BUSINESS INTELLIGENCE COSTS AND FIRM PERFORMANCE: EVIDENCE FROM SELECTED TOP ECOWAS’ BANKS
Journal of Economics and Trade, Volume 5, Issue 1,
Page 1-17
Abstract
The impact of business Intelligence costs on the performance of selected top ECOWAS’ banks that made Banker Magazine’s 2017 list of 1000 Global banks was investigated using descriptive-quantitative research approach and based on economic theoretical framework. Purposeful sampling technique was adopted based on availability of data. Data were extracted from sampled banks’ audited annual reports from 2012 to 2016. Six literature-backed hypotheses were developed and tested using OLS multiple regression analysis. The study revealed that: (i) computer hardware cost did not have significant effect on profitability; but, it had significant negative effects on value added and productivity of sampled banks; (ii) software cost had significant negative effect on profitability, and significant positive effects on value added and productivity of sampled banks; (iii) total equity had significant positive effects on profitability, value added, and productivity of sampled banks; (iv) bank age did not have significant effect on profitability; but, it had significant positive effects on value added and productivity of sampled banks; and (v) In cumulative terms, business intelligence cost had significant effect on profitability only; and it is negative. Practically, the study suggests that BI cost does not have significant positive effect on firms’ financial performance as it decreased profitability by circa 40%. Theoretically, the importance of economic model in BI studies has being highlighted. Far reaching recommendations have being proposed.
Keywords:
- Business intelligence
- performance
- productivity
- profitability
- value added.
How to Cite
WAHUA, L., & AHLIJAH, Y. (2020). BUSINESS INTELLIGENCE COSTS AND FIRM PERFORMANCE: EVIDENCE FROM SELECTED TOP ECOWAS’ BANKS. Journal of Economics and Trade, 5(1), 1–17. Retrieved from https://ikprress.org/index.php/JET/article/view/5035
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Available:https://www.sisense.com
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Popovič A, Turk T, Jaklič J. Conceptual model of business intelligence systems. Management Journal. 2010;15(1):5-30.
Johansson S, Nilsson M. The intelligent business - An assessment of business intelligence practices in large Swedish organizations. A project submitted to Lund University Sweden for the award of Master of Science in Business and Economics; 2013.
Fink L, Yogev N, Even A. Business intelligence and organizational learning: An empirical investigation of value creation processes; 2016.
Available:http://dx.doi.org/10.1016/j.im.2016.03.009
Brynjolfsson E, Hitt LM, Kim HH. Strength in numbers: how does data-driven decision-making affect firm performance; 2011.
DOI: 10.2139/ssrn.1819486
Wixom BH, Watson HJ. An empirical investigation of the factors affecting data warehousing success. MIS Q. 2001;25(1):17-41.
Eckerson WW. Pervasive business intelligence: Techniques and technologies to deploy BI on an enterprise scale, The Data Warehousing Institute; 2008.
Available:http://tdwi.org/research/2008/07/bpr 3qpervasive-business-intelligence.aspx
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.
Lorenzetti C. Business intelligence systems in the financial industry; 2010.
[Retrieved 28/06/2018]
Available:https://www.politesi.polimi.it
/bitstream/10589/6123/1/2010_12_Lorenzetti.pdf
Sabbour S, Lasi H, Tessin P. Business intelligence and strategic decision simulation. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering. 2012;6(1):108-115
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Available:http://www.mrcproductivity.com
/blog/2015/03/7-practical-ways-to-improve-bi- user-adoption/
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Holley A. 7 reasons why business intelligence is vital to business success; 2015.
[Retrieved June 25, 2018]
Available:https://www.maximizer.com
/blog/7-reasons-why-business-intelligence-is-vital to business-success/
Kudyba RS, Hoptroff R. Data mining and business intelligence: A guide to productivity. Idea Group Publishing; 2001.
Watson H, Goodhue D, Wixon B. The benefits of data warehousing: Why some organizations realize exceptional payoffs. Information & Management. 2002;39(6):491-502.
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.
Liautaud B. E-business intelligence: turning information into knowledge into profit. McGraw-Hill; 2000.
Kalakota R, Robinson M. E-business 2.0 – Roadmap for success. Addison-Wesley, NY; 2001.
Ghazamfari M, Jafari M, Rouhami S. A tool to evaluate the business intelligence of enterprise systems. Scientia Iranica. 2011;18(6):1579-1590.
