1,720,959 research outputs found
L’adoption de l’industrie 4.0 dans le secteur textile : Cartographie des dynamiques scientifiques mondiales (2013–2025)
No full textIn a context of rapid industrial transformation and growing sustainability demands, Industry 4.0 has emerged as a key technological paradigm for the textile sector, historically characterized by labor intensity and low digitalization. Despite its potential for automation, flexibility, and traceability, adoption remains partial and uneven. This study addresses a major gap in the literature by providing a systematic and longitudinal mapping of the barriers to Industry 4.0 adoption in the global textile industry.
Using a quantitative bibliometric approach, 150 Scopus-indexed articles (2013–2025) were analyzed with Bibliometrix (R), Biblioshiny, and VOSviewer. The study combines performance analyses (output, citations, H-index) with structural analyses (co-occurrence, co-citation, and bibliographic coupling) to identify the field’s main themes, key contributors, and persistent barriers.
The results indicate an average annual growth rate of 24.6% and reveal four thematic clusters: (1) intelligent production technologies, (2) sustainability and circularity, (3) organizational adoption models, and (4) innovations in smart and functional textiles. The main obstacles are interconnected: high investment costs, lack of data science skills, limited interoperability, cybersecurity risks, and socio-technical resistance. The analysis also highlights geographic asymmetries, as Southern countries often produce knowledge that remains under-cited. Artificial intelligence emerges as a transversal enabler applied to predictive maintenance, simulation, automation, and adaptive management.
Overall, this study calls for a systemic reassessment of the barriers to adoption, integrating human, technical, and institutional dimensions. It offers a structured framework to guide the textile industry’s transition toward a smarter, more sustainable, and resilient model.
Classification JEL : O33, L67, M11, Q56
Paper type : Theoretical ResearchDans un contexte de transformation industrielle rapide et de quête de durabilité, l’Industrie 4.0 s’impose comme un paradigme technologique clé pour le secteur textile, historiquement marqué par une forte intensité de main-d’œuvre et une faible digitalisation. Malgré ses atouts en automatisation, flexibilité et traçabilité, son adoption demeure partielle et inégale. Cette recherche comble une lacune de la littérature en proposant une cartographie systématique et longitudinale des barrières à l’adoption des technologies 4.0 dans l’industrie textile mondiale.
En mobilisant une approche bibliométrique quantitative, 150 articles indexés dans Scopus (2013–2025) ont été analysés à l’aide des outils Bibliometrix (R), Biblioshiny et VOSviewer. L’étude combine des analyses de performance (production, citations, H-index) et de structure (co-occurrence, co-citation, couplage bibliographique) afin d’identifier les principaux thématiques, acteurs et freins du champ.
Les résultats révèlent une croissance annuelle moyenne de 24,6 % et une structuration en quatre clusters : (1) technologies de production intelligentes, (2) durabilité et circularité, (3) modèles organisationnels d’adoption et (4) innovations textiles et e-textiles. Les freins principaux apparaissent interconnectés : coûts d’investissement élevés, déficit de compétences en data science, interopérabilité limitée, cybersécurité et résistances sociotechniques. L’étude montre aussi une concentration géographique inégale, les pays du Sud produisant des connaissances souvent sous-citées. L’intelligence artificielle émerge comme un axe transversal appliqué à la maintenance prédictive, la simulation, l’automatisation et la gestion adaptative.
En conclusion, cette étude propose une lecture systémique des barrières et offre un cadre structurant pour une transition vers un modèle textile plus intelligent, durable et résilient.
JEL Classification : O33, L67, M11, Q56
Type du papier : Recherche Théoriqu
L’adoption de l’industrie 4.0 dans le secteur textile : Cartographie des dynamiques scientifiques mondiales (2013–2025)
No full textIn a context of rapid industrial transformation and growing sustainability demands, Industry 4.0 has emerged as a key technological paradigm for the textile sector, historically characterized by labor intensity and low digitalization. Despite its potential for automation, flexibility, and traceability, adoption remains partial and uneven. This study addresses a major gap in the literature by providing a systematic and longitudinal mapping of the barriers to Industry 4.0 adoption in the global textile industry.
