Main Article Content
As the need to quantify the contribution of business intelligence deployment on the optimization of firms in general (and banks in particular) continues to take critical dimension among business and policy makers, this parametric quantitative research investigates the association between business intelligence and performance using 2010 – 2019 (10 years) audited data of the Standard Banks of South Africa Limited. The theoretical underpin of the work is the famous Technology-Organisation-Environment (TOE) theory. The net book values of computer hardware and software measured the technological dimension of the TOE framework). The size (total assets) of Standard Bank of South Africa measured the organisational dimension of the TOE framework. Finally, employees (total personnel cost) measured the environmental dimension of the TOE framework. Profitability and Shareholder value measured the performance of the bank within the period under study. Descriptive and inferential quantitative research analyses were carried out with the aid of the Statistical Package for Social Sciences (SPSS); and the Pearson correlation analysis established that; (i) software investment, bank size, and employee cost have significant positive association with profitability and shareholder value; (ii) hardware investment has significant negative association with profitability and shareholder value; and (iii) Employee quality (staff cost) has the highest significant positive effect on both profitability and shareholder value. The relevance of the TOE theory is established in this study; and the need for banks to optimize their TOE investment mix in order to maximize their profitability and shareholder value was stressed. The study recommends that further comparative studies within and across industries and countries to be carried out for better generalization of the findings of this work or vice versa.
Standard Bank of South Africa Limited. The standard bank of South Africa annual report 2019; 2020.
Available: https:// reporting.standardbank.com/
Popovič A, Turk T, Jaklič J. Conceptual model of business intelligence systems. Management Journal. 2015;15(1):5-30.
Wixom B, Watson H. The BI-based organisation. International Journal of Business Intelligence Research. 2010;1(1):13- 28.
Davenport TH. BI and organizational decisions. International Journal of Business Intelligence Research. 2010;1(1):1-12.
Lorenzetti C.(2010) Business intelligence systems in the financial industry. Available:https://www.politesi.polimi.it/bitstream/10589/6123/1/2010_12_Lorenzetti.pdf
Fink L, Yogev N, Even A. Business intelligence and organizational learning: An empirical investigation of value creation processes. Information and Management. 2017;54(1):38-56.
Brynjolfsson E, Hitt LM, Kim HH. Strength in numbers: how does data-driven decision-making affect firm performance?; 2011.
Rama J, Zhangb C, Koroniosc A. The implications of big data analytics on business intelligence: A qualitative study in China. Procedia Computer Science. 2016;86(1):221-226.
Lyke-Ho-Gland. Three measures for capturing the value of data-driven decisions; 2017.
Wahua L. Banks’ governance, country institutions, and capital adequacy in Nigeria. Asian Journal of Arts, Humanities and Social Studies. 2020;3(1):1-14.
Wahua L, Ahlijah Y. Business intelligence costs and firm performance: Evidence from top selected ECOWAS’ banks. Journal of Economics and Trade. 2020;5(1):1-17.
Lautenbach P, Johnston K, Adeniran-Ogundipe T. Factors influencing business intelligence and analytics usage extent in South African organisations. South African Journal of Business Management. 2017;48(3):23–33.
Lebied M. Why data driven decision making is your path to business success; 2017.
Micheni EM. Diffusion of big data and analytics in developing countries; 2015. Corpus ID: 167872804.
Awa HO, Ukoha O, Emecheta BC. Integrating TAM and TOE frameworks and expanding their characteristic constructs for e-commerce adoption by SMEs. Informing Science & IT Education Conference (InSITE). Informing Science Institute; 2012.
Idowu SA, Osofisan AO. Cloud computing and sustainable development in higher education. Journal of Emerging Trends in Computing and Information Sciences. 2012;3(11).
Oracle White Paper. Oracle’s cloud solutions for higher education and research; 2011.
Jain A, Pandey US. Role of cloud computing in higher education. International Journal of Advanced Research in Computer Science and Software Engineering. 2013;3(7):966-972.
Ahmad Z. Business intelligence for sustainable competitive advantage: The case of telecommunications companies in Malaysia. This thesis is presented for the Degree of Doctor of Philosophy of Curtin University of Technology; 2011.
