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Although various stakeholder groups frequently advocate and call for greater heterogeneity among directors and managers, it remains unknown whether team heterogeneity can be beneficial for audit committee to exercise the auditor selection functions. This study aims to address this question.

Drawing on a sample of domestically listed nonfinancial A-share firms in China from 2008 to 2022, the authors empirically examine whether and how firm’s audit committee heterogeneity associates with the selection of auditors.

Firms with higher levels of audit committee heterogeneity are more likely to be associated with lower-quality auditors. Further examination reveals the mediating role of risk-taking: higher levels of heterogeneity are associated with higher levels of risk-taking, influencing firms to employ lower-quality auditors. Moreover, the authors document that increased audit committee heterogeneity is associated with more audit committee meetings and lower audit efficiency, and that hiring lower-quality auditors can influence the market value of firms with high audit committee heterogeneity.

To the best of the authors’ knowledge, this study is the first to examine whether and how audit committee erogeneity associates with the selection of auditors. Moreover, because China is a high-power distance, collectivism-oriented, more relations-based (i.e. guanxi-based) than rules-based society, it is critical to examine the influence of team heterogeneity based on the unique cultural context and transitional nature of China’s business environment.

Although a growing body of research has found evidence of anti-public sector bias – a negative stereotype that the performance of the public sector is worse than that of the private sector, whether this phenomenon is universal across situations is still an unknown topic to be further discussed. The purpose of this study is to examine how citizens assess the public sector in Chinese context, as well as the source and deep logic of this evaluation.

Through two survey experiments (N = 1,375), this paper empirically explores how citizens evaluate the public sector in the Chinese context and the cross-context stability of this attitude. At the same time, two focus group interviews (N = 12) are used to deeply analyze the source and internal logic of this attitude.

In China, there is no anti-public sector bias in the public evaluation of the public sector. On the contrary, there is a certain degree of pro-public sector preference, that is, the public has a more positive view of the public sector than the private sector. We also find that this preference has strong cross-context stability, which will not be affected by different performance information or failure public events with different severity. The study also finds that Chinese citizens' preference for the public sector is not only the product of traditional political culture, but also the result of authoritarian propaganda. More importantly, it is a personal rational choice based on institutional performance.

This study provides a good example to explore how the public views the public sector in countries where the proportion of public sector supply far exceeds that of non-public sector. The results increase the academic understanding of how citizens assess the public sector and why this evaluation comes into being. The results enlighten significance for future theoretical research on the main body of public service providers and their performance views. It also provides micro-evidence of behavioral public performance for cross-sectoral comparison between public sector and private sector.

The objective of this study is to investigate how the implementation of an Emission Trading Scheme (ETS) influences an ETS-regulated firm’s level of earnings smoothness.

Using a staggered difference-in-differences model based on China’s ETS pilots commencing in 2013, this study investigates how the implementation of ETS pilots affects regulated firms’ earnings smoothing relative to non-regulated firms. The sample period spans from 2008 to 2019. This model incorporates time-invariant firm-specific heterogeneity, time-specific heterogeneity, and a series of firm characteristics to establish causality. Robustness tests justify findings.

The results show that after implementing an ETS pilot, regulated firms increase their earnings smoothness relative to non-regulated firms. Regulated firms strategically smooth their earnings to obtain additional financial resources and meet compliance costs arising from an ETS. Further analysis reveals that regulated firms’ earnings smoothing activity is a function of environmental regulations, managerial integrity, and capital market incentives.

This study deviates from past research focusing on the environmental consequences of ETS by indicating that an ETS affects regulated firms’ financial reporting decisions. Specifically, regulated firms resort to earnings smoothing as a short-term exit strategy from financing concerns arising from environmental regulations. This finding expands prior literature primarily focusing on the effect of tax and financial reporting regulations on earnings smoothness. This study also indicates that firms utilize earning smoothing to lower their short-term cost of capital, which enables them to access additional financing at a lower cost and reconfigure their operations to meet stakeholder environmental demands.