Gangadharan GR, Swami SN. Business intelligence systems: Design and implementation strategies. A Paper presented at International Conference on Information Technology Initiatives. Cavtat, Croatia; 2004.
Chang E. Advanced business intelligence technologies, trust, reputation, and recommendation systems. A Paper presented at the 7th Business Intelligence Conference, Sydney, Australia; 2006.
Mbawuni J, Nimako SG. Critical factors underlying students’ choice of institution for graduate programmes: Empirical evidence from Ghana. International Journal of Higher Education. 2015;4(1):120– 135.
Fosu FF, Poku K. Exploring the factors that influence students’ choice of higher education in Ghana. European Journal of Business and Management. 2014;6(28):209-22011.
Williams S, Williams N. The profit impact of business intelligence. Elsevier Inc; 2007.
Gibson M, Arnott D, Jagielska I. Evaluating the intangible benefits of business intelligence: Review & research agenda. Proceedings of the 2004 IFIP International Conference on Decision Support Systems (DSS2004), Decision Support in an Uncertain and Complex World 2004. 2005;295–305.
Lonnqvist A, Pirttimaki V. The measurement of business intelligence. Information Systems Management. 2006;23(1):32-40.
Davenport TH, Short JE. Information technology and business process redesign. Operations Management: Critical Perspectives on Business and Management. 2003;1(1):1-27.
Sawka K. The analyst's corner: are we valuable? Competitive Intelligence Magazine. 2000;3(2):53.
Herring J. Measuring the value of competitive intelligence: Accessing & communication CI's value to your organization: SCIP Monograph Series, Alexandria: SCIP; 1996.
Al-ma'aitah MA. The role of business intelligence tools in decision making process. International Journal of Computer Applications. 2013;73(13):24-31.
Moss L, Atre S. Business intelligence roadmap: The complete project lifecycle for decision support applications. Addison Wesley Information Technology Series, Reading, MA; 2003.
Pirttimäki V. Business intelligence as a managerial tool in large Finnish companies. Tampere: Tampere University of Technology; 2007.
Sclater N, Webb M, Danson M. The future of data-driven decision-making; 2017.
[Retrieved June 25, 2018]
Available:https://www.jisc.ac.uk/reports
/the-future-of-data-driven-decision-making
Cornerstone Information System. Quantitative data-driven insights to make better business decisions; 2017.
[Retrieved 25/06/2018]
Available:https://www.ciswired.com
/quantitative-data-driveninsights-to-make-better-business-decisions/
PwC. PwC's global data and analytics survey; 2016.
[Retrieved June 25, 2018]
Available:https://www.pwc.com
/us/en/services/consulting/analytics/big-decision-survey.html
Owusu A. Business intelligence systems and bank performance in Ghana: The balanced scorecard approach. Cogent OA Journal; 2017.
Available:https://doi.org/10.1080/23311975.2017.1364056
Teoh A, Rajendran M, Lim K. Predictors and outcome of business intelligence system implementation: A perspective of manufacturers in Malaysia. Research Journal of Applied Sciences, Engineering and Technology. 2014;8(18):1980-1993.
DOI: 10.19026/rjaset.8.1190
Richards G, Yeoh W, Chong AY, Popovič A. An empirical study of business intelligence impact on corporate performance management. 2014 Proceedings. Paper 341; 2014.
Available:http://aisel.aisnet.org/pacis2014/341
Hartl K, Jacob O, Mbep FL, Budree A, Fourie L. The impact of business intelligence on corporate performance management. 49th Hawaii International Conference on System Sciences; 2016.
DOI: 10.1109/HICSS.2016.625
Kakhki MD, Palvia P. Effect of business intelligence and analytics on business performance. Twenty-second Americas Conference on Information Systems, San Diego; 2016.
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.
Elbashir M, Collier PA, Davern MJ. Measuring the effects of business intelligence systems: The relation-ship between business process and organizational performance. International Journal of Accounting Information Systems. 2008;9(2008):135–153.
DOI: 10.1016/j.accinf.2008.03.001
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
Grover V, Teng JTC, Segars AH, Fiedler K. The influence of information technology diffusion and business process change on perceived productivity: The IS executive's perspective. Information & Management. 1998;34(3):141-159.
Kappelman L, McLean E, Luftman J, Johnson V. Key issues of IT organizations and their leadership: The 2013 SIM IT trends study, MIS Q. Executive. 2013;12(4):227-240.
USCLibraries. Organizing your social sciences research paper: quantitative methods. The University of Southern Califonia Library; 2018.
Available:https://libguides.usc.edu/writingguide
Jones & Bartlett Learning LLC (n.d.). Quantitative research designs: Experimental, quasi experimental, and descriptive.
[Accessed 27 June 2018]
Available:http://www.jblearning.com/
Babbie ER. The practice of social research. 12th ed. Belmont, CA: Wadsworth Cengage, Brians; 2010.
McNabb DE. Research methods in public administration and nonprofit management: Quantitative and qualitative approaches. 2nd ed. Armonk, NY: M.E. Sharpe; 2008.
Singh K. Quantitative social research methods. Los Angeles, CA: Sage; 2007.
Mugwang’a FA. Determinants of capital adequacy of commercial banks in Kenya. Thesis submitted to University of Nairobi Business School for the award of MBA; 2014.
Grove SK, Burns N, Gray JR. The practice of nursing research: appraisal, synthesis and generation of evidence. St. Louis, MO: Elsevier Saunders; 2013.
Larson N, Story M, Eisenberg ME, Neumark-Sztainer D. Secular trends in meal and snack patterns among adolescents from 1999 to 2010. Journal of the Academy of Nutrition and Diabetics. 2016;116(1):240–250.
Beridze T. Boardroom gender diversity and firm financial performance: Evidence from the banking sector in Georgia. Dissertation for Master in Finance from Universidade do Port; 2016.
Ajide FM, Aderemi AA. The effects of corporate social responsibility activity disclosure on corporate profitability: Empirical evidence from Nigerian commercial banks. Journal of Economics and Finance (IOSR-JEF). 2014;2(6):17-25.
Daniel K. The effect of corporate social responsibility on financial performance of commercial banks in Kenya. Being a Thesis submitted to University of Nairobi Business School for the award of M.Sc in Finance; 2014.
Velnampy T. Value added, productivity and performance of few selected companies in SriLanka, Indian Journal of Commerce and Management. 2011;2(6).
Agalega E. Public sector accounting and finance. Ziphin Business World, 2nd Edition, Koforidua; 2014.
King J. Top 1000 World Banks - South Africans stay strong as Nigeria slumps in Africa; 2017.
[Retrieved on September 24, 2018]
Available:https://www.thebanker.com
/Top-1000World Banks/
Top-1000-World-Banks-South-
Africans-stay-strong-as-Nigeria-slumps-inAfrica?ct=true
Laerd Dissertation. Purposive sampling; 2012.
[Retrieved on July 5, 2018]
Available:http://dissertation.laerd.com
/purposive-sampling.php#homogenous
Wagschal U, Jäckle S. Aggregate data analysis. In B. Badie, D. BergSchlosser, & L. Morlino (Eds.), International encyclopedia of politicalscience. Thousand Oaks, CA: SAGE Publications, Inc. 2011;54-58.
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.
Aspal PK, Nazneen A. An empirical analysis of capital adequacy in the Indian private sector banks. American Journal of Research Communication. 2014;2(11):28-42.
[ISSN: 2325-4076]
Garson GD. Hierarchical linear modeling: guide and applications. Thousand Oaks, CA: Sage Publications, Inc; 2012.
Frost J. Multicollinearity in regression analysis: Problems, detection, and solutions; 2017.
[Retrieved September 20, 2018]
Available:http://statisticsbyjim.com/regression/multicollinearity in-regression-analysis/
Brother SPSS. Multicollinearity test example using SPSS; 2015.
[Retrieved May 9, 2019]
Available:www.spsstests.com/2015/03/multicollinearity-test-example-using.html
Kajananthan R. Effect of corporate governance on capital structure: Case of the Srilankan listed manufacturing companies. Journal of Arts, Science & Commerce. 2012;4(1):63- 71.
Tabachnick BG, Fidell LS. Using multivariate statistics (4th ed.). Needleham Heights, MA: Allyn and Bacon; 2001.
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) held at Colombo, Sri Lanka; 2015.
Pourhosein MR, Kol AA, Vishkaii BM, Jourshari FP. Investigate the relationship between institutional ownership in Tehran stock exchange. International Journal of Economics and Financial Issues. 2017;7(3): 276-285.
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