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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 pandemic COVID-19 has affected every sector of an economy in every possible way. Banking sector of Bangladesh has been affected by it badly. The purpose of this paper is to find out the impact of COVID-19 on the liquidity and financial health of the listed banks in Bangladesh.

Liquidity ratios are calculated to measure the liquidity condition of the banks and revised Altman's Z-Score Model for non-manufacturing companies is used to measure the financial health. The ratios are compared before and during the COVID-19 periods to assess the impact.

The findings of this study indicate a deterioration of liquidity position and financial health of the listed banks after the emergence of this pandemic. Though the banks have poor liquidity ratios and financial health prior to the emergence of this pandemic, they have decreased more in the second quarter of 2020. Most of the banks have poor liquidity ratios and cash position. The listed Islamic Banks have poor financial health than the listed Commercial Banks and all the banks belong to the red zone in all the quarters.

The results of this study will have policy implications for companies and regulators of money market.

This paper is a pioneer initiative in assessing the impact of COVID-19 pandemic on liquidity and financial health based on empirical data.

To provide a brief survey of the literature directed towards the analysis of warranty claim data.

For convenience, this survey of the analysis of warranty claims data is somewhat arbitrarily be classified by topics as follows: age‐based claims analysis, aggregated warranty claims analysis, marginal counts of claims analysis, warranty claims analysis by using covariates, estimation of lifetime distribution using supplementary data, two‐dimensional warranty, warranty costs analysis, sales lag and reporting lag analysis, and forecasts of warranty claims.

Emphasis is placed on a discussion of different kinds of warranty claims data selected from reviews and on a comparison of the statistical models and methods used to analyze such data.

Since the literature on product warranty data is vast, more work on this problem is needed.

This review points out why warranty claims data is important and gives a survey of the literature pertaining to the analysis of such data. The emphasis is on the analysis of minimal databases of real warranty data, constructed by combining information from different sources, which can be collected economically and efficiently through service networks. The research is applicable for those responsible for product reliability, product design decisions and warranty management in manufacturing industries.

The paper reviews different statistical models and methods used to analyze warranty claims data. The statistical models and methods presented are be valuable and meaningful tools for product reliability and warranty management and analysis.

The purpose of this paper is to study the wear behavior of as‐cast (AC) and heat treated (HT) triple particle size (TPS) silicon carbide (SiC) reinforced aluminum alloy‐based metal matrix composites (SiC_p/Al‐MMC).

Al‐MMCs were prepared using 20 vol.% SiC reinforcement into aluminum metal matrix and developed using a stir casting process. Stir casting is a primary process of composite production whereby the reinforcement ingredient material is incorporated into the molten metal by stirring. The TPS composite consist of SiC of three different sizes viz., coarse, intermediate, and fine. The solution heat treatment was done on AC composite at 540°C for 4 h followed by precipitation treatment. The wear test was carried out using a pin‐on‐disc type tribo‐test machine under dry sliding condition. A mathematical analysis was also done for power factor values based on wear and friction results. The wear morphology of the damaged surface was also studied using optical microscope and scanning electron microscope (SEM) in this investigation.

The test results showed that HT composite exhibited better wear resistance properties compared to AC composite. It is anticipated that heat treatment could be an effective method of optimizing the wear resistance properties of the developed Al‐MMC material.

This paper provides a way to enhance the wear behavior of automotive tribo‐components such as brake rotor (disc and drum), brake pad, piston cylinder, etc.

This paper compares the wear behavior of AC and HT TPS reinforced Al‐MMC material under dry sliding condition.

The aim is to study the tribological behavior of dual particle size (DPS) and triple particle size (TPS) SiC reinforced aluminum alloy‐based metal matrix composites – MMCs (Al/SiC_p MMC).

Al‐MMCs with DPS and TPS of SiC were prepared using 20 wt% SiC and developed using stir‐casting process. The TPS composite consist of three different sizes of SiC and DPS composite consist of two different sizes of SiC. The tribological test was carried out using a pin‐on‐disc type tribo‐test machine under dry sliding condition.

The TPS composite exhibited better wear resistance properties compared to DPS composite. It is anticipated that when a composite is integrated with small, intermediate and large SiC particle sizes (which is known as TPS) within the same composite could be an effective method of optimizing the wear resistance properties of the developed material.

This study provides a way to enhance the tribological behavior of automotive tribo‐components such as brake rotor, piston, cylinder, etc.

This investigation compares the tribological behavior of DPS and TPS SiC reinforced aluminum MMCs.

The purpose of this paper is to evaluate the effect of thiourea (TU) on corrosion resistance property for rolled and recrystallized E-34 microalloyed steels by using electrochemical polarization techniques.

To perceive the effect of TU on the corrosion inhibition efficiency, various concentrations of TU (from 1 × 10 − 4 to 1 × 10 − 2M) and different temperatures (20, 30 and 400°C) in 1N sulfuric acid are used.

It is found that TU has significant inhibition effect on corrosion process. Moreover, it reveals that both the inhibitor concentrations and temperatures have a strong influence on the corrosion prevention efficiency of inhibitor. Thermodynamics studies confirm that the inhibitor adsorption follows the Langmuir adsorption isotherm model.

To the best of the authors’ knowledge, it is the first work that has been disclosed the corrosion inhibitory effect of TU for recrystallized E-34 microalloyed steels in acidic media.

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.

The paper aims to examine the level of agricultural credit by commercial banks and the determinants that influence the commercial banks to the increased level of agricultural credit through the pressures of the institutional environment.

The study selects seventeen sample commercial banks following the market capitalization method and investigates a total of 85 annual reports during the period from 2013 to 2017. The study conducts a pooled regression to conclude the proposed hypotheses.

The present study finding indicates that the average of agricultural credits to total credits is 2.25% among the sample commercial banks. The study finds a positive significant association between board gender diversity, foreign director, management team and agricultural credit. Furthermore, the study has found that the role of the deposit in enhancing agricultural credit is positive. On the other hand, the association between independent directors, profitability and agricultural credits is negative.

The study is based on secondary data with five firm-year observations of commercial banks. The study finding is based on commercial banks, so it should not be generalized to non-bank financial institutions.

The study emphasizes policymakers’ attention towards the level of agricultural credit and determinants that influence the level of agricultural credit by commercial banks in emerging markets.

The key contribution of the study is to focus on the reformist role of the determinants in promoting the increased level of agricultural credit in the emerging markets.