Hasil untuk "Employee participation in management. Employee ownership. Industrial democracy. Works councils"

Antoine BONNEMAIN

L’article aborde le travail soutenable sous l’angle de la qualité du travail et de son impact sur la santé des travailleurs et la santé publique, à partir d’une expérimentation sociale réalisée en psychologie du travail. L’auteur s’appuie sur une recherche en entreprise pour montrer comment la possibilité donnée aux travailleurs d’agir sur leur travail et les conditions de sa réalisation peut avoir des effets importants sur la santé publique et sur la protection de la nature. Pour cela, de nouvelles méthodes de délibération, associées à une évolution des dispositions légales en matière de droit du travail, s’avèrent indispensables.

Borna Barkhordar, Ali Jahangiri, Davoud Hosseinpour et al.

Purpose: Now that more than a decade has passed since the implementation of the law "Supporting Knowledge-Based Companies and Institutions and Commercializing Innovations and Inventions", the issue of evaluating the level of achievement of the predetermined goals in this law and analyzing the quality of achievements regarding their positive and negative effects on the target community is of great importance. The present study was conducted with the aim of presenting an evaluation model of supporting policy for knowledge-based companies and institutions. Methodology: In this qualitative research, the statistical population of the research consisted of two parts: the first were documents, plans, drafts, the text of the law, regulations, instructions, correspondence and administrative orders related to the law and the second were managers, experts, policymakers and implementers of the policy. The collection of research data was conducted using a documentary-library method and fieldwork through semi-structured interviews with academic and executive experts in the field of policymaking. The sampling method was snowball and purposeful theoretical saturation, through which we obtained the opinions from fifteen experts. We analyzed the research data using content analysis method and Atlas T software, version 7. Findings: Based on the research results, 363 basic themes, thirty-two organizing themes, and nine comprehensive themes were discovered and presented in the formwork of a model with such key structural items as policy functions, desirable characteristics, evaluation structure, environmental factors, evaluation scheduling, appropriateness, efficiency, effectiveness, and usefulness. Originality: In this study, we considered semantic commonalities of evaluation literature and, in accordance with the context and subject of evaluation, the desired evaluation approach and model of supporting policy for knowledge-based companies and institutions was presented. Implications: formulating quantitative, specific, and measurable objectives for supporting policy in knowledge-based companies and institutions is the suggestion of this research.

Kristi Topollai, Tolga Dimlioglu, Anna Choromanska et al.

Contract management involves reviewing and negotiating provisions, individual clauses that define rights, obligations, and terms of agreement. During this process, revisions to provisions are proposed and iteratively refined, some of which may be problematic or unacceptable. Automating this workflow is challenging due to the scarcity of labeled data and the abundance of unstructured legacy contracts. In this paper, we present a modular framework designed to streamline contract management through a retrieval-augmented generation (RAG) pipeline. Our system integrates synthetic data generation, semantic clause retrieval, acceptability classification, and reward-based alignment to flag problematic revisions and generate improved alternatives. Developed and evaluated in collaboration with an industry partner, our system achieves over 80% accuracy in both identifying and optimizing problematic revisions, demonstrating strong performance under real-world, low-resource conditions and offering a practical means of accelerating contract revision workflows.

Suyash Fulay, Sercan Demir, Galen Hines-Pierce et al.

A large share of retail investors hold public equities through mutual funds, yet lack adequate control over these investments. Indeed, mutual funds concentrate voting power in the hands of a few asset managers. These managers vote on behalf of shareholders despite having limited insight into their individual preferences, leaving them exposed to growing political and regulatory pressures, particularly amid rising shareholder activism. Pass-through voting has been proposed as a way to empower retail investors and provide asset managers with clearer guidance, but it faces challenges such as low participation rates and the difficulty of capturing highly individualized shareholder preferences for each specific vote. Randomly selected assemblies of shareholders, or ``investor assemblies,'' have also been proposed as more representative proxies than asset managers. As a third alternative, we propose artificial intelligence (AI) enabled representatives trained on individual shareholder preferences to act as proxies and vote on their behalf. Over time, these models could not only predict how retail investors would vote at any given moment but also how they might vote if they had significantly more time, knowledge, and resources to evaluate each proposal, leading to better overall decision-making. We argue that shareholder democracy offers a compelling real-world test bed for AI-enabled representation, providing valuable insights into both the potential benefits and risks of this approach more generally.

