As adversarial hacker exploits proliferate the media, researchers call for more studies examining their culture, behaviours, and defence mechanisms. This study aims to understand the nature of hackers through their knowledge-sharing behaviours in a coopetitive environment. This understanding may expose emerging threats and cybercriminal activities, which are beneficial to building information security defences. Also, extracting knowledge from darknet sources can help organisations anticipate, defend, and respond to cyber-attacks. Hence, we incorporate hacker forum data and perform a detailed analysis informed by foundational social capital theory. We explore hacker knowledge-sharing patterns with respect to the malicious knowledge shared. Further, we use nonlinear approaches to understand the dynamic relationships in an environment that is both cooperative and competitive. The findings seek to clarify how forms of social capital and coopetitive factors function differently to facilitate or impede knowledge sharing in a darknet context. Specifically, the findings indicate that the sharing of malicious knowledge is non-linear, and that knowledge control (i.e., reduced knowledge sharing) occurs with malicious knowledge as it relates to status and structural positioning.

Bring your own applications (BYOA) in organizational networks introduces security risks. We examine whether individuals who BYOA at work are motivated to use multi-factor authentication (MFA) to protect the organization’s security, a practice we describe as bring your own security (BYOS). The behaviors are examined through dual theoretical lenses (a) intrinsic motivation, capturing the personal satisfaction related to the use, and (b) perspective-taking, capturing the user’s ability to consider information security from the organization’s perspective. We analyzed survey data of 154 samples using partial least squares structural equation modeling (PLS-SEM). Our results demonstrate that while the intrinsic motivation path is instrumental in influencing MFA use, perspective-taking offers significant explanatory power to information security behaviors. This article extends the current knowledge of the impact of perspective-taking on voluntary security behavior, which makes a novel and significant contribution to cybersecurity research and practice.

Increased rewards and reduced risks drive illicit networks. Cybercriminals seek to avoid risks, including detection, arrests, sanctions, and violence. Hence, they employ several strategies to avoid operational risks when accessing darknet sites, communicating, making payments, shipping and delivery, and resolving disputes. Each operation involves transactions that should work together, such that when a transaction is disrupted, it increases its risk and reduces the crime’s benefits. In this study, we examine these risks, and their avoidance strategies, in an attempt to highlight transactions that could be disrupted. We develop a typology of darknet risks, a parsimonious conceptual model abstracted from the literature on risk originators (internal or external) and risk targets (vendors or buyers). This study further clarifies how risk avoidance strategies may be disrupted to increase the risks and reduce the reward of darknet crime.

While there is an explosion in data analytic activity, certain behavioral aspects of a data analyst’s modeling efforts are not well understood. Through the lens of effort minimization theory, this research considers how data analysts make decisions to engage with more data as a part of their modeling process, and the effects that modeling tools have on these decisions. Previous research found that decision aids have no effect on reducing effort in terms of the amount of information referenced and processed. However, our results indicate that there are indeed significant differences in the amount of data analyzed with less effort-intensive modeling methods, and increasing amounts of available data. These results offer new implications for effort minimization research and organizations engaged in data analytics.