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This chapter introduces a new theoretical framework for developing emotion-related abilities according to the emotional intelligence (EI) construct definition of Mayer, Salovey, and Caruso (2006). The awareness, reflection, and management (ARM) model has been devised and demonstrates a triadic cycle of emotional ARM relating to affect, cognition, and behavior. The ARM model constitutes an approach to nurture emotion-related abilities (ability EI) and responds to criticism raised by Zeidner, Matthews, and Roberts (2009). The ARM Theory was corroborated by both learning theory and schools of counselling (SOC). The potential to develop emotion-related abilities in emotional awareness, reflection and reasoning, coping and management is discussed.

Purpose: The world has been impacted distinctively by the Covid-19 outbreak. The economic recession in 2020 and 2021 is predicted to become the most intense since World War II. This chapter explores the effects of the Covid-19 epidemic crisis on entrepreneurship and different activities, emphasizing developing economies.

Design/methodology/approach: The work aims first on the major crises that could impact entrepreneurial activity triggered by the pandemic. The authors briefly study the literature on the previous health crisis and its impacts on entrepreneurship. Then, under Covid-19, the authors illustrated the navigational prospects for creativity and entrepreneurship.

Findings: Our research indicates that the pandemic’s impact on creativity and entrepreneurship would be adverse but not hugely harmful. A new opportunity ecosystem under COVID-19 is respected and researched for innovation and entrepreneurship. Simultaneously, the incentives provided by the COVID-19 pandemic for consumer e-commerce adoption in Saudi Arabia are being addressed.

Originality/value: This is probably the first chapter of the book focusing on the impact on the Covid-19 pandemic entrepreneurship. It directly discusses the effects of the Covid-19 pandemic on both the global economy and entrepreneurship dynamics. It also recommends fascinating future-oriented, relevant research areas about helping entrepreneurs solve it and what new prospects are developing for them.

This chapter draws on longitudinal and cross-sessional research to explore the use of Bottery's variant of portrait methodology in understanding the individual experiences of a group of Chinese top-up students when they study a one-year top-up programme in a UK university. The rationale for using Bottery's variant was to understand only the perceptions of these students in their particular context, during a specific period of time in their life. Data were collected three times using semi-structured interviews, and individual portraits were produced after each set of interviews, based on the interview transcripts. The findings of the research and also the comments of the participants suggest that the use of portrait methodology helped enhance the trustworthiness of the research and also self-awareness and self-reflection of the participants. Researchers may face challenges when adopting Bottery's variant of portrait methodology in research, in particular relating to portrait writing and rapport building with the participants.

Scholars and practitioners in the OB literature nowadays appreciate that emotions and emotional regulation constitute an inseparable part of work life, but the HRM literature has lagged in addressing the emotional dimensions of life at work. In this chapter therefore, beginning with a multi-level perspective taken from the OB literature, we introduce the roles played by emotions and emotional regulation in the workplace and discuss their implications for HRM. We do so by considering five levels of analysis: (1) within-person temporal variations, (2) between persons (individual differences), (3) interpersonal processes; (4) groups and teams, and (5) the organization as a whole. We focus especially on processes of emotional regulation in both self and others, including discussion of emotional labor and emotional intelligence. In the opening sections of the chapter, we discuss the nature of emotions and emotional regulation from an OB perspective by introducing the five-level model, and explaining in particular how emotions and emotional regulation play a role at each of the levels. We then apply these ideas to four major domains of concern to HR managers: (1) recruitment, selection, and socialization; (2) performance management; (3) training and development; and (4) compensation and benefits. In concluding, we stress the interconnectedness of emotions and emotional regulation across the five levels of the model, arguing that emotions and emotional regulation at each level can influence effects at other levels, ultimately culminating in the organization’s affective climate.

This chapter proposes a fuzzy approach to forecasting using a financial data set. The methodology used is multiple objective linear programming (MOLP). Selecting an individual forecast based on a single objective may not make the best use of available information for a variety of reasons. Combined forecasts may provide a better fit with respect to a single objective than any individual forecast. We incorporate soft constraints and preemptive additive weights into a mathematical programming approach to improve our forecasting accuracy. We compare the results of our approach with the preemptive MOLP approach. A financial example is used to illustrate the efficacy of the proposed forecasting methodology.

This chapter presents the survey of selected linear and mixed integer programming multi-objective portfolio optimization. The definitions of selected percentile risk measures are presented. Some contrasts and similarities of the different types of portfolio formulations are drawn out. The survey of multi-criteria methods devoted to portfolio optimization such as weighting approach, lexicographic approach, and reference point method is also presented. This survey presents the nature of the multi-objective portfolio problems focuses on a compromise between the construction of objectives, constraints, and decision variables in a portfolio and the problem complexity of the implemented mathematical models. There is always a trade-off between computational time and the size of an input data, as well as the type of mathematical programming formulation with linear and/or mixed integer variables.