Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/294459 
Year of Publication: 
2023
Citation: 
[Journal:] Cogent Business & Management [ISSN:] 2331-1975 [Volume:] 10 [Issue:] 2 [Article No.:] 2215566 [Year:] 2023 [Pages:] 1-18
Publisher: 
Taylor & Francis, Abingdon
Abstract: 
Semantic analysis is becoming increasingly important not only in computing but also in the business world. The purpose of the current study is to apply semantic network analysis to the service industry, one of the economic sectors. To learn more about the crowded environment in the service sector, the study interviewed customers and employees by using dyad approach in the service sector. The data collected was analyzed using a text mining approach in Python library and Ucinet software. The text data collected through interviews was analyzed using multiple techniques like sentiment analysis, centrality analysis, and CONCOR analysis. The results from the two data sets of interviews with employees and consumers revealed certain effects and behavior that they exhibit in a crowded environment. When providing services to consumers in a crowded environment, employees experience a variety of behavioral changes, whether due to physical, psychological, emotional, habitual, or work-related factors. Additionally, findings show that crowding has an emotional and psychological impact on customers' behavioral responses. The study offers important implications of text analysis for business intelligence.
Subjects: 
centrality analysis
perceived crowding
semantic network analysis
service sector
text mining
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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