IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v10y2023i1d10.1057_s41599-023-02122-x.html
   My bibliography  Save this article

From insights to impact: leveraging data analytics for data-driven decision-making and productivity in banking sector

Author

Listed:
  • Raazia Gul

    (Faculty of Management Sciences, SZABIST University)

  • Mamdouh Abdulaziz Saleh Al-Faryan

    (University of Portsmouth
    Consultant in Economics and Finance)

Abstract

This research stems from the disturbing phenomenon known as digital transformation and the colossal data creation. A vast amount of data has been produced as a result of increased usage of digital technologies to generate commercial value. However, data does not hold any value, so organizations are motivated to invest in data analytics and make informed decisions to enhance their performance. Against this backdrop, this study investigates the impact of data-driven decision-making (DDDM) on productivity in the presence of data analytics capability of Pakistan’s banking sector. We explore this link based on innovation diffusion theory using Instrumental Variable Two-Stage Least Square. A composite index of DDDM was developed based on primary data collected through online survey. This index is then regressed on actual productivity measures for 2016–2020. The findings suggest that banks exploiting analytics and adopting DDDM methods results in an increase in productivity of about 9–10%. It further indicates that DDDM adoption with an investment in data analytics leads to enhanced productivity.

Suggested Citation

  • Raazia Gul & Mamdouh Abdulaziz Saleh Al-Faryan, 2023. "From insights to impact: leveraging data analytics for data-driven decision-making and productivity in banking sector," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-8, December.
    • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02122-x
    DOI: 10.1057/s41599-023-02122-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-023-02122-x
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-023-02122-x?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. Raazia Gul & Nazima Ellahi, 2021. "The nexus between data analytics and firm performance," Cogent Business & Management, Taylor & Francis Journals, vol. 8(1), pages 1923360-192, January.
    2. Acharya, Abhilash & Singh, Sanjay Kumar & Pereira, Vijay & Singh, Poonam, 2018. "Big data, knowledge co-creation and decision making in fashion industry," International Journal of Information Management, Elsevier, vol. 42(C), pages 90-101.
    3. Erik Brynjolfsson & Kristina McElheran, 2016. "The Rapid Adoption of Data-Driven Decision-Making," American Economic Review, American Economic Association, vol. 106(5), pages 133-139, May.
    4. Mikalef, Patrick & Boura, Maria & Lekakos, George & Krogstie, John, 2019. "Big data analytics and firm performance: Findings from a mixed-method approach," Journal of Business Research, Elsevier, vol. 98(C), pages 261-276.
    5. Jalil, Abdul & Feridun, Mete & Ma, Ying, 2010. "Finance-growth nexus in China revisited: New evidence from principal components and ARDL bounds tests," International Review of Economics & Finance, Elsevier, vol. 19(2), pages 189-195, April.
    6. Prasanna Tambe, 2014. "Big Data Investment, Skills, and Firm Value," Management Science, INFORMS, vol. 60(6), pages 1452-1469, June.
    7. Martín-Oliver, Alfredo & Salas-Fumás, Vicente, 2008. "The output and profit contribution of information technology and advertising investments in banks," Journal of Financial Intermediation, Elsevier, vol. 17(2), pages 229-255, April.
    8. Nafis Alam & Lokesh Gupta & Abdolhossein Zameni, 2019. "Fintech and Islamic Finance," Springer Books, Springer, number 978-3-030-24666-2, December.
    9. Daud, Siti Nurazira Mohd & Ahmad, Abd Halim, 2023. "Financial inclusion, economic growth and the role of digital technology," Finance Research Letters, Elsevier, vol. 53(C).
    Full references (including those not matched with items on IDEAS)