Using a quantitative bibliometric approach, 150 Scopus-indexed articles (2013–2025) were analyzed with Bibliometrix (R), Biblioshiny, and VOSviewer. The study combines performance analyses (output, citations, H-index) with structural analyses (co-occurrence, co-citation, and bibliographic coupling) to identify the field’s main themes, key contributors, and persistent barriers.
The results indicate an average annual growth rate of 24.6% and reveal four thematic clusters: (1) intelligent production technologies, (2) sustainability and circularity, (3) organizational adoption models, and (4) innovations in smart and functional textiles. The main obstacles are interconnected: high investment costs, lack of data science skills, limited interoperability, cybersecurity risks, and socio-technical resistance. The analysis also highlights geographic asymmetries, as Southern countries often produce knowledge that remains under-cited. Artificial intelligence emerges as a transversal enabler applied to predictive maintenance, simulation, automation, and adaptive management.
Overall, this study calls for a systemic reassessment of the barriers to adoption, integrating human, technical, and institutional dimensions. It offers a structured framework to guide the textile industry’s transition toward a smarter, more sustainable, and resilient model.
Classification JEL : O33, L67, M11, Q56
Paper type : Theoretical ResearchDans un contexte de transformation industrielle rapide et de quête de durabilité, l’Industrie 4.0 s’impose comme un paradigme technologique clé pour le secteur textile, historiquement marqué par une forte intensité de main-d’œuvre et une faible digitalisation. Malgré ses atouts en automatisation, flexibilité et traçabilité, son adoption demeure partielle et inégale. Cette recherche comble une lacune de la littérature en proposant une cartographie systématique et longitudinale des barrières à l’adoption des technologies 4.0 dans l’industrie textile mondiale.
En mobilisant une approche bibliométrique quantitative, 150 articles indexés dans Scopus (2013–2025) ont été analysés à l’aide des outils Bibliometrix (R), Biblioshiny et VOSviewer. L’étude combine des analyses de performance (production, citations, H-index) et de structure (co-occurrence, co-citation, couplage bibliographique) afin d’identifier les principaux thématiques, acteurs et freins du champ.
Les résultats révèlent une croissance annuelle moyenne de 24,6 % et une structuration en quatre clusters : (1) technologies de production intelligentes, (2) durabilité et circularité, (3) modèles organisationnels d’adoption et (4) innovations textiles et e-textiles. Les freins principaux apparaissent interconnectés : coûts d’investissement élevés, déficit de compétences en data science, interopérabilité limitée, cybersécurité et résistances sociotechniques. L’étude montre aussi une concentration géographique inégale, les pays du Sud produisant des connaissances souvent sous-citées. L’intelligence artificielle émerge comme un axe transversal appliqué à la maintenance prédictive, la simulation, l’automatisation et la gestion adaptative.
En conclusion, cette étude propose une lecture systémique des barrières et offre un cadre structurant pour une transition vers un modèle textile plus intelligent, durable et résilient.
JEL Classification : O33, L67, M11, Q56
Type du papier : Recherche Théoriqu
Using SVM for Smart Direct Marketing (SDM): A case of predicting bank customers interested in the Term Deposits
No full textAbstract:
The objective of this study is to reveal how important and necessary it has become to adopt the new methods and technical of Data Mining derived from artificial intelligence (AI), and this in the process of marketing banking products and precisely that of Term Deposit (TD). Therefore, in our research study we adopted the Support Vector Machine (SVM) method, and we succeeded through the process of modeling borrowed by the techniques of Data Mining (DM) to build a model that will allow us to predict the behavior of bank customers toward term deposits, for this the SVM method has been applied on a database of customers of a bank which includes historical responses of customers during a marketing campaign of the product DT.
