ICT evaluation models and performance of medium and small enterprises

Open access

Abstract

Building on prior research related to (1) impact of information communication technology (ICT) and (2) operational risk management (ORM) in the context of medium and small enterprises (MSEs), the focus of this study was to investigate the relationship between (1) ICT operational risk management (ORM) and (2) performances of MSEs. To achieve the focus, the research investigated evaluating models for understanding the value of ICT ORM in MSEs. Multiple regression, Repeated-Measures Analysis of Variance (RM-ANOVA) and Repeated-Measures Multivariate Analysis of Variance (RM-MANOVA) were performed. The findings of the distribution revealed that only one variable made a significant percentage contribution to the level of ICT operation in MSEs, the Payback method (β = 0.410, p < .000). It may thus be inferred that the Payback method is the prominent variable, explaining the variation in level of evaluation models affecting ICT adoption within MSEs. Conclusively, in answering the two questions (1) degree of variability explained and (2) predictors, the results revealed that the variable contributed approximately 88.4% of the variations in evaluation models affecting ICT adoption within MSEs. The analysis of variance also revealed that the regression coefficients were real and did not occur by chance

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Balbas A. (2007). Mathematical Methods in Modern Risk Measurement: A Survey. Applied Mathematics 101(2) 205-219.

  • Basel (2004). Basel Committee on Banking Supervision. New Basel Capital Accord Operational Risk.

  • Burget C. Ruschendorf L.(2006). Consistent risk measures for portfolio vectors. Insurance: Mathematics and Economics 38 289-297.

  • Calder A.(2006). Information security based on ISO 27001. Amersfoort - NL: Van Haren.

  • CAS (2003). Casualty Actuarial Society. Overview of Enterprise Risk”. NY; 2003.

  • Cody R.P. Smith J.K.. Applied statistics and the SAS programming language. Upper Saddle River NJ: Prentice; 2005.

  • Conner F.W. Coviello A.W. (2004). Information security governance: A call to action. The Corporate Governance Task Force. Retrieved Jan 9 2010 from: http:// www.cyberpartnership.org/InfoSecGov4_04.2004.

  • Froot K.A. Stein J.C. (1998). Risk management capital budgeting and capital structure policy for fi nancial institutions: An integrated approach. Journal of Financial Economics 47 55- 82.

  • Lam J. (2006). Emerging best practices in developing key risk indicators and ERM reporting. James Lam and Associates.

  • Liebenberg A. Hoyt R. (2003). The determinants of enterprise risk management: Evidence from the appointment of chief risk offi cers. Risk Management and Insurance Review 6(1) 37-52.

  • Meyers L.S. Gamst G. Guarino A.J. (2006). Applied multivariate research. Thousand Oaks CA: Sage.

  • Raykov T. Marcoulides G.A. (2008). An introduction to applied multivariate analysis.New York NY: Rutledge.

  • SA (2006) South Africa. Department of Trade and Industry. Micro Finance Regulatory Council (MFRC). Retrieved February 21 2009 from The DTI 2006.

  • Stoney C.. (2007). Risk management: a guide to its relevance and application in quality management and enhancement. Leeds Metropolitan University.

  • Tabachnick B.G.(2008). Multivariate statistics: an introduction and some applications. Invited workshop presented to the American Psychology - Law Society Jacksonville FL.

  • ITGI (2007). IT Governance Institute ITGI CobiT 4. Executive Summary. IT Governance Institute.

  • Tabachnick B.G. Fidell L.S. (2007) Using multivariate statistics. (5th Ed). Boston: Allyn and Bacon.

Search
Journal information
Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 191 106 2
PDF Downloads 98 64 3