IDEAS home Printed from https://ideas.repec.org/a/fgv/eaerae/v59y2019i6a80773.html
   My bibliography  Save this article

Intenção de adoção de big data na cadeia de suprimentos: Uma perspectiva brasileira

Author

Listed:
  • Queiroz, Maciel M.
  • Pereira, Susana Carla Farias

Abstract

Big data applications have been remodeling several business models and provoking strong radical transformations in supply chain management (SCM). Supported by the literature on big data, supply chain management, and the unified theory of acceptance and use of technology (UTAUT), this study aims to evaluate the variables that influence the intention of Brazilian SCM professionals to adopt big data. To this end, we adapted and validated a previously developed UTAUT model. A survey of 152 supply chain respondents revealed that facilitating conditions (e.g., IT infrastructure) have a high influence on their intention to adopt big data. However, social influence and performance expectancy showed no significant effect. This study contributes to the practical field, offering valuable insights for decision-makers considering big data projects. It also contributes to the literature by helping minimize the research gap in big data in the Brazilian context.

Suggested Citation

  • Queiroz, Maciel M. & Pereira, Susana Carla Farias, 2019. "Intenção de adoção de big data na cadeia de suprimentos: Uma perspectiva brasileira," RAE - Revista de Administração de Empresas, FGV-EAESP Escola de Administração de Empresas de São Paulo (Brazil), vol. 59(6), December.
    • Handle: RePEc:fgv:eaerae:v:59:y:2019:i:6:a:80773
    as

    Download full text from publisher

    File URL: http://bibliotecadigital.fgv.br/ojs/index.php/rae/article/view/80773
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gunasekaran, Angappa & Papadopoulos, Thanos & Dubey, Rameshwar & Wamba, Samuel Fosso & Childe, Stephen J. & Hazen, Benjamin & Akter, Shahriar, 2017. "Big data and predictive analytics for supply chain and organizational performance," Journal of Business Research, Elsevier, vol. 70(C), pages 308-317.
    2. Gustavo Cattelan Nobre & Elaine Tavares, 2017. "Scientific literature analysis on big data and internet of things applications on circular economy: a bibliometric study," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 463-492, April.
    3. 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.
    4. Craig R. Carter & Dale S. Rogers & Thomas Y. Choi, 2015. "Toward the Theory of the Supply Chain," Journal of Supply Chain Management, Institute for Supply Management, vol. 51(2), pages 89-97, April.
    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. Usama Awan & Robert Sroufe & Muhammad Shahbaz, 2021. "Industry 4.0 and the circular economy: A literature review and recommendations for future research," Business Strategy and the Environment, Wiley Blackwell, vol. 30(4), pages 2038-2060, May.
    2. 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.
    3. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    4. Pan Liu & Shu-ping Yi, 2018. "Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era," Annals of Operations Research, Springer, vol. 270(1), pages 255-271, November.
    5. Norzalita Abd Aziz & Fei Long & Wan Mohd Hirwani Wan Hussain, 2023. "Examining the Effects of Big Data Analytics Capabilities on Firm Performance in the Malaysian Banking Sector," IJFS, MDPI, vol. 11(1), pages 1-13, January.
    6. 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.
    7. 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.
    8. Aydiner, Arafat Salih & Tatoglu, Ekrem & Bayraktar, Erkan & Zaim, Selim & Delen, Dursun, 2019. "Business analytics and firm performance: The mediating role of business process performance," Journal of Business Research, Elsevier, vol. 96(C), pages 228-237.
    9. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.
    10. 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.
    11. 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.
    12. S. Vijayakumar Bharathi, 2017. "Prioritizing and Ranking the Big Data Information Security Risk Spectrum," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 183-201, September.
    13. 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.
    14. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Papadopoulos, Thanos & Luo, Zongwei & Wamba, Samuel Fosso & Roubaud, David, 2019. "Can big data and predictive analytics improve social and environmental sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 534-545.
    15. Xue, Fujing & Li, Xiaoyu & Zhang, Ting & Hu, Nan, 2021. "Stock market reactions to the COVID-19 pandemic: The moderating role of corporate big data strategies based on Word2Vec," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    16. Biman Darshana Hettiarachchi & Stefan Seuring & Marcus Brandenburg, 2022. "Industry 4.0-driven operations and supply chains for the circular economy: a bibliometric analysis," Operations Management Research, Springer, vol. 15(3), pages 858-878, December.
    17. Mohammad Reza Seddigh & Sajjad Shokouhyar & Fatemeh Loghmani, 2023. "Approaching towards sustainable supply chain under the spotlight of business intelligence," Annals of Operations Research, Springer, vol. 324(1), pages 937-970, May.
    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. Ashaari, Mohamed Azlan & Singh, Karpal Singh Dara & Abbasi, Ghazanfar Ali & Amran, Azlan & Liebana-Cabanillas, Francisco J., 2021. "Big data analytics capability for improved performance of higher education institutions in the Era of IR 4.0: A multi-analytical SEM & ANN perspective," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    20. Dawn Iacobucci & Maria Petrescu & Anjala Krishen & Michael Bendixen, 2019. "The state of marketing analytics in research and practice," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(3), pages 152-181, September.

    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:fgv:eaerae:v:59:y:2019:i:6:a:80773. 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: Núcleo de Computação da FGV EPGE (email available below). General contact details of provider: https://edirc.repec.org/data/eagvfbr.html .

    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.