CENTERIS - Conference on ENTERprise Information Systems

Big Data Analytics: Technologies, Value, Models and Challenges

Workshop to be held in conjunction with CENTERIS - Conference on ENTERprise Information Systems.
Already in its tenth edition, the conference will be held in Portugal.

AIS Affiliated Conference |

Workshop Chairs

Samuel Fosso Wamba,, Toulouse Business School, France

Workshop Description

Big data analytics (BDA) is defined as “a holistic approach to manage, process and analyze 5 Vs (i.e., volume, variety, velocity, veracity and value) in order to create actionable insights for sustained value delivery, measuring performance and establishing competitive advantages” (Fosso Wamba, Akter, Edwards, Chopin, & Gnanzou, 2015). It is emerging as the "next big thing" in management. Some scholars have gone so far as suggesting that BDA is the "next management revolution" (McAfee & Brynjolfsson, 2012), and thus generating huge attention from both practitioners and academics. For example, IDC(2016) predicts that the worldwide big data and business analytics revenues estimated at $130.1 billion in 2016 will be about $203 billion in 2020. The consulting firm added that this growth represents “a compound annual growth rate (CAGR) of 11.7% through 2020”. Despite the fact that the potential of BDA is tremendous, very few empirical studies have been conducted to assess the real business value of BDA at the individual, firm, supply chain and society levels (Fosso Wamba, Ngai, Riggins, & Akter, 2017). Indeed, there is little research in the domain of data driven personalization, service product innovation, supply chain visibility, dynamic pricing, security and fraud detections (Akter & Wamba, 2016). Some other burning issues, such as analytics climate, privacy, surveillance and democracy are still unanswered(Akter, Wamba, Gunasekaran, Dubey, & Childe, 2016).
The main objective of this workshop to invite scholars and practitioners to look at the ways and means to identify and capture business value from BDA in terms of innovative business models, improved decisions making, improved intra-and inter-organizational performance, and competitive advantage.

Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 1-22. doi:10.1007/s12525-016-0219-0br>Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113-131. doi:
Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (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. doi:
Fosso Wamba, S., Ngai, E. W. T., Riggins, F., & Akter, S. (2017). Guest editorial-Transforming operations and production management using big data and business analytics: future research directions. International Journal of Operations & Production Management, 37(1), 2-9. doi:doi:10.1108/IJOPM-07-2016-0414
IDC. (2016). Double-Digit Growth Forecast for the Worldwide Big Data and Business Analytics Market Through 2020 Led by Banking and Manufacturing Investments, According to IDC Retrieved from
McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard business review, 90(10), 4.

Workshop Topics and Areas of Interest

* Big data analytics enabled-business process innovation at the individual, firm, supply chain and society levels
* Assessment of the effect of big data analytics on the decision-making processes in operations
* Assessment of facilitators and inhibitors of big data analytics adoption for supply chain management processes
* In-depth & longitudinal case studies and pilot studies on the implementation of IT infrastructure to support big data initiatives for improved operations management, lean & agile operations, quality management in operations and supply chain management
* Facilitation of innovative electronic business models and operations by using big data analytics in various sectors (e.g., healthcare, retail industry, and manufacturing)
* Enabling smart cities, smart organizations and smart homes using big data analytics
* New theory development to explain the adoption and use of big data in operations at the individual, firm, supply chain and society levels
* Challenges related to big data analytics-enabled end-to-end supply chain transformation

Workshop Scientific Committee

Samuel Fosso Wamba, Toulouse Business School, France
Ygal Bendavid, The Université du Québec à Montréal (UQAM), Canada
Shahriar Akter, University of Wollongong, Australia
Katina Michael, University of Wollongong, Australia
Jean Kamdjoug. Université Catholique d'Afrique Centrale, Cameroun
Eric Ngai, The Hong Kong Polytechnic University, Hong Kong
Rameshwar Dubey, Symbiosis International University,India and South University of Science and Technology of China
Fred Riggins, North Dakota State University, USA
Angappa Gunasekaran, University of Massachusetts Dartmouth, USA
Gary Graham, University Business School, UK
Wojciech Piotrowicz, University of Oxford, UK

Workshop Important Dates

Deadline for paper submission: June 19, 2017
Notification of acceptance/rejection: July 3, 2017
Revised version due date: July 24, 2017
Conference: (available soon)

Submission Procedure

Please use the following link to submit your paper:

Submit workshop paper

Paper format

Manuscripts must be written in English. Each manuscript should not exceed the maximum number of pages predefined for each submission type, considering the format available:

Download template and guidelines.

  • Publications

    Authors of selected papers will be invited to extend their papers for publication in international journals and in edited books.

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