Probit Regression Model Research Articles

A Study on the Socio-economic Factors Influencing Women’s Involvement in SHG Programmes in Ambedkar Nagar District of Uttar Pradesh, India

In the present paper, an attempt has been made to understand the socio-economic conditions of the SHG members and factors determining women’s participation in the SHG programme, utilizing pre-structured and pre-tested schedules to collect information from Ambekar Nagar district. Employed proportionate random sampling as well as purposive technique, 120 respondents were selected from two blocks i.e., Akbarpur and Bhiti based on higher number of SHGs. The data were analyzed with the help of suitable statistical tools like percentage, mean and binary probit models, to find the factors that effect the women’s participation in SHG programme. Results revealed that 40% of the respondents were illiterate, with the remainder having varying levels of education, predominantly at the primary level. Family size was primarily medium (5-6 members), and landholdings averaged 1.4 hectares, reflecting a predominantly agricultural setting. Livestock ownership, particularly buffaloes and goats, contributed significantly to household livelihoods. The study employed a probit regression model to analyze the determinants of women’s participation in SHGs, with factors such as age, family size, landholding, education, and net non-farm income examined. The results indicated that age and net non-farm income were statistically significant in influencing participation, with older women and those with higher non-farm income being less likely to engage in SHGs. Conversely, larger family size positively impacted participation. The findings suggest that rural development initiatives should prioritize younger women, expand education opportunities, and promote farm-based income generation to enhance SHG participation and, consequently, women's empowerment.

Analysis of agricultural technology adoption: the use of improved maize seeds and its determinants in Ethiopia, evidence from Eastern Amhara

Agriculture is the major source of income and employment creation for majority of least developed countries. Ethiopia as one of least developed countries of sub-Saharan Africa, majority of its citizen’s livelihood is predominantly dependent on agricultural products, more specifically on the production of cereal crops (Alemayehu et al. in Adv Crop Sci Technol 6(1):1–9, 2018). This study is conducted to identify the determinants of household to adopt and use improved maize seed, one of highly cultivated cereal crops in Eastern Amhara of Ethiopia. The data used in this study are secondary data collected by the agriculture office of Amhara regional state from 3864 household farmers living in the study area in 2022 fiscal year. To address the objective of the study, both descriptive and econometric methods of data analysis are applied. Because of the binary nature of adoption to be either adaptor or non-adaptor, probit regression model is applied. The probit model result shows that being male-headed household, rural credit provision, access to irrigation and the use of fertilizers affect the tendency of adopting improved maize seeds are positively related, while total arable land size owned by farmers negatively affects adoption status. Based on this result, the researcher recommends that government and other institutions should be invited to facilitate and directly involve in providing credit, irrigation, and fertilizers in the study area.

Drivers of adult educational engagement in Ecuadorian prisons: A gender perspective

This research paper reports an investigation on the determinants of educational engagement among the whole Ecuadorian prison population as collected in the penitentiary census conducted in 2022. By employing a probit regression model, the analysis reveals distinct patterns: while age and ethnicity significantly influence participation among male inmates, environmental factors like overcrowding play a pivotal role for female prisoners. Moreover, higher levels of formal education negatively correlate with educational engagement across both genders. These findings underscore the need for targeted policies that address both individual and environmental barriers to educational participation within Ecuadorian prisons. By understanding these dynamics, policymakers can develop effective interventions to improve prison conditions and enhance rehabilitation outcomes, contributing to broader societal goals of justice and sustainable development.

Impacts of blockchain cooperation project announcements on corporate stock market values: a loose coupling theory perspective

Purpose As a disruptive technology, blockchain technology brings new opportunities and challenges to operations management. We aim to examine the influences of blockchain cooperation project announcements (BCPAs) on firms’ stock market reactions in an emerging market. From 2016 to 2021, a total of 113 BCPAs of listed firms from the Chinese A-share market are selected as samples.Design/methodology/approach This study is based on the loose coupling theory and uses the event study method and probit regression analysis.Findings We find BCPAs positively affect the firm’s stock price on the day they are released. Compared with vertical BCPAs in a supply chain, horizontal BCPAs exert a more positive market reaction. Moreover, a BCPA with a partner within a shorter geographical distance exerts a more positive influence on market reaction. Contrary to the intuition of the decentralized blockchain feature that one-to-many cooperation leads to better benefits, one-to-one BCPAs exert a more positive effect on market reaction than one-to-many BCPAs. We further find that (1) industry type has a certain impact on cooperation mode selection, and (2) manufacturing firms are more inclined to choose one-to-one cooperation than those in service industry.Originality/value We focus on the impact of blockchain cooperative announcements and additionally use the probit regression models to analyze the influencing factors of cooperation mode selection and find the critical role of the industry type, which complements the existing empirical research on blockchain announcements and is conducive to provide decision-making reference for managers.

