A comparison of features in a crowdsourced phishing warning system

• Published in: Information systems journal (Oxford, England) Vol. 31; no. 3; pp. 473 - 513
• Main Authors: Nguyen, Christopher, Jensen, Matthew L., Durcikova, Alexandra, Wright, Ryan T.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01-05-2021; Blackwell Publishing Ltd
• Subjects: Accuracy; Anxiety; Crowdsourcing; Cybercrime; Electronic mail; human‐automation interaction; Messages; Phishing; report source; warning; Warning systems
• ISSN: 1350-1917; 1365-2575
• DOI: 10.1111/isj.12318

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.