IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v97y2019icp160-173.html
   My bibliography  Save this article

Unlocking the drivers of big data analytics value in firms

Author

Listed:
  • Côrte-Real, Nadine
  • Ruivo, Pedro
  • Oliveira, Tiago
  • Popovič, Aleš

Abstract

Although big data analytics (BDA) is considered the next “frontier” in data science by creating potential business opportunities, the way to extract those opportunities is unclear. This paper aims to understand the antecedents of BDA value at a firm level. The authors performed a study using a mixed methodology approach. First, by carrying out a Delphi study to explore and rank the antecedents affecting the creation of BDA value. Based on the Delphi results, we propose an empirically validated model supported by a survey conducted on 175 European firms to explain the antecedents of BDA sustained value. The results show that the proposed model explains 62% of BDA sustained value at the firm level, where the most critical contributor is BDA use. We provide directions for managers to support their decisions on BDA strategy definition and refinement. For academics, we extend BDA value literature and outline some potential research opportunities.

Suggested Citation

  • Côrte-Real, Nadine & Ruivo, Pedro & Oliveira, Tiago & Popovič, Aleš, 2019. "Unlocking the drivers of big data analytics value in firms," Journal of Business Research, Elsevier, vol. 97(C), pages 160-173.
    • Handle: RePEc:eee:jbrese:v:97:y:2019:i:c:p:160-173
    DOI: 10.1016/j.jbusres.2018.12.072
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296318306908
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2018.12.072?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Delen, Dursun & Zolbanin, Hamed M., 2018. "The analytics paradigm in business research," Journal of Business Research, Elsevier, vol. 90(C), pages 186-195.
    2. William Yeoh & Andy Koronios & Jing Gao, 2008. "Managing the Implementation of Business Intelligence Systems: A Critical Success Factors Framework," International Journal of Enterprise Information Systems (IJEIS), IGI Global, vol. 4(3), pages 79-94, July.
    3. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    4. Akkermans, Henk A. & Bogerd, Paul & Yucesan, Enver & van Wassenhove, Luk N., 2003. "The impact of ERP on supply chain management: Exploratory findings from a European Delphi study," European Journal of Operational Research, Elsevier, vol. 146(2), pages 284-301, April.
    5. Kevin Zhu & Kenneth L. Kraemer, 2005. "Post-Adoption Variations in Usage and Value of E-Business by Organizations: Cross-Country Evidence from the Retail Industry," Information Systems Research, INFORMS, vol. 16(1), pages 61-84, March.
    6. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    7. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    8. Gary C. Moore & Izak Benbasat, 1991. "Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation," Information Systems Research, INFORMS, vol. 2(3), pages 192-222, September.
    9. Ashish Gupta & Amit Deokar & Lakshmi Iyer & Ramesh Sharda & Dave Schrader, 2018. "Big Data & Analytics for Societal Impact: Recent Research and Trends," Information Systems Frontiers, Springer, vol. 20(2), pages 185-194, April.
    10. Wamba, Samuel Fosso & Gunasekaran, Angappa & Akter, Shahriar & Ren, Steven Ji-fan & Dubey, Rameshwar & Childe, Stephen J., 2017. "Big data analytics and firm performance: Effects of dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 356-365.
    11. Shirish Jeble & Rameshwar Dubey & Stephen J. Childe & Thanos Papadopoulos & David Roubaud & Anand Prakash, 2018. "Impact of big data and predictive analytics capability on supply chain sustainability," Post-Print hal-02061341, HAL.
    12. Norman Dalkey & Olaf Helmer, 1963. "An Experimental Application of the DELPHI Method to the Use of Experts," Management Science, INFORMS, vol. 9(3), pages 458-467, April.
    13. Ritu Agarwal & Vasant Dhar, 2014. "Editorial —Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research," Information Systems Research, INFORMS, vol. 25(3), pages 443-448, September.
    14. Côrte-Real, Nadine & Oliveira, Tiago & Ruivo, Pedro, 2017. "Assessing business value of Big Data Analytics in European firms," Journal of Business Research, Elsevier, vol. 70(C), pages 379-390.
