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Understanding the impact of big data on firm performance: The necessity of conceptually differentiating among big data characteristics

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  • Ghasemaghaei, Maryam

Abstract

This study uses the resource-based view to explore the impact of data volume, data velocity, and data variety, which are the main characteristics of big data, on firm performance and the mediating roles of data value and data veracity on these relationships. To test the research model, we collected data from 143 top and middle level managers in the United States. The findings show that data variety positively improves data value generation, whereas data volume and data velocity do not impact it. Additionally, while data volume negatively impacts data veracity, data velocity and data variety positively impact it. Findings indicate the necessity of conceptually differentiating among big data characteristics in investigating their impacts on firm outcomes instead of treating big data as a holistic variable. The study provides useful insights for researchers and managers willing to better understand the role of big data characteristics in influencing firm performance.

Suggested Citation

  • Ghasemaghaei, Maryam, 2021. "Understanding the impact of big data on firm performance: The necessity of conceptually differentiating among big data characteristics," International Journal of Information Management, Elsevier, vol. 57(C).
    • Handle: RePEc:eee:ininma:v:57:y:2021:i:c:s0268401219310394
    DOI: 10.1016/j.ijinfomgt.2019.102055
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    Cited by:

    1. Falana, Gbenga Ayodele & Olusola Esther (PhD) & Dagunduro, Muyiwa Emmanuel, 2023. "Effect of Big Data on Accounting Information Quality in Selected Firms in Nigeria," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 7(3), pages 789-806, March.
    2. Oduro, Stephen & De Nisco, Alessandro & Mainolfi, Giada, 2023. "Do digital technologies pay off? A meta-analytic review of the digital technologies/firm performance nexus," Technovation, Elsevier, vol. 128(C).
    3. Perdana, Arif & Lee, Hwee Hoon & Koh, SzeKee & Arisandi, Desi, 2022. "Data analytics in small and mid-size enterprises: Enablers and inhibitors for business value and firm performance," International Journal of Accounting Information Systems, Elsevier, vol. 44(C).
    4. Lutfi, Abdalwali & Alrawad, Mahmaod & Alsyouf, Adi & Almaiah, Mohammed Amin & Al-Khasawneh, Ahmad & Al-Khasawneh, Akif Lutfi & Alshira'h, Ahmad Farhan & Alshirah, Malek Hamed & Saad, Mohamed & Ibrahim, 2023. "Drivers and impact of big data analytic adoption in the retail industry: A quantitative investigation applying structural equation modeling," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    5. 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.

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