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The purpose of this paper is to explore the matching relationship between factor endowment and industrial structure, and its impact on economic growth.

The assortative matching method is developed to quantitatively measure the matching between factor endowment and industrial structure. A series of empirical tests are then carried out to evaluate the impact on the economic development of the matching.

1) The matching between factor endowment and industrial structure has a significantly positive impact on economic growth. (2) Economic growth reaches its maximum when the gap between the two sectors narrows to zero. (3) This effect is particularly significant for countries with higher GDP per capita and GNI per capita. (4) The results remain robust after employing a series of tests.

Aggressive industrial policies are not desirable. The optimal industrial structure is the one that complied with the comparative advantage of the given factor endowment in the economy.

So far, there has been a significant lack of an applicable quantitative indicator for measuring the matching between factor endowment and industrial structure, which is essential for conducting empirical tests and providing evidence for related economic theories.

The purpose of this study is to explore ways to establish an effective coordinative mechanism to ensure the effective practice of career coaching in innovativeness and creativity training programs in higher education.

This study aims to explore a successful case – that of Wenzhou University College – to elucidate how to establish an effective coordinative mechanism. The Delphi method is an interview approach for obtaining and analyzing case data.

According to research results, first, the causes of conflict were found and an attempt was made to solve these by using a personal system and training program structure revision; the main reasons for the conflicts are an overemphasis on “experts’ experience leading” and a “cognitive gap” between experts as coaches and teaching faculties; second, to decrease conflict, a revision of the training program structure will aid the improvement in the specialization for experts and teaching faculties; and third, revision of one’s personal system requires the teaching faculties to be from within the industries or companies facilitating the process of these faculties gaining practical experience; moreover, it will be easier if experts are the ones who are communicating how to re-design training programs and cooperative modes.

This study provides a coordinative mechanism for the effective practice of innovativeness and creativity training in higher education institutions. On the basis of research results, higher education institutions can effectively develop a training program of innovativeness and creativity based on the concept of career coaching.

The primary purpose is to empower financial institutions in AI integration decisions. By combining QSFS and the Golden Cut technique, the study establishes a robust foundation for assessing AI progress effects, aligning implementation with performance goals, and promoting technical innovation. Dimensions explored include AI-related workforce competency, technological adaption, and ethical AI practices, crucial components within the BSC framework for technological innovation.

This study employs a distinctive approach, integrating the Balanced Scorecard (BSC) framework with Quantum Spherical Fuzzy Sets (QSFS) and the Golden Cut approach to explore the dynamic landscape of AI deployment. The integration addresses uncertainties, enhancing impact assessment accuracy amid ambiguity associated with AI outcomes. QSFS and the Golden Cut technique together facilitate precise identification of thresholds and crucial values.

The research delves into the intricate relationship between enduring financial stability and AI progress, recognizing technology's crucial influence on financial decision-making. Findings underscore technology's significant impact on financial institutions' AI integration decisions. This novel approach provides a strong quantitative basis, offering insights into workforce competency, technological adaption, and ethical AI practices.

Despite valuable contributions, the study acknowledges limitations, such as potential biases and generalizability concerns, emphasizing the need for cautious interpretation and suggesting future research directions. Recognizing the research's boundaries and complexities in studying AI deployment in financial institutions underscores the need for ongoing exploration.

The research's originality lies in presenting an innovative methodology, integrating BSC, QSFS, and the Golden Cut, providing a unique perspective for decision-making. Contributions extend beyond academia, offering practical insights to enhance AI strategic implementation in the financial industry. This novel approach enriches the technology and finance discourse, fostering theoretical and practical advancements.

This paper aims to conduct a systematic literature review on the fintech services and financial inclusion of the developing nations that particularly focuses on lower middle-income group nations (LMIGN) and upper middle-income group nations (UMIGN) to highlight the research areas that have not received attention and present opportunities for future research.

