Exploring the effects of consumers’ trust : a predictive model for satisfying buyers’ expectations based on sellers’ behaviour in the marketplace

AlSheikh, S 2019, Exploring the effects of consumers’ trust : a predictive model for satisfying buyers’ expectations based on sellers’ behaviour in the marketplace , PhD thesis, University of Salford.

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Abstract

In recent years, Consumer-to-Consumer (C2C) marketplaces have become very popular among Internet users. However, compared to traditional Business-to-Consumer (B2C) stores, most modern C2C marketplaces are reported to be associated with stronger negative sentiments among consumers. On the other hand, these negative sentiments are a result of sellers’ inability to meet buyers’ expectations. These negative emotions are also linked to the low trust relationship among sellers and buyers in C2C marketplaces. The growth of these negative emotions might jeopardize buyers’ decisions to opt for C2C marketplaces in their future purchase intentions. In the present study, the concept of trust is explained in a situation characterised by the following aspects: One party (the trustor) is willing to rely on the actions of another party (the trustee) in a situation in the future to meet his/her expectations. Based on the buyer’s and seller’s behaviour in the C2C marketplace, we were able to quantify the trust emotion found in text. We also performed text mining on Airbnb, a rich source of data in C2C interactions, to quantify the trust level in host descriptions of offered facilities. Specifically, the research questions addressed the possibility to infer trust from C2C interactions on Airbnb, as well as whether it is possible to infer trust from emotions such as joy and fear. The data are acquired from Ashville, and Boston in the USA, Vancouver in Canada and Manchester in the UK. In line with our expectations, the results of the analysis demonstrate that negative guest feedback in Airbnb reviews is stronger when the description of the host’s property expresses the emotion of joy only. Conversely, negative guest sentiments in reviews are the weakest when the host sentiment expressed in Airbnb listings is mixed and expresses different balanced emotions (e.g., joy and fear).

Item Type: Thesis (PhD)
Contributors: Meziane, F (Supervisor)
Schools: Schools > School of Computing, Science and Engineering
Depositing User: USIR Admin
Date Deposited: 21 Apr 2021 13:13
Last Modified: 27 Aug 2021 21:49
URI: http://usir.salford.ac.uk/id/eprint/59474

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