Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/294006 
Year of Publication: 
2023
Citation: 
[Journal:] European Management Review [ISSN:] 1740-4762 [Volume:] 21 [Issue:] 1 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2023 [Pages:] 83-102
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
Current online marketplaces, characterized by a high number of sellers and the velocity of offerings, make service differentiation difficult for sellers. One particularly promising avenue for sellers (in this study: individuals) beyond classical demand-side approaches (i.e., prices) is to employ linguistic descriptions of their offerings. Yet, it remains mostly unclear what constitutes “successful” linguistic strategies. To elaborate on this, the current paper mines more than 2000 unique service offerings on Fiverr.com, a leading online marketplace for freelance services. By distinguishing between different service categories (i.e., hedonic and utilitarian services) and other characteristics of individual sellers (e.g., the origin of a seller), the paper analyzes the linguistic service descriptions via the Linguistic Inquirer and Word Counts (LIWC) and provides an empirical taxonomy of linguistic styles among individuals. Although the paper is novel and explorative, a few interesting insights can be obtained. First, there are significant linguistic differences in how sellers describe their service offerings depending on the service category (hedonic/utilitarian). Second, linguistic proxies of complexity, namely, words per sentence, six-letter words, and the overall word count (i.e., increasing informational content) as well as signals of analytical language, appear to be a beneficial strategy for sellers. Third, a linguistic strategy aimed at matching (congruence) of service categories (hedonic/utilitarian) and linguistic styles (analytical/emotional) appears to be beneficial. The results have important implications for creating linguistic strategies in online marketplaces focused on services on the supply side.
Subjects: 
computer‐aided‐content‐analysis
platforms
signaling
text‐mining
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

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