A HYBRID LINGUISTIC FUZZY MULTIPLE CRITERIA GROUP SELECTION OF A CHIEF ACCOUNTING OFFICER

Abstract

In the preceding decade, economic and social costs brought by financial statement fraud have shaken markets, devastated investment portfolios and reduced confidence in financial reporting. A financial department is special in the way it needs to conform to standards. Many individual attributes considered for the selection of a chief accounting officer, such as organisational skills, personality, leadership etc. This paper focuses on a fuzzy multi-criteria decision making (MCDM) algorithm, which integrates the principles of fusion of fuzzy information, additive ratio assessment method with fuzzy numbers (ARAS-F), fuzzy weighted-product model and analytic hierarchy process (AHP). The proposed method is apt to manage information assessed using both linguistic and numerical scales in a decision making problem with a group of information sources. The computational procedure is illustrated through the problem related to the selection of a chief accounting officer.