Data Science and Productivity Analytics

1. Front Matter

   Pages i-x

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2. Data Envelopment Analysis and Big Data: Revisit with a Faster Method

   • Dariush Khezrimotlagh, Joe Zhu

   Pages 1-34

3. Data Envelopment Analysis (DEA): Algorithms, Computations, and Geometry

   • José H. Dulá

   Pages 35-56

4. An Introduction to Data Science and Its Applications

   • Alex Rabasa, Ciara Heavin

   Pages 57-81

5. Identification of Congestion in DEA

   • Mahmood Mehdiloo, Biresh K. Sahoo, Joe Zhu

   Pages 83-119

6. Data Envelopment Analysis and Non-parametric Analysis

   • Gabriel Villa, Sebastián Lozano

   Pages 121-160

7. The Measurement of Firms’ Efficiency Using Parametric Techniques

   • Luis Orea

   Pages 161-199

8. Fair Target Setting for Intermediate Products in Two-Stage Systems with Data Envelopment Analysis

   • Qingxian An, Haoxun Chen, Beibei Xiong, Jie Wu, Liang Liang

   Pages 201-226

9. Fixed Cost and Resource Allocation Considering Technology Heterogeneity in Two-Stage Network Production Systems

   • Tao Ding, Feng Li, Liang Liang

   Pages 227-249

10. Efficiency Assessment of Schools Operating in Heterogeneous Contexts: A Robust Nonparametric Analysis Using PISA 2015

    • Jose Manuel Cordero, Cristina Polo, Rosa Simancas

    Pages 251-277

11. A DEA Analysis in Latin American Ports: Measuring the Performance of Guayaquil Contecon Port

    • Emilio J. Morales-Núñez, Xavier R. Seminario-Vergara, Sonia Valeria Avilés-Sacoto, Galo Eduardo Mosquera-Recalde

    Pages 279-309

12. Effects of Locus of Control on Bank’s Policy—A Case Study of a Chinese State-Owned Bank

    • Cong Xu, Guo-liang Yang, Jian-bo Yang, Yu-wang Chen, Hua-ying Zhu

    Pages 311-335

13. A Data Scientific Approach to Measure Hospital Productivity

    • Babak Daneshvar Rouyendegh (B. Erdebilli), Asil Oztekin, Joseph Ekong, Ali Dag

    Pages 337-358

14. Environmental Application of Carbon Abatement Allocation by Data Envelopment Analysis

    • Anyu Yu, Simon Rudkin, Jianxin You

    Pages 359-389

15. Pension Funds and Mutual Funds Performance Measurement with a New DEA (MV-DEA) Model Allowing for Missing Variables

    • Maryam Badrizadeh, Joseph C. Paradi

    Pages 391-413

16. Sharpe Portfolio Using a Cross-Efficiency Evaluation

    • Mercedes Landete, Juan F. Monge, José L. Ruiz, José V. Segura

    Pages 415-439

This book includes a spectrum of concepts, such as performance, productivity, operations research, econometrics, and data science, for the practically and theoretically important areas of ‘productivity analysis/data envelopment analysis’ and ‘data science/big data’. Data science is defined as the collection of scientific methods, processes, and systems dedicated to extracting knowledge or insights from data and it develops on concepts from various domains, containing mathematics and statistical methods, operations research, machine learning, computer programming, pattern recognition, and data visualisation, among others.

Examples of data science techniques include linear and logistic regressions, decision trees, Naïve Bayesian classifier, principal component analysis, neural networks, predictive modelling, deep learning, text analysis, survival analysis, and so on, all of which allow using the data to make more intelligent decisions. On the other hand, it is without a doubtthat nowadays the amount of data is exponentially increasing, and analysing large data sets has become a key basis of competition and innovation, underpinning new waves of productivity growth. This book aims to bring a fresh look onto the various ways that data science techniques could unleash value and drive productivity from these mountains of data.

Researchers working in productivity analysis/data envelopment analysis will benefit from learning about the tools available in data science/big data that can be used in their current research analyses and endeavours. The data scientists, on the other hand, will also get benefit from learning about the plethora of applications available in productivity analysis/data envelopment analysis.

• Productivity Analysis
• Data Science
• Data Envelopment Analysis (DEA)
• Big Data
• Efficiency
• Parametric Analytics
• Combinatory Analytics
• Multidimensional Scaling
• Data Visualization

• School of Management, University of Bradford, Bradford, UK

  Vincent Charles

• Center of Operations Research, University Miguel Hernandez, Elche, Spain

  Juan Aparicio

• Foisie Business School, Worcester Polytechnic Institute, Worcester, USA

  Joe Zhu

Vincent Charles is an experienced researcher in the field of Artificial Intelligence and Management Science, currently with the School of Management, University of Bradford. He has more than two decades of teaching, research, and consultancy experience, having been a full professor and director of research for more than a decade. He has published over 130 research outputs. He is a recipient of many international academic honours and awards.

Juan Aparicio is an Associate Professor at the Department of Statistics, Mathematics an Information Technology of the University Miguel Hernandez, Elche (Alicante), Spain. He is the director of the Center of Operations Research and is also Co-Chair (with Knox Lovell) of the Santander Chair on Efficiency and Productivity. He has published over 100 research contributions, mainly on Data Envelopment Analysis, Efficiency and Productivity Analysis.


Joe Zhu is Professor of Operations Analytics in the Foisie Business School, Worcester Polytechnic Institute. He is an internationally recognized expert in methods of performance evaluation and benchmarking using Data Envelopment Analysis (DEA), and his research interests are in the areas of operations and business analytics, productivity modeling, and performance evaluation and benchmarking. He has published and co-edited several books focusing on performance evaluation and benchmarking using DEA and developed the DEA Frontier software.

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