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首页> 外文期刊>The USV Annals of Economics and Public Administration >Determinants of key performance indicators (KPIs) of private sector banks in Sri Lanka: an application of exploratory factor analysis
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Determinants of key performance indicators (KPIs) of private sector banks in Sri Lanka: an application of exploratory factor analysis

机译：斯里兰卡私营部门银行关键绩效指标（KPI）的决定因素：探索性因素分析的应用

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An efficient banking system facilitates linkage between mobilization and use of resources, which accelerates the process of economic growth. It is a widely accepted belief that a banking system which relies on a wide array of banking products, is able to carry out this function because it increases the efficiency of a banking systems to a large extent by offering a broader and flexible arrange of services to the benefits of both borrowers and investors. Meanwhile, there are no comprehensive and empirical researches in that field especially in banking sector. In an attempt to fill in this gap, the present s tudy is conducted determinants of key performance indicators (KPIs) of private sector banks in Sri Lanka with samples of hundred respondents in twelve branches in North and Eastern Provinces. Data were collected through a five points Likert type summated ra ting scales of questionnaire from strongly disagree (1) to strongly agree (5) were adopted to identify indicators. Sophisticated statistical model as Exploratory Factor Analysis (EFA) has been used. The results show that eight factors extracted from the analysis that together accounted 73.781% of the total variance. These factors were categorized as 1) Accident Ratio (AR); (2) Opportunity Succession Rate (OSR); (3) Cash Flow (CF); (4) Return on Capital Employed (ROCE); (5) Customer Satisfaction Rate (CSR); (6) Overall Equipment Effectiveness (OEE); (7) Return on Investment (ROI); (8) Internal Promotion (IP).
机译：高效的银行系统促进了动员和资源使用之间的联系，从而加快了经济增长的进程​​。人们普遍认为，依靠多种银行产品的银行系统能够执行此功能，因为它通过向银行提供更广泛，更灵活的服务安排而在很大程度上提高了银行系统的效率。借款人和投资者的利益。同时，在该领域，特别是在银行部门，还没有全面的经验研究。为了填补这一空白，本研究对斯里兰卡私营部门银行的关键绩效指标（KPI）进行了确定，并从北部和东部省的12个分支机构中抽取了数百名受访者作为样本。通过对问卷的五分李克特式汇总评分量表收集数据，从强烈不同意（1）到强烈同意（5）来确定指标。使用了探索性因素分析（EFA）的复杂统计模型。结果表明，从分析中提取的八个因素合起来占总方差的73.781％。这些因素分类为：1）事故率（AR）; （2）机会继承率（OSR）; （3）现金流量（CF）; （4）使用资本回报率（ROCE）; （5）客户满意度（CSR）; （6）总体设备效率（OEE）; （7）投资回报率; （8）内部晋升（IP）。

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