An evaluative framework for research on the performance effects of information technology investment
ICIS '89 Proceedings of the tenth international conference on Information Systems
Journal of Management Information Systems
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Profitability and Marketability of the Top 55 U.S. Commercial Banks
Management Science - Special issue on the performance of financial Institutions
Fuzzy efficiency measures in data envelopment analysis
Fuzzy Sets and Systems
Fuzzy DEA: a perceptual evalution method
Fuzzy Sets and Systems
Data Envelopment Analysis: Theory, Methodology and Application
Data Envelopment Analysis: Theory, Methodology and Application
Data Envelopment Analysis: A Comprehensive Text with Models, Applications References, and DEA-Solver Software with Cdrom
Idea and Ar-Idea: Models for Dealing with Imprecise Data in Dea
Management Science
Information Systems Research
Measuring Information Technology's Indirect Impact on Firm Performance
Information Technology and Management
Fuzzy data envelopment analysis and its application to location problems
Information Sciences: an International Journal
Evaluation of information technology investment: a data envelopment analysis approach
Computers and Operations Research
Mathematical and Computer Modelling: An International Journal
Fuzzy stochastic data envelopment analysis with application to base realignment and closure (BRAC)
Expert Systems with Applications: An International Journal
A concept of fuzzy input mix-efficiency in fuzzy DEA and its application in banking sector
Expert Systems with Applications: An International Journal
International Journal of Fuzzy System Applications
Computers and Industrial Engineering
Chance-constrained DEA models with random fuzzy inputs and outputs
Knowledge-Based Systems
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Data envelopment analysis (DEA) is a widely used mathematical programming approach for evaluating the relative efficiency of decision making units (DMUs) in organizations. Crisp input and output data are fundamentally indispensable in traditional DEA evaluation process. However, the input and output data in real-world problems are often imprecise or ambiguous. In this study, we present a four-phase fuzzy DEA framework based on the theory of displaced ideal. Two hypothetical DMUs called the ideal and nadir DMUs are constructed and used as reference points to evaluate a set of information technology (IT) investment strategies based on their Euclidean distance from these reference points. The best relative efficiency of the fuzzy ideal DMU and the worst relative efficiency of the fuzzy nadir DMU are determined and combined to rank the DMUs. A numerical example is presented to demonstrate the applicability of the proposed framework and exhibit the efficacy of the procedures and algorithms.