معضل ابعاد واحدهای تصمیم گیری
ترجمه نشده

معضل ابعاد واحدهای تصمیم گیری

عنوان فارسی مقاله: معضل ابعاد واحدهای تصمیم گیری: یک رویکرد ساده برای افزایش قدرت متفاوت تحلیل پوششی داده ها
عنوان انگلیسی مقاله: The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis
مجله/کنفرانس: مجله اروپایی درباره تحقیقات عملیاتی – European Journal of Operational Research
رشته های تحصیلی مرتبط: مدیریت
گرایش های تحصیلی مرتبط: بانکداری
کلمات کلیدی فارسی: تجزیه و تحلیل پوششی داده ها، عملکرد، تابلوهای گردشی چاپی، بانکداری، بهترین شهرها
کلمات کلیدی انگلیسی: Data envelopment analysis، Performance، Printed circuit boards، Banking، Best cities
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.ejor.2019.06.025
دانشگاه: Buckingham Business School, University of Buckingham, Buckingham MK18 1EG, United Kingdom
صفحات مقاله انگلیسی: 12
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.712 در سال 2018
شاخص H_index: 226 در سال 2019
شاخص SJR: 2.205 در سال 2018
شناسه ISSN: 0377-2217
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E13530
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1. Introduction

2. Motivating example

3. Modeling

4. Cases

5. Conclusion

Acknowledgments

References

بخشی از مقاله (انگلیسی)

Abstract

Data envelopment analysis (DEA) is a technique for identifying the best practices of a given set of decision-making units (DMUs) whose performance is categorized by multiple performance metrics that are classified as inputs and outputs. Although DEA is regarded as non-parametric, the sample size can be an issue of great importance in determining the efficiency scores for the evaluated units, empirically, when the use of too many inputs and outputs may result in a significant number of DMUs being rated as efficient. In the DEA literature, empirical rules have been established to avoid too many DMUs being rated as efficient. These empirical thresholds relate the number of variables with the number of observations. When the number of DMUs is below the empirical threshold levels, the discriminatory power among the DMUs may weaken, which leads to the data set not being suitable to apply traditional DEA models. In the literature, the lack of discrimination is often referred to as the “curse of dimensionality”. To overcome this drawback, we provide a simple approach to increase the discriminatory power between efficient and inefficient DMUs using the well-known pure DEA model, which considers either inputs only or outputs only. Three real cases, namely printed circuit boards, Greek banks, and quality of life in Fortune’s best cities, have been discussed to illustrate the proposed approach.

Introduction

Data envelopment analysis (DEA) is an excellent management science tool that measures the relative performance of a set of entities or decision-making units (DMUs) with multiple performance measures that are classified as inputs and outputs. Nevertheless, problems of discrimination between efficient and inefficient DMUs often arise when there is a relatively large number of performance measures (variables) when compared to the number of DMUs; this may lead to efficient units being incorrectly classified as inefficient and inefficient units being misclassified as efficient. As Adler and Yazhemsky (2010, p. 283) showed, “the latter occurs particularly frequently with small data sets under the assumption of variable returns-to-scale”. In the literature, the lack of discrimination is often referred to as the “curse of dimensionality” (e.g., Adler & Golany, 2007; Daraio & Simar, 2007). The lack of discriminating power has important implications, as in practice it can limit the managerial insights that can be drawn (Ghasemi, Ignatius, & Rezaee, 2019). In this sense, regarding the number of DMUs (sample size), it is quite clear that there are advantages to having larger data sets, as at a given number of DMUs, the efficiency score of each DMU can rely heavily on the number of variables (inputs and outputs) (Cinca & Molinero, 2004) – as such, the greater the number of variables, the less discerning the DEA analysis is (Jenkins & Anderson, 2003). Nevertheless, the literature indicates some empirical rules regarding the number of DMUs versus the number of inputs and outputs.