Natasha Winkler-Titus, Charlene Gerber, Vera Ngalo
Orientation: Remote working has allowed employees greater flexibility in fulfilling their tasks; but with the recent rapid shift to working from home for a significant proportion of the workforce, the common notions about remote work should be revisited, especially in a developing world context.
Research purpose: The aim of the research was to test the relationship between remote work challenges (i.e. procrastination, loneliness and work–home interference) and remote work characteristics (social support and job autonomy) and its effects on employee well-being, while controlling for workload and self-discipline.
Motivation for the study: Although remote working is not new, the changing work context has emphasised the opportunities in flexibility and job opportunities. However, challenges exist especially around the mental well-being of workers.
Research approach/design and method: A mixed method approach was followed with a cross-sectional quantitative survey as the primary focus, with an open-ended question added. Data were collected in the finance industry during the coronavirus disease 2019 (COVID-19) enforced lockdown period, when employees were compelled to work from home owing to the national lockdown restrictions.
Main findings: The study confirmed the hypothesis that employee well-being will be more pronounced during remote work if employees have fewer remote work challenges and are exposed to positive remote work characteristics. The study further found that the relationship between remote work challenges and well-being is influenced by self-discipline and workload.
Practical/managerial implications: Managers will do well to consider time and place dimensions for work models in their context, but must be mindful of the challenges and opportunities.
Contribution/value-add: The findings of the study are grounded in elements of self-determination theory, and the main implication for practice is the insight on how to facilitate a working environment that promotes well-being in the context of remote working.
Conceição Gomes, Cátia Malheiros, Luís Lima Santos
et al.
The complexity of companies in the restaurant industry is clear, and various techniques can be used to make decisions. The analysis of performance and the optimization of restaurant menus are considered important, which is why several approaches can be used. The objective of this study is to achieve financial sustainability in the restaurant industry through menu performance analysis and identifying strategies to improve menu profitability. A qualitative methodology of a dual case study was adopted by comparing a restaurant within a hotel and a street restaurant. The results show that for restaurant owners and managers, these approaches are useful, simple, and pertinent for measuring the performance of the restaurant menu and consequently improving results. The originality of this research lies in the fact that three analysis models were applied simultaneously, allowing for an in-depth analysis of the profitability of the menus being analyzed. This study identified the most profitable items for each restaurant and the items that needed to be changed to contribute more to the profitability of the restaurant’s menu, resulting in practical implications. Through theoretical implications, this study corrects the limited knowledge about performance through the restaurant menu, creating a starting point for knowledge spreading to society. In conclusion, this research is one of the first to bridge the gap between theory and practice, taking several approaches to assess restaurant menu performances, which can be useful in restaurants to promote sustainability.
Abbie WINTON, Alejandro CASTILLO, Eva HERMAN
et al.
Drawing on 12 case studies across 10 countries of how trade unions and collective bargaining institutions supported front-line workers in healthcare, social care and food retail, this article finds that pre-existing or new collective bargaining or social dialogue forums provided important avenues for employee voice on pandemic management. Trade unions also supported marginalized front-line workers through multiple tactics, though most initiatives predated the pandemic and often depended upon gaining active state support, which was not always possible. Trade unions were thus pursuing sword-of-justice objectives, though they were sometimes less open to revaluing front-line work already covered by collectively negotiated grading structures.
Labor. Work. Working class, Personnel management. Employment management
Fullchis Nurtjahjani, Forbis Ahamed, Ayu F. Puspita
et al.
Orientation: Higher education institutions have the potential to produce quality human resources. For this reason, it is necessary to monitor the quality of its teaching workforce.
Research purpose: The purpose of this study is to investigate the influence that transformational leadership and organisational culture have on the performance of lecturers and to examine the possible moderating effect that honest conduct may have on the link between these two factors.
Motivation for the study: This study was conducted because the author wants to know the influence that transformational leadership and organisational culture have on the performance of lecturers through moderating sincere behaviour.
Research approach/design and method: This study is considered explanation-grounded research. The sample consists of 170 Indonesian academics working at various universities. This research makes use of a technique known as structural equation modelling-partial least squares (SEM-PLS).