Farm characteristics and exogenous factors influencing the choice to buy land in Italy

Access to land is one of the key factors of farm growth, while at the same time, related research is characterised by important gaps, in particular facing the change over time in the nature and role of drivers of the land market. The objective of this research is to identify the endogenous and exogenous factors that affect the decision to purchase land in Italy between 2013 and 2020. Five probit regression models were implemented to understand the role of a set of different determinants in land investment decisions. The results show that factors related to capital in machinery and equipment, energy production and the presence of a successor are endogenous factors that positively influence the purchase decision. The ratio of rented land to utilised agricultural area and of family work units to total work units are endogenic factors that negatively influence the purchase decision. Exogenous factors related to the cost of capital and inflation rate affect the purchase of land in an opposite way, negatively and positively respectively. The role of Utilised Agricultural Area and Gross Saleable Production per hectare varies depending on the specialisation considered. The research can support policymakers in designing policies to promote the survival and growth of farms, as well as to facilitate land investment by reducing barriers to land acquisition.

WILLINGNESS TO ADOPT INDEX BASED CATTLE INSURANCE IN THE FACE OF CLIMATE CHANGE: EVIDENCE FROM SMALLHOLDER CATTLE FARMERS IN THE POLOKWANE LOCAL MUNICIPALITY, SOUTH AFRICA

This paper analysed the determinants of smallholder cattle farmers’ willingness to adopt Index-Based Cattle Insurance (IBCI) being climate change adaptive strategy in the Polokwane Municipality of South Africa. A multi-stage purposive sampling procedure was employed to collect primary data using structured questionnaires from 110 smallholder cattle farmers. The results revealed that about 89% of the sampled farmers were willing to adopt IBCI, while about 11% weren’t willing to adopt IBCI. The Probit regression model results revealed that marital status, farming experience, access to extension services, off-farm income and land ownership had a positive and significant influence on the willingness to adopt IBCI. However, educational level, household size, experience of cattle loss and farm size significantly influenced willingness to adopt IBCI negatively. Therefore, this study recommends workshops for raising IBCI awareness, government subsidy on insurance premiums and finally, improved market access and extension services for smallholder cattle farmers for sustainable livelihoods.

Mixtures of Probit Regression Models with Overlapping Clusters

Studies with binary outcomes on a heterogeneous population are quite common. Typically, the heterogeneity is modelled through varying effect coefficients within some binary regression setting combined with a clustering procedure. Most of the existing methods assign statistical units to distinct and non-overlapping clusters. However, there are scenarios where units exhibit a more complex organization and the clusters can be thought as partially overlapping. In this case, the standard approach does not work. In this paper, we define a mixture of regression models that allows overlapping clusters. This approach involves an overlap function that maps the regression coefficients, either at the unit or response level, of the parent clusters into the coefficients of the multiple allocation clusters. In order to deal with this intrinsic heterogeneity, regression analyses have to be stratified for different groups of observations or clusters. We present a computationally efficient Monte Carlo Markov Chain (MCMC) scheme for the case of a mixture of probit regressions. A simulation study shows the overall performance of the method. We conclude with two illustrative examples of modelling voting behavior, involving United States (US) Supreme Court justices over a number of topics and members of the United Kingdom (UK) parliament over divisions related to Brexit. These applications provide insights on the usefulness of the method in real applications. The method described can be extended to the case of a generic mixture of multivariate generalized linear models under overlapping clusters.

The median effective concentration of ropivacaine for ultrasound-guided anterior iliopsoas muscle space block in the elderly undergoing hip surgery: a dose-finding study.