Investment decisions hold immense significance for investors and eventually affect their portfolio performance. Investors are advised to weigh the costs and benefits associated with every decision in order to make rational investment decisions. However, behavioral finance research reveals that investors' choices often stem from a blend of economic, psychological and sociological factors, leading to irrationality. Moreover, environmental, social and corporate governance (ESG) factors, aligned with behavioral finance hypotheses, also sway opinions and stock prices. Hence, this study aims to identify how individual equity investors prioritize key determinants of investment decisions in the Indian stock market.

The current research gathered data from 391 individual equity investors through a structured questionnaire. Thereafter, a fuzzy analytic hierarchy process (F-AHP) was used to meet the purpose of the research.

Information availability, representative heuristics belonging to psychological factors and macroeconomic indicators falling under economic factors were discovered to be the three most prioritized criteria, whereas environmental issues within the realm of ESG factors, recommendations of brokers or investment consultants of sociological factors, and social issues belonging to ESG factors were found to be the least prioritized criteria, respectively.

Only active and experienced individual equity investors were surveyed in this study. Furthermore, with a sample size of 391 participants, the study was confined to individual equity investors in one nation, India.

This research has implications for individual investors, institutional investors, market regulators, corporations, financial advisors, portfolio managers, policymakers and society as a whole.

To the best of the authors' knowledge, no real attempt has been made to comprehend how active and experienced individual investors prioritize critical determinants of investment decisions by taking economic, psychological, sociological and ESG factors collectively under consideration.

The purpose of this research is to examine generative artificial intelligence (AI) user continuance intention based on the stimulus-organism-response model.

We adopted a mixed method of structural equation modeling and fuzzy-set qualitative comparative analysis to conduct data analysis.

The results found that generative AI content quality (perceived personalization, perceived accuracy and perceived credibility) and system quality (perceived interactivity, perceived anthropomorphism and perceived intelligence) affect sense of empowerment and satisfaction, both of which further determine continuance intention.

Extant research has identified the effect of flow, trust and parasocial interaction on generative AI user continuance, but it has seldom disclosed the internal decisional process of generative AI user continuance intention. This research tries to fill this gap, and the results enrich the extant research on generative AI user continuance.

This study aims to investigate the trend of research on universities and accountability (UAA) in Southeast Asian (SEA) countries.

A total of 115 journal articles, conference papers, books and book chapters were obtained from the Scopus database spanning the years 1996–2023. These documents were subsequently analyzed using bibliometric methods.

The majority of UAA in SEA documents were published between 2007 and 2023 (106 documents, 92.19%). Scholars from both SEA countries and outside the region, particularly Australia, co-authored UAA in SEA documents. While scholars from Vietnam contributed the highest number of UAA in SEA publications (30 documents), scholars from Australia received the highest number of citations (878 citations). Collaboration between Vietnam and Australia emerged as the most productive partnership in conducting studies on UAA in SEA. Additionally, UAA in SEA documents were published not only in education-related outlets but also in other sectors, particularly in public policy. Furthermore, studies on UAA in different countries exhibited both similar and dissimilar interest keywords.

This study represents the first bibliometric analysis focusing on UAA in SEA literature. The insights and implications derived from this study are valuable for future researchers, university leaders and policymakers.

The present study aims to investigate the impact of internal control effectiveness on supply chain management efficiency (SCME) and capital allocation efficiency for companies listed in the Tehran Stock Exchange (TSE). In addition, it investigates the mediating role of supply chain management efficiency in the relationship between internal controls and capital allocation efficiency.

The data about 191 companies in 2014–2022 were examined. The sales per inventory ratio was used to calculate SCME. The present study also applied the Generalized Method of Moments (GMM) for endogeneity concerns.

The results showed that internal control effectiveness has a significant positive effect on SCME. Moreover, internal control effectiveness and SCME significantly positively affect capital allocation efficiency. SCME has a mediating role in the relationship between internal control effectiveness and capital allocation efficiency. These findings remained robust even after several robustness tests. In addition, this study tested the results' robustness by dividing data into the pre-COVID-19 and post-COVID-19 years. The previous results were also confirmed according to the robustness test of COVID-19.

