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Understanding Perceived Trust to Reduce Regret
Author(s) -
Costante Elisa,
Hartog Jerry,
Petković Milan
Publication year - 2015
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12025
Subject(s) - regret , internet privacy , trustworthiness , perception , precondition , computer science , domain (mathematical analysis) , web of trust , computational trust , computer security , psychology , social science , reputation , sociology , mathematical analysis , mathematics , neuroscience , machine learning , programming language
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., security , look&feel , and privacy ) 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.