For the manipulation tool we used, we chose the Python programming language, recognized by its power in modeling and exploiting DM techniques. For the methodology adopted, we started with a pre-treatment and cleaning of the data to keep only the significant explanatory variables and also for any extreme or aberrant variables, subsequently the modeling was carried out by adopting the DM process, at the end of this process we were able to measure the perfection and the level of predictability of our obtained model, thus we obtained the accuracy of 93% using the metric Accuracy, Thus according to the ROC curve we have that AUC=98, this reflects the performance of our obtained model. So, we were able to build a model that will help bankers make decisions in terms of predicting customers interested in the term deposit product.
JEL Classification : C02, C19, C35, C55, C6, M31,G2
Paper type: Empirical researchAbstract:
The objective of this study is to reveal how important and necessary it has become to adopt the new methods and technical of Data Mining derived from artificial intelligence (AI), and this in the process of marketing banking products and precisely that of Term Deposit (TD). Therefore, in our research study we adopted the Support Vector Machine (SVM) method, and we succeeded through the process of modeling borrowed by the techniques of Data Mining (DM) to build a model that will allow us to predict the behavior of bank customers toward term deposits, for this the SVM method has been applied on a database of customers of a bank which includes historical responses of customers during a marketing campaign of the product DT.
For the manipulation tool we used, we chose the Python programming language, recognized by its power in modeling and exploiting DM techniques. For the methodology adopted, we started with a pre-treatment and cleaning of the data to keep only the significant explanatory variables and also for any extreme or aberrant variables, subsequently the modeling was carried out by adopting the DM process, at the end of this process we were able to measure the perfection and the level of predictability of our obtained model, thus we obtained the accuracy of 93% using the metric Accuracy, Thus according to the ROC curve we have that AUC=98, this reflects the performance of our obtained model. So, we were able to build a model that will help bankers make decisions in terms of predicting customers interested in the term deposit product.
JEL Classification : C02, C19, C35, C55, C6, M31,G2
Paper type: Empirical researc
L’adoption de l’industrie 4.0 dans le secteur textile : Cartographie des dynamiques scientifiques mondiales (2013–2025)
No full textIn a context of rapid industrial transformation and growing sustainability demands, Industry 4.0 has emerged as a key technological paradigm for the textile sector, historically characterized by labor intensity and low digitalization. Despite its potential for automation, flexibility, and traceability, adoption remains partial and uneven. This study addresses a major gap in the literature by providing a systematic and longitudinal mapping of the barriers to Industry 4.0 adoption in the global textile industry.
Using a quantitative bibliometric approach, 150 Scopus-indexed articles (2013–2025) were analyzed with Bibliometrix (R), Biblioshiny, and VOSviewer. The study combines performance analyses (output, citations, H-index) with structural analyses (co-occurrence, co-citation, and bibliographic coupling) to identify the field’s main themes, key contributors, and persistent barriers.
The results indicate an average annual growth rate of 24.6% and reveal four thematic clusters: (1) intelligent production technologies, (2) sustainability and circularity, (3) organizational adoption models, and (4) innovations in smart and functional textiles. The main obstacles are interconnected: high investment costs, lack of data science skills, limited interoperability, cybersecurity risks, and socio-technical resistance. The analysis also highlights geographic asymmetries, as Southern countries often produce knowledge that remains under-cited. Artificial intelligence emerges as a transversal enabler applied to predictive maintenance, simulation, automation, and adaptive management.
Overall, this study calls for a systemic reassessment of the barriers to adoption, integrating human, technical, and institutional dimensions. It offers a structured framework to guide the textile industry’s transition toward a smarter, more sustainable, and resilient model.
Classification JEL : O33, L67, M11, Q56
Paper type : Theoretical ResearchDans un contexte de transformation industrielle rapide et de quête de durabilité, l’Industrie 4.0 s’impose comme un paradigme technologique clé pour le secteur textile, historiquement marqué par une forte intensité de main-d’œuvre et une faible digitalisation. Malgré ses atouts en automatisation, flexibilité et traçabilité, son adoption demeure partielle et inégale. Cette recherche comble une lacune de la littérature en proposant une cartographie systématique et longitudinale des barrières à l’adoption des technologies 4.0 dans l’industrie textile mondiale.
