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This paper searches the impact of trade credit on firms’ performance in 15 sub-Saharan African (SSA) countries. In particular, we also examine whether trade credit can advance in an environment of higher financial inclusion rate. We achieve this by using a fixed effect estimation model and solving endogeneity by GMM and IV-2SLS methods by utilizing firm level data from the Bloomberg terminal from 2010 to 2023. Baseline results summarize that firm performance grows with the level of trade credit finance. We find a firm’s industry and SSA blocs heterogeneity the trade credit-firm performance nexus. Additionally, financial inclusion alone is positive and when interacted with trade credit it becomes significant. The major implication among others is that trade credit facilitates more firm performance in a situation of higher financial inclusion level.

The coal industry in Indonesia is a capital-intensive sector characterized by long operating cycles and high dependence on global commodity price fluctuations. These conditions create substantial liquidity needs, making efficient working capital management crucial. This study aims to examine the effect of Supply Chain Finance (SCF) on firm performance in Indonesia’s coal production sub-industry. SCF in this study is measured using the Cash Conversion Cycle (CCC) and its components, Days Sales Outstanding (DSO), Days Inventory Outstanding (DIO), and Days Payable Outstanding (DPO). Firm performance is proxied by Return on Assets (ROA), while the control variables include firm size (SIZE), leverage (LEV), and sales growth (SALEG). The main hypothesis proposes that SCF has a positive effect on firm performance. This research employs secondary data from 19 coal production companies listed on the Indonesia Stock Exchange (IDX) during the 2020-2024 period. The analysis is conducted using panel data regression with the Random Effect Model (REM) approach, estimated through the Generalized Least Square (XTGLS) method.The results show that SCF has no significant effect on firm performance, while leverage (LEV) and sales growth (SALEG) have a negative and significant effect. The coefficient of determination (R²) of 27.06% indicates that the model explains 27.06% of the variation in firm performance. These findings suggest that SCF efficiency in the coal industry does not directly enhance profitability, likely due to the long cash conversion cycle and high capital requirements inherent in this sector.

Research Originality: This study investigates the relationship between inventory management and profitability within the textiles industry in Bangladesh, emphasizing the key factors that significantly enhance the industry's profitability. Research Objectives: This analysis investigates how various factors, including total assets, total equity, total revenue, share capital, retained earnings, finished items, work in progress, and raw materials, impact profit after tax (PAT). Research Methods: The data spans from 2011 to 2023 and encompasses 46 textile companies listed on the Dhaka Stock Exchange (DSE). To conduct this study, different models like Pooled Ordinary Least Square (POLS), Second Stage Least Square (2SLS), Generalized Method of Moments (GMM) are utilized. Empirical Results: Through the application of techniques including Pooled Ordinary Least Squares (POLS), Two-Stage Least Squares (2SLS), and the Generalized Method of Moments (GMM), we have established that total assets exhibit a significant negative correlation with PAT in both the POLS and 2SLS models. On the other hand, there is a notable positive correlation between total revenue and retained earnings on PAT across all 2SLS, POLS, and GMM models. The share capital shows a negative correlation with PAT in all three models: 2SLS, POLS, and GMM. The main independent variable, which is our fundamental focus, shows that finished goods have a positive correlation with PAT; however, this correlation lacks significance. The raw materials exhibited a negative correlation with PAT, and this relationship is significant. The correlation between Packing Materials and PAT is notably positive. This study highlights the importance of strategies that could facilitate sustainable growth in the textile industry.

Research Originality: This study investigates the relationship between inventory management and profitability within the textiles industry in Bangladesh, emphasizing the key factors that significantly enhance the industry's profitability. Research Objectives: This analysis investigates how various factors, including total assets, total equity, total revenue, share capital, retained earnings, finished items, work in progress, and raw materials, impact profit after tax (PAT). Research Methods: The data spans from 2011 to 2023 and encompasses 46 textile companies listed on the Dhaka Stock Exchange (DSE). To conduct this study, different models like Pooled Ordinary Least Square (POLS), Second Stage Least Square (2SLS), Generalized Method of Moments (GMM) are utilized. Empirical Results: Through the application of techniques including Pooled Ordinary Least Squares (POLS), Two-Stage Least Squares (2SLS), and the Generalized Method of Moments (GMM), we have established that total assets exhibit a significant negative correlation with PAT in both the POLS and 2SLS models. On the other hand, there is a notable positive correlation between total revenue and retained earnings on PAT across all 2SLS, POLS, and GMM models. The share capital shows a negative correlation with PAT in all three models: 2SLS, POLS, and GMM. The main independent variable, which is our fundamental focus, shows that finished goods have a positive correlation with PAT; however, this correlation lacks significance. The raw materials exhibited a negative correlation with PAT, and this relationship is significant. The correlation between Packing Materials and PAT is notably positive. This study highlights the importance of strategies that could facilitate sustainable growth in the textile industry.

This paper searches the impact of trade credit on firms’ performance in 15 sub-Saharan African (SSA) countries. In particular, we also examine whether trade credit can advance in an environment of higher financial inclusion rate. We achieve this by using a fixed effect estimation model and solving endogeneity by GMM and IV-2SLS methods by utilizing firm level data from the Bloomberg terminal from 2010 to 2023. Baseline results summarize that firm performance grows with the level of trade credit finance. We find a firm’s industry and SSA blocs heterogeneity the trade credit-firm performance nexus. Additionally, financial inclusion alone is positive and when interacted with trade credit it becomes significant. The major implication among others is that trade credit facilitates more firm performance in a situation of higher financial inclusion level.

The coal industry in Indonesia is a capital-intensive sector characterized by long operating cycles and high dependence on global commodity price fluctuations. These conditions create substantial liquidity needs, making efficient working capital management crucial. This study aims to examine the effect of Supply Chain Finance (SCF) on firm performance in Indonesia’s coal production sub-industry. SCF in this study is measured using the Cash Conversion Cycle (CCC) and its components, Days Sales Outstanding (DSO), Days Inventory Outstanding (DIO), and Days Payable Outstanding (DPO). Firm performance is proxied by Return on Assets (ROA), while the control variables include firm size (SIZE), leverage (LEV), and sales growth (SALEG). The main hypothesis proposes that SCF has a positive effect on firm performance. This research employs secondary data from 19 coal production companies listed on the Indonesia Stock Exchange (IDX) during the 2020-2024 period. The analysis is conducted using panel data regression with the Random Effect Model (REM) approach, estimated through the Generalized Least Square (XTGLS) method.The results show that SCF has no significant effect on firm performance, while leverage (LEV) and sales growth (SALEG) have a negative and significant effect. The coefficient of determination (R²) of 27.06% indicates that the model explains 27.06% of the variation in firm performance. These findings suggest that SCF efficiency in the coal industry does not directly enhance profitability, likely due to the long cash conversion cycle and high capital requirements inherent in this sector.