Main findings: The study findings show a significant relationship between transformational leadership culture in the workplace and the impact on lecturer success. Furthermore, the results of the study suggest that sincerity plays a moderating function in the connection between transformative leadership and organisational culture on lecturer performance.
Practical/managerial implications: The policy makers in higher education institutions could be able to implement policies for lecturers in improving lecturer performance through the application of transformational leadership and organisational culture.
Contribution/value-add: The results of this research provide valuable insights for decision makers in an effort to improve lecturer performance.
The rapid expansion of cross-border e-commerce (CBEC) has created significant opportunities for small- and medium-sized sellers, yet financing remains a critical challenge due to their limited credit histories. Third-party logistics (3PL)-led supply chain finance (SCF) has emerged as a promising solution, leveraging in-transit inventory as collateral. We propose an advanced credit risk management framework tailored for 3PL-led SCF, addressing the dual challenges of credit risk assessment and loan size determination. Specifically, we leverage conditional generative modeling of sales distributions through Quantile-Regression-based Generative Metamodeling (QRGMM) as the foundation for risk measures estimation. We propose a unified framework that enables flexible estimation of multiple risk measures while introducing a functional risk measure formulation that systematically captures the relationship between these risk measures and varying loan levels, supported by theoretical guarantees. To capture complex covariate interactions in e-commerce sales data, we integrate QRGMM with Deep Factorization Machines (DeepFM). Extensive experiments on synthetic and real-world data validate the efficacy of our model for credit risk assessment and loan size determination. This study explores the use of generative models in CBEC SCF risk management, illustrating their potential to strengthen credit assessment and support financing for small- and medium-sized sellers.
Capital adequacy is an important part of a company's financial performance, because achieving an optimal capital adequacy ratio (CAR) indicates that the company has sufficient capital to fund each of its operations. Sufficient capital allows the company to easily innovate, so that it can develop the company's productivity. Interestingly, CAR is influenced by various factors, so it is necessary to study academically the factors that influence CAR. The purpose of this study is to determine the factors that influence CAR. This study uses a quantitative approach with empirical methods using Bank BTN data for the 2010-2020 period. The results show that this model contributes 81% to changes in CAR, partially showing that ROA has a negative effect on CAR, meaning that the lower the ROA, the higher the CAR, LDR has a negative effect on CAR, meaning that the lower the LDR, the higher the CAR, and the NPL has an effect negative to CAR means that the lower the NPL, the higher the CAR. This indicates that increases or decreases in ROA, LDR, and NPL have an impact on changes in CAR at Bank BTN.
The purpose of this study to analyze the effect of cognitive image, affective image and unique image on interest of visits and the impact on visiting decision. The population used in this study are tourist who had visited the Menganti Beach tourist attraction, Kebumen Regency, at least once. The number of samples used in this study were 110 respondents. The method of data collection is done through a questionnaire. This study uses Structural Equation Modeling (SEM) analysis techniques with AMOS 22.0 analysis tool. The results of this study show that cognitive image has a positive and significant effect on interest of visits, cognitive image does not have significant on visiting decision, affective image has a positive and significant effect on interest of visits, affective image does not have significant on visiting decision, unique image has a positive and significant effect on interest of visits, unique image does not have significant on visiting decision, and interest of visits has a positive and significant effect on visiting decision.
Keywords: Expectancy Theory, Cognitive Image, Affective Image, Unique Image, Visit Intention, Visiting Decision
Background: To manage growth opportunities effectively and to make a significant impact on superior longterm performance, it is necessary to analyze firm value and diagnose its determinants. Increasing profit, providing prosperity to the company's stakeholders, and improving company value are the goals of every company's business. Purpose: The paper aims to build a model of the company's optimal value by assessing company performance based on financial statement analysis of European companies over the period 2015-2020. Study design/methodology/approach: The impact of financial indicators such as financial leverage, profitability, size, liquidity, growth, and asset tangibility on company value was thoroughly considered. The empirical research was founded on a sample of 158 Eastern and Western European companies, generating 948 observations. Panel regression analysis was conducted. Findings/conclusions: The obtained results revealed that debt-to-assets ratio, return on equity, and assets tangibility have a significant adverse effect on company value, whereas the return on assets and firm size have a significant favorable effect. The obtained conclusions should serve as a beneficial tool for the strategy of reaching the targeted market company's value and ensuring the company's future viability by the market. Hence, stakeholders could assess the perspective of the future company's development and strengthen the importance and influence of financial variables on the company's value. Limitations/future research: The research limitations, which are also opportunities for future research, are aimed at the investigation of company value indicators at the level of individual European economies or industries. One should look at the company's value factors before and after the Covid-19 pandemic and consider a longer time in the company's business. Other financial determinants that affect the value of the company could be considered, and the company value could be measured by some other indicators. Also, the influence of nonfinancial determinants on the company value could be researched.
