ABSTRACT
Nowadays, big data analytics (BDA) have widely used in our business environment as an undeniable function for firms to not only survive in turbulence but also have the opportunity to be ahead of their major competitors. One of the promising aspects of BDA relates to its influence on innovation performance. In line, the present study proposed a conceptual model in order to investigate the relationship between BDA use and innovation performance by considering the role of dynamic capability (DC) theory. In this research, we consider firm agility in terms of DC theory and decompose it into three main factors contacting sensing agility, decision making agility, and acting agility. The research model and required data were analyzed using Partial Least Squares (PLS)/Structured Equation Modelling (SEM). The outcome of this study indicates that firms would be able to increase their innovation performance from a DC theory. This study also shows that BDA use has a positive influence on sensing agility of firms.
- Vidgen, R., Shaw, S., and Grant, D.B. 2017. Management challenges in creating value from business analytics. European Journal of Operational Research. 261, 2 (2017), 626--639.Google ScholarCross Ref
- Kiron, D. and Shockley, R. 2011. Creating business value with analytics. MIT Sloan Management Review. 53, 1 (2011), 57.Google Scholar
- Rouhani, S., et al. 2016. The impact model of business intelligence on decision support and organizational benefits. Journal of Enterprise Information Management. 29, 1 (2016), 19--50.Google ScholarCross Ref
- Rouhani, S., et al. 2018. Business intelligence systems adoption model: an empirical investigation. Journal of Organizational and End User Computing (JOEUC). 30, 2 (2018), 43--70.Google Scholar
- Wamba, S.F., et al. 2015. How 'big data'can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics. 165, (2015), 234--246.Google ScholarCross Ref
- Ghasemaghaei, M. 2019. Are firms ready to use big data analytics to create value? The role of structural and psychological readiness. Enterprise Information Systems, (2019), 1--25.Google Scholar
- Shollo, A. and Galliers, R.D. 2016. Towards an understanding of the role of business intelligence systems in organisational knowing. Information Systems Journal. 26, 4 (2016), 339--367.Google Scholar
- Chen, H., Chiang, R.H., and Storey, V.C. 2012. Business intelligence and analytics: From big data to big impact. MIS quarterly. 36, 4 (2012).Google Scholar
- Torres, R., Sidorova, A., and Jones, M.C. 2018. Enabling firm performance through business intelligence and analytics: A dynamic capabilities perspective. Information & Management. 55, 7 (2018), 822--839.Google ScholarCross Ref
- Seddon, P.B., et al. 2017. How does business analytics contribute to business value? Information Systems Journal. 27, 3 (2017), 237--269.Google Scholar
- Akter, S., et al. 2016. How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics. 182, (2016), 113--131.Google ScholarCross Ref
- Raguseo, E. 2018. Big data technologies: An empirical investigation on their adoption, benefits and risks for companies. International Journal of Information Management. 38, 1 (2018), 187--195.Google Scholar
- Kowalczyk, M. and Buxmann, P. 2015. An ambidextrous perspective on business intelligence and analytics support in decision processes: Insights from a multiple case study. Decision Support Systems. 80, (2015), 1--13.Google ScholarDigital Library
- Wamba, S.F., et al. 2017. Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research. 70, (2017), 356--365.Google ScholarCross Ref
- Mikalef, P., et al. 2019. Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research. 98, (2019), 261--276.Google ScholarCross Ref
- Gunasekaran, A., et al. 2017. Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research. 70, (2017), 308--317.Google ScholarCross Ref
- Ghasemaghaei, M., Hassanein, K., and Turel, O. 2017. Increasing firm agility through the use of data analytics: The role of fit. Decision Support Systems. 101, (2017), 95--105.Google ScholarDigital Library
