Data Science for Better Productivity

Today’s global competition and rapid development of information technology has led to the creation of massive amounts of data that are, moreover, exponentially increasing day by day. Analysing these large data sets is a key basis of competition and innovation, and supports new waves of productivity growth. This has challenged organizations to find novel ways to analyse and use the data to make more intelligent decisions and increase their productivity. Data Science, encompassing a collection of scientific methods, processes, and systems, is one of today’s most interesting fields of research and allows organizations to extract knowledge or insights from these existent mountains of data. As such, PRODUCTIVITY ANALYSIS/DATA ENVELOPMENT ANALYSIS and DATA SCIENCE/BIG DATA are areas of growing interest to researchers and practitioners alike, with a significant body of research focusing on either of them. It is, nevertheless, not too bold to say studies dedicated to addressing both the fields at the same time are rather scarce.

This Special Issue encourages original research papers of high quality that focus on novel ways of using the existent data science techniques and/or the development of novel data science techniques to improve and/or unleash value and drive productivity from large data sets, and with practical applications in various domains. Empirical research studies, as well as the development of new or modified methodologies to address challenging and emerging issues in DATA SCIENCE/BIG DATA and PRODUCTIVITY ANALYSIS/DATA ENVELOPMENT ANALYSIS, are of considerable interest.

Contributions from both the academic and the practitioner communities are encouraged.

TOPICS AND AREAS covered include but are not limited to:

  • Performance
  • Productivity
  • Operations Research
  • Econometrics
  • Machine Learning
  • Data Science
  • Data Visualization
  • Computer Programming
  • Pattern Recognition, and so on.

DATA SCIENCE TECHNIQUES covered include but are not limited to:

  • Linear & Logistic Regression
  • Mathematical Programming
  • Decision Trees
  • Bayes Classifiers
  • Principal Component Analysis
  • Data Envelopment Analysis
  • Neural Networks
  • Predictive Modelling
  • Deep Learning
  • Text Analysis
  • Survival Analysis, and so on

Submission instructions

Authors are invited to submit their manuscripts at https://mc.manuscriptcentral.com/ors-jors on or before the indicated submission deadline.

All submissions will undergo a blind peer-reviewed process. The corresponding author, on behalf of all authors, must declare that the manuscript has not been previously published, has not been accepted for publication, nor is currently under consideration for publication elsewhere.

For further information or clarifications about this Call for Papers, please do not hesitate to contact the Special Issue Editors directly.

The Special Issue is scheduled for publication in December 2019.

Dates for your diary

  • 31/10/2018 – Submission Deadline
  • 28/02/2019 – Notification of first round of review results
  • 30/11/2019 – Final acceptances after second round of review

Editorial information

  • Guest Editor: Vincent Charles, Professor of Management Science and Director of Research, Buckingham Business School, University of Buckingham(charles@buckingham.ac.uk )
  • Guest Editor: Juan Aparicio, Aparicio, Professor and Director of Center of Operations Research (CIO), Universidad Miguel Hernandez de Elche,(aparicio@umh.es)
  • Guest Editor: Joe Zhu, Professor of Operations Analytics, Foisie Business School, Worcester Polytechnic Institute(jzhu@wpi.edu)
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