IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-02051276.html
   My bibliography  Save this paper

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

Author

Listed:
  • David Roubaud

    (MRM - Montpellier Research in Management - UPVM - Université Paul-Valéry - Montpellier 3 - UPVD - Université de Perpignan Via Domitia - Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School - UM - Université de Montpellier, Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School)

  • Rameshwar Dubey

    (Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School)

  • Cyril Foropon

    (Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School)

  • Angappa Gunasekaran

    (CSUB - California State University [Bakersfield])

  • Stephen J. Childe

    (Plymouth University)

  • Zongwei Luo

    (SUSTech - Southern University of Science and Technology)

  • Fosso Wamba Samuel

    (Groupe École Supérieure de Commerce de Toulouse - ESCT)

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.

Suggested Citation

  • David Roubaud & Rameshwar Dubey & Cyril Foropon & Angappa Gunasekaran & Stephen J. Childe & Zongwei Luo & Fosso Wamba Samuel, 2018. "Examining the role of big data and predictive analytics on collaborative performance in context to sustainable consumption and production behaviour," Post-Print hal-02051276, HAL.
    • Handle: RePEc:hal:journl:hal-02051276
    DOI: 10.1016/j.jclepro.2018.06.097
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aziz Barhmi & Omar Hajaji, 2023. "Multidisciplinary Approach to Supply Chain Resilience: Conceptualization and Scale Development," Central European Business Review, Prague University of Economics and Business, vol. 2023(5), pages 43-69.
    2. Yi Wang & Yafei Yang & Zhaoxiang Qin & Yefei Yang & Jun Li, 2023. "A Literature Review on the Application of Digital Technology in Achieving Green Supply Chain Management," Sustainability, MDPI, vol. 15(11), pages 1-18, May.
    3. Showimy Aldossari & Umi Asma’ Mokhtar & Ahmad Tarmizi Abdul Ghani, 2023. "Factor Influencing the Adoption of Big Data Analytics: A Systematic Literature and Experts Review," SAGE Open, , vol. 13(4), pages 21582440231, December.
    4. Chen, Jiandong & Gao, Ming & Shahbaz, Muhammad & Cheng, Shulei & Song, Malin, 2021. "An improved decomposition approach toward energy rebound effects in China: Review since 1992," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    5. Taiwen Feng & Hongyan Sheng, 2023. "Identifying the equifinal configurations of prompting green supply chain integration and subsequent performance outcome," Business Strategy and the Environment, Wiley Blackwell, vol. 32(8), pages 5234-5251, December.
    6. Paulina Permatasari & Assyifa Szami Ilman & Carol Ann Tilt & Dian Lestari & Saiful Islam & Rita Helbra Tenrini & Arif Budi Rahman & Agunan Paulus Samosir & Irwanda Wisnu Wardhana, 2021. "The Village Fund Program in Indonesia: Measuring the Effectiveness and Alignment to Sustainable Development Goals," Sustainability, MDPI, vol. 13(21), pages 1-30, November.
    7. Cuomo, Maria Teresa & Tortora, Debora & Colosimo, Ivan & Ricciardi Celsi, Lorenzo & Genovino, Cinzia & Festa, Giuseppe & La Rocca, Michele, 2023. "Segmenting with big data analytics and Python: A quantitative exploratory analysis of household savings," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    8. Song, Malin & Zhu, Shuai & Wang, Jianlin & Zhao, Jiajia, 2020. "Share green growth: Regional evaluation of green output performance in China," International Journal of Production Economics, Elsevier, vol. 219(C), pages 152-163.
    9. Chauhan, Chetna & Kaur, Puneet & Arrawatia, Rakesh & Ractham, Peter & Dhir, Amandeep, 2022. "Supply chain collaboration and sustainable development goals (SDGs). Teamwork makes achieving SDGs dream work," Journal of Business Research, Elsevier, vol. 147(C), pages 290-307.