In order to improve perioperative pain and reduce the adverse outcome of severe pain in elderly hip fractures, anterior iliopsoas muscle space block (AIMSB) can be used clinically to reduce pain. The aim of the study is to investigate the 50% effective concentration (EC50) of ropivacaine for ultrasound-guided anterior iliopsoas space block in elderly with hip fracture. A total of 27 patients were enrolled with aged ≥65 years, American society of Anesthesiologists (ASA) physical status classification II-III and undergoing Total Hip Arthroplasty (THA). We measured the EC50 using Dixon's up-and-down method. Ultrasound-guided AIMSB was performed preoperatively with an initial concentration of 0.2% in the first patient. After a successful or unsuccessful postoperative analgesia, the concentration of local anesthetic was decreased or increased 0.05%, respectively in the next patient. The successful block effect was defined as no sensation to pinprick in the area with femoral nerve, obturator nerve, and lateral femoral cutaneous nerve in 30 min. Meanwhile, the EC50 of ropivacaine was determined by using linear model, linear-logarithmic model, probit regression model, and centered isotonic regression. A total of 12 patients (48%) had a successful block. All patients with a successful block had a postoperative visual analog scale score of <4 in the 12 h. The estimated EC50 values in linear model, linear-logarithmic model, probit regression model, and centered isotonic regression (a nonparametric method) were 0.268%, 0.259%, 0.277%, and 0.289%. The residual standard error of linear model was the smallest (0.1245). The EC50 of ropivacaine in anterior iliopsoas space block under ultrasound guidance is 0.259-0.289%.

Compensation incentives, geographical distance, and the successful exit of indirect CVCs

PurposeThe literature suggests that increasing the intensity of compensation incentives for corporate venture capital (CVC) managers can contribute to successful exits of direct CVCs. This study explores the impact of compensation incentives on the successful exits of indirect CVCs under different geographical distances between parent companies and indirect CVC managers.Design/methodology/approachThe authors observed the compensation terms of CVC managers through investment announcements made by listed companies and used a probit regression model to test the hypotheses from a sample of 241 investment events with indirect CVCs in China.FindingsThe results show that if parent companies are geographically close to the managers of indirect CVCs, increasing the intensity of compensation incentives for managers will help the successful exit of indirect CVCs. However, if parent companies are not geographically close to indirect CVC managers, increasing the intensity of compensation incentives for managers will not promote the successful exit of indirect CVCs.Originality/valueThis study contributes significantly to the CVC literature. First, it sharpens our understanding of the differences in operational mechanisms between direct and indirect CVCs. Second, we find that the threshold returns of indirect CVC managers are non-negligible compensation incentives. Finally, the empirical evidence supports that in indirect CVC investments, the geographical distance between parent companies and managers is concerning because it affects whether compensation incentives contribute to the successful exit of indirect CVCs.

Predicting Student Dropout Rates Using Supervised Machine Learning: Insights from the 2022 National Education Accessibility Survey in Somaliland

High student dropout rates are a critical issue in Somaliland, significantly impeding educational progress and socioeconomic development. This study leveraged data from the 2022 National Education Accessibility Survey (NEAS) to predict student dropout rates using supervised machine learning techniques. Various algorithms, including logistic regression (LR), probit regression (PR), naïve Bayes (NB), decision tree (DT), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN), were employed to analyze the survey data. The analysis revealed school dropout rate of 12.67%. Key predictors of dropout included student’s grade, age, school type, household income, and type of housing. Logistic regression and probit regression models highlighted age and student’s grade as critical predictors, while naïve Bayes and random forest models underscored the significance of household income and housing type. Among the models, random forest demonstrated the highest accuracy at 95.00%, indicating its effectiveness in predicting dropout rates. The findings from this study provide valuable insights for educational policymakers and stakeholders in Somaliland. By identifying and understanding the key factors influencing dropout rates, targeted interventions can be designed to enhance student retention and improve educational outcomes. The dominant role of demographic and educational factors, particularly age and student’s grade, underscores the necessity for focused strategies to reduce dropout rates and promote inclusive education in Somaliland.

Combined Influence of Multiple Factors on Lung Cancer and Anxiety: A Probit Model Analysis

