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We adopt an information risk view and argue that higher levels of pledge risk incurred by insiders incentivize opportunistic financial disclosure and impair the quality of information available to analysts to forecast firm performance.

We sample Chinese listed companies from 2010 to 2022. Following the literature, we apply established models to measure and test analysts’ forecasting accuracy/dispersion related to controlling shareholders pledging equity and the amount of margin call pressure. Analyst characteristics and nonfinancial disclosures proxied by CSR reports are also examined as factors likely to influence the relationship between pledge risk and analysts’ forecast quality.

We find that analysts’ earnings predictions are less accurate and more dispersed as the proportion of shares pledged (pledge ratio) increases and in combination with greater margin call pressure. Pledge ratios are significantly associated with several information risk proxies (i.e. earnings permanence, accruals quality, audit quality, financial restatements, related party transactions and internal control weaknesses), validating the channel through which equity pledges undermine analysts’ forecast quality. The results also demonstrate that forecast quality declines for a wide variety of analysts’ attributes, including high- and low-quality analysts and analysts from small and large brokerage firms. Importantly, nonfinancial disclosures, as proxied by CSR reporting, improve analysts’ forecasts.

We extend the literature by demonstrating that incremental pledge risk increases non-diversifiable information risk; all non-pledging shareholders pay a premium through more diverse and less accurate earnings forecasts. Our study provides important policy implications with economically significant costs to investors associated with insider equity pledges. Our results highlight the benefits of nonfinancial disclosures in China, which has implications for the current debate on the global convergence of CSR reporting.

Artificial Intelligence (AI) is widely used in higher education teaching and learning. This trend of AI integration will continue to emerge within the education system. To stay ahead of the curve in the education realm, educators shall involve the usage of AI in teaching activities, assessments, and learning experiences. This book chapter aimed to highlight the challenges faced by higher education while proposing refined solutions to educators in involving the usage of AI tools in their teaching pedagogy. The suggested solutions were drafted from three different perspectives of AI to be integrated into its curriculum, namely the 3As triadic approach. Activities are identified as the essential asset to fostering a collaborative learning environment encouraging students to make use of cocreated AI in activities creation. Meanwhile, assessment preparation requires rigorous restrictions on AI usage to portray higher order thinking skills while abiding by ethical guidelines and legal framework. Academic content is prudent in ensuring latest findings and research are credible while bridging the gap between interactive academic materials with students' learning outcomes. Concurrently, we highlighted several approaches to achieve AI collaborative learning and enhance students' experience.

Artificial Intelligence (AI)-powered technologies are revolutionising the landscape of education, ushering in a myriad of possibilities and challenges. This article delves into the dual nature of AI-driven tools in education, spotlighting pivotal advancements like automated grading, personalised learning algorithms, online monitoring, content filtering, AI-based learning tools, Chat Generative Pre-trained Transformer (ChatGPT) and standardised testing (ST) platforms. Ultimately, the examination reveals a spectrum of advantages, risks and considerations associated with AI-driven educational applications. Employing the PRISMA protocol, this study systematically reviews peer-reviewed literature concerning the implementation and ethical implications of AI in higher education. The analysis incorporates 36 scholarly articles, uncovering the entrepreneurial advantages of AI, such as tailored learning experiences, self-assessment opportunities, heightened efficiency, skill enhancement and reduced educational disparities. Concurrently, the research identifies potential hazards, including user profiling, plagiarism, academic integrity breaches and excessive reliance on technology that may hinder creative learning dynamics. Crucial concerns emerge, encompassing the possible devaluation of educators' roles, privacy issues inherent in personalised learning platforms and the intrusive nature of online surveillance. Additionally, the study highlights biases embedded within AI algorithms and apprehensions regarding job displacement within the academic community. To steer AI integration responsibly within higher education, the investigation explores ethical frameworks and models, offering pragmatic suggestions for institutions. Recommendations advocate for a balanced approach, emphasising judicious AI utilisation and the formulation of institutional policies. This chapter's distinctiveness lies in its innovative stance, striving to reconcile the technical and entrepreneurial benefits of AI applications with the preservation of creativity in higher education contexts.

The main purpose of this chapter is to study and analyze the impact of metaverse (mixed reality) on health tourism in the major tourist destinations in India and the global market. This chapter is made as a case study with detailed mentions of factors for tourism promotion for metaverse, sustainability, and crisis management. This chapter has tried to explain the role of the metaverse in tourism marketing and the cognitive level of influence the metaverse possesses among tourists which makes them prefer repeated visits. Also, the chapter tried to assess the existing conditions in the tourism industry which positively or negatively impact the health sector due to the role of metaverse. This chapter ends with giving inspirational notes to future researchers to examine the conceptual understanding of metaverse as a major tool for destination tourism emphasizing health, wellness, and happiness.

