Reference Hub4
Using Case Data to Ensure ‘Real World’ Input Validation within Fuzzy Set Theory Models

Using Case Data to Ensure ‘Real World’ Input Validation within Fuzzy Set Theory Models

Sara Denize, Sharon Purchase, Doina Olaru
ISBN13: 9781466600959|ISBN10: 1466600950|EISBN13: 9781466600966
DOI: 10.4018/978-1-4666-0095-9.ch004
Cite Chapter Cite Chapter

MLA

Denize, Sara, et al. "Using Case Data to Ensure ‘Real World’ Input Validation within Fuzzy Set Theory Models." Fuzzy Methods for Customer Relationship Management and Marketing: Applications and Classifications, edited by Andreas Meier and Laurent Donzé, IGI Global, 2012, pp. 61-89. https://doi.org/10.4018/978-1-4666-0095-9.ch004

APA

Denize, S., Purchase, S., & Olaru, D. (2012). Using Case Data to Ensure ‘Real World’ Input Validation within Fuzzy Set Theory Models. In A. Meier & L. Donzé (Eds.), Fuzzy Methods for Customer Relationship Management and Marketing: Applications and Classifications (pp. 61-89). IGI Global. https://doi.org/10.4018/978-1-4666-0095-9.ch004

Chicago

Denize, Sara, Sharon Purchase, and Doina Olaru. "Using Case Data to Ensure ‘Real World’ Input Validation within Fuzzy Set Theory Models." In Fuzzy Methods for Customer Relationship Management and Marketing: Applications and Classifications, edited by Andreas Meier and Laurent Donzé, 61-89. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-0095-9.ch004

Export Reference

Mendeley
Favorite

Abstract

Fuzzy set theory models have considerable potential to address complex marketing and B2B problems, but for this methodology to be accepted, models require validation. However, there is relatively little detail in the literature dealing with validation of fuzzy simulation in marketing. This limitation is compounded by the difficulty of using case-based and qualitative evidence (data to which fuzzy models are well suited) when applying more general validation. The chapter illustrates a fuzzy model validation process using small-N cased based data and concludes with recommendations to assist researchers in validating their fuzzy models.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.