Production management. Operations management, Personnel management. Employment management
Autonomous intersection management (AIM) poses significant challenges due to the intricate nature of real-world traffic scenarios and the need for a highly expensive centralised server in charge of simultaneously controlling all the vehicles. This study addresses such issues by proposing a novel distributed approach to AIM utilizing multi-agent reinforcement learning (MARL). We show that by leveraging the 3D surround view technology for advanced assistance systems, autonomous vehicles can accurately navigate intersection scenarios without needing any centralised controller. The contributions of this paper thus include a MARL-based algorithm for the autonomous management of a 4-way intersection and also the introduction of a new strategy called prioritised scenario replay for improved training efficacy. We validate our approach as an innovative alternative to conventional centralised AIM techniques, ensuring the full reproducibility of our results. Specifically, experiments conducted in virtual environments using the SMARTS platform highlight its superiority over benchmarks across various metrics.
Bastien Baldacci, Philippe Bergault, Olivier Guéant
This paper introduces a novel methodology for the pricing and management of share buyback contracts, overcoming the limitations of traditional optimal control methods, which frequently encounter difficulties with high-dimensional state spaces and the intricacies of selecting appropriate risk penalty or risk aversion parameter. Our methodology applies optimized heuristic strategies to maximize the contract's value. The computation of this value utilizes classical methods typically used for pricing path-dependent options. Additionally, our approach naturally leads to the formulation of a $Δ$-hedging strategy and disentangles therefore the repurchase strategy from the hedging of the payoff.
Pablo Fondo-Ferreiro, Saber Mhiri, Cristina López-Bravo
et al.
Global optimization of access point (AP) assignment to user terminals requires efficient monitoring of user behavior, fast decision algorithms, efficient control signaling, and fast AP reassignment mechanisms. In this scenario, software defined networking (SDN) technology may be suitable for network monitoring, signaling, and control. We recently proposed embedding virtual switches in user terminals for direct management by an SDN controller, further contributing to SDN-oriented access network optimization. However, since users may restrict terminal-side traffic monitoring for privacy reasons (a common assumption by previous authors), we infer user traffic classes at the APs. On the other hand, since handovers will be more frequent in dense small-cell networks (e.g., mmWave-based 5G deployments will require dense network topologies with inter-site distances of ~150-200 m), the delay to take assignment decisions should be minimal. To this end, we propose taking fast decisions based exclusively on extremely simple network-side application flow-type predictions based on past user behavior. Using real data we show that a centralized allocation algorithm based on those predictions achieves network utilization levels that approximate those of optimal allocations. We also test a distributed version of this algorithm. Finally, we quantify the elapsed time since a user traffic event takes place until its terminal is assigned an AP, when needed.
Investigating the optimal nature of social interactions among actors (e.g., people or firms), who seek to achieve certain mutually-agreed objectives, has been the subject of extensive academic research. Using the relational models theory (describing all social interactions as combinations of four basic sociality ingredients: Communal Sharing, Authority Ranking, Equality Matching, and Market Pricing), the common approach revolves around qualitative arguments for determining sociality configurations most effective in realizing specific purposes, at times supplemented by empirical data. In the current treatment, we formulate this question as a mathematical optimization problem, in order to quantitatively derive the most suitable combination of sociality forms for dyadic actors, which optimizes their mutually-agreed objective. For this purpose, we develop an analytical framework of the (meta)relational models theory, and demonstrate that combining the four sociality forms to define a specific meaningful social situation inevitably prompts an inherent tension among them, codified by a single elementary and universal metarelation. In analogy with financial portfolio management, we subsequently introduce the concept of Social Relations Portfolio (SRP) management, and propose a generalizable methodology capable of quantitatively identifying the efficient SRP, which, in turn, enables effective stakeholder and change management initiatives. As an important illustration, the methodology is applied to the Triple Bottom Line (Profit, People, Planet) paradigm to derive its efficient SRP. This serves as a guide to practitioners for precisely measuring, monitoring, reporting and steering stakeholder and change management efforts concerning Corporate Social Responsibility (CSR) and Environmental, Social and Governance (ESG) within and / or across organizations.
