FACTORS INFLUENCING BIG DATA ANALYTICS ADOPTION: THE INDIAN MANAGEMENT ACCOUNTANTS’ PERSPECTIVE
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With advances in computing and informatics, the management accounting domain is undergoing a strategic shift, and the role of management accountants has transformed from a strategic advisor to a strategic partner within businesses. Big data analytics (BDA) is a promising technological innovation with immense potential to improve the entire management accounting value chain. However, it involves several positive and negative factors that drive BDA adoption in real-world applications. This study investigates the influence of key factors driving the adoption of BDA by Indian cost and management accountants (CMAs) in management accounting work practices. Utilizing an online survey instrument, quantitative data from 242 respondents engaged in industry or practice were collected. The research is based on the Technology-Organization-Environment (TOE) framework and uses Partial Least Squares Structural Equation Modelling (PLS-SEM) in R. The results reveal that relative advantage (β = 0.428, p < 0.01), compatibility (β = 0.286, p < 0.01) and vendor support (β = 0.212, p < 0.01) have statistically significant positive effects on BDA adoption. At the same time, complexity shows a negative but statistically insignificant association (β = –0.005) with BDA adoption. The model further demonstrates high explanatory power (R2 = 0.671 and Adj R2 = 0.666), without unnecessary inflation or the inclusion of variables. The outcome of this work has improved understanding of the relevant factors in the technical, organizational, and environmental dimensions of BDA adoption. It provides actionable insights for managers, management accountants, organizations, professional bodies, and third parties engaged in providing BDA technology solutions, helping them tap into the enormous opportunities in BDA.
JEL Classification Codes: M40, M41, M150, M490, M410.
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Abdelwahed, A. S., Abu-Musa, A. A., Badawy, H. A., & Moubarak, H. (2025). Unleashing the beast: The impact of big data and data analytics on the auditing profession—Evidence from a developing country. Future Business Journal, 11(1), 1–18. https://doi.org/10.1186/s43093-024-00420-7
Aghakishi, A. (2023, December 6). Big data in management accounting. SSRN. https://ssrn.com/abstract=4718516
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113–131. https://doi.org/10.1016/j.ijpe.2016.08.018
Aldossari, S., Mokhtar, U. A., & Abdul Ghani, A. T. (2023). Factors influencing the adoption of big data analytics: A systematic literature and experts review. SAGE Open, 13(4), 1–25. https://doi.org/10.1177/21582440231217902
Alquhaif, A. S., & Al-Mamary, Y. H. (2025). Examining factors influencing the adoption of accounting information systems: An analysis of behavioral intentions and usage behavior. Human Systems Management, 44(3), 401–420. https://doi.org/10.1177/01672533241297453
Al-shanableh, N., Alzyoud, M., Alomar, S., Kilani, Y., Nashnush, E., Al-Hawary, S., & Al-Momani, A. (2024). The adoption of big data analytics in Jordanian SMEs: An extended technology–organization environment framework with diffusion of innovation and perceived usefulness. International Journal of Data and Network Science, 8(2), 753–764. https://doi.org/10.5267/j.ijdns.2024.1.003
Babalghaith, R., & Aljarallah, A. (2024). Factors affecting big data analytics adoption in small and medium enterprises. Information Systems Frontiers, 26(6), 2165–2187. https://doi.org/10.1007/s10796-024-10538-2
Bag, S., Gupta, S., Kumar, A., & Sivarajah, U. (2022). Big data analytics: Implementation challenges in Indian manufacturing supply chains. Computers in Industry, 125, Article 103368. https://doi.org/10.1016/j.compind.2020.103368
Baker, J. (2012). The technology–organization–environment framework. In Y. K. Dwivedi, M. R. Wade, & S. L. Schneberger (Eds.), Information systems theory: Explaining and predicting our digital society (Vol. 1, pp. 231–245). Springer. https://doi.org/10.1007/978-1-4419-6108-2_12
Barnes, S. J., Guo, Y., & Chan, J. (2022). Big data analytics for sustainability: Insight through technological innovation. Information & Management, 59(5), Article 103627. https://doi.org/10.1016/j.im.2022.103627
Begum, F., & Rahman, M. M. (2026). Impact of strategic management accounting on firm's financial performance: Evidence from Bangladesh's manufacturing sector. International Journal of Accounting & Finance Review, 17(1), 1–14. https://doi.org/10.46281/ijafr.v17i1.291
Caesarius, L. M., & Hohenthal, J. (2018). Searching for big data: How incumbents explore a possible adoption of big data technologies. Scandinavian Journal of Management, 34(2), 129–140. https://doi.org/10.1016/j.scaman.2017.12.002
Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4–39. https://doi.org/10.1080/07421222.2015.1138364
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
Dai, H.-N., Wang, H., Xu, G., Wan, J., & Imran, M. (2020). Big data analytics for manufacturing internet of things: Opportunities, challenges and enabling technologies. Enterprise Information Systems, 14(9–10), 1279–1303. https://doi.org/10.1080/17517575.2019.1633689
Danielsen, F., Olsen, D., & Framnes, V. A. (2021). Toward an understanding of big data analytics and competitive performance. Scandinavian Journal of Information Systems, 33(1), 155–192.