En mobilisant une approche bibliométrique quantitative, 150 articles indexés dans Scopus (2013–2025) ont été analysés à l’aide des outils Bibliometrix (R), Biblioshiny et VOSviewer. L’étude combine des analyses de performance (production, citations, H-index) et de structure (co-occurrence, co-citation, couplage bibliographique) afin d’identifier les principaux thématiques, acteurs et freins du champ.
Les résultats révèlent une croissance annuelle moyenne de 24,6 % et une structuration en quatre clusters : (1) technologies de production intelligentes, (2) durabilité et circularité, (3) modèles organisationnels d’adoption et (4) innovations textiles et e-textiles. Les freins principaux apparaissent interconnectés : coûts d’investissement élevés, déficit de compétences en data science, interopérabilité limitée, cybersécurité et résistances sociotechniques. L’étude montre aussi une concentration géographique inégale, les pays du Sud produisant des connaissances souvent sous-citées. L’intelligence artificielle émerge comme un axe transversal appliqué à la maintenance prédictive, la simulation, l’automatisation et la gestion adaptative.
En conclusion, cette étude propose une lecture systémique des barrières et offre un cadre structurant pour une transition vers un modèle textile plus intelligent, durable et résilient.
JEL Classification : O33, L67, M11, Q56
Type du papier : Recherche Théoriqu
L’utilisation de la méthode KPV émanant de l’intelligence artificielle pour la prédiction de la solvabilité des clients bancaires
Get PDFLa gestion du risque de crédit est un sujet important pour les banques et les établissements socio-économiques qui recueillent d’énormes quantités de données, dans l’intention de rendre obsolète la mauvaise décision. Dans cet article, nous avons étudié le comportement du classificateur KPV (K plus Proche Voisin) à la prédiction de la solvabilité des clients d’une banque. Ce classificateur serve à trouver la classe d’un nouveau client qui désire obtenir un crédit auprès d’une banque. À cet effet nous avons utilisé une base de données des clients d’une banque qui comporte des clients solvables et non-solvable.
Étant donné que la méthode utilisée relevant des techniques de l’intelligence artificielle, nous avons utilisé le langage Python comme outil de modélisation, par conséquent, nous avons commencé notre processus de modélisation par un prétraitement des données, par la suite nous avons exploré les différents résultats obtenus par les différentes distances, afin que nous puissions choisir la meilleure valeur de K, ensuite nous avons évalué et comparé les différents modèles de prédiction obtenus. Au terme du processus suivi, nous avons pu conclure que le modèle obtenu par la méthod
Using SVM for Smart Direct Marketing (SDM): A case of predicting bank customers interested in the Term Deposits
No full textAbstract:
The objective of this study is to reveal how important and necessary it has become to adopt the new methods and technical of Data Mining derived from artificial intelligence (AI), and this in the process of marketing banking products and precisely that of Term Deposit (TD). Therefore, in our research study we adopted the Support Vector Machine (SVM) method, and we succeeded through the process of modeling borrowed by the techniques of Data Mining (DM) to build a model that will allow us to predict the behavior of bank customers toward term deposits, for this the SVM method has been applied on a database of customers of a bank which includes historical responses of customers during a marketing campaign of the product DT.
For the manipulation tool we used, we chose the Python programming language, recognized by its power in modeling and exploiting DM techniques. For the methodology adopted, we started with a pre-treatment and cleaning of the data to keep only the significant explanatory variables and also for any extreme or aberrant variables, subsequently the modeling was carried out by adopting the DM process, at the end of this process we were able to measure the perfection and the level of predictability of our obtained model, thus we obtained the accuracy of 93% using the metric Accuracy, Thus according to the ROC curve we have that AUC=98, this reflects the performance of our obtained model. So, we were able to build a model that will help bankers make decisions in terms of predicting customers interested in the term deposit product.
