Skip to main content
Kent Academic Repository

Big Data and Predictive Analytics for Supply Chain and Organizational Performance

Gunasekaran, Angappa, Papadopoulos, Thanos, Dubey, Rameshwar, Fosso Wamba, Samuel, Childe, Stephen J., Hazen, Benjamin, Akhter, Shahriar (2016) Big Data and Predictive Analytics for Supply Chain and Organizational Performance. Journal of Business Research, 70 . pp. 308-317. ISSN 0148-2963. (doi:10.1016/j.jbusres.2016.08.004) (KAR id:57171)

PDF Author's Accepted Manuscript
Language: English
Download this file
(PDF/702kB)
[thumbnail of JBR accepted.pdf]
Preview
Request a format suitable for use with assistive technology e.g. a screenreader
XML Word Processing Document (DOCX) Author's Accepted Manuscript
Language: English
Download this file
(XML Word Processing Document (DOCX)/144kB)
[thumbnail of JBR accepted.docx]
Request a format suitable for use with assistive technology e.g. a screenreader
PDF Author's Accepted Manuscript
Language: English

Restricted to Repository staff only
[thumbnail of JBR.pdf]
Official URL:
http://dx.doi.org/10.1016/j.jbusres.2016.08.004

Abstract

Scholars acknowledge the importance of big data and predictive analytics (BDPA) in achieving business value and firm performance. However, the impact of BDPA assimilation on supply chain (SCP) and organizational performance (OP) has not been thoroughly investigated. To address this gap, this paper draws on resource-based view. It conceptualizes assimilation as a three stage process (acceptance, routinization, and assimilation) and identifies the influence of resources (connectivity and information sharing) under the mediation effect of top management commitment on big data assimilation (capability), SCP and OP. The findings suggest that connectivity and information sharing under the mediation effect of top management commitment are positively related to BDPA acceptance, which is positively related to BDPA assimilation under the mediation effect of BDPA routinization, and positively related to SCP and OP. Limitations and future research directions are provided.

Item Type: Article
DOI/Identification number: 10.1016/j.jbusres.2016.08.004
Subjects: H Social Sciences
T Technology
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Thanos Papadopoulos
Date Deposited: 11 Sep 2016 12:02 UTC
Last Modified: 05 Nov 2024 10:47 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/57171 (The current URI for this page, for reference purposes)

University of Kent Author Information

Papadopoulos, Thanos.

Creator's ORCID: https://orcid.org/0000-0001-6821-1136
CReDIT Contributor Roles:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.