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Using Business Analytics for SME Business Model Transformation under Pandemic Time Pressure

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  • Efpraxia D. Zamani

    (The University of Sheffield)

  • Anastasia Griva

    (National University of Ireland Galway)

  • Kieran Conboy

    (National University of Ireland Galway)

Abstract

The COVID-19 pandemic has had an unprecedented impact on many industry sectors, forcing many companies and particularly Small Medium Enterprises (SMEs) to fundamentally change their business models under extreme time pressure. While there are claims that technologies such as analytics can help such rapid transitions, little empirical research exists that shows if or how Business Analytics (BA) supports the adaptation or innovation of SMEs’ business models, let alone within the context of extreme time pressure and turbulence. This study addresses this gap through an exemplar case, where the SME actively used location-based business analytics for rapid business model adaptation and innovation during the Covid-19 crisis. The paper contributes to existing theory by providing a set of propositions, an agenda for future research and a guide for SMEs to assess and implement their own use of analytics for business model transformation.

Suggested Citation

  • Efpraxia D. Zamani & Anastasia Griva & Kieran Conboy, 2022. "Using Business Analytics for SME Business Model Transformation under Pandemic Time Pressure," Information Systems Frontiers, Springer, vol. 24(4), pages 1145-1166, August.
    • Handle: RePEc:spr:infosf:v:24:y:2022:i:4:d:10.1007_s10796-022-10255-8
    DOI: 10.1007/s10796-022-10255-8
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    References listed on IDEAS

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    Cited by:

    1. Efpraxia D. Zamani & Conn Smyth & Samrat Gupta & Denis Dennehy, 2023. "Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review," Annals of Operations Research, Springer, vol. 327(2), pages 605-632, August.
    2. Kenneth E. Poole & Allison Forbes & Nichelle Williams, 2023. "Applied Regional Economic Research Can Improve Development Strategies and Drive Better Outcomes," Economic Development Quarterly, , vol. 37(1), pages 85-95, February.
    3. He Huang & Liwei Zhong & Ting Shen & Huixin Wang, 2022. "Performance prediction and optimization for healthcare enterprises in the context of the COVID-19 pandemic: an intelligent DEA-SVM model," Journal of Combinatorial Optimization, Springer, vol. 44(5), pages 3778-3791, December.
    4. Nisreen Ameen & Jyoti Choudrie & Paul Jones & Amitabh Anand, 2022. "Innovative Technologies and Small-Medium Sized Enterprises in Times of Crisis," Information Systems Frontiers, Springer, vol. 24(4), pages 1055-1060, August.

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