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This study estimates returns to higher education across different fields in Thailand for 2019 and 2021, accounting for field selection endogeneity. The comparison offers insights into the impact of the pandemic and other economic shocks on the returns.

The study applies an econometric causal framework, integrating economic theory with empirical analysis using data from Thailand’s socioeconomic surveys in 2019 and 2021. A multinomial treatment effects model with sample selection correction is used to estimate the impact of different fields of study on income, accounting for selection both into higher education in different fields and into employment, addressing potential biases from ability sorting and sample selection.

The study finds variations in returns to education across fields. In 2019, teaching offered the highest returns on average, followed by healthcare. Social sciences, business and computer-related fields showed moderate returns, while the combined group of science, agriculture, engineering and architecture had non-significant returns, indicating a low weighted average across these diverse fields. In 2021, healthcare exhibited the highest return due to pandemic-driven demand. Across both years, controlling for occupation reduced the estimated returns by approximately 50%, highlighting the role of occupational status in mediating educational returns.

This study uniquely applies an econometric causal framework to analyze returns to higher education by field of study in Thailand. It offers insights for policymakers to align educational programs with labor market demand and emphasizes the importance of data-driven decisions in responding to disruptions.

This research aims to examine the link between distributed leadership and team ambidexterity and the sequential mediation of team climate innovation and knowledge management in this relationship.

This study draws on a sample of 546 knowledge workers involved within 157 service research and development (R&D) teams of knowledge-intensive firms (KIFs) and uses partial least squares-structural equation modelling (PLS-SEM) through SMART PLS 4 to analyse the data collected.

Findings reveal that distributed leadership has a significant direct impact on team ambidexterity. Besides, they indicate that team climate innovation and knowledge management in teams mediate this link. Results also highlight the sequential mediation of team climate innovation and knowledge management in teams, linking distributed leadership to team ambidexterity.

This study explores the relationship between distributed leadership and ambidexterity at the team level and proposes a sequential mediation model linking these variables through team climate innovation and knowledge management in teams. It offers practical insights for KIFs’ managers on the importance of using a distributed leadership approach and building a team climate innovation to motivate R&D teams, encourage dynamic participation in knowledge management practices and cultivate both exploitative and exploratory learning activities.

Green innovation in human-centric smart manufacturing (HSM-GI) has emerged as a new paradigm in innovation management for Industry 5.0. The evaluation analysis method is crucial for measuring the development progress and guiding continual improvements of HSM-GI. Since this process of HSM-GI can be regarded as complex and interactive, a holistic picture is often required to describe the interrelations of its antecedents and consequences. In this respect, this study aims to construct a causality network indicator system and proposes a synergy evaluation method for HSM-GI.

Firstly, based on the Driver force-State-Response (DSR) causal-effect framework, this study constructs a holistic indicator system to analyze the interactions between environmental and human concerns of HSM-GI. Secondly, owing to the imprecision of human cognition and synergy interaction in the evaluation process, a flexible hesitant fuzzy (HF) superiority-inferiority synergetic evaluation method is presented. This method quantifies the strengths of causal relationships and expresses the incentives and constraints attitudes of humans. Finally, the proposed framework is applied to six HSMs in the electronic technology industry.

The driving force and state of the HSM-GI system exhibit an upward trend, while the response continues to decline due to changing market demands. The order and synergy degree have shown an increasing trend during 2021–2023, particularly significant for BOE and Haier Smart Home. HSM-GI systems with higher scores mostly have functional coordination and a coherent synergy structure.

This study demonstrates the proposed approach’s applicability and assists policymakers in formulating targeted strategies for green innovation systems.

The development and presentation of a framework that integrates modern methods for detecting, assessing and mitigating mental health issues in the context of dynamic and adverse changes in social networks.

This viewpoint is based on a literature review of current advancements in the field. The use of causal discovery and causal inference methods forms the foundation for applying all the techniques included in the framework (machine learning, deep learning, explainable AI as well as large language models and generative AI). Additionally, an analysis of network effects and their influence on users’ emotional states is conducted.

The synergy of all methods used in the framework, combined with causal analysis, opens new horizons in predicting and diagnosing mental health disorders. The proposed framework demonstrates its applicability in providing additional analytics for the studied subjects (individual traits and factors that worsen mental health). It also proves its ability to identify hidden factors and processes.

The proposed framework offers a novel perspective on addressing mental health issues in the context of rapidly evolving digital platforms. Its flexibility allows for the adaptation of tools and methods to various scenarios and user groups. Its application can contribute to the development of more accurate algorithms that account for the impact of negative (including hidden) external factors affecting users. Furthermore, it can assist in the diagnostic process.

This study demonstrates the necessary and significant role of national formal institutional frameworks in shaping the quality of e-governance in Asian countries. Moreover, it presents a robust model of e-governance as a necessary and significant driver of sustainable human development.

This study applied the cross-lagged panel method in path modelling and conducted competing model and necessary condition analyses to test the lagged, necessary and positive effects of formal institutions on the level of e-governance and sustainable human development in 45 Asian countries from 2012 to 2022.

Formal governance institutions have necessary direct and indirect (through e-governance development) causal effects on a country’s sustainable human development.

Future studies should explore how informal institutions such as culture, industry and government norms and practices shape the extent of e-governance development and sustainable socio-economic development in Asia and beyond over time.

A renewed focus on the institutional fundamentals of governance and development should be the legislative priority of policymakers and leaders of Asian countries.

Proactive digital citizen engagement in institutional building in respective countries is critical to developing sound, human-development-centred institutional governance in Asia.

