Propensity Score Matching and Marketing: A Scientometric Study, Tree of Science Analysis, and Research Perspectives

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dc.creatorMoyano-Londoño, Gabriel Antonio
dc.creatorHernández, Jhon Edwar
dc.creatorPava Idárraga, Mario Andrés
dc.date2026-05-08
dc.descriptionObjective: This study aimed to analyze the evolution of scientific production; collaboration networks; and seminal, structural, and recent studies, as well as future research perspectives on the use of Propensity Score Matching (PSM) in marketing.Design/Methodology: A total of 417 documents were retrieved from the Scopus and Web of Science databases and then analyzed using Bibliometrix, Tree of Science, and Gephi. To ensure reproducibility and transparency, elements of the PRISMA 2020 statement were applied throughout the study.Findings: Scientometric mapping indicated a steady increase in academic output since 2012, with the United States, China, and Germany leading in the number of publications. Furthermore, the analysis revealed three main clusters: (i) the measurement of impact on marketing contracts and crop productivity, (ii) consumer behavior and business planning, and (iii) effect evaluation. In addition, key driving topics were identified, including risk marketing and consumer behavior.Conclusions: The findings highlight PSM as an essential tool for evaluating marketing strategies. Moreover, its application across the identified clusters significantly reduces selection bias and enables more robust causal inferences in studies related to consumer behavior, organizational management, and rural development.Originality: This study represents the first scientometric analysis combining Bibliometrix, Tree of Science, and Gephi to examine PSM in marketing research. Consequently, the approach provides a comprehensive characterization of the field’s development, revealing both impact measurement strategies and emerging research directions in marketing.en-US
dc.descriptionObjetivo: el objetivo de este estudio fue analizar la evolución de la producción científica, las redes de colaboración, los estudios seminales, estructurales y recientes, así como las perspectivas de investigación sobre el uso del Propensity Score Matching en el campo del marketing.Diseño/metodología: el estudio se adelantó a partir de la recopilación de 417 documentos de las bases de datos de Scopus y Web of Science, los cuales fueron procesados con herramientas como Bibliometrix, Tree of Science y Gephi, y, para garantizar reproducibilidad y transparencia investigativa, se adoptaron elementos de la declaración PRISMA 2020.Resultados: el mapeo científico permitió identificar un crecimiento sostenido de la producción académica desde 2012, liderado por Estados Unidos, China y Alemania. Por su parte, el análisis de la información reveló tres clústeres principales centrados en la medición de impacto sobre los contratos de comercialización y la productividad de los cultivos, el comportamiento del consumidor y la planificación empresarial, así como en la evaluación de efectos. Asimismo, se identificaron temas motores como el marketing de riesgo y la conducta del consumidor.Conclusiones: esta investigación revela que el Propensity Score Matching es una herramienta esencial para la evaluación de estrategias de marketing. De igual forma, que su uso transversal en los clústeres analizados aporta significativamente a la reducción de sesgos de selección y permite generar inferencias causales más sólidas y confiables en estudios relacionados con el comportamiento del consumidor, la gestión organizacional y el desarrollo rural.Originalidad: esta investigación es el primer estudio cienciométrico en el que se implementaron herramientas como Bibliometrix, Tree of Science y Gephi para analizar el uso del Propensity Score Matching en el campo del marketing, posibilitando la caracterización integral del desarrollo investigativo y revelando tanto estrategias de medición de impacto como perspectivas de investigación en el mundo del mercadeo. es-ES
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dc.identifierhttps://revistas.itm.edu.co/index.php/revista-cea/article/view/3479
dc.identifier10.22430/24223182.3479
dc.languageeng
dc.publisherInstitución Universitaria ITMen-US
dc.relationhttps://revistas.itm.edu.co/index.php/revista-cea/article/view/3479/4067
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dc.rightsCopyright (c) 2026 Gabriel Antonio Moyano-Londoño, Jhon Edwar Hernández, Mario Andrés Pava Idárragaen-US
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0en-US
dc.sourceRevista CEA; Vol. 12 No. 29 (2026); Art. e3479en-US
dc.sourceRevista CEA; Vol. 12 Núm. 29 (2026); Art. e3479es-ES
dc.source2422-3182
dc.source2390-0725
dc.subjectcomportamiento del consumidores-ES
dc.subjectevaluación de impactoes-ES
dc.subjectMarketinges-ES
dc.subjectPropensity Score Matchinges-ES
dc.subjectconsumer behavioren-US
dc.subjectimpact evaluationen-US
dc.subjectmarketingen-US
dc.subjectpropensity score matchingen-US
dc.titlePropensity Score Matching and Marketing: A Scientometric Study, Tree of Science Analysis, and Research Perspectivesen-US
dc.titlePropensity Score Matching y marketing: estudio cienciométrico, árbol de la ciencia y perspectivas de investigaciónes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion

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