This chapter critically explores the transformative role of Artificial Intelligence (AI) in higher education, highlighting both the opportunities it presents and the challenges it imposes. Focused on pedagogical enhancement, it details how AI-driven adaptive learning systems, tutors, and curriculum adjustments contribute to personalized education. It further examines AI's efficiency in streamlining administrative tasks, like enrollment and support services, while addressing significant ethical concerns, including data privacy and algorithmic bias. Moreover, the impact of AI on research and development is discussed, showcasing its potential to revolutionize scientific discovery through advanced data analysis. A strategic framework is proposed for stakeholders to effectively navigate AI integration and digital transformation, emphasizing a balanced approach that prioritizes innovation, ethical considerations, and quality in the digital era of higher education.

Charitable giving in China has moved from being subjected to government attention and public skepticism to receiving government encouragement and public support. The role played by political connections in philanthropy is indisputable, although very few studies have explored their association from the perspective of the country’s first Charity Law of 2016. This study aims to contribute to the ongoing debate about the 2016 Charity Law and offers an understanding of the future trends in corporate charitable giving.

Using empirical analysis of data collected from listed companies in China, this study analyzes the impact of political connections on corporate charitable giving before and after the 2016 Charity Law. The study adopts three leading theories from previous research into corporate charitable giving and political connections: corporate social responsibility, resource dependence theory and stakeholder theory. A conceptual framework is outlined, and hypotheses are formulated accordingly.

The results show that political connections have a substantial positive impact on corporate charitable giving, both before and after the implementation of the 2016 Charity Law, which has significantly promoted and increased the amount and proportion of charitable giving. Although the 2016 Charity Law attempted to weaken the political connections of enterprises, the influence of political connections on corporate charitable giving has proved difficult to diminish or eliminate, as charity is dominated by the state.

This study explores the association between political connections and corporate charitable giving from the perspective of China’s Charity Law of 2016.

The purpose of our study is to investigate the effects of politically-connected boards (PCBs) on over-(under-)investment in labor. We also examine the impacts of the supervisory board (SB)’s optimal tenure on the association between PCBs and over-investment in labor.

We constructed the proxy for PCBs using a dummy variable set to 1 (one) if a firm has politically-connected boards and zero (0) otherwise. For the robustness check, we used the number of politically-connected members on the boards as the proxy for PCBs.

We find that the presence of PCBs reduces over-investment in labor. Consistent with our prediction, we found no significant association between PCBs and under-investment in labor. We also find that the SB with optimal tenure strengthens the negative association between PCBs and over-investment in labor. In our channel analysis, we find that the presence of PCB mitigates over-investment in labor through a higher dividend payout ratio.

Due to the unavailability of data in firms’ annual reports regarding the number of poorly-skilled and highly skilled employees, we were not able to examine the effect of low-skilled and high-skilled employees on over-investment in labor. Also, we were not able to examine over-(under-)investment in labor by drawing a distinction between general (generalist) and firm-specific human capital (specialist) as suggested by Sevcenko, Wu, and Kacperczyk (2022). Generally, it is more difficult for managers to hire highly-skilled employees, specialists in particular, thereby driving the choice of either over- or under-investing in the labor forces. In addition, in the firms’ annual reports, there is no information regarding temporary employees. Therefore, if and when such data become available, this would provide another avenue for future research.

Our study offers several practical implications and insights to stakeholders (e.g. insiders or management, shareholders, investors, analysts and creditors) in the following ways. First, our study highlights significant differences between capital investment and labor investment. For instance, labor investment is considered an expense rather than an asset (Wyatt, 2008) because, although such investment is human capital and is not recognized on the firm’s balance sheet (Boon et al., 2017). In addition, labor investment is characterized by: its flexibility which enables firms to make frequent adjustments (Hamermesh, 1995; Dixit & Pindyck, 2012; Aksin et al., 2015), its non-homogeneity since every employee is unique (Luo et al., 2020), its direct impact on morale and productivity of a firm (Azadegan et al., 2013; Mishina et al., 2004; Tatikonda et al., 2013), and its financial outlay which affects the ongoing cash flows of a firm (Sualihu et al., 2021; Khedmati et al., 2020; Merz & Yashiv, 2007). Second, our findings reveal that the presence of PCBs could help to reduce over-investment in labor. However, if managers of a firm choose to under-invest in labor in order to obtain better profit in the short-term through cost saving, they should be aware of the potential consequences of facing a financial loss when a new business opportunity suddenly arises which requires a larger labor force. Third, our findings help stakeholders to re-focus on the labor investment. This is crucial due to the fact that labor investment is often neglected by those stakeholders because the expenditure of labor investment is not recognized on the firm’s balance sheet as an asset. Instead, it is written off as an expense in the firm’s income statement. Fourth, our findings also provide insightful information to stakeholders, suggesting that an SB with optimal tenure is more committed to a firm, and this factor plays an important role in strengthening the negative association between PCBs and over-investment in labor.