This paper adopts a systematic approach to examine 65 research articles published from 2016 to 2021, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

The study identifies research gaps in two key themes: backward and outward linkages. In backward linkages, the literature on UMIGN should pay attention to the behavioural patterns associated with lending, investment and market provision-related fintech services. Further research is needed to understand the relationship between fintech services on the usage and quality dimension of financial inclusion in both LMIGN and UMIGN. For outward linkages, future research work should explore the role of fintech and financial inclusion in the development of LMIGN. This study provides valuable insights and guides future research directions by comprehensively mapping the existing studies.

This study does not use quantitative tools, such as meta and bibliometric analysis, to validate the findings.

This research paper offers new perspectives that introduce a novel framework for analysing literature on fintech, financial inclusion and its impact on the overall development of UMIGN and LMIGN.

By investigating trust in the organization as a mediator and traditionality as a moderator, this study aims to examine the effect perceived organizational exploitation poses on frontline hotel employees’ service performance.

A three-wave survey that targets 219 supervisor–subordinate dyads from four Chinese hotels was conducted to test the hypotheses. The authors used SPSS 20.0 and AMOS 21.0 to analyze the data and verify the theoretical model.

This study found that perceived organizational exploitation exerts a destructive impact on frontline hotel employees’ service performance. Trust in the organization is a full mediator of the link connecting perceived organizational exploitation to service performance. Furthermore, traditionality weakens perceived organizational exploitation’s impact on trust in the organization and subsequent service performance.

The authors’ findings remind hotels to cease exploiting their employees to avoid compromising service performance. Hotels should also endeavor to instill trust among employees toward the hotel and allocate more attention to employees with lower levels of traditionality.

First, to the best of the authors’ knowledge, this study is among the first to explore the impact of perceived organizational exploitation on frontline hotel employees’ service performance. Second, this study reveals a novel mechanism underlying the connection between perceived organizational exploitation and service performance. Finally, this study identifies frontline hotel employees’ traditionality as a vital moderator that mitigates the negative relationships among perceived organizational exploitation, trust in the organization and service performance.

As academic social Q&A networking websites become more popular, scholars are increasingly using them to meet their information needs by asking academic questions. However, compared with other types of social media, scholars are less active on these sites, resulting in a lower response quantity for some questions. This paper explores the factors that help explain how to ask questions that generate more responses and examines the impact of different disciplines on response quantity.

The study examines 1,968 questions in five disciplines on the academic social Q&A platform ResearchGate Q&A and explores how the linguistic characteristics of these questions affect the number of responses. It uses a range of methods to statistically analyze the relationship between these linguistic characteristics and the number of responses, and conducts comparisons between disciplines.

The findings indicate that some linguistic characteristics, such as sadness, positive emotion and second-person pronouns, have a positive effect on response quantity; conversely, a high level of function words and first-person pronouns has a negative effect. However, the impacts of these linguistic characteristics vary across disciplines.

This study provides support for academic social Q&A platforms to assist scholars in asking richer questions that are likely to generate more answers across disciplines, thereby promoting improved academic communication among scholars.

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

This study aims to explore the relationship between firm risk, capital structure, cost of equity capital and social and environmental sustainability during the COVID-19 pandemic for companies listed on Tehran Stock Exchange.

To this aim, the information about 190 companies in 2014–2020 was retrieved to be analyzed. The total risk and systematic risk were used as the indicators of company risk; the industry-adjusted earnings price ratio (IndEP) and GORDON were used for the cost of equity capital. To measure social sustainability and environmental sustainability, the procedure suggested by Arianpoor and Salehi (2020) was used.

Underleveraged firms have had a lower total risk during the COVID-19 pandemic, while overleveraged firms have not had a higher risk during this time. In overleveraged firms, using systematic risk has a negative impact on social sustainability during the COVID-19 pandemic. In overleveraged firms, using total risk and systematic risk has a significant negative impact on environmental sustainability in the pandemic. Besides, overleveraged firms have a lower cost of equity capital (IndEP) during COVID-19.

To the best of the authors’ knowledge, no similar study has so far examined the joint impact of COVID-19 and corporate risk on social and environmental sustainability and also the joint impact of COVID-19 and capital structure on the cost of equity. This study contributes to the related literature by providing corporations with insightful post-pandemic directions on capital structure decisions and social and environmental activities. Furthermore, this research and the relevant findings can help understand and develop social responsibility in Iran as a developing country.

