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ROISI - ISSN 2634-1468 - © ISTE Ltd
The journal aims at providing a space for the publication of disciplinary or interdisciplinary methodological or applied French-speaking research, in the field of information systems engineering. The contributions formalize the design, implementation, and evaluation of information systems. The journal aims to promote and energize stimulating and high-quality research in the emerging themes of information systems. The language of publication is French and, exceptionally, English.
Scientific Board
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Guillaume CABANAC
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Nadira LAMMARI
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L’objectif de la revue est de fournir un espace pour la publication de recherches francophones disciplinaires ou interdisciplinaires, méthodologiques ou appliquées autour de l’ingénierie des systèmes d’information. Les contributions ont pour but de formaliser la conception, la mise en œuvre et l’évaluation des systèmes d’information. La revue vise à promouvoir et dynamiser des recherches stimulantes et de haute qualité dans les thématiques émergentes des systèmes d’information. La langue de publication est le français et, à titre exceptionnel, l’anglais.
Conseil scientifique
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Guillaume CABANAC
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Nadira LAMMARI
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Ce numéro spécial, « Apports et limites de l’intelligence artificielle pour la gestion des connaissances tacites en entreprise », reprend une sélection retravaillée de trois des huit contributions ayant été présentées à l’atelier "Connaissances Tacites" (KM-IA).
Digital artificial intelligence (AI) is ubiquitous and constantly interacts with humans, drawing on both their explicit and tacit knowledge. Unlike humans, who possess both formalized knowledge and a wealth of tacit knowledge shaped by experience, AI does not hold any intrinsic knowledge. It generates responses by exploiting algorithmic models and accumulated datasets, but encounters limitations in understanding and reproducing tacit knowledge, which is often unarticulated and highly context-dependent. However, AI could play a key role in the articulation and transmission of such knowledge. By interacting with humans, it may assist in structuring informal knowledge, identifying recurring patterns in decision-making, and facilitating the exchange of expertise within organizations. Inspired by the concept of "Ba" defined by Nonaka, which describes a shared space that fosters knowledge creation, AI could act as a catalyst for formalizing certain aspects of tacit knowledge, while simultaneously raising major epistemological challenges, such as bias and the opacity of AI models. In this article, we analyze the capabilities and limitations of AI in addressing informal knowledge. We explore the mechanisms by which it could contribute to the emergence of a hybrid intelligence, combining human reasoning with algorithmic assistance, and discuss the practical, ethical, and equity-related implications of this interaction, particularly in domains where intuition and experience are essential, such as medicine, education, and strategic decision-making.
This study explores the capture and valorization of tacit knowledge within scientific organizations, using BRGM as a case study. Facing the strategic challenge of knowledge management, we examine the transformation of indi-vidual knowledge into a collective asset via a three-pronged methodological approach: theoretical framework, use of AI tools to identify and transcribe this knowledge, and proposal of a CBR architecture with an AI agent ("beregem") to solve problems in geosciences. This research contributes to the management of scientific tacit knowledge through AI-based solutions.
This paper explores the perpetuation of practitioners’ tacit knowledge in the context of projects aimed at designing Artificial Intelligence (AI) uses in organizations. By comparing an interdisciplinary review of the state of the art on tacit knowledge with an observational field study of 7 application cases in France and Switzerland, this article sheds light on the dynamics of capturing practitioners’ tacit knowledge during the design and operation of AI models and highlights three areas for consideration: (1) the emergence of new devices for translating practitioners’ know-how into data models and capturing tacit knowledge through the maieutic carried out in the design phase, (2) the difficulty of taking unconscious tacit knowledge into account when judging AI in use, revealing issues of interpretability, cognitive bias and trust, and (3) the capture of knowledge, including tacit knowledge, as the primary goal of Data Science projects. But this capture may not be desired by the practitioners or even introduce an intermediation that prevents the development of further tacit knowledge derived from real-life experience in favour of that linked to the use of AI. These considerations lead to the improvement of tacit knowledge perpetuation devices, as long as their legitimacy is justified, and the risks are mitigated.
Ce numéro spécial de la “Revue ouverte d’ingénierie des systèmes d’information” rassemble des articles étendus des éditions 2023 et 2024 du congrès INFORSID.
The Circular Economy consists in producing goods and services in a sustainable way by limiting the consumption and waste of resources and the production of waste. Pressure from laws, stakeholders and customers leads organization to review their practices and adopt the circular economy principles to improve the circularity of their supply chains. In practice, companies look for a way to make their supply chains more circular. A successful transition towards circular supply chains requires continual measurement of progress towards circularity. The main contribution of this paper is a method called CircuSChain that aims at guiding organizations in order to evaluate and design more circular supply chains. The method, formalized in the form of an intentional process model and a product meta-model, is based on the use of a generic model of circular supply chains, a serious game to simulate the structure and operating of a circular supply chain, and a circularity indicator to calculate the circularity of a supply chain. This paper extends a previous article [KUR 23] by detailing these tools used by the method as well as all the protocols used in the different strategies proposed by the method.
Business processes digitalization is a crucial challenge for companies, providing the opportunity to enhance their efficiency, quality, and execution speed. This transformation cannot be fully achieved without a thorough mastery of individual expert knowledge, collaborative management, and knowledge formalization, as well as a complete adoption of new technologies. This paper proposes a methodological approach based on knowledge elicitation for the design of formal, consensual, and shared ontologies. A binomial analysis of the acceptability of digital technologies complements this process to better understand the requirements and concerns of experts and to propose appropriate solutions. The proposed approach is experimentally tested on industrial collaboration projects in the field of manufacturing (associating knowledge sources from multinational companies) and in the field of viticulture (associating explicit knowledge and implicit knowledge acquired through observation.
Educational information systems make it possible to observe learners’ learning traces and to carry out analyses of their behaviour or to predict their success. In this work, we study how Process Mining can be used in contextual recommender systems. We are focusing in particular on Trace Clustering, which aims to group together traces with similar dynamics. Our contributions concern the definition of an architecture for recommendation that uses Trace Clustering and the characterization of the learning styles of the identified groups. We validate our proposal on data collected from an introductory course in UI programming.
Faced with the development of big data and its application to personal data, the European legislator has provided a protective legal framework: the "General Data Protection Regulation" (GDPR). When it came into force on May 25, 2018, the issue of obtaining consent prior to any processing of personal data was at the heart of the concerns of targeted advertising, particularly programmatic advertising, and of performance analysis. This has led to the emergence of service providers specialized in the creation of consent collection interfaces, the Consent Management Platforms (CMP), but also to the multiplication of dark patterns aiming at forcing the obtaining of such consent. In this research, we identified the dark patterns used by a set of press sites and then used the dark pattern typology of Gray and his co-authors to classify the designs. We then discussed, on the one hand, their legality and, on the other, their ethics (from the point of view of the utilitarian and deontological approaches). Finally, we discuss the best ways of combating the abuses observed. In particular, we demonstrate the existence of a grey area that allows professionals to maximise, sometimes temporarily, the amount of personal data collected.
Editorial Board
Editor in Chief
Isabelle COMYN-WATTIAU
ESSEC Business School
[email protected]
Vice Editor in Chief
Christine VERDIER
Université Grenoble Alpes
[email protected]
Olivier TESTE
IRIT, Université de Toulouse
[email protected]