process for extracting useful knowledge from volumes of data Communications of the ACM 39 11 1996 27 34 (Pubitemid 126428076)
14. J. Fuller, and K. Matzler Customer delight and market segmentation: an application of the three-factor theory of customer satisfaction on life style groups Tourism Management 29 1 2008 116 126 (Pubitemid 47535082)
15. B. Galitsky, and J.L. de la Rosa Concept-based learning of human behaviour for customer relationship management Information Sciences 181 2011 2016 2035
16. B. Ganter, and R. Wille Formal Concept Analysis, Mathematical Foundations 1999 Springer
17. R. Hanson Determining attributes importance Quirk's Marketing Research Review 6 October 1992 16 18
18. C. Homburg, N. Koschate, and W.D. Hoyer Do satisfied customers really pay more? a study of the relationship between customer satisfaction and willingness to pay Journal of Marketing 69 2 2005 84 96 (Pubitemid 40554520)
19. Q. Hu, Z. Xie, and D. Yu Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation Pattern Recognition 40 12 2007 3509 3521 (Pubitemid 47223272)
20. Q. Hu, D. Yu, and Z. Xie Information-preserving hybrid data reduction based on fuzzy-rough techniques Pattern recognition letters 27 5 2006 414 423 (Pubitemid 43242919)
21. J.A. Hyung Evaluating customer aid functions of online stores with agent-based models of customer behaviour and evolution strategy Information Sciences 180 9 2010 1555 1570
22. R. Johnston The determinant of service quality: satisfiers and dissatisfies International Journal of Service Industry management 6 5 1995 53 71 (Pubitemid 39026060)
23. G.H. John, R. Kohavi, and K. Pfleger Irrelevant features and the subset selection problem Proceedings of 11th International Conference on Machine Learning 1994 Morgan Kaufman New Brunswick, NJ 121 129
24. N. Kano, S. Nobuhiku, T. Fumio, T. Shinichi, Attractive quality and must-be quality, The Journal of the Japanese Society for Quality Control (1984) 39-48.
25. K. Kira, L. Rendell, A practical approach to feature selection, in: Proceedings of Ninth International Conference on Machine Learning, Aberdeen, Scotland, 1992, pp. 249-256.
26. I. Kononenko, Estimating features: analysis and extensions of RELIEF, in: Proceedings of European Conference on Machine Learning, 1994, pp. 171-182.
27. W. Lee, B. Shih, and L. Tu The application of Kano's model for improving web-based learning performance Frontiers in Education (FIE 2002) 2002 pp. T3E-27-T3E-32
28. K. Li, Y.S. Liu, Rough set based attribute reduction approach in data mining, in: Proceedings of the First International Conference on Machine Learning and Cybernetics, vol. 1, 2002, pp. 60-63.
29. J.J.H. Liou, and G.H. Tzeng A dominance-based rough set approach to customer behaviour in the airline market Information Sciences 180 11 2010 2230 2238
30. A.K. Lynch, and D.W. Rasmussenz Measuring the impact of crime on house prices Applied Economics 33 2001 1981 1989 (Pubitemid 34002351)
31. K. Matzler, F. Bailom, H.H. Hinterhuber, B. Renzl, and J. Pichler The asymmetric relationship between attribute-level performance and overall customer satisfaction: a reconsideration of the importance-performance analysis Industrial Marketing Management 33 4 2004 271 277 (Pubitemid 38457882)
32. V. Mittal, and W.A. Kamakura Satisfaction, repurchase intent, and repurchase behaviour: investigating the moderate effect of customer characteristics Journal of Marketing Research 38 February 2001 131 142
33. P.M. Narendra, and K. Fukunaga A branch and bound algorithm for feature subset selection IEEE Transactions on Computer C-26 9 1977 917 922 (Pubitemid 8550870)
34. E. Nassiri-Mofakham, M.A. Nematbakhsh, A. Baraani-Dastjerdi, and N. Ghasem-Aghaee Electronic promotion to new customers using mkNN learning Information Sciences 179 3 2009 248 266
35. G. Pandula, and B. Busacca The asymmetric impact of price-attribute performance on overall price evaluation International Journal of Service Industry Management 16 1 2005 28 54 (Pubitemid 40688845)
36. M. Paulssen, A. Sommerfeld, Modeling the nonlinear relationship between satisfaction and loyalty with structural equation models, in: Proceedings of the 29th Annual Conference From Data and Information Analysis to Knowledge Engineering, Gesellschaft für Klassifikation e.V., University of Magdeburg, 2005, pp. 574-581.
37. Z. Pawlak Rough sets International Journal of Computer and Information Sciences 11 1982 341 356
38. Z. Pawlak, and A. Skowron Rudiments of rough sets Information Sciences 177 1 2007 3 27 (Pubitemid 44556698)
39. Z. Pawlak, and A. Skowron A rough sets: some extensions Information Sciences 177 1 2007 28 40
40. J.R. Quinlan Induction of decision trees Machine Learning 1 1986 81 106
41. H.W. Shin, and S.Y. Sohn Segmentation of stock trading customers according to potential value Expert systems with applications 27 1 2004 27 33
42. M.R. Sikonja, and K. Vanhoof Evaluation of ordinal features at value level Data Mining and Knowledge Discovery 14 2 2007 225 243 (Pubitemid 46418256)
43. R. Srikant, R. Agrawal, Mining generalized association rules. VLDB'95, 1995, pp. 407-419.
44. B. Stauss, B. Hentshel, Feature-based versus incident-based measurement of service quality: result of an empirical study in German car service industry, in: P. Kunst, J. Lemmink, (Eds.), Quality Management in Services, Van Gorcum, Assen, 1992, pp. 59-78.
45. R.W. Swiniarski, and A. Skowron Rough set methods in feature selection and recognition Pattern Recognition Letters 24 6 2003 833 849 (Pubitemid 35391801)
46. H. Takeuchi, L.V. Subramaniam, T. Nasukawa, and S. Roy Getting insights from the voices of customers: conversation mining at a contact centre Information Sciences 179 11 2009 1584 1591
47. K. Thangavel, and A. Pethalakshmi Dimensionality reduction based on rough set theory: a review Applied Soft Computing 9 1 2009 1 12
48. R. Weber, Customer segmentation for banks and insurance groups with fuzzy clustering techniques, in: J.F. Baldwin, (Ed.), Proceedings of Fuzzy Logic, Wiley, New York, NY., 1996, pp. 187-196.
49. D.R. Wittink, and L.R. Bayer The measurement imperative Marketing Research 6 1994 14 23
50. W.Z. Wu Attribute reduction based on evidence theory in incomplete decision systems Information Sciences 178 5 2008 1355 1371