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This study aims to present the cost and revenue efficiency evaluation models in data envelopment analysis in the presence of fuzzy inputs, outputs and their prices that the prices are also fuzzy. This study applies the proposed approach in the energy sector of the oil industry.

This study proposes a value-based technology according to fuzzy input-cost and revenue-output data, and based on this technology, the authors propose an approach to calculate fuzzy cost and revenue efficiency based on a directional distance function approach. These papers incorporated a decision-maker’s (DM) a priori knowledge into the fuzzy cost (revenue) efficiency analysis.

This study shows that the proposed approach obtains the components of fuzzy numbers corresponding to fuzzy cost efficiency scores in the interval [0, 1] corresponding to each of the decision-making units (DMUs). The models presented in this paper satisfies the most important properties: translation invariance, translation invariance, handle with negative data. The proposed approach obtains the fuzzy efficient targets corresponding to each DMU.

In the proposed approach, by selecting the appropriate direction vector in the model, we can incorporate preference information of the DM in the process of evaluating fuzzy cost or revenue efficiency and this shows the efficiency of the method and the advantages of the proposed model in a fully fuzzy environment.

The purpose of this paper is threefold. First, to explore the importance of specific work environment facets for the overall job satisfaction (JS) of primary full-time permanent teachers (PTs) and substitute/temporary teachers (STs). Second, to highlight the similarity or difference in JS patterns among PTs and STs. Third, to provide guidelines for effective evidence-based human resource management (HRM) interventions targeting to boost PTs and STs JS levels by considering: (1) the perceived importance of individual work facets for them and (2) the school's performance in providing a satisfactory work environment.

Data on overall and important JS facets (i.e. satisfaction with opportunities for self-fulfillment, work intensity/load, salary/income, leadership and collegial relations) were collected from a sample of 438 PTs and STs in Greece. Moreover, MUSA, a method that combines Multi-Criteria Decision (MCDA) and Importance-Performance Analysis (IPA), was applied to uncover the critical work environment facets of PTs and STs overall JS that call for interventions.

The findings suggest that PTs seem to value, more than STs, the transactional and economic aspects of the school environment. More precisely, on the part of PTs, self-fulfillment and salary/income are the main contributors to their JS, whilst leadership is the least important facet of JS. For STs self-fulfillment and collegial relationships are the aspects of work that contribute the most to their overall JS, whilst salary/income is the least important contributor. The study results further indicate that self-fulfillment is the strong attribute of Greek schools' work environment in boosting TJS regardless teachers' status, whilst salary/income and workload are potential threats.

It is one of the few studies that provide insights into the differing JS patterns of STs and PTs through the application of a MCDA/IPA method. Therefore, it offers evidence-based guidelines that take into consideration both the school's performance (overall and facet JS) and importance of core aspects of the work experience for STs and PTs.

This study explores job search dynamics among employed individuals. We focus on factors related to employees initiating their job searches and changing its intensity.

We conducted a qualitative study with 41 employed job seekers using semi-structured interviews.

Our analysis reveals six catalysts that initiate employee job searches and nine modulators that influence fluctuations in job search effort. We highlight the significant roles of social actors, resource dynamics, and personal, professional, and environmental factors in shaping employees’ job searches.

The diverse sample strengthens theory building but may not represent all employed job seekers. Future research would benefit from longitudinal designs.

This study underscores the influence of social actors on employee job search processes. Our findings offer valuable insights for policymakers and employers seeking to support employed job seekers and proactively manage voluntary turnover.

This research advances our understanding of employee job search beyond its traditional role in voluntary turnover. By integrating Conservation of Resources (COR) theory, we provide a framework for understanding employee job search initiation and modulation.

The e-commerce boom presents new challenges for last-mile delivery (LMD), which may be mitigated by new delivery technologies. This paper evaluates the impact of mobile parcel lockers (MPL) on costs and CO₂ equivalent (CO₂e) emissions in existing LMD networks, which include home delivery and shipments to stationary parcel lockers.

To describe customers’ preferences, we design a multinomial logit model based on recipients’ travel distance to pick-up locations and availability at home. Based on route cost estimation, we define the operating costs for MPLs. We devise a mathematical model with binary decision variables to optimize the location of MPLs.

Our study demonstrates that integrating MPLs leads to additional cost savings of 8.7% and extra CO₂e emissions savings of up to 5.4%. Our analysis of several regional clusters suggests that MPLs yield benefits in highly populous cities but may result in additional emissions in more rural areas where recipients drive longer distances to pick-ups.

This paper designs a suitable operating model for MPLs and demonstrates environmental and economic savings. Moreover, it adds recipients’ availability at home to receive parcels improving the accuracy of stochastic demand. In addition, MPLs are evaluated in the context of several regional clusters ranging from large cities to rural areas. Thus, we provide managerial guidance to logistics service providers how and where to deploy MPLs.

This study combines different perspectives on herding, viewing it as a social network heuristic in comparison to other heuristics. The purpose is to use the heuristic view of herding as found in early literature and test it on grounds of efficiency and payoff, in essence, combining the heuristic and rational agent view of herding. The simulated double auction setting includes agents embedded in a social network, allowing for an examination of herding alongside rational behaviour and imperfect signals.