First, our findings provide a valuable understanding of the effects of PCBs on over-(under-)investment in labor. Stakeholders could use information disclosed in the financial statements of a publicly-listed firm to determine the extent of the firm’s investment in labor and PCBs, and compare this information with similar firms in the same industry sector. Second, our findings give a better understanding of the association between investment in labor and political connections , which are human and social capital that could determine the long-term survival and success of a firm. Third, for shareholders, the appointment of board members with political connections is an important strategic decision to build political capital, which is likely to have a long-term impact on the financial performance of a firm; therefore, it requires thoughtful consultation with firm insiders.

Our findings highlight the role of PCBs in reducing over-investment in labor. These findings are significant because both investment in labor and political connections as human and social capital can play an important role in determining the long-term survival and success of a firm.

Intellectual capital (IC) and venture capital (VC) play an important role in enterprise development. While the literature has investigated the relationship between IC and the profitability of companies, the relationship among IC, VC and enterprise value (EV) is still not well understood.

Drawing insights from the literature, we develop a few testable hypotheses about the relationships among IC, VC and EV. Using the panel data of companies listed in the Chinese stock market from 2009 to 2019, we employ fixed-effects regression models to test these hypotheses.

We find that IC has a significant positive effect on long-term EV. VC is found to have a positive direct effect on long-term EV but has a negative direct effect when its moderating effect with IC is considered. To explain this finding, we develop a simple economic model and provide an over-investment perspective.

We believe this paper can shed light on pro-venture investment policies in China, as well as provide indications for similar policies around the world.

Artificial Intelligence (AI) in education is a rapidly emerging technology that has revolutionised teaching and learning, administrative tasks and research in higher education. The emergence of AI in higher education has impacted the evolving roles of faculty and students, how data is examined and how results are delivered. In this chapter, the different aspects of the role of AI and the transformative power of AI in both academic and administrative spheres are revealed. The case studies presented and future directions reveal AI's capability in transforming education and preparing for an AI-driven workforce. The personalisation of learning experiences, automation of administrative tasks, enhancement of research and impact on instructional design, all made possible through AI, reveal the possibility of tackling long-standing challenges in education, such as accessibility, engagement and efficiency. In the AI-enhanced future that higher education institutions navigate, it is vital to hold on to the principle that AI should be used as a tool for empowerment, innovation and transformation, ensuring that learning goes through the required progress.

Drawing on the reciprocity principle of social exchange theory situated within Service-dominant Logic, this study aims to examine how customers’ perception of knowledge sharing in co-production, their inherent scepticism and prosocial orientation relate to their willingness to co-create and provide feedback on services. The authors also explored the interplay between these factors to identify conditions in configurations comprising scepticism, which may help navigate its adverse effects.

The authors surveyed 556 online and offline mobile payment service users. They used a combination of partial least squares structural equation modelling (PLS-SEM) to assess the relationships among variables, and fuzzy-set qualitative comparative analysis (fsQCA) to identify configurations associated with feedback behaviour.

The study determined that customer perception of co-production knowledge sharing is positively associated with willingness to co-create and feedback behaviour. Additionally, prosocial orientation positively affects this relationship, while scepticism has an adverse effect. Willingness to co-create mediates the relationship between customer perception of co-production knowledge sharing and feedback behaviour. The fsQCA findings revealed configurations for potentially navigating doubts regarding feedback. To encourage valuable customer feedback, businesses may consider promoting a collaborative and supportive atmosphere, emphasising shared advantages or building trust even among hesitant and doubtful individuals.

This study uniquely examines how both prosocial tendencies and scepticism relate to customer feedback behaviour in co-creation by using a hybrid PLS-SEM/fsQCA approach to identify co-existing conditions in configurations comprising scepticism that may help navigate its adverse effects and leverage customer feedback for business improvement.