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A modified ANP and fuzzy inference system based approach for risk assessment of in-house and third party e-procurement systems

A modified ANP and fuzzy inference system based approach for risk assessment of in-house and... PurposeThe increasing complexity and dynamism of new technology implemented or to implement have imposed substantial uncertainties and subjectivities in the risk assessment process. This paper aims to present a risk assessment methodology for e-procurement implementation based on modified analytic network process (ANP) coupled with fuzzy inference systems.Design/methodology/approachANP is modified in such a way that the experts can provide necessary data precise numerical value, a range of numerical values, a linguistic term or a fuzzy number. The proposed methodology incorporates knowledge and judgements obtained from experts to carry out identification of risk factors and to assess the risk magnitude of the identified risk factors based on factor index, risk likelihood and risk severity.FindingsRisk magnitude of third party systems are found to be minor with a belief of 100 per cent, and for in-house systems, the risk is found to be between minor with a belief of 30 per cent and major of 70 per cent. The results indicate that by using the proposed methodology, the technological risk assessment of new technology can be done effectively and efficiently.Research limitations/implicationsUsing the results of this study, the practitioners can better know the pros and cons of implementing both in-house and third party e-procurement systems.Originality/valueThe modified ANP is used mainly to structure and prioritize the diverse risk factors. Finally, an illustrative example on technological risk assessment of both in-house and third party e-procurement systems is used to demonstrate the applicability of the proposed methodology in real life situations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Strategic Outsourcing: An International Journal Emerald Publishing

A modified ANP and fuzzy inference system based approach for risk assessment of in-house and third party e-procurement systems

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1753-8297
DOI
10.1108/SO-12-2015-0030
Publisher site
See Article on Publisher Site

Abstract

PurposeThe increasing complexity and dynamism of new technology implemented or to implement have imposed substantial uncertainties and subjectivities in the risk assessment process. This paper aims to present a risk assessment methodology for e-procurement implementation based on modified analytic network process (ANP) coupled with fuzzy inference systems.Design/methodology/approachANP is modified in such a way that the experts can provide necessary data precise numerical value, a range of numerical values, a linguistic term or a fuzzy number. The proposed methodology incorporates knowledge and judgements obtained from experts to carry out identification of risk factors and to assess the risk magnitude of the identified risk factors based on factor index, risk likelihood and risk severity.FindingsRisk magnitude of third party systems are found to be minor with a belief of 100 per cent, and for in-house systems, the risk is found to be between minor with a belief of 30 per cent and major of 70 per cent. The results indicate that by using the proposed methodology, the technological risk assessment of new technology can be done effectively and efficiently.Research limitations/implicationsUsing the results of this study, the practitioners can better know the pros and cons of implementing both in-house and third party e-procurement systems.Originality/valueThe modified ANP is used mainly to structure and prioritize the diverse risk factors. Finally, an illustrative example on technological risk assessment of both in-house and third party e-procurement systems is used to demonstrate the applicability of the proposed methodology in real life situations.

Journal

Strategic Outsourcing: An International JournalEmerald Publishing

Published: Jun 20, 2016

References