Dubey, R., Bryde, D. J., Foropon, C., Tiwari, M., & Dwivedi, Y. K. (2022). Role of big data analytics capabilities to improve sustainable competitive advantage of MSME service firms during COVID-19. Journal of Business Research, 148, 378–389. https://doi.org/10.1016/j.jbusres.2022.05.009
Elawadly, H. S. H. (2026). Comprehensive analysis of digitalization in management accounting: A bibliometric and coding analysis. Journal of Financial Reporting and Accounting. Advance online publication. https://doi.org/10.1108/JFRA-01-2025-0002
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Fortune Business Insights. (2025). Big data technology market size, share & industry analysis, 2025–2032. https://www.fortunebusinessinsights.com/big-data-technology-market-100144
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
Gangwar, H. (2018). Understanding the determinants of big data adoption in India: An analysis of the manufacturing and services sectors. Information Resources Management Journal, 31(4), 1–22. https://doi.org/10.4018/IRMJ.2018100101
Gangwar, H., Date, H., & Ramaswamy, R. (2015). Understanding determinants of cloud computing adoption using an integrated TAM–TOE model. Journal of Enterprise Information Management, 28(1), 107–130. https://doi.org/10.1108/JEIM-08-2013-0065
Ghaleb, E. A. A., Dominic, P. D. D., Singh, N. S. S., & Naji, G. M. A. (2023). Assessing the big data adoption readiness role in healthcare between technology impact factors and intention to adopt big data. Sustainability, 15(15), Article 11521. https://doi.org/10.3390/su151511521
Grosu, V., Cosmulese, C. G., Socoliuc, M., Ciubotariu, M. S., & Mihaila, S. (2023). Testing accountants' perceptions of the digitizationdigitization of the profession and profiling the future professional. Technological Forecasting and Social Change, 193, Article 122630. https://doi.org/10.1016/j.techfore.2023.122630
Hair, J. F., & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), Article 100027. https://doi.org/10.1016/j.rmal.2022.100027
Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer Nature. https://doi.org/10.1007/978-3-030-80519-7
Hair, J. F., Ringle, C. M., Gudergan, S. P., Fischer, A., Nitzl, C., & Menictas, C. (2019). Partial least squares structural equation modeling-based discrete choice modeling: An illustration in modeling retailer choice. Business Research, 12(1), 115–142. https://doi.org/10.1007/s40685-018-0072-4
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2018). Advanced issues in partial least squares structural equation modeling. Sage.
