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This is a survey paper of the recent literature on the application of semiparametric–econometric advances to testing for functional form of the environmental Kuznets curve (EKC). The EKC postulates that there is an inverted U-shaped relationship between economic growth (typically measured by income) and pollution; that is, as economic growth expands, pollution increases up to a maximum and then starts declining after a threshold level of income. This hypothesized relationship is simple to visualize but has eluded many empirical investigations. A typical application of the EKC uses panel data models, which allows for heterogeneity, serial correlation, heteroskedasticity, data pooling, and smooth coefficients. This vast literature is reviewed in the context of semiparametric model specification tests. Additionally, recent developments in semiparametric econometrics, such as Bayesian methods, generalized time-varying coefficient models, and nonstationary panels are discussed as fruitful areas of future research. The cited literature is fairly complete and should prove useful to applied researchers at large.

Purpose: Risk is a multifaceted concept, and its identification requires complex approaches that are often misunderstood. The consequence is that decisions are based on limited perception rather than the full value and meaning of what risk is, as a result, the way it is being tackled is incorrect. The individuals are often limited in their perceptions and ideas and do not embrace the full multifaceted nature of risk. Regulators and individuals want to follow norms and checklists or overuse models, simulations, and templates, thereby reducing responsibility for decision-making. At the same time, the wider use of technology and rules reduces the critical thinking of individuals. We advance the automation process by building robots that follow protocols and forget about the part of risk assessment that cannot be programed. Therefore, with this study, the objective of this study was to discover how people define risk, the influencing factors of risk perception and how they behave toward this perception. The authors also determine how the perception differed with age, gender, marital status, education level and region. The novelty of the research is related to individual risk perception during COVID-19, as this is a new and unknown phenomenon. Methodology: The research is based on the analysis of the self-administered purposely designed questionnaires we distributed across different social media platforms between February and June 2020 in Europe and in some cases was carried out as a interview over communication platforms such as “Skype,” “Zoom” and “Microsoft Teams.” The questionnaire was divided into four parts: Section 1 was designed to collect demographic information from the participants; Section 2 included risk definition statements obtained from literature and a preliminary discussion with peers; Section 3 included risk behavior statements; and Section 4 included statements on risk perception experiences. A five-point Likert Scale was provided, and participants were required to answer along a scale of “1” for “Strongly Agree” to “5” for “Strongly Disagree.” Participants also had the option to elaborate further and provide additional comments in an open-ended box provided at the end of the section. 466 valid responses were received. Thematic analysis was carried out to analyze the interviews and the open-ended questions, while the questionnaire responses were analyzed using various quantitative methods on IBM SPSS (version 23). Findings: The results of the analysis indicate that individuals evaluate the risk before making a decision and view risk as both a loss and opportunity. The study identifies nine factors influencing risk perception. Nevertheless, it must be emphasized that we can continue to develop models and rules, but as long as the risk is not understood, we will never achieve anything.

American depository receipts (ADRs) listed on the New York Stock Exchange during the 1990s and 2000s are compared to determine how well they performed versus the US index and respective regional indexes utilizing three-year holding period excess returns. Results suggest that ADRs listed in the 2000s perform better than those in the 1990s. Also, seasoned equity offerings performed better than initial public offerings. Regression analysis indicated the best predictors of ADR performance are the returns of the respective regional index where the ADR-listing firm is headquartered, the date of issue (2000s vs 1990s), and whether the ADR was from an emerging economy.

Creativity and innovation have been buzzwords of managerial discourse over the last few decades as they contribute to the long-term survival and competitiveness of firms. Given the non-linear, causally ambiguous, and intangible nature of all innovation-related phenomena, management scholars have been trying to uncover factors that contribute to creativity and innovation from multiple lenses ranging from organizational behavior at the micro-level to strategic management at the macro-level. Along with important and insightful developments in these research streams that evolved independently from one another, human resource management (HRM) research – especially from a strategic perspective – has only recently started to contribute to a better understanding of both creativity and innovation. The goal of this chapter is to review the contributions of strategic HRM research to an improved understanding of creativity at the individual-level and innovation at the firm-level. In organizing this review, the authors rely on the open innovation funnel as a metaphor to review research on both HRM practices and HRM systems that contribute to creativity and innovation. In the last section, the authors focus on more recent developments in HRM research that focus on ambidexterity – as a way for HRM to simultaneously facilitate exploration and exploitation. This chapter concludes with a discussion of future research directions.

In this chapter, we will review the history, deregulation, policy reforms, and airline consolidations and mergers of the Chinese airline industry. The measurement of airline competition in China’s domestic market will also be discussed. Although air deregulation is still ongoing, the Chinese airline industry has become a market-driven business subject to some mild regulations. Then, we will review the impressive development of the high-speed rail (HSR) network in China and its effects on the domestic civil aviation market. In general, previous studies have found that the introduction of HSR services has a significant negative impact on airfare and air travel demand in China. The rapidly expanding network of HSR has important policy implications for Chinese airlines.

Purpose: The insurance business is confronted with coordination difficulties that necessitate a high level of mobility, flexibility, and the capacity to analyse heterogeneous, location-dependent data from different sources and qualities. Recent innovations in emerging technologies have given the insurance industry new organisational options. When coupled with data analytics, crowdsourcing in the insurance industry facilitates solving complex issues with the wisdom of crowds. The notion of incorporating crowdsourcing and big data into the mainstream activities of insurance management is developed in this article, as are the ramifications and gains of collective intelligence achieved by Crowdsourcing and the added value of crowdsourcing insurance activities.

Design/methodology/approach: This chapter is a conceptual work that builds on relevant literature.

Findings: This chapter analyses what insurance industry managers should consider when coordinating crowdsourced activities and how they may benefit from collective intelligence combined with data analytics in terms of efficient and real-time response management for the insurance industry. Furthermore, it is demonstrated how they may use crowdsourcing to exploit information and benefit from invoking additional resources and eliminating the institutional voids present in the industry.

Practical implications: Exemplary applications that take advantage of crowdsourcing and data analytics would help the insurance sector respond flexibly, efficiently, and effectively in real time.

Originality/value: This chapter offers new collaborative ways to enhance the decision-making of insurance industry managers. The relevance of overcoming institutional voids is expanded, and repercussions from the given framework are suggested using data analytics.

In this paper, I explore what shapes the identities of digital nomads (DNs), a class of remote workers who travel and work concurrently. Through extensive fieldwork and interviews with 50 digital nomads conducted in seven coworking hostels in Mexico in 2022, I construct a theory of DN identity. I base this upon the frequent transformations they undergo in their Circumstances, which regularly change their worker identity.

DNs relinquish traditional social determinants of identity, such as nationality and religion. They define their personal identities by their passions and interests, which are influenced by the people they meet. DNs exist in inherently transitive social spaces and, without rigid social roles to fulfil, they represent themselves authentically. They form close relationships with other long-term travellers to combat loneliness and homesickness. Digital nomads define their worker identities around their location independence. This study shows that DNs value their nomadic lifestyle above promotions and financial gain. They define themselves by productivity and professionalism to ensure the sustainability of their lifestyle. Furthermore, digital nomad coworking hubs serve focused, individual work, leaving workplace politics and strict ‘office image’ norms behind. Without fixed social and professional roles to play, digital nomads define themselves personally according to their ever-evolving passions and the sustainability of their nomadic life. Based on these findings, I present a cyclical framework for DN identity evolution which demonstrates how relational, logistical, and socio-personal flux evolves DN’s worker identities.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.