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********************* CALL FOR PAPERS *********************
SUBMISSION DUE DATE: May 15, 2017
REVIEWER FIRST REPORTS: July 15, 2017
REVISED PAPER SUBMISSION: September 15, 2017
REVIEWER SECOND REPORTS: November 30, 2017
FINAL MANUSCRIPT SUBMISSIONS TO PUBLISHER: December 15, 2017
SPECIAL ISSUE ON Big Data Analytics in Logistics and Supply Chain Management
International Journal of Logistics Management
Guest Editors:
Professor Samuel Fosso Wamba, Toulouse Business School, France
Professor Angappa Gunasekaran, University of Massachusetts Dartmouth, USA
Professor Eric Ngai, Hong Kong Polytechnic University, Hong Kong
Professor Thanos Papadopoulos, Kent Business School, University of Kent, UK
Introduction:
Big data analytics (BDA) is a holistic approach to managing, processing and analyzing the 5V data-
related dimensions (i.e., volume, variety, velocity, veracity and value) to create actionable insights for
delivering sustained value, measuring performance and establishing competitive advantage (Fosso
Wamba, Akter et al. 2015). The high business potential of BDA has been acknowledged by scholars
(Mayika, Chui et al. 2011; Strawn, 2012; Gobble, 2013). For example, big data analytics can improve firm
operational and strategic capabilities (Hazen, Skipper et al. 2016), enhance supply chain processes
(Hazen, Boone et al. 2014) and, thus, overall supply chain performance (Hazen, Boone et al. 2014; Hazen,
Skipper et al. 2016). Also, big data may transform manufacturing activities through automation, real-
time process monitoring and measurement as well as detection and diagnosis of production issues, and
thus leading to improved firm performance (e.g., low downtime costs, improved quality management,
logistics and order fulfilment cycles) (George, Haas et al. 2014). However so far very few studies have
examined how and why BDA impacts on operational-, firm- and supply chain-level outcomes.
Recommended Topics:
The topics to be discussed in this special issue include but are not limited to the following:
evaluation of the impact of BDA on logistics and supply chain management processes and
performance
evaluation of inhibitors and facilitators of BDA for supply chain management
evaluation of the impact of BDA on different organizational and supply chain levels
longitudinal case studies and pilot studies on the implementation and use of IT to support
BDA for improved operations and supply chain management
emergence of new business models based on the use of BDA in supply chain management
empirical studies on the business value of BDA in operations and supply chains
development and use of alternative theories to explain BDA adoption and use in operations
and supply chain management
empirical studies on the use of resources and capabilities for BDA in operations and supply
chain management
empirical studies on the use of BDA to analyze social media data (e.g., Twitter, Facebook)
for supply chain management optimization
Talent management in the context of BDA
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Submission Procedure
Prospective authors are invited to submit papers for this special thematic issue on “Big Data Analytics
in Logistics and Supply Chain Management” on or before June 15, 2017. All submissions must be original
and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE
JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at
http://emeraldgrouppublishing.com/products/journals/author_guidelines.htm?id=ijlm PRIOR TO
SUBMISSION at: https://mc.manuscriptcentral.com/ijlm.
About International Journal of Logistics Management
Editorial objectives
To provide executives and teachers with reports of current developments in the field of logistics
and supply chain management.
To facilitate the interchange of information about logistics and supply chain management among
business planners and researchers on a world-wide basis.
To provide a platform for new thinking on the problems and techniques of logistics and supply chain
management.
