Research trends in digital marketing and data-driven marketing: A bibliometric analysis
The accelerated development of digital technologies and the Internet stimulates the transition from the traditional to the digital model of marketing. The study presents a bibliometric analysis to examine international publications in the field of digital marketing (DM) and data-driven marketing (DD...
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| Format: | Article |
| Language: | English |
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Ural State University of Economics
2025-01-01
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| Series: | Управленец |
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| Online Access: | https://upravlenets.usue.ru/en/issues-2024/1717 |
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| _version_ | 1841556270025277440 |
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| author | Tuğçe Acar Kara Ferhat Orman |
| author_facet | Tuğçe Acar Kara Ferhat Orman |
| author_sort | Tuğçe Acar Kara |
| collection | DOAJ |
| description | The accelerated development of digital technologies and the Internet stimulates the transition from the traditional to the digital model of marketing. The study presents a bibliometric analysis to examine international publications in the field of digital marketing (DM) and data-driven marketing (DDM) with a holistic approach, determine the current level of research interest in the topic under review and identify key development trends. The fundamental principles of bibliometrics and scientometric management constitute the methodological framework of the paper. Among the research methods applied are statistical and scientometric analyses. The data used in the study were retrieved from the Web of Science. Bibliometric analysis was carried out with 1,541 and 58 articles on digital and data-driven marketing, respectively. The data set covers the period of 2006–2024, when the first study on digital marketing emerged, and the period of 2003–2024 for data-driven marketing. The obtained data were processed using the R Project. The findings indicate a constantly growing research interest in the concepts under consideration. We have identified journals with most publications on digital marketing (Sustainability) and data-driven marketing (Journal of Business Research); established the countries publishing most studies on DM (USA) and DDM (China); and revealed the most frequently used keywords in DM (‘impact’) and DDM (‘management’). The results of keywords’ occurrence analysis were utilized to create thematic maps for visualizing motor, niche, basic and emerging/declining research areas in the field of digital and data driven marketing. |
| format | Article |
| id | doaj-art-4618dd854eea404fa5b6c7ff15e16c3e |
| institution | Kabale University |
| issn | 2218-5003 2686-7923 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Ural State University of Economics |
| record_format | Article |
| series | Управленец |
| spelling | doaj-art-4618dd854eea404fa5b6c7ff15e16c3e2025-01-07T10:16:43ZengUral State University of EconomicsУправленец2218-50032686-79232025-01-01156485910.29141/2218-5003-2024-15-6-4Research trends in digital marketing and data-driven marketing: A bibliometric analysisTuğçe Acar Kara0Ferhat Orman1Istanbul Aydın University, Istanbul, Türkiye Istanbul Beykent University, Istanbul, TürkiyeThe accelerated development of digital technologies and the Internet stimulates the transition from the traditional to the digital model of marketing. The study presents a bibliometric analysis to examine international publications in the field of digital marketing (DM) and data-driven marketing (DDM) with a holistic approach, determine the current level of research interest in the topic under review and identify key development trends. The fundamental principles of bibliometrics and scientometric management constitute the methodological framework of the paper. Among the research methods applied are statistical and scientometric analyses. The data used in the study were retrieved from the Web of Science. Bibliometric analysis was carried out with 1,541 and 58 articles on digital and data-driven marketing, respectively. The data set covers the period of 2006–2024, when the first study on digital marketing emerged, and the period of 2003–2024 for data-driven marketing. The obtained data were processed using the R Project. The findings indicate a constantly growing research interest in the concepts under consideration. We have identified journals with most publications on digital marketing (Sustainability) and data-driven marketing (Journal of Business Research); established the countries publishing most studies on DM (USA) and DDM (China); and revealed the most frequently used keywords in DM (‘impact’) and DDM (‘management’). The results of keywords’ occurrence analysis were utilized to create thematic maps for visualizing motor, niche, basic and emerging/declining research areas in the field of digital and data driven marketing.https://upravlenets.usue.ru/en/issues-2024/1717digital marketingdata-driven marketingbibliometric analysisthematic mapkeyword analysis |
| spellingShingle | Tuğçe Acar Kara Ferhat Orman Research trends in digital marketing and data-driven marketing: A bibliometric analysis Управленец digital marketing data-driven marketing bibliometric analysis thematic map keyword analysis |
| title | Research trends in digital marketing and data-driven marketing: A bibliometric analysis |
| title_full | Research trends in digital marketing and data-driven marketing: A bibliometric analysis |
| title_fullStr | Research trends in digital marketing and data-driven marketing: A bibliometric analysis |
| title_full_unstemmed | Research trends in digital marketing and data-driven marketing: A bibliometric analysis |
| title_short | Research trends in digital marketing and data-driven marketing: A bibliometric analysis |
| title_sort | research trends in digital marketing and data driven marketing a bibliometric analysis |
| topic | digital marketing data-driven marketing bibliometric analysis thematic map keyword analysis |
| url | https://upravlenets.usue.ru/en/issues-2024/1717 |
| work_keys_str_mv | AT tugceacarkara researchtrendsindigitalmarketinganddatadrivenmarketingabibliometricanalysis AT ferhatorman researchtrendsindigitalmarketinganddatadrivenmarketingabibliometricanalysis |