Les auteurs examinent la capacité des syndicats et de la négociation collective d’entreprise à améliorer les conditions de travail dans les usines de confection approvisionnant les marques et distributeurs multinationaux. Ils montrent, à partir de données recueillies par le programme Better Work au Bangladesh, au Cambodge, à Haïti, en Indonésie, en Jordanie, au Nicaragua et au Viet Nam, que leur rôle peut être positif, même dans les pays qui restreignent la liberté syndicale et la négociation collective. Ils confirment ainsi en partie que l’implication des institutions et acteurs locaux permet d’améliorer le respect des normes du travail du côté production des chaînes d’approvisionnement mondiales.
Darul Wiyono, Deshinta Arrova Dewi, Ema Ambiapuri
et al.
Purpose: This research explores the impact of Environmental, Social, and Governance (ESG) practices on employee performance and well-being in private higher education institutions in Bandung, West Java. It seeks to provide insights into how effective ESG integration can enhance organizational performance and employee satisfaction. Methodology: A quantitative approach employing Partial Least Squares Structural Equation Modeling (PLS-SEM) analyzed the relationships among constructs: environmental practices, social practices, governance practices, and the dependent variable, employee performance and well-being. Data were collected from 270 respondents through stratified random sampling across various administrative roles in 138 private higher education institutions. Findings: The results showed that environmental practices positively impact employee performance and well-being, with social practices also contributing. Governance practices mediate and amplify these effects. These findings emphasize the importance of integrating sustainable practices into organizational strategies to improve employee outcomes and overall institutional performance. Originality: This study explores the impact of ESG practices on employee performance in private higher education institutions, focusing on sustainability to enhance engagement and productivity. Unlike previous research, which focused primarily on corporate sectors or public universities, it emphasizes the unique challenges of private institutions, particularly in Bandung, offering new insights into governance practices as mediators.
We propose a statistical model to estimate population proportions under the survey variable cause model (Groves 2006), the setting in which the characteristic measured by the survey has a direct causal effect on survey participation. For example, we estimate employee satisfaction from a survey in which the decision of an employee to participate depends on their satisfaction. We model the time at which a respondent 'arrives' to take the survey, leveraging results from the counting processes literature that has been developed to analyze similar problems with survival data. Our approach is particularly useful for nonresponse bias analysis because it relies on different assumptions than traditional adjustments such as poststratification, which assumes the common cause model, the setting in which external factors explain the characteristic measured by the survey and participation. Our motivation is the Federal Employee Viewpoint Survey, which asks federal employees whether they are satisfied with their work organization. Our model suggests that the sample proportion overestimates the proportion of federal employees that are not satisfied with their work organization even after adjustment by poststratification. Employees that are not satisfied likely select into the survey, and this selection cannot be explained by personal characteristics like race, gender, and occupation or work-place characteristics like agency, unit, and location.
Employee turnover refers to an individual's termination of employment from the current organization. It is one of the most persistent challenges for firms, especially those ones in Information Technology (IT) industry that confront high turnover rates. Effective prediction of potential employee turnovers benefits multiple stakeholders such as firms and online recruiters. Prior studies have focused on either the turnover prediction within a single firm or the aggregated employee movement among firms. How to predict the individual employees' turnovers among multiple firms has gained little attention in literature, and thus remains a great research challenge. In this study, we propose a novel deep learning approach based on job embeddedness theory to predict the turnovers of individual employees across different firms. Through extensive experimental evaluations using a real-world dataset, our developed method demonstrates superior performance over several state-of-the-art benchmark methods. Additionally, we estimate the cost saving for recruiters by using our turnover prediction solution and interpret the attributions of various driving factors to employee's turnover to showcase its practical business value.
Abstract
Purpose: The current research aimed to determine the dimensions and components of ambi/ tridexterity universities in Iran.
Methodology: The study was based on interpretive paradigm. It was also developmental in terms of its applied purpose, descriptive in terms of the nature of method, and qualitative in terms of fragmentary data. The statistical population consisted of the organizational experts and faculty members of the country's universities. A statistical sample of twenty-seven people was determined according to the characteristics and conditions required for the members of the Delphi panel and through the combination of targeted and accessible methods. To collect data, we applied closed and open-ended questionnaires and semi-structured interviews were used. Data were analyzed based on fuzzy Delphi approach and thematic analysis.
