Research on prostitution has flourished over the last few decades with a new emphasis on the online sex market. To study the online market, researchers have utilized qualitative methods to investigate escort review websites. Coding for violent rhetoric and perceived intimacy are two conventional techniques that have been utilized using qualitative methods. Other areas investigated include the overall characteristics of sex buyers, price variation, and socialization of buyers. What has lacked in current literature has been the use of quantitative methods to study what factors influence the presence of positive reviews. To expand on limited existing literature utilizing quantitative methods, the current study used data from an escort review website titled Punternet.com for the years 2015 through 2017. Punternet is a public website where sex buyers (punters in the UK) can review sex workers and discuss aspects of sex buying culture in community forums. Factors that influence the presence of positive reviews were investigated including various sexual acts (oral, anal, vaginal, manual, group sex), session variables (length, cost, location, climax, condom usage, and intimacy), and a factor related to the type of sex worker (escort). Symbolic interactionism theory was used to explain the presence of intimacy within the reviews. The results from a binary logistic regression indicate that a variety of different factors are significant in regard to influencing the presence of positive reviews. For example, the odds that positive reviews occurred were increased when intimacy, or perceived intimacy, occurred throughout the sessions and whether at least one climax occurred. Future research in this area should continue to use quantitative methods to investigate what factors influence intimacy within the sessions, due to the high significance finding in the present research.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-7466 |
| Date | 01 January 2019 |
| Creators | Christensen, Ashley |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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