Lung cancer remains a leading cause of cancer-related mortality worldwide, significantly impacting public health. Concurrently, anxiety is a prevalent psychological disorder known to influence the development and progression of various cancers. This research paper aims to examine the combined influence of various factors, including demographic characteristics, environmental exposures, and physical conditioning data, on the incidence of lung cancer and anxiety. Given the binary nature of both lung cancer and anxiety outcomes, the analyses will first employ separate binary probit regression models to identify significant predictors for each condition independently. Then, joint modeling techniques will be implemented with dual purposes. First, by comparing the results of the individual and joint models, the reliability and robustness of the findings will be enhanced through cross-validation. Second, joint models will enable an investigation into potential endogeneity between lung cancer and anxiety. Addressing this endogeneity is crucial as it can potentially improve model robustness and provide deeper insights into the interrelationship between these two health outcomes. For the study purpose, the demographic factors considered include age and gender. Environmental factors encompass smoking history, alcohol consumption, and peer pressure. Physical conditioning data includes pre-existing health conditions such as yellow fingers, anxiety, chronic disease, fatigue, allergy, wheezing, coughing, shortness of breath, swallowing difficulty, and chest pain. By leveraging advanced statistical modeling techniques, this research seeks to uncover nuanced relationships and potential causal pathways that may exist between lung cancer and anxiety. The findings from this study will contribute to the existing body of knowledge by providing additional case study showing the relationship among a multitude of factors on lung cancer and anxiety, respectively. Also, the endogeneity checking among lung cancer and anxiety may enhance the efficiency of models done by others in the future. The research paper's analysis on lung cancer incidences reveals that age and wheezing are significant predictors. In examining anxiety levels, smoking emerged as a significant predictor, indicating a higher likelihood of anxiety among smokers. In addition, the joint probit models confirmed these findings, with age and wheezing significantly predicting lung cancer incidence, and smoking significantly predicting anxiety levels. No significant endogeneity was observed between lung cancer and anxiety, suggesting that these health outcomes are influenced by different sets of factors. These findings underscore the importance of considering demographic, environmental, and physical conditioning data in understanding and addressing lung cancer and anxiety, and checking their potential endogeneity

Layoff Factors Analysis: Evidence from 2021 China General Social Survey

This paper analyzes factors that explain company layoffs, given the individual layoff data in 2021, China. Using the Probit regression model, we find that gender inequality exists in layoffs, an employee’s work experience becomes less critical in the company’s layoff decisions, and how an employee’s health reasons affect work affects its probability of being laid off. Since we consider a significant endogeneity issue with education, using parents’ education as an instrumental variable suggests that political status cannot be a significant advantage for employees to lower the chance of being laid off. Moreover, evidence implies that policymakers encouraging the pursuit of higher educational degrees can foster stability in the labor market.

Assessing the current status of food security under climate variability and the role of household-level adaption strategies near Ghibe III hydroelectric dam, Ethiopia

The food security problem is exacerbated by low production and crop loss in many parts of the world and Ethiopia in particular. Prior literature less documented the role of innovative adaptation strategies to climate variability in ensuring food security. This study investigated the current status of food security and the role of adaptation strategies in the context of climate unpredictability in the Ghibe III dam area, in Southern Ethiopia. To achieve the intended objective, data were collected from 175 adapters and 209 non-adapters through multi-stage sampling using a questionnaire supplemented with key informant interviews and focus group discussions. A probit regression model was used to determine the factors affecting farmers’ adaptation. Propensity score matching (PSM) was employed to evaluate the impact of the adaptation strategies on food security. Food security was measured by the food balance sheet model (HBSM)). The rainfall anomaly index and Mann–Kendall tests were employed for the analysis of rainfall and temperature. The findings showed that throughout 30 years, temperatures were rising while rainfall was falling. Receiving early warning weather systems, credit services, extension services, family size, and farm size had a significant impact on adaptation strategies at a 1% level. Adapters showed an increase in food security (7–9%) than non-adapters. Adapters consumed 2101.6 mean calories per day/adult equivalent compared to non-adapters who consumed 1151.5 kcal. Conservation agriculture and climate-resilient crop varieties should be implemented by farmers supported by local-level government bodies at large.

Security matters: Empowering e-commerce in Sri Lanka through customer insights

In the fast-paced, post-COVID digital world, e-commerce presents promising prospects for significant advancement. However, customers often feel uncertain due to persistent concerns about the robustness of security measures safeguarding e-commerce platforms. The primary objective of our study was to identify factors affecting the security of e-commerce platforms based on the perceptions of Sri Lankan customers. This research was conducted using data collected from Sri Lankan e-commerce users via both online and offline surveys. An ordered probit regression model was utilised, demonstrating that transaction security, privacy, vendor system security, and platform quality positively impact the perceived security of e-commerce. The e-commerce industry in Sri Lanka is expected to see growth and an increased user penetration rate. The findings of this study are anticipated to assist e-commerce business owners and policymakers in addressing critical security issues, namely vulnerabilities in transactional security, low privacy, inadequate system security, and poor e-commerce platform quality. These improvements are expected to build trust and credibility among consumers, maximising e-commerce success.