Findings: Based on the analysis of the interview texts, the dimensions of an ambi/ tridexterity university include: a. exploitation dimension with seven components of organizational intelligence, empowerment, social capital, talent management, communication, meritocracy and organizational culture; b. exploration dimension with five components of flexibility of human resources, opportunity creating leadership style, creativity and innovation, training and development, and strategic targeting; c. sustainability dimension with four components of sustainable regional growth, commitment to sustainability, environmental measures and explanation of sustainability criteria.
Originality: Findings show that ample studies exist in the field of ambidexterity; however, most of them have only looked at the concept of ambidextrous organization emphasizing more on the balance between or the combination of exploitation and exploration dimensions. While, the present research, in addition to dealing with the concept of ambidextrous university, has proposed the third dimension of "sustainability" as an innovation in the concept of ambidextrous university. As such, we have promoted the concept to trinaldextrous university.
Economic growth, development, planning, Employee participation in management. Employee ownership. Industrial democracy. Works councils
Understanding employee behavior in a workplace is critical for enhancing overall organizational performance. Despite numerous efforts to improve work environments, many organizations still need help with challenges primarily rooted in unaddressed issues or poorly understood behavioral patterns. In this paper, we have focused on recognizing this pattern and the dynamics of complex systems in organizational behavior and studying how factors influence the systems overall behavior using Wolframs Cellular Automata theory. Over the cycle, we observe how leadership influences team dynamics, influences the organization, and drives employee behavior to foster a positive environment. Keywords: Cellular Automata, Employee Behavior, team dynamics, Leadership Influence.
Amidst growing uncertainty and frequent restructurings, the impacts of employee exits are becoming one of the central concerns for organizations. Using rich communication data from a large holding company, we examine the effects of employee departures on socialization networks among the remaining coworkers. Specifically, we investigate how network metrics change among people who historically interacted with departing employees. We find evidence of ``breakdown" in communication among the remaining coworkers, who tend to become less connected with fewer interactions after their coworkers' departure. This effect appears to be moderated by both external factors, such as periods of high organizational stress, and internal factors, such as the characteristics of the departing employee. At the external level, periods of high stress correspond to greater communication breakdown; at the internal level, however, we find patterns suggesting individuals may end up better positioned in their networks after a network neighbor's departure. Overall, our study provides critical insights into managing workforce changes and preserving communication dynamics in the face of employee exits.
Jarring events inspiring reflection, known as ``shocks" in the literature, are the motive force in explaining changes in employee embeddedness and retention within the unfolding model of labor turnover. Substantial research effort has examined strategies for insulating valued employees from adverse shocks. However, this paper provides empirical evidence that unambiguously negative shocks can increase employee retention when underlying firm and employee incentives with respect to these shocks are aligned. Using survival analysis on a unique data set of 466,236 communication records and 45,873 employment spells from 21 trucking companies, we show how equipment-related shocks tend to increase the duration of employment. Equipment shocks also generate paradoxically positive sentiments that demonstrate an increase in employees' affective commitment to the firm. Our results highlight the important moderating role aligned incentives have in how shocks ultimately translate into retention. Shared hardships strengthen bonds in employment as in other areas.
Success in todays data-driven corporate climate requires a deep understanding of employee behavior. Companies aim to improve employee satisfaction, boost output, and optimize workflow. This research study delves into creating synthetic data, a powerful tool that allows us to comprehensively understand employee performance, flexibility, cooperation, and team dynamics. Synthetic data provides a detailed and accurate picture of employee activities while protecting individual privacy thanks to cutting-edge methods like agent-based models (ABMs), Generative Adversarial Networks (GANs), and statistical models. Through the creation of multiple situations, this method offers insightful viewpoints regarding increasing teamwork, improving adaptability, and accelerating overall productivity. We examine how synthetic data has evolved from a specialized field to an essential resource for researching employee behavior and enhancing management efficiency. Keywords: Agent-Based Model, Generative Adversarial Network, workflow optimization, organizational success
Employee attrition poses significant costs for organizations, with traditional statistical prediction methods often struggling to capture modern workforce complexities. Machine learning (ML) advancements offer more scalable and accurate solutions, but large language models (LLMs) introduce new potential in human resource management by interpreting nuanced employee communication and detecting subtle turnover cues. This study leverages the IBM HR Analytics Attrition dataset to compare the predictive accuracy and interpretability of a fine-tuned GPT-3.5 model against traditional ML classifiers, including Logistic Regression, k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, AdaBoost, and XGBoost. While traditional models are easier to use and interpret, LLMs can reveal deeper patterns in employee behavior. Our findings show that the fine-tuned GPT-3.5 model outperforms traditional methods with a precision of 0.91, recall of 0.94, and an F1-score of 0.92, while the best traditional model, SVM, achieved an F1-score of 0.82, with Random Forest and XGBoost reaching 0.80. These results highlight GPT-3.5's ability to capture complex patterns in attrition risk, offering organizations improved insights for retention strategies and underscoring the value of LLMs in HR applications.