Challenges in the supply chain often hinder capital allocation efficiency. In addition, enterprises should try to establish strong internal controls to ensure SCME. Therefore, the relationship between internal control effectiveness, SCME and capital allocation efficiency is complex and underscores the importance of robust internal controls in optimizing resource allocation within organizations. Interestingly, this topic has not been extensively researched in accounting and business research, and there is a lack of empirical evidence on these effects. Consequently, this study aims to fill the gap and identify potential opportunities for new research directions.

This paper identifies consumer acceptance criteria of artificial intelligence (AI)-enabled products and services in the business. We first investigate the existing three models. They are the technology acceptance model (TAM), the unified theory of acceptance and use of technology (UTAUT) and the consumer acceptance of technology (CAT). We then discuss the applicability of these three models for AI-enabled products and services. Finally, we outline the shortcomings of the models and propose an AI-enabled product and service acceptance model (AIEPSAM). We also validate the proposed AIEPSAM model with empirical results using primary survey data.

To understand the customer’s point of view on AI applications in products and services, we identify some critical factors and present a conceptual framework of consumers' acceptance criteria based on existing literature, prior research and prominent technology management theories. Then, the study broadens the horizon beyond established principles associated with technology acceptance to accommodate AI-specific factors/variables like data privacy, explainability and apparent opacity of algorithms. In this paper, we propose an AIEPSAM and validate that model with primary survey data.

We argue that although TAM, UTAUT and CAT models are generally applicable to explain consumers' attitudes towards technology, these models alone are insufficient to encompass the entire spectrum of AI-related issues that must not be ignored. The proposed model, namely AIEPSAM, accommodates the limitations of the existing models and modifies the CAT model to make it suitable for the acceptance of AI technology.

We attempt to articulate the consumer acceptance criteria of AI-enabled products and services and discover useful insights, leading to the critical examination of TAM, UTAUT and CAT models and formulating AIEPSAM with validation through primary survey data. This study is not to criticize the TAM and other technology acceptance models but to incorporate AI-specific factors into those models. Through this study, we propose the required modifications in the existing technology acceptance models considering the AI-specific additional factors. The AIEPSAM will assist companies in building AI-enabled products and services and better understanding the technology emergence (TE) and technology opportunities (TO).

Previous research has not considered the impact on human frontline employees (FLEs) of altered employee–customer relationships in the presence of a service robot (i.e. an intrusion challenge), nor how FLEs may respond. The purpose of this study is to explore the task allocation strategies by human frontline employees’ (FLE) work well-being responses within the intrusion challenge.

This study employs a mixed-method approach, whereby an in-depth qualitative study (Study 1, n = 15) is followed by a quantitative field study (Study 2, n = 81).

Results indicate that FLEs experience the intrusion challenge, impacting social, purpose, physical and community well-being. Study 1 reveals that while service robots trigger this challenge, FLEs use them for task allocation to maintain their initial work well-being state. Study 2 shows that using robots instead of colleagues positively affects FLE work well-being.

Service robots, as a task allocation strategy by FLEs, can be used to empower FLEs by assisting them to preserve their work well-being within the intrusion challenge.

This study is one of the first to involve FLEs from a live service robot site, where data is based on personal lived experiences rather than anticipated experiences. This is the first study to investigate how FLEs respond to the intrusion challenge.

The purpose of this study is to present a systematic and comprehensive review of personalized, adaptive and semantic e-learning systems.

Preferred reporting items of systematic reviews and meta-analyses guidelines have been used for a thorough insight into associated aspects of e-learning that complement the e-learning pedagogies and processes. The aspects of e-learning systems have been reviewed comprehensively such as personalization and adaptivity, e-learning and semantics, learner profiling and learner categorization, which are handy in intelligent content recommendations for learners.

The adoption of semantic Web based technologies would complement the learner’s performance in terms of learning outcomes.

The evaluation of the proposed framework depends upon the yearly batch of learners and recording is a cumbersome/tedious process.

E-Learning systems may have diverse and positive impact on society including democratized learning and inclusivity regardless of socio-economic or geographic status.

A preliminary framework of an ontology-based e-learning system has been proposed at a modular level of granularity for implementation, along with evaluation metrics followed by a future roadmap.