IDEAS home Printed from https://ideas.repec.org/a/blg/msudev/v14y2022i2p27-33n6.html
   My bibliography  Save this article

Innovation Management: Is Big Data Necessarily Better Data?

Author

Listed:
  • JAHAN Sakila Akter

    (Independent University, Bangladesh)

  • SAZU Mesbaul Haque

    (Case Western Reserve University, USA)

Abstract

This study explores the relationship between firms’ application of data analytics (specifically it’s attributes) with the innovative performance of business. The other objective is to assess if large volume of data is necessarily more effective to drive business innovation. The study collected data through questionnaire survey from management staffs of 250 companies in both developed and developing economies. Statistical tools such as T-test and multiple regression methods were used to analyse the data. The study found suggestive proof demonstrating that data analytics is a pertinent determinant of a firm being innovator and bring innovative products and services to the market. The study also found that large volume of data is not necessarily better data to drive innovation. The findings imply that firms must utilize big data analytics to stay innovative and have a competitive advantage. Unlike previous studies that approached big data as whole, this study addresses various components of big data such as variety, volume, velocity, and their individual impacts on innovation in businesses across the developed economies.

Suggested Citation

  • JAHAN Sakila Akter & SAZU Mesbaul Haque, 2022. "Innovation Management: Is Big Data Necessarily Better Data?," Management of Sustainable Development, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 14(2), pages 27-33, December.
  • Handle: RePEc:blg:msudev:v:14:y:2022:i:2:p:27-33:n:6
    DOI: https://doi.org/10.54989/msd-2022-0013
    as

    Download full text from publisher

    File URL: https://msdjournal.org/wp-content/uploads/vol14issue2-5.pdf
    Download Restriction: no

    File URL: https://libkey.io/https://doi.org/10.54989/msd-2022-0013?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Hau L. Lee, 2018. "Big Data and the Innovation Cycle," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1642-1646, September.
    3. Shengbin Hao & Haili Zhang & Michael Song, 2019. "Big Data, Big Data Analytics Capability, and Sustainable Innovation Performance," Sustainability, MDPI, vol. 11(24), pages 1-15, December.
    4. Bresciani, Stefano & Ciampi, Francesco & Meli, Francesco & Ferraris, Alberto, 2021. "Using big data for co-innovation processes: Mapping the field of data-driven innovation, proposing theoretical developments and providing a research agenda," International Journal of Information Management, Elsevier, vol. 60(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. Hua Zhang & Shaofeng Yuan, 2023. "How and When Does Big Data Analytics Capability Boost Innovation Performance?," Sustainability, MDPI, vol. 15(5), pages 1-19, February.
    2. Li, Lixu & Ye, Fei & Zhan, Yuanzhu & Kumar, Ajay & Schiavone, Francesco & Li, Yina, 2022. "Unraveling the performance puzzle of digitalization: Evidence from manufacturing firms," Journal of Business Research, Elsevier, vol. 149(C), pages 54-64.
    3. Hongyi Mao & Jiang Lu, 2023. "Big Data Management Capabilities and Green Innovation: A Dynamic Capabilities View," Sustainability, MDPI, vol. 15(19), pages 1-27, October.
    4. Sakila Akter JAHAN & Mesbaul Haque SAZU, 2022. "The Impact of Data Analytics on High Efficiency Supply Chain Management," CECCAR Business Review, Body of Expert and Licensed Accountants of Romania (CECCAR), vol. 3(7), pages 62-72, July.
    5. Guixiang Cao & Xintong Fang & Ying Chen & Jinghuai She, 2023. "Regional Big Data Application Capability and Firm Green Technology Innovation," Sustainability, MDPI, vol. 15(17), pages 1-29, August.
    6. Mariani, Marcello M. & Fosso Wamba, Samuel, 2020. "Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies," Journal of Business Research, Elsevier, vol. 121(C), pages 338-352.
    7. Long Xue & Qianyu Zhang & Xuemang Zhang & Chengyu Li, 2022. "Can Digital Transformation Promote Green Technology Innovation?," Sustainability, MDPI, vol. 14(12), pages 1-20, June.
    8. Linlin Zheng & Yashi Dong & Jineng Chen & Yuyi Li & Wenzhuo Li & Miaolian Su, 2022. "Impact of Crisis on Sustainable Business Model Innovation—The Role of Technology Innovation," Sustainability, MDPI, vol. 14(18), pages 1-28, September.
    9. Rampersad, Giselle, 2020. "Robot will take your job: Innovation for an era of artificial intelligence," Journal of Business Research, Elsevier, vol. 116(C), pages 68-74.
    10. Behl, Abhishek & Gaur, Jighyasu & Pereira, Vijay & Yadav, Rambalak & Laker, Benjamin, 2022. "Role of big data analytics capabilities to improve sustainable competitive advantage of MSME service firms during COVID-19 – A multi-theoretical approach," Journal of Business Research, Elsevier, vol. 148(C), pages 378-389.
    11. Haili Zhang & Yufan Wang & Michael Song, 2019. "Does Competitive Intensity Moderate the Relationships between Sustainable Capabilities and Sustainable Organizational Performance in New Ventures?," Sustainability, MDPI, vol. 12(1), pages 1-18, December.
    12. Li, Lei & Lin, Jiabao & Ouyang, Ye & Luo, Xin (Robert), 2022. "Evaluating the impact of big data analytics usage on the decision-making quality of organizations," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    13. Fink, Alexander A. & Klöckner, Maximilian & Räder, Tobias & Wagner, Stephan M., 2022. "Supply chain management accelerators: Types, objectives, and key design features," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    14. Bargoni, Augusto & Santoro, Gabriele & Messeni Petruzzelli, Antonio & Ferraris, Alberto, 2024. "Growth hacking: A critical review to clarify its meaning and guide its practical application," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    15. Singh, Anjali & Lim, Weng Marc & Jha, Sumi & Kumar, Satish & Ciasullo, Maria Vincenza, 2023. "The state of the art of strategic leadership," Journal of Business Research, Elsevier, vol. 158(C).
    16. David Hidalgo-Carvajal & Ruth Carrasco-Gallego & Gustavo Morales-Alonso, 2021. "From Goods to Services and from Linear to Circular: The Role of Servitization’s Challenges and Drivers in the Shifting Process," Sustainability, MDPI, vol. 13(8), pages 1-26, April.
    17. 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.
    18. 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.
    19. Sergi, Bruno S. & Ključnikov, Aleksandr & Popkova, Elena G. & Bogoviz, Aleksei V. & Lobova, Svetlana V., 2022. "Creative abilities and digital competencies to transitioning to Business 4.0," Journal of Business Research, Elsevier, vol. 153(C), pages 401-411.
    20. Hou, Tingting & Luo, Xin (Robert) & Ke, Dan & Cheng, Xusen, 2022. "Exploring different appraisals in deviant sharing behaviors: A mixed-methods study," Journal of Business Research, Elsevier, vol. 139(C), pages 496-509.

    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:blg:msudev:v:14:y:2022:i:2:p:27-33:n:6. 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: Camelia Oprean-Stan (email available below). General contact details of provider: https://edirc.repec.org/data/feulbro.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.