Background & Purpose: The issue of work-family conflict has received much attention in today's organizations, because it affects the organizational success and the personal life of employees. Studies in this field emphasize its two-dimensionality: work family conflict (WFC) and family- work conflict (FWC). Therefore, the research seeks to identify the affecting factors and consequences of both of the conflicts.
Methodology: This interpretive research has used the systematic literature review (SLR) method to answer the main research question. Its information sources include related articles that have been published in international scientific databases from 1992 to 2023 and entered the systematic review process based on acceptance criteria. Finally, after evaluating their quality based on Q and H indexes of publications, 91 articles were analyzed using coding method.
Findings: The identified antecedents of work-family conflict were classified into four categories: nature of job, time-related antecedents of the job, job requirements and work conditions, and social pressure of the job, which lead to two categories of consequences: weakening of the family role and Weakening the quality of family life. The antecedents affecting family-work conflict are also classified into three categories: family status of employees, role-oriented family antecedents, and family social pressure. The consequences of these antecedents include: psychological job consequences, behavior-oriented job consequences, and performance-oriented job consequences.
Conclusion: The findings of this research can help managers and organizations to better understand the antecedents and consequences related to both areas of the conflict so that they can effectively take steps to solve the problems of the working and family life of employees. Additionally, the findings of this study, by integratingng the findings of previous studies, have helped to improve the richness of existing knowledge in this field.
Employee participation in management. Employee ownership. Industrial democracy. Works councils
Mohammad Hossein Roozbeh, Morteza Mousakhani, Hamed Rahmani
Purpose: Identifying and developing employees' competencies is one of the most important measures to advance human resources management goals. Therefore, the current research was conducted to identify the advantages of the gamification tool for selecting and promoting professional managers in evaluation centers with the intention of investigating a new method to determine the competencies of managers.
Methodology: In terms of the purpose, this research was applied and regarding type, it was qualitative. It intended to investigate the degree of compatibility of the competencies indicated in the Executive Directives No. 1657363 on the selection and appointment of professional managers enacted by the State Employment and Administration Organization. The statistical population includes 5 experts and evaluators and 10 managers of Evaluation Centers. A survey including interviews and a Delphi questionnaire was used to collect data.
Findings: The findings of the research show that gamification can be used as a suitable tool to identify competencies such as analytical thinking and problem-solving among the existing Assessment Centers.
Economic growth, development, planning, Employee participation in management. Employee ownership. Industrial democracy. Works councils
Background & Purpose: Human Resources Analytics (HRA) was considered to replace decision-making based on intuition and experience with data-driven decisions, thereby connecting human resources functions with business outcomes through quantitative analysis. As this area is still in its infancy, it requires extensive examination from both theoretical and practical perspectives. To contribute to the literature on this topic, this study seeks to review the evolution of research and extract the intellectual-conceptual foundations of human resources analytics.Methodology: In relation to the article's purpose, relevant research published between 1991 and 2022 was extracted from the Web of Science database and analyzed using VOS Viewer software. Descriptive, co-authorship, co-citation, and keyword co-occurrence analyses were performed on the collected data. Specifically, co-citation analysis was used to extract the intellectual-conceptual foundations of the subject.Findings: Research findings revealed that in terms of quantity, the highest number of articles were published on this topic in 2017. The highest number of citations was recorded in 2022. Ulrich and Dave are the most cited researchers, Human Resource Management was the most cited journal, and the University of Southern California was the most cited university. Co-citation analysis, which expresses intellectual-conceptual foundations in the field, presented the literature on human resources analytics in the form of four clusters. The first cluster showed the "nature and necessity of human resources analytics", the second cluster focused on the discussion of "the link between human resources analytics and technology", the third cluster was formed around the topic of "human resources analytics and data science", and finally the fourth cluster was called “the dark strain of human resource analytics.Conclusion: The topic of human resource analytics has a research history of about two decades in the world, although the process of published research in this field has been very fast. The structural dispersion of the research of the four clusters counted in this research shows that a so-called "bird's eye view" is required in human resources analytics. Furthermore, the most important weakness of research or even projects carried out in the field of human resources analytics is the limitation of work to people with human resources expertise or technical expertise, which deserves cooperation between these two types of expertise in the form of interdisciplinary collaboration.
