Corporate governance and extent of corporate sustainability practice: the role of investor activism

Purpose Following the unceasing pressure on companies to adopt sustainable business practices to mitigate climate effect, this study aims to examine corporate governance (CG) attributes and role of activist investors in influencing extensive sustainability practice for firms in the developed and emerging climes. Design/methodology/approach Using a panel ordered probit regression analysis for 368 companies over 2016 to 2019, the study examined CG attributes that drive extensive corporate sustainability practice. The study addressed endogeneity bias using STATA Extended panel ordered probit regression model with endogenous covariates. Findings The result showed CG attributes is critical for firms, and activist investors play a critical role in driving extensive sustainability practice. Findings further reveal the extent of adoption is relatively low in the emerging climes but showed sign of improvement over the years examined. Research limitations/implications The study focused mainly on larger firms operating in different sectors globally. Hence, findings cannot be generalized for small sized entities. Practical implications The study provides an insightful explanation regarding the extensive sustainability practices and the vital role assumed by activist investors. Social implications The increasing number of companies responding to Carbon Disclosure Project and consequent improvement in scores indicates a corporate commitment to ensuring a sustainable future. Originality/value This research offers significant insights to the extent discussion on attributes of CG critical for sustainability practice. The findings ascertain useful tools to aid the continued adoption of sound sustainability practices around the globe.

What Drives Climate Change Adaptation Practices in Smallholder Farmers? Evidence from Potato Farmers in Indonesia

The potato is the third most consumed crop globally after rice and wheat, but climate change has often disrupted its production. Therefore, adaptation practices are needed to maintain potato productivity. This study investigates the determinants of on- and off-farm climate change adaptation practices among smallholder farmers in Indonesia, considering adaptation intensity, which has not discussed in previous literature. The cross-sectional data were collected from 302 smallholder potato farmers in East Java, Indonesia, analyzed by a multivariate probit model to estimate the determinants. An ordered probit model was subsequently employed to understand the intensity factors. The findings indicated that the significant factors that affect farmers’ choice of on-farm adaptations were the farmers’ education, their participation in farmers’ groups, agricultural-related infrastructure, and agriculture output prices. Meanwhile, the off-farm adaptations were significantly affected by the farmers’ education, employed family members, agriculture-related infrastructure, and livestock ownership. The ordered probit model also suggested that participation in farmers groups and agricultural-related infrastructure were the most significant factors that encouraged adaptation. Therefore, adaptation planning should consider these factors to optimally improve farmers’ adaptation capacity.

The performance of tramway service from the users' viewpoint: A comparative analysis between two Moroccan cities

The Moroccan government has recently promoted sustainable public transport projects such as tramway services namely in the two largest cities of country, Casablanca and Rabat-Salé. Since its launch, the tramway service is in-creasingly present in citizens' daily lives in both cities. To maintain its attractiveness, operators and transport authori-ties should examine the performance of tramway service from user’s point of view. That is, an in-depth understanding of how passengers perceive service quality and what make them satisfied. The purpose of this study is to compare the performance of tramway service in the two cities based on the opinions of a sample size of 613 and 435 individuals in each city. The outcome of this peer comparison allows to determine the strengths and weaknesses of provided service and identify priorities for improvement in each city. Regarding the methodology, we adopted a two-step approach to achieve our research purpose. The first stage intends to compare users' perceptions regarding Service Quality Attrib-utes (SQAs) and overall satisfaction and to identify any significant differences between the two cities. To this end, we applied, in the first stage, a student t-test of two independent samples. The second stage employs an ordered probit regression model to identify the most important SQA; i.e., which most influence the overall satisfaction, and improve-ments priorities for the current service tramway. The results showed that, in average, passengers found service quality as good and are satisfied with tramway service in both cities. Tram vehicles’ is the best appreciated service attribute in both cities while Comfort in Rabat-Salé and Lines’ connectivity in Casablanca are the least appreciated. Moreover, the service performance of Rabat-Salé tramway exceeds that of Casablanca tramway in terms of service Availability, ser-vice Reliability, Fares level, Tram vehicle, Drivers’ competence, Lines’ connectivity, and overall satisfaction. On the other hand, we found that among top six important attributes, Reliability and Administrators should be prioritized for improvement in Casablanca; and staff, Lines, Comfort, and Administrators in Rabat-Salé. Results showed that im-provements in all these service aspects would increase significantly overall user’s satisfaction.

