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Theory building with big data-driven research – Moving away from the “What” towards the “Why”

Author

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  • Kar, Arpan Kumar
  • Dwivedi, Yogesh K.

Abstract

Data availability and access to various platforms, is changing the nature of Information Systems (IS) studies. Such studies often use large datasets, which may incorporate structured and unstructured data, from various platforms. The questions that such papers address, in turn, may attempt to use methods from computational science like sentiment mining, text mining, network science and image analytics to derive insights. However, there is often a weak theoretical contribution in many of these studies. We point out the need for such studies to contribute back to the IS discipline, whereby findings can explain more about the phenomenon surrounding the interaction of people with technology artefacts and the ecosystem within which these contextual usage is situated. Our opinion paper attempts to address this gap and provide insights on the methodological adaptations required in “big data studies” to be converted into “IS research” and contribute to theory building in information systems.

Suggested Citation

  • Kar, Arpan Kumar & Dwivedi, Yogesh K., 2020. "Theory building with big data-driven research – Moving away from the “What” towards the “Why”," International Journal of Information Management, Elsevier, vol. 54(C).
    • Handle: RePEc:eee:ininma:v:54:y:2020:i:c:s0268401220311257
    DOI: 10.1016/j.ijinfomgt.2020.102205
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    Citations

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    Cited by:

    1. Taiga Saito & Shivam Gupta, 2022. "Big Data Applications with Theoretical Models and Social Media in Financial Management," CIRJE F-Series CIRJE-F-1205, CIRJE, Faculty of Economics, University of Tokyo.
    2. Shivam Gupta & Théo Justy & Shampy Kamboj & Ajay Kumar & Eivind Kristoffersen, 2021. "Big data and firm marketing performance: Findings from knowledge-based view," Post-Print hal-03609916, HAL.
    3. Taiga Saito & Shivam Gupta, 2022. "Big data applications with theoretical models and social media in financial management," CARF F-Series CARF-F-550, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    4. Alex V. Teixeira & Denis Alcides Rezende, 2023. "A Multidimensional Information Management Framework for Strategic Digital Cities: A Comparative Analysis of Canada and Brazil," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(1), pages 107-121, March.
    5. Arpan Kumar Kar & Shalini Nath Tripathi & Nishtha Malik & Shivam Gupta & Uthayasankar Sivarajah, 2023. "How Does Misinformation and Capricious Opinions Impact the Supply Chain - A Study on the Impacts During the Pandemic," Annals of Operations Research, Springer, vol. 327(2), pages 713-734, August.
    6. Xing, Yunfei & Wang, Xiwei & Qiu, Chengcheng & Li, Yueqi & He, Wu, 2022. "Research on opinion polarization by big data analytics capabilities in online social networks," Technology in Society, Elsevier, vol. 68(C).
    7. Amit Kumar Kushwaha & Ruchika Pharswan & Prashant Kumar & Arpan Kumar Kar, 2023. "How Do Users Feel When They Use Artificial Intelligence for Decision Making? A Framework for Assessing Users’ Perception," Information Systems Frontiers, Springer, vol. 25(3), pages 1241-1260, June.
    8. C, Deep Prakash & Majumdar, Adrija, 2023. "Predicting sports fans’ engagement with culturally aligned social media content: A language expectancy perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    9. Arpan Kumar Kar & Amit Kumar Kushwaha, 2023. "Facilitators and Barriers of Artificial Intelligence Adoption in Business – Insights from Opinions Using Big Data Analytics," Information Systems Frontiers, Springer, vol. 25(4), pages 1351-1374, August.
    10. Dwivedi, Yogesh K & Shareef, Mahmud A & Akram, Muhammad S & Bhatti, Zeeshan A & Rana, Nripendra P, 2022. "Examining the effects of enterprise social media on operational and social performance during environmental disruption," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    11. Prabhsimran Singh & Surleen Kaur & Abdullah M. Baabdullah & Yogesh K. Dwivedi & Sandeep Sharma & Ravinder Singh Sawhney & Ronnie Das, 2023. "Is #SDG13 Trending Online? Insights from Climate Change Discussions on Twitter," Information Systems Frontiers, Springer, vol. 25(1), pages 199-219, February.
    12. Geissinger, Andrea & Laurell, Christofer & Öberg, Christina & Sandström, Christian, 2023. "Social media analytics for innovation management research: A systematic literature review and future research agenda," Technovation, Elsevier, vol. 123(C).
    13. Islam, Towhidul & Meade, Nigel & Carson, Richard T. & Louviere, Jordan J. & Wang, Juan, 2022. "The usefulness of socio-demographic variables in predicting purchase decisions: Evidence from machine learning procedures," Journal of Business Research, Elsevier, vol. 151(C), pages 324-338.
    14. Maryia Zaitsava & Elona Marku & Maria Chiara Guardo & Azar Shahgholian, 2023. "A fine-grained perspective on big data knowledge creation: dimensions, insights, and mechanism from a pilot study," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 547-573, June.
    15. Tat‐Dat Bui & Jiun‐Wei Tseng & Thi Phuong Thuy Tran & Hien Minh Ha & Ming K. Lim & Ming‐Lang Tseng, 2023. "Circular supply chain strategy in Industry 4.0: The canned food industry in Vietnam," Business Strategy and the Environment, Wiley Blackwell, vol. 32(8), pages 6047-6073, December.

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