JEL Classification : C02, C19, C35, C55, C6, M31,G2
Paper type: Empirical researchAbstract:
The objective of this study is to reveal how important and necessary it has become to adopt the new methods and technical of Data Mining derived from artificial intelligence (AI), and this in the process of marketing banking products and precisely that of Term Deposit (TD). Therefore, in our research study we adopted the Support Vector Machine (SVM) method, and we succeeded through the process of modeling borrowed by the techniques of Data Mining (DM) to build a model that will allow us to predict the behavior of bank customers toward term deposits, for this the SVM method has been applied on a database of customers of a bank which includes historical responses of customers during a marketing campaign of the product DT.
For the manipulation tool we used, we chose the Python programming language, recognized by its power in modeling and exploiting DM techniques. For the methodology adopted, we started with a pre-treatment and cleaning of the data to keep only the significant explanatory variables and also for any extreme or aberrant variables, subsequently the modeling was carried out by adopting the DM process, at the end of this process we were able to measure the perfection and the level of predictability of our obtained model, thus we obtained the accuracy of 93% using the metric Accuracy, Thus according to the ROC curve we have that AUC=98, this reflects the performance of our obtained model. So, we were able to build a model that will help bankers make decisions in terms of predicting customers interested in the term deposit product.
JEL Classification : C02, C19, C35, C55, C6, M31,G2
Paper type: Empirical researc
Using SVM for Smart Direct Marketing (SDM): A case of predicting bank customers interested in the Term Deposits
No full textAbstract:
The objective of this study is to reveal how important and necessary it has become to adopt the new methods and technical of Data Mining derived from artificial intelligence (AI), and this in the process of marketing banking products and precisely that of Term Deposit (TD). Therefore, in our research study we adopted the Support Vector Machine (SVM) method, and we succeeded through the process of modeling borrowed by the techniques of Data Mining (DM) to build a model that will allow us to predict the behavior of bank customers toward term deposits, for this the SVM method has been applied on a database of customers of a bank which includes historical responses of customers during a marketing campaign of the product DT.
For the manipulation tool we used, we chose the Python programming language, recognized by its power in modeling and exploiting DM techniques. For the methodology adopted, we started with a pre-treatment and cleaning of the data to keep only the significant explanatory variables and also for any extreme or aberrant variables, subsequently the modeling was carried out by adopting the DM process, at the end of this process we were able to measure the perfection and the level of predictability of our obtained model, thus we obtained the accuracy of 93% using the metric Accuracy, Thus according to the ROC curve we have that AUC=98, this reflects the performance of our obtained model. So, we were able to build a model that will help bankers make decisions in terms of predicting customers interested in the term deposit product.
JEL Classification : C02, C19, C35, C55, C6, M31,G2
Paper type: Empirical researchAbstract:
The objective of this study is to reveal how important and necessary it has become to adopt the new methods and technical of Data Mining derived from artificial intelligence (AI), and this in the process of marketing banking products and precisely that of Term Deposit (TD). Therefore, in our research study we adopted the Support Vector Machine (SVM) method, and we succeeded through the process of modeling borrowed by the techniques of Data Mining (DM) to build a model that will allow us to predict the behavior of bank customers toward term deposits, for this the SVM method has been applied on a database of customers of a bank which includes historical responses of customers during a marketing campaign of the product DT.
For the manipulation tool we used, we chose the Python programming language, recognized by its power in modeling and exploiting DM techniques. For the methodology adopted, we started with a pre-treatment and cleaning of the data to keep only the significant explanatory variables and also for any extreme or aberrant variables, subsequently the modeling was carried out by adopting the DM process, at the end of this process we were able to measure the perfection and the level of predictability of our obtained model, thus we obtained the accuracy of 93% using the metric Accuracy, Thus according to the ROC curve we have that AUC=98, this reflects the performance of our obtained model. So, we were able to build a model that will help bankers make decisions in terms of predicting customers interested in the term deposit product.
JEL Classification : C02, C19, C35, C55, C6, M31,G2
Paper type: Empirical researc
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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