Duygu Kabakci-Zorlu, Fabio Lorenzi, John Sheehan et al.

We propose an interactive system using foundation models and user-provided technical documents to generate Failure Mode and Effects Analyses (FMEA) for industrial equipment. Our system aggregates unstructured content across documents to generate an FMEA and stores it in a relational database. Leveraging this tool, the time required for creation of this knowledge-intensive content is reduced, outperforming traditional manual approaches. This demonstration showcases the potential of foundation models to facilitate the creation of specialized structured content for enterprise asset management systems.

Morteza Ghodratipour, Ali Asghar pourezat, Mojtaba Kiaei

Purpose:  This research was intended to examine the governance challenges of the National Pension Fund and to design a periodic performance evaluation system for the Fund. Methodology: The research method was exploratory, and it was practical in terms of purpose. The statistical population consisted of fifteen experts in the field of pension funds. The data were collected using interviews and the model was designed through triple coding grounded theory. The number of experts was determined based on theoretical saturation. Findings: The analysis of the text of the interviews with the experts led to six main categories and thirty concepts. Findings show that the most important challenges of the governance of the National Pension Fund of a country include the following items: the inadequacy of the pension infrastructure, unfavourable work development and uncertainties in the Fund, short span of managerial appointments, fluctuations in the money exchange rate, high replacement rate of the personnel and low support rate,  lack of meritocracy in managerial appointments, rarity and even  lack of support measures at the community level, and lack of  government accountability and obligations to strengthen the Fund. Regarding the political and commercial turbulence in the external environment as well as the need for strategic thinking and quality internal monitoring and supervision, we suggest that planning, support, financial, insurance, managerial, political, human, corrective, supervisory, and retirement strategies be implemented to make periodic evaluation of the Fund's performance possible. Originality/ value: By presenting a comprehensive model including the challenges of the National Pension Fund governance, the causes of such challenges, and the external and internal factors affecting them, this research has achieved strategies to improve the current conditions of the National Pension Fund and the implementation of its periodic performance evaluation system. This outcome not only can be considered as a new step for the improvement of the National Pension Fund performance, but also can function as a milestone for other pension funds. Recommendations: We suggest that the strategies presented in this research be implemented in the National Pension Fund and be modified, if necessary, after the evaluation of their effects. It is also necessary to conduct more research in the field of the performance evaluation of other government funds.

Ben Armstrong, Kate Larson

We argue that there is a strong connection between ensemble learning and a delegative voting paradigm -- liquid democracy -- that can be leveraged to reduce ensemble training costs. We present an incremental training procedure that identifies and removes redundant classifiers from an ensemble via delegation mechanisms inspired by liquid democracy. Through both analysis and extensive experiments we show that this process greatly reduces the computational cost of training compared to training a full ensemble. By carefully selecting the underlying delegation mechanism, weight centralization in the classifier population is avoided, leading to higher accuracy than some boosting methods. Furthermore, this work serves as an exemplar of how frameworks from computational social choice literature can be applied to problems in nontraditional domains.

Matthew J. Schneider, Rufus Rankin, Prabir Burman et al.

The M6 Competition assessed the performance of competitors using a ranked probability score and an information ratio (IR). While these metrics do well at picking the winners in the competition, crucial questions remain for investors with longer-term incentives. To address these questions, we compare the competitors' performance to a number of conventional (long-only) and alternative indices using standard industry metrics. We apply factor models to measure the competitors' value-adds above industry-standard benchmarks and find that competitors with more extreme performance are less dependent on the benchmarks. We also uncover that most competitors could not generate significant out-performance compared to randomly selected long-only and long-short portfolios but did generate out-performance compared to short-only portfolios. We further introduce two new strategies by picking the competitors with the best (Superstars) and worst (Superlosers) recent performance and show that it is challenging to identify skill amongst investment managers. We also discuss the incentives of winning the competition compared to professional investors, where investors wish to maximize fees over an extended period of time.

Karoline Wiesner, Samuel Bien, Matthew C. Wilson

A critical issue for society today is the emergence and decline of democracy worldwide. It is unclear, however, how democratic features, such as elections and civil liberties, influence this change. Democracy indices, which are the standard tool to study this question, are based on the a priori assumption that improvement in any individual feature strengthens democracy overall. We show that this assumption does not always hold. We use the V-Dem dataset for a quantitative study of electoral regimes worldwide during the 20th century. We find a so-far overlooked trade-off between election quality and civil liberties. In particular, we identify a threshold in the democratisation process at which the correlation between election quality and civil liberties flips from negative to positive. Below this threshold we can thus clearly separate two kinds of non-democratic regimes: autocracies that govern through tightly controlled elections and regimes in which citizens are free but under less certainty -- a distinction that existing democracy indices cannot make. We discuss the stabilising role of election quality uncovered here in the context of the recently observed decline in democracy score of long-standing democracies, so-called `democratic backsliding' or `democratic recession'.

