Understanding Perceived Trust to Reduce Regret. (21st January 2014)

• Record Type: Journal Article
• Authors: Costante, Elisa; den Hartog, Jerry; Petković, Milan
• Is Part Of: Computational intelligence. Volume 31:Number 2(2015:May)
• Page Start: 327
• Page End: 347
• Publication Date: 2014-01-21
• Subjects: Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12025
• Languages: English
• ISSNs: 0824-7935

Abstract

<abstract abstract-type="main" id="coin12025-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="coin12025-para-0001">Trust is fundamental for promoting the use of online services, such as e‐commerce or e‐health. Understanding how users perceive trust online is a precondition to create trustworthy marketplaces. In this article, we present a domain‐independent general trust perception model that helps us to understand how users make online trust decisions and how we can help them in making the right decisions, which minimize future regret. We also present the results of a user study describing the weight that different factors in the model (e.g., <italic>security</italic>, <italic>look&amp;feel</italic>, and <italic>privacy</italic>) have on perceived trust. The study identifies the existence of a positive correlation between the user's knowledge and the importance placed on factors such as security and privacy. This indicates that the impact factors as security and privacy have on perceived trust is higher in users with higher knowledge.</p> </abstract>