Hasil untuk "Business ethics"

Lucile Favero, Juan Antonio Pérez-Ortiz, Tanja Käser et al.

Artificial intelligence (AI) is rapidly being integrated into educational contexts, promising personalized support and increased efficiency. However, growing evidence suggests that the uncritical adoption of AI may produce unintended harms that extend beyond individual learning outcomes to affect broader societal goals. This paper examines the societal implications of AI in education through an integrative framework with four interrelated dimensions: cognition, agency, emotional well-being, and ethics. Drawing on research from education, cognitive science, psychology, and ethics, we synthesize existing evidence to show how AI-driven cognitive offloading, diminished learner agency, emotional disengagement, and surveillance-oriented practices can mutually reinforce one another. We argue that these dynamics risk undermining critical thinking, intellectual autonomy, emotional resilience, and trust, capacities that are foundational both for effective learning and also for democratic participation and informed civic engagement. Moreover, AI's impact is contingent on design and governance: pedagogically aligned, ethically grounded, and human-centered AI systems can scaffold effortful reasoning, support learner agency, and preserve meaningful social interaction. By integrating fragmented strands of prior research into a unified framework, this paper advances the discourse on responsible AI in education and offers actionable implications for educators, designers, and institutions. Ultimately, the paper contends that the central challenge is not whether AI should be used in education, but how it can be designed and governed to support learning while safeguarding the social and civic purposes of education.

Lynnette Hui Xian Ng, Ethan Pan, Michael Miller Yoder et al.

A growing suite of research illustrates the negative impact of social media bots in amplifying harmful information with widespread social implications. Social bot detection algorithms have been developed to help identify these bot agents efficiently. While such algorithms can help mitigate the harmful effects of social media bots, they operate within complex socio-technical systems that include users and organizations. As such, ethical considerations are key while developing and deploying these bot detection algorithms, especially at scales as massive as social media ecosystems. In this article, we examine the ethical implications for social bot detection systems through three pillars: training datasets, algorithm development, and the use of bot agents. We do so by surveying the training datasets of existing bot detection algorithms, evaluating existing bot detection datasets, and drawing on discussions of user experiences of people being detected as bots. This examination is grounded in the FATe framework, which examines Fairness, Accountability, and Transparency in consideration of tech ethics. We then elaborate on the challenges that researchers face in addressing ethical issues with bot detection and provide recommendations for research directions. We aim for this preliminary discussion to inspire more responsible and equitable approaches towards improving the social media bot detection landscape.

Valerii Paliiev, Dmytro Kozlovskyi, Vadym Tuhai et al.

The purpose of this study is to provide a comprehensive scientific rationale and quantitative modelling of strategies for managing antifragility and crisis resilience in Ukrainian agricultural enterprises in the face of escalating global risks. The research develops an integrated model that combines short–term crisis response with long–term strategic development. The methodology is based on a systems approach to risk analysis, incorporating official statistics, expert interview, and multifactor regression modeling of logistics costs. The methodology combines a systems–based risk analysis using official statistics, a single semi-structured interview with the director of LLC “Agropartner” (Ukraine), and multifactor modelling of logistics costs. A multiple linear regression of logistics –plus –storage costs per ton on diesel price, distance to port, enterprise scale, and an antifragility indicator was estimated using n=50 observations (2022–2023). Coefficient signs and significance are consistent with theory, and robustness checks confirm the results. The results demonstrate that integrating antifragility with resilience significantly reduces transportation and storage costs, while also enhancing financial efficiency under uncertainty. The scientific novelty lies in the development of a quantitative model that evaluates the effectiveness of antifragile measures in the Ukrainian context. The practical significance is the creation of a tool for agricultural enterprises to improve strategic planning, optimize logistics, and justify investment decisions, as well as to inform public policies aimed at strengthening the resilience of the agricultural sector.

