Exploring the effects of consumers’ trust : a predictive model for satisfying buyers’ expectations based on sellers’ behaviour in the marketplace
Bibliographic record
Abstract
In recent years, Consumer-to-Consumer (C2C) marketplaces have become very popular among \nInternet users. However, compared to traditional Business-to-Consumer (B2C) stores, most \nmodern C2C marketplaces are reported to be associated with stronger negative sentiments \namong consumers. On the other hand, these negative sentiments are a result of sellers’ inability \nto meet buyers’ expectations. These negative emotions are also linked to the low trust \nrelationship among sellers and buyers in C2C marketplaces. The growth of these negative \nemotions might jeopardize buyers’ decisions to opt for C2C marketplaces in their future \npurchase intentions. \nIn the present study, the concept of trust is explained in a situation characterised by the \nfollowing aspects: One party (the trustor) is willing to rely on the actions of another party (the \ntrustee) in a situation in the future to meet his/her expectations. Based on the buyer’s and \nseller’s behaviour in the C2C marketplace, we were able to quantify the trust emotion found in \ntext. We also performed text mining on Airbnb, a rich source of data in C2C interactions, to \nquantify the trust level in host descriptions of offered facilities. Specifically, the research \nquestions addressed the possibility to infer trust from C2C interactions on Airbnb, as well as \nwhether it is possible to infer trust from emotions such as joy and fear. The data are acquired \nfrom Ashville, and Boston in the USA, Vancouver in Canada and Manchester in the UK. In \nline with our expectations, the results of the analysis demonstrate that negative guest feedback \nin Airbnb reviews is stronger when the description of the host’s property expresses the emotion \nof joy only. Conversely, negative guest sentiments in reviews are the weakest when the host \nsentiment expressed in Airbnb listings is mixed and expresses different balanced emotions \n(e.g., joy and fear).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".