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The purpose of our paper is to empirically examine the conjectures, which prior literature suggests, that employees work more productively in socially responsible companies and employees are willing to work for less when they work for these companies.

This study uses ordinary least squares regression to examine the relationship between corporate social responsibility (CSR) and employee performance and between CSR and employee cost. Further, 2SLS is used to address the endogeneity issue.

The results indicate a positive relation between CSR and employee performance, suggesting that employees in socially responsible companies generate better operating performance than their peers in less socially responsible companies. Findings also reveal that socially responsible companies incur higher labor cost.

First, the CSR ratings constructed by KLD Inc. are an approximate measure of CSR performance. Better CSR measures may yield stronger results. Additionally, the sample firms in our study are relatively large firms. Caution needs be exercised when readers generalize these conclusions. Finally, this sample only consists of public firms. Whether these conclusions hold in private firms remains unknown. The above issues can be investigated in future studies.

The findings of our study should interest managers who contemplate engaging in socially responsible activities, investors and financial analysts who assess firm performance and policymakers who design and implement guidelines on CSR programs.

This is the first paper that directly tests the association between CSR and employee performance and cost. Thus, this study contributes to the CSR literature by offering evidence to show a positive effect of CSR on employee performance. It also contributes to the management accounting literature.

This study aims to investigate the monitoring role of ownership structure (OWS) on real earnings management (REM) practices; previous studies primarily examined the effect of OWS on accrual-based earnings management.

The sample of this study is 490 companies listed on the Malaysian Stock Exchange during the period 2013–2016 (1,960 company-year observations). The regression of a feasible generalized least square was used for data analysis. The authors use three regression models ordinary least squares, panel-corrected standard errors and Driscoll–Kraay standard errors to corroborate the findings and also examine alternative REM measures.

Analysis of the data shows that family, foreign and institutional ownership has a positive link with the quality of financial reporting and, to a large extent, is capable of alleviating REM. The findings also indicate that some form of OWS significantly affects REM, corroborating existing theories on corporate governance (CG) and the perspectives of practitioners.

The evidence concerns the significant role played by the OWS in reducing REM activities. The findings are useful in support of regulatory activities, particularly in the design of policies to regulate the OWS. The results may also provide useful insights to inform other policymakers, investors, shareholders and researchers about the active role of family, foreign and institutional investors in monitoring Malaysia's public listed companies (PLCs) to strengthen CG practices. This also leads to less REM and enhances the quality of financial reporting.

To the authors' knowledge, this work is pioneering research from a developing country, specifically from Malaysia, to investigate the manner in which all possible OWSs influence REM. More importantly, the study recommends that regulators and researchers do not envisage OWS as a holistic phenomenon.

Vector Autoregression (VAR) has been a standard empirical tool used in macroeconomics and finance. In this paper we discuss how to compare alternative VAR models after they are estimated by Bayesian MCMC methods. In particular we apply a robust version of deviance information criterion (RDIC) recently developed in Li, Zeng, and Yu (2014b) to determine the best candidate model. RDIC is a better information criterion than the widely used deviance information criterion (DIC) when latent variables are involved in candidate models. Empirical analysis using US data shows that the optimal model selected by RDIC can be different from that by DIC.

The purpose of this paper is to investigate whether analysts’ optimism affects the stock crash risk.

The sample covers 49,246 firm-year observations for the period between 1995 and 2015. The authors use OLS regressions with firm and year fixed effects for analyses.

The study finds that there is a positive association between analysts’ optimism and stock crash risk. Such a positive impact is more pronounced for firms with opaque information environment and for analysts who are considered ex ante credible.

The results indicate that analysts’ optimism can be an important source of stock crash risk.

The findings can be useful for informational users of analyst reports. Given that information provided by analysts might have negative consequences, the empirical results can be useful in assessing future stock return behaviors.

This paper has the potential to shed light on the large literature of crash risk. Prior studies suggest that crash is driven by the agency tension between shareholders and managers. It remains possible that crashes could be caused by overpriced stocks in the absence of bad news hoarding. The paper investigates crash from a perspective, financial analysts, that is underexplored.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.

The purpose of this study is to deepen our understanding of the well-being of transient organizations/groups and to use this to develop a novel conceptual framework of gig worker well-being during times of crisis.

A qualitative approach was adopted combining in-depth semi-structured interviews and daily diaries. Twenty-two workers working in the sharing economy were recruited. Thematic analysis was conducted for the diary and interview data.

The findings illustrate a complex picture of sharing economy workers’ four dimensions of well-being, including physical, subjective, psychological and social well-being. A number of the COVID-19 pandemic contexts, such as more time, restriction, economic recession and uncertainty, were seen to influence these workers’ well-being in different ways including both positive and negative impacts. The precarious nature of gig work within the sharing economy was also found influential, which includes flexibility, uncertainty, temporality and diversity. Furthermore, the specific contexts of the hospitality, tourism and event industry (such as labor-intensive, low esteem, self-value and purpose in life) had also impacted gig workers physical and psychological well-being in various ways.

This study complements the gig workers’ view of the sharing economy by investigating their well-being during the COVID-19 pandemic. In addition, this study reveals the complex and various influences hospitality, tourism and events industry contexts made, amplified by the pandemic. Methodologically, the daily diary approach applied in this research has captured gig workers’ instant feelings and thoughts, which enriches the current understanding of gig workers’ well-being.

From the findings and the newly developed conceptual framework, practical implications are proposed focusing on how the tourism, hospitality and event industries should look after their gig workers’ well-being in the COVID-ized environment. From the physical well-being perspective, businesses should consider partnering with gym operators to provide corporate packages or discounted membership to their gig workers. From psychological well-being perspective, a recognition system integrating gig workers would be useful to strengthen gig workers’ perception of value in their jobs. In addition, technology can be used to introduce more resources to their gig workers, particularly when distancing.

A conceptual framework is developed, which captures the influence of both “internal” and “external” determinants of gig worker well-being during times of crisis. This research contributes to theory by developing a framework of well-being in the context of the sharing economy, as well as explicitly addressing how the uncertainty and precariousness of sharing economy work and the hospitality, tourism and event industry contexts relate to well-being. This model is likely to have applicability beyond COVID-19 as the pandemic made clear many existing challenges – rather than just simply creating new ones.

OUR new features of record and reminiscence appear to have been appreciated by our readers; and, as this number shows, we continue with increased pages and are endeavouring to extend our scope to meet every kind of library interest. There is an atmosphere, of change and, as some think, of crisis, in library matters, especially in those of the public library. The winter to which our minds turn in mid‐September is likely to be interesting and may bring decisions of various kinds. We hope to reflect them, and, as is our invariable custom, invite readers to use us to express their views as well as their experiences.

The normal error distribution for the observations and log-volatilities in a stochastic volatility (SV) model is replaced by the Student-t distribution for robustness consideration. The model is then called the t-t SV model throughout this paper. The objectives of the paper are twofold. First, we introduce the scale mixtures of uniform (SMU) and the scale mixtures of normal (SMN) representations to the Student-t density and show that the setup of a Gibbs sampler for the t-t SV model can be simplified. For example, the full conditional distribution of the log-volatilities has a truncated normal distribution that enables an efficient Gibbs sampling algorithm. These representations also provide a means for outlier diagnostics. Second, we consider the so-called t SV model with leverage where the observations and log-volatilities follow a bivariate t distribution. Returns on exchange rates of Australian dollar to 10 major currencies are fitted by the t-t SV model and the t SV model with leverage, respectively.