Vusi Mpungane, Tshepo Tlapana

The primary objective of this study was to investigate how parents in the eThekwini region perceive food advertising targeted at minors on television. The study also aimed to understand the impact of television food promotions on minor’s eating habits and food preferences from the parent’s perspective. The potential implications of this study's findings on marketing strategies, particularly those targeting children, are significant and could lead to a shift in advertising practices. This quantitative cross-sectional study, which involved a nonprobability sampling method, specifically convenience sampling, focused on a subset of the larger eThekwini population, particularly 400 parents with minors residing in the region. The data was analyzed using SPSS Version 27 software, employing descriptive statistics to summarize the findings. The study revealed that many respondents are highly concerned about food advertising on children’s television shows. In particular, 94.1% of respondents believe that there are too many food advertisements in programs aimed at minors, indicating that they feel excessive food marketing targets children. Additionally, 94.4% of respondents expressed concerns about food product advertising, especially the use of celebrities to promote food items. Reliability was assessed by calculating Cronbach’s alpha coefficient, which produced scores of 0.814 and 0.929, respectively. These results demonstrate a high level of reliability for the measuring instrument used in the study. It is recommended that governments introduce stringent regulations to discourage the marketing and promotion of food products to minors, focusing instead on promoting healthy eating and overall well-being. Importantly, regulations are urgently needed to encourage food manufacturers to improve child-targeted advertising and help combat childhood obesity.

Ali Nour Eldin, Benjamin Dalmas, Walid Gaaloul

In recent years, there has been a growing interest in the verification of business process models. Despite their lack of formal characterization, these models are widely adopted in both industry and academia. To address this issue, formalizing the execution semantics of business process modeling languages is essential. Since data and process are two facets of the same coin, and data are critical elements in the execution of process models, this work introduces Proving an eXecutable BPMN injected with data, BPMN-ProX. BPMN-ProX is a low-code testing framework that significantly enhances the verification of data-aware BPMN. This low-code platform helps bridge the gap between non-technical experts and professionals by proposing a tool that integrates advanced data handling and employs a robust verification mechanism through state-of-the-art model checkers. This innovative approach combines theoretical verification with practical modeling, fostering more agile, reliable, and user-centric business process management.

Francesco Vinci, Gyunam Park, Wil van der Aalst et al.

Business Process Simulation (BPS) refers to techniques designed to replicate the dynamic behavior of a business process. Many approaches have been proposed to automatically discover simulation models from historical event logs, reducing the cost and time to manually design them. However, in dynamic business environments, organizations continuously refine their processes to enhance efficiency, reduce costs, and improve customer satisfaction. Existing techniques to process simulation discovery lack adaptability to real-time operational changes. In this paper, we propose a streaming process simulation discovery technique that integrates Incremental Process Discovery with Online Machine Learning methods. This technique prioritizes recent data while preserving historical information, ensuring adaptation to evolving process dynamics. Experiments conducted on four different event logs demonstrate the importance in simulation of giving more weight to recent data while retaining historical knowledge. Our technique not only produces more stable simulations but also exhibits robustness in handling concept drift, as highlighted in one of the use cases.

Valeria Cesaroni, Eleonora Pasqua, Piercosma Bisconti et al.

AI-based technologies have significant potential to enhance inclusive education and clinical-rehabilitative contexts for children with Special Educational Needs and Disabilities. AI can enhance learning experiences, empower students, and support both teachers and rehabilitators. However, their usage presents challenges that require a systemic-ecological vision, ethical considerations, and participatory research. Therefore, research and technological development must be rooted in a strong ethical-theoretical framework. The Capability Approach - a theoretical model of disability, human vulnerability, and inclusion - offers a more relevant perspective on functionality, effectiveness, and technological adequacy in inclusive learning environments. In this paper, we propose a participatory research strategy with different stakeholders through a case study on the ARTIS Project, which develops an AI-enriched interface to support children with text comprehension difficulties. Our research strategy integrates ethical, educational, clinical, and technological expertise in designing and implementing AI-based technologies for children's learning environments through focus groups and collaborative design sessions. We believe that this holistic approach to AI adoption in education can help bridge the gap between technological innovation and ethical responsibility.

Shahaf Bassan, Shlomit Gur, Sergey Zeltyn et al.