- Lehrer, C., et al. 2017. How big data analytics enables service innovation: materiality, affordance, and the individualization of service. Journal of Management Information Systems. 35, 2 (2017), 424--460.Google Scholar
- Akter, S., et al. 2019. Analytics-based decision-making for service systems: A qualitative study and agenda for future research. International Journal of Information Management. 48, (2019), 85--95.Google ScholarCross Ref
- Davenport, T.H., Enterprise analytics: Optimize performance, process, and decisions through big data. 2012, Upper Saddle River, New Jersey: FT Press Operations Management.Google Scholar
- Ji-fan Ren, S., et al. 2017. Modelling quality dynamics, business value and firm performance in a big data analytics environment. International Journal of Production Research. 55, 17 (2017), 5011--5026.Google Scholar
- Popovič, A., et al. 2016. The impact of big data analytics on firms' high value business performance. Information Systems Frontiers, (2016), 1--14.Google Scholar
- Teece, D.J., Pisano, G., and Shuen, A. 1997. Dynamic capabilities and strategic management. Strategic management journal. 18, 7 (1997), 509--533.Google Scholar
- Liu, H., et al. 2013. The impact of IT capabilities on firm performance: The mediating roles of absorptive capacity and supply chain agility. Decision Support Systems. 54, 3 (2013), 1452--1462.Google ScholarDigital Library
- Helfat, C.E. and Peteraf, M.A., Understanding dynamic capabilities: progress along a developmental path. 2009, Sage publications Sage UK: London, England.Google Scholar
- Pavlou, P.A. and El Sawy, O.A. 2011. Understanding the elusive black box of dynamic capabilities. Decision sciences. 42, 1 (2011), 239--273.Google Scholar
- Park, Y., El Sawy, O.A., and Fiss, P. 2017. The role of business intelligence and communication technologies in organizational agility: a configurational approach. Journal of the association for information systems. 18, 9 (2017), 1.Google Scholar
- Teece, D., Peteraf, M., and Leih, S. 2016. Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California Management Review. 58, 4 (2016), 13--35.Google ScholarCross Ref
- Erevelles, S., Fukawa, N., and Swayne, L. 2016. Big Data consumer analytics and the transformation of marketing. Journal of Business Research. 69, 2 (2016), 897--904.Google ScholarCross Ref
- Ashrafi, A. and Zare Ravasan, A. 2018. How market orientation contributes to innovation and market performance: the roles of business analytics and flexible IT infrastructure. Journal of Business & Industrial Marketing. 33, 7 (2018), 970--983.Google Scholar
- Joshi, K.D., et al. 2010. Changing the competitive landscape: Continuous innovation through IT-enabled knowledge capabilities. Information Systems Research. 21, 3 (2010), 472--495.Google ScholarDigital Library
- Sharma, R., Mithas, S., and Kankanhalli, A., Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations. 2014, Taylor & Francis.Google Scholar
- Shamim, S., et al. 2018. Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: A dynamic capabilities view. Information & Management, (2018).Google Scholar
- Ashrafi, A., et al. 2019. The role of business analytics capabilities in bolstering firms' agility and performance. International Journal of Information Management. 47, (2019), 1--15.Google ScholarCross Ref
- Hair, J.J.F., et al., A primer on partial least squares structural equation modeling (PLS-SEM). 2017: Sage Publications.Google Scholar
- Henseler, J., Hubona, G., and Ray, P.A. 2016. Using PLS path modeling in new technology research: updated guidelines. Industrial management & data systems. 116, 1 (2016), 2--20.Google Scholar
- Henseler, J., Ringle, C.M., and 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 (2015), 115--135.Google Scholar
- Cohen, J. 1992. Quantitative methods in psychology: A power primer. Psychol. Bull. 112, (1992), 1155--1159.Google Scholar
- Aydiner, A.S., et al. 2019. Business analytics and firm performance: The mediating role of business process performance. Journal of Business Research. 96, (2019), 228--237.Google ScholarCross Ref
Index Terms
- An empirical investigation on big data analytics (BDA) and innovation performance
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