In each round of the simulation, levels of homophily, density and fractions of types of agents is set and agents are allowed to follow their respective heuristics under those conditions. Characteristics of the social network, such as the size, levels of different homophilies, density and fractions of different types of agents are varied randomly to gauge their effect on the performance of herders vis-à-vis others and the overall market efficiency through simulation based approach. The data used for the study has been developed in Python and linear models are estimated using R.

Herding decreases total surplus in private value double auctions, but herders are not worse off than other agents and perform equally in common value auctions. Further, herders and random offerers reduce payoffs of other agents as well, and herding effects the surplus per transaction and not the quantum.

This study explores herding as a strategic behaviour coexisting with rationality and other strategies in specific circumstances. It presents intriguing findings on the impact of herding on individual outcomes and market efficiency, raising new avenues for future research. Implication to research includes a dent on the “sieve” argument of markets rooting out irrationality and from it, a policy implication that follows is the need for corrective measures as markets cannot self-correct this, given herders do not perform worse than others.

The study links the phenomenon of herding to the dynamics of social networks and heuristic-based learning mechanisms that sets apart this research from the majority of existing literature, which predominantly conceptualizes herding as an outcome derived from a perfect Bayesian Equilibrium and a rational learning process.

This study is aimed at decoding the impact of supportive leadership behavior (leader–member exchange [LMX]) on job satisfaction (JS) through the mediating role of the work–nonwork interface (work-to-nonwork conflict [WNC] and work-to-nonwork enrichment [WNE]), within the work-from-home context in India.

Multiphased data collected from 232 full-time working Indian dual-working parents (with one or more children) were analyzed using structural equation modeling.

Overall, the hypothesized model receives empirical support from the data. LMX positively influenced WNE and simultaneously negatively influenced WNC. WNE, in turn, positively impacted JS, and WNC negatively influenced JS. Results supported only the mediating role of WNE between LMX and JS but not WNC. Women reported greater JS than men, and respondents staying in a joint family reported decreased WNC.

The current study takes a multiphased, multidomain approach to understand the underlying mechanisms of leadership’s impact while working from home.

By adopting a tailored approach, organizations can ensure better alignment between employee goals and the desired outcomes of the organization. This entails considering extended family requirements and designing HR interventions and strategies that accommodate the specific challenges faced by dual-working parents.

This study helps to shed light on the sparsely researched arena of the role of leadership in the work-from-home context, more so for Indian dual-working households. Hence, it makes significant contributions to theory and practice.

Surface acoustic wave (SAW) sensors have attracted great attention worldwide for a variety of applications in measuring physical, chemical and biological parameters. However, stability has been one of the key issues which have limited their effective commercial applications. To fully understand this challenge of operation stability, this paper aims to systematically review mechanisms, stability issues and future challenges of SAW sensors for various applications.

This review paper starts with different types of SAWs, advantages and disadvantages of different types of SAW sensors and then the stability issues of SAW sensors. Subsequently, recent efforts made by researchers for improving working stability of SAW sensors are reviewed. Finally, it discusses the existing challenges and future prospects of SAW sensors in the rapidly growing Internet of Things-enabled application market.

A large number of scientific articles related to SAW technologies were found, and a number of opportunities for future researchers were identified. Over the past 20 years, SAW-related research has gained a growing interest of researchers. SAW sensors have attracted more and more researchers worldwide over the years, but the research topics of SAW sensor stability only own an extremely poor percentage in the total researc topics of SAWs or SAW sensors.

Although SAW sensors have been attracting researchers worldwide for decades, researchers mainly focused on the new materials and design strategies for SAW sensors to achieve good sensitivity and selectivity, and little work can be found on the stability issues of SAW sensors, which are so important for SAW sensor industries and one of the key factors to be mature products. Therefore, this paper systematically reviewed the SAW sensors from their fundamental mechanisms to stability issues and indicated their future challenges for various applications.

This is an account of student experiences with the 2020/2021 Johnson & Johnson (J&J) Women in Science, Technology, Engineering, Mathematics, Manufacturing and Design (WiSTEM²D) intervention programme at the University of Limerick (UL).

The WiSTEM²D programme supports female undergraduate students undertaking science, technology, engineering, mathematics, manufacturing and design (STEM²D) courses, helping them develop and grow in their degrees, research and beyond.

During the programme, data were gathered from students in the form of qualitative information, which was used to analyse perceptions of the programme. This analysis revealed that students found the WiSTEM²D programme beneficial to them and that it improved their confidence levels, career knowledge and overall networking abilities.

WiSTEM²D is one of several initiatives globally that are attempting to empower and encourage women to be active in STEM.

This study analyses the relationship between the use of work–family benefits and job satisfaction (JS). Furthermore, it proposes that work-to-family conflict (WFC) and work-to-family enrichment (WFE) play a mediating role in this relationship. The purpose of this paper is to address these issues.

Data are gathered from 1,051 employees of Colombian organisations. Partial least squares path modelling is used.

The results show that the perception of WFE to a greater extent and the WFC perception, to a lesser extent, are significant mediators in the relationship between the use of benefits and JS.

This study justifies investments and initiatives on the adoption and promotion of work–family benefits. Moreover, it provides practical clues on how to boost JS: WFC and WFE are variables to be considered.

This study proposes a multiple mediation model to analyse the relationship between the actual use of work–family benefits and JS from a family perspective. It contributes to the literature in examining antecedents of JS, highlighting the role of WFE.

This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively.

Here, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used.

Then, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance.

In this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.