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R. Lombardo, “Data Mining and Explorative Multivariate data Analysis for Customer Satisfaction Study,” In: A. S. Koyuncugil and N. Ozgulbas, Eds., Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection, Information Science Publishing, Hershey, 2010, pp. 243-266. doi:10.4018/978-1-61692-865-0.ch013

has been cited by the following article:

• TITLE: Data Mining and Exploratory Data Analysis for the Evaluation of Job Satisfaction

  AUTHORS: Rosaria Lombardo, Ermelinda Della Valle

  KEYWORDS: Customer Relationship Management, Relationship Value Management, Job Satisfaction, Social Enterprise, Data Mining, Ordered Multiple Correspondence Analysis

  JOURNAL NAME: iBusiness, Vol.3 No.4, December 9, 2011

  ABSTRACT: In this paper we face off the relationship value management which is a theme in the relationship marketing literature gaining increasing attention in the last decade. The main aim of this study is to evaluate job quality and in particular employees’ satisfaction of non-profit enterprises by using, among different exploratory data analysis tools, ordered multiple correspondence analysis which is a part of corporate data mining. We focus attention on Ordered Multiple Correspondence Analysis (OMCA), recently proposed in statistical literature, to monitor (dis)satisfaction in different times or spaces. In particular we present a new strategy based on OMCA which allows to deal with ordered variables (Likert items) taking into account other qualitative information of job (kind of job contract, type of incentives, etc.) affecting the overall satisfaction of employees.