Status | 已发表Published |
Title | Examining the role of big data and predictive analytics on collaborative performance in context to sustainable consumption and production behaviour |
Creator | |
Date Issued | 2018-09-20 |
Source Publication | Journal of Cleaner Production
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ISSN | 0959-6526 |
Volume | 196Pages:1508-1521 |
Abstract | 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. |
Keyword | Collaboration Inter-organizational fit Resource complementarity Sustainable consumption Sustainable operations Sustainable production |
DOI | 10.1016/j.jclepro.2018.06.097 |
URL | View source |
Indexed By | SCIE ; SSCI |
Language | 英语English |
WOS Research Area | Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology |
WOS Subject | Green & Sustainable Science & Technology ; Engineering, Environmental ; Environmental Sciences |
WOS ID | WOS:000444364400129 |
Scopus ID | 2-s2.0-85048757480 |
Citation statistics | |
Document Type | Journal article |
Identifier | http://repository.uic.edu.cn/handle/39GCC9TT/6901 |
Collection | Research outside affiliated institution |
Corresponding Author | Luo, Zongwei |
Affiliation | 1.Montpellier Business School,Montpellier Research in Management,Montpellier,2300 Avenue des Moulins,34185,France 2.School of Business and Public Administration,California State University,Bakersfield,Bakersfield 9001 Stockdale Highway,93311-1022,United States 3.Plymouth Business School,Plymouth University,Plymouth,PL4 8AA,United Kingdom 4.Computer Science and Engineering Department,Southern University of Science and Technology of China,Shenzen,1088 Xueyuan Blvd.,518055,China 5.Toulouse Business School,Toulouse University,Toulouse,20 Boulevard Lascrosses,31068,France |
Recommended Citation GB/T 7714 | Dubey, Rameshwar,Gunasekaran, Angappa,Childe, Stephen J.et al. Examining the role of big data and predictive analytics on collaborative performance in context to sustainable consumption and production behaviour[J]. Journal of Cleaner Production, 2018, 196: 1508-1521. |
APA | Dubey, Rameshwar., Gunasekaran, Angappa., Childe, Stephen J., Luo, Zongwei., Wamba, Samuel Fosso., .. & Foropon, Cyril. (2018). Examining the role of big data and predictive analytics on collaborative performance in context to sustainable consumption and production behaviour. Journal of Cleaner Production, 196, 1508-1521. |
MLA | Dubey, Rameshwar,et al."Examining the role of big data and predictive analytics on collaborative performance in context to sustainable consumption and production behaviour". Journal of Cleaner Production 196(2018): 1508-1521. |
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