Phishing, a prevalent cyber threat leveraging social engineering, poses significant challenges in the digital landscape. Despite advancements in security technologies, phishing continues to exploit human vulnerabilities, underscoring the need to understand how individuals detect such attacks. Existing research often assumes phishing detection occurs in isolation, overlooking real-world multitasking contexts where competing cognitive demands can hinder detection. This study fills this gap by examining phishing detection in the multitasking context and theorizing the relevant cognitive mechanisms and phishing-specific factors that influence phishing detection performance. Drawing on the memory-for-goals theory, we investigate the effects of working memory load (WML) from the primary task, goal activation (GA) towards phishing detection on performance, and message framing of phishing attacks. Findings from two online experiments reveal that increased WML impairs detection accuracy, while GA improves performance and reduces the negative impact of WML; furthermore, GA plays a more significant role in gain-framed phishing emails compared to loss-framed ones. Our research shifts the focus from message characteristics to the influence of multitasking on phishing detection. The results highlight the need for context-aware interventions that better align with real-world user behaviour, which provides a foundation for designing more effective phishing defences in multitasking digital environments.

Phishing attacks, often disguised as legitimate communications, exploit human psychology to compromise security. Research on phishing susceptibility suggests that the way phishing messages are framed acts as an external motivational cue, enticing individuals to click on malicious links. However, these studies often overlook the role of individuals’ internal motivational orientation and its interplay with cognitive constraints and message design. The paper investigates how a promotion vs. prevention focus interacts with message framing (gain vs. loss), working memory load (WML), and need for cognition (NFC) to shape phishing detection. Across two experiments, we find that prevention-focused individuals exhibit higher detection accuracy for phishing emails but lower detection accuracy for legitimate emails. For phishing emails, this motivational gap is more pronounced under conditions of low WML or among low-NFC individuals, particularly when emails include gain-framed content. For legitimate emails, the gap is especially salient under low WML and when evaluating messages with gain-framed content. Our findings advance phishing research by demonstrating the joint influence of motivation, cognition, and message design, and offer practical insights for personalized cybersecurity training that adapts to users’ cognitive and motivational profiles to reduce cyber risks.

As firms move towards data-driven decision-making using algorithmic systems, concerns are raised regarding the lack of transparency of these systems which could have ramifications related to users’ trust and the potential for provoking discriminatory decisions. Although previous research has developed methods to improve algorithmic transparency, little empirical evidence exists regarding the extent of the effectiveness of these approaches. Drawing upon Rest’s theory of ethical decision-making and the literature on algorithmic transparency and bias, we investigate the effectiveness of feature importance (FI), a common transparency-enhancing approach, which illustrates the nature and the weights of the features utilised by an algorithm. Through an online experiment employing a fictitious tool that provided recommendations for selecting employees for a promotion-related training programme, we find that FI is effective when biased recommendations include direct discrimination (i.e. when individuals are treated less favourably on protected grounds such as gender); but is of little assistance when discrimination is indirect (i.e. when a criterion or practice that is apparently neutral, disadvantages a group of individuals who are of a protected class). Additionally, we propose a new transparency approach, using aggregated demographic information, to accompany FI in indirect discrimination circumstances and report the results of testing its effects.

This study investigates the synergistic impacts of online and offline social participation on older adults’ subjective well-being outcomes. By drawing on the socio-emotional selectivity perspective, we conduct a cohort analysis using the Canadian Longitudinal Study on Ageing data and find that while online participation alone may increase loneliness, engaging in offline participation will make online participation beneficial. Loneliness serves as a mediating mechanism such that engaging in both online and offline social participation can indirectly enhance satisfaction with life by reducing loneliness. We further find a significant moderating effect of social support, which mitigates the negative impact of loneliness on life satisfaction.

To build a solid foundation on which to understand and combat threats to information systems, researchers need to look past technical security issues and explore why hackers do what they do. Based on General Deterrence Theory and the Theory of Reasoned Action, a structural model is proposed and validated that examines attraction and detraction factors towards a hack. From a motivational perspective, individual characteristics (mastery and curiosity), peer influence and the nature of the task itself are shown to impact hacker’s attitudes. Specifically, we uncover an interesting non-linear relationship between hacking task complexity and a hacker’s attitude towards a hack. From a deterrence perspective, while hackers consider the likelihood of being caught, the severity of punishment/sanctions does not have a significant effect on hackers’ intention to engage in a hacking task. When we better understand what motivates and demotivates these highly skilled users, we gain insights to avoid becoming targets.