4.7 Article

Examining the role of big data and predictive analytics on collaborative performance in context to sustainable consumption and production behaviour

期刊

JOURNAL OF CLEANER PRODUCTION
卷 196, 期 -, 页码 1508-1521

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2018.06.097

关键词

Sustainable consumption; Sustainable production; Collaboration; Inter-organizational fit; Resource complementarity; Sustainable operations

资金

  1. National Natural Science Foundation of China [6171101169]
  2. Guangdong Natural Science Foundation [2015A030313782]
  3. Guangdong Education Bureau Fund [2017KTSCX166]
  4. Science and Technology Innovation Committee Foundation of Shenzhen [JCYJ20170817112037041]

向作者/读者索取更多资源

The organizations engaged in sustainable development programmes are increasingly paying serious attention towards synergetic relationships between focal firms and their partners to achieve the goal of sustainable consumption and production (SCP) via big data and predictive analytics (BDPA). The study examines the role of BDPA in collaborative performance (CP) among the partners engaged in sustainable development programme to achieve the goal of SCP. The study further investigates the contingent effect of organization fit on the impact of BDPA on CP. We used variance based structural equation modelling (PLS SEM) to test research hypotheses using a sample of 190 respondents working in auto-components manufacturing organizations in India drawn from the ACMA and Dun & Bradstreet databases. The results indicate that BDPA has a significant positive impact on the CP among partners and the organizational compatibility and resource complementarity have positive moderating effects on the path joining BDPA and CP. The study contributes to the understanding of BDPA and collaboration literature in the context of sustainable development. These findings extend the dynamic capability view (DCV) to create a better understanding of contemporary applications of big data and predictive analytics capability, while also providing theoretically grounded directions to managers who seek to use information processing technologies to continuously improve the collaboration in supply chain networks. We have also noted some of the limitations of our study and identified numerous further research directions. (C) 2018 Elsevier Ltd. All rights reserved.

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