Tasks in Predictive Business Process Monitoring (PBPM), such as Next Activity Prediction, focus on generating useful business predictions from historical case logs. Recently, Deep Learning methods, particularly sequence-to-sequence models like Long Short-Term Memory (LSTM), have become a dominant approach for tackling these tasks. However, to enhance model transparency, build trust in the predictions, and gain a deeper understanding of business processes, it is crucial to explain the decisions made by these models. Existing explainability methods for PBPM decisions are typically *post-hoc*, meaning they provide explanations only after the model has been trained. Unfortunately, these post-hoc approaches have shown to face various challenges, including lack of faithfulness, high computational costs and a significant sensitivity to out-of-distribution samples. In this work, we introduce, to the best of our knowledge, the first *self-explaining neural network* architecture for predictive process monitoring. Our framework trains an LSTM model that not only provides predictions but also outputs a concise explanation for each prediction, while adapting the optimization objective to improve the reliability of the explanation. We first demonstrate that incorporating explainability into the training process does not hurt model performance, and in some cases, actually improves it. Additionally, we show that our method outperforms post-hoc approaches in terms of both the faithfulness of the generated explanations and substantial improvements in efficiency.

Lucas Taufer

A finalidade deste ensaio é apresentar algumas contribuições de David Hume à discussão do tema razões para agir a partir dos textos “Da liberdade e necessidade”, oriundo de Uma investigação sobre o entendimento humano, e “Dos princípios gerais da moral” e “Sobre o sentimento moral”, ambos oriundos de Uma investigação sobre os princípios da moral. Partindo da provocação de Bernard Williams em sua descrição do que seria um modelo “sub-humeano” de concepção sobre razões para agir, intentamos apresentar qual seria o modelo “propriamente humeano” de compreensão sobre a temática. Os esforços da tentativa estão expostos em três momentos respectivamente orientados à reconstrução do argumento humeano. As relações entre sentimentos, razão, ações, motivação para agir e juízos morais serão discutidas, na primeira seção, enquanto tributárias do debate sobre as disposições de entendimento sobre a liberdade e a necessidade que dizem respeito às tratativas humeanas de compreender a condição humana em seus aspectos epistemológicas. Já na segunda e na terceira, buscamos expor a compreensão possível dos conceitos acima mencionados nos quadros da filosofia moral humeana propriamente dita. A partir disso, podemos afirmar que, em Hume, as razões para agir, isto é, os elementos que se caracterizam como motivadores das ações e dos comportamentos, podem ser ditas como residentes, em última instância, na subjetividade da condição humana e originárias da sensibilidade humana, constituída por sua vez pelas paixões, emoções, vontade, desejos e sentimentos morais.

Andrea Zugarini, Andrew Zamai, Marco Ernandes et al.

Albeit Natural Language Processing has seen major breakthroughs in the last few years, transferring such advances into real-world business cases can be challenging. One of the reasons resides in the displacement between popular benchmarks and actual data. Lack of supervision, unbalanced classes, noisy data and long documents often affect real problems in vertical domains such as finance, law and health. To support industry-oriented research, we present BUSTER, a BUSiness Transaction Entity Recognition dataset. The dataset consists of 3779 manually annotated documents on financial transactions. We establish several baselines exploiting both general-purpose and domain-specific language models. The best performing model is also used to automatically annotate 6196 documents, which we release as an additional silver corpus to BUSTER.

Roy Abitbol, Eyal Cohen, Muhammad Kanaan et al.

As artificial intelligence (AI) continues to rapidly advance, there is a growing demand to integrate AI capabilities into existing business applications. However, a significant gap exists between the rapid progress in AI and how slowly AI is being embedded into business environments. Deploying well-performing lab models into production settings, especially in on-premise environments, often entails specialized expertise and imposes a heavy burden of model management, creating significant barriers to implementing AI models in real-world applications. KModels leverages proven libraries and platforms (Kubeflow Pipelines, KServe) to streamline AI adoption by supporting both AI developers and consumers. It allows model developers to focus solely on model development and share models as transportable units (Templates), abstracting away complex production deployment concerns. KModels enables AI consumers to eliminate the need for a dedicated data scientist, as the templates encapsulate most data science considerations while providing business-oriented control. This paper presents the architecture of KModels and the key decisions that shape it. We outline KModels' main components as well as its interfaces. Furthermore, we explain how KModels is highly suited for on-premise deployment but can also be used in cloud environments. The efficacy of KModels is demonstrated through the successful deployment of three AI models within an existing Work Order Management system. These models operate in a client's data center and are trained on local data, without data scientist intervention. One model improved the accuracy of Failure Code specification for work orders from 46% to 83%, showcasing the substantial benefit of accessible and localized AI solutions.