This paper investigates the application of Feature-Enriched Generative Adversarial Networks (FE-GAN) in financial risk management, with a focus on improving the estimation of Value at Risk (VaR) and Expected Shortfall (ES). FE-GAN enhances existing GANs architectures by incorporating an additional input sequence derived from preceding data to improve model performance. Two specialized GANs models, the Wasserstein Generative Adversarial Network (WGAN) and the Tail Generative Adversarial Network (Tail-GAN), were evaluated under the FE-GAN framework. The results demonstrate that FE-GAN significantly outperforms traditional architectures in both VaR and ES estimation. Tail-GAN, leveraging its task-specific loss function, consistently outperforms WGAN in ES estimation, while both models exhibit similar performance in VaR estimation. Despite these promising results, the study acknowledges limitations, including reliance on highly correlated temporal data and restricted applicability to other domains. Future research directions include exploring alternative input generation methods, dynamic forecasting models, and advanced neural network architectures to further enhance GANs-based financial risk estimation.
This study focuses on the concept of organizational inclusion, which goes beyond diversity management, the dominant paradigm in the field of public administration. Although several studies in public administration mention the importance of inclusion, none of these studies have empirically tested its association with performance beyond diversity management. Data for this study comes from a survey conducted among public managers in Texas agencies. The study finds that diversity management alone is insufficient for improving workplace performance. What is required instead is an approach that promotes greater inclusion of employees in ways that takes their views into account and promotes self-esteem. The results show that productive workplaces exist when employees are encouraged to express their opinions, and their input is sought before making important organizational decisions. This requires supportive leadership and empowering employees with information and resources that will help them make important decisions about their jobs.
Hossein Firouzi, Javad Rezaeian, Mohammad Mehdi Movahedi
et al.
This paper presents a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic, incorporating dimensions of sustainability. The objectives of the model are as follows: 1) Minimizing the costs associated with building facilities and waste treatment centers, vehicle fuel costs, and environmental costs due to pollutant emissions; 2) Maximizing the energy generated from the waste combustion process; 3) Minimizing the risk of virus transmission resulting from inadequate waste management; and 4) Maximizing the number of job opportunities in the established centers. It is important to note that existing uncertainties are addressed through the application of fuzzy set theory. Given the multi-objective nature of the model, two multi-objective algorithms, namely the Pareto archive-based Krill Herd Algorithm and Non-dominated Sorting Genetic Algorithm II (NSGA-II), are employed to solve the defined problem. The results indicate that the proposed Krill Herd Algorithm converges to a solution with higher quality and dispersion compared to NSGA-II. Additionally, through a comparison of the spacing index and running time of the two algorithms, it is observed that NSGA-II explores the solution space with higher uniformity and solves the model in less time.IntroductionHospital waste encompasses a broad spectrum of both hazardous and non-hazardous materials. The management of hospital waste involves the development of a suitable supply chain network for handling waste generated in the healthcare sector. Improper disposal or mishandling of contaminated waste not only contributes to environmental pollution but also poses a risk of transferring viral pathogens to healthcare and recycling personnel. Research has shown that inadequate disposal of medical waste can lead to the transmission of up to 30% of hepatitis B, 1-3% of hepatitis C, and 0.3% of HIV infections from patients to healthcare workers. This paper aims to design a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic while considering the dimensions of sustainability.Literatur ReviewIn recent years, various studies have delved into the complexities of medical and hospital waste management, proposing mathematical models to address this intricate issue. The current study is built upon the work of Valizadeh et al. (2021). In their paper, a hybrid mathematical modeling approach was introduced, featuring a Bi-level programming model specifically tailored for infectious waste management during the COVID-19 pandemic. The outcomes revealed that, at the higher level of the model, governmental decisions aiming to minimize total costs associated with infectious waste management were crucial. This involved the conversion of collected infectious waste into energy, with