Recent studies regarding auditor experience generally focus on auditor overall experience in accounting, auditing, finance and related fields (Hardies et al., 2014), auditor sector and domain experience (Bedard and Biggs, 1991; Hammersley, 2006), auditor experience as CPA (Ye et al., 2014; Sonu et al., 2016) or big N experience (Chi and Huang, 2005; Gul et al., 2013; Zimmerman, 2016) or auditors’ international working experience (Chen et al., 2017). But there is little attention paid to where auditors obtained their experience from? And how do auditors with government experience affect audit quality (AQ)? This paper aims to present the effect of auditors with government experience on AQ.

The authors used Turkish publicly traded firms in Borsa Istanbul between the year 2008 and 2015 to test the hypothesis. The sample comprises 1,067 observations and eight years. Two main proxies of government experience are used in this paper. The first proxy is auditor’s government experience in the past. The second proxy is the continuous variable which is “the logarithmic value of the number of years of government experience”. Further, auditor overall experience in auditing, accounting, finance and other related fields are also used as a control variable. Audit reporting aggressiveness, audit reporting lag and discretionary accruals are used as proxies of AQ. Besides this, the authors adopted the model to estimate the probability of selecting a government-experienced auditor, and they presented the regression results with the addition of inverse Mills ratio.

The main findings are consistent with conjecture. Government-experienced auditors do not enhance AQ. They are aggressive, and they complete audit work slowly and they cannot detect discretionary accruals effectively. Spending more time in a government agency makes them more aggressive and slow, and they do not detect earnings management practices. The Heckman estimation results regarding the variable of interest are also consistent with the main estimation results. In addition, the authors found in predicting government-experienced auditor choice that family firms, domestic firms and firms that reported losses (larger firms, older firms) are more (less) likely to choose government-experienced auditors.

This study has some limitations. The authors used a small sample to test the impact of government-experienced auditors on AQ because of data access problems. Much data used in this study were collected manually. Earnings quality was calculated using only discretionary accruals. Real activities manipulation was not used as the proxy of AQ in this paper. The findings from emerging markets might not generalize to the developed countries because the Turkish audit market is developing compared to Continental Europe or USA.

The findings are considered for independent audit firms. Audit firms may employ new graduates and train them to offer more qualified audit work for their clients. The results do not mean that government-experienced auditors should not work in an audit firm, or that they should not establish an audit firm. It is clear that government-experienced auditors provide low AQ in terms of audit reporting aggressiveness, audit report lag and discretionary accruals. But as they operate more in the independent audit sector, they will become successful and provide qualified audit work. One other thing we can say is that it is perhaps better for government-experienced auditors to work in the tax department of independent audit firms.

This paper tries to fill the gap in the literature regarding the effect of auditor experience on AQ and concentrates on a different type of experience: Auditors with government experience.

Although CLS has been implemented in China for over a century, it is barely known to educators internationally. The purpose of this paper is to synthesize the salient characteristics of Chinese lesson study (CLS), introduce the major themes of this special issue, and invite dialogues about the theories and practices of CLS.

The authors of this editorial paper conducted an extensive literature review on CLS, analyzed the contents and methods of the existing research categorically, compared CLS with other models of LS globally, and present this special issue articles and their major contributions thematically. The theoretical framework of the paper relies mainly on cultural theories and theories on research paradigms such as improvement science, which explain why and how CLS functions in Chinese education system over time.

Existing studies suggest that CLS is a deliberate practice for developing instructional expertise, a research methodology for linking research and practice, and an improvement science for instruction and school improvement system wide. In addition to the theorization of CLS, this special issue also introduces some adaptations of CLS outside of China such as the USA and Italy.

This paper, for the first time, spells out some salient features of CLS, and discusses issues in adapting CLS in other parts of the world. It will enrich the understanding of LS theories and practices in China and promotes trans-cultural development of LS internationally.