Editor-in-Chief: Dr Benjamin Hazen
Air Force Institute of Technology, USA
benjamin.hazen@live.com
All inquiries should be directed to the attention of:
Samuel Fosso Wamba
Guest Editor
E-mail: fossowam@gmail.com
All manuscript submissions to the special issue should be sent through the online submission system:
https://mc.manuscriptcentral.com/ijlm
* * * * * *
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Samuel Fosso Wamba is a Professor in the Department of Information, Operations and Management
Sciences at The Toulouse Business School. Prior, he was Associate Professor at NEOMA Business
School, France and Senior lecturer in the School of Information Systems & Technology (SISAT),
University of Wollongong, Australia. He earned an MSc in mathematics, from the University of
Sherbrooke in Canada, an MSc in e-commerce from HEC Montreal, Canada, and a Ph.D. in
industrial engineering, from the Polytechnic School of Montreal, Canada. His current research
focuses on business value of IT, business analytics, big data, inter-organisational system (e.g., RFID
technology) adoption and use, e-government (e.g., open data), supply chain management, electronic
commerce and mobile commerce. He has published papers in a number of international conferences
and journals. He is organizing special issues on IT related topics for many top journals.
Angappa Gunasekaran is a Professor and Dean at the Charlton College of Business, University of
Massachusetts, Dartmouth. Dr. Gunasekaran has held academic positions in UK, Australia,
Finland, India and Canada. He has over 250 articles published in peer-reviewed journals. He has
presented about 50 papers and published 50 articles in conferences and given a number of invited
talks in many countries. He is on the Editorial Board of several journals. He has organized several
international workshops and conferences in the emerging areas of operations management and
information systems. He is currently interested in researching logistics and supply chain
management. Dr. Gunasekaran has been the founding Director of Business Innovation Research
Center (BIRC) since 2006.
Eric W. T. Ngai, PhD
Prof. Eric Ngai is a Professor in the Department of Management and Marketing at The Hong Kong
Polytechnic University. His current research interests are in the areas of E-commerce, Supply Chain
Management, Decision Support Systems and RFID Technology and Applications. He has over 100
refereed international journal publications including MIS Quarterly, Journal of Operations
Management, Decision Support Systems, IEEE Transactions on Systems, Man and Cybernetics,
Production & Operations Management, and others. He is an Associate Editor of European Journal of
Information Systems and Information & Management. He serves on editorial board of four
international journals. Prof. Ngai has attained an h-index of 20, and received 1190 citations, ISI
Web of Science.
Thanos Papadopoulos is a Professor and Co-Director of the MBA in Kent Business School at the
University of Kent, UK. Previously, he was Associate Professor in Sussex School of Business,
Management, and Economics at the University of Sussex, UK. He holds a PhD from Warwick
Business School, UK, an MSc from Athens University of Economics and Business, Greece, and a
Diploma (Equivalent to MEng) from the Computer Engineering and Informatics Department at the
University of Patras, Greece. His research areas lie in Big Data, and the deployment of information
systems/technology within organizations and supply chains. His articles have been published in,
inter alia, British Journal of Management, International Journal of Operations and Production
Management, International Journal of Production Economics, Journal of Strategic Information
Systems, Journal of Business Research, IEEE Transactions on Engineering Management, and
Production Planning and Control.
References:
Fosso Wamba, S., S. Akter, et al. (2015). "How ‘big data’ can make big impact: Findings from a systematic
review and a longitudinal case study." International Journal of Production Economics 165: 234-246.
George, G., M. R. Haas, et al. (2014). BIG DATA AND MANAGEMENT. Academy of Management Journal,
Academy of Management. 57: 321-326.
Gobble, M. M. (2013). "Big Data: The Next Big Thing in Innovation." Research Technology Management
56(1): 64-66.
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Hazen, B. T., C. A. Boone, et al. (2014). "Data quality for data science, predictive analytics, and big data in
supply chain management: An introduction to the problem and suggestions for research and
applications." International Journal of Production Economics 154: 72-80.
Hazen, B. T., J. B. Skipper, et al. (2016). "Big data and predictive analytics for supply chain sustainability: A
theory-driven research agenda." Computers & Industrial Engineering.
Manyika, J., M. Chui, et al. (2011). Big data: The next frontier for innovation, competition, and productivity,
McKinsey Global Institute.
Strawn, G. O. (2012). "Scientific Research: How Many Paradigms?" EDUCAUSE Review 47(3): 26.