the generated revenue being reinvested back into the system. The findings indicated that, through energy production from waste during the COVID-19 pandemic, approximately 34% of the total costs related to waste collection and transportation could be offset. The uniqueness of this study lies in its consideration of three sustainability dimensions: risk, vehicle routing, energy production, employment, and emission of polluting gases. Consequently, the novelty of this research, when compared to previous studies and the article by Valizadeh et al. (2021), is evident in several aspects. It introduces an integrated multi-objective positioning-routing model for the supply chain of waste management under pandemic conditions, taking into account sustainability dimensions, notably the economic aspect, and employs meta-heuristic algorithms for model resolution.MethdologyTo ensure the proper management of hospital waste, the waste is categorized into two groups: infectious and non-infectious waste. It is assumed that waste in hospitals and health centers is segregated and placed in infectious and non-infectious waste bins. The collected waste undergoes further processing: infectious waste is transported to incineration centers, where it is burned and converted into electrical energy, while non-infectious waste is sent to waste recycling centers, where it is reprocessed and returned to the production cycle in the industry. A multi-objective mathematical model is presented to integrate location-routing decisions in the supply chain of hospital waste management, with the following modeling assumptions:Waste segregation at the source helps prevent all waste from becoming viral, reducing the spread of viruses through waste.The risk of spreading viruses is assumed to be relatively equal for each type of waste.Two types of vehicles are considered for transporting waste: the first type carries non-infectious waste, while the second type carries infectious waste.The number of cars, waste collectors, and the capacity of waste incinerators are considered constant in this study.The mathematical model is multi-objective, with the objectives being to optimize the three dimensions of sustainability (economic, social, and environmental).The economic goal is to minimize system costs, including the cost of site location, recycling, collection, segregation of non-infectious waste, and incineration.The environmental goal is to minimize the emission of pollutants in the transportation and processing system in various facilities, as well as to maximize the production of electrical energy.The social goal is to minimize the risk of virus transmission and maximize the employment rate.Results and DiscussionThis research presents a multi-objective mathematical model for the reverse supply chain of hospital waste management during the COVID-19 pandemic in Iran and solves it. The pandemic period is considered a time of maximum utilization of health centers and waste disposal. In this context, a three-objective mathematical model was initially introduced. To solve the model, the krill herd optimization algorithm was employed. The performance of the krill herd optimization algorithm was scientifically and practically evaluated by comparing it with the well-known NSGA-II algorithm. After designing the model, both the multi-objective krill herd algorithm based on Pareto Archive and the NSGA-II algorithm were utilized to solve the model. The results of solving the model demonstrated that the proposed krill herd algorithm, designed in combination with VNS, effectively solved the model and determined the optimal solution within a boundary. Comparing the results of this algorithm with those obtained by the renowned NSGA-II algorithm revealed that the krill herd algorithm produced solutions of much higher quality.ConclusionThe comparison of the Index of dispersion between the two algorithms indicates that the krill herd optimization algorithm explores more points in the solution space, leading to a lower probability of getting stuck in local optima compared to the NSGA-II algorithm. On the other hand, the index of uniformity for the NSGA-II algorithm is lower than that of the krill herd algorithm (lower values are better), suggesting that the multi-objective genetic algorithm explores the solution space more uniformly. Considering the execution time of the two algorithms, it was observed that the NSGA-II algorithm solved the model in less time. Additionally, the increasing trend of execution time in both algorithms confirms the NP-HARD nature of the hospital waste management problem. According to the output of the MATLAB software, considering the presented model, the results affirm the capability to optimally select hospital waste recycling centers.
Orientation: The manufacturing sector in Kenya has been experiencing employee safety and productivity issues despite adopting safety programmes and laws regulating employee safety. Employee safety attitudes significantly worsen workplace safety and productivity problems.
