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This paper aims to investigate the association of COVID-19 confirmed cases and deaths with mental health, unemployment and financial markets-related search terms for the USA, the UK, India and worldwide using Google Trends.

The authors use Spearman’s rank correlation coefficients to assess the relationship between relative search volumes (RSVs) and mental health, unemployment and financial markets-related search terms, with the total confirmed COVID-19 cases as well as deaths in the USA, UK, India and worldwide. The sample period starts from the day 100 cases were reported for the first time, which is 7 March 2020, 13 March 2020, 23 March 2020 and 28 January 2020 for the US, the UK, India and worldwide, respectively, and ends on 25 June 2020.

The results indicate a significant increase in anxiety, depression and stress leading to sleeping disorders or insomnia, further deteriorating mental health. The RSVs of employment are negatively significant, implying that people are hesitant to search for new jobs due to being susceptible to exposure, imposed lockdown and social distancing measures and changing employment patterns. The RSVs for financial terms exhibit the varying associations of COVID-19 cases and deaths with the stock market, loans, rent, etc.

This study has implications for the policymakers, health experts and the government. The state governments must provide proper medical facilities and holistic care to the affected population. It may be noted that the findings of this study only lead us to conclude about the relationship between COVID-19 cases and deaths and Google Trends searches, and do not as such indicate the effect on actual behaviour.

To the best of the authors’ knowledge, this is the first attempt to investigate the relationship between the number of COVID-19 cases and deaths in the USA, UK and India and at the global level and RSVs for mental health-related, job-related and financial keywords.

This study aims to exploit Tobin’s Q model of investment to examine the relationship between corporate governance and green innovation.

The study is based on a sample of 3,896 firms from 2002 to 2021, covering 45 countries worldwide. The authors adopt Tobin’s Q model to conceptualize the relationship between corporate governance and investment in green research and development (R&D). The authors argue that agency costs and financial market frictions affect corporate investment and are fundamental factors in R&D activities. By limiting agency conflicts, effective governance favors efficiency, facilitates access to external financing and encourages green innovation. The authors analyzed the causal effect by using the system-generalized method of moments (system-GMM).

The results reveal that the better the corporate governance, the more the firm invests in green R&D. A 1%-point increase in the corporate governance ratings leads to an increase in green R&D expenses to the total asset ratio of about 0.77 percentage points. In addition, an increase in the score of each dimension (strategy, management and shareholder) of corporate governance results in an increase in the probability of green product innovation. Finally, green innovation is positively related to firm environmental performance, including emission reduction and resource use efficiency.

The findings provide implications to support managers and policymakers on how to improve sustainability through corporate governance. Governance mechanisms will help resolve agency problems and, in turn, encourage green innovation.

Understanding the impact of corporate governance on green innovation may help firms combat climate change, a crucial societal concern. The present study helps achieve one of the precious UN’s sustainable development goals: Goal 13 on climate action.

This study goes beyond previous research by adopting Tobin’s Q model to examine the relationship between corporate governance and green R&D investment. Overall, the results suggest that effective corporate governance is necessary for environmental efficiency.

This study seeks to explicate how institutional disruptions impact multinational corporation (MNC) subsidiary control choices. It uses institutional theory to understand the influence of formal and informal institutions across countries on the type of control system employed in an MNC manufacturing subsidiary.

This study’s sample is based on a unique dataset from five trustworthy sources. We use multi-level models to account for the hierarchical nature of the sample of 1,630 multinational subsidiaries spread across 26 host countries by firms from 21 home countries.

The institutional distance between the host and the home country has a negative relationship with strategic control. In contrast, the home country’s power distance has a positive relationship with strategic control.

Study findings indicate the need to incorporate formal and informal institutional elements in the control system’s conceptual framing and design. This notion complements existing visualizations of optimizing MNC controls through extant articulations of minimizing governance costs through organizational design choices or strategic needs.

The direction of causality between job satisfaction and job performance (known as the holy grail of industrial psychologists) is undetermined and related research findings in different organizational contexts are mixed. Based on the ample literature, mainly from Western countries, on the relationship between job satisfaction and job performance, a non-recursive bow pattern model was utilized to investigate the direct relationship between these two variables in an Asia–Pacific higher education system.

This study is quantitative in approach and survey in design. Additionally, to meet the statistical requirements of non-recursive bow pattern analysis, the authors added welfare as a theory-driven instrumental variable to introduce exogenous variability. Using the efficient partial least squares (PLSe2) estimator, the authors fitted the model to the data collected from 2008 academics affiliated with Malaysian public universities and polytechnics.

The results showed that while job satisfaction is considerably influenced by welfare, it is not a significant predictor of job performance directly. In addition, a meaningful positive correlation between the disturbance terms of job satisfaction and job performance was observed, suggesting the existence of other factors that could increase both job satisfaction and job performance. The findings' theoretical and practical implications are discussed, and a list of theory-driven evidenced-based policies in this regard is provided.

This is the first study to test a non-recursive bow pattern model and examine the holy grail of industrial psychology based on the PLSe2 methodology, as a parametric approach to partial least squares (PLS), in a higher education context. This study also provides higher education researchers with the advantages of the PLSe2 method, especially in causal-predictive modeling, in the context of applied higher education research.

This paper aims to investigate the performance of OECD countries' long-term productivity during the period of 1975–2018.

This study employed different approaches to evaluate how efficiency scores vary with changes in inputs and outputs: Data Envelopment Analysis (CRS, VRS and FDH), TOPSIS and TOPSIS of these scores.

The findings suggest that, during the period of this study, countries with higher freedom of religion and with Presidential democracy regimes are positively associated with higher productivity.

To the best of the authors’ knowledge, this is the first study that uses efficiency models to assess the productivity levels of OECD countries based on several contextual variables that can potentially affect it.