Employees are both the first line of defense in organizations and a significant source of vulnerability. Behavioral research in information security (InfoSec) has predominantly studied the compliance of employees with organizational directives. Less understood are ‘shadow security practices’ – a related category of behavior where employees adopt InfoSec workarounds, albeit to still comply with organizational security needs. We develop a model of the antecedents of employees’ intentions to engage in shadow security practices and empirically test our model through a sample of 433 office workers. Results of our structural equation modeling analysis reveal that both InfoSec overload and psychological empowerment increase intentions to adopt shadow security measures, whereas perceived transparency of organizational InfoSec (through InfoSec communication) reduces this intention. Furthermore, we find that these constructs are interrelated and that InfoSec overload can be increased by both psychological empowerment and InfoSec transparency . Our study develops the theoretical understanding of the important yet under-researched concept of shadow security and presents practical recommendations to effectively manage organizational InfoSec through these factors.

The primary goal of higher education institutions is to support all students in the pursuit of academic success, which requires timely assistance for ‘at risk’ students. The adoption of learning management systems results in a large amount of data that can be collected, processed and utilised to improve the students’ learning experiences. This research examines the potential applications of analytics techniques for extracting insights from student-generated content in an academic setting. It showcases how different text analytics techniques, from descriptive content analysis, semantic network analysis, to topic modelling support the discovery of new insights from unstructured, user-generated data. We looked at 968 letters written by ‘at risk’ students in an Australian-based university in Southeast Asia to examine the difficulties the students faced, which led to their academic failure. The results show that time management, family, learning, assessment, and subjects were the leading causes of poor performance, but in a more nuanced way than was expected. Students often faced multiple challenges, one led to another, which resulted in the failing grades. Our study contributes a set of effective text analytics techniques for extracting insights from student data, providing the preliminary guidelines for an information system to detect early at risk students.