Harman, H. H. (1967). Modern factor analysis. University of Chicago Press.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Irfan, M., & Wang, M. (2019). Data-driven capabilities, supply chain integration and competitive performance: Evidence from the food and beverages industry in Pakistan. British Food Journal, 121(11), 2708–2729. https://doi.org/10.1108/BFJ-02-2019-0131
Jackson, D., Michelson, G., & Munir, R. (2023). Developing accountants for the future: New technology, skills, and the role of stakeholders. Accounting Education, 32(2), 150–177. https://doi.org/10.1080/09639284.2022.2057195
Kapoor, K. K., Dwivedi, Y. K., & Williams, M. D. (2015). Empirical examination of the role of three sets of innovation attributes for determining adoption of IRCTC mobile ticketing service. Information Systems Management, 32(2), 153–173. https://doi.org/10.1080/10580530.2015.1018776
Karina, R., Siti-Nabiha, A. K., & Jurnali, T. (2025). Big data integration in performance management and control: A socio-technical perspective. Journal of Accounting & Organizational Change, 22(2), 313–332. https://doi.org/10.1108/JAOC-07-2024-0238
Kumar, S., Luthra, S., & Haleem, A. (2013). Customer involvement in greening the supply chain: An interpretive structural modeling methodology. Journal of Industrial Engineering International, 9(1), Article 6. https://doi.org/10.1186/2251-712X-9-6
Lai, Y., Sun, H., & Ren, J. (2018). Understanding the determinants of big data analytics adoption in logistics and supply chain management: An empirical investigation. The International Journal of Logistics Management, 29(2), 676–703. https://doi.org/10.1108/IJLM-06-2017-0153
Lutfi, A., Alsyouf, A., Almaiah, M. A., Al-Khasawneh, A., Alshira'h, A. F., Alshirah, M. H., Saad, M., & Ibrahim, N. (2022). Factors influencing the adoption of big data analytics in the digital transformation era: Case study of Jordanian SMEs. Sustainability, 14(3), Article 1802. https://doi.org/10.3390/su14031802
Maroufkhani, P., Tseng, M. L., Iranmanesh, M., Ismail, W. K. W., & Khalid, H. (2020). Big data analytics adoption: Determinants and performances among small to medium-sized enterprises. International Journal of Information Management, 54, Article 102190. https://doi.org/10.1016/j.ijinfomgt.2020.102190
Mukherjee, A., Sharma, U., & Liu, J. (2025). Big data analytics role in shaping the work of accounting function and accounting professionals. Journal of Accounting & Organizational Change, 21(7), 272–295. https://doi.org/10.1108/JAOC-08-2024-0255
Munir, S., Abdul Rasid, S. Z., Aamir, M., Jamil, F., & Ahmed, I. (2022). Big data analytics capabilities and innovation: Effect of dynamic capabilities, organizational culture and role of management accountants. Foresight, 25(1), 41–66. https://doi.org/10.1108/FS-08-2021-0161
Pereira, L. M., Sanchez Rodrigues, V., & Freires, F. G. M. (2024). Use of partial least squares structural equation modeling (PLS-SEM) to improve plastic waste management. Applied Sciences, 14(2), Article 628. https://doi.org/10.3390/app14020628
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
Ramachandran, R. (2019). Big data analytics and the management accountant. The Management Accountant Journal, 54(5), 40–43. https://doi.org/10.33516/maj.v54i5.40-43
Ray, S., Danks, N. P., & Velasquez-Estrada, J. M. (2020). seminr: Domain-specific language for building and estimating structural equation models (Version 2.3.7) [R package]. https://cran.r-project.org/web/packages/seminr/
Renu, & Goyal, N. (2026). Impacts of artificial intelligence and big data analytics on accounting practices and professional roles. International Journal of Research in Commerce and Management Studies, 8(1), 585–606.
Richardson, V., Teeter, R., & Terrell, K. (2019). Data analytics for accounting. McGraw-Hill Education.
