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This paper aims to perform empirical analysis to test whether less severe agency conflict between managers and controlling shareholders may improve family firms’ corporate and stock liquidity, compared to non-family firms.

The authors use the ordinary least square and two-stage generalized method of moments regression analyses. They also use match-paired design for robustness check.

Focusing on Standard & Poor’s 500 firms, the authors find that family firms are more conservative by hoarding more corporate liquid assets (as measured by accounting balance sheet liquidity ratios) than their peer non-family firms to prevent underinvestment from external costly finance. These family firms also exhibit higher level of stock liquidity and lower liquidity risk as measured by effective bid–ask spread than non-family firms. The results are consistent with the motivation that organizations (i.e. family firms in this study) whose shareholders can efficiently monitor that their managers are associated with higher level of corporate liquidity and stock liquidity, and lower level of liquidity risk.

This study contributes to the literature on liquidity (both corporate liquidity and stock liquidity) and ownership structure, more broadly corporate governance. It provides insights into corporate and stock liquidity within a unique ownership context: family firms versus non-family firms. Family firms in the USA are subject to both Type I (agency problems arising from the separation of ownership and control) and Type II agency problems (agency conflict arising between majority and minority shareholders). It is an ongoing debate whether family firms suffer more or less agency problems from one type versus the other than non-family firms. The finding that family firms have higher corporate and stock liquidity is consistent with that family firms being subject to less severe agency conflict due to separation of ownership from control.

This chapter draws from a three-year ethnographic study focused on the educational and community interactions among working- and middle-class ethnic Chinese immigrants in a mid-western town in the United States. Aihwa Ong (1999) argues that “Chineseness” is a fluid, cultural practice manifested within the Chinese diaspora in particular ways that relate to globalization in late modernity, immigrants’ cultural background, their place in the social structure in their home society, and their new social class status in the context they enter. The study extends research focused on the complexities of social reproduction within larger global flows of Chinese immigrants. First, we describe how Chinese immigrants’ social status in their countries of origin in part shapes middle and working-class group’s access to cultural capital and positions in the social structure of their post-migration context. Second, we trace groups’ negotiation of their relational race and class positioning in the new context (Ong, 1999) that is often invisible in the processes of social reproduction. Third, we describe how both groups must negotiate national, community, and schooling conceptions of the model minority concept (Lee, 1996) that shapes Asian-American’s lived realities in the United States; yet the continuing salience of their immigrant experience, home culture, and access to cultural capital (Bourdieu, 2007) means that they enact the “model minority” concept differently. The findings suggest the complexity of Chinese immigrants’ accommodation of and resistance to normative ideologies and local structures that cumulatively contribute to social reproduction on the basis of class.

The purpose of this study is to examine how pension risk shifting can be explained and constrained by debt component in chief executive officer (CEO) compensation and to explore whether a CEO’s relatively large holdings of inside debt to equity compensation would result in a well-funded pension status.

The authors use two-stage least-squares model to control the potential unobserved and uncontrolled firm characteristics that could drive both CEO inside debt determinants and firm pension funding status.

This paper finds a positive relationship between the CEO inside debt ratio and firm funding status. Additional tests show a positive association between the CEO inside debt ratio and financial slack measures and a negative association between this ratio and financial constraint measure. Additional evidence also shows that the CEO inside debt ratio is negatively associated with other contemporaneous investment activities. Overall, the findings suggest that CEO inside debt creates managerial incentives that can affect pension funding decisions and decrease pension risk shifting.

One of the difficulties facing the compensation literature is the unobservable nature of the entire compensation negotiation and design process. Pension funding status is another challenging topic given that management has discretion over the pension assumptions and the calculations themselves are complicated. Therefore, the determinants of pension status used in this paper are not all-inclusive. Although a two-stage least-squares methodology is applied to mitigate endogeneity, it is still possible that an omitted variable problem exists in both cases.

This study provides direct evidence of the executive debt-like compensation’s effect on pension risk-shifting behavior and pension funding decisions and also contributes to the literature that investigates the association between CEO inside debt and firm risk by examining the trade-off between pension funding and other contemporaneous investment activities.

