The Impact of Business Intelligence and Analytics Adoption on Decision Making Effectiveness and Managerial Work Performance
DOI:
https://doi.org/10.47743/saeb-2023-0012Keywords:
Business Intelligence and Analytics, data-driven culture, decision-making effectiveness, individual work performance.Abstract
Business Intelligence and Analytics systems have the capability to enable organizations to better comprehend their business and to increase the quality of managerial decisions, and consequently improve their performance. Recently, organizations have embraced the idea that data becomes a core asset, and this belief also changes the culture of the organization; data and analytics now determine a data-driven culture, which makes way for more effective data-driven decisions. To the best of our knowledge, there are few studies that investigate the effects of BI&A adoption on individual decision-making effectiveness and managerial work performance. This paper aims to contribute to bridging this gap by providing a research model that examines the relationship between BI&A adoption and manager’s decision-making effectiveness and then his individual work performance. The research model also theorizes that a data-driven culture promotes the BI&A adoption in the organization. Using specific control variables, we also expect to observe differences between different departments and managerial positions, which will provide practical implications for companies that work on BI&A adoption.
References
Alexander, D. T., & Lyytinen, K. (2019). Organizing Around Big Data: Organizational Analytic Capabilities for Improved Performance. Paper presented at the Proceedings of the 40th International Conference on Information Systems, Munich.
Anderson-Lehman, R., & Watson, H. (2004). Continental airlines flies high with real-time business intelligence. Management Information Systems Quarterly, 3(4), 163-176.
Appelbaum, D., Kogan, A., Vasarhelyi, M., & Yan, Z. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, 29-44. http://dx.doi.org/10.1016/j.accinf.2017.03.003
Assur, N., & Rowshankish, K. (2022). The data-driven enterprise of 2025. Retrieved from https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-data-driven-enterprise-of-2025
Battleson, D. A., West, B. C., Kim, J., Ramesh, B., & Robinson, P. S. (2016). Achieving Dynamic Capabilities with Cloud Computing: An Empirical Investigation. European Journal of Information Systems, 25(3), 209-230. http://dx.doi.org/10.1057/ejis.2015.12
Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, 57(2), 103168. http://dx.doi.org/10.1016/j.im.2019.05.003
Bokman, A., Fiedler, L., Perrey, J., & Pickersgill, A. (2014). Five facts: How customer analytics boosts corporate performance. Retrieved from https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance
Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, 3-11.
Calof, J., Richards, G., & Smith, J. (2015). Foresight, Competitive Intelligence and Business Analytics - Tools for Making Industrial Programmes More Efficient. Foresight-Russia, 9(1), 68-81. http://dx.doi.org/10.17323/1995-459X.2015.1.68.81
Cao, G., & Duan, Y. (2014). A path model linking business analytics, data-driven culture, and competitive advantage. Paper presented at the ECIS 2014 Proceedings - 22nd European Conference on Information Systems, Tel Aviv, Israel.
Chae, B., Yang, C., Olson, D., & Sheu, C. (2014). The Impact of Advanced Analytics and Data Accuracy on Operational Performance: A Contingent Resource Based Theory (RBT) Perspective. Decision Support Systems, 59(1), 119-126. http://dx.doi.org/10.1016/j.dss.2013.10.012
Chae, B. K., & Olson, D. L. (2013). Business Analytics for Supply Chain: A Dynamic-Capabilities Framework. International Journal of Information Technology & Decision Making, 12(1), 9-26. http://dx.doi.org/10.1142/S0219622013500016
Chatterjee, S., Chaudhuri, R., & Vrontis, D. (2021). Does data-driven culture impact innovation and performance of a firm? An empirical examination. Annals of Operations Research. http://dx.doi.org/10.1007/s10479-020-03887-z
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. Management Information Systems Quarterly, 36(4), 1165-1188. http://dx.doi.org/10.2307/41703503
Cosic, R., Shanks, G., & Maynard, S. (2012). Towards a business analytics capability maturity model. Paper presented at the Proceedings of the 23rd Australasian Conference on Information Systems, Geelong.
Davenport, T. H. (2006). Competing on analytics. Harv Bus Rev, 84(1), 98-107, 134.
Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Boston: Harvard Business School Press.
Davenport, T. H., Harris, J. G., & Morison, R. (2010). Analytics at Work: Smarter Decisions, Better Results. Boston: Harvard Business School Press.
De Saulles, M. (2019). What exactly is a data-driven organization? . Retrieved from https://www.cio.com/article/217767/what-exactly-is-a-data-driven-organization.html
DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information & Management, 3(1), 60-95. http://dx.doi.org/10.1287/isre.3.1.60
Djerdjouri, M., & Mehailia, A. (2017). Adopting Business Analytics to Leverage Enterprise Data Assets. Paper presented at the Springer Proceedings in Business and Economics. Leadership, Innovation and Entrepreneurship as Driving Forces of the Global Economy.
