General and Familiar Trust in Websites Knowledge

Technology & Policy, 09/2010, Volume 23, Issue 3, p.311-331, 2010
General and Familiar Trust in Websites Knowledge
Coye Cheshire, Judd Antin, Karen Cook, Elizabeth Churchill, Coye Cheshire, Judd Antin, Karen Cook, Elizabeth Churchill

When people rely on the web to gather and distribute information, they can build a sense of trust in the websites with which they interact. Understanding the correlates of trust in most websites (general website trust) and trust in websites that one frequently visits (familiar website trust) is crucial for constructing better models of risk perception and online behavior.

We conducted an online survey of active Internet users and examined the associations between the two types of web trust and several independent factors: information technology competence, adverse online events, and general dispositions to be trusting or cautious of others.

Using a series of nested ordered logistic regression models, we find positive associations between general trust, general caution, and the two types of web trust.

The positive effect of information technology competence erases the effect of general caution for general website trust but not for familiar website trust, providing evidence that general trust and self-reported competence are stronger associates of general website trust than broad attitudes about prudence. Finally, the experience of an adverse online event has a strong, negative association with general website trust, but not with familiar website trust.

We discuss several implications for online behavior and suggest website policies that can help users make informed decisions about interacting with potentially risky websites.

Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.