Customer behavior and decision making in the refurbishment industry‐a data mining approach

Abstract The study of consumer behavior in the refurbishment industry is crucial to the business operation of firms, but there is a lack of research in this regard. With reference to the EKB model specific to consumer behavior, this paper discusses the relationship among consumption characteristics, firm selection behavior and satisfaction degree of refurbishment customers. 242 valid questionnaire copies were collected from refurbishment customers, and analyzed using Decision Tree Analysis and Association Rules in Data Mining. The research results show that, over half of the customers tend to entrust the refurbishment to well‐reputed firms. Moreover, the integrity of refurbishment equipment, response of refurbishment personnel, professionalism and confidence are key elements in service quality (SQ). The best marketing policy for the customers is one which provides more attractive services. These research findings may provide a useful reference for innovative refurbishment firms in their decision‐making.

[1]  Steven A. Taylor,et al.  Measuring Service Quality: A Reexamination and Extension , 1992 .

[2]  Gilbert A. Churchill,et al.  Consumer Socialization: A Theoretical and Empirical Analysis , 1978 .

[3]  J. Jacoby,et al.  Consumer Behavior , 2024 .

[4]  Homi Kharas,et al.  The Service Revolution in South Asia , 2010 .

[5]  Mao-Lin Chiu,et al.  Information and IN-formation: Information mining for supporting collaborative design , 2005 .

[6]  Ling-Feng Hsieh,et al.  A service quality measurement architecture for hot spring hotels in Taiwan , 2008 .

[7]  B. Thuraisingham.  A primer for understanding and applying data mining , 2000 .

[8]  Jen‐Rong Lee,et al.  UTILIZING DATA MINING TO DISCOVER KNOWLEDGE IN CONSTRUCTION ENTERPRISE PERFORMANCE RECORDS , 2008 .

[9]  Edmundas Kazimieras Zavadskas,et al.  Multivariant design and multiple criteria analysis of building refurbishments , 2005 .

[10]  O. Williamson,et al.  Markets and Hierarchies: Analysis and Antitrust Implications. , 1977 .

[11]  M. Etzel,et al.  The Index of Consumer Sentiment toward Marketing , 1986 .

[12]  Charly Kleissner.  Data mining for the enterprise , 1998, Proceedings of the Thirty-First Hawaii International Conference on System Sciences.

[13]  I. M. Johnstone,et al.  Periodic refurbishment and reductions in national costs to sustain dwelling services , 2001 .

[14]  Edmundas Kazimieras Zavadskas,et al.  The concept model of sustainable buildings refurbishment , 2008 .

[15]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[16]  W. J. Regan.  The Service Revolution , 1963 .

[17]  Charles O. Egbu,et al.  Refurbishment management practices in the shipping and construction industries — lessons to be learned: Comparative study of refurbishment management practices conducted to extend the boundaries of knowledge and encourage transfer of information between the sectors , 1996 .

[18]  Gregory Piatetsky-Shapiro,et al.  The KDD process for extracting useful knowledge from volumes of data , 1996, CACM.

[19]  Anne Aikivuori.  Periods and demand for private sector housing refurbishment , 1996 .

[20]  A. Parasuraman,et al.  SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. , 1988 .

[21]  S. C. Hui,et al.  Data mining for customer service support , 2000, Inf. Manag..

[22]  Mikhail V. Kiselev.  PolyAnalyst 2.0: Combination of Statistical Data Preprocessing and Symbolic KDD Technique. , 1995 .

[23]  Yi-Kai Juan,et al.  A hybrid approach using data envelopment analysis and case-based reasoning for housing refurbishment contractors selection and performance improvement , 2009, Expert Syst. Appl..

[24]  M Johns,et al.  Customer satisfaction: the case for measurement. , 1995, The Journal of audiovisual media in medicine.