On the Decision Structures and Knowledge Discovery for ANP Modeling

Lawrence W. Lan¹, Wei-Wen Wu², Yu-Ting Lee²

¹Department of Tourism Management, Ta Hwa University of Science and Technology, Qionglin.
²Department of Business and Tourism Planning, Ta Hwa University of Science and Technology, Qionglin.

DOI: 10.4236/ijis.2013.31A003                

Abstract

This paper proposes an integrative framework for network-structured analytic network process (ANP) modeling. The underlying rationales include: 1) creating the measuring items for the complex decision problems; 2) applying factor analysis to reduce the complex measuring items into fewer constructs; 3) employing Bayesian network classifier technique to discover the causal directions among constructs; 4) using partial least squares path modeling to test the causal relationships among the items-constructs. The proposed framework is implemented for knowledge discovery to a case of high-tech companies’ enterprise resource planning (ERP) benefits and satisfaction in Hsinchu Science Park,Taiwan. The results show that the proposed framework for ANP modeling can reach a satisfactory level of convergent reliability and validity. Based on the findings, pragmatic implications to the ERP venders are discussed. This study has shed new light on the long neglected, yet critical, issue on decision structures and knowledge discovery for ANP modeling.

Keywords

Analytic Network Process; Bayesian Network Classifier; Enterprise Resource Planning; Knowledge Discovery; Partial Least Squares Path Modeling

Conflicts of Interest

The authors declare no conflicts of interest.

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