IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i12p7316-d839241.html
   My bibliography  Save this article

Pull and Push Factors of Data Analytics Adoption and Its Mediating Role on Operational Performance

Author

Listed:
  • Luther Yuong Qai Chong

    (Faculty of Business, Economics and Accountancy, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia)

  • Thien Sang Lim

    (Faculty of Business, Economics and Accountancy, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia)

Abstract

Despite the influx of data analytics (DA) practices among firms, their impact on operational performance remains ambiguous. This study examined the pull and push factors affecting the data analytics adoption (DAA) from the theoretical perspectives of the technology–organization–environment (TOE) model, theory of perceived risk (TPR), and resource-based view (RBV). The study analyzed data from 169 firms on the basis of the positivist paradigm and employed the partial least square to run the reflective–formative two-stage analysis. Accordingly, the results indicated that the three TOE model aspects exhibited a positive direct impact on DAA and indirectly impacted operational performance through DAA. However, the perceived risk did not display a similar effect in both situations. This study further revealed that the environment push factor had more explanatory power than the perceived risk pull factor, suggesting that a conducive TOE environment would motivate DAA, subsequently enhancing operational performance. The study provided valuable empirical evidence on the determinants of DAA and its subsequent effect on firms’ operational performance. Uniquely, the study also contributed to the literature from the perspective of higher-order-construct analysis in examining the determinants of DAA and its effect on operational performance. Furthermore, the mediation analysis covered the interaction of indirect-path coefficients, minimizing errors in interpreting the mediation effect.