The study presents robust necessary condition models that offer more nuanced explanations of the institutional imperatives of enabling Asian countries to strengthen their e-governance towards sustainable human development.

Researchers have worked diligently to find the factors that foster organizational trust, but the causal relationships between the enablers of organizational trust have remained unexplored. Therefore, the purpose of this study is to map and analyze the causal link structures of organizational trust enablers.

Data were gathered from employees working in Indian manufacturing organizations. The decision-making trial and evaluation laboratory (DEMATEL) approach was used to test the cause-and-effect linkages among organizational trust enablers.

The empirical evidence shows that 6 out of 14 enablers are causal, namely, organizational justice, person–organization fit, corporate citizenship, positive leadership behavior, relational quality and job satisfaction.

The findings of the study offer a deeper understanding of identified enablers of organizational trust and their linkages with other outcome enablers. Furthermore, the findings provided in the current study will assist top authorities, practitioners and HR managers in focusing on the select causal organizational trust enablers. In addition, the ranking established in this study will help organizations in directing their efforts and resources toward the few select enablers that help to facilitate other consequent enablers of organizational trust. In other words, the results of the study would help in gaining the advantages of efficiency in fostering trust at work.

By providing the empirically valid causal framework of organizational trust enablers, the present study makes a novel contribution to the field. Also, the findings of this study would help organizational policymakers, HR managers and organizational behavior practitioners in developing a better understanding of inculcating trust at work. Furthermore, the use of these enablers will help to foster a trustworthy environment at work.

This study aims to develop cause-and-effect relationships among the critical success factors (CSFs) of fintech adoption and rank these CSFs based on their importance in the model.

To achieve the objectives of the study, the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) approach was used. The data was collected from 16 experts using a questionnaire.

The findings demonstrated the interrelationships among the CSFs. In total, 16 critical factors were recognized as causal factors, and the remaining eight were considered effect factors. The CSFs were ranked based on their importance in fintech adoption.

This study is novel as it investigates CSFs of fintech adoption using FDEMATEL, and it contributes to understanding the nature of these factors and how they affect fintech adoption. The findings propose a significant basis to deepen fintech adoption and deliver a clue to design a practical framework for fintech adoption.

In this paper, we explore the role of education in household financial technology (FinTech) adoption.

Using representative nationwide household data from the 2017 China Household Finance Survey, we employ the change in China’s compulsory schooling law in the 1980s as an instrumental variable for educational attainment.

We find that among Chinese households, education has statistically significant and economically important effects on the use of various FinTech services, including digital banking, mobile payment, digital wealth management and digital consumer credit. Further analysis indicates that exogeneous increases in education lead to higher levels of financial literacy and social trust, both of which are potential drivers of FinTech adoption. Our findings provide new insights into the importance of education for household financial decision-making and technology adoption.

The contribution of our study is mainly twofold. First, we provide evidence on the role of education in household financial decision making. Second, this study adds to the literature on household adoption of technological innovation in finance. Our findings are also policy-relevant.

This paper examines the effect of bond market development on economic growth of selected developing countries from 1990 to 2020. Previous studies provide inconsistent results on the effect of bond market development on economic growth. Some results reveal positive effects while others show negative effects of bond market development on economic growth. These conflicting findings have motivated research.

The autoregressive distributed lag (ARDL) and co-integration methods are used for analysis. The gross domestic product per capita proxies economic growth while government bond capitalisation and corporate bond capitalisation measure bond market development.

The findings unveil a long-term effect within the series. The results disclose that government bond capitalisation, trade openness and inflation positively affect economic growth while corporate bond capitalisation and domestic credit to the private sector presents negative effects on economic growth.

The results propose that the governments should issue more bonds to raise funds for long-term economic growth initiatives. The governments should promote bond market development such that the corporate bonds issued boost economic growth by limiting lengthy documentations and bottlenecks in the bond market listing and issue procedures. The policymakers and regulatory authorities should implement policies which attract investors and encourage companies' listing in the countries' bond markets.

The study’s findings add value that government bond capitalisation positively impacts economic growth, while corporate bond capitalisation negatively affects economic growth in developing countries.

This paper aims to investigate the causal effects that the cutting-edge US monetary, regulatory, financial regulatory and national security policy uncertainty indices exert on the highly representative S&P500 stock index from January 1985 to August 2022 during crises versus noncrisis periods.

Vector autoregressive methodologies are adopted to trace causality and reverse causality between alternative major sources of uncertainty in the USA and the major US stock index.

Intriguingly, it is revealed that shocks in monetary, regulatory, financial regulatory and national security uncertainties generate negative impacts on the S&P500 in the first two months in crash periods, whereas only the monetary and national security innovations lower this stock index in normal times. Notably, in terms of reverse causality, the S&P500 is found to be influential by lowering all types of uncertainties in normal times while temporarily decreases financial regulation uncertainty in bear markets. Notably, financial regulation is found to exhibit the tightest linkages with the S&P500 index as uncertainty in financial regulation increases competition by new forms of investments (such as cryptocurrencies) and renders the stock market less stable.

This study casts light on the influential factors that generate effects on stock markets during crises among a range of potential uncertainty sources. Moreover, the impact of stock markets on the risk stemming from monetary, regulatory, financial regulatory and national security issues is examined. Providing insights into the interconnectedness of a spectrum of US uncertainties with the US financial markets permits to provide a clearer picture of the interlinkages among difficultly-traced determinants of instability in the financial system and the main representative of this system in the advanced US economy and how these are affected by bear tendencies. This provides a roadmap for better action taking by policymakers and investors during crises through the lens of comparison with normal times.