Purpose While Austrian foreign direct investment (FDI) in Asian economies experiences a rising trend, the following question arises: Why does Austria invest more in certain economies over others? This study intends to assess the factors that drive Austrian investment in Asian economies. Design/methodology/approach Based on the ownership, location and internalization framework and the knowledge capital approach, this study hypothesizes that knowledge capital significantly attracts FDI from Austria. Meanwhile, this study applies the panel-corrected standard error method to analyze data for 11 Asian economies from 1990 to 2022. Findings After considering endogeneity, the results show a positive and significant correlation between expenditure in research and development per gross domestic product (GDP) in the host economies and FDI inflow from Austria. In addition, the study reveals that factors such as market size, trade openness and natural resources in the host economies significantly influence Austria’s FDI, which indicates that Austrian investors fall into the three main FDI typologies: market-seeking, resources-seeking and efficiency-seeking. Originality/value This study fills the literature gap by becoming the first to analyze the determinants of Austrian FDI in Asian economies, thus enriching our understanding of Austria’s global investment pattern.

Purpose The development of green manufacturing has become essential to achieve sustainable development and modernize the nation’s manufacturing and production capacity without increasing nonrenewable resource consumption and pollution. This study investigates the effect of green industrial practices on technical efficiency for Thai manufacturers. Design/methodology/approach The study uses stochastic frontier analysis (SFA) to estimate the stochastic frontier production function (SFPF) and inefficiency effects model, as pioneered by Battese and Coelli (1995). Findings This study shows that, on average, Thai manufacturing firms have experienced declining returns-to-scale production and relatively low technical efficiency. However, it is estimated that Thai manufacturing firms with a green commitment obtained the highest technical efficiency, followed by those with green activity, green systems and green culture levels, compared to those without any commitment to green manufacturing practices. Finally, internationalization and skill development can significantly improve technical efficiency. Practical implications Green industry policy mixes will be vital for driving structural reforms toward a more environmentally friendly and sustainable economic system. Furthermore, circular economy processes can promote firms' production efficiency and resource use. Originality/value To the best of the authors' knowledge, this study is the first to investigate the effect of green industry practices on the technical efficiency of Thai manufacturing enterprises. This study also encompasses analyses of the roles of internationalization, innovation and skill development.

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 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.

"Purpose – This paper develops a new model of policy development for the creative industries in a transitional economy setting. These sectors could potentially make a significant contribution to the continuing growth of the Vietnamese economy; however, they are currently held back by a lack of policies designed to support them Design/methodology/approach – The paper uses data collected from a mixed-methods study of the creative and cultural sectors in Vietnam. The paper combines quantitative results from a mapping project with ethnographic observations and several qualitative interviews to identify the policy needs of the sector. Findings – The paper develops the INCITE model of policy development composed of four parts: education and human resources, infrastructure, intellectual property rights and freedom of speech. Originality/value – The paper contributes to our understanding of the kinds of policies needed to support the creative industries by exploring their development in an economy transitioning from a state planned economy to a market-driven one."