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Depression imposes substantial individual and societal economic costs, including lower productivity and higher healthcare use. However, while the relationship between employment and mental health has been explored, less is known about the potentially countervailing effects of different types of economic inactivity on depression among older individuals. The authors employ a series of models, including fixed effects panel data models and matching on rich data from the English Longitudinal Study of Ageing (ELSA) to investigate whether different types of inactivity might have heterogenous effects on depression. The authors find that whereas transitions to involuntary inactivity (unemployment) do not appear to have a perceivable effect on depression, transitions to voluntary inactivity (retirement) seem to decrease it.

This chapter of the book aims to introduce multiobjective linear programming (MLP) as an optimum tool to find the best quality engineering techniques (QET) in the main domains of supply chain management (SCM). The importance of finding the best quality techniques in SCM elements in the shortest possible time and at the least cost allows all organizations to increase the power of experts’ analysis in supply chain network (SCN) data under cost-effective conditions. In other words, this chapter aims to introduce an operations research model by presenting MLP for obtaining the best QET in the main domains of SCM. MLP is one of the most determinative tools in this chapter that can provide a competitive advantage. Under goal and system constraints, the most challenging task for decision-makers (DMs) is to decide which components to fund and at what levels. The definition of a comprehensive target value among the required goals and determining system constraints is the strength of this chapter. Therefore, this chapter can guide the readers to extract the best statistical and non-statistical techniques with the application of an operations research model through MLP in supply chain elements and shows a new innovation of the effective application of operations research approach in this field. The analytic hierarchy process (AHP) is a supplemental tool in this chapter to facilitate the relevant decision-making process.

The study's objective was to address the factors impacting the employability attributes of fresh graduates in the Kingdom of Bahrain while considering the various challenges. This study used a quantitative approach which employed the questionnaire tool, and data were collected by using a convenience sampling method. The study sample comprised n = 385 respondents from different industries, including manufacturing, banking and finance, hospitality, healthcare, oil and gas, and real estate sectors of Bahrain. Data gathered from questionnaire were analyzed using Statistical Package for the Social Sciences (SPSS), where descriptive and inferential statistics was used to analyze the data. The results of the study showed for the major Hypothesis 1 that the demographic variables have no significant statistical impact on employment attributes of the new fresh graduates. Moreover, findings suggest that null hypothesis for major Hypothesis 2 has been rejected as applied academic skills and critical thinking skills have no significant impact on employability attribute of fresh graduates in the Kingdom of Bahrain. Null hypothesis for major Hypothesis 3 has been accepted as findings suggest that technology use skills (β ₁ = 0.080), system thinking skills (β ₂ = 0.210), communication skills (β ₃ = 0.402), and information skills (β ₄ = −0.100) which are an antecedent of workplace skills, have significant statistical impact on employability attribute of fresh graduates in Kingdom of Bahrain. Lastly, null hypothesis for major Hypothesis 4 has been accepted as findings suggest that interpersonal skills (β ₅ = 0.229) which are an antecedent of effective relationship have significant statistical impact on employability attribute of fresh graduates in the Kingdom of Bahrain.

The authors examine whether or not applicants and recipients of federal disability insurance (DI) inflate their self-assessed health (SAH) problems relative to others. To do this, the authors employ a technique which uses anchoring vignettes. This approach allows them to examine how various cohorts of the population interpret survey questions associated with subjective self-assessments of health. The results of the analysis suggest that DI participants do inflate the severity of a given health problem, but by a small but significant degree. This tendency to exaggerate the severity of disability problems is much more apparent among those with more education (especially those with a college degree). In contrast, racial minorities tend to underestimate severity ratings for a given disability vignette when compared to their white peers.

The author develops a bilateral Nash bargaining model under value uncertainty and private/asymmetric information, combining ideas from axiomatic and strategic bargaining theory. The solution to the model leads organically to a two-tier stochastic frontier (2TSF) setup with intra-error dependence. The author presents two different statistical specifications to estimate the model, one that accounts for regressor endogeneity using copulas, the other able to identify separately the bargaining power from the private information effects at the individual level. An empirical application using a matched employer–employee data set (MEEDS) from Zambia and a second using another one from Ghana showcase the applied potential of the approach.