Nianjun Zhou, Dhaval Patel, Shuxin Lin et al.

This study introduces a novel approach to Industrial Asset Management (IAM) by incorporating Conditional-Based Management (CBM) principles with the latest advancements in Large Language Models (LLMs). Our research introduces an automated model-building process, traditionally reliant on intensive collaboration between data scientists and domain experts. We present two primary innovations: a taxonomy-guided prompting generation that facilitates the automatic creation of AI solution recipes and a set of LLM pipelines designed to produce a solution recipe containing a set of artifacts composed of documents, sample data, and models for IAM. These pipelines, guided by standardized principles, enable the generation of initial solution templates for heterogeneous asset classes without direct human input, reducing reliance on extensive domain knowledge and enhancing automation. We evaluate our methodology by assessing asset health and sustainability across a spectrum of ten asset classes. Our findings illustrate the potential of LLMs and taxonomy-based LLM prompting pipelines in transforming asset management, offering a blueprint for subsequent research and development initiatives to be integrated into a rapid client solution.

Seyed Abbas Alavi, Saeed Mosavi, Mohsen Sadeghi Nasab et al.

Background & Purpose: The success of knowledge-based organizations depends on the empowerment of its knowledge-based human resources. The more capable these organizations are in terms of human resources, the more successful they will be in achieving their goals. The purpose of this article is to design and explain the model of human resource empowerment in the Methodology: Department of Research and Self-Sufficiency Jihad of Iran's Army. Methodology: The research is developmental in terms of purpose and practical in terms of results, and it is qualitative and quantitative in terms of the type of data. The participants of the qualitative part include scientific experts and experts in the field of human resources management and executive experts of AJA's Research and Self-Sufficiency Jihad Department, who were selected by a non-probability judgmental and purposeful sampling method, and in the quantitative part, they also include knowledge workers who were selected by a random sampling method. In the qualitative part, the data collection tools included semi-structured interviews, and in the quantitative part, a researcher-made questionnaire was used. In the quantitative part, thematic analysis was used to analyze the data, and in the quantitative part, structural equation modeling was used to validate the model. Findings: The model of empowerment of the target society included the main categories of knowledge empowerment, professional empowerment, functional empowerment, moral empowerment, social empowerment, military empowerment, and cognitive empowerment, and the relationship between them and their constituent subcategories have been confirmed quantitatively. Conclusion: The professional nature of human resources working in knowledge-based defense organizations makes their empowerment program multidimensional and comprehensive. The model presented in this research can be used as a practical guide for developing human resources development programs in this field.

Markus Utke, Ulrike Schmidt-Kraepelin

Liquid democracy with ranked delegations is a novel voting scheme that unites the practicability of representative democracy with the idealistic appeal of direct democracy: Every voter decides between casting their vote on a question at hand or delegating their voting weight to some other, trusted agent. Delegations are transitive, and since voters may end up in a delegation cycle, they are encouraged to indicate not only a single delegate, but a set of potential delegates and a ranking among them. Based on the delegation preferences of all voters, a delegation rule selects one representative per voter. Previous work has revealed a trade-off between two properties of delegation rules called anonymity and copy-robustness. To overcome this issue we study two fractional delegation rules: Mixed Borda branching, which generalizes a rule satisfying copy-robustness, and the random walk rule, which satisfies anonymity. Using the Markov chain tree theorem, we show that the two rules are in fact equivalent, and simultaneously satisfy generalized versions of the two properties. Combining the same theorem with Fulkerson's algorithm, we develop a polynomial-time algorithm for computing the outcome of the studied delegation rule. This algorithm is of independent interest, having applications in semi-supervised learning and graph theory.

