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Authors: Cvetkoska, Violeta 
Palamidovska-Sterjadovska, Nikolina 
Ciunova-shuleska, Anita 
Bogoevska-gavrilova, Irena 
Keywords: bibliometric analysis, customer engagement, keyword co-occurrence analysis, mapping
Issue Date: 11-Nov-2022
Publisher: Faculty of Economics-Skopje, Ss. Cyril and Methodius University in Skopje
Conference: 3rd international conference "Economic and Business Trends Shaping the Future"
Abstract: The purpose of this study is to provide a bibliometric analysis of customer engagement (CE) research in the period 2006-2021 by using the PRISMA protocol for systematic reviews and by relying on a set of CE-related keywords. Bibliometric analysis refers to the quantitative study of bibliographic material that provides a general picture of a research field. By using a bibliometric analysis, the most relevant research in a particular field can be provided and the newest research trends can be identified. This study will provide a detailed overview of the evolution of relevant literature and the status of CE research over the past 15 years by using VOSviewer software for creating, visualizing, and exploring bibliometric maps of science. The concept of CE emerged in the marketing literature around 2005 followed by an increased number of research conducted in various contexts and fields, from customer and firm perspectives, etc., linking customer engagement to different marketing concepts such as customer satisfaction. Additionally, some of the researchers conceptualized customer engagement as a behavioral concept whereas others conceptualized it as a psychological concept. Based on the need for further clarification of this concept, a systematic review through bibliometric analysis was conducted and the results of descriptive analysis (distribution of articles by year, top five journals based on the number of published articles, top ten most cited articles, and country co-authorship network visualization) are presented. Additionally, the results from keywords co-occurrence analysis based on text mining in the abstracts are shown. Moreover, a machine learning algorithm for logistic regression in Power BI Desktop was performed to identify independent variables associated with greater citations of CE research. The results of the performed bibliometric analysis can be used by marketing scholars as a basis for future CE research.
Appears in Collections:Conference Proceedings: Economic and Business Trends Shaping the Future

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