Implementing Data Clustering to Identify Capital Allocation for Small and Medium Sized Enterprises (SMEs) - Hidayah, Amelia (UI Scholars Hub, 2021)
Abstract: N/A. Manuscript type: Original Research. Research Aims: Analyzing the investigated factors includes the firm size and the industry sector that influence capital allocation for MSMEs using the K-means clustering technique. Design/methodology/approach: The initial step for the research is the data preparation. It is covering 20 sectors and consists of 6,666 pieces of data. The modified data are analysed using the K-means clustering technique. Research Findings: MSMEs are divided into three clusters, with each cluster exhibiting different characteristics in terms of assets, sales, and industry sectors. However, the number of employees was not found to be significant in the analysis. Theoretical Contribution/Originality: The size of MSMEs is defined as the total assets, sales, or number of employees. It should be considered by financial institutions when assessing the viability of awarding a loan to that MSMEs. Moreover industry characteristics affect finance institutions to grant a loan to firm. Practitioner/Policy Implication: Financial decision makers, banks, financial institutions, and government advisors alike can determine capital allocation for MSMEs based on the clustering profile created in this study. Research limitation/Implications: This study constructs MSMEs grouping model that relies on investigated factors, including the firm size and the industry sector in order to determine the capital allocation to MSMEs. The future research regarding the capital allocation schemes for the MSMEs clustering profile are possible to do by adding new data, such as ownership type, owner-manager characteristics, and macroeconomic factors.