IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v58y2020i5p1574-1593.html
   My bibliography  Save this article

A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context

Author

Listed:
  • Shradha A. Gawankar
  • Angappa Gunasekaran
  • Sachin Kamble

Abstract

The use of digital technologies such as ‘internet of things’ and ‘big data analytics’ have transformed the traditional retail supply chains into data-driven retail supply chains referred to as ‘Retail 4.0.’ These big data-driven retail supply chains have the advantage of providing superior products and services and enhance the customers shopping experience. The retailing industry in India is highly competitive and eager to transform into the environment of retail 4.0. The literature on big data in the supply chain has mainly focused on the applications in manufacturing industries and therefore needs to be further investigated on how the big data-driven retail supply chains influence the supply chain performance. Therefore, this study investigates how the retailing 4.0 context in India is influencing the existing supply chain performance measures and what effect it has on the organisational performance. The findings of the study provide valuable insights for retail supply chain practitioners on planning BDA investments. Based on a survey of 380 respondents selected from retail organisations in India, this study uses governance structure as the moderating variable. Implications for managers and future research possibilities are presented.

Suggested Citation

  • Shradha A. Gawankar & Angappa Gunasekaran & Sachin Kamble, 2020. "A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context," International Journal of Production Research, Taylor & Francis Journals, vol. 58(5), pages 1574-1593, March.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:5:p:1574-1593
    DOI: 10.1080/00207543.2019.1668070
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2019.1668070
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2019.1668070?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Bag, Surajit & Dhamija, Pavitra & Singh, Rajesh Kumar & Rahman, Muhammad Sabbir & Sreedharan, V. Raja, 2023. "Big data analytics and artificial intelligence technologies based collaborative platform empowering absorptive capacity in health care supply chain: An empirical study," Journal of Business Research, Elsevier, vol. 154(C).
    2. Kamble, Sachin S. & Gunasekaran, Angappa & Ghadge, Abhijeet & Raut, Rakesh, 2020. "A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation," International Journal of Production Economics, Elsevier, vol. 229(C).
    3. Ahmed, Adeel & Bhatti, Sabeen Hussain & Gölgeci, Ismail & Arslan, Ahmad, 2022. "Digital platform capability and organizational agility of emerging market manufacturing SMEs: The mediating role of intellectual capital and the moderating role of environmental dynamism," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    4. Papanagnou, Christos & Seiler, Andreas & Spanaki, Konstantina & Papadopoulos, Thanos & Bourlakis, Michael, 2022. "Data-driven digital transformation for emergency situations: The case of the UK retail sector," International Journal of Production Economics, Elsevier, vol. 250(C).
    5. Santosh Kumar Srivastava & Surajit Bag, 2023. "Recent Developments on Flexible Manufacturing in the Digital Era: A Review and Future Research Directions," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 483-516, December.
    6. Ray Qing Cao & Dara G. Schniederjans & Vicky Ching Gu, 2021. "Stakeholder sentiment in service supply chains: big data meets agenda-setting theory," Service Business, Springer;Pan-Pacific Business Association, vol. 15(1), pages 151-175, March.
    7. Ilona Skačkauskienė & Juliana Smirnova, 2022. "Review of Possibilities for Evaluating the Performance of an Organization in the Aspect of Greenness," Energies, MDPI, vol. 15(19), pages 1-18, September.
    8. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    9. Ola G. El‐Taliawi & Nihit Goyal & Michael Howlett, 2021. "Holding out the promise of Lasswell's dream: Big data analytics in public policy research and teaching," Review of Policy Research, Policy Studies Organization, vol. 38(6), pages 640-660, November.
    10. Fakhrul Hasan & Mary Fiona Ross Bellenstedt & Mohammad Raijul Islam, 2023. "Demand and Supply Disruptions During the Covid-19 Crisis on Firm Productivity," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(1), pages 87-105, March.
    11. Margherita Bernabei & Marco Eugeni & Paolo Gaudenzi & Francesco Costantino, 2023. "Assessment of Smart Transformation in the Manufacturing Process of Aerospace Components Through a Data-Driven Approach," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(1), pages 67-86, March.

    More about this item

    Statistics

    Access and download statistics

    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:taf:tprsxx:v:58:y:2020:i:5:p:1574-1593. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    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.