Petrini M, Pozzebon M. Managing sustainability with the support of business intelligence: Integrating socio-environmental indicators and organizational context. Journal of Strategic Information System. 2009;18(2009):178-191.
Wahua L, Tsekpo S, Anyamele J. Governance and employee productivity of selected Nigerian banks: does gender diversity matter? Asian Journal of Arts, Humanities and Social Studies. 2018;1(1):19-39.
Wahua L. Corporate governance, financial soundness and economic development: empirical evidence from Malaysia, Indonesia, and Turkey. Text of paper presented at the 2nd Annual International Conference on Accounting and Finance, ICAF 2015, At Colombo, Sri Lanka; 2015.
Velnampy T. Value added, productivity and performance of few selected companies in Sri Lanka, Indian Journal of Commerce and Management. 2011;2(6).
Hayes A. Valued added; 2020.
Namisiko P, Munialo C, Nyongesa S. Towards an optimization framework for e-learning in developing countries: a case of private universities in Kenya. Journal of Computer Science and Information Technology. 2014;2(2):131-148.
Kebande VR, Sigar KO, Odongo GY. Meta-modeling cloud computing architecture in distance learning. International Journal of Computer Science Issues. 2013;10(3):66-72.
Laisheng X, Zhengxia W. Cloud computing: a new business paradigm for e-learning. Third International Conference on Measuring Technology and Mechatronics Automation. 2011;716-719.
Angeles R. Using the technology-organization-environment framework and Zuboff’s concepts for understanding environmental sustainability and RFID: Two case studies. International Journal of Social, Education, Economics and Management Engineering. 2013;7 (11):1599-1608.
Přikrylová D. Business intelligence models for capturing and analysis of enterprise marketing data. Master thesis submitted to Masaryk University, Faculty of Informatics; 2016.
Oei MHH. Acceptance of operational business intelligence in organisations developing a framework describing the context and powers at play involved in achieving OpBI acceptance in organisations; 2014. Corpus ID: 166647261
Johansson S, Nilsson M. The intelligent business: an assessment of business intelligence practices in large Swedish organisations. Master Thesis submitted to Lund University; 2013.
Amoako BT. The importance of business intelligence as a decision-making tool: Case study electricity company of Ghana; 2013. Corpus ID: 166940121
USC Libraries Organizing your social sciences research paper: Quantitative methods. The University of Southern Califonia Library; 2018.
Babbie ER. The Practice of Social Research. 12th ed. Belmont, CA: Wadsworth Cengage, Brians; 2010.
Kefas NN. An evaluation of corporate governance practices on board member selection and recruitment in Namibian state owned enterprises. A Research Project submitted for the degree of Masters in Leadership and Change Management in the Harold Pupkewitz Graduate School of Business, Polytechnic of Namibia; 2014.
Ankomah ET. The impact of covid-19 pandemic on the overall performance of fidelity bank (Ghana) in 2020. Being a thesis submitted to Accra Business School in partial fulfillment of the requirements for the award of Master of Business Administration (MBA) by Accra Business School, Ghana; 2020.
Roozitalab M, Sayadi MK. Investigating the effect of business intelligence on business value creation. Journal of Soft Computing and Decision Support System. 2018;5(5):43-48.
Wehner C. Accounting for computer software costs; 2020.
Wahua L, Ezeilo FI. Effects of environmental, social and governance imperatives on the performance of selected listed mortgage banks in Nigeria. International Journal of Economics. Business and Management. 2021;13(4):34-48.
Garsonm GD. Hierarchical linear modeling: Guide and applications. Thousand Oaks, CA: Sage Publications, Inc; 2012.
Owusu A. Business intelligence systems and bank performance in Ghana: The balanced scorecard approach. Cogent Business & Management. 2017;4:1364056.
Kaplan RS, Norton DP. The balanced scorecard— Measures that drive performance. Harvard Business Review. 1992;70(1):71– 79.
Sclater N, Webb M, Danson M. The future of data-driven decision-making; 2017.