    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. Nicolas Ameye & Jacques Bughin & Nicolas van Zeebroeck, 2024. "From experimentation to scaling: what shapes the funnel of AI adoption?," ULB Institutional Repository 2013/378623, ULB -- Universite Libre de Bruxelles.
    2. Han Bu & Zhou Xun & Sha Cai, 2024. "Big data and inter-firm wage disparities: theory and evidence from China," Economic Change and Restructuring, Springer, vol. 57(4), pages 1-36, August.
    3. Sun, Pengfei & Yuan, Chunhui & Li, Xiaolong & Di, Jia, 2024. "Big data analytics, firm risk and corporate policies: Evidence from China," Research in International Business and Finance, Elsevier, vol. 70(PB).
    4. 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).
    5. Elisabetta Raguseo & Claudio Vitari & Federico Pigni, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," Post-Print hal-03032504, HAL.
    6. Kristina McElheran & J. Frank Li & Erik Brynjolfsson & Zachary Kroff & Emin Dinlersoz & Lucia Foster & Nikolas Zolas, 2024. "AI adoption in America: Who, what, and where," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 375-415, March.
    7. Rodepeter, Elisa & Gschnaidtner, Christoph & Hottenrott, Hanna, 2023. "Big data and start-up performance," ZEW Discussion Papers 23-061, ZEW - Leibniz Centre for European Economic Research.
    8. Elisa Rodepeter & Christoph Gschnaidtner & Hanna Hottenrott, 2024. "Big Data and Start-up Performance," Working Papers 232, Bavarian Graduate Program in Economics (BGPE).
    9. Lynn Wu & Lorin Hitt & Bowen Lou, 2020. "Data Analytics, Innovation, and Firm Productivity," Management Science, INFORMS, vol. 66(5), pages 2017-2039, May.
    10. Geylani, Pinar Celikkol & Park, Timothy A. & Restrepo, Brandon J., 2024. "The Role of Managerial and Organizational Practices in Explaining Productivity Differences: A Study of U.S. Food Manufacturing Firms," 2024 Annual Meeting, July 28-30, New Orleans, LA 343704, Agricultural and Applied Economics Association.
    11. Raguseo, Elisabetta & Vitari, Claudio & Pigni, Federico, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," International Journal of Production Economics, Elsevier, vol. 229(C).
    12. Rodepeter, Elisa & Gschnaidtner, Christoph & Hottenrott, Hanna, 2023. "Big data and start-up performance," ZEW Discussion Papers 23-061, ZEW - Leibniz Centre for European Economic Research.
    13. Charles Hoffreumon & Chris Forman & Nicolas van Zeebroeck, 2024. "Make or buy your artificial intelligence? Complementarities in technology sourcing," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 452-479, March.
    14. Christian Peukert & Imke Reimers, 2022. "Digitization, Prediction, and Market Efficiency: Evidence from Book Publishing Deals," Management Science, INFORMS, vol. 68(9), pages 6907-6924, September.
    15. Lynn Wu & Bowen Lou & Lorin Hitt, 2019. "Data Analytics Supports Decentralized Innovation," Management Science, INFORMS, vol. 65(10), pages 4863-4877, October.
    16. Perdana, Arif & Lee, Hwee Hoon & Koh, SzeKee & Arisandi, Desi, 2022. "Data analytics in small and mid-size enterprises: Enablers and inhibitors for business value and firm performance," International Journal of Accounting Information Systems, Elsevier, vol. 44(C).
    17. Elisabetta Raguseo & Claudio Vitari & Federico Pigni, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," Grenoble Ecole de Management (Post-Print) hal-03032504, HAL.
    18. Prasanna Tambe & Xuan Ye & Peter Cappelli, 2020. "Paying to Program? Engineering Brand and High-Tech Wages," Management Science, INFORMS, vol. 66(7), pages 3010-3028, July.
    19. Claudio Vitari & Elisabetta Raguseo, 2016. "Big data value and financial performance: an empirical investigation [Digital data, dynamic capability and financial performance: an empirical investigation in the era of Big Data]," Post-Print halshs-01923271, HAL.
    20. Labro, Eva & Lang, Mark & Omartian, James D., 2023. "Predictive analytics and centralization of authority," Journal of Accounting and Economics, Elsevier, vol. 75(1).

    More about this item

    Statistics

    Access and download statistics

    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:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02122-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.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.