Impact of Big Data Analytics on Organizational Performance: The Role of Business Analytics, Decision-Making Quality and Sustainability
DOI:
https://doi.org/10.63062/trt/V24.069Keywords:
Big Data Analytics, Organizational Performance, Business Analytics Capacity, Decision-Making Quality, Sustainable Product DevelopmentAbstract
The research analyzes Big Data Analytics effects on organizational results through studies of business analytics capability improvement alongside better decision quality and sustainable product development outcomes. Organizations that utilize big data efficiently improve both their operational speed and their ability to process and utilize data which enables better decisions and innovative sustainable practices. Evidence shows that big data integration as a strategic business process component creates substantial organizational improvements which supply essential knowledge for professionals and academics alike. The research demonstrates strong evidence of performance gains yet points to the necessity for additional investigations about these relationships throughout different industry sectors.
References
Agustí, M. A., Aguilar-Caro, R., Galán, J. L., & Acedo, F. J. (2024). Dynamic resource management and slack resources. Management Decision, 62(13), 223–242. https://doi.org/10.1108/MD-01-2023-0119
Ahmad, M. A., Asaad, M. N., Saad, R., Iteng, R., & Rahim, M. K. I. A. (2016). The mediating effect of sustainable product development on the relationship between quality management practices and organizational performance: Empirical study of Malaysian automotive industry. 020010. https://doi.org/10.1063/1.4960850
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016a). How can firms improve 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., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016b). 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
Al-Ansaari, Y., Bederr, H., & Chen, C. (2015). Strategic orientation and business performance: An empirical study in the UAE context. Management Decision, 53(10), 2287–2302. https://doi.org/10.1108/MD-01-2015-0034
Ali, S., Poulova, P., Yasmin, F., Danish, M., Akhtar, W., & Usama Javed, H. M. (2020). How Big Data Analytics Boosts Organizational Performance: The Mediating Role of the Sustainable Product Development. Journal of Open Innovation: Technology, Market, and Complexity, 6(4), 190. https://doi.org/10.3390/joitmc6040190
Aydiner, A. S., Tatoglu, E., Bayraktar, E., & Zaim, S. (2019). Information system capabilities and firm performance: Opening the black box through decision-making performance and business-process performance. International Journal of Information Management, 47, 168–182. https://doi.org/10.1016/j.ijinfomgt.2018.12.015
Barney, J. (1991). Resources, firm resources, and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
Bayrak, T. (2015). A Review of Business Analytics: A Business Enabler or Another Passing Fad. Procedia - Social and Behavioral Sciences, 195, 230–239. https://doi.org/10.1016/j.sbspro.2015.06.354
Bharadwaj, Anandhi, Bharadwaj, A., El Sawy, O. A., University of Southern California, Pavlou, P. A., Temple University, Venkatraman, N., & Boston University. (2013). Digital Business Strategy: Toward a Next Generation of Insights. MIS Quarterly, 37(2), 471–482. https://doi.org/10.25300/MISQ/2013/37:2.3
Bitmiş, M. G., & Ergeneli, A. (2015). How Psychological Capital Influences Burnout: The Mediating Role of Job Insecurity. Procedia - Social and Behavioral Sciences, 207, 363–368. https://doi.org/10.1016/j.sbspro.2015.10.106
Chatterjee, S., Rana, N. P., & Dwivedi, Y. K. (2024). How does business analytics contribute to organisational performance and business value? A resource-based view. Information Technology & People, 37(2), 874–894. https://doi.org/10.1108/ITP-08-2020-0603
Chen, Y., Jin, Q., Fang, H., Lei, H., Hu, J., Wu, Y., Chen, J., Wang, C., & Wan, Y. (2019). Analytic network process: Academic insights and perspectives analysis. Journal of Cleaner Production, 235, 1276–1294. https://doi.org/10.1016/j.jclepro.2019.07.016
Curry, E., Auer, S., Berre, A. J., Metzger, A., Perez, M. S., & Zillner, S. (Eds.). (2022). Technologies and Applications for Big Data Value. Springer International Publishing. https://doi.org/10.1007/978-3-030-78307-5
Davenport, T. H. (2018). From analytics to artificial intelligence. Journal of Business Analytics, 1(2), 73–80. https://doi.org/10.1080/2573234X.2018.1543535
Davenport, T. H. & Harris, J. G. (2007). Competing on analytics: The new science of winning. Harvard Business Press.
Dwivedi, R., Nerur, S., & Balijepally, V. (2023). Exploring artificial intelligence and big data scholarship in information systems: A citation, bibliographic coupling, and co-word analysis. International Journal of Information Management Data Insights, 3(2), 100185. https://doi.org/10.1016/j.jjimei.2023.100185
Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897–904. https://doi.org/10.1016/j.jbusres.2015.07.001
Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, 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
Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2019). Big data analytics capabilities and knowledge management: Impact on firm performance. Management Decision, 57(8), 1923–1936. https://doi.org/10.1108/MD-07-2018-0825
Fosso, W. S., Akter, S., Edwards, A., Chopin, G., & Gnanzou D. (2015). How ‘big data’ can have big impact. Journal of Business Research, 70, 356–365.
Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246. https://doi.org/10.1016/j.ijpe.2014.12.031
Frederiksen, A. (2009). Competing on analytics: The new science of winning. Total Quality Management & Business Excellence, 20(5), 583–583. https://doi.org/10.1080/14783360902925454
Ghasemaghaei, M., Hassanein, K., & Turel, O. (2017). Increasing firm agility through the use of data analytics: The role of fit. Decision Support Systems, 101, 95–105. https://doi.org/10.1016/j.dss.2017.06.004
Giovanis, A., & Athanasopoulou, P. (2018). Understanding lovemark brands: Dimensions and effect on Brand loyalty in high-technology products. Spanish Journal of Marketing - ESIC, 22(3), 272–294. https://doi.org/10.1108/SJME-07-2018-0035
Grossman, R. L., & Siegel, K. P. (2014). Organizational Models for Big Data and Analytics. Journal of Organization Design, 3(1), 20. https://doi.org/10.7146/jod.9799
Gunasekaran, A., & Spalanzani, A. (2012). Sustainability of manufacturing and services: Investigations for research and applications. International Journal of Production Economics, 140(1), 35–47. https://doi.org/10.1016/j.ijpe.2011.05.011
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308-317. https://doi.org/10.1016/j.jbusres.2016.08.004
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
Helfat, C. E., & Raubitschek, R. S. (2018). Dynamic and integrative capabilities for profiting from innovation in digital platform-based ecosystems. Research Policy, 47(8), 1391–1399. https://doi.org/10.1016/j.respol.2018.01.019
Helfat, C. E., & Winter, S. G. (2011). Untangling Dynamic and Operational Capabilities: Strategy for the (N)ever‐Changing World. Strategic Management Journal, 32(11), 1243–1250. https://doi.org/10.1002/smj.955
Howard, M. C., & Rose, J. C. (2019). Refining and extending task–technology fit theory: Creation of two task–technology fit scales and empirical clarification of the construct. Information & Management, 56(6), 103134. https://doi.org/10.1016/j.im.2018.12.002
Jantunen, A., Tarkiainen, A., Chari, S., & Oghazi, P. (2018). Dynamic capabilities, operational changes, and performance outcomes in the media industry. Journal of Business Research, 89, 251–257. https://doi.org/10.1016/j.jbusres.2018.01.037
Kanwal, S., Al Mamun, A., Wu, M., Bhatti, S. M., & Ali, M. H. (2024). Corporate social responsibility: A Driver for green organizational climate and workplace pro-environmental behavior. Heliyon, 10(19), e38987. https://doi.org/10.1016/j.heliyon.2024.e38987
Ke, M., & Shi, Y. (2014). Big Data, Big Change: In the Financial Management. Open Journal of Accounting, 03(04), 77–82. https://doi.org/10.4236/ojacct.2014.34009
Kesavan, S., & Kushwaha, T. (2020). Field Experiment on the Profit Implications of Merchants’ Discretionary Power to Override Data-Driven Decision-Making Tools. Management Science, 66(11), 5182–5190. https://doi.org/10.1287/mnsc.2020.3743
Kim, G., Shin, B., & Kwon, O. (2012). Investigating the Value of Sociomaterialism in Conceptualizing IT Capability of a Firm. Journal of Management Information Systems, 29(3), 327–362. https://doi.org/10.2753/MIS0742-1222290310
Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), 387–394. https://doi.org/10.1016/j.ijinfomgt.2014.02.002
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics && the journey from insights to value. MIT Sloan Management Review, 52 (2), 21-32. https://sloanreview.mit.edu/article/big-data-analytics-and-the-path-from-insights-to-value/
Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020a). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50, 57–70. https://doi.org/10.1016/j.ijinfomgt.2019.04.003
Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020b). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50, 57–70. https://doi.org/10.1016/j.ijinfomgt.2019.04.003
Li, T. (Carol), & Chan, Y. E. (2019). Dynamic information technology capability: Concept definition and framework development. The Journal of Strategic Information Systems, 28(4), 101575. https://doi.org/10.1016/j.jsis.2019.101575
López-Muñoz, J. F., & Escribá-Esteve, A. (2017). An upper echelons perspective on information technology business value. European Research on Management and Business Economics, 23(3), 173–181. https://doi.org/10.1016/j.iedeen.2017.02.003
Luftman, J., Derksen, B., Dwivedi, R., Santana, M., Zadeh, H. S., & Rigoni, E. (2015). Influential it Management Trends: An International Study. Journal of Information Technology, 30(3), 293–305. https://doi.org/10.1057/jit.2015.18
Luoma, J. (2016). Model-based organizational decision making: A behavioral lens. European Journal of Operational Research, 249(3), 816–826. https://doi.org/10.1016/j.ejor.2015.08.039
