Exploring the effects of consumers’ trust : a predictive model for satisfying buyers’ expectations based on sellers’ behaviour in the marketplace
type: CreativeWork
description: In recent years, Consumer-to-Consumer (C2C) marketplaces have become very popular amongInternet users. However, compared to traditional Business-to-Consumer (B2C) stores, mostmodern C2C marketplaces are reported to be associated with stronger negative sentimentsamong consumers. On the other hand, these negative sentiments are a result of sellers’ inabilityto meet buyers’ expectations. These negative emotions are also linked to the low trustrelationship among sellers and buyers in C2C marketplaces. The growth of these negativeemotions might jeopardize buyers’ decisions to opt for C2C marketplaces in their futurepurchase intentions.In the present study, the concept of trust is explained in a situation characterised by thefollowing aspects: One party (the trustor) is willing to rely on the actions of another party (thetrustee) in a situation in the future to meet his/her expectations. Based on the buyer’s andseller’s behaviour in the C2C marketplace, we were able to quantify the trust emotion found intext. We also performed text mining on Airbnb, a rich source of data in C2C interactions, toquantify the trust level in host descriptions of offered facilities. Specifically, the researchquestions addressed the possibility to infer trust from C2C interactions on Airbnb, as well aswhether it is possible to infer trust from emotions such as joy and fear. The data are acquiredfrom Ashville, and Boston in the USA, Vancouver in Canada and Manchester in the UK. Inline with our expectations, the results of the analysis demonstrate that negative guest feedbackin Airbnb reviews is stronger when the description of the host’s property expresses the emotionof joy only. Conversely, negative guest sentiments in reviews are the weakest when the hostsentiment expressed in Airbnb listings is mixed and expresses different balanced emotions(e.g., joy and fear).
abstract: In recent years, Consumer-to-Consumer (C2C) marketplaces have become very popular amongInternet users. However, compared to traditional Business-to-Consumer (B2C) stores, mostmodern C2C marketplaces are reported to be associated with stronger negative sentimentsamong consumers. On the other hand, these negative sentiments are a result of sellers’ inabilityto meet buyers’ expectations. These negative emotions are also linked to the low trustrelationship among sellers and buyers in C2C marketplaces. The growth of these negativeemotions might jeopardize buyers’ decisions to opt for C2C marketplaces in their futurepurchase intentions.In the present study, the concept of trust is explained in a situation characterised by thefollowing aspects: One party (the trustor) is willing to rely on the actions of another party (thetrustee) in a situation in the future to meet his/her expectations. Based on the buyer’s andseller’s behaviour in the C2C marketplace, we were able to quantify the trust emotion found intext. We also performed text mining on Airbnb, a rich source of data in C2C interactions, toquantify the trust level in host descriptions of offered facilities. Specifically, the researchquestions addressed the possibility to infer trust from C2C interactions on Airbnb, as well aswhether it is possible to infer trust from emotions such as joy and fear. The data are acquiredfrom Ashville, and Boston in the USA, Vancouver in Canada and Manchester in the UK. Inline with our expectations, the results of the analysis demonstrate that negative guest feedbackin Airbnb reviews is stronger when the description of the host’s property expresses the emotionof joy only. Conversely, negative guest sentiments in reviews are the weakest when the hostsentiment expressed in Airbnb listings is mixed and expresses different balanced emotions(e.g., joy and fear).
author: AlSheikh, S
language: en
date published: 2019
date modified: 2026-07-24T04:26:14Z
provider: U. of Salford
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: U. of Salford