Fuzzy measure theoretical approach to screening product innovations

Divakaran Liginlal, Sudha Ram, Lucien Duckstein

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Variety of decision models have been proposed in contemporary literature to tackle the problem of screening product innovations. Although linear models have gained considerable attention and recommendation, contemporary literature contains strong evidence in support of nonlinear noncompensatory models. In this paper, the authors first demonstrate how fuzzy measures, which are defined on subsets of decision attributes, and their Choquet-integral formulation, which exhibits both compensatory and noncompensatory properties, have meaningful behavioral interpretations within the context of new-product screening. Then, they show how to address the complex problem of building such measures by applying a learning algorithm that relies on methods of judgment analysis. An accompanying case study demonstrates how organizations may customize a new product decision aid and fine tune their business strategy as actual results accrue. Finally, the authors present the results of analytical studies to compare the Choquet-integral model with other noncompensatory models, such as Martino's extended scoring model and Einhorn's conjunctive model, and heuristic approaches, such as Tversky's EBA and the lexicographic method. For the new-product-decision scenario considered in the study, the Choquet-integral model provided the best fit, measured by Pearson's rank order correlation coefficient, with all of the competing models.

Original languageEnglish (US)
Pages (from-to)577-591
Number of pages15
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
Volume36
Issue number3
DOIs
StatePublished - May 2006

Fingerprint

Fuzzy Measure
Screening
Innovation
Choquet Integral
Model
Rank order
Decision Model
Scoring
Correlation coefficient
Demonstrate
Nonlinear Model
Learning Algorithm
Recommendations
Linear Model
Attribute
Heuristics
Scenarios
Learning algorithms
Subset
Formulation

Keywords

  • Choquet integral
  • Decision support systems
  • Fuzzy measure
  • New-product screening
  • Noncompensatory models

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Human-Computer Interaction
  • Theoretical Computer Science
  • Computational Theory and Mathematics

Cite this

Fuzzy measure theoretical approach to screening product innovations. / Liginlal, Divakaran; Ram, Sudha; Duckstein, Lucien.

In: IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, Vol. 36, No. 3, 05.2006, p. 577-591.

Research output: Contribution to journalArticle

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