Huan Wang, Yan-Fu Li, Min Xie

Prognostics and health management (PHM) is essential for industrial operation and maintenance, focusing on predicting, diagnosing, and managing the health status of industrial systems. The emergence of the ChatGPT-Like large-scale language model (LLM) has begun to lead a new round of innovation in the AI field. It has extensively promoted the level of intelligence in various fields. Therefore, it is also expected further to change the application paradigm in industrial PHM and promote PHM to become intelligent. Although ChatGPT-Like LLMs have rich knowledge reserves and powerful language understanding and generation capabilities, they lack domain-specific expertise, significantly limiting their practicability in PHM applications. To this end, this study explores the ChatGPT-Like LLM empowered by the local knowledge base (LKB) in industrial PHM to solve the above limitations. In addition, we introduce the method and steps of combining the LKB with LLMs, including LKB preparation, LKB vectorization, prompt engineering, etc. Experimental analysis of real cases shows that combining the LKB with ChatGPT-Like LLM can significantly improve its performance and make ChatGPT-Like LLMs more accurate, relevant, and able to provide more insightful information. This can promote the development of ChatGPT-Like LLMs in industrial PHM and promote their efficiency and quality.

Wenyuan Yang, Gongxi Zhu, Yuguo Yin et al.

Federated learning allows multiple parties to collaborate in learning a global model without revealing private data. The high cost of training and the significant value of the global model necessitates the need for ownership verification of federated learning. However, the existing ownership verification schemes in federated learning suffer from several limitations, such as inadequate support for a large number of clients and vulnerability to ambiguity attacks. To address these limitations, we propose a cryptographic signature-based federated learning model ownership verification scheme named FedSOV. FedSOV allows numerous clients to embed their ownership credentials and verify ownership using unforgeable digital signatures. The scheme provides theoretical resistance to ambiguity attacks with the unforgeability of the signature. Experimental results on computer vision and natural language processing tasks demonstrate that FedSOV is an effective federated model ownership verification scheme enhanced with provable cryptographic security.

Saied Sehhat, Maghssod Amiri, Mehdi Yazdanshenas et al.

Background & Purpose: Line managers’ HR role is considerably highlighted, and HR devolution is a definite future in organizational landscape. Accordingly, the principal objective of this study is to present a comprehensive framework of antecedents of strengthening line managers’ HR role and uncover its multi-level outcomes.Methodology: In line with the purpose of this study, all indicators, concepts, and categories of HR devolution were identified using the meta-synthesis qualitative research method. 113 manuscripts were identified by searching through authenticate scientific datasets sources. Finally, the data from 46 relevant research conducted in this field were selected and analyzed.Findings: Using seven-step model of Sandelowski and Barroso, the findings of 46 previous studies related to the objectives of the study were reviewed, aggregated, combined, and interpreted. A total number of 28 codes, five themes, and two dimensions were identified and validated through Kappa Cohen coefficient. The results indicate that precursors of HR devolution can be categorized into two main dimensions including organizational and psychological factors. Additionally, devolving HR to the line can affect outcome at three individuals, group, and organizational levels.Conclusion: Utilization of line managers’ HR role creates several competitive advantages in various levels of organization. Nonetheless, it should be noticed that an equal and concurrent attention to both organizational factors and line managers’ psychological elements is of utmost important. Moreover, the framework offered in this study which sheds light on different aspects of HR devolution, provides a basis for future studies

Sahand Akbari, Aryan Gholipour, Abbas Nargesiyan

Background & Purpose: Nowadays, there are different generations of employees in the workplaces. Each generation has its own characteristics and effective management of these generations is a major challenge for organizations. Therefore, recognizing and understanding these characteristics is essential in order to managing these generations. Performance management is one of the most important functions of human resource management, which in this research has been investigated in generation Z as the generation after generation Y that few studies have been done about it. Therefore, the purpose of this research is to identify the essentials of performance management system of generation Z employees in the capital market. Methodology: For this purpose, thematic analysis method has been used as one of the qualitative research methods. The research paradigm is interpretive and semi-structured interviews were used as a research tool to collect raw data from Generation Z employees in the capital market. These employees were selected by purposive sampling of the type of maximum diversity. Findings: Total of 26 interviews after analysis led to the identification of 4 main themes, 12 secondary themes and 114 basic themes, which are based on the four main phases of performance management. Conclusion: The results show the importance of all four phases of the performance management system for generation Z employees, and if organizations continue to fail to implement the standard of this system, it will bring about destructive outcomes like turnover. Finally, research limitations and suggestions for future research are provided.