Research purpose: The study determined the intervening effect of workplace safety attitudes on the relationship between workplace safety and employee productivity in manufacturing firms in Kenya.
Motivation for the study: Manufacturing firms adopt new technologies that expose employees to new safety risks, while globalisation has led to a diverse workforce with diverse safety attitudes.
Research approach/design and method: This study is grounded on the risk homeostasis theory; it adopted a cross-sectional survey research design guided by a positivist research philosophy. The target population comprised 853 manufacturing firms registered with the Kenya Association of Manufacturers. A sample of 124 firms distributed across the 14 sub-sectors in the manufacturing sector was obtained using a statistical formula to ensure all sectors were represented. Regression analysis was carried out in four steps to assess the intervening effect of workplace safety attitude on the relationship between workplace safety attitude and employee productivity.
Main findings: The coefficients were significant in each step, therefore leading to the conclusion that employee safety attitude significantly intervened in the relationship between workplace safety and employee productivity.
Practical/managerial implications: The study offers managerial insights into the situational position of workplace safety, employee safety attitudes and employee productivity.
Contribution/value-add: The study provides epistemological insights on the impact of employee safety attitudes on workplace safety and employee productivity.
The objective of this work is to identify and visualize the intellectual structure of the Lean Manufacturing research field. To achieve this, a bibliometric analysis will be carried out that combines a performance and co-occurrence analysis, to identify and analyze the relationships between the topics that have had the greatest impact on the construction of the knowledge base of this discipline. To achieve this objective, a bibliometric analysis was carried out that began with a search on the Web of Science (WOS) platform with the Lean Manufacturing theme. From this database, the references were exported and subsequently processed using the Bibliometrix software, which allows for both an analysis of indexes and the number of articles, which showed us that it was not until 2012 that a progressive publication of works on the subject begins, of productivity by country, with the USA appearing as the country with the greatest number of publications and the greatest number of citations on the topic analyzed, and of authors and journals with the greatest publications, as well as a co-occurrence analysis, which shows us the keywords most used by the authors and the thematic areas where they are most published, the results of which yielded five clusters or groups of keywords led by the terms Lean Management, Lean, Industry 4.0, Value Stream Map and Six Sigma.
In the field of Prognostics and Health Management (PHM), recent years have witnessed a significant surge in the application of machine learning (ML). Despite this growth, the field grapples with a lack of unified guidelines and systematic approaches for effectively implementing these ML techniques and comprehensive analysis regarding industrial open-source data across varied scenarios. To address these gaps, this paper provides a comprehensive review of ML approaches for diagnostics and prognostics of industrial systems using open-source datasets from PHM Data Challenge Competitions held between 2018 and 2023 by PHM Society and IEEE Reliability Society and summarizes a unified ML framework. This review systematically categorizes and scrutinizes the problems, challenges, methodologies, and advancements demonstrated in these competitions, highlighting the evolving role of both conventional machine learning and deep learning in tackling complex industrial tasks related to detection, diagnosis, assessment, and prognosis. Moreover, this paper delves into the common challenges in PHM data challenge competitions by emphasizing data-related and model-related issues and evaluating the limitations of these competitions. The potential solutions to address these challenges are also summarized. Finally, we identify key themes and potential directions for future research, providing opportunities and prospects for next-generation ML-PHM development in PHM domain.
Paraskevi Nousi, Loukia Avramelou, Georgios Rodinos
et al.
Financial portfolio management describes the task of distributing funds and conducting trading operations on a set of financial assets, such as stocks, index funds, foreign exchange or cryptocurrencies, aiming to maximize the profit while minimizing the loss incurred by said operations. Deep Learning (DL) methods have been consistently excelling at various tasks and automated financial trading is one of the most complex one of those. This paper aims to provide insight into various DL methods for financial trading, under both the supervised and reinforcement learning schemes. At the same time, taking into consideration sentiment information regarding the traded assets, we discuss and demonstrate their usefulness through corresponding research studies. Finally, we discuss commonly found problems in training such financial agents and equip the reader with the necessary knowledge to avoid these problems and apply the discussed methods in practice.