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Talent management in higher education institutes is an underexplored topic. Only a small portion of talent management publications is focussed on describing talent management in higher education institutes. In this chapter, we give an overview of the most important topics in the talent management literature in general and link it to what is known about these issues in higher education. It discusses the definition of talent and talent management, the talent management process and the multilevel outcomes of talent management, the fairness and justice issues related to talent management and the importance of embedding the analysis of talent management in its broader organizational and institutional context. In the final part of this introduction chapter, we will explain how the talent management topics are discussed in the subsequent chapters of this book.

The aim of our study is to explore the nexus between the gender composition of board and firm financial performance. We use the data of 114 listed banks from 10 Asian emerging economies. Data were extracted from the DataStream for the year 2012–2021. We apply fixed effect model to analyze the data. In addition, we use generalized method of moments (GMM) to verify our main findings. We find that both proxies of board gender composition which are the proportion of female board members and the percentage of female executives on the board have a significant impact on banks' financial performance. Findings suggest that female representation on board provides more insights of monitoring and optimal advisory capabilities and, therefore, gender-diversified board enhances firm performance. Females are more active in business matters and take more interests to fulfill their responsibilities. The results of our study provide useful signals for corporate and regulatory policymakers. Board gender disparities between enterprises should be better understood by all stakeholders to have the optimal combination of board members that ultimately lead to better performance of the firm.

The COVID-19 pandemic has posed many challenges in almost all sectors around the globe. Because of the pandemic, government entities responsible for managing health-care resources face challenges in managing and distributing their limited and valuable health resources. In addition, severe outbreaks may occur in a small or large geographical area. Therefore, county-level preparation is crucial for officials and organizations who manage such disease outbreaks. However, most COVID-19-related research projects have focused on either state- or country-level. Only a few studies have considered county-level preparations, such as identifying high-risk counties of a particular state to fight against the COVID-19 pandemic. Therefore, the purpose of this research is to prioritize counties in a state based on their COVID-19-related risks to manage the COVID outbreak effectively.

In this research, the authors use a systematic hybrid approach that uses a clustering technique to group counties that share similar COVID conditions and use a multi-criteria decision-making approach – the analytic hierarchy process – to rank clusters with respect to the severity of the pandemic. The clustering was performed using two methods, k-means and fuzzy c-means, but only one of them was used at a time during the experiment.

The results of this study indicate that the proposed approach can effectively identify and rank the most vulnerable counties in a particular state. Hence, state health resources managing entities can identify counties in desperate need of more attention before they allocate their resources and better prepare those counties before another surge.

To the best of the authors’ knowledge, this study is the first to use both an unsupervised learning approach and the analytic hierarchy process to identify and rank state counties in accordance with the severity of COVID-19.

The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility.

This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area.

The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area.

This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.

This study aims to unravel a potential determinant of employee engagement in family firms. In particular, we focus on the role of the CEO by studying the influence of CEO transformational leadership on employee engagement. Moreover, we look into the potential mediating psychological safety might play in this relationship.

Based on an extensive literature review, we propose that there will be a significant positive relationship between family firm CEOs’ transformational leadership and the level of employee engagement. We argue that psychological safety will serve as an underlying mechanism explaining this positive relationship. We empirically tested our research model using quantitative data collected through a questionnaire, completed by 508 employees from Belgian family firms.

The results confirm the positive relationship between CEOs’ transformational leadership and employee engagement. Moreover, these results show that the degree of psychological safety mediates this relationship.

This study forms a significant contribution to family firm literature. Until now, even though existing studies on employee engagement in general are numerous, we had little to no knowledge of the factors influencing employee engagement taking into account the unique context of family firms. With this study, we take an important step in this matter. In addition, this study also contributes to the general literature on employee engagement, since previous studies on the impact of leadership on employee engagement tended to focus on the role of the immediate supervisors and not the CEO.

The purpose of this study is to examine the buyers’ preferences influencing the purchase of privately developed affordable housing in Kolkata and to determine whether unsold houses result from misalignment with these preferences.

The literature review and user-opinion survey identified 119 independent variables that indicate buyers’ preferences. A questionnaire survey of 383 households in affordable housing units from 32 housing complexes in Kolkata recorded buyers’ preferences and satisfaction against the independent variables grouped under five levels of characteristics. The product weights of variables derived from the rank sum method and percentage satisfaction give the Utility Score. Multivariate regression and univariate linear regressions were conducted to determine the significance of each Level of characteristics and each variable, identifying the significant variables that would affect the sale of affordable houses.

The multivariate regression analysis has indicated that 68.56% of the variation in the percentage of unsold houses was explained by the five utility scores, which affirms that misalignment with buyers’ preferences significantly affects the sale of privately developed affordable houses. Furthermore, building and neighbourhood-level utility show the highest significance as predictors, while city-level and miscellaneous utility have moderate significance, but housing complex-level utility lacks statistical significance.

