Abstract
With the emergence of information and communication technologies, organizations worldwide have been putting in meaningful efforts towards developing and gaining business insights by combining technology capability, management capability and personnel capability to explore data potential, which is known as big data analytics (BDA) capability. In this context, variables such as sensing capability—which is related to the organization’s ability to explore the market and develop opportunities—and analytics culture—which refers to the organization’s practices and behavior patterns of its analytical principles—play a fundamental role in BDA initiatives. However, there is a considerable literature gap concerning the effects of BDA-enabled sensing capability and analytics culture on organizational outcomes (i.e., customer linking capability, financial performance, market performance, and strategic business value) and on how important the organization’s analytics culture is as a mediator in the relationship between BDA-enabled sensing capability and organizational outcomes. Therefore, this study aims to investigate these relationships. And to attain this goal, we developed a conceptual model supported by dynamics capabilities, BDA, and analytics culture. We then validated our model by applying partial least squares structural equation modeling. The findings showed not only the positive effect of the BDA-enabled sensing capability and analytics culture on organizational outcomes but also the mediation effect of the analytics culture. Such results bring valuable theoretical implications and contributions to managers and practitioners.
Similar content being viewed by others
References
Aboelmaged, M., & Mouakket, S. (2020). Influencing models and determinants in big data analytics research: A bibliometric analysis. Information Processing and Management, 57(4), 102234. https://doi.org/10.1016/j.ipm.2020.102234.
Akter, S., Bandara, R., Hani, U., Fosso Wamba, S., Foropon, C., & Papadopoulos, T. (2019). Analytics-based decision-making for service systems: A qualitative study and agenda for future research. International Journal of Information Management, 48, 85–95. https://doi.org/10.1016/j.ijinfomgt.2019.01.020.
Akter, S., & Fosso Wamba, S. (2019). Big data and disaster management: a systematic review and agenda for future research. Annals of Operations Research, 283(1–2), 939–959. https://doi.org/10.1007/s10479-017-2584-2.
Akter, S., Fosso Wamba, S., & Dewan, S. (2017). Why PLS-SEM is suitable for complex modelling? An empirical illustration in big data analytics quality. Production Planning and Control, 28(11–12), 1011–1021. https://doi.org/10.1080/09537287.2016.1267411.
Akter, S., Fosso Wamba, S., 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.
Akter, S., & Fosso Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 26(2), 173–194. https://doi.org/10.1007/s12525-016-0219-0.
Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2020). Transforming business using digital innovations: the application of A.I., blockchain, cloud and data analytics. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03620-w.
Alinaghian, L., Kim, Y., & Srai, J. (2020). A relational embeddedness perspective on dynamic capabilities: A grounded investigation of buyer-supplier routines. Industrial Marketing Management, 85(February 2019), 110–125. https://doi.org/10.1016/j.indmarman.2019.10.003
Aloysius, J. A., Hoehle, H., Goodarzi, S., & Venkatesh, V. (2018). Big data initiatives in retail environments: Linking service process perceptions to shopping outcomes. Annals of Operations Research, 270(1–2), 25–51. https://doi.org/10.1007/s10479-016-2276-3.
Alshanty, A. M., & Emeagwali, O. L. (2019). Market-sensing capability, knowledge creation and innovation: The moderating role of entrepreneurial-orientation. Journal of Innovation & Knowledge, 4(3), 171–178. https://doi.org/10.1016/j.jik.2019.02.002.
Arunachalam, D., Kumar, N., & Kawalek, J. P. (2018). Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114, 416–436. https://doi.org/10.1016/j.tre.2017.04.001.
Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17, 99–120. https://doi.org/10.1177/014920639101700108.
Bayighomog Likoum, S. W., Shamout, M. D., Harazneh, I., & Abubakar, A. M. (2020). Market-Sensing Capability, Innovativeness, Brand Management Systems, Market Dynamism, Competitive Intensity, and Performance: an Integrative Review. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-018-0561-x.
Bloomberg. (2020). Big data and business analytics market size is projected to reach USD 512.04 Billion by 2026 | Valuates Reports. Retrieved June 9, 2020, from https://www.bloomberg.com/press-releases/2020-02-11/big-data-and-business-analytics-market-size-is-projected-to-reach-usd-512-04-billion-by-2026-valuates-reports.
