Email phishing and signal detection: How persuasion principles and personality influence response patterns and accuracy.

Phishing is a social engineering tactic where a malicious actor impersonates a trustworthy third party with the intention of tricking the user into divulging sensitive information. Previous social engineering research in a real-world setting has shown an interaction between the personality of the target and the persuasion principle used. This study investigated whether this interaction is present in the realm of email phishing. Additionally, a signal detection theory framework was used to evaluate how the various persuasion principles influence accuracy, sensitivity (d'), and response criterion placement. A personality inventory and an email identification task (phishing or legitimate) were used. These data support previous findings that high extroversion is predictive of increased susceptibility to phishing attacks. The various persuasions principles elicited diverse response criterions and sensitivities, though all investigated persuasion principles resulted in a liberal decision criterion, except one. These findings are interpreted and discussed.

Who's the real expert here? Pedigree's unique bias on trust between human and automated advisers.

OBJECTIVE: We assessed the effects of source type bias (human or automation) on adviser trust in a dual adviser decision-making task. BACKGROUND: Source type and reliability's effects on adviser trust have been studied in a dual-adviser context, but the influence of pedigree (perceived expertise) across source types lacked robust investigation. As situations with two decision-aids of uneven pedigree can easily arise, it is critical to understand how operators are biased towards a decision-aid of a certain source type and pedigree. METHOD: A decision-making task similar to the paradigm of Convoy Leader (Lyons and Stokes, 2012) was given to participants, where a military convoy route had to be selected in the presence of IEDs and insurgent activity. We measured behavioral reliance and trust attitudes. Pedigree was manipulated via controlled adviser descriptions, in a manner consistent with past investigations (Madhavan and Wiegmann, 2007a). RESULTS: We found a trust bias towards the human adviser, reversed only when there is a far greater pedigree in the automated adviser. Trust attitudes were also strongly indicative of reliance behaviors. CONCLUSION: Pedigree is a strong influencer of trust in a decision-aid and biased towards human advisers. Trust is highly predictive of reliance decisions. APPLICATION: System designers must take care with how "expert" automation is portrayed, particularly if it is used in conjunction with other human advisers (e.g.: conflicting advice from air-traffic control and an onboard system).