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

The social process of Big Data and predictive analytics use for logistics and supply chain... 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/approachThe 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.FindingsFindings 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 implicationsThis 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/valueThe 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Physical Distribution & Logistics Management Emerald Publishing

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

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References (74)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0960-0035
DOI
10.1108/ijpdlm-01-2018-0041
Publisher site
See Article on Publisher Site

Abstract

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/approachThe 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.FindingsFindings 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 implicationsThis 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/valueThe 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.

Journal

International Journal of Physical Distribution & Logistics Management – Emerald Publishing

Published: Aug 30, 2019

Keywords: Big Data; Supply chain management; Grounded theory; Predictive analytics; Retail supply chain

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