Self-service technologies: A bibliometric analysis

Shruti Thakral and Vishal Kamra ()
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Shruti Thakral: Amity College of Commerce and Finance, Amity University, Uttar Pradesh, India
Vishal Kamra: Amity School of Business, Amity University, Uttar Pradesh, India

International Journal of Financial Engineering (IJFE), 2024, vol. 11, issue 04, 1-20

Abstract: In this paper, we applied bibliometric methods to analyze the field of self-service technology (SST). Our goal was to identify the most impactful publications and journals, comprehend the key topics studied as well as the emerging trends and forecast future research trends in this domain. The Scopus database was used to extract 490 documents related to SST between 2000 and 2023. Our analyses include the most cited papers, sources of publications, bibliographic coupling, and topic modeling. As the body of literature continues to expand, we identified the most influential source “Journal of Service Marketing†which holds a prominent position in this area. The citation analysis revealed the highly influential papers that have made significant contributions to their respective fields. Notably, Meuter [Meuter, ML, AL Ostrom, RI Roundtree and MJ Bitner (2000). Self-Service Technologies: Understanding Customer Satisfaction with Technology-Based Service Encounters] in the realm of SST emerged as the most frequently cited paper, exerting substantial influence in academic literature. We applied bibliometric coupling in this field, which resulted in the discovery of seven clusters in SST. These clusters shed light on various aspects associated with these topics, offering valuable insights. The findings of this research offer valuable insights to study researchers by bringing clarity to this research field. Finally, the study presents the implications and recommendations for researchers. The findings of this paper offer valuable insights to study researchers by bringing clarity to the fields of the study.

Keywords: Self-service technology; SSTs; bibliometric analyses; bibliographic coupling; topic modeling (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S2424786324420076

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