This study addresses a research gap in privately developed affordable housing in Kolkata, enhancing understanding of buyer preferences in this segment.

The paper aims to present an experimental and numerical investigation of the load–settlement behavior of soil reinforced by stone column, as well as to evaluate the plane strain unit cell model for the analysis of stone columns.

The numerical analysis was done using both axisymmetric and plane strain models. The elastic perfectly plastic behavior of Mohr–Coulomb was adopted for both soil and column material. The numerical results of this study were validated by the comparison with the in-situ measurements of a full-scale loading test on a stone column. This study also evaluated the effect of different parameters involved in the design of a stone column, including Young’s modulus of the column material, column diameter, spacing between the stone columns and Poisson’s ratio of the column material.

After the numerical simulation, the results from both axisymmetric and plane strain models are quite comparable. In addition, the numerical results revealed that the stone column with low spacing, a large diameter and a high Young’s modulus indicated better behavior against the settlement.

The axisymmetric unit cell model was used in many numerical studies on the behavior of stone columns. In the present work, a field load test on stone column was simulated using a plane strain unit cell model. This research adds that the plane strain unit cell model can be used to predict the settlement of reinforced soil with stone columns.

Although social media has gained prominence as a communication and marketing tool in various sectors, its adoption and utilization within the construction industry remain relatively underexplored. Therefore, this study fills this gap by evaluating the level of awareness and the extent of adoption of social media within the Nigerian construction industry, shedding light on its current status and potential impact.

This objective was attained via a quantitative research approach that utilized a structured questionnaire to obtain responses from construction professionals such as architects, builders, engineers, quantity surveyors and estate managers. Frequencies and percentages and the mean item score (MIS) were used to analyze the questionnaire responses and assess the overall awareness and adoption of social media among construction professionals. Additionally, the Kruskal–Wallis H-test provided valuable insights into the variations in social media adoption levels among different professional categories within the construction industry.

The results indicate that construction professionals possess a generally high level of awareness regarding various social media platforms. However, despite this awareness, the extent of adoption does not align with the level of awareness, suggesting that adoption rates are not as widespread as anticipated.

The findings of this study underscore the importance of not just awareness but also effective adoption and utilization of social media platforms. While awareness is a crucial first step, construction firms should focus on implementing strategies to encourage greater adoption and integration of these platforms into their daily operations. This can go a long way in bridging the awareness – adoption gap which was revealed in this study.

While the limited existing research on social media in the construction industry has predominantly concentrated on areas such as marketing, addressing the root causes of fatalities, data environment tools and business branding, none have undertaken a thorough evaluation of social media awareness and adoption within the sector. This study fills a critical gap by narrowing its focus to the adoption dynamics and the technology’s potential impact on communication, collaboration and knowledge sharing among construction professionals.

People with intellectual disability die prematurely and from avoidable causes. Innovative solutions and proactive strategies have been limited in addressing this disparity. This paper aims to detail the process of developing a risk stratification tool to identify those individuals who are higher risk of premature mortality.

This study used population health management principles to conceptualise a risk stratification tool for avoidable deaths in people with intellectual disability. A review of the literature examined the existing evidence of causes of death in people with intellectual disability. A qualitative methodology using focused groups of specialist clinicians was used to understand the factors that contributed towards avoidable deaths in people with intellectual disability. Delphi groups were used for consensus on the variables for inclusion in the risk stratification tool (Decision Support Tool for Physical Health).

A pilot of the Decision Support Tool for Physical Health within specialist intellectual disability service demonstrated effective utility and acceptability in clinical practice. The tool has also demonstrated good face and construct validity. A further study is currently being completed to examine concurrent and predictive validity of the tool.

To the best of the authors’ knowledge, this is the only study that has used a systematic approach to designing a risk stratification tool for identifying premature mortality in people with intellectual disability. The Decision Support Tool for Physical Health in clinical practice aims to guide clinical responses and prioritise those identified as at higher risk of avoidable deaths.

Police procedural justice is essential in shaping police legitimacy and public willingness to cooperate, yet factors that affect police fair treatment of citizens are not fully understood. Using the data of the National Police Research Platform (NPRP), Phase II, this study examines the effects of three key organizational factors (i.e. effective leadership, supervisory justice and department process fairness) on officers’ procedural justice in police stops.

Innovatively, this study links police data with citizens’ data and conducts multilevel analyses on the effects of a host of citizen, officer, incident, and, importantly, agency characteristics on officer behaviors during over 5,000 police stops nested within 48 police agencies.

The results showed that the fairness of the departmental process had a positive effect on officer procedural justice, while the fairness of the supervisor was inversely associated with procedural justice on the street.

The linked data demonstrated that organizational fairness affected street procedure justice.