Employee participation in management. Employee ownership. Industrial democracy. Works councils
Employee attrition is an important and complex problem that can directly affect an organisation's competitiveness and performance. Explaining the reasons why employees leave an organisation is a key human resource management challenge due to the high costs and time required to attract and keep talented employees. Businesses therefore aim to increase employee retention rates to minimise their costs and maximise their performance. Machine learning (ML) has been applied in various aspects of human resource management including attrition prediction to provide businesses with insights on proactive measures on how to prevent talented employees from quitting. Among these ML methods, the best performance has been reported by ensemble or deep neural networks, which by nature constitute black box techniques and thus cannot be easily interpreted. To enable the understanding of these models' reasoning several explainability frameworks have been proposed. Counterfactual explanation methods have attracted considerable attention in recent years since they can be used to explain and recommend actions to be performed to obtain the desired outcome. However current counterfactual explanations methods focus on optimising the changes to be made on individual cases to achieve the desired outcome. In the attrition problem it is important to be able to foresee what would be the effect of an organisation's action to a group of employees where the goal is to prevent them from leaving the company. Therefore, in this paper we propose the use of counterfactual explanations focusing on multiple attrition cases from historical data, to identify the optimum interventions that an organisation needs to make to its practices/policies to prevent or minimise attrition probability for these cases.
PurposeMost people associate ownership with the ability to control something. In the USA, employee share (or stock) ownership plans (ESOPs) are one of the principal forms of employee ownership. However, most ESOPs give employees very limited rights of control over the company they own. This paper explore this conflict by examining theories of property and ownership to determine whether the right to participate in decision-making is inherent in the idea of ownership as it is generally understood. Ultimately, the author argues that the law governing ESOPs should be revised to give employees a larger role in the governance of their companies.Design/methodology/approachThis paper considers the concept of ownership both historically and analytically. The author examines the roots of property theory in the work of John Locke and contemporary theorists, as well as contemporary theorizing about ownership.FindingsThere are two kinds of ownership: legal ownership and psychological ownership. In legal ownership, the right to participation is inherent but alienable, so one can legally be an owner of something but have no right of participation. Psychological ownership primarily arises from a sense of control. Legal ownership confers some part of the bundle of rights associated with property. Psychological ownership conveys a feeling of efficacy, responsibility and control, but no formal rights. The author argues that, for employee ownership to be more than mere property-holding, it must include meaningful participation in decision-making, including governance.Research limitations/implicationsThis paper is only concerned with ESOPs in the USA. Although the findings may be applicable, it does not address other forms of employee ownership or employee ownership outside of the USA.Practical implicationsPeople associate ownership with the ability to control something, so when workers are told they own their company but then find they have few control rights, it may undermine their sense of ownership. This then has negative implications for the company's success. To ensure meaningful levels of governance rights, policy-makers should revise the laws governing ESOPs to require greater involvement by employees.Social implicationsClarifying ambiguities around ownership will help support arguments for affording employee-owners greater control rights in their companies, which will have various spill-over effects.Originality/valuePractitioners and scholars alike deploy the term, “ownership” but ascribe different meanings to it. The distinction between legal and psychological ownership is largely lacking in the ESOP literature. Clarifying this distinction will help to move the discussion forward regarding employee participation in ESOPs. In addition, the paper provides an original analysis of property that demonstrates the importance of the right to control, showing that the traditional ESOP structure may violate important aspects of that right.
Samad Barani, Yaghob Rashnoadi, Hamed Khamehchi
et al.
