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The family time is being an important element in assisting to empower the mutual feeling in connecting both love and compassion among the family members. The copying initiative to spend the quality time with family is supposed to embed the feeling of security, the family values with confidence, in order to strengthen the social intelligence. However, due to the pandemic age with its outstanding challenges on being less socially connected, the more affection as the real impact toward the reduction on social interaction requires the attempts to restore the process with the sufficient link between emotional and social intelligence. The aim of this chapter attempts to examine the family quality time maintenance for children’s social intelligence in order to fully comprehend the strategic way of particular issue identification in affecting the children, by the suggested proper solution amidst the pandemic age. The empirical data from the qualitative interview among 12 public educators were employed by exploring their beliefs and practices in maintaining the family time quality for their children social intelligence support. The finding of this chapter reveals that there are three aspects of maintaining the family time for children’s social intelligence support, consisting of the technical skills to improve the family relationships, the communication on the feelings to care for being close relationships and emotional intimacy to advance the family contact in broadening the comfortable spend of time with emotional integrity and openness. The value of this chapter is to give an insightful value on the knowledge enrichment about the strategic maintenance of family time quality for children’s social intelligence. Offering the understandable suggestion together with an effective method to bring the family time into being closer is supposed to lead to the emotional intelligence among the peer-family members, mainly parent–children relationship amidst the pandemic age.

To explore the feasibility of utilizing the Bar-On mixed model of emotional–social intelligence as a framework for the competencies and traits needed for reference and information services librarians. Through a survey of the literature, the author created a baseline list of competencies, which was compared and contrasted with the abilities, traits, and competencies that comprise the Bar-On model of emotional–social intelligence. The author conducted a pilot study with a small group (n = 10) of reference and user services librarians who took the EQ-i 2.0. The competencies and traits of reference and user services librarians identified in the literature compare favorably with those measured by the EQ-i 2.0. Overall, a majority of the participants (70%) obtained a total score on the EQ-i 2.0 in the mid or high range. Composite scales with the highest overall mean scores were decision-making and self-perception. Subscales with the highest scores included the following: impulse control, self-actualization, social responsibility, problem solving, and reality testing. As a pilot study, it was conducted using a small population of academic reference and user services librarians. Further research should be conducted utilizing a larger population of reference and user services librarians or librarians who have been recognized as exemplary in reference librarianship. The findings of this study could assist pre-service and in-service reference and user services librarians in further developing their emotional–social intelligence competencies and abilities by identifying areas where improvements could occur.

Purpose: In recent times, ‘artificial intelligence (AI)’ has been pervasive even in organisations or at home. AI is defined as programming computers or other technological devices to act, react, respond, or assist the same way humans do. AI has undeniably made people’s lives easier. In organisations, the impact of AI is even more visible. The main aim of this chapter is to examine the significant role of future work skill’s (FWS) each component in the field of on-growing automation. The focus will be especially on emotional and social intelligence (ESI) (a key component of FWS) while adopting AI.

Need of the Study: In terms of human resource management (HRM), AI is useful for people management, payroll services, staff monitoring and improving the recruiting network, among other things. Even managers put their organisation’s job openings on the web and get applicant resumes electronically. People and employees in the organisation have become more advanced and innovative due to AI. A device obtains employee attendance, and human resource (HR) can track their employees and their organisation’s workforce data. HR has now been awarded more authority to manage and fix their employee’s problems because of AI. In a rapidly changing world, AI is affecting all aspects. AI is yearning to automate all of the jobs.

Methodology: Now a question arises how we can stay relevant in AI economic development? As humans, we learned that every issue is a problem of optimisation because we simply require human skills to develop, create and innovate new things. Therefore, researchers recognised that adopting sustainable growth skills encourages people to continue learning throughout their lives. Moreover, AI has enabled machines the ability to learn over time. Still, they will never be able to develop new ideas like human intelligence. A machine can use only one fixed data algorithm. Now humans have made significant progress in various fields with the help of FWS; without integrated computer sciences, brain science would not make such an outstanding achievement. On the other hand, human minds are masters of their intelligence, such as creativity, complex problem-solving, cognitive thinking, ESI and communication. Breakthrough human mind are masters of algorithms represented people have to understand new trends of technology around us, and the best way to move forward is to be aware, adapt and update skills.

Practical Implications: However, AI is required because, regardless of technological advancements, AI is leading Industry 4.0. The industry’s transformation is in 4.0, and hopefully, 5.0 will jump on board soon. Undoubtedly, AI should streamline the process and eliminate redundancy or administrative tasks.

Finding: AI can be more effective in organisations if they incorporate other FWS, particularly the soft human ESI skills, whereas AI is present everywhere, we can still not neglect FWS, especially ESI. So, this chapter highlights the important role of soft skills, that is, ESI and FWS, while adapting AI for an effective HRM.

