The nexus of mindfulness, affect, and information processing in phishing identification: An empirical examination

Phishing, the most pervasive cyber-attack, is a threat to both organizations and individuals, leaving phishing identification the most crucial anti-phishing weapon for all internet users. Individuals’ choice of information processing strategies results in differing accuracy of phishing identification. As an antecedent of phishing identification, the effect of mindfulness training has been researched. However, the influence of dispositional and domain-specific mindfulness, along with an individual's affective state, which drives the choice of information processing strategies which in turn affect one's phishing detection, has not yet been given sufficient empirical and theoretical scrutiny. This study thus identifies and analyzes the antecedents (heuristic and systematic information processing, dispositional and domain mindfulness, and affective state) and behavioral consequences (phishing detection accuracy), drawing on the heuristic-systematic model of information processing, mindful decision-making, and affect-and-persuasion literature. A scenario-based survey experiment was conducted to reveal how dispositional and contextual mindfulness and affectivity influence information processing mechanisms and, consequently, affect phishing detection accuracy. The study aims to contribute to the existing information security literature by examining the novel connections between dispositional and domain mindfulness and their influence on individual users' information processing strategies and phishing detection accuracy. Further, it intends to contribute to phishing training and awareness activities by identifying the function of cognitive-affective (affective states, trait mindfulness) and cognitive-behavioral (domain mindfulness) factors on the choice of information processing modes and phishing detection accuracy. Also, the study indicates that leveraging affective states could enhance the effectiveness of automatic filters in combating phishing attempts.