Background & Purpose: Today, the classical structures and approaches of organization and management have often lost their effectiveness in the face of increasing environmental changes, and organizations are forced to abandon and forget these traditional approaches. The purpose of this study is to identify the antecedents and consequences of purposeful organizational forgetfulness using a meta-analysis approach.Methodology: The present study is developmental in terms of purpose and descriptive regarding method. The study population consists of scientific articles and master’s theses in the field of targeted organizational forgetting from 2011 to 2020, from which 45 cases were entered through meta-analysis process with a non- random purposive sampling method. The selected studies were analyzed using CMA2 software. The reliability of the research includes the reliability of identification that was obtained through the agreement of the referees in selecting and categorizing the researches, the reliability of coding was achieved through the agreement of the referees on the use of specific concepts for coding variables, and the reliability of significant level and effect size was gained through an agreement in measuring the effect size between the two meta- analysts.Findings: Findings suggested that the variables of information technology, organizational learning, knowledge management, organizational intelligence, transformational leadership and servant leadership have the highest impact factor among the antecedents, and variables of organizational change, staff empowerment, knowledge management, organizational learning, organizational agility and organizational performance have the highest impact factor among the consequences of targeted organizational forgetfulness.Conclusion: By using the antecedents identified in the present study, organizations can take steps to break the dependence on their outdated habits, structure, knowledge and technology, and be ready to accept changes and new approaches in the field of organization.
Employee participation in management. Employee ownership. Industrial democracy. Works councils
Saeed Nosratabadi, Roya Khayer Zahed, Vadim Vitalievich Ponkratov
et al.
Background/Purpose: The use of artificial intelligence (AI) models for data-driven decision-making in different stages of employee lifecycle (EL) management is increasing. However, there is no comprehensive study that addresses contributions of AI in EL management. Therefore, the main goal of this study was to address this theoretical gap and determine the contribution of AI models to EL. Methods: This study applied the PRISMA method, a systematic literature review model, to ensure that the maximum number of publications related to the subject can be accessed. The output of the PRISMA model led to the identification of 23 related articles, and the findings of this study were presented based on the analysis of these articles. Results: The findings revealed that AL algorithms were used in all stages of EL management (i.e., recruitment, on-boarding, employability and benefits, retention, and off-boarding). It was also disclosed that Random Forest, Support Vector Machines, Adaptive Boosting, Decision Tree, and Artificial Neural Network algorithms outperform other algorithms and were the most used in the literature. Conclusion: Although the use of AI models in solving EL problems is increasing, research on this topic is still in its infancy stage, and more research on this topic is necessary.
Jahaniar Bamdad Sufi, Mohmmadtaghi Taghavifard, Hamed Dehghanan
et al.
Background & Purpose: Due to the increasing expansion of holding structures in Iranian organizations, it is necessary for these companies to be able to properly assess the human resource management system of their subsidiaries, given the importance of human capital in creating value for companies. Thus, the purpose of this study is to provide a model for strategic evaluation and management of human resource management system in holding companies.Methodology: The research method is applied in terms of purpose and is a sequential exploratory mixed study regarding data collection. In the first part, by reviewing the literature and research, the initial model was calculated based on the CIPP framing framework, and then the opinions of the focus group, including 12 experts in the field of human resources, were used to complete the model. The AHP method has also been used to weigh the components and dimensions; Acceptable validity and reliability were achieved in qualitative and quantitative sections.Findings: The field section of the model includes internal and external environment factors. Inputs of the model consists of orientation of strategies, role human resources manager, and human resources organization components. The processes section includes components of human resource risk management, knowledge and innovation management, management of organizational culture and values, development of capabilities and talents, planning and recruitment, training and learning, maintenance and compensation of services, performance management and promotion, safety and health, human resource information system, interaction and communication with stakeholders. Also, the output section contains procedural factors, resulting factors, and organizational effect factors.Conclusion: Holding organizations can use this model to strategically evaluate the human resource management system of their subsidiaries. In this regard, it is essential, firstly, to adapt and possibly localize the dimensions of this model in their industry in addition to form a steering committee, and to strategize the strategic measures for the development of this model in their collection.
Employee participation in management. Employee ownership. Industrial democracy. Works councils
Employee rostering is a process of assigning available employees to open shifts. Automating it has ubiquitous practical benefits for nearly all industries, such as reducing manual workload and producing flexible, high-quality schedules. In this work, we develop a hybrid methodology which combines Mixed-Integer Linear Programming (MILP) with scatter search, an evolutionary algorithm, having as use case the optimization of employee rostering for Swissgrid, where it is currently a largely manual process. The hybrid methodology guarantees compliance with labor laws, maximizes employees' preference satisfaction, and distributes workload as uniformly as possible among them. Above all, it is shown to be a robust and efficient algorithm, consistently solving realistic problems of varying complexity to near-optimality an order of magnitude faster than an MILP-alone approach using a state-of-the-art commercial solver. Several practical extensions and use cases are presented, which are incorporated into a software tool currently being in pilot use at Swissgrid.