Cognitive functioning and sustained internet use amid the COVID-19 pandemic: longitudinal evidence from older adults in Switzerland

This study aimed to investigate the relationship between pre-pandemic objective and subjective cognitive functioning and sustained Internet use during the pandemic among older adults in Switzerland. Data from 1299 respondents of the Survey of Health, Ageing and Retirement in Europe (SHARE) in 2019/2020 and a supplementary technology use questionnaire during the pandemic in 2021 were used. Cognitive functioning was assessed in 2019/2020 through objective measures (delayed and immediate memory, verbal fluency) and self-rated memory. Sustained Internet use was defined as having used the Internet at least once in the past seven days in 2019/2020 and reporting daily or weekly use in 2021. We found that 73.1% of respondents consistently used Internet between 2019/2020 and 2021. Using multivariable probit regression models controlling for sociodemographic and health variables, we found that higher global cognition z-scores, especially in immediate and delayed memory, were associated with a higher likelihood of sustained Internet use. Additionally, respondents with good, very good, or excellent self-rated memory were more likely to sustain their Internet use. These findings highlight the potential critical role of cognitive health in shaping older adults’ digital engagement, suggesting that cognitive assessments and training should be further considered in digital literacy initiatives for this population.

The association between rs6859 in NECTIN2 gene and Alzheimer's disease is partly mediated by pTau.

Emerging evidence suggests a connection between vulnerability to infections and Alzheimer's disease (AD). The nectin cell adhesion molecule 2 (NECTIN2) gene coding for a membrane component of adherens junctions is involved in response to infections, and its single nucleotide polymorphism (SNP) rs6859 was significantly associated with AD risk in several human cohorts. It is unclear, however, how exactly rs6859 influences the development of AD pathology. The aggregation of hyperphosphorylated tau protein (pTau) is a key pathological feature of neurodegeneration in AD, which may be induced by infections, among other factors, and potentially influenced by genes involved in both AD and vulnerability to infections, such as NECTIN2. We conducted a causal mediation analysis (CMA) on a sample of 708 participants in the Alzheimer's disease Neuroimaging Initiative (ADNI). The relationship between rs6859 and Alzheimer's disease (AD), with AD (yes/no) as the outcome and pTau-181 levels in the cerebrospinal fluid (CSF) acting as a mediator in this association, was assessed. Adjusted estimates from the probit and linear regression models were used in the CMA model, where an additive model considered an increase in dosage of the rs6859 A allele (AD risk factor). The increase in dose of allele A of the SNP rs6859 resulted in about 0.144 increase per standard deviation (SD) of pTau-181 (95% CI: 0.041, 0.248, p < 0.01). When included together in the probit model, the change in A allele dose and each standard deviation change in pTau-181 predicted 6.84% and 9.79% higher probabilities for AD, respectively. In the CMA, the proportion of the average mediated effect was 17.05% and was higher for the risk allele homozygotes (AA), at 19.40% (95% CI: 6.20%, 43.00%, p < 0.01). The sensitivity analysis confirmed the evidence of a robust mediation effect. This study reported a new potential causal relationship between pTau-181 and AD. We found that the association between rs6859 in the NECTIN2 gene and AD is partly mediated by pTau-181 levels in CSF. The rest of this association may be mediated by other factors. Our finding sheds light on the complex interplay between genetic susceptibility, protein aggregation, and neurodegeneration in AD. Further research, using other biomarkers, is needed to uncover the remaining mechanisms of the association between the NECTIN2 gene and AD.

Scientific misconduct responsibility attribution: An empirical study on byline position and team identity in Chinese medical papers.

The primary objective of this inquiry was to explore the nexus between authorship attribution in medical literature and accountability for scientific impropriety while assessing the influence of authorial multiplicity on the severity of sanctions imposed. Probit regression models were employed to scrutinize the impact of authorship on assuming accountability for scientific misconduct, and unordered multinomial logistic regression models were used to examine the influence of authorship and the number of bylines on the severity of punitive measures. First authors and corresponding authors were significantly more likely to be liable for scientific misconduct than other authors and were more likely to be penalized particularly severely. Furthermore, a negative correlation was observed between the number of authors' affiliations and the severity of punitive measures. Authorship exerts a pronounced influence on the attribution of accountability in scientific research misconduct, particularly evident in the heightened risk of severe penalties confronting first and corresponding authors owing to their principal roles. Hence, scientific research institutions and journals must delineate authorship specifications meticulously, ascertain authors' contributions judiciously, bolster initiatives aimed at fostering scientific research integrity, and uphold an environment conducive for robust scientific inquiry.

