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Big Data Analytics in Building the Competitive Intelligence of Organizations

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  • Ranjan, Jayanthi
  • Foropon, Cyril

Abstract

Over recent years, organizations have started to capitalize on the significant use of Big Data and emerging technologies to analyze, and gain valuable insights linked to, decision-making processes. The process of Competitive Intelligence (CI) includes monitoring competitors with a view to delivering both actionable and meaningful intelligence to organizations. In this regard, the capacity to leverage and unleash the potential of big data tools and techniques is one of various significant components of successfully steering CI and ultimately infusing such valuable knowledge into CI strategies. In this paper, the authors aim to examine Big Data applications in CI processes within organizations by exploring how organizations deal with Big Data analytics, and this study provides a context for developing Big Data frameworks and process models for CI in organizations. Overall, research findings have indicated a preference for a rather centralized informal process as opposed to a clear formal structure for CI; the use of basic tools for queries, as opposed to reliance on dedicated methods such as advanced machine learning; and the existence of multiple challenges that companies currently face regarding the use of big data analytics in building organizational CI.

Suggested Citation

  • Ranjan, Jayanthi & Foropon, Cyril, 2021. "Big Data Analytics in Building the Competitive Intelligence of Organizations," International Journal of Information Management, Elsevier, vol. 56(C).
    • Handle: RePEc:eee:ininma:v:56:y:2021:i:c:s0268401220314304
    DOI: 10.1016/j.ijinfomgt.2020.102231
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    Citations

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    Cited by:

    1. 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.
    2. Adilson Carlos Yoshikuni & Rajeev Dwivedi & Ronaldo Gomes Dultra-de-Lima & Claudio Parisi & José Carlos Tiomatsu Oyadomari, 2023. "Role of Emerging Technologies in Accounting Information Systems for Achieving Strategic Flexibility through Decision-Making Performance: An Exploratory Study Based on North American and South American," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(2), pages 199-218, June.
    3. Shafique, Muhammad Noman & Yeo, Sook Fern & Tan, Cheng Ling, 2024. "Roles of top management support and compatibility in big data predictive analytics for supply chain collaboration and supply chain performance," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    4. Showimy Aldossari & Umi Asma’ Mokhtar & Ahmad Tarmizi Abdul Ghani, 2023. "Factor Influencing the Adoption of Big Data Analytics: A Systematic Literature and Experts Review," SAGE Open, , vol. 13(4), pages 21582440231, December.
    5. Luqman, Adeel & Wang, Liangyu & Katiyar, Gagan & Agarwal, Reeti & Mohapatra, Amiya Kumar, 2024. "Unpacking associations between positive-negative valence and ambidexterity of big data. Implications for firm performance," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    6. Oliver Schaer & Nikolaos Kourentzes & Robert Fildes, 2022. "Predictive competitive intelligence with prerelease online search traffic," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3823-3839, October.
    7. Juan J. Lull & Roberto Cervelló-Royo & José Luis Galdón, 2024. "Crossroads between Big Data and entrepreneurship: current key trends," International Entrepreneurship and Management Journal, Springer, vol. 20(4), pages 2763-2790, December.
    8. Alshawawreh, Ali Ra’Ed & Liébana-Cabanillas, Francisco & Blanco-Encomienda, Francisco Javier, 2024. "Impact of big data analytics on telecom companies' competitive advantage," Technology in Society, Elsevier, vol. 76(C).
    9. Govindan, Kannan & Kannan, Devika & Jørgensen, Thomas Ballegård & Nielsen, Tim Straarup, 2022. "Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    10. Maria Hoffmann Jensen & John Stouby Persson & Peter Axel Nielsen, 2023. "Measuring benefits from big data analytics projects: an action research study," Information Systems and e-Business Management, Springer, vol. 21(2), pages 323-352, June.
    11. Oesterreich, Thuy Duong & Anton, Eduard & Teuteberg, Frank & Dwivedi, Yogesh K, 2022. "The role of the social and technical factors in creating business value from big data analytics: A meta-analysis," Journal of Business Research, Elsevier, vol. 153(C), pages 128-149.
    12. Yang, Li & Zou, Haobo & Shang, Chao & Ye, Xiaoming & Rani, Pratibha, 2023. "Adoption of information and digital technologies for sustainable smart manufacturing systems for industry 4.0 in small, medium, and micro enterprises (SMMEs)," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    13. 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.
    14. Korayim, Diana & Chotia, Varun & Jain, Girish & Hassan, Sharfa & Paolone, Francesco, 2024. "How big data analytics can create competitive advantage in high-stake decision forecasting? The mediating role of organizational innovation," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    15. Hassani, Abdeslam & Mosconi, Elaine, 2022. "Social media analytics, competitive intelligence, and dynamic capabilities in manufacturing SMEs," Technological Forecasting and Social Change, Elsevier, vol. 175(C).

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