This paper aims to examine the effect of dividend regulation on cost stickiness (i.e. the asymmetric change in firm expense between sales increase and sales decrease) and explore the underlying mechanism.

Based on the quasi-natural experiment of the Guideline for Dividend Policy of Listed Companies issued by the Shanghai Stock Exchange (SSE) in 2013, the authors employ a difference-in-difference model to investigate the impact of dividend regulation on cost stickiness.

The authors find that the cost stickiness of treatment group firms has decreased significantly when compared with control group firms after the dividend regulation. Moreover, this effect is more pronounced among firms in lower marketization regions, in lower competition industries and those with less analyst coverage and lower cash flow levels. Further analyses show that dividend regulation reduces the cost stickiness of firms by mitigating agency problems. Finally, the conclusion holds after several robust tests, including controlling for firm fixed effect, propensity score matching (PSM), placebo test and reconstruction of expense variable.

This paper confirms that dividend regulation serves an important role in corporate governance, which reduces firms' agency costs and thereby decreases cost stickiness. The conclusions shed light on the dividend policies of listed companies and capital market regulation in the future.

For the provision of smart library services to end users, tools of the Internet of Things (IoT) play a significant role. The study aims to discover the factors influencing the adoption of IoT in university libraries, investigate the impact of IoT on university library services and identify challenges to adopt IoT applications in university libraries.

A systematic literature review was carried out to address the objectives of the study. The 40 most relevant research papers published in the world’s leading digital databases were selected to conduct the study.

The findings illustrated that rapid growth in technology, perceived benefits, the networked world and the changing landscape of librarianship positively influenced the adoption of IoT in university libraries. The study also displayed that IoT supported library professionals to initiate smart library services, assisted in service efficiency, offered context-based library services, provided tracking facilities and delivered effective management of library systems. Results also revealed that a lack of technical infrastructure, security and privacy concerns, a lack of technological skills and unavailability of policy and strategic planning caused barriers to the successful adoption of IoT applications in university libraries.

The study has provided theoretical implications through a valuable addition to the current literature. It has also offered managerial implications for policymakers to construct productive policies for the implementation of IoT applications in university libraries for the attainment of fruitful outcomes. Finally, the study provides a baseline for understanding the adoption of IoT in academic libraries.

The purpose of the study is to analyse the impact of COVID-19 on the hospitality industry during the rise of worldwide pandemic crises using Twitter analysis. The study is based on 57,794 English-language tweets mined from Twitter from 1 April 2020 to 15 October 2020. Based on thematic and sentiment analysis, the study found that overall sentiments expressed on Twitter were negative. This chapter contributes to existing knowledge about the COVID-19 crisis and broadens the respondents’ understanding of the potential impacts of the crisis on the most vulnerable tourism and hospitality industry. This research emphasises the sustainable revival of the hospitality industry.

The use of artificial intelligence (AI) in management research is still nascent and has primarily focused on content analyses of text data. Some method scholars have begun to discuss the potential benefits of far broader applications; however, these discussions have not led yet to a wave of corresponding AI applications by management researchers. This chapter explores the feasibility and the potential value of using AI for a very specific methodological task: the reliable and efficient capturing of higher-level psychological constructs in management research. It introduces the capturing of basic emotions and emotional authenticity of entrepreneurs based on their macro- and microfacial expressions during pitch presentations as an illustrative example of related AI opportunities and challenges. Thus, this chapter provides both motivation and guidance to management scholars for future applications of AI to advance management research.

Cryptocurrency, an important application of blockchain technology, has gradually circulated, and its use has become widespread. While cryptocurrency is growing rapidly, potential risks are simultaneously emerging. Users thus may abandon their usage behavior of cryptocurrency, hindering the future development of cryptocurrency. While prior studies focus more on the intention to use cryptocurrency in the pre-adoption phase, less studies pay attention to discontinuance usage intention in the post-adoption phase. To fill this knowledge gap, this stfudy aims to explore factors that cause discontinuance usage intention regarding cryptocurrency.