    10. Yagi, Michiyuki & Kokubu, Katsuhiko, 2020. "A Framework of Sustainable Consumption and Production from the Production Perspective: Application to Thailand and Vietnam," MPRA Paper 103931, University Library of Munich, Germany.
    11. Raut, Rakesh D. & Mangla, Sachin Kumar & Narwane, Vaibhav S. & Dora, Manoj & Liu, Mengqi, 2021. "Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    12. Bag, Surajit & Rahman, Muhammad Sabbir & Srivastava, Gautam & Shore, Adam & Ram, Pratibha, 2023. "Examining the role of virtue ethics and big data in enhancing viable, sustainable, and digital supply chain performance," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    13. Oesterreich, Thuy Duong & Anton, Eduard & Teuteberg, Frank & Dwivedi, Yogesh K, 2022. "The role of the social and technical factors in creating business value from big data analytics: A meta-analysis," Journal of Business Research, Elsevier, vol. 153(C), pages 128-149.
    14. Muhammad Adeel Munir & Amjad Hussain & Muhammad Farooq & Muhammad Salman Habib & Muhammad Faisal Shahzad, 2023. "Data-Driven Transformation: The Role of Ambidexterity and Analytics Capability in Building Dynamic and Sustainable Supply Chains," Sustainability, MDPI, vol. 15(14), pages 1-37, July.
    15. Biman Darshana Hettiarachchi & Stefan Seuring & Marcus Brandenburg, 2022. "Industry 4.0-driven operations and supply chains for the circular economy: a bibliometric analysis," Operations Management Research, Springer, vol. 15(3), pages 858-878, December.
    16. Xu, Jinou & Pero, Margherita & Fabbri, Margherita, 2023. "Unfolding the link between big data analytics and supply chain planning," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    17. Bag, Surajit & Dhamija, Pavitra & Bryde, David J. & Singh, Rajesh Kumar, 2022. "Effect of eco-innovation on green supply chain management, circular economy capability, and performance of small and medium enterprises," Journal of Business Research, Elsevier, vol. 141(C), pages 60-72.
    18. Sara Rye, 2022. "Analysis of the Disparity between Recurring and Temporary Collaborative Performance: A Literature Review between 1994 and 2021," Logistics, MDPI, vol. 6(4), pages 1-16, October.
    19. Kazancoglu, Yigit & Sagnak, Muhittin & Mangla, Sachin Kumar & Sezer, Muruvvet Deniz & Pala, Melisa Ozbiltekin, 2021. "A fuzzy based hybrid decision framework to circularity in dairy supply chains through big data solutions," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    20. Ualison Rébula de Oliveira & Thaís Stiegert Meireles Gomes & Geovani Gabizo de Oliveira & Júlio Cesar Andrade de Abreu & Murilo Alvarenga Oliveira & Aldara da Silva César & Vicente Aprigliano Fernande, 2022. "Systematic Literature Review on Sustainable Consumption from the Perspective of Companies, People and Public Policies," Sustainability, MDPI, vol. 14(21), pages 1-26, October.
    21. Huaide Wen & Jun Dai, 2021. "The Change of Sources of Growth and Sustainable Development in China: Based on the Extended EKC Explanation," Sustainability, MDPI, vol. 13(5), pages 1-19, March.
    22. Nadine Bachmann & Shailesh Tripathi & Manuel Brunner & Herbert Jodlbauer, 2022. "The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals," Sustainability, MDPI, vol. 14(5), pages 1-33, February.
    23. Ma, Shuaiyin & Zhang, Yingfeng & Lv, Jingxiang & Ge, Yuntian & Yang, Haidong & Li, Lin, 2020. "Big data driven predictive production planning for energy-intensive manufacturing industries," Energy, Elsevier, vol. 211(C).
    24. Philipp Korherr & Dominik Kanbach, 2023. "Human-related capabilities in big data analytics: a taxonomy of human factors with impact on firm performance," Review of Managerial Science, Springer, vol. 17(6), pages 1943-1970, August.
    25. Chen, Jiandong & Gao, Ming & Mangla, Sachin Kumar & Song, Malin & Wen, Jie, 2020. "Effects of technological changes on China's carbon emissions," Technological Forecasting and Social Change, Elsevier, vol. 153(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-02051276. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.