IOSR-JEF   Volume-16 ~ Issue-4 ~ Jul. – Aug. 2025

Abstract: Indonesia's economic development heavily relies on human capital quality, particularly through education and skilled labour development. Understanding the relationship between education, skilled labour, and economic outcomes is crucial for policy formulation. This study examines the impact of education and skilled labour on per capita income in Indonesia over the period 2000-2024, employing advanced time series econometric analysis. The research utilises an ARIMAX (Autoregressive Integrated Moving Average with Exogenous variables) model with skilled labour as the exogenous variable. This approach was selected for its robustness in handling time series data characteristics......

Keywords: Per Capita Income; Education; Skilled Labor; ARIMAX.

Abstract: This paper shows the work that was done on linear regression during the development and assessment of a machine learning based predictive model to determine the success of cloud computing adoption in indigenous Small and Medium-sized Enterprises (SMEs) in Ghana. The study will establish important indigenous drivers of adoption, build a predictive model, design a system prototype and measure its behaviour based on Mean Absolute Percentage Error (MAPE.....

Keywords: Cloud computing adoption, Indigenous SMEs, Machine learning, Linear regression, Predictive model, Ghana, Technological adoption, MAPE, Digital transformation.

[1] Ackah, C., & Asiamah, M. (2020). Financial Sector Reforms And Economic Growth In Ghana. African Development Review, 32(2), 156–170.
[2] Adusei, M. (2015). Bank Profitability: Insights From The Rural Banking Industry In Ghana. Cogent Economics & Finance, 3(1), 1078270.
[3] Adusei, M., & Nyarko-Baasi, M. (2018). Technological Innovations And Bank Profitability In Ghana: Evidence From Panel Data. African Journal Of Economic And Management Studies, 9(3), 332–346.
[4] Agyei, P. M. (2021). Business Intelligence Adoption In The Ghanaian Public Sector: Opportunities And Challenges. Journal Of African Business, 22(3), 359–377.
[5] Al Nawayseh, M., Al-Hayek, H., & Almajali, D. (2022). The Impact Of Integrating Decision Support Systems And Expert Judgments On Forecasting Accuracy In Financial Institutions. Journal Of Enterprise Information Management, 35(7), 1783–1801.
Https://Doi.Org/10.1108/JEIM-08-2021-0356

Abstract: With the quick development of China's economy, the traditional extensive growth model has resulted in severe environmental issues, increasingly highlighting the conflict between economic growth and ecological conservation. Against this backdrop, adjusting the original development model, fostering the enhancement and refinement of the economic framework, and adopting low-carbon, environmentally friendly green development strategies have become particularly urgent for China. To align with this trend, green finance has emerged as a mechanism to support sustainable development by leveraging the capital-guiding role of the financial sector, among which green credit stands out as one of the critical financial instruments garnering considerable attention. Based on data from.....

Keywords: green credit policy, enterprise performance, The multi-time period difference-in-differences model

[1] Li Z, Liao G,Wang Z,Et Al.Green Loan And Subsidy For Promoting Clean Production Innovation[J].Journal Of Cleaner Production,2018,187:421-431.
[2] Paul Thompson And Christopher J Cowton .Bringing The Environment Into Bank Lending:Implications For Environmental Reporting[J].The British Accounting Review,2003,36(2):197-218.
[3] Flammer C.Corporate Green Bonds[J].Journal Of Financial Economics,2021,142(2):499-516.
[4] Xu Xuejun, Qi Qiuyue. The Impact Of Green Credit Policies On Corporate Financing Ability—An Empirical Study Based On The PSM-DID Model [J]. Public Finance Research, 2022, (03): 82-96.
[5] Wu Lichao, Wang Rui, Fang Xinyi, Et Al. Green Credit Policy And Export Enterprises' Green Technological Innovation [J]. Research And Development Management, 2022, 34(04): 66-80. DOI: 10.13581/J.Cnki.Rdm.20211690.

Abstract: Background: A number of empirical studies on performance of Microfinance banks have been carried out though inconclusively. While cost efficiency is seen to influence the financial performance of Microfinance banks (MFBs), other factors seem to influence this relationship either directly or indirectly. Empirical studies indicate that liquidity risk is a key determinant of any financial institution’s performance. MFBs today are making losses while only a very small percentage record little or no profits at all. Stakeholders which include the Kenya government, financiers, clients and potential clients/investors are concerned about profitability trends of these pivotal financial institutions.....

Keywords: Liquidity Risk, Cost Efficiency, Financial Performance, Microfinance Banks

[1]. Serrano – Cinca, C, Gutierrez – Nieto B, Reyes NM. A Social And Environmental Approach To Microfinance Credit Scoring. Journal Of Cleaner Production. 2016; 112(5): 3504 – 3513.
[2]. Abdullah BM, Murad MW, Hasan MM. A Decisions Dynamics Model Of Cost Efficiency And Corporate Governance. Journal Of Developing Areas. 2017; 49(2): 379– 405.
[3]. Muriithi JG, Waweru KM. Liquidity Risk And Financial Performance Of Commercial Banks In Kenya. International Journal Of Economics And Finance. 2017; 9(3): 256 – 264.
[4]. Jagogo A, Rop E. Liquidity Risk Management And Financial Performance Of State-Owned Enterprises In Kenya. International Journal Of Finance And Accounting. 2021; 6(2): 12 – 28.
[5]. Ehiedu VC. Analysis Of Micro Prudential Determinants Of Capital Adequacy In Deposit Money Banks. International Journal Of Management And Entrepreneurship Research. 2022; 4(11): 398 – 415..