Purpose This study revisits the relationship between environmental, social and governance (ESG) activities and firm performance. More importantly, it tests whether this relationship is moderated by critical yet underexplored factors such as stakeholder engagement, financial constraints, and religiosity. Design/methodology/approach A wide range of estimation techniques, including pooled ordinary least squares (OLS), fixed effects, system generalized method of moments (GMM) and propensity score matching-difference-in-differences (PSM-DiD), are employed to investigate such issues in a large sample of firms from 31 countries. Findings ESG performance has a positive and significant impact on firm performance. While stakeholder engagement positively moderates this relationship, financial constraints and religiosity negatively moderate it. Interestingly, this positive linkage is driven by environmental and social performance rather than governance performance. Practical implications Firms should proactively engage in ESG initiatives and consider the intervening influences of stakeholder engagement, financial constraints and religiosity in making decisions to invest in ESG activities. Furthermore, our findings can help policymakers understand the financial consequences of ESG practices, which can be helpful in designing new policies to further promote corporate engagement in ESG practices. Originality/value First, our research findings help reconcile the long-standing debate about the value impact of ESG. Second, our paper investigates relatively new aspects of the ESG-firm performance relationship. Third, our study offers more insight into the ESG literature by showing that not all ESG dimensions equally impact firm performance.

Purpose The study compares the impacts of mixed syndication venture capital (VC) investment and private VC (PVC) investment on the transitional performance indicators of intangible assets, fixed assets, liabilities and number of employees in Estonia. It also examines the impact of mixed syndication on investees' sales and profit. Design/methodology/approach This study conducted panel data regression analyses based on the dataset consists of yearly data from 2006 to 2015 for more than 187,000 unlisted firms in Estonia. Findings Results showed that mixed syndication had a significant positive effect on the number of employees of investees but not on investees' sales and profit. PVC investment had a significant positive effect on investee sales but not on the transitional performance indicators of investees. Originality/value The study has two unique research contributions. First, it investigates the impact of syndicated investment on investees' transitional performance indicators in addition to performance indicators. Second, it focuses on Estonia, an emerging country that has somewhat achieved success in fostering information and communications technology startups and is one of the earliest emerging countries to implement a mixed syndication VC investment policy.

Purpose This study aims to unlock the path of growth for sustainable economic development and accomplish the government's vision 2030 by ameliorating the productivity of the manufacturing sector in Laos. Design/methodology/approach This study applied cross-sectional data of 2,009 firms from the national firm survey, namely the Economic Census Survey (ECS), in 2012/13 in addition to employing the stochastic frontier analysis (SFA) to assess the production frontier and factors behind the technical inefficiency to arrive at policy recommendations. Findings The study found that the efficiency level varied across subindustries with an average of 72.51% in full potential production. Out of the five classified groups, Sub4 (chemical and plastic) was found to be the most efficient manufacturer, while the rest in order are Sub1 (food and beverage), Sub5 (furniture and others), Sub2 (garment and textile), and Sub3 (paper and printing), providing the evidence to improve the technical efficiency. This study discovered that the firm's size, accounting system and credit access are crucial to enhancing the production efficiency of all sampling firms. However, these factors might be subject to specific industries. Practical implications For the implication to the business community and policymakers, the findings of this study could be a reference in terms of which areas they should concentrate on to improve the technical efficiency as a part of productivity in the manufacturing industry. For instance, it suggests that firms could improve their production efficiency by introducing the accounting system, laborers' skills (education of managers) and engaging in international trade activities. Additionally, it asks policymakers to help private firms by improving the infrastructure, credit access, training and trade facilitation. Originality/value It is believed that, as the major contribution in Lao literature, this study is the first research applying the largest data from the national survey – the Lao ECS – examining the technical efficiency in the manufacturing sector in the country, and overcoming the gap of the previous research which recruited few policy variables and applied a small sample size in one specific industry. Therefore, the findings of this study impart more insights into the analysis, providing more effective and credible recommendations to policymakers and firms to improve their technical efficiency and, consequently, their competitiveness.

Purpose The purpose of this study is to examine the financial autonomy that affects the financial accounting information quality of public organizations. This study also tests the impact of the financial autonomy on support from leadership. How this impact has affected elements of accounting information systems such as hardware, software, communications technology and chief accountant to support providing the quality of the financial accounting information. Design/methodology/approach The research model is in the SEM form and measurement models are reflective scales so this study applies the PLS-SEM analysis technique on the Smart PLS 3.2.7 software to test the research hypotheses. Analytical data is collected through survey questionnaires with observed variables measured using the typical 7-point Likert scales. The result obtained after cleaning the data includes 164 Vietnamese public organizations with the different levels of the financial autonomy. Findings This research has three primary findings: firstly, FA has a positive direct effect on FAIQ and SL. Secondly, SL influences FAIQ through four mediate variables including AM, HW, SW and CN. Finally, SL also acts as a mediate variable in the relationship of FA and FAIQ. Originality/value This is one of the first empirical studies to examine the role of financial autonomy in leadership support to improve the quality of the accounting information in the public sector in the context of the Vietnamese government is promoting the financial autonomy of public organizations.

Purpose Financial health of a corporation is a great concern for every investor level and decision-makers. For many years, financial solvency prediction is a significant issue throughout academia, precisely in finance. This requirement leads this study to check whether machine learning can be implemented in financial solvency prediction. Design/methodology/approach This study analyzed 244 Dhaka stock exchange public-listed companies over the 2015–2019 period, and two subsets of data are also developed as training and testing datasets. For machine learning model building, samples are classified as secure, healthy and insolvent by the Altman Z-score. R statistical software is used to make predictive models of five classifiers and all model performances are measured with different performance metrics such as logarithmic loss (logLoss), area under the curve (AUC), precision recall AUC (prAUC), accuracy, kappa, sensitivity and specificity. Findings This study found that the artificial neural network classifier has 88% accuracy and sensitivity rate; also, AUC for this model is 96%. However, the ensemble classifier outperforms all other models by considering logLoss and other metrics. Research limitations/implications The major result of this study can be implicated to the financial institution for credit scoring, credit rating and loan classification, etc. And other companies can implement machine learning models to their enterprise resource planning software to trace their financial solvency. Practical implications Finally, a predictive application is developed through training a model with 1,200 observations and making it available for all rational and novice investors (Abdullah, 2020). Originality/value This study found that, with the best of author expertise, the author did not find any studies regarding machine learning research of financial solvency that examines a comparable number of a dataset, with all these models in Bangladesh.