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This study aims to explore COVID-19-related accounting disclosures using sociological disclosure analysis (SDA) within the context of the developing economy of Bangladesh.

COVID-19-related accounting disclosures from listed banks’ annual reports have been examined using three levels of SDA: textual, contextual and sociological interpretations. Data were gathered from the banks’ 2019 and 2020 annual reports. The study uses the legitimacy theory as its theoretical framework.

The research reveals a substantial shift in corporate disclosures due to COVID-19, marked by a significant increase from 2019 to 2020. Despite regulatory and professional directives for COVID-19-specific disclosures, notable non-compliance is evident in subsequent events, going concern, fair value, financial instruments and more. Instead of assessing the implications of COVID-19 and making disclosures, companies used positive, vague and subjective wording to legitimize non-disclosure.

The study’s insights can inform regulators and policymakers in crafting effective guidelines for future crisis-related reporting like COVID-19. The research adds to the literature by methodologically using SDA to explore pandemic-specific disclosures, uncovering the interplay between disclosures, legitimacy and stakeholder engagement.

This study represents a pioneering effort in investigating COVID-19-specific disclosures. Moreover, it uses the SDA methodology along with the legitimacy theory to analyze accounting disclosures associated with COVID-19.

The imperative to conserve resources and minimize operational expenses has spurred a notable increase in the adoption of lean manufacturing within the context of the circular economy across diverse industries in recent years. However, a notable gap exists in the research landscape, particularly concerning the implementation of lean practices within the pharmaceutical industry to enhance circular economy performance. Addressing this void, this study endeavors to identify and prioritize the pivotal drivers influencing lean manufacturing within the pharmaceutical sector.

The outcome of this rigorous examination highlights that “Continuous Monitoring Process for Sustainable Lean Implementation,” “Management Involvement for Sustainable Implementation” and “Training and Education” emerge as the most consequential drivers. These factors are deemed crucial for augmenting circular economy performance, underscoring the significance of management engagement, training initiatives and a continuous monitoring process in fostering a closed-loop practice within the pharmaceutical industry.

The findings contribute valuable insights for decision-makers aiming to adopt lean practices within a circular economy framework. Specifically, by streamlining the process of developing a robust action plan tailored to the unique needs of the pharmaceutical sector, our study provides actionable guidance for enhancing overall sustainability in the manufacturing processes.

This study represents one of the initial efforts to systematically identify and assess the drivers to LM implementation within the pharmaceutical industry, contributing to the emerging body of knowledge in this area.

The objective of this study is to investigate the barriers hindering the integration of lean manufacturing (LM) practices within the furniture industry of Bangladesh. The traditional operational paradigms in this sector have posed substantial challenges to the effective implementation of LM. In this study, the barriers of implementing LM in the furniture business are examined, aiming to provide a systematic understanding of the barriers that must be addressed for a successful transition.

The research reveals that “Fragmented Industry Structure,” “Resistance to Lean Practices” and “Inadequate Plant Layout and Maintenance”, emerged as the foremost barriers to LM implementation in the furniture industry. Additionally, “Insufficient Expert Management,” “Limited Technical Resources” and “Lack of Capital Investment” play significant roles.

The outcomes of this study provide valuable insights into the furniture industry, enabling the development of strategies for effective LM implementation. One notable challenge in lean implementation is the tendency to revert to established practices when confronted with barriers. Therefore, this transition necessitates informed guidance and leadership. In addition to addressing these internal challenges, the scope of lean implementation should be broadened.

This study represents one of the initial efforts to systematically identify and assess the barriers to LM implementation within the furniture industry of Bangladesh, contributing to the emerging body of knowledge in this area.

This paper aims to evaluate the performance of the multiple linear regression (MLR) using a fixed-effects model (FE) and artificial neural network (ANN) models to predict the level of customer deposits on a sample of Tunisian commercial banks.

Training and testing datasets are developed to evaluate the level of customer deposits of 15 Tunisian commercial banks over the 2002–2021 period. This study uses two predictive modeling techniques: the MLR using a FE model and ANN. In addition, it uses the mean absolute error (MAE), R-squared and mean square error (MSE) as performance metrics.

The results prove that both methods have a high ability in predicting customer deposits of 15 Tunisian banks. However, the ANN method has a slightly higher performance compared to the MLR method by considering the MAE, R-squared and MSE.

The findings of this paper will be very significant for banks to use additional management support to forecast the level of their customers' deposits. It will be also beneficial for investors to have knowledge about the capacity of banks to attract deposits.

This paper contributes to the existing literature on the application of machine learning in the banking industry. To the author's knowledge, this is the first study that predicts the level of customer deposits using banking specific and macroeconomic variables.