Boone, C. A., Hazen, B. T., Skipper, J. B., & Overstreet, R. E. (2018). A framework for investigating optimization of service parts performance with big data. Annals of Operations Research, 270(1), 65–74. https://doi.org/10.1007/s10479-016-2314-1.
Day, G. S. (1994). The Capabilities of Market-Driven Organizations. Journal of Marketing, 58(4), 37–52. https://doi.org/10.1177/002224299405800404.
Duan, Y., Cao, G., & Edwards, J. S. (2020). Understanding the impact of business analytics on innovation. European Journal of Operational Research, 281(3), 673–686. https://doi.org/10.1016/j.ejor.2018.06.021.
Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019a). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource‐based view and big data culture. British Journal of Management, 30, 341–361. https://doi.org/10.1111/1467-8551.12355.
Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., et al. (2019b). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2019.107599
Dubey, R., Gunasekaran, A., Childe, S. J., Luo, Z., Fosso Wamba, S., Roubaud, D., & Foropon, C. (2018). Examining the role of big data and predictive analytics on collaborative performance in context to sustainable consumption and production behaviour. Journal of Cleaner Production, 196, 1508–1521. https://doi.org/10.1016/j.jclepro.2018.06.097.
Dubey, R., Gunasekaran, A., Childe, S. J., Roubaud, D., Fosso Wamba, S., Giannakis, M., & Foropon, C. (2019c). Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. International Journal of Production Economics, 210(January), 120–136. https://doi.org/10.1016/j.ijpe.2019.01.023.
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
Fosso Wamba, S., Dubey, R., Gunasekaran, A., & Akter, S. (2020a). The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2019.09.019
Fosso Wamba, S., Gunasekaran, A., Akter, S., Ren, S. J., Dubey, R., & Childe, S. J. (2017a). Big data analytics and firm performance: Effects of dynamic capabilities ☆. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009.
Fosso Wamba, S., Gunasekaran, A., Dubey, R., & Ngai, E. W. T. (2018). Big data analytics in operations and supply chain management. Annals of Operations Research, 270(1–2), 1–4. https://doi.org/10.1007/s10479-018-3024-7.
Fosso Wamba, S., Ngai, E.W. T., Riggins, F., & Akter, S. (2017b). Guest editorial. International Journal of Operations & Production Management, 37(1), 2–9. https://doi.org/10.1108/IJOPM-07-2016-0414.
Fosso Wamba, S., Queiroz, M. M., & Trinchera, L. (2020b). Dynamics between blockchain adoption determinants and supply chain performance: An empirical investigation. International Journal of Production Economics, 229(September 2019), 107791. https://doi.org/10.1016/j.ijpe.2020.107791.
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.
Grant, R. M. (1991). The resource-based theory of competitive advantage: Implications for strategy formulation. California Management Review, 33(3), 114–135. https://doi.org/10.2307/41166664.
Gregor, S., Martin, M., Fernandez, W., Stern, S., & Vitale, M. (2006). The transformational dimension in the realization of business value from information technology. Journal of Strategic Information Systems, 15(3), 249–270. https://doi.org/10.1016/j.jsis.2006.04.001.
Guha, S., & Kumar, S. (2018). Emergence of big data research in operations management, information systems, and healthcare: Past contributions and future roadmap. Production and Operations Management, 27(9), 1724–1735. https://doi.org/10.1111/poms.12833
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064. https://doi.org/10.1016/j.im.2016.07.004.
Gupta, S., Altay, N., & Luo, Z. (2019). Big data in humanitarian supply chain management: a review and further research directions. Annals of Operations Research, 283(1), 1153–1173. https://doi.org/10.1007/s10479-017-2671-4.
Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks: Sage Publications.
Hair, J. F. Jr., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203.
Hair, J. F. Jr., Sarstedt, M., Hopkins, L., & Kuppelwieser, G., V (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128.
Hallikainen, H., Savimäki, E., & Laukkanen, T. (2020). Fostering B2B sales with customer big data analytics. Industrial Marketing Management, 86, 90–98. https://doi.org/10.1016/j.indmarman.2019.12.005.
Hassna, G., & Lowry, P. B. (2018). Big data capability, customer agility, and organization performance: A dynamic capability perspective. In Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018, (December).
Hazen, B. T., Skipper, J. B., Boone, C. A., & Hill, R. R. (2018). Back in business: operations research in support of big data analytics for operations and supply chain management. Annals of Operations Research, 270(1), 201–211. https://doi.org/10.1007/s10479-016-2226-0.