Suggested Citation

  • Luther Yuong Qai Chong & Thien Sang Lim, 2022. "Pull and Push Factors of Data Analytics Adoption and Its Mediating Role on Operational Performance," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7316-:d:839241
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/12/7316/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/12/7316/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Boulaksil, Youssef, 2016. "Safety stock placement in supply chains with demand forecast updates," Operations Research Perspectives, Elsevier, vol. 3(C), pages 27-31.
    2. Surabhi Verma & Sushil Chaurasia, 2019. "Understanding the Determinants of Big Data Analytics Adoption," Information Resources Management Journal (IRMJ), IGI Global, vol. 32(3), pages 1-26, July.
    3. Danese, Pamela & Kalchschmidt, Matteo, 2011. "The role of the forecasting process in improving forecast accuracy and operational performance," International Journal of Production Economics, Elsevier, vol. 131(1), pages 204-214, May.
    4. Elisabetta Raguseo & Claudio Vitari, 2018. "Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects," International Journal of Production Research, Taylor & Francis Journals, vol. 56(15), pages 5206-5221, August.
    5. Raut, Rakesh D. & Mangla, Sachin Kumar & Narwane, Vaibhav S. & Dora, Manoj & Liu, Mengqi, 2021. "Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    6. Arunachalam, Deepak & Kumar, Niraj & Kawalek, John Paul, 2018. "Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 416-436.
    7. Ploypailin Kijkasiwat & Pongsutti Phuensane, 2020. "Innovation and Firm Performance: The Moderating and Mediating Roles of Firm Size and Small and Medium Enterprise Finance," JRFM, MDPI, vol. 13(5), pages 1-15, May.
    8. Raguseo, Elisabetta, 2018. "Big data technologies: An empirical investigation on their adoption, benefits and risks for companies," International Journal of Information Management, Elsevier, vol. 38(1), pages 187-195.
    9. Kevin Zhu & Kenneth L. Kraemer & Sean Xu, 2006. "The Process of Innovation Assimilation by Firms in Different Countries: A Technology Diffusion Perspective on E-Business," Management Science, INFORMS, vol. 52(10), pages 1557-1576, October.
    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. Hemlata Gangwar, 2018. "Understanding the Determinants of Big Data Adoption in India: An Analysis of the Manufacturing and Services Sectors," Information Resources Management Journal (IRMJ), IGI Global, vol. 31(4), pages 1-22, October.
    12. Sarstedt, Marko & Hair, Joseph F. & Cheah, Jun-Hwa & Becker, Jan-Michael & Ringle, Christian M., 2019. "How to specify, estimate, and validate higher-order constructs in PLS-SEM," Australasian marketing journal, Elsevier, vol. 27(3), pages 197-211.
    13. 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.
    14. 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.
    15. Khayer, Abul & Talukder, Md. Shamim & Bao, Yukun & Hossain, Md. Nahin, 2020. "Cloud computing adoption and its impact on SMEs’ performance for cloud supported operations: A dual-stage analytical approach," Technology in Society, Elsevier, vol. 60(C).
    16. Eder, Lauren B. & Igbaria, Magid, 2001. "Determinants of intranet diffusion and infusion," Omega, Elsevier, vol. 29(3), pages 233-242, June.
    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. Md Ahsan Uddin Murad & Dilek Cetindamar & Subrata Chakraborty, 2022. "Identifying the Key Big Data Analytics Capabilities in Bangladesh’s Healthcare Sector," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    2. Rajesh Chidananda Reddy & Biplab Bhattacharjee & Debasisha Mishra & Anandadeep Mandal, 2022. "A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy," Information Systems and e-Business Management, Springer, vol. 20(1), pages 223-255, March.
    3. Bag, Surajit & Rahman, Muhammad Sabbir & Srivastava, Gautam & Shore, Adam & Ram, Pratibha, 2023. "Examining the role of virtue ethics and big data in enhancing viable, sustainable, and digital supply chain performance," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    4. AlNuaimi, Bader Khamis & Khan, Mehmood & Ajmal, Mian M., 2021. "The role of big data analytics capabilities in greening e-procurement: A higher order PLS-SEM analysis," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    5. Kristoffersen, Eivind & Mikalef, Patrick & Blomsma, Fenna & Li, Jingyue, 2021. "The effects of business analytics capability on circular economy implementation, resource orchestration capability, and firm performance," International Journal of Production Economics, Elsevier, vol. 239(C).
    6. 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).
    7. Lutfi, Abdalwali & Alrawad, Mahmaod & Alsyouf, Adi & Almaiah, Mohammed Amin & Al-Khasawneh, Ahmad & Al-Khasawneh, Akif Lutfi & Alshira'h, Ahmad Farhan & Alshirah, Malek Hamed & Saad, Mohamed & Ibrahim, 2023. "Drivers and impact of big data analytic adoption in the retail industry: A quantitative investigation applying structural equation modeling," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    8. Changchun Zhu & Jianguo Du & Fakhar Shahzad & Muhammad Umair Wattoo, 2022. "Environment Sustainability Is a Corporate Social Responsibility: Measuring the Nexus between Sustainable Supply Chain Management, Big Data Analytics Capabilities, and Organizational Performance," Sustainability, MDPI, vol. 14(6), pages 1-20, March.
    9. 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.
    10. Wang, Yonggui & Tian, Qinghong & Li, Xia & Xiao, Xiaohong, 2022. "Different roles, different strokes: How to leverage two types of digital platform capabilities to fuel service innovation," Journal of Business Research, Elsevier, vol. 144(C), pages 1121-1128.
    11. Meadows, Maureen & Merendino, Alessandro & Dibb, Sally & Garcia-Perez, Alexeis & Hinton, Matthew & Papagiannidis, Savvas & Pappas, Ilias & Wang, Huamao, 2022. "Tension in the data environment: How organisations can meet the challenge," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Maya Vachkova & Arsalan Ghouri & Haidy Ashour & Normalisa Binti Md Isa & Gregory Barnes, 2023. "Big data and predictive analytics and Malaysian micro-, small and medium businesses," SN Business & Economics, Springer, vol. 3(8), pages 1-28, August.
    17. Rialti, Riccardo & Zollo, Lamberto & Ferraris, Alberto & Alon, Ilan, 2019. "Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    18. 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.
    19. 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.
    20. 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.

    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:jsusta:v:14:y:2022:i:12:p:7316-:d:839241. 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.