Analytics Culture and Absorptive Capacity as Mediators of Big ‎Data Value Creation in SMEs

  • Authors

    • Yanamadala Durga Prasad Research Scholar, Department of Computer Engineering & Applications,‎ IET, Mangalayatan University, Beswan, Aligarh-202146, Uttar Pradesh, India‎
    • Dr Sumit Singh Sonker Assistant Professor, Department of Computer Engineering & Applications IET, Mangalayatan University, Beswan , Aligarh-202146,Uttar Pradesh, India
    https://doi.org/10.14419/p3aazc61

    Received date: September 19, 2025

    Accepted date: October 27, 2025

    Published date: November 4, 2025

  • SME; Big Data; Analytics; Reliability; Culture
  • Abstract

    This paper examines the ways small and medium firms derive value from big data analytics. It notes that many firms invest in analytics ‎technology. Yet many struggle to turn data into strategic value. The research asks whether analytics culture and absorptive capacity help. ‎Analytics culture involves using data in daily decisions. Absorptive capacity involves finding, learning, and using outside knowledge. The ‎model tests human and technological analytics capabilities. It tests analytics culture and absorptive capacity as mediators. The results show ‎that both mediators matter. Analytics culture amplifies the effect of capabilities on business value. Absorptive capacity strengthens this effect. ‎Together, these factors help firms translate analytics investments into measurable outcomes. The findings add new empirical evidence for ‎SMEs. Results show that technology alone is not enough. Firms need culture and learning routines. Managers should develop analytics ‎mindsets and promote knowledge sharing and external learning. These steps help align analytics with organizational goals and results. The ‎model demonstrated strong fit and predictive accuracy. The mediators partly explain the process through which capabilities lead to value. ‎The research provides clear recommendations for managers in SMEs. Leaders should align technology, people, and processes. Training and ‎hiring matter. Leaders should reward knowledge sharing. Firms should develop routines to learn from partners. The research adds to theory ‎and practice. Limitations include the single-country, cross-sectional design. Future work should use other countries and longitudinal data‎.

