IDEAS home Printed from https://ideas.repec.org/a/gam/jecomi/v11y2023i3p99-d1103580.html
   My bibliography  Save this article

Business Intelligence (BI) in Firm Performance: Role of Big Data Analytics and Blockchain Technology

Author

Listed:
  • Mladen Pancić

    (Faculty of Economics in Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
    These authors contributed equally to this work.)

  • Dražen Ćućić

    (Faculty of Economics in Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
    These authors contributed equally to this work.)

  • Hrvoje Serdarušić

    (Faculty of Economics in Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
    These authors contributed equally to this work.)

Abstract

The analysis of the causes or drivers of the adoption of big data analytics and blockchain and their subsequent influence on firm performance has become a significant need as a direct result of the rapidly expanding popularity of business intelligence. The purpose of this research is to present a model that investigates the direct and indirect influence of business intelligence on firm performance through the mediating roles of the adoption of big data analytics and blockchain. The analysis is based on data collected from a representative sample of 387 employees from 12 Information technology (IT) firms operating in Croatia. The study investigates these connections using a structural equation modeling. The findings showed that business intelligence has a direct and significant influence on firm performance. In addition, business intelligence significantly and positively influenced the adoption of big data analytics and blockchain and, in turn, firm performance. Additionally, the adoption of big data analytics and blockchain technology signified and positively mediated the relationship between business intelligence and firm performance. Both the mediations were partial. Finally, the study also provides managerial implications, limitations and future directions.

Suggested Citation

  • Mladen Pancić & Dražen Ćućić & Hrvoje Serdarušić, 2023. "Business Intelligence (BI) in Firm Performance: Role of Big Data Analytics and Blockchain Technology," Economies, MDPI, vol. 11(3), pages 1-19, March.
    • Handle: RePEc:gam:jecomi:v:11:y:2023:i:3:p:99-:d:1103580
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7099/11/3/99/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7099/11/3/99/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Shuiye Niu & Honglong Zhuo & Kelei Xue, 2019. "DfRem-Driven Closed-Loop Supply Chain Decision-Making: A Systematic Framework for Modeling Research," Sustainability, MDPI, vol. 11(12), pages 1-19, June.
    3. Muntean, Mihaela & Cabau, Liviu Gabriel, 2011. "Business Intelligence Approach In A Business Performance Context," MPRA Paper 29914, University Library of Munich, Germany.
    4. Paul Chou, 2018. "Workplace Social Support and Attitude toward Enterprise Resource Planning System: A Perspective of Organizational Change," International Journal of Information Systems and Social Change (IJISSC), IGI Global, vol. 9(1), pages 58-76, January.
    5. Joon-Seok Kim & Nina Shin, 2019. "The Impact of Blockchain Technology Application on Supply Chain Partnership and Performance," Sustainability, MDPI, vol. 11(21), pages 1-17, November.
    6. Dash, Ganesh & Paul, Justin, 2021. "CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    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. 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.

    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. Tabesh, Pooya & Mousavidin, Elham & Hasani, Sona, 2019. "Implementing big data strategies: A managerial perspective," Business Horizons, Elsevier, vol. 62(3), pages 347-358.
    2. Kautish, Pradeep & Paço, Arminda & Thaichon, Park, 2022. "Sustainable consumption and plastic packaging: Relationships among product involvement, perceived marketplace influence and choice behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    3. Aaltonen, Aleksi Ville & Alaimo, Cristina & Kallinikos, Jannis, 2021. "The making of data commodities: data analytics as an embedded process," LSE Research Online Documents on Economics 110296, London School of Economics and Political Science, LSE Library.
    4. Hongyi Sun & Bingqian Zhang & Wenbin Ni, 2022. "A Hybrid Model Based on SEM and Fuzzy TOPSIS for Supplier Selection," Mathematics, MDPI, vol. 10(19), pages 1-19, September.
    5. Alfiero, Simona & Battisti, Enrico & Ηadjielias, Elias, 2022. "Black box technology, usage-based insurance, and prediction of purchase behavior: Evidence from the auto insurance sector," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    6. Michela Arnaboldi, 2018. "The Missing Variable in Big Data for Social Sciences: The Decision-Maker," Sustainability, MDPI, vol. 10(10), pages 1-18, September.
    7. Anike Sult & Janice Wobst & Rainer Lueg, 2024. "The role of training in implementing corporate sustainability: A systematic literature review," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(1), pages 1-30, January.
    8. Brewis, Claire & Dibb, Sally & Meadows, Maureen, 2023. "Leveraging big data for strategic marketing: A dynamic capabilities model for incumbent firms," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    9. Leogrande, Angelo, 2021. "The Destruction of Price-Representativeness," MPRA Paper 111239, University Library of Munich, Germany.
    10. Damminda Alahakoon & Rashmika Nawaratne & Yan Xu & Daswin Silva & Uthayasankar Sivarajah & Bhumika Gupta, 2023. "Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities," Information Systems Frontiers, Springer, vol. 25(1), pages 221-240, February.
    11. Taiwen Feng & Hongyan Sheng, 2023. "Identifying the equifinal configurations of prompting green supply chain integration and subsequent performance outcome," Business Strategy and the Environment, Wiley Blackwell, vol. 32(8), pages 5234-5251, December.
    12. Yihan Huang & Daehwan Kim, 2023. "How Does Service Quality Improve Consumer Loyalty in Sports Fitness Centers? The Moderating Role of Sport Involvement," Sustainability, MDPI, vol. 15(17), pages 1-24, August.
    13. Syed Abdul Rehman Khan & Arsalan Zahid Piprani & Zhang Yu, 2022. "Digital technology and circular economy practices: future of supply chains," Operations Management Research, Springer, vol. 15(3), pages 676-688, December.
    14. Thanh Tung Ha & Thanh Chuong Nguyen & Sy Sua Tu & Minh Hieu Nguyen, 2023. "Investigation of Influential Factors of Intention to Adopt Electric Vehicles for Motorcyclists in Vietnam," Sustainability, MDPI, vol. 15(11), pages 1-16, May.
    15. Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2021. "Data Analytics Diffusion in the UK Renewable Energy Sector: An Innovation Perspective," Post-Print hal-03781046, HAL.
    16. Correa, Juan C. & Garzón, Wilmer & Brooker, Phillip & Sakarkar, Gopal & Carranza, Steven A. & Yunado, Leidy & Rincón, Alejandro, 2019. "Evaluation of collaborative consumption of food delivery services through web mining techniques," Journal of Retailing and Consumer Services, Elsevier, vol. 46(C), pages 45-50.
    17. Cheng-Feng Cheng & Chien-Che Huang & Ming-Chang Lin & Ta-Cheng Chen, 2023. "Exploring Effectiveness of Relationship Marketing on Artificial Intelligence Adopting Intention," SAGE Open, , vol. 13(4), pages 21582440231, December.
    18. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    19. Anthony Martins & Maryam Abbasi & Pedro Martins & Filipe Sá, 2022. "BigData oriented to business decision making: a real case study in constructel," Computational and Mathematical Organization Theory, Springer, vol. 28(3), pages 271-291, September.
    20. Andreas Schwab & Zhu Zhang, 2019. "A New Methodological Frontier in Entrepreneurship Research: Big Data Studies," Entrepreneurship Theory and Practice, , vol. 43(5), pages 843-854, September.

    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:gam:jecomi:v:11:y:2023:i:3:p:99-:d:1103580. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.