Maliha, R. (2023). The big data analytical capability and firm performance: Mediating role of supply chain performance. The Asian Bulletin of Big Data Management, 2(1), 36–48. https://doi.org/10.62019/abbdm.v2i1.23
Masa’deh, R., Obeidat, B. Y., & Tarhini, A. (2016). A Jordanian empirical study of the associations among transformational leadership, transactional leadership, knowledge sharing, job performance, and firm performance: A structural equation modelling approach. Journal of Management Development, 35(5), 681–705. https://doi.org/10.1108/JMD-09-2015-0134
Mikalef, P., & Krogstie, J. (2020). Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities. European Journal of Information Systems, 29(3), 260–287. https://doi.org/10.1080/0960085X.2020.1740618
Mikalef, P., & Pateli, A. (2017). Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA. Journal of Business Research, 70, 1–16. https://doi.org/10.1016/j.jbusres.2016.09.004
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., Pappas, I. O., Krogstie, J., & Pavlou, P. A. (2020a). Big data and business analytics: A research agenda for realizing business value. Information & Management, 57(1), 103237. https://doi.org/10.1016/j.im.2019.103237
Mikalef, P., Pappas, I. O., Krogstie, J., & Pavlou, P. A. (2020b). Big data and business analytics: A research agenda for realizing business value. Information & Management, 57(1), 103237. https://doi.org/10.1016/j.im.2019.103237
Modgil, S., Gupta, S., Sivarajah, U., & Bhushan, B. (2021). Big data-enabled large-scale group decision making for circular economy: An emerging market context. Technological Forecasting and Social Change, 166, 120607. https://doi.org/10.1016/j.techfore.2021.120607
Oliveira, R. D., Leitão, J., & Alves, H. (2020). Corporate Governance and Sustainability in HEIs. In E. Sengupta, P. Blessinger, & T. S. Yamin (Eds.), Innovations in Higher Education Teaching and Learning (pp. 177–191). Emerald Publishing Limited. https://doi.org/10.1108/S2055-364120200000022016
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
Rahman, I. A., Memon, A. H., & Karim, A. T. A. (2013). Examining Factors Affecting Budget Overrun of Construction Projects Undertaken through Management Procurement Method Using PLS-sem Approach. Procedia - Social and Behavioral Sciences, 107, 120–128. https://doi.org/10.1016/j.sbspro.2013.12.407
Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30–40. https://doi.org/10.1016/j.dss.2015.10.006
Schuberth, F., Hubona, G., Roemer, E., Zaza, S., Schamberger, T., Chuah, F., Cepeda-Carrión, G., & Henseler, J. (2023). The choice of structural equation modeling technique matters: A commentary on Dash and Paul (2021). Technological Forecasting and Social Change, 194, 122665. https://doi.org/10.1016/j.techfore.2023.122665
Sena, V., Bhaumik, S., Sengupta, A., & Demirbag, M. (2019). Big Data and Performance: What Can Management Research Tell us? British Journal of Management, 30(2), 219–228. https://doi.org/10.1111/1467-8551.12362
Themistocleous, M., & Papadaki, M. (Eds.). (2020). Information Systems: 16th European, Mediterranean, and Middle Eastern Conference, EMCIS 2019, Dubai, United Arab Emirates, December 9–10, 2019, Proceedings (Vol. 381). Springer International Publishing. https://doi.org/10.1007/978-3-030-44322-1
Van Kollenburg, G. H., Van Es, J., Gerretzen, J., Lanters, H., Bouman, R., Koelewijn, W., Davies, A. N., Buydens, L. M. C., Van Manen, H.-J., & Jansen, J. J. (2020). Understanding chemical production processes by using PLS path model parameters as soft sensors. Computers & Chemical Engineering, 139, 106841. https://doi.org/10.1016/j.compchemeng.2020.106841
Viaene, S. (2013). Data Scientists Aren’t Domain Experts. IT Professional, 15(6), 12–17. https://doi.org/10.1109/MITP.2013.93
Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626–639. https://doi.org/10.1016/j.ejor.2017.02.023
Volberda, H. W., Khanagha, S., Baden-Fuller, C., Mihalache, O. R., & Birkinshaw, J. (2021). Strategizing in a digital world: Overcoming cognitive barriers, reconfiguring routines and introducing new organizational forms. Long Range Planning, 54(5), 102110. https://doi.org/10.1016/j.lrp.2021.102110
Zhan, Y., & Tan, K. H. (2020). An analytic infrastructure for harvesting big data to enhance supply chain performance. European Journal of Operational Research, 281(3), 559–574. https://doi.org/10.1016/j.ejor.2018.09.018
Zhang, S., O’Connor, P., & Gardiner, E. (2023). Some rules should be broken: Developing a measure of constructive rule beliefs. Personality and Individual Differences, 207, 112145. https://doi.org/10.1016/j.paid.2023.112145
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