Duan, Y., Cao, G., & Edwards, J. (2020). Understanding the Impact of Business Analytics on Innovation. European Journal of Operational Research, 281(3), 673-686. http://dx.doi.org/10.1016/j.ejor.2018.06.021
Fricker, S., Kreisler, C., & Tam, L. (2012). An exploration of the application of PLS path modeling approach to creating a summary index of respondent burden. Retrieved from https://www.bls.gov/osmr/research-papers/2012/pdf/st120050.pdf
Frisk, J. E., Lindgren, R., & Mathiassen, L. (2014). Design matters for decision makers: Discovering IT investment alternatives. European Journal of Information Systems, 23(4), 442-461. http://dx.doi.org/10.1057/ejis.2013.13
Ghasemaghaei, M. (2019). Does Data Analytics Use Improve Firm Decision Making Quality? The Role of Knowledge Sharing and Data Analytics Competency. Decision Support Systems, 120(May), 14-24. http://dx.doi.org/10.1016/j.dss.2019.03.004
Ghasemaghaei, M., Ebrahimi, S., & Hassanein, K. (2018). Data analytics competency for improving firm decision making performance. The Journal of Strategic Information Systems, 27(1), 101-113. http://dx.doi.org/10.1016/j.jsis.2017.10.001
Gibson, M., Arnott, D., & Jagielska, I. (2004). Evaluating the intangible benefits of Business Intelligence: review and research agenda. Paper presented at the Proceedings of the 2004 IFIP International Conference on Decision Support Systems, Prato, Italy.
Gupta, M., & George, J. F. (2016). Toward the Development of a Big Data Analytics Capability. Information & Management, 53(8), 1049-1064. http://dx.doi.org/10.1016/j.im.2016.07.004
Hindle, G. A., & Vidgen, R. (2018). Developing a business analytics methodology: A case study in the foodbank sector. European Journal of Operational Research, 268(3), 836-851. http://dx.doi.org/10.1016/j.ejor.2017.06.031
Holsapple, C., Lee-Post, A., & Pakath, R. (2014). A unified foundation for business analytics. Decision Support Systems, 64(August), 130-141. http://dx.doi.org/10.1016/j.dss.2014.05.013
Hou, C. (2012). Examining the effect of user satisfaction on system usage and individual performance with business intelligence systems: An empirical study of Taiwan’s electronics industry. International Journal of Information Management, 32(6), 560-573. http://dx.doi.org/10.1016/j.ijinfomgt.2012.03.001
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, 138(November), 113382. http://dx.doi.org/10.1016/j.dss.2020.113382
Kiron, D., Ferguson, R., & Prentice, P. (2013). Innovating with Analytics. MIT Sloan Management Review, 54(1), 47-52.
Kitchens, B., Dobolyi, D., Li, J., & Abbasi, A. (2018). Advanced Customer Analytics: Strategic Value Through Integration of Relationship-Oriented Big Data. Journal of Management Information Systems, 35(2), 540-574. http://dx.doi.org/10.1080/07421222.2018.1451957
Koopmans, L., Bernaards, C., Hildebrandt, V., van Buuren, S., van der Beek, A. J., & de Vet, H. C. W. (2012). Development of an individual work performance questionnaire. International Journal of Productivity and Performance Management, 62(1), 6-28. http://dx.doi.org/10.1108/17410401311285273
Laursen, G. H. N., & Thorlund, J. (2010). Business Analytics for Managers: Taking Business Intelligence beyond Reporting. New Jersey: John Wiley and Sons.
Leidner, D. E., & Elam, J. J. (1993). Executive information systems: their impact on executive decision making. Journal of Management Information Systems, 10(3), 139-155.
Madhala, P., Li, H., & Helander, N. (2021). Understanding the Antecedents and Consequences of Data Analytics Capabilities: A Literature Review in IS field. Paper presented at the PACIS 2021 Proceedings, Dubai.
Matzler, K., Bailom, F., & Mooradian, T. (2007). Intuitive Decision Making. MIT Sloan Management Review, 49(1), 13-15.
Oliveira, M. P. V., McCormack, K., & Trkman, P. (2012). Business analytics in supply chains - The contingent effect of business process maturity. Expert Systems with Applications, 39(5), 5488-5498. http://dx.doi.org/10.1016/j.eswa.2011.11.073
Passlick, J., Guhr, N., Lebek, B., & Breitner, M. H. (2020). Encouraging the use of self-service business intelligence - An examination of employee-related influencing factors. Journal of Decision Systems, 29(1), 1-26. http://dx.doi.org/10.1080/12460125.2020.1739884
Sanders, G., & Courtney, J. (1985). A Field Study of Organizational Factors Influencing DSS Success. Management Information Systems Quarterly, 9(1), 77-93. http://dx.doi.org/10.2307/249275
Schläfke, M., Silvi, R., & Möller, K. (2012). A framework for business analytics in performance management. International Journal of Productivity and Performance Management, 62(1), 110-122. http://dx.doi.org/10.1108/17410401311285327
Seddon, P. B., Constantinidis, D., Tamm, T., & Dod, H. (2017). How does business analytics contribute to business value? Information Systems Journal, 27(3), 237-269. http://dx.doi.org/10.1111/isj.12101
Shanks, G., & Bekmamedova, N. (2012). Achieving benefits with business analytics systems: An evolutionary process perspective. Journal of Decision Systems, 21(3), 231-244. http://dx.doi.org/10.1080/12460125.2012.729182
Sharda, R., Delen, D., & Turban, E. (2015). Business Intelligence and Analytics. Systems for decision support (10th ed.): Pearson.
Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming Decision-Making Processes: A Research Agenda for Understanding the Impact of Business Analytics on Organisations. European Journal of Information Systems, 23(4), 433-441. http://dx.doi.org/10.1057/ejis.2014.17
Sharma, R., Reynolds, P., Scheepers, R., Seddon, P. B., & Shanks, G. (2010). Business Analytics and Competitive Advantage: A Review and Research Agenda. In A. Respicio, F. Adam, G. Phillips, C. Teixeira, & J. Telhada (Eds.), Bridging the Socio-Technical Gap in Decision Support Systems: Challenges for the Next Decade (pp. 187-198). Amsterdam, Netherlands.
Someh, I., Shanks, G., & Davern, M. (2019). Reconceptualizing synergy to explain the value of business analytics systems. Journal of Information Technology, 34(4), 371-391. http://dx.doi.org/10.1177/0268396218816210
Sundaram, S., Schwarz, A., Jones, E., & Chin, W. W. (2007). Technology use on the front line: How information technology enhances individual performance. Journal of the Academy of Marketing Science, 35(1), 101-112. http://dx.doi.org/10.1007/s11747-006-0010-4
Tan, F. T. C., Guo, Z., Cahalane, M., & Cheng, D. (2016). Developing Business Analytic Capabilities for Combating E-Commerce Identity Fraud: A Study of Trustev’s Digital Verification Solution. Information & Management, 53(7), 878-891. http://dx.doi.org/10.1016/j.im.2016.07.002
Tavera Romero, C. A., Ortiz, J. H., Khalaf, O. I., & Ríos Prado, A. (2021). Business Intelligence: Business Evolution after Industry 4.0. Sustainability (Basel), 13(18), 10026. http://dx.doi.org/10.3390/su131810026
Trieu, V.-H., Cockcroft, S., & Perdana, A. (2018). Decision-making performance in big data era: the role of actual Business Intelligence systems use and affecting external constraints Deakin University. Retrieved from https://hdl.handle.net/10536/DRO/DU:30115523
Trkman, P., McCormack, K., de Oliveira, M. P. V., & Ladeira, M. B. (2010). The impact of business analytics on supply chain performance. Decision Support Systems, 49(3), 318-327. http://dx.doi.org/10.1016/j.dss.2010.03.007
Troilo, M., Bouchet, A., Urban, T. L., & Sutton, W. A. (2016). Perception, reality, and the adoption of business analytics: Evidence from North American professional sport organizations. Omega, 59(PA), 72-83. http://dx.doi.org/10.1016/j.omega.2015.05.011
Tushman, M. L., Kahn, A., Porray, M. E., & Binns, A. (2017). Change management is becoming increasingly data-driven. Companies aren’t ready. Harvard Business Review.
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. http://dx.doi.org/10.1016/j.ejor.2017.02.023
Wang, F., Raisinghani, M. S., Mora, M., & Forrest, J. (2022). Effective Decision Support in the Big Data Era. International Journal of Decision Support System Technology, 14(1), 1-16. http://dx.doi.org/10.4018/IJDSST.286683
Wang, Y., & Byrd, T. A. (2017). Business Analytics-Enabled Decision Making Effectiveness through Knowledge Absorptive Capacity in Health Care. Journal of Knowledge Management, 21(3), 517-539. http://dx.doi.org/10.1108/JKM-08-2015-0301
Wedel, M., & Kannan, P. K. (2016). Marketing Analytics for Data-Rich Environments. Journal of Marketing, 80(6), 97-121. http://dx.doi.org/10.1509/jm.15.0413
Wixom, B., Yen, B., & Relich, M. (2013). Maximizing Value from Business Analytics. MIS Quarterly Executive, 12(2), 61-71.
Yin, J., & Fernandez, V. (2020). A systematic review on business analytics. Journal of Industrial Engineering and Management, 13(2), 283-295. http://dx.doi.org/10.3926/jiem.3030
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Luminita Hurbean, Florin Militaru, Mihaela Muntean, Doina Danaiata
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All accepted papers are published on an Open Access basis.
The Open Access License is based on the Creative Commons license.
The non-commercial use of the article will be governed by the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License as currently displayed on https://creativecommons.org/licenses/by-nc-nd/4.0
Under the Creative Commons Attribution-NonCommercial-NoDerivatives license, the author(s) and users are free to share (copy, distribute and transmit the contribution) under the following conditions:
1. they must attribute the contribution in the manner specified by the author or licensor,
2. they may not use this contribution for commercial purposes,
3. they may not alter, transform, or build upon this work.
This work is licenced under a 
