As firms increasingly rely on big data to guide decisions and manage strategic responsiveness, the need for constructing and measuring big data analytics capability (BDAC) has gone up accordingly. Though scholarly interest in BDAC is continuing to grow, however, how it is conceptualized and operationalized in empirical research remains inconsistent. This study aims to examine how BDAC constructs are specified and validated in empirical research. To this end, by following transparent and replicable procedures for search, screening, selection, and coding, the study conducted a structured review of 44 empirical studies published between 2020 and 2024. The findings indicate a diversity in conceptualization, though over two-thirds of studies have conceptualized big data analytics capabilities as a bundle of related but distinct capabilities. From measurement model specifications perspective, literature synthesis highlights that the overwhelming majority of studies have relied on reflective models, and that too with limited theoretical justification. This review contributes by eliciting prevailing methodological tensions and offering systematic guidance to assist more conceptually harmonious and analytically stronger BDAC studies. Practical implications are also discussed for researchers and practitioners seeking to develop more credible and strategically aligned analytics capability assessments. The review acknowledges the limitations with regard to shorter temporal review period and exclusion of non-SEM-based research, and calls for future research to embrace broader methodological approaches and context-sensitive modeling strategies.
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.
AL-Khatib, A. W., & Ramayah, T. (2023). Big data analytics capabilities and supply chain performance: testing a moderated mediation model using partial least squares approach. Business Process Management Journal, 29(2), 393–412. https://doi.org/10.1108/BPMJ-04-2022-0179
Ashaari, M. A., Singh, K. S. D., Abbasi, G. A., Amran, A., & Liebana-Cabanillas, F. 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, 173. https://doi.org/10.1016/j.techfore.2021.121119
Cepeda, G., Roldán, J. L., Sabol, M., Hair, J., & Chong, A. Y. L. (2024). Emerging opportunities for information systems researchers to expand their PLS-SEM analytical toolbox. In Industrial Management and Data Systems (Vol. 124, Issue 6, pp. 2230–2250). Emerald Publishing. https://doi.org/10.1108/IMDS-08-2023-0580
Ciampi, F., Demi, S., Magrini, A., Marzi, G., & Papa, A. (2021). Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation. Journal of Business Research, 123(June 2020), 1–13. https://doi.org/10.1016/j.jbusres.2020.09.023
Coltman, T., Devinney, T. M., Midgley, D. F., & Venaik, S. (2008). Formative versus reflective measurement models: Two applications of formative measurement. Journal of Business Research, 61(12), 1250–1262. https://doi.org/10.1016/j.jbusres.2008.01.013
Gao, J., & Sarwar, Z. (2022). How do firms create business value and dynamic capabilities by leveraging big data analytics management capability? Information Technology and Management. https://doi.org/10.1007/s10799-022-00380-w
Garmaki, M., Gharib, R. K., & Boughzala, I. (2023). Big data analytics capability and contribution to firm performance: the mediating effect of organizational learning on firm performance. Journal of Enterprise Information Management. https://doi.org/10.1108/JEIM-06-2021-0247
Ghasemaghaei, M., & Calic, G. (2019). Can big data improve firm decision quality? The role of data quality and data diagnosticity. Decision Support Systems, 120(March), 38–49. https://doi.org/10.1016/j.dss.2019.03.008
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information and Management, 53(8), 1049–1064. https://doi.org/10.1016/j.im.2016.07.004
Hafiz Hanafiah, M. (2020). FORMATIVE VS. REFLECTIVE MEASUREMENT MODEL: GUIDELINES FOR STRUCTURAL EQUATION MODELING RESEARCH. International Journal of Analysis and Applications, 18(5), 876–889. https://doi.org/10.28924/2291-8639
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
Huynh, M. T., Nippa, M., & Aichner, T. (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, 197(October), 122884. https://doi.org/10.1016/j.techfore.2023.122884
Jarvis, C. B., Mackenzie, S. B., Podsakoff, P. M., Giliatt, N., & Mee, J. F. (2003). A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research. In Journal of Consumer Research (Vol. 30, Issue 2, pp. 199–218). https://doi.org/10.1086/376806
Lozada, N. (2022). Unveiling the effects of big data analytics capability on innovation capability through absorptive capacity?: why more and better insights matter. https://doi.org/10.1108/JEIM-02-2021-0092
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information and Management, 57(2), 103169. https://doi.org/10.1016/j.im.2019.05.004
Petter, S., Straub, D., & Rai, A. (2007a). Specifying Formative Constructs in Information Systems Research (Vol. 31, Issue 4).
Petter, S., Straub, D., & Rai, A. (2007b). Specifying Formative Constructs in Information Systems Research Qiarterjy Specifying Formative Constructs in Information Systems Research1 (Vol. 31, Issue 4).
Roy, S., Marsillac, E., Tarafdar, M., Ragu-Nathan, T. S., & Marsillac, E. (2012). Misspecification of Reflective and Formative Constructs in Operations and Manufacturing Management Research. The Electronic Journal of Business Research Methods, 10, 34–52. www.ejbrm.com
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
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. In British Journal of Management (Vol. 14, Issue 3, pp. 207–222). https://doi.org/10.1111/1467-8551.00375
Varsha P S, Chakraborty, A., & Kar, A. K. (2024). How to Undertake an Impactful Literature Review: Understanding Review Approaches and Guidelines for High-impact Systematic Literature Reviews. In South Asian Journal of Business and Management Cases (Vol. 13, Issue 1, pp. 18–35). SAGE Publications Ltd. https://doi.org/10.1177/22779779241227654
Xu, T., & Liu, X. (2024). Achieving manufacturing supply chain resilience: the role of paradoxical leadership and big data analytics capability. Journal of Manufacturing Technology Management, 35(2), 205–225. https://doi.org/10.1108/JMTM-05-2023-0206
Zhang, H., & Yuan, S. (2023). How and When Does Big Data Analytics Capability Boost Innovation Performance? Sustainability (Switzerland), 15(5). https://doi.org/10.3390/su15054036
Shehzad, A., & Rozan, M. Z. A., Miskon, S. (2025). Are we Capturing the True Nature of Big Data Analytics Capabilities? A Review from Concept to Construct. International Journal of Academic Research in Business and Social Sciences, 15(8), 1568–1579.
Copyright: © 2025 The Author(s)
Published by Knowledge Words Publications (www.kwpublications.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode
