A framework for assessing readiness of adopting business intelligence in small and medium sized enterprises

Abstract

There has been a rise in complexity of making effective and timely business decisions in the current highly competitive markets. For this reason, Data-driven decision-making using Business Intelligence (BI) applications have attracted many organizations worldwide. However, despite these applications being suited for use in most organizations regardless of size, only the larger enterprises have reached a stage of maturity in BI use while small and medium-size enterprises (SMEs) still lag behind. Whilst many academic researchers have conducted BI research focused on large organizations, literature relating to BI adoption within SMEs has remained limited. This research presents a study which was aimed at proposing a model, presenting salient factors for identifying the current state of readiness for the adoption BI by SMEs in Kenya and the enabling factors that impact BI adoption. The research also aimed at providing a better and clearer understanding of BI adoption within SMEs by reviewing and analyzing current BI literature. To undertake this research, we sampled 280 respondents from three strata (SMEs) i.e. Hotels, microfinance and hospitals and pharmaceuticals which had an overall population size of 950. With a response rate of 73%, a justified analysis of the 205 responses received was done to test the hypotheses under Information Evolution theory. Data was collected using structured questionnaire that has 35 questions which were completed by different decision makers from different areas of operations in respective selected SMEs. Data which responded to the Likert scale questions was then uploaded to STATA for further analysis using the Structural Equation Model (SEM) and variance analysis to test the stated hypothesis The results of this study revealed that a majority of SMEs (47%) in Kenya are willing to invest in personnel and technology in order to provide a better data processing options to clients irrespective of the SME annual revenue, number of employees, and nature of business and years of operation. On the other hand, 15% have already adopted BI while 38% are not ready to adopt BI. Further Structured Equation Model (SEM) analysis showed a significant and positive relationship between all indicators adopted for data collection and the three factors, Technology, Organization and Environment that affect adoption of business Intelligence by SMEs. This research will help managers in SMEs to assess their data processing needs and capabilities and make decision on whether to adopt BI systems and consequently assess their readiness for adoption if such a decision is made. We recommend further studies on this subject to focus on mixed of Technology Diffusion and TOE to find out how constructs derived from the two models would generate the concept of Business Intelligence adoption.