Based on the net valence framework theoretically grounded on the theory of reason action, a dilemmatic dual-factor model is proposed to figure out cryptocurrency users' discontinuance usage intention from the perceived risk and perceived benefit. This study identifies four potential risks and three potential benefits that affect perceived risk and benefit. The model with nine hypotheses were developed, and research data were collected by a survey method. A total of 343 valid responses were received, and PLS-SEM with SmartPLS was utilized to test the nine hypotheses, with seven hypotheses supported empirically.

Our findings demonstrate that financial, legal and operational risks are critical to increase users' perceived risk, and perceived usefulness and seamless transactions play important roles in enhancing users' perceived benefit. Moreover, while perceived risk can increase users' discontinuance usage intention to cryptocurrency, perceived benefit can mitigate such intention.

This study contributes nascent knowledge to the literature by examining factors that influence discontinuous usage intention in regard to cryptocurrencies, to firms that have issued or attempted to issue cryptocurrencies and to the potential users of cryptocurrencies by adjusting the mode of operation and investment strategies and reducing user costs, achieving a win-win situation for firms and users.

Introduction: The insurance industry has unprecedented growth, and the demand for insurance has outgrown in the recent past due to the prevailing pandemic. The companies have a large base of the data set at their disposal, and companies must appropriately handle these data to come out with valuable solutions. Data mining enables insurance companies to gain an insightful approach to map strategies and gain competitive advantage, thus strengthening the profits that will allow them to identify the effectiveness of back-propagation neural network (BPNN) and support vector machines (SVMs) for the companies considered under study. Data mining techniques are the data-driven extraction techniques of information from large data repositories, thus discovering useful patterns from the voluminous data (Weiss & Indurkya, 1998).

Purpose: The present study is performed to investigate the comparative performance of BPNNs and SVMs for the selected Indian insurance companies.

Methodology: The study is conducted by extracting daily data of Indian insurance companies listed on the CNX 500. The data were then transformed into technical indicators for predictive model building using BPNN and SVMs. The daily data of the selected insurance companies for four years, that is, 1 April 2017 to 21 March 2021, were used for this. The data were further transformed into 90 data sets for different periods by categorising them into biannual, annual, and two-year collective data sets. Additionally, the comparison was made for the models generated with the help of BPNNs and SVMs for the six Indian insurance companies selected under this study.

Findings: The findings of the study exhibited that the predictive performance of the BPNN and SVM models are significantly different from each other for SBI data, General Insurance Corporation of India (GICRE) data, HDFC data, New India Assurance Company Ltd. (NIACL) data, and ICICIPRULI data at a 5% level of significance.

This study reveals the green building development path and analyzes the optimal government subsidy equilibrium through evolutionary game theory and numerical simulation. This was done to explore the feasible measures and optimal incentives to achieve higher levels of green building in China.

First, the practice of green building in China was analyzed, and the specific influencing factors and incentive measures for green building development were extracted. Second, China-specific evolutionary game models were constructed between developers and homebuyers under the market regulation and government incentive mechanism scenarios, and the evolutionary paths were analyzed. Finally, real-case numerical simulations were conducted, subsidy impacts were mainly analyzed and optimal subsidy equilibriums were solved.

(1) Simultaneously subsidizing developers and homebuyers proved to be the most effective measure to promote the sustainability of green buildings. (2) The sensitivity of developers and homebuyers to subsidies varied across scenarios, and the optimal subsidy level diminished marginally as building greenness and public awareness increased. (3) The optimal subsidy level for developers was intricately tied to the building greenness benchmark. A higher benchmark intensified the developer’s responsiveness to losses, at which point increasing subsidies were justified. Conversely, a reduction in subsidy might have been appropriate when the benchmark was set at a lower level.

The expeditious advancement of green buildings holds paramount importance for the high-quality development of the construction industry. Nevertheless, the pace of green building expansion in China has experienced a recent deceleration. Drawing insights from the practices of green building in China, the exploration of viable strategies and the determination of optimal government subsidies stand as imperative initiatives. These endeavors aim to propel the acceleration of green building proliferation and materialize high-quality development at the earliest juncture possible.

The model is grounded in China’s green building practices, which makes the conclusions drawn more specific. Furthermore, research results provide practical references for governments to formulate green building incentive policies.