Helfat, C. E., & Peteraf, M. A. (2009). Understanding dynamic capabilities: Progress along a developmental path. Strategic Organization, 7(1), 91–102. https://doi.org/10.1177/1476127008100133.
Jha, A. K., Agi, M. A. N., & Ngai, E. W. T. (2020). A note on big data analytics capability development in supply chain. Decision Support Systems. https://doi.org/10.1016/j.dss.2020.113382
Kiron, D., Prentice, P. K., & Ferguson, R. B. (2014). The analytics mandate. MIT Sloan Management Review, 55, 1–25.
Kock, N. (2019). From composites to factors: Bridging the gap between PLS and covariance-based structural equation modelling. Information Systems Journal, 29(3), 674–706. https://doi.org/10.1111/isj.12228.
Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28, 227–261. https://doi.org/10.1111/isj.12131
Krishnamoorthi, S., & Mathew, S. K. (2018). Business analytics and business value: A comparative case study. Information and Management, 55(5), 643–666. https://doi.org/10.1016/j.im.2018.01.005.
Lin, C., & Kunnathur, A. (2019). Strategic orientations, developmental culture, and big data capability. Journal of Business Research, 105(November 2018), 49–60. https://doi.org/10.1016/j.jbusres.2019.07.016
Liu, P., & Yi, S. (2018). Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era. Annals of Operations Research, 270(1), 255–271. https://doi.org/10.1007/s10479-018-2783-5.
Malesios, C., Dey, P. K., & Abdelaziz, F. B. (2018). Supply chain sustainability performance measurement of small and medium sized enterprises using structural equation modeling. Annals of Operations Research. https://doi.org/10.1007/s10479-018-3080-z
Manupati, V. K., Schoenherr, T., Ramkumar, M., Wagner, S. M., Pabba, S. K., & Singh, I. R. R. (2019). A blockchain-based approach for a multi-echelon sustainable supply chain. International Journal of Production Research. https://doi.org/10.1080/00207543.2019.1683248
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity, technical report. McKinsey Global Institute.
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98(February), 261–276. https://doi.org/10.1016/j.jbusres.2019.01.044.
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2020a). Big data analytics capabilities and innovation: The mediating role of dynamic capabilities and moderating effect of the environment. British Journal of Management, 30(2), 272–298. https://doi.org/10.1111/1467-8551.12343
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020b). 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.
Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and E-Business Management, 16(3), 547–578. https://doi.org/10.1007/s10257-017-0362-y.
Mikalef, P., Pateli, A., & van de Wetering, R. (2020c). I.T. architecture flexibility and I.T. governance decentralisation as drivers of IT-enabled dynamic capabilities and competitive performance: The moderating effect of the external environment. European Journal of Information Systems, 00(00), 1–29. https://doi.org/10.1080/0960085X.2020.1808541.
Mishra, D., Gunasekaran, A., Papadopoulos, T., & Childe, S. J. (2018). Big Data and supply chain management: a review and bibliometric analysis. Annals of Operations Research, 270(1–2), 313–336. https://doi.org/10.1007/s10479-016-2236-y.
Moqbel, M., & Kock, N. (2018). Unveiling the dark side of social networking sites: Personal and work-related consequences of social networking site addiction. Information & Management, 55(1), 109–119. https://doi.org/10.1016/j.im.2017.05.001.
Morgan, N. A., Slotegraaf, R. J., & Vorhies, D. W. (2009). Linking marketing capabilities with profit growth. International Journal of Research in Marketing, 26(4), 284–293. https://doi.org/10.1016/j.ijresmar.2009.06.005.
Nam, D., Lee, J., & Lee, H. (2019). Business analytics use in CRM: A nomological net from I.T. competence to CRM performance. International Journal of Information Management, 45, 233–245. https://doi.org/10.1016/j.ijinfomgt.2018.01.005.
Newbert, S. L. (2007). Empirical research on the resource-based view of the firm: an assessment and suggestions for future research. Strategic management journal, 28(2), 121–146. https://doi.org/10.1002/smj.573.
Nunnally, J. C. (1978). Psychometric Theory (2nd ed.). New York: McGraw-Hill, Ed.
Peng, D. X., & Lai, F. (2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30(6), 467–480. https://doi.org/10.1016/j.jom.2012.06.002
Phillips-Wren, G., & Hoskisson, A. (2015). An analytical journey towards big data. Journal of Decision Systems, 24(1), 87–102. https://doi.org/10.1080/12460125.2015.994333.