    15. Nicky J. Welton & Howard H. Z. Thom, 2015. "Value of Information," Medical Decision Making, , vol. 35(5), pages 564-566, July.
    16. Lin, Yini & Wu, Lei-Yu, 2014. "Exploring the role of dynamic capabilities in firm performance under the resource-based view framework," Journal of Business Research, Elsevier, vol. 67(3), pages 407-413.
    17. Akter, Shahriar & Wamba, Samuel Fosso & Gunasekaran, Angappa & Dubey, Rameshwar & Childe, Stephen J., 2016. "How to improve firm performance using big data analytics capability and business strategy alignment?," International Journal of Production Economics, Elsevier, vol. 182(C), pages 113-131.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shet, Sateesh.V. & Poddar, Tanuj & Wamba Samuel, Fosso & Dwivedi, Yogesh K., 2021. "Examining the determinants of successful adoption of data analytics in human resource management – A framework for implications," Journal of Business Research, Elsevier, vol. 131(C), pages 311-326.
    2. Radicic, Dragana & Petković, Saša, 2023. "Impact of digitalization on technological innovations in small and medium-sized enterprises (SMEs)," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    3. Dignity Paradza & Olawande Daramola, 2021. "Business Intelligence and Business Value in Organisations: A Systematic Literature Review," Sustainability, MDPI, vol. 13(20), pages 1-27, October.
    4. Shah, Tushar R., 2022. "Can big data analytics help organisations achieve sustainable competitive advantage? A developmental enquiry," Technology in Society, Elsevier, vol. 68(C).
    5. Tsionas, Mike & Patel, Pankaj C. & Guedes, Maria João, 2022. "Endogenous efficiency of the dynamic profit maximization in the intertemporal production models of venture behavior," International Journal of Production Economics, Elsevier, vol. 246(C).
    6. Akter, Shahriar & Hossain, Md Afnan & Sajib, Shahriar & Sultana, Saida & Rahman, Mahfuzur & Vrontis, Demetris & McCarthy, Grace, 2023. "A framework for AI-powered service innovation capability: Review and agenda for future research," Technovation, Elsevier, vol. 125(C).
    7. Rengarajan, Srinath & Moser, Roger & Narayanamurthy, Gopalakrishnan, 2021. "Strategy tools in dynamic environments – An expert-panel study," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    8. Mihai BOGDAN & Anca BORZA, 2019. "Big Data Analytics and Organizational Performance: A Meta-Analysis Study," Management and Economics Review, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 4(2), pages 1-13, June.
    9. Volkmar, Gioia & Fischer, Peter M. & Reinecke, Sven, 2022. "Artificial Intelligence and Machine Learning: Exploring drivers, barriers, and future developments in marketing management," Journal of Business Research, Elsevier, vol. 149(C), pages 599-614.
    10. Gebhardt, Maximilian & Spieske, Alexander & Kopyto, Matthias & Birkel, Hendrik, 2022. "Increasing global supply chains’ resilience after the COVID-19 pandemic: Empirical results from a Delphi study," Journal of Business Research, Elsevier, vol. 150(C), pages 59-72.
    11. El-Haddadeh, Ramzi & Osmani, Mohamad & Hindi, Nitham & Fadlalla, Adam, 2021. "Value creation for realising the sustainable development goals: Fostering organisational adoption of big data analytics," Journal of Business Research, Elsevier, vol. 131(C), pages 402-410.
    12. Morimura, Fumikazu & Sakagawa, Yuji, 2023. "The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    13. 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.
    14. Rengarajan, Srinath & Narayanamurthy, Gopalakrishnan & Moser, Roger & Pereira, Vijay, 2022. "Data strategies for global value chains: Hybridization of small and big data in the aftermath of COVID-19," Journal of Business Research, Elsevier, vol. 144(C), pages 776-787.
    15. Reis, Carolina & Ruivo, Pedro & Oliveira, Tiago & Faroleiro, Paulo, 2020. "Assessing the drivers of machine learning business value," Journal of Business Research, Elsevier, vol. 117(C), pages 232-243.
    16. Judita Peterlin & Maja Meško & Vlado Dimovski & Vasja Roblek, 2021. "Automated content analysis: The review of the big data systemic discourse in tourism and hospitality," Systems Research and Behavioral Science, Wiley Blackwell, vol. 38(3), pages 377-385, May.
    17. Nan Wang & Wenxuan Xie & Yalan Huang & Zhenzhong Ma, 2023. "Big Data capability and sustainability oriented innovation: The mediating role of intellectual capital," Business Strategy and the Environment, Wiley Blackwell, vol. 32(8), pages 5702-5720, December.
