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This study was conducted to analyze the effects of machining parameters on the specific energy consumption in the computerized numerical control lathe turning operation of a hardened alloy steel roll at low cutting speeds. The aim was to minimize its consumption.

The design matrix was based on three variable factors at three levels. Response surface methodology was used for the analysis of experimental results. Optimization was carried out by using the desirability function and genetic algorithm. A multiple regression model was used for relationship build-up.

According to desirability function, genetic algorithm and multiple regression analysis, optimal machining parameters were cutting speed 40 m/min, feed 0.2 mm/rev and depth of cut 0.50 mm, which resulted in minimal specific energy consumption of 0.78, 0.772 and 0.78 kJ/mm³, respectively. Correlation analysis and multiple regression model found a quadratic relationship between specific energy consumption with power consumption and material removal rate.

In the past, many researchers have developed mathematical models for specific energy consumption, but these models were developed at high cutting speed, and a majority of the models were based on the material removal rate as the independent variable. This research work developed a mathematical model based on the machining parameters as an independent variable at low cutting speeds, for a new type of large-sized hardened alloy steel roll. A multiple regression model was developed to build a quadratic relationship of specific energy consumption with power consumption and material removal rate. This work has a practical application in hot rolling industry.

This study aims to investigate the relationship between managerial corporate social responsibility (CSR) mindset and business performance in small- and medium-sized enterprises (SMEs), focusing on the mediating role of CSR practices and the moderating influence of institutional forces.

A structured questionnaire was administered to 221 SME managers. The data was analysed using the Hayes process in SPSS to test the hypothesized relationships.

This study found that a managerial CSR mindset significantly improves operational and financial business performance. In addition, CSR practices mediate the relationship between managerial CSR mindset and business performance. Furthermore, institutional forces moderate this relationship, highlighting the critical role of external factors in shaping SME performance.

The findings suggest that SME managers should adopt a proactive managerial CSR mindset and integrate CSR into their core strategies to enhance business performance. Moreover, managers must be responsive to institutional forces as they adjust their strategy to meet external pressures to ensure sustainable performance.

This study demonstrates the theoretical explanation of how CSR practices serve as a conduit through which a managerial CSR mindset improves business performance under varying conditions of institutional forces.

This research develops a theoretical framework to understand the role of big data analytics capability (BDAC) in enhancing supply chain sustainability and examines the moderating effect of green supply chain management (GSCM) practices on this relationship.

Guided by the dynamic capability view (DCV), we formulated a theoretical model and research hypotheses. We used partial least square-based structural equation modeling (PLS-SEM) to analyze data collected from 159 survey responses from Bangladeshi ready-made garments (RMG).

The statistical analysis revealed that BDAC positively impacts all three dimensions of supply chain sustainability: economic, social and environmental. Additionally, GSCM practices significantly moderate the relationship between BDAC and supply chain sustainability.

This study makes unique contributions to the operations and supply chain management literature by providing empirical evidence and theoretical insights that extend beyond the focus on single sustainability dimensions. The findings offer valuable guidelines for policymakers and managers to enhance supply chain sustainability through BDAC and GSCM practices.

This study advances the current understanding of supply chain sustainability by integrating BDAC with GSCM practices. It is among the first to empirically investigate the combined effects of BDAC on the three dimensions of sustainability – economic, social and environmental – while also exploring the moderating role of GSCM practices. By employing the DCV, this research offers a robust theoretical framework highlighting the dynamic interplay between technological and environmental capabilities in achieving sustainable supply chain performance.