In this chapter, the authors critically examine the application of unmeasured latent method factors (ULMFs) in human resource and organizational behavior (HROB) research, focusing on addressing common method variance (CMV). The authors explore the development and usage of ULMF to mitigate CMV and highlight key debates concerning measurement error in the HROB literature. The authors also discuss the implications of biased effect sizes and how such bias can lead HR professionals to oversell interventions. The authors provide evidence supporting the effectiveness of ULMF when a specific assumption is held: a single latent method factor contributes to the data. However, the authors dispute this assumption, noting that CMV is likely multidimensional; that is, it is complex and difficult to fix with statistical methods alone. Importantly, the authors highlight the significance of maintaining a multidimensional view of CMV, challenging the simplification of a CMV as a single source. The authors close by offering recommendations for using ULMFs in practice as well as more research into more complex forms of CMV.

International Financial Reporting Standards (IFRS) on microfinance services are important for several reasons, such as global financial integration, investor confidence, risk assessment, access to capital markets, creditworthiness, alignment with best practices, international partnerships, legal compliance, capacity building, and risk mitigation. The data is based on reports from all commercial banks in Uzbekistan. The authors developed the econometric equation using the autoregressive distributed lag (ARDL) model. This research hypothesizes that the bank's net profit is positively related to the bank's deposit practices. High net profit in commercial banks indicates financial strength and stability, which can increase customer trust in the bank. Depositors are more likely to entrust their funds to banks with strong financial performance because this increases their deposits' security. Consequently, higher net profit is expected to be associated with increased deposit practices. The factors are econometrically analyzed based on the conceptual model created by the author. The results of this study are of practical importance for developing remote banking services in the banking system of Uzbekistan. In conclusion, the econometric model provides insight into the relationship between various independent variables (loan operations, microfinance services, and net profit) and the dependent variable (deposit operations) for commercial banks.

Financial technologies form the heart of considerable disruptive innovation. Fintech is the emerging financial infrastructure for modern business. Big data are the feedstock for artificial intelligence (AI) that drives many fintech sectors – start-up finance, commodities and investment instrumentation, payment systems, currencies, exchange markets/trading platforms, market-failure response forensics, underwriting, syndication, risk assessment, advisory services, banking, financial intermediaries, transaction settlement, corporate disclosure, and decentralized finance. This chapter demonstrates how analyzing big data, largely processed through cloud computing, drives fintech innovations, scholarship, forensics, and public policy. Despite their apparent virtues, some fintech mechanisms can externalize various social costs: flawed designs, opacity/obscurity, social media (SM) influences, cyber(in)security, and other malfunctions. Fintech suffers regulatory lag, the delay following the introduction of novel fintechs and later assessment, development, and deployment of reliable regulatory mechanisms. Big data can improve fintech practices by balancing three key influences: (1) fintech incentives, (2) market failure forensics, and (3) developing balanced public policy resolutions to fintech challenges.

Technologies like virtual reality (VR), augmented reality (AR), and artificial intelligence (AI) impact the decision-making process related to the creation of tourist offers. The aim of this chapter is to analyse the exigency of application technologies in the tourism industry from the aspect of business, in order to demonstrate the readiness of certain organisations to apply alternative reality technologies. This chapter analyses the attitudes of top, middle, and operational managers in 3-star, 4-star, and 5-star hotels at the regional level of Dubrovnik-Neretva county in order to assess the necessity for businesses in the hotel industry to adopt alternative reality technologies as well as the key benefits and opportunities that come with it. Empirical research was conducted using a convenience purposive sample of managers in order to determine the relationship between the application of specific technologies and tourism organisations. Statistical methods were used to evaluate the data in order to test the importance of the respondent’s examination attitudes. The results reflect the state of hotel organisations in terms of the usefulness that technologies as alternative forms of reality can provide them in business and point to the increasing adaptation of the tourist sector for the application of such technologies.