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The social process of Big Data and predictive analytics use for logistics and supply chain management

Annibal Sodero (Department of Marketing and Logistics, The Ohio State University, Columbus, Ohio, USA)
Yao Henry Jin (Department of Management, Miami University, Oxford, Ohio, USA)
Mark Barratt (Department of Management, Marquette University, Milwaukee, Wisconsin, USA)

International Journal of Physical Distribution & Logistics Management

ISSN: 0960-0035

Article publication date: 30 August 2019

Issue publication date: 30 August 2019

Abstract

Purpose

The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations.

Design/methodology/approach

The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC.

Findings

Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations.

Practical implications

This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place.

Originality/value

The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area.

Keywords

Acknowledgements

Publishers Note: The publisher would like to inform readers that the following special issue paper was mistakenly published as part of a regular issue. This error was introduced as part of the editorial process, and the publisher sincerely apologises for this error. The paper will remain in its current issue. The affected paper is as follows.

Sodero, A., Jin, Y. and Barratt, M. (2019), “The social process of Big Data and predictive analytics use for logistics and supply chain management”, International Journal of Physical Distribution & Logistics Management, Vol. 49 No. 7, pp. 706-726, doi: 10.1108/IJPDLM-01-2018-0041.

The affected paper was originally intended to publish as part of ‘SCM 4.0: Supply Chain Management in the Digital Age’ Guest Edited by Erik Hofmann, Henrik Sternberg, Haozhe Chen, Alexander Pflaum and Gunter Prockl.

The publisher would like to take this opportunity to thank the Guest Editors for their time and effort.

Citation

Sodero, A., Jin, Y.H. and Barratt, M. (2019), "The social process of Big Data and predictive analytics use for logistics and supply chain management", International Journal of Physical Distribution & Logistics Management, Vol. 49 No. 7, pp. 706-726. https://doi.org/10.1108/IJPDLM-01-2018-0041

Publisher

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Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited