The purpose of this study was to investigate the influence of corporate social responsibility (CSR) on an employee’s negative behavior, in addition to its intermediating mechanism (i.e., mediators and moderator) in the relationship. This paper proposes that CSR may diminish an employee’s negative behavior, such as counterproductive work behavior. Relying on the context–attitude–behavior framework, this study investigated the mediators and moderator of the relationship between CSR and counterproductive work behavior. Specifically, this study hypothesized that not only does CSR diminish the level of counterproductive work behavior by sequentially boosting the level of employees’ organizational trust and commitment, but their work overload also negatively moderates the association between CSR and organizational trust. Utilizing three-wave time-lagged online survey data from 342 employees in South Korean companies, this study tested the hypotheses by building a moderated mediation model with structural equation modeling analysis. The results indicate that CSR decreases the level of employees’ counterproductive work behavior through enhancing their organizational trust and commitment. Moreover, work overload negatively moderates the association between CSR and organizational trust. The findings of this study make theoretical and practical contributions to the CSR literature.

The increasing amount and complexity of Cyber security attacks in recent years have made text analysis and data mining techniques an important factor in discovering features of such attacks and detecting future security threats. In this paper, we report on the results of a recent case study that involved the analysis of a community data set collected from five small and medium companies in Korea. The data set represents Cyber security incidents and response actions. We investigated in the study the kind of problems concerned with the prediction of response actions to future incidents from features of past incidents. Our analysis is based on text mining methods, such as n-gram and bag-of-words, as well as on machine learning algorithms for the classification of incidents and their response actions. Based on the results of the study, we also suggest an experience-sharing model, which we use to demonstrate how companies may share their trained classifiers without the sharing of their individual data sets in a collaborative environment.

Purpose: This paper aims to provide an in-depth exploration of the code of silence in Korean policing and its relationship to perceptions of disciplinary fairness.Design/methodology/approach: The authors separately surveyed 370 Korean police officers in 2008 and 356 Korean police officers in 2019. The respondents were asked to evaluate seven hypothetical scenarios measuring different types of police misconduct from police corruption to the use of excessive force.Findings: The results demonstrated that the strength of the code of silence decreased over a decade. The code of silence seems to protect less serious examples of police misconduct more strongly than more serious examples of police misconduct. Furthermore, the extent of the code of silence and perceptions of discipline severity are closely related in situations in which the expected discipline is evaluated by officers as too harsh. When police officers evaluated the expected discipline as fair, they were less likely to adhere to the code of silence than when they evaluated the expected discipline as too harsh, providing support for the simple justice model. On the other hand, the results are mixed for comparisons of the code of silence among respondents who evaluated discipline as fair and those who evaluated discipline as too lenient.Originality/value: This is one of few studies focusing on the potential changes in the code of silence over time and on its relationship with the perception of disciplinary fairness. South Korea has conducted a reform of the police (the Grand Reform) in the late 1990s and more recently enacted the new laws regulating police misconduct. This study relies on two independent surveys of the same population of police officersto empirically assesses potential changes resulting from these societal and organizational transformations.

In this study, we evaluated one of the modern automated steganalysis tools, Stegdetect, to study its false negative rates when analysing a bulk of images. In so doing, we used JPHide method to embed a randomly generated messages into 2000 JPEG images. The aim of this study is to help digital forensics analysts during their investigations by means of providing an idea of the false negative rates of Stegdetect. This study found that (1) the false negative rates depended largely on the tool’s sensitivity values, (2) the tool had a high false negative rate between the sensitivity values from 0.1 to 3.4 and (3) the best sensitivity value for detection of JPHide method was 6.2. It is therefore recommended that when analysing a huge bulk of images forensic analysts need to take into consideration sensitivity values to reduce the false negative rates of Stegdetect.