University of Illinois Urbana-Champaign

Nonfinancial Indicators in Performance Evaluation of MNE's; Exploring the Use of Fuzzy Outranking Relations

Simga, Can

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Description

Title
Nonfinancial Indicators in Performance Evaluation of MNE's; Exploring the Use of Fuzzy Outranking Relations
Author(s)
Simga, Can
Issue Date
1987
Doctoral Committee Chair(s)
Schoenfeld, Hanns-Martin H.,
Department of Study
Accountancy
Discipline
Accountancy
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Business Administration, Accounting
Abstract
  • During the last two decades, multinational enterprises (MNE's) gained an increasing importance in the world economy as evidenced by the significant increase in their investments. Consequently, now the accounting system is responsible for generating reports for the objective of planning, implementing and evaluating the operations.
  • The objectives of this study were to develop a performance evaluation model that relates nonfinancial indicators such as the total productivity growth index (TPI), change in the number of employees index (CNE) and the market share growth index (MSI) to return-on-investment (ROI) and to determine the temporal relationships as well; and to utilize multicriteria fuzzy outranking relations to appraise the performance of an MNE; and through this analysis perhaps to gain some insight as regard to the manager's perception of different activities.
  • Five different models are used by employing cross sectional regression analyses for the sample MNE's. Although the findings demonstrated the effect of lag, no stable model has been determined i.e., the lag structure and significant variables were different during the period under study (1980-1984).
  • Fuzzy outranking relations helped to form clusters of companies according to their domination structure. To determine the outranking relation three different sets of weights are used i.e., equal, eigenvector and entropy. The entropy weights showed that controllable factors such as TPI and CNE have more certainty and weighted higher than the others.
  • It seems to be necessary to carry out a time series analysis to determine a stable model over time in order to be able to utilize it for predictive purposes.
Graduation Semester
1987
Type of Resource
text
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http://hdl.handle.net/2142/71408

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