Alireza Tanhayee

Background & Purpose: In today's society, one of the main duties of the university should be to train responsible citizens who not only feel self-worth but also consider themselves members of society. In addition, these citizens should not only face and solve problematic situations but also be able to take on various responsibilities in society and have high social participation. Accordingly, the purpose of this study is to examine the role of the university in educating a responsible citizen and to provide effective solutions.Methodology: This research is applied in terms of purpose and has been done using a qualitative approach and content analysis. Participants of this study are university administrators, professors, experts, and specialists (employees of universities, related institutions and organizations) with a doctoral degree and at least 15 years of service. For sampling, the snowball targeted method has been used to the extent of theoretical saturation.Findings: The analysis of the information collected in the interviews using open and axial coding indicates that the most important fundamental harms in reducing the effectiveness of the administrators of the universities of the Islamic Republic of Iran in educating responsible citizens include: the uncontrolled spread of quantitativeism in the development of universities and the weakness of quality, the weak relationship among academia, society, and industry, and the wrong system of promoting professors. Besides, the most important and effective ways to achieve this goal include: rethinking institutional values of university, establishing educational sites in the city with the help of other social institutions (municipality), reviewing the functions of higher education by specialists and experts in educational sciences.Conclusion: Applying strategies to improve the role of the university in educating responsible citizens can provide the ground for sustainable development in country by producing and promoting sustainable order and strengthening, deepening, and developing the values ​​of democracy in society.

Shrutarv Awasthi, Nils Gramse, Christopher Reining et al.

This work aims at showing that it is feasible and safe to use a swarm of Unmanned Aerial Vehicles (UAVs) indoors alongside humans. UAVs are increasingly being integrated under the Industry 4.0 framework. UAV swarms are primarily deployed outdoors in civil and military applications, but the opportunities for using them in manufacturing and supply chain management are immense. There is extensive research on UAV technology, e.g., localization, control, and computer vision, but less research on the practical application of UAVs in industry. UAV technology could improve data collection and monitoring, enhance decision-making in an Internet of Things framework and automate time-consuming and redundant tasks in the industry. However, there is a gap between the technological developments of UAVs and their integration into the supply chain. Therefore, this work focuses on automating the task of transporting packages utilizing a swarm of small UAVs operating alongside humans. MoCap system, ROS, and unity are used for localization, inter-process communication and visualization. Multiple experiments are performed with the UAVs in wander and swarm mode in a warehouse like environment.

Markus Voggenreiter, Ulrich Schöpp

Integrating security activities into the software development lifecycle to detect security flaws is essential for any project. These activities produce reports that must be managed and looped back to project stakeholders like developers to enable security improvements. This so-called Feedback Loop is a crucial part of any project and is required by various industrial security standards and models. However, the operation of this loop presents a variety of challenges. These challenges range from ensuring that feedback data is of sufficient quality over providing different stakeholders with the information they need to the enormous effort to manage the reports. In this paper, we propose a novel approach for treating findings from security activity reports as belief in a Knowledge Base (KB). By utilizing continuous logical inferences, we derive information necessary for practitioners and address existing challenges in the industry. This approach is currently evaluated in industrial DevOps projects, using data from continuous security testing.

Dafeng Zhu, Bo Yang, Yuxiang Liu et al.

Owing to large industrial energy consumption, industrial production has brought a huge burden to the grid in terms of renewable energy access and power supply. Due to the coupling of multiple energy sources and the uncertainty of renewable energy and demand, centralized methods require large calculation and coordination overhead. Thus, this paper proposes a multi-energy management framework achieved by decentralized execution and centralized training for an industrial park. The energy management problem is formulated as a partially-observable Markov decision process, which is intractable by dynamic programming due to the lack of the prior knowledge of the underlying stochastic process. The objective is to minimize long-term energy costs while ensuring the demand of users. To solve this issue and improve the calculation speed, a novel multi-agent deep reinforcement learning algorithm is proposed, which contains the following key points: counterfactual baseline for facilitating contributing agents to learn better policies, soft actor-critic for improving robustness and exploring optimal solutions. A novel reward is designed by Lagrange multiplier method to ensure the capacity constraints of energy storage. In addition, considering that the increase in the number of agents leads to performance degradation due to large observation spaces, an attention mechanism is introduced to enhance the stability of policy and enable agents to focus on important energy-related information, which improves the exploration efficiency of soft actor-critic. Numerical results based on actual data verify the performance of the proposed algorithm with high scalability, indicating that the industrial park can minimize energy costs under different demands.