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Examining the relationship between blockchain capabilities and organizational performance in the Indian banking sector
No full textFNEGE 2, ABS 3International audienceBlockchain has enormous capabilities to transform the traditional business models in countless ways. Banks in India are building collaborative blockchain ecosystems to create an innovative business model and disrupt the traditional one to create further competitive advantages. The purpose of this study is to examine the relationship between blockchain capabilities (BCC), competitive advantages (CA) and organizational performance (OP). Further, to evaluate the mediating role of CA on the relationship between BCC and OP. In this context, scientific research model consisting of a hypothesis has been developed from the existing literature. The proposed model was tested using statistical data collected from Blockchain specialists, blockchain product marketing managers, experts of future and emergent technology and VP/AVP/Chief Manager/Branch head of banks/ financial Analyst/divisional Managers who are involved in planning and deployment of practical blockchain in banking/financial sector. Data was analyzed and tested through AMOS 22.0 and process macro using a sample of 289 responses. Our empirical result indicated that there is a significant positive relationship between BCC, CA and OP. Furthermore, relationship of BCC and OP partially mediated CA. This paper presents originality and contributes towards the body of knowledge on this subject to understand the relationship and mediation role of CA on the relationship between BCC and OP in Indian banking sector
Artificial intelligence-based group decision making to improve knowledge transfer: the case of distance learning in higher education
No full textFNEGE 4, ABS 1International audienceIn this paper, we propose a method based on multicriteria classification and a dominancebased rough set approach (DRSA) to support teachers in decision making. The objective is to use teachers’ knowledge and preferences to identify ‘atrisk students’, i.e. students who are likely to drop out, and ‘leader students’, i.e. students who are likely to help their peers, in distance learning. The proposed method is composed of two phases: phase I builds collective decision rules from teachers’ preferences, and phase II classifies students into two decision classes: ‘atrisk students’ and ‘leader students’. This method was designed, tested, and validated in higher education, with teachers who have acquired rich experience in teaching in online-synchronous mode since the Covid-19 pandemic
Windkessel model-based Physics Informed Neural Networks for Blood Flow Estimation and Cardiovascular Parameters Assessment
No full textCardiovascular system modeling is crucial due to the alarming rise in cardiovascular diseases worldwide. According to the WHO, cardiovascular diseases are the leading cause of death globally, accounting for 17.9 million deaths annually. This statistic underscores the need to better understand cardiovascular physiology and pathophysiology through accurate modeling and characterization. Arterial models vary in complexity, from simple yet less interpretable to highly intricate but precise ones. A prominent example is the arterial Windkessel model introduced by [1], which includes single and multiple-compartment models [2]. In practical applications, models offering interpretable parameters and manageable complexity are preferred. Estimating Blood Flow (BF) within the cardiovascular system is crucial to understanding its function and pathology [7]. However, measuring BF directly can be challenging due to the complex nature of blood circulation and the limitations of available measurement techniques. This report proposes a physics-informed neural network (PINN) approach for simultaneous BF and cardiovascular parameters estimation. Our proposed method evaluates the Windkessel model as a cardiovascular model, considering both 2 and 3 elements Windkessel models with integer and fractional-order derivatives. Fractional-order derivatives[3], incorporating non-integer orders, extend the concept of differentiability, capturing non-local and memory effects through fractional-order space and time derivatives
Replacer la décision IVG : la concurrence nouvelle des juridictions judiciaires et administratives
No full textInternational audienc
Dynamic structural reconfiguration for building supply chain viability under labor shortage
No full textInternational audienceThe outbreak of the COVID-19 pandemic has directly caused the infection of employees. In the post-COVID-19 era, supply chains (SCs) still face the challenge of labor shortage, due to a series of pandemics. As industry production has been significantly impacted by the uncertain labor capacity, we investigate a new SC viability building problem under labor dynamics, where the SC includes multiple suppliers and a labor-intensive manufacturer. The concerned problem comprehensively includes applying infection control measures, employing a backup supplier and establishing risk mitigation inventory (RMI) to build a viable SC. To portray the dynamics of the labor capacity, we innovatively propose a labor dynamics model based on the classic susceptible-infected-susceptible model. To hedge against disruption risks and uncertain demand, we propose a mixed-integer linear programming model integrated with optimal control methodology. Especially, our optimal control framework can dynamically fulfill SC structural reconfiguration by timely selecting appropriate suppliers to build SC viability. Then an efficient solution method is developed. Numerical experiments validate the efficiency of our algorithm