Ignace Haaz

Between practical ethics, which seeks to define a wide range of ethical norms and ways of ethical reasoning on firm philosophical basis, including the definition of the foundation of ethics, and business ethics, environmental ethics or health ethics the difference is only about the degree we get to apply practically ethics. The Business Ethics Center of Corvinus University of Budapest, lead by Prof. Laszlo Zsolnai, takes all these levels very seriously. The external observer who would want to review the activities of the Center would immediately get in trouble if all that he would expect is either great theories on practical ethics, or only concrete observations on how, in some precise context, ethics is applied by economic science professionals. Indeed, as we shall review, the 30 Years Report of the Business Ethics Center of Corvinus University Budapest presents the most important conferences and workshops that they organized, describes significant books they published, and summarizes the main findings of their decades-long research. Crucially, the functioning of the Center is based on the conviction that ethics is a relevant aspect of all levels of economic activity, from individual and organizational to societal and global. Business ethics is practiced by the Center as an action-oriented, interdisciplinary scientific inquiry where normative and descriptive elements are intermingled.

Patricia Martínez García de Leaniz, Sandra Castro-González

This special issue of the Management Letters / Cuadernos de Gestión is dedicated to presenting those articles that are included as part of the special issue on consumer behavior and sustainable issues and that highlight marketing’s important role in encouraging sustainable consumption. The first part of this editorial presents six articles that structure this special issue. The second part examines the concept of sustainable marketing and the most effective ways to change consumer behaviour to become more sustainable.

Olena Chygryn, Cetin Bektas, Oleksii Havrylenko

The acceleration of globalisation processes and increasing countries' energy interdependence are required to ensure national energy security and independence. That demands investigating and developing processes and approaches for sustainable transformation of the global energy sector. The article aims to perform a complex review and investigation of the academic environment to analyse the trends and features of scientific publications devoted to new trends and tendencies in the smart energy industry transformation. To provide a categorical and theoretical background on the key scientific publications’ trends, the paper conducted a bibliometric analysis of scientific publications about smart energy management and sustainable energy sector. The subject of investigation is publications on smart energy management and the sustainable energy sector. The article represented the results of bibliometric analysis using the Scopus tools analytics and VOSViewer tools. The investigation answered the central question of the key academic and research tendencies in the smart energy development and sustainable transformation field. Thus, qualitative, and quantitative trends describe the academic tendencies to spread smart and sustainable technologies in the energy industry. Using the Scopus scientometric database, a system of more than 5000 academic texts in the determined area was created from 2001 to 2022. Such countries as India, China, the USA, the UK, Germany, Italy, Canada, South Korea, France represent the analysed scientific area. Describing the key trends and clusters has allowed understanding and systemised the dominant trends in the development of scientific publications in the field of management of sustainable development processes, spreading the IOT processes, and renewable energy.

S.Yu. , T.P. , K.Ye.

HR managers are specialists who adjust all processes of search, adaptation, motivation, development and evaluation of company personnel, so they must perform their professional duties in accordance with established standards of moral ethics. The article examines the level of definition of the concepts of "professional ethics" and "HR-management" in the works of scientists, and proves the need to consider the general concept of "professional ethics in HR management". A number of ethical issues in HR-management that arise during employment, determination of rewards and benefits in labor relations, as well as possible consequences of unethical behavior that manifests itself in the form of business risk and can negatively affect the activities of the business entity are highlighted. The process of ensuring compliance with professional ethics through personnel policy, adoption of ethical codes and establishment of organizational structure is outlined. Measures are proposed to implement the norms of professional ethics in the personnel sphere in the forms of establishing uniform rules for the activities of the company's employees; giving priority to the professional development of each staff member and taking into account his moral and psychological state to ensure competitiveness in the market; implementation of the principles of inclusion and diversity in the selection of personnel, their development and motivation; conducting relevant trainings; ensuring the confidentiality of personnel information; development of local codes of ethics adapted to the conditions of the enterprise. Thus, compliance with ethics in HR-management ensures the company's image, customer loyalty and increased attractiveness among qualified employees on the labor market.