Exploring injury severity of pedestrian-vehicle crashes at intersections: unbalanced panel mixed ordered probit model

Although the pedestrian deaths have been declining in recent years, the pedestrian-vehicle death rate in Croatia is still pretty high. This study intended to explore the injury severity of pedestrian-vehicle crashes with panel mixed ordered probit model and identify the influencing factors at intersections. To achieve this objective, the data were collected from Ministry of the Interior, Republic of Croatia from 2015 to 2018. Compared to the equivalent random-effects and random parameter ordered probit models, the proposed model showed better performance on goodness-of-fit, while capturing the impact of exogenous variables to vary among the intersections, as well as accommodating the heterogeneity issue due to unobserved effects. Results revealed that the proposed model can be considered as an alternative to deal with the heterogeneity issue and to decide the factor determinants. The results may provide beneficial insight for reducing the injury severity of pedestrian-vehicle crashes.

Will Smog Cause Mental Health Problems? Indication from a Microsurvey of 35 Major Cities in China

Using the survey data of 21,861 participants from 35 major cities in China in 2018 and 2019, the effect of air quality on participants’ mental health was empirically tested based on the ordered probit model. The results showed that smog can significantly influence the mental health of participants. The better the air quality, the better the participants’ mental health, while poor air quality results in poor mental health. The older and higher-paid participants demonstrated poorer mental health. Additionally, for different health conditions, the air quality had different effects on the participants’ mental health. The healthier the participants, the more sensitive their mental health to changes in air pollution; the poorer the physical condition of the participants, the less sensitive their mental health to changes in air quality. Therefore, we need to more comprehensively and scientifically understand the effect of air quality on health. We need to pay attention not only to the adverse effects of smog on participants’ physical health, but also to its effects on participants’ mental health to improve both the physical and mental health of participants by improving the air quality.

Impacts of COVID-19 on patterns of meat and seafood consumption: evidence from Australia

PurposeIn 2020, mechanisms to limit the chain of transmission of coronavirus disease 2019 (COVID-19) in Australia led to widespread restrictions on population mobility and business operations. Such conditions provide a natural experiment that may help to provide insights into consumer behaviour and future trends in food consumption. The overall objective of this study is to explore the possible impacts of COVID-19 on meat consumption patterns in Australia, both in the short and medium term, and to explore whether there have been impacts on the underlying drivers for consumption.Design/methodology/approachThe research reported in this paper analyses the impacts of COVID-19 on meat and seafood consumption in Australia, drawing on a national random survey of 1,200 participants in June 2020. Survey data on past and current consumption rates are compared to respondent estimates of their future consumption behaviour, and ordered probit models are used to identify whether consumption changes can be explained by socio-demographic, attitudinal or economic factors.FindingsTwo potential scenarios were evaluated to explore future consumption trends. The first “acceleration” scenario is that the restrictions would encourage people to speed up existing declines in meat consumption, perhaps taking more account of credence factors such as health, animal welfare and environmental issues. The second “transformation” scenario is that people will change consumption patterns, perhaps moving more towards home-cooked meals and increased consumption. Slightly stronger support was found for the transformation scenario, indicating that consumption rates for most meats and seafood will be stable or increase over the next five years.Originality/valueThis study capitalises on changed social and economic settings generated by COVID-19 to test the effects on consumption of meat (chicken, beef, pork, lamb) and seafood at a national level. Ordered probit models are applied to evaluate participant data on their future intentions for meat consumption to test two scenarios, finding stronger support for the “transformative” scenario than the “accelerate” scenario.