Trust-assuring arguments to enhance consumer trust in internet stores : an experimental investigation
Bibliographic record
Abstract
A trust-assuring argument refers to "a claim and its supporting statements used in an Internet store to address trust related issues." Whether it is statements placed on a website about a store's privacy policy or a symbol representing third-party assurances, we cannot assume a priori that such presence will necessarily increase consumer trust. To analyse and test the effectiveness of trust-assuring arguments in promoting consumer trust in Internet stores, and also to delineate guidelines for effective implementation of these arguments, a series of three interrelated studies have been conducted. Drawing from a model of trust and the customer resource life cycle, the first study identifies the important trust related issues (or concerns) about which Internet stores need to provide arguments in order to increase consumer trust. It categorizes the identified issues into four groups: issues related to personal information, customer service, product price/ quality, and store presence. In the second study, Toulmin's model of argumentation is proposed as a useful method of constructing trust-assuring arguments to amplify the effects of the arguments on consumer trust in Internet stores. Three forms of arguments have been identified based on Toulmin's model of argumentation in our study and their effects on consumer trust in Internet stores have been investigated in a laboratory experiment. The results suggest that the application of Toulmin's model can bolster the effects of trust-assuring arguments on consumer trust in Internet stores. The third study compares the relative influence of a store's trust-assuring arguments on consumer trust to that of third party certifications, by analyzing three factors: the content of the arguments, the sources of the arguments, and the relevance of the argument topics to consumers' personal interests. The main focus of the study involves identifying the conditions in which one feature (either a store's trust-assuring arguments or third party certifications) is more effective than the other. The results of a laboratory experiment suggest that when the relevance of the argument topics to a consumer's personal interests is high, a store's trust-assuring arguments are as effective in increasing consumer trust in the store as third party certifications with equivalent content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".