The importance of humans to the successful delivery of construction projects has led to the emergence of research attention on construction workforce management. As such, this chapter uncovers emotional intelligence (EI) and the external environment as critical aspects of workforce management practices that have not gained substantial attention in past workforce management studies. While some theories and models (existing outside the construction domain) have considered the external environment, none of these models is specific to the construction industry. Furthermore, EI has received less attention within existing workforce management models. Through a review of related studies and theories, this chapter noted that the EI of construction workers and their senior management is crucial to the performance of these workers and the ultimate performance of their organisations. In the same vein, since construction organisations do not operate in silos, the external environment significantly influences the operations of organisations in the construction industry. The environment exact pressures that can influence workforce management practices and technological innovations construction organisations adopt.

In this chapter, we explore the interrelationships between team member cultural intelligence (CQ) and multinational team functioning and performance. We argue that CQ, an individual's capability to adapt to different cultural contexts, can be enhanced through experience working in a multinational team, suggesting that CQ is not simply a stable individual difference. We propose a conceptual framework, and demonstrate empirical support through a longitudinal study, that links the effectiveness of team experience to shared norms and positive performance feedback. Additionally, we present evidence that mean level of team member CQ predicts intragroup trust, cohesion, and performance for the multinational team.

This chapter examines EI, presents a history of EI including the various models, and a discussion of the three streams approach to classifying EI literature. The author advocates for the efficacy of the Stream One Ability Model (SOAM) of EI citing previous authors and literature. The commonly used SOAM instruments are discussed in light of recent studies. The discussion turns to alternate tests of the SOAM of EI including Situational Judgment Tests (SJTs). Recommendations include an analysis of SOAM instruments, a new approach to measurement, and increased use of SJTs to capture the four-branch ability model of EI.

Dewey, through his contributions to pragmatism (America’s sole original philosophy), has long been considered relative to symbolic interactionism (SI), which emerged from that philosophy. His impact on SI, while falling short of those of Mead and Cooley, has mainly come from (and has been limited to) concepts and insights developed in Human Nature and Conduct: An Introduction to Social Psychology (1922/1957) and his earlier, seminal, article, “The Reflex Arc Concept in Psychology,” published in 1896 during his tenure at the University of Chicago (1894–1904). SI, however, has wrongly ignored Dewey’s political theory, especially his concept of domination. In order to rectify this inattention, I summarize the social and historical contexts that motivated Dewey’s turn toward domination; outline the radical nature of his political theory; illustrate similarities of his political theory with Marx’s; expatiate on his concept of domination, including his argument for social practices to reduce surplus domination; and explicate the theoretical and political implications of taking his political theory seriously.

The sudden rise of the socio-political importance of security that has marked the twenty-first century entails a commensurate empowerment of the intelligence apparatus. This chapter takes the Investigatory Powers Act 2016 as a vantage point from where to address the political significance of this development. It provides an account of the powers the Act grants intelligence agencies, concluding that it effectively legalizes their operational paradigm. Further, the socio-legal dynamics that informed the Act lead the chapter to conclude that Intelligence has become a dominant apparatus within the state. This chapter pivots at this point. It seeks to identify, first, the reasons of this empowerment; and, second, its effects on liberal-democratic forms, including the rule of law. The key reason for intelligence empowerment is the adoption of a pre-emptive security strategy, geared toward neutralizing threats that are yet unformed. Regarding its effects on liberal democracy, the chapter notes the incompatibility of the logic of intelligence with the rule of law. It further argues that the empowerment of intelligence pertains to the rise of a new threat-based governmental logic. It outlines the core premises of this logic to argue that they strengthen the anti-democratic elements in liberalism, but in a manner that liberalism is overcome.

Universities are considered as learning institutions and their output is knowledge. Their main objectives are to promote knowledge and to integrate three main roles: (1) teaching and learning toward an award; (2) research and publication; and (3) activities centred toward work-based learning. Researchers generally categorize knowledge in three dimensions, cognitive, functional and social competence which are clearly consistent with the French paradigm- savoir, savoir faire, and savoir être. Delamare Le Deist and Winterton (2007) acknowledged that knowledge, that is, understanding is captured by cognitive competence, skills are captured by functional competence and behavioral and attitudinal competencies are captured by social competencies. This chapter describes some basic concepts of social competence in the tertiary education and examines the relationship that exists among knowledge, knowledge management, and social competence. Achieving personal goals and at the same time maintaining positive relationship over time and across situations is one of the main definition of social competence, as brought forward by Rubin and Rose-krasner (1992). Social competence also embraces all the social, emotional and cognitive knowledge and skills individuals require to achieve their goals and to be effective in their relations with others (Kostlenik et al., 2014).