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

IEEE Computing Conference 2018, London, UK

Regularization of the Kernel Matrix via Covariance Matrix Shrinkage Estimation

The kernel trick concept, formulated as an inner product in a feature space, facilitates powerful extensions to many well-known algorithms. While the kernel matrix involves inner products in the feature space, the sample covariance matrix of the data requires outer products. Therefore, their spectral properties are tightly connected. This allows us to examine the kernel matrix through the sample covariance matrix in the feature space and vice versa. The use of kernels often involves a large number of features, compared to the number of observations. In this scenario, the sample covariance matrix is not well-conditioned nor is it necessarily invertible, mandating a solution to the problem of estimating high-dimensional covariance matrices under small sample size conditions. We tackle this problem through the use of a shrinkage estimator that offers a compromise between the sample covariance matrix and a well-conditioned matrix (also known as the "target") with the aim of minimizing the mean-squared error (MSE). We propose a distribution-free kernel matrix regularization approach that is tuned directly from the kernel matrix, avoiding the need to address the feature space explicitly. Numerical simulations demonstrate that the proposed regularization is effective in classification tasks.