    18. Mihai BOGDAN & Anca BORZA, 2019. "Big Data Analytics As A Strategic Capability: A Systematic Review," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 13(1), pages 575-583, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ghasemaghaei, Maryam & Calic, Goran, 2019. "Does big data enhance firm innovation competency? The mediating role of data-driven insights," Journal of Business Research, Elsevier, vol. 104(C), pages 69-84.
    2. Ghasemaghaei, Maryam & Calic, Goran, 2020. "Assessing the impact of big data on firm innovation performance: Big data is not always better data," Journal of Business Research, Elsevier, vol. 108(C), pages 147-162.
    3. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    4. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    5. Candice WALLS & Brian BARNARD, 2020. "Success Factors of Big Data to Achieve Organisational Performance: Theoretical Perspectives," Expert Journal of Business and Management, Sprint Investify, vol. 8(1), pages 1-16.
    6. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.
    7. Ashrafi, Amir & Zare Ravasan, Ahad & Trkman, Peter & Afshari, Samira, 2019. "The role of business analytics capabilities in bolstering firms’ agility and performance," International Journal of Information Management, Elsevier, vol. 47(C), pages 1-15.
    8. Patrick Mikalef & Ilias O. Pappas & John Krogstie & Michail Giannakos, 2018. "Big data analytics capabilities: a systematic literature review and research agenda," Information Systems and e-Business Management, Springer, vol. 16(3), pages 547-578, August.
    9. Nguyen Anh Khoa Dam & Thang Le Dinh & William Menvielle, 2019. "A systematic literature review of big data adoption in internationalization," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(3), pages 182-195, September.
    10. Elisabetta Raguseo & Claudio Vitari, 2017. "Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects," Grenoble Ecole de Management (Post-Print) halshs-01923259, HAL.
    11. Liedong, Tahiru Azaaviele & Rajwani, Tazeeb & Lawton, Thomas C., 2020. "Information and nonmarket strategy: Conceptualizing the interrelationship between big data and corporate political activity," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    12. Conboy, Kieran & Mikalef, Patrick & Dennehy, Denis & Krogstie, John, 2020. "Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda," European Journal of Operational Research, Elsevier, vol. 281(3), pages 656-672.
    13. Zhang, Yucheng & Hou, Zhongwei & Yang, Feifei & Yang, Miles M. & Wang, Zhiling, 2021. "Discovering the evolution of resource-based theory: Science mapping based on bibliometric analysis," Journal of Business Research, Elsevier, vol. 137(C), pages 500-516.
    14. Elisabetta Raguseo & Claudio Vitari, 2017. "Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects," Post-Print halshs-01923259, HAL.
    15. Mihai BOGDAN & Anca BORZA, 2019. "Big Data Analytics and Organizational Performance: A Meta-Analysis Study," Management and Economics Review, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 4(2), pages 1-13, June.
    16. Vicky Ching Gu & Bin Zhou & Qing Cao & Jeffery Adams, 2021. "Exploring the relationship between supplier development, big data analytics capability, and firm performance," Annals of Operations Research, Springer, vol. 302(1), pages 151-172, July.
    17. Ciampi, Francesco & Demi, Stefano & Magrini, Alessandro & Marzi, Giacomo & Papa, Armando, 2021. "Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation," Journal of Business Research, Elsevier, vol. 123(C), pages 1-13.
    18. Alberto Bertello & Alberto Ferraris & Stefano Bresciani & Paola Bernardi, 2021. "Big data analytics (BDA) and degree of internationalization: the interplay between governance of BDA infrastructure and BDA capabilities," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 25(4), pages 1035-1055, December.
    19. Morimura, Fumikazu & Sakagawa, Yuji, 2023. "The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    20. Brinch, Morten & Gunasekaran, Angappa & Fosso Wamba, Samuel, 2021. "Firm-level capabilities towards big data value creation," Journal of Business Research, Elsevier, vol. 131(C), pages 539-548.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbrese:v:97:y:2019:i:c:p:160-173. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.