Informal Employment and High Burden of Out-of-Pocket Healthcare Payments among Older Workers: Evidence from Longitudinal Ageing Study in India.

India's economy is among the fastest growing in the world. However, a large share of informal workforce is a common characteristic of country's economy, comprises a significant portion of most of its labour markets. This workforce often receives low wages and lacks benefits such as strong social security and health coverage for all. The majority of healthcare spending in India is private. As India's population ages and the informal sector expands, it is expected that many of these workers will continue to work beyond the retirement age to bear their own healthcare costs due to lack of savings, pensions and the precarious nature of their employment. In this context, this study estimates the burden of Out-of-Pocket (OOP) payments on India's informal older workers compared to their formal counterparts, using data from the first wave of the nationally representative Longitudinal Ageing Study in India. According to estimates from the Two-part regression model, informal older workers pay, on an average, INR 1113 (p<0.01) and INR 55 (p<0.05) less than their formal counterparts for inpatient and outpatient care, respectively. However, probit regression models revealed that the burden of combined (both inpatient and outpatient) OOP payments exceeding (by 40%, 20%, and 10%) of their income is significantly higher among informal older workers compared to formal older workers. The study underscores the need for strengthening of universal health insurance schemes to ensure everyone has access to medical services without experiencing financial hardship. It also advocates for policies specifically tailored towards informal workers, considering their unique challenges with regard to livelihoods and healthcare security. In particular, this encompasses bolstering the existing social security and healthcare system, and related policies for ensuring financial security against OOP payments, especially for informal workers and all the population in general.

Air Quality Prediction and Ranking Assessment Based on Bootstrap-XGBoost Algorithm and Ordinal Classification Models

Along with the rapid development of industries and the acceleration of urbanisation, the problem of air pollution is becoming more serious. Exploring the relevant factors affecting air quality and accurately predicting the air quality index are significant in improving the overall environmental quality and realising green economic development. Machine learning algorithms and statistical models have been widely used in air quality prediction and ranking assessment. In this paper, based on daily air quality data for the city of Xi’an, China, from 1 October 2022 to 30 September 2023, we construct support vector regression (SVR), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), random forests (RF), neural network (NN) and long short-term memory (LSTM) models to analyse the influence of the air quality index for Xi’an and to conduct comparative tests. The predicted values and 95% prediction intervals of the AQI for the next 15 days for Xi’an, China, are given based on the Bootstrap-XGBoost algorithm. Further, the ordinal logit regression and ordinal probit regression models are constructed to evaluate and accurately predict the AQI ranks of the data from 1 October 2023 to 15 October 2023 for Xi’an. Finally, this paper proposes some suggestions and policy measures based on the findings of this paper.

The influence of the board’s size, independence and sociodemography on the firm’s climate change orientation: evidence from the GALPLACC index

PurposeThis paper aims to explore the relationship between board composition and a firm’s commitment to combatting climate change. Specifically, this study investigates how various characteristics of the board, namely its size and presence of independent directors, and of the directors themselves, including gender diversity, age, educational background and national homogeneity, affect the corporate-level climate change orientation. From a theoretical standpoint, the authors take a cross-fertilizing perspective, bridging upper echelons theory with agency, resource dependence and critical mass theories.Design/methodology/approachThe study uses ordered probit regression models on a hand-collected multi-country and multi-industry sample of 35 listed firms included in the Global Climate Change Liquid Equity Index (GALPLACC) provided by ECPI. This index is particularly relevant as it focuses on firms that have demonstrated a commitment to climate change, providing a robust dataset for the analysis.FindingsThe findings underscore the importance of disentangling various characteristics of corporate boards and directors. Specifically, the orientation toward climate change is negatively influenced by both board size and having a higher number of independent directors, while it is positively affected by reaching a critical mass of women on the board. Conversely, factors such as average age, educational background and the level of national homogeneity do not show significant effects.Originality/valueThis paper has an exploratory nature and contributes to the ongoing debate on the crucial, yet controversial role played by board-level and directors’ sociodemographic characteristics in shaping a firm’s environmental stance. Moreover, this study offers potential recommendations for policymakers regarding board composition to enhance firms’ climate change orientation.