Richins, G., Stapleton, A., Stratopoulos, T. C., & Wong, C. (2017). Big data analytics: Opportunity or threat for the accounting profession? Journal of Information Systems, 31(3), 63–79. https://doi.org/10.2308/isys-51805
Rikhardsson, P., & Yigitbasioglu, O. (2018). Business intelligence and analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems, 29, 37–58. https://doi.org/10.1016/j.accinf.2018.03.001
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Salleh, K. A., Janczewski, L. J., & Beltrán, F. (2015). SEC-TOE framework: Exploring security determinants in big data solutions adoption. In Proceedings of the Pacific Asia Conference on Information Systems (PACIS 2015) (Paper 203). Association for Information Systems. https://aisel.aisnet.org/pacis2015/203
Sarbhai, A., & Khare, V. (2024). Leveraging big data for enhanced strategic planning and improved firm performance: A study of Indian organizations. Metamorphosis, 23(1), 77–89. https://doi.org/10.1177/09726225241249525
Saud, I. M., Sofyani, H., Utami, T. P., Mukhlish, M., & Fathmaningrum, E. S. (2025). Big data analytics-based auditing adoption in public sector: Indonesian evidence. Cogent Business & Management, 12(1), Article 2454320. https://doi.org/10.1080/23311975.2025.2454320
Schmidt, P. J., Riley, J., & Swanson Church, K. (2020). Investigating accountants' resistance to move beyond Excel and adopt new data analytics technology. Accounting Horizons, 34(4), 165–180. https://doi.org/10.2308/HORIZONS-19-154
Sharma, M., Gupta, R., Sehrawat, R., Jain, K., & Dhir, A. (2023). The assessment of factors influencing big data adoption and firm performance: Evidences from emerging economy. Enterprise Information Systems, 17(12), Article 2218160. https://doi.org/10.1080/17517575.2023.2218160
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of big data challenges and analytical methods. Journal of Business Research, 70, 263–286. https://doi.org/10.1016/j.jbusres.2016.08.001
Spraakman, G., Sanchez-Rodriguez, C., & Tuck-Riggs, C. A. (2021). Data analytics by management accountants. Qualitative Research in Accounting & Management, 18(1), 127–147. https://doi.org/10.1108/QRAM-11-2019-0122
Tambuskar, D. P., Jain, P., & Narwane, V. S. (2024). An exploration into the factors influencing the implementation of big data analytics in sustainable supply chain management. Kybernetes, 53(5), 1710–1739. https://doi.org/10.1108/K-07-2022-1057
Tehseen, S., Ramayah, T., & Sajilan, S. (2017). Testing and controlling for common method variance: A review of available methods. Journal of Management Sciences, 4(2), 142–168. https://doi.org/10.20547/jms.2014.1704202
Tiron-Tudor, A., & Deliu, D. (2021). Big data's disruptive effect on job profiles: Management accountants' case study. Journal of Risk and Financial Management, 14(8), Article 376. https://doi.org/10.3390/jrfm14080376
Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
Varma, A. (2018). Big data usage intention of management accountants: Blending the utility theory with the theory of planned behavior in an emerging market context. Theoretical Economics Letters, 8(13), 2803–2817. https://doi.org/10.4236/tel.2018.813176
Varma, A., Piedepalumbo, P., & Mancini, D. (2021). Big data and accounting: A bibliometric study. The International Journal of Digital Accounting Research, 21(27), 203–238. https://doi.org/10.4192/1577-8517-v21_8
Verma, S., & Bhattacharyya, S. S. (2017). Perceived strategic value-based adoption of big data analytics in emerging economy: A qualitative approach for Indian firms. Journal of Enterprise Information Management, 30(3), 354–382. https://doi.org/10.1108/JEIM-10-2015-0099
Verma, S., & Chaurasia, S. (2019). Understanding the determinants of big data analytics adoption. Information Resources Management Journal, 32(3), 1–26. https://doi.org/10.4018/IRMJ.2019070101
Vysotskaya, A., & Prokofieva, M. (2025). Management accounting and data analytics: Technology acceptance from the educational perspective. Accounting Education, 34(3), 410–433. https://doi.org/10.1080/09639284.2024.2338140
Yadegaridehkordi, E., Hourmand, M., Nilashi, M., Shuib, L., Ahani, A., & Ibrahim, O. (2018). Influence of big data adoption on manufacturing companies' performance: An integrated DEMATEL-ANFIS approach. Technological Forecasting and Social Change, 137, 199–210. https://doi.org/10.1016/j.techfore.2018.07.043
Zhu, S., Dong, T., & Luo, X. (2021). A longitudinal study of the actual value of big data and analytics: The role of industry environment. International Journal of Information Management, 60, Article 102389. https://doi.org/10.1016/j.ijinfomgt.2021.102389