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Big data analytics and business failures in data-Rich environments: An organizing framework

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  • Amankwah-Amoah, Joseph

Abstract

In view of the burgeoning scholarly works on big data and big data analytical capabilities, there remains limited research on how different access to big data and different big data analytic capabilities possessed by firms can generate diverse conditions leading to business failure. To fill this gap in the existing literature, an integrated framework was developed that entailed two approaches to big data as an asset (i.e. threshold resource and distinctive resource) and two types of competences in big data analytics (i.e. threshold competence and distinctive/core competence). The analysis provides insights into how ordinary big data analytic capability and mere possession of big data are more likely to create conditions for business failure. The study extends the existing streams of research by shedding light on decisions and processes in facilitating or hampering firms’ ability to harness big data to mitigate the cause of business failures. The analysis led to the categorisation of a number of fruitful avenues for research on data-driven approaches to business failure.

Suggested Citation

  • Amankwah-Amoah, Joseph, 2019. "Big data analytics and business failures in data-Rich environments: An organizing framework," MPRA Paper 91264, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:91264
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    Cited by:

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    2. Mohamed Saeudy & Ali Meftah Gerged & Khaldoon Albitar, 2022. "Accounting Perspectives on The Business Value of Big Data During and Beyond The COVID-19 Pandemic," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 21(2), pages 174-199, June.
    3. Yasmin, Mariam & Tatoglu, Ekrem & Kilic, Huseyin Selcuk & Zaim, Selim & Delen, Dursun, 2020. "Big data analytics capabilities and firm performance: An integrated MCDM approach," Journal of Business Research, Elsevier, vol. 114(C), pages 1-15.
    4. Fernando Zambrano Farias & María del Carmen Valls Martínez & Pedro Antonio Martín-Cervantes, 2021. "Explanatory Factors of Business Failure: Literature Review and Global Trends," Sustainability, MDPI, vol. 13(18), pages 1-26, September.
    5. Khaled Naser Yousef Magableh & Selvi Kannan & Aladeen Yousef Rashid Hmoud, 2024. "Innovation Business Model: Adoption of Blockchain Technology and Big Data Analytics," Sustainability, MDPI, vol. 16(14), pages 1-25, July.
    6. Sumera Ahmad & Suraya Miskon & Rana Alabdan & Iskander Tlili, 2020. "Towards Sustainable Textile and Apparel Industry: Exploring the Role of Business Intelligence Systems in the Era of Industry 4.0," Sustainability, MDPI, vol. 12(7), pages 1-23, March.
    7. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    8. Adomako, Samuel & Amankwah-Amoah, Joseph & Tarba, Shlomo Y. & Khan, Zaheer, 2021. "Perceived corruption, business process digitization, and SMEs’ degree of internationalization in sub-Saharan Africa," Journal of Business Research, Elsevier, vol. 123(C), pages 196-207.
    9. Mansour Alyahya & Meqbel Aliedan & Gomaa Agag & Ziad H. Abdelmoety, 2023. "Understanding the Relationship between Big Data Analytics Capabilities and Sustainable Performance: The Role of Strategic Agility and Firm Creativity," Sustainability, MDPI, vol. 15(9), pages 1-17, May.
    10. 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.

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    More about this item

    Keywords

    big data analytics; technology; innovation management; big data; business failure;
    All these keywords.

    JEL classification:

    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L2 - Industrial Organization - - Firm Objectives, Organization, and Behavior

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