Sidra Nasir, Rizwan Ahmed Khan, Samita Bai

In the past decade, the deployment of deep learning (Artificial Intelligence (AI)) methods has become pervasive across a spectrum of real-world applications, often in safety-critical contexts. This comprehensive research article rigorously investigates the ethical dimensions intricately linked to the rapid evolution of AI technologies, with a particular focus on the healthcare domain. Delving deeply, it explores a multitude of facets including transparency, adept data management, human oversight, educational imperatives, and international collaboration within the realm of AI advancement. Central to this article is the proposition of a conscientious AI framework, meticulously crafted to accentuate values of transparency, equity, answerability, and a human-centric orientation. The second contribution of the article is the in-depth and thorough discussion of the limitations inherent to AI systems. It astutely identifies potential biases and the intricate challenges of navigating multifaceted contexts. Lastly, the article unequivocally accentuates the pressing need for globally standardized AI ethics principles and frameworks. Simultaneously, it aptly illustrates the adaptability of the ethical framework proposed herein, positioned skillfully to surmount emergent challenges.

Rui Zhu, Quanzhou Hu, Wenxin Li et al.

Business Process Management (BPM) is gaining increasing attention as it has the potential to cut costs while boosting output and quality. Business process document generation is a crucial stage in BPM. However, due to a shortage of datasets, data-driven deep learning techniques struggle to deliver the expected results. We propose an approach to transform Conditional Process Trees (CPTs) into Business Process Text Sketches (BPTSs) using Large Language Models (LLMs). The traditional prompting approach (Few-shot In-Context Learning) tries to get the correct answer in one go, and it can find the pattern of transforming simple CPTs into BPTSs, but for close-domain and CPTs with complex hierarchy, the traditional prompts perform weakly and with low correctness. We suggest using this technique to break down a difficult CPT into a number of basic CPTs and then solve each one in turn, drawing inspiration from the divide-and-conquer strategy. We chose 100 process trees with depths ranging from 2 to 5 at random, as well as CPTs with many nodes, many degrees of selection, and cyclic nesting. Experiments show that our method can achieve a correct rate of 93.42%, which is 45.17% better than traditional prompting methods. Our proposed method provides a solution for business process document generation in the absence of datasets, and secondly, it becomes potentially possible to provide a large number of datasets for the process model extraction (PME) domain.

Henri-Corto Stoeklé, Achille Ivasilevitch, Christian Hervé

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N. Zhang

Abstract Because of COVID-19 in the world, enterprises and consumers pay more and more attention to environmental protection, food safety and health issues. The purpose of this paper is to take China's food company as an example to study the impact of CSR on customer loyalty, mediating effects of company image and customer satisfaction, and moderating effects of COVID-19. The result shows that during COVID-19, company image and customer satisfaction have significant mediating effects, and COVID-19 positively moderate the impact of CSR on customer satisfaction.

Hanan Shteingart, Gerben Oostra, Ohad Levinkron et al.

Data science has the potential to improve business in a variety of verticals. While the lion's share of data science projects uses a predictive approach, to drive improvements these predictions should become decisions. However, such a two-step approach is not only sub-optimal but might even degrade performance and fail the project. The alternative is to follow a prescriptive framing, where actions are "first citizens" so that the model produces a policy that prescribes an action to take, rather than predicting an outcome. In this paper, we explain why the prescriptive approach is important and provide a step-by-step methodology: the Prescriptive Canvas. The latter aims to improve framing and communication across the project stakeholders including project and data science managers towards a successful business impact.