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This instant study explores the relationship between weight-based teasing and depressive symptoms in Indian college students. It further investigates the moderating effect of gratitude on depressive symptoms occurring due to weight-based teasing.

The study is theoretically based on Fredrickson's broaden-and-built theory (2001). PROCESS macro in IBM SPSS v21 was used to analyze the effect of gratitude in moderation of weight-based teasing and depressive symptoms. The study used correlation and regression analysis to assess the relationship between weight-based teasing and depressive symptoms.

The study has confirmed that weight-based teasing results in the development of depressive symptoms in Indian college students. The study has also revealed that gratitude casts a significant moderating effect on depression due to weight-based teasing, i.e. a reduction in regression weight of weight-based teasing.

This study is the first of its kind in India and will significantly add to the national literature on teasing and depression. Further, the study will help stakeholders like educators and policymakers to formulate psychological programs based on positive psychology 2.0 and gratitude to combat the rising issue of body shaming in India.

The purpose of this study was to evaluate the impact of a waqf-based microfinance program on clients’ well-being during the COVID-19 pandemic.

This study obtained primary data from a survey distributed to 282 respondents, consisting of 150 clients and 132 nonclients of the Bank Wakaf Mikro (BWM) Al Kausar in Indonesia. This study constructed a well-being index (WBI) and compared clients’ and nonclients’ WBI before and during the pandemic using the difference-in-differences (DID) method. DID measures the effect of a treatment in a “treatment group” versus a “control group” using data from two periods.

This study found that clients and nonclients alike experienced an increase in well-being throughout the pandemic, but the increase was greater for clients than for nonclients. This study argues that the waqf-based microfinance program run by Bank Waqf Mikro model can assist their clients – as more vulnerable groups in society – to maintain their well-being during the pandemic.

To ensure the effectiveness of waqf-based microfinance programs in diverse settings, this study should include more respondents from different institutions.

This research has several practical recommendations, particularly for integrating Islamic charity for microfinance. The findings of this study suggest that the BWM model, which combines three institutions – the government, zakat groups and Islamic boarding schools (pesantrens) – can play a substantial role in enhancing the welfare of its members during the pandemic.

This study contributes to the body of knowledge on Islamic microfinance by providing empirical evidence of the importance of waqf-based microfinance in reducing the pandemic’s impact on clients well-being.

The Covid-19 pandemic appears to have engendered heterogeneous effects on individuals’ labour market prospects. This paper focuses on two possible sources of a heterogeneous exposition to labour market risks associated with the pandemic outbreak: the routine task content of the job and the teleworkability. To evaluate whether these dimensions played a crucial role in amplifying employment and wage gaps among workers, we focus on the case of Italy, the first EU country hit by Covid-19.

Investigating the actual effect of the pandemic on workers employed in jobs with a different degree of teleworkability and routinization, using real microdata, is currently unfeasible. This is because longitudinal datasets collecting annual earnings and the detailed information about occupations needed to capture a job’s routine task content and teleworkability are not presently available. To simulate changes in the wage distribution for the year 2020, we have employed a static microsimulation model. This model is built on data from the Statistics on Income and Living Conditions (IT-SILC) survey, which has been enriched with administrative data and aligned with monthly observed labour market dynamics by industries and regions.

We measure the degree of job teleworkability and routinization with the teleworkability index (TWA) built by Sostero et al. (2020) and the routine-task-intensity index (RTI) developed by Cirillo et al. (2021), respectively. We find that RTI and TWA are negatively and positively associated with wages, respectively, and they are correlated with higher (respectively lower) risks of a large labour income drop due to the pandemic. Our evidence suggests that labour market risks related to the pandemic – and the associated new types of earnings inequality that may derive – are shaped by various factors (including TWA and RTI) instead of by a single dimension. However, differences in income drop risks for workers in jobs with varying degrees of teleworkability and routinization largely reduce when income support measures are considered, thus suggesting that the redistributive effect of the emergency measures implemented by the Italian government was rather effective.

No studies have so far investigated the effect of the pandemic on workers employed in jobs with a different degree of routinization and teleworkability in Italy. We thus investigate whether income drop risks in Italy in 2020 – before and after income support measures – differed among workers whose jobs are characterized by a different degree of RTI and TWA.

As perceived corporate social responsibility (CSR) has become one of the long-term sustainable development strategies for many companies, this paper investigates the interrelationships between sustainable values (SVs), perceived CSR and customer behaviour in the restaurant industry.

To gather data needed to meet the study aims, we conducted an online survey of restaurant patrons in China, and used partial least squares structural equation modelling (PLS-SEM) to analyse the proposed models and test the hypotheses.

The obtained findings confirm that the three studied SV dimensions (equality, respect for nature and shared responsibility) have positive effects on perceived CSR, which in turn significantly affects word of mouth (WOM). Moreover, perceived CSR mediates the relationship between the three SVs and WOM but has no impact on the relationship between SVs and revisit intention.

The results of this study have practical implications for managers in the restaurant industry. Restaurant managers can prioritize their CSR efforts based on the relative importance of SV dimensions, which is useful in generating positive WOM.

Even though the importance of CSR is widely recognised, it is insufficiently studied from the perspective of restaurant customers. This research not only addresses this gap, but also expands the current understanding of SVs and their impact on CSR.

In a virtual interconnected digital space, the metaverse encompasses various virtual environments where people can interact, including engaging in business activities. Machine learning (ML) is a strategic technology that enables digital transformation to the metaverse, and it is becoming a more prevalent driver of business performance and reporting on performance. However, ML has limitations, and using the technology in business processes, such as accounting, poses a technology governance failure risk. To address this risk, decision makers and those tasked to govern these technologies must understand where the technology fits into the business process and consider its limitations to enable a governed transition to the metaverse. Using selected accounting processes, this study aims to describe the limitations that ML techniques pose to ensure the quality of financial information.

A grounded theory literature review method, consisting of five iterative stages, was used to identify the accounting tasks that ML could perform in the respective accounting processes, describe the ML techniques that could be applied to each accounting task and identify the limitations associated with the individual techniques.

This study finds that limitations such as data availability and training time may impact the quality of the financial information and that ML techniques and their limitations must be clearly understood when developing and implementing technology governance measures.

The study contributes to the growing literature on enterprise information and technology management and governance. In this study, the authors integrated current ML knowledge into an accounting context. As accounting is a pervasive aspect of business, the insights from this study will benefit decision makers and those tasked to govern these technologies to understand how some processes are more likely to be affected by certain limitations and how this may impact the accounting objectives. It will also benefit those users hoping to exploit the advantages of ML in their accounting processes while understanding the specific technology limitations on an accounting task level.