Pinsonneault, A., & Kraemer, K. (1993). Survey research methodology in management information systems: An assessment. Journal of Management Information Systems, 10(2), 75–105. https://doi.org/10.1080/07421222.1993.11518001
Prasad, N., Gopalakrishnan, N., & Roger, N. (2020). Management of humanitarian relief operations using satellite big data analytics: the case of Kerala floods. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03593-w.
Prasad, S., Zakaria, R., & Altay, N. (2018). Big data in humanitarian supply chain networks: a resource dependence perspective. Annals of Operations Research, 270(1), 383–413. https://doi.org/10.1007/s10479-016-2280-7.
Queiroz, M. M., & Telles, R. (2018). Big data analytics in supply chain and logistics: an empirical approach. International Journal of Logistics Management, 29(2), 767–783. https://doi.org/10.1108/IJLM-05-2017-0116.
Queiroz, M. M., Wamba, F., Machado, S., & Telles, R. (2020). Smart production systems drivers for business process management improvement: An integrative framework. Business Process Management Journal. https://doi.org/10.1108/BPMJ-03-2019-0134
Raguseo, E., & Vitari, C. (2018). Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects. International Journal of Production Research, 56(15), 5206–5221. https://doi.org/10.1080/00207543.2018.1427900.
Rapp, A., Trainor, K. J., & Agnihotri, R. (2010). Performance implications of customer-linking capabilities: Examining the complementary role of customer orientation and CRM technology. Journal of Business Research, 63(11), 1229–1236. https://doi.org/10.1016/j.jbusres.2009.11.002.
Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH. Retrieved from https://www.smartpls.com.
Rumelt, R. P. (1984). Towards a strategic theory of the firm. In R. B. Lamb (Ed.), Competitive Strategic Management (pp. 556–570). Englewood Cliffs: Prentice-Hall.
See-To, E. W. K., & Ngai, E. W. T. (2018). Customer reviews for demand distribution and sales nowcasting: a big data approach. Annals of Operations Research, 270(1), 415–431. https://doi.org/10.1007/s10479-016-2296-z.
Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society Series B (Methodological), 36(2), 111–147.
Teece, D., & Pisano, G. (1994). The Dynamic Capabilities of Firms: an Introduction. Industrial and Corporate Change, 3(3), 537–556. https://doi.org/10.1093/icc/3.3.537-a.
Teece, D., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z.
Teece, D. J. (2007). Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640.
Tippins, M. J., & Sohi, R. S. (2003). I.T. competency and firm performance: Is organizational learning a missing link? Strategic Management Journal, 24(8), 745–761. https://doi.org/10.1002/smj.337.
Upadhyay, P., & Kumar, A. (2020). The intermediating role of organizational culture and internal analytical knowledge between the capability of big data analytics and a firm’s performance. International Journal of Information Management, 52(February), 102100. https://doi.org/10.1016/j.ijinfomgt.2020.102100.
Vieira, A. A. C., Dias, L. M. S., Santos, M. Y., Pereira, G. A. B., & Oliveira, J. A. (2019). Simulation of an automotive supply chain using big data. Computers and Industrial Engineering. https://doi.org/10.1016/j.cie.2019.106033
Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84. https://doi.org/10.1111/jbl.12010.
Wang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98–110. https://doi.org/10.1016/j.ijpe.2016.03.014.
Wang, N., Liang, H., Zhong, W., Xue, Y., & Xiao, J. (2012). Resource structuring or capability building? An empirical study of the business value of information technology. Journal of Management Information Systems, 29(2), 325–367. https://doi.org/10.2753/MIS0742-1222290211.
Wang, Y., Kung, L. A., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3–13. https://doi.org/10.1016/j.techfore.2015.12.019.
Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5(2), 171–180. https://doi.org/10.1002/smj.4250050207.
Zhan, Y., Tan, K. H., Li, Y., & Tse, Y. K. (2018). Unlocking the power of big data in new product development. Annals of Operations Research, 270(1–2), 577–595. https://doi.org/10.1007/s10479-016-2379-x.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Fosso Wamba, S., Queiroz, M.M., Wu, L. et al. Big data analytics-enabled sensing capability and organizational outcomes: assessing the mediating effects of business analytics culture. Ann Oper Res 333, 559–578 (2024). https://doi.org/10.1007/s10479-020-03812-4
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-020-03812-4