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A comparison of features in a crowdsourced phishing warning system
Author(s) -
Nguyen Christopher,
Jensen Matthew L.,
Durcikova Alexandra,
Wright Ryan T.
Publication year - 2021
Publication title -
information systems journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.635
H-Index - 89
eISSN - 1365-2575
pISSN - 1350-1917
DOI - 10.1111/isj.12318
Subject(s) - crowdsourcing , phishing , judgement , context (archaeology) , computer science , internet privacy , warning system , data science , world wide web , the internet , paleontology , telecommunications , political science , law , biology
Initial research on using crowdsourcing as a collaborative method for helping individuals identify phishing messages has shown promising results. However, the vast majority of crowdsourcing research has focussed on crowdsourced system components broadly and understanding individuals' motivation in contributing to crowdsourced systems. Little research has examined the features of crowdsourced systems that influence whether individuals utilise this information, particularly in the context of warnings for phishing emails. Thus, the present study examined four features related to warnings derived from a mock crowdsourced anti‐phishing warning system that 438 participants were provided to aid in their evaluation of a series of email messages: the number of times an email message was reported as being potentially suspicious, the source of the reports, the accuracy rate of the warnings (based on reports) and the disclosure of the accuracy rate. The results showed that crowdsourcing features work together to encourage warning acceptance and reduce anxiety. Accuracy rate demonstrated the most prominent effects on outcomes related to judgement accuracy, adherence to warning recommendations and anxiety with system use. The results are discussed regarding implications for organisations considering the design and implementation of crowdsourced phishing warning systems that facilitate accurate recommendations.