Sufficiency as a mode of collective anti-consumption: the case of an anarchist cooperative
No full textInternational audienc
Comportements déviants des salariés sur les médias sociaux : les effets sur l’attitude des consommateurs vis-à-vis de la marque
No full textFNEGE 3International audience• Research objectivesIn an era where social media is an integral part of individuals’ daily lives, brands are questioning how to manage their employees’ online presence. Employees increasingly use social media in ways that, intentionally or accidentally, can harm the company’s image. This research explores the factors influencing consumers’ attitudes toward a brand following the dissemination of deviant employee behavior on socialmedia.• MethodologyAn empirical study based on 21 semi-structured interviews was conducted. Participants reacted to six crisis scenarios.• ResultsFour key factors emerge: (1) Attribution of behavior to the brand: Consumers seek to understand the brand’s role in the deviant behavior; (2) Consumer sensitivity: Respondents’ values influence their perception of employees’ deviant behavior; (3) Company response: Inadequate crisis communication worsens negative perceptions; (4) Breach of brand promise: Employees’ deviant behavior has a stronger impact on consumer attitudes when it challenges the brand promise.• Managerial implicationsThe findings offer practical recommendations, both preventive and corrective, to mitigate the negative impact of such behaviors on brand image.• OriginalityThis study contributes to the literature on brand crises by examining an emerging phenomenon: deviant employee behaviors disseminated on social media.• ObjectifsÀ l’heure où les médias sociaux font partie de la vie quotidienne des individus, les marques se posent la question de la gestion de la présence en ligne de leurs employés. Les salariés utilisent de plus en plus les médias sociaux avec des propos ou des comportements qui, accidentellement ou délibérément, peuvent nuire à l’image de l’entreprise. Cette recherche explore les facteurs influençant l’attitude des consommateurs envers une marque après la diffusion de comportements déviants de collaborateurs sur les médias sociaux.• MéthodologieUne étude empirique basée sur 21 entretiens semi-directifs a été menée. Les participants ont réagi à six scénarios de crise.• RésultatsQuatre facteurs clés émergent : (1) attribution du comportement à la marque : les consommateurs cherchent à comprendre le rôle de la marque dans le comportement déviant ; (2) sensibilité des consommateurs : les valeurs personnelles des répondants influencent leur perception du comportement déviant des salariés ; (3) réponse de l’entreprise : une communication de crise inadaptée aggrave les perceptions négatives ; (4) rupture de la promesse de marque : le comportement déviant des salariés a des effets plus marqués sur l’attitude des consommateurs lorsqu’il remet en cause la promesse de la marque.• Implications managérialesLes résultats offrent des recommandations pratiques, tant préventives que curatives, pour limiter les impacts négatifs de tels comportements sur l’image de marque.• OriginalitéCette étude enrichit la littérature sur les crises de marque en examinant un phénomène émergent : les comportements déviants des salariés diffusés sur les médias sociaux
La neutralité politique des édifices publics, un principe politiquement neutre ?
No full textInternational audienc
Women and Economy of mobility : historiographical review and archival perspectives
No full textInternational audienc
Morphological traits and machine learning for genetic lineage prediction of two reef-building corals
No full textInternational audienceIntegrating multiple lines of evidence that support molecular taxonomy analysis has proven to be a robust method for species delimitation in scleractinian corals. However, morphology often conflicts with genetic approaches due to high phenotypic plasticity and convergence. Understanding morphological variation among species is crucial to studying coral distribution, life history, ecology, and evolution. Here, we present an application of Random Forest models for coral species identification based on morphological annotation of the corallum and corallites. We show that the integration of molecular and morphological trait analysis can be improved using machine learning. Morphological traits were documented for Porites and Pocillopora coral species that were collected and genotyped through genome-wide, genetical hierarchical clustering, and coalescence analyses for the Tara Pacific Expedition. While Porites only included three tentative species, most Pocillopora species were accounted by included specimens from the western Indian Ocean, tropical Southwestern Pacific, and southeast Polynesia. Two Random Forest models per genus were trained on the morphological annotations using the genetic lineage labels. One model was developed for in-situ image identification and used corallum traits measured from in-situ photographs. Another model for integrative species identification combined corallum and corallite data measured on scanning electron micrographs. Random Forest models outperformed traditional dimension reduction methods like PCA and FAMD followed by k-means and hierarchical clustering by classifying the correct genetic lineage despite morphological clusters overlapping. This machine learning approach is reproducible, cost-effective, and accessible, reducing the need for taxonomic expertise. It can complement molecular and phylogenetic studies and support image identification, highlighting its potential to advance a coral integrative taxonomy workflow