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Environmental degradation resulting from human activities may adversely affect human health in multiple ways. Until now, policies aimed at mitigating environmental problems such as climate change, environmental pollution and damage to biodiversity have failed to clearly identify and drive the potential benefits of these policies on health. The conducted study assesses and demonstrates how specific environmental policies and instruments influence perceived human health in order to ensure input for a data-driven decision process.

The study was conducted for the 2004–2020 period in European Union (EU) countries with the use of dynamic panel data modeling. Verification of specific policies' impact on dependent variables allows to indicate this their effectiveness and importance. As a result of the computed dynamic panel data models, it has been confirmed that a number of significant and meaningful relationships between the self-perceived health index and environmental variables can be identified.

There is a strong positive impact of environmental taxation on the health index, and the strength of this relationship causes effects to be observed in the very short term, even the following year. In addition, the development of renewable energy sources (RES) and the elimination of fossil fuels from the energy mix exert positive, although milder, effects on health. The reduction of ammonia emissions from agriculture and reducing noise pollution are other health-supporting factors that have been shown to be statistically valid. Results allow to identify the most efficient policies in the analyzed area in order to introduce those with the best results or a mix of such measures.

The results of the authors' research clearly indicate the health benefits of measures primarily aimed at improving environmental factors, such as environmental taxes in general. The authors have also discovered an unexpected negative impact of an increase in the share of energy taxes in total taxes on the health index. The presented study opens several possibilities for further investigation, especially in the context of the rapidly changing geopolitical environment and global efforts to respond to environmental and health challenges. The authors believe that the outcome of the authors' study may provide new arguments to policymakers pursuing solutions that are not always easily acceptable by the public.

This study explored how organizational leaders at different hierarchical levels may communicatively enhance employees' health and well-being. Drawing on interdisciplinary research, it proposed a model that connects health-oriented leadership communication at supervisory and executive levels with remote workers' self-care and stress levels during the COVID-19 pandemic.

Data collected through a survey of 363 full-time United States (US) employees were analyzed to test the model.

Results showed health-oriented communication at the two leadership levels directly influenced employees' self-care, which in turn reduced their stress levels. Further, executive leaders' health-oriented leadership communication indirectly impacted remote workers' self-care through its positive association with supervisors' health-oriented leadership communication.

This study offers much-needed guidelines for executive leaders, supervisors and communication practitioners seeking to meet employees' growing expectations for a healthy work environment in today's post-pandemic era.

Although the literature has established organizational leadership as a vital determinant for a healthy workforce, few studies have explored leaders' health-specific communication to enhance employee health. This study is the first to conceptualize health-oriented leadership communication at dual hierarchical levels and uncover its influence on employees. The results suggested the importance of health-oriented leadership communication across hierarchical levels in building a healthy workplace.

This study aims to investigate the impact of regular Kung Fu and Taekwondo practice on the health and quality of life among elderly individuals in the Maule region, Chile.

The authors designed a 12-week Kung Fu and Taekwondo workshop with activities suitable for their age. Through semistructured interviews (at the beginning and the end of the workshop), along with periodic monitoring of vital signs and cardiovascular components, the authors observed an improvement in participants’ physical (strength, speed of reaction and flexibility) and psychological conditions (self-esteem and resilience), quality of life (relationships with family and friends and ability to deal with stressful events in working life) and health (waist circumference, percentage of oxygen saturation in blood, blood pressure, among other values).

From these results, the authors affirm that this workshop improves health and physical condition and helps the participants develop the coping capacity to deal with stressful situations and complicated interpersonal relationships. In this sense, the authors conclude that Kung Fu and Taekwondo as regular sports activities can benefit senior citizens’ aging process.

This research is based on an original study project.

Buying directly from farmers online has become increasingly popular in recent years. This study aims to investigate the effect of the interaction between various consumption values that drive consumers to buy directly from farmers online. The proposed conceptual framework suggests that consumers who buy online directly from farmers are driven by an interaction of weighted individualistic consumption value (i.e. an integration of values such as saving money, getting quality and fresh produce) and collectivistic values (pro-environmental behaviour and ethnocentric perception).

Data were collected using a representative sample of 576 consumers via an online access panel and analysed using AMOS SEM.

A weighted individualistic consumption value affects consumer attitudes and, consequently, consumers' intention to buy agri-food products directly from farmers. Nonetheless, individualistic consumption value is more effective in enhancing attitudes among consumers with high pro-environmental behaviour. Moreover, ethnocentric perception lowers the effect of individualistic consumption value on attitudes and enhances the positive effect of attitudes on buying intention.

This study contributes to the literature on consumer online behaviour when buying food products directly from farmers. Its originality lies in the effect of interacting individualistic and collectivistic consumption values to explain consumer motivation for this behaviour.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

Classical unit root tests are known to suffer from potentially crippling size distortions, and a range of procedures have been proposed to attenuate this problem, including the use of bootstrap procedures. It is also known that the estimating equation’s functional form can affect the outcome of the test, and various model selection procedures have been proposed to overcome this limitation. In this chapter, the authors adopt a model averaging procedure to deal with model uncertainty at the testing stage. In addition, the authors leverage an automatic model-free dependent bootstrap procedure where the null is imposed by simple differencing (the block length is automatically determined using recent developments for bootstrapping dependent processes). Monte Carlo simulations indicate that this approach exhibits the lowest size distortions among its peers in settings that confound existing approaches, while it has superior power relative to those peers whose size distortions do not preclude their general use. The proposed approach is fully automatic, and there are no nuisance parameters that have to be set by the user, which ought to appeal to practitioners.

Research on the relationship between customer bargaining power and supplier performance in supplier–customer relationships has flourished in recent decades. This study aims to empirically investigate whether product market overlap (PMO) in a supply chain moderates the effect of customer bargaining power on supplier profitability.

This study uses large-scale secondary data from multiple databases. Econometric panel data techniques are used to test the hypotheses.

The results show that PMO in a supplier–customer relationship and PMO in supplier–supplier relationships both exacerbate the negative effect of the bargaining power of customers on supplier profitability.

This study contributes to the field of supply chain management. This study brings new insights into the ongoing debate surrounding the relationship between customer bargaining power and supplier profitability. The study also contributes to the literature on supply chain networks by showing the impact of indirect supply chain relationships.

Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic error v and a one-sided inefficiency random component u. When v or u has a nonstandard distribution, such as v follows a generalized t distribution or u has a χ2 distribution, the likelihood function can be complicated or untractable. This chapter introduces using indirect inference to estimate the SF models, where only least squares estimation is used. There is no need to derive the density or likelihood function, thus it is easier to handle a model with complicated distributions in practice. The author examines the finite sample performance of the proposed estimator and also compare it with the standard ML estimator as well as the maximum simulated likelihood (MSL) estimator using Monte Carlo simulations. The author found that the indirect inference estimator performs quite well in finite samples.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.