IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v158y2023ics0148296323000474.html
   My bibliography  Save this article

Customer-centered data power: Sensing and responding capability in big data analytics

Author

Listed:
  • Tseng, Hsiao-Ting

Abstract

In recent years, data-driven strategy formulation has become a trend in business to gain competitive advantages. Therefore, a company’s customer perception and responsiveness play an important role. Previous research has not explored how to effectively use big data to quantitatively understand customers. Thus, this study collects top managers’ opinions from different companies and applies a quantitative method to empirically examine the proposed model to enhance operations. This finding suggests that using big data analysis tools effectively enhances customer sensing and response capabilities. Furthermore, customer sensing and responding capabilities significantly lead to new product success. The implications are discussed considering both theoretical and practical perspectives.

Suggested Citation

  • Tseng, Hsiao-Ting, 2023. "Customer-centered data power: Sensing and responding capability in big data analytics," Journal of Business Research, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:jbrese:v:158:y:2023:i:c:s0148296323000474
    DOI: 10.1016/j.jbusres.2023.113689
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296323000474
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2023.113689?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. Xu, Zhenning & Frankwick, Gary L. & Ramirez, Edward, 2016. "Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective," Journal of Business Research, Elsevier, vol. 69(5), pages 1562-1566.
    2. 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.
    3. Dominik Krimpmann & Anna Stühmeier, 2017. "Big Data and Analytics: Why an IT Organization Requires Dedicated Roles to Drive Sustainable Competitive Advantage," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 8(3), pages 79-92, July.
    4. Qaisar Ali & Hakimah Yaacob & Shazia Parveen & Zaki Zaini, 2021. "Big data and predictive analytics to optimise social and environmental performance of Islamic banks," Environment Systems and Decisions, Springer, vol. 41(4), pages 616-632, December.
    5. Ciampi, Francesco & Demi, Stefano & Magrini, Alessandro & Marzi, Giacomo & Papa, Armando, 2021. "Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation," Journal of Business Research, Elsevier, vol. 123(C), pages 1-13.
    6. 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.
    7. Cheng-Kui Huang & Tawei Wang & Tzu-Yen Huang, 2020. "Initial Evidence on the Impact of Big Data Implementation on Firm Performance," Information Systems Frontiers, Springer, vol. 22(2), pages 475-487, April.
    8. Sepasgozar, Samad M.E. & Hawken, Scott & Sargolzaei, Sharifeh & Foroozanfa, Mona, 2019. "Implementing citizen centric technology in developing smart cities: A model for predicting the acceptance of urban technologies," Technological Forecasting and Social Change, Elsevier, vol. 142(C), pages 105-116.
    9. 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.
    10. Roberts, Nicholas & Grover, Varun, 2012. "Investigating firm's customer agility and firm performance: The importance of aligning sense and respond capabilities," Journal of Business Research, Elsevier, vol. 65(5), pages 579-585.
    11. Zhang, Haisu & Wu, Fang & Cui, Anna Shaojie, 2015. "Balancing market exploration and market exploitation in product innovation: A contingency perspective," International Journal of Research in Marketing, Elsevier, vol. 32(3), pages 297-308.
    12. Tseng, Hsiao-Ting & Aghaali, Niloofar & Hajli, Dr Nick, 2022. "Customer agility and big data analytics in new product context," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    13. Santos, Maribel Yasmina & Oliveira e Sá, Jorge & Andrade, Carina & Vale Lima, Francisca & Costa, Eduarda & Costa, Carlos & Martinho, Bruno & Galvão, João, 2017. "A Big Data system supporting Bosch Braga Industry 4.0 strategy," International Journal of Information Management, Elsevier, vol. 37(6), pages 750-760.
    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. Mansour Alyahya & Meqbel Aliedan & Gomaa Agag & Ziad H. Abdelmoety, 2023. "Understanding the Relationship between Big Data Analytics Capabilities and Sustainable Performance: The Role of Strategic Agility and Firm Creativity," Sustainability, MDPI, vol. 15(9), pages 1-17, May.

    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. 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.
    2. Blasco-Arcas, Lorena & Lee, Hsin-Hsuan Meg & Kastanakis, Minas N. & Alcañiz, Mariano & Reyes-Menendez, Ana, 2022. "The role of consumer data in marketing: A research agenda," Journal of Business Research, Elsevier, vol. 146(C), pages 436-452.
    3. 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.
    4. Anke Joubert & Matthias Murawski & Markus Bick, 2023. "Measuring the Big Data Readiness of Developing Countries – Index Development and its Application to Africa," Information Systems Frontiers, Springer, vol. 25(1), pages 327-350, February.
    5. 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).
    6. 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.
    7. 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.
    8. Osama Musa Ali Al-Darras & Cem Tanova, 2022. "From Big Data Analytics to Organizational Agility: What Is the Mechanism?," SAGE Open, , vol. 12(2), pages 21582440221, June.
    9. 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.
    10. Mihai BOGDAN & Anca BORZA, 2020. "Big Data Analytics And Firm Performance: A Text Mining Approach," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 14(1), pages 549-560, November.
    11. Nguyen Anh Khoa Dam & Thang Le Dinh & William Menvielle, 2019. "A systematic literature review of big data adoption in internationalization," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(3), pages 182-195, September.
    12. 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.
    13. Aljumah, Ahmad Ibrahim & Nuseir, Mohammed T. & Alam, Md. Mahmudul, 2021. "Traditional Marketing Analytics, Big Data Analytics, Big Data System Quality and the Success of New Product Development," OSF Preprints 9auec, Center for Open Science.
    14. Gupta, Shivam & Kar, Arpan Kumar & Baabdullah, Abdullah & Al-Khowaiter, Wassan A.A., 2018. "Big data with cognitive computing: A review for the future," International Journal of Information Management, Elsevier, vol. 42(C), pages 78-89.
    15. 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.
    16. Huynh, Minh-Tay & Nippa, Michael & Aichner, Thomas, 2023. "Big data analytics capabilities: Patchwork or progress? A systematic review of the status quo and implications for future research," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    17. Shet, Sateesh.V. & Poddar, Tanuj & Wamba Samuel, Fosso & Dwivedi, Yogesh K., 2021. "Examining the determinants of successful adoption of data analytics in human resource management – A framework for implications," Journal of Business Research, Elsevier, vol. 131(C), pages 311-326.
    18. Jianmin Song & Senmao Xia & Demetris Vrontis & Arun Sukumar & Bing Liao & Qi Li & Kun Tian & Nengzhi Yao, 2022. "The Source of SMEs’ Competitive Performance in COVID-19: Matching Big Data Analytics Capability to Business Models," Information Systems Frontiers, Springer, vol. 24(4), pages 1167-1187, August.
    19. Elisa Rodepeter & Christoph Gschnaidtner & Hanna Hottenrott, 2024. "Big Data and Start-up Performance," Working Papers 232, Bavarian Graduate Program in Economics (BGPE).
    20. Mansour Alyahya & Meqbel Aliedan & Gomaa Agag & Ziad H. Abdelmoety, 2023. "Understanding the Relationship between Big Data Analytics Capabilities and Sustainable Performance: The Role of Strategic Agility and Firm Creativity," Sustainability, MDPI, vol. 15(9), pages 1-17, May.

    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:eee:jbrese:v:158:y:2023:i:c:s0148296323000474. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    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.