  • References

    1. A. C. Ikegwu, H. F. Nweke, C. V. Anikwe, U. R. Alo, and O. R. Okonkwo, “Big data analytics for data-driven industry: a review of data sources, tools, challenges, solutions, and research directions,” Cluster Computing, vol. 25, no. 5, pp. 3343–3387, 2022. https://doi.org/10.1007/s10586-022-03568-5.
    2. D. Blazquez and J. Domenech, “Big data sources and methods for social and economic analyses,” Technological Forecasting and Social Change, vol. 130, pp. 99–113, 2018. https://doi.org/10.1016/j.techfore.2017.07.027.
    3. R. Jiwat and Z. L. Zhang, “Adopting big data analytics (bda) in business-to-business (b2b) organizations–development of a model of needs,” Journal of Engineering and Technology Management, vol. 63, p. 101676, 2022. https://doi.org/10.1016/j.jengtecman.2022.101676.
    4. E. Raguseo, “Big data technologies: An empirical investigation on their adoption, benefits and risks for companies,” International Journal of Information Management, vol. 38, no. 1, pp. 187–195, 2018. https://doi.org/10.1016/j.ijinfomgt.2017.07.008.
    5. E. Bryan, Q. Bernier, M. Espinal, and C. Ringler, “Making climate change adaptation programmes in sub-saharan africa more gender responsive: insights from implementing organizations on the barriers and opportunities,” Climate and Development, vol. 10, no. 5, pp. 417–431, 2018. https://doi.org/10.1080/17565529.2017.1301870.
    6. G. Stalk, Jr, P. Evans, and L. E. Shulman, “Competing on capabilities,” Own the Future: 50 Ways to Win from the Boston Consulting Group, pp. 41–51, 2012. https://doi.org/10.1002/9781119204084.ch5.
    7. N. A. Dela Cruz, A. C. B. Villanueva, L. A. Tolin, S. Disse, R. Lensink, and H. White, “Protocol: Effects of interventions to improve access to financial services for micro-, small-and medium-sized enterprises in low-and middle-income countries: An evidence and gap map,” Campbell Systematic Reviews, vol. 19, no. 3, p. e1341, 2023. https://doi.org/10.1002/cl2.1341.
    8. A. Pearce, D. Pons, and T. Neitzert, “Implementing lean—outcomes from sme case studies,” Operations Research Perspectives, vol. 5, pp. 94–104, 2018. https://doi.org/10.1016/j.orp.2018.02.002.
    9. H. Wu, “Intuition in investment decision-making across cultures,” Journal of Behavioral Finance, vol. 23, no. 1, pp. 106–122, 2022. https://doi.org/10.1080/15427560.2020.1848839.
    10. K. Ramakrishna, S. Balaji, and M. S. Kumar, “The impact of hr analytics on organisational culture and employee engagement.,” Journal of Advanced Zoology, vol. 45, no. 6, 2024. https://doi.org/10.53555/jaz.v45i6.4851.
    11. H. D. Jonsdottir, “The utilization of bda in digital marketing strategies of international b2b organizations from a dynamic capability´ s perspective: A qualitative case study,” 2024.
    12. P. Korherr and D. Kanbach, “Human-related capabilities in big data analytics: a taxonomy of human factors with impact on firm performance,” Review of Managerial Science, vol. 17, no. 6, pp. 1943–1970, 2023. https://doi.org/10.1007/s11846-021-00506-4.
    13. R. N. Lussier and J. R. Hendon, Human resource management: Functions, applications, and skill development. Sage publications, 2025.
    14. J. Hair and A. Alamer, “Partial least squares structural equation modelling (pls-sem) in second language and education research: Guidelines using an applied example,” Research Methods in Applied Linguistics, vol. 1, no. 3, p. 100027, 2022. https://doi.org/10.1016/j.rmal.2022.100027.
    15. L. C. Vieira, M. L. Handojo, and C. O. Wilke, “Medium-sized protein language models perform well at transfer learning on realistic datasets: Lc vieira et al.,” Scientific Reports, vol. 15, no. 1, p. 21400, 2025. https://doi.org/10.1038/s41598-025-05674-x.
    16. N. Kling, S. Haugk, and H. Gebauer, “Towards a data-driven organisation: Making data a strategic knowledge asset in smes,” Journal of the Knowledge Economy, pp. 1–19, 2025. https://doi.org/10.1007/s13132-025-02631-x.
    17. M. Gupta and J. F. George, “Toward the development of a big data analytics capability,” Information & Management, vol. 53, no. 8, pp. 1049–1064, Dec. 2016. https://doi.org/10.1016/j.im.2016.07.004.
    18. W. M. Cohen and D. A. Levinthal, “Absorptive capacity: A new perspective on learning and innovation,” Administrative Science Quarterly, vol. 35, no. 1, pp. 128–152, 1990. https://doi.org/10.2307/2393553.
    19. A. Braganza, L. Brooks, D. Nepelski, M. Ali, and R. Moro, “Resource management in big data initiatives: Processes and dynamic capabilities,” Journal of Business Research, vol. 70, pp. 328–337, Jan. 2017. https://doi.org/10.1016/j.jbusres.2016.08.006.
    20. B. Wernerfelt, “The resource-based view of the firm: Ten years after,” Strategic Management Journal, vol. 16, no. 3, pp. 171–174, 1995. https://doi.org/10.1002/smj.4250160303.
    21. J. Barney, “Firm resources and sustained competitive advantage,” Journal of Management, vol. 17, no. 1, pp. 99–120, 1991. https://doi.org/10.1177/014920639101700108.
    22. D. J. Teece, “Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance,” Strategic Management Journal, vol. 28, no. 13, pp. 1319–1350, 2007. https://doi.org/10.1002/smj.640.
    23. A. S. Bharadwaj, “A resource-based perspective on information technology capability and firm performance: An empirical investigation,” MIS Quarterly, vol. 24, no. 1, pp. 169–196, 2000. https://doi.org/10.2307/3250983.
    24. S. F. Wamba, S. Akter, A. Edwards, G. Chopin, and D. Gnanzou, “Big data analytics and firm performance: Effects of dynamic capabilities,” Journal of Business Research, vol. 70, pp. 356–365, 2017. https://doi.org/10.1016/j.jbusres.2016.08.009.
    25. D. Kiron, P. K. Prentice, and R. B. Ferguson, “The analytics mandate,” MIT Sloan Management Review, vol. 55, no. 4, pp. 1–25, 2014.
    26. G. Cao, Y. Duan, and G. Li, “Linking business analytics to decision making effectiveness: A path model analysis,” IEEE Transactions on Engineering Management, vol. 62, no. 3, pp. 384–395, Aug. 2015.r. https://doi.org/10.1109/TEM.2015.2441875.
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  • How to Cite

    Prasad , Y. D. ., & Sonker , D. S. S. . (2025). Analytics Culture and Absorptive Capacity as Mediators of Big ‎Data Value Creation in SMEs. International Journal of Basic and Applied Sciences, 14(7), 130-138. https://doi.org/10.14419/p3aazc61