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This research focuses on the distinct group of high-status employees commonly referred to as workplace vigilantes, and conceptually investigates how unethical requests by these individuals impact the behavior and attitude of other employees over time using the social identity theory.

For developing a conceptual model, literature from the domains of social identity, organizational behavior and general management was searched through Google Scholar. To search the literature, some key terms such as “unethical activities”, “Islamic work ethics” and “social identity” were searched and analyzed.

Using the social identity theory, a conceptual process model is developed which suggests that when high-status employees propose unethical requests to employees, individuals with high morality are likely to refuse those unethical requests to protect their self-categorizations. However, taking the unfair advantage of their illegitimate powers, high-status employees are likely to eventually make wrong judgments and give unnecessary punishments to moral employees. It is further argued that consistent victimization is likely to negatively impact the social identity of such employees and leads to irritability in moral employees, particularly when such individuals are unable to get the requisite social support from their leaders.

While a considerable body of literature has focused on the antecedents and consequences of intense unethical business practices and the crucial role of leaders in such activities, limited attention has been given to the role of other employees and how they engage in mild unethical misconduct regularly, which is the key focus of this research. The novel conceptual framework needs to be tested in diverse contexts for further development and validation.

The paper aims to explore the relationship between accounting and racial violence through an investigation of sharecropping in the postbellum American South.

A range of primary sources including peonage case files of the US Department of Justice and the archives of the National Association for the Advancement of Colored People (NAACP) are utilised. Data are analysed by reference to Randall Collins' theory of violence. Consistent with this theory, a micro-sociological approach to examining violent encounters is employed.

It is demonstrated that the production of alternative or competing accounts, accounting manipulation and failure to account generated interactions where confrontational tension culminated in bluster, physical attacks and lynching. Such violence took place in the context of potent racial ideologies and institutions.

The paper is distinctive in its focus on the interface between accounting and “actual” (as opposed to symbolic) violence. It reveals how accounting processes and traces featured in the highly charged emotional fields from which physical violence could erupt. The study advances knowledge of the role of accounting in race relations from the late nineteenth century to the mid-twentieth century, a largely unexplored period in the accounting history literature. It also seeks to extend the research agenda on accounting and slavery (which has hitherto emphasised chattel slavery) to encompass the practice of debt peonage.

It has been suggested that homicides in Jamaica are partly driven by conflicts among criminals over funds coming from international lottery scams; most of these funds are channeled into the country via remittances. This study aims to determine the empirical relationship between remittances and homicides in Jamaica over the period 1985–2019.

The authors apply an error correction modelling framework while accounting for indicators of changes in socioeconomic conditions.

There are two. First, the authors find from impulse response analysis of the long-run dynamics that an increase in remittances is associated with an increase in homicides, and vice versa. Second, the authors find that there is bidirectional Granger causality between remittances and homicides in the short run.

Two important implications are that policies should be strengthened to channel remittances to productive and legal investment opportunities and that greater efforts may be needed to stem the flow of funds coming from international lottery scamming and other illegal activities.

This is the first study that examines the dynamic relationship between remittances and homicides in Jamaica from a robust statistical perspective.

Abuja, the capital city of Nigeria, is one of the fastest growing capital cities in sub-Saharan Africa. Recently, the city is experiencing an alarming rate of burglary and violent crimes, while the city planning management frameworks lacks adequate and effective crime mapping, monitoring and management techniques necessary for achieving liveable and safe environment for habitation despite its grandiose spatial planning and aesthetically appealing architectural design as a modern city. Based on police crime records (2007–2018) and geospatial analysis, this paper aims to provide adequate understanding on the interplay of land use configuration and burglary crime formation in residential neighbourhoods of Abuja, Nigeria.

The methods used for the purpose of data collection includes; field survey, Block Environmental Inventory, while inferential statistics and Geographic Information System tools was used for data analysis. The analysis established that Lagos, Nsukka and Enugu Streets are hotspots; while Chief Palace street, Ladoke Akintola and Oka-Akoko streets were found to be cold spots.

This study, however, established the applicability of crime pattern, opportunity theory and routine activity theory in understanding the rising burglary crime events in the study areas and the link between physical characteristics of street block typology and burglary crime pattern. The results of the analysis has in a way affirmed the positions of the theories, while disagreed with them in cases where the results indicated contrary outcome.

This paper concluded with inference drawn from the results that supported mixed-use development but with built-in crime prevention through environmental design strategies as effective burglary crime prevention mechanisms that contribute to crime rate reduction.

Social networks (SNs) have recently evolved from a means of connecting people to becoming a tool for social engineering, radicalization, dissemination of propaganda and recruitment of terrorists. It is no secret that the majority of the Islamic State in Iraq and Syria (ISIS) members are Arabic speakers, and even the non-Arabs adopt Arabic nicknames. However, the majority of the literature researching the subject deals with non-Arabic languages. Moreover, the features involved in identifying radical Islamic content are shallow and the search or classification terms are common in daily chatter among people of the region. The authors aim at distinguishing normal conversation, influenced by the role religion plays in daily life, from terror-related content.

This article presents the authors' experience and the results of collecting, analyzing and classifying Twitter data from affiliated members of ISIS, as well as sympathizers. The authors used artificial intelligence (AI) and machine learning classification algorithms to categorize the tweets, as terror-related, generic religious, and unrelated.

The authors report the classification accuracy of the K-nearest neighbor (KNN), Bernoulli Naive Bayes (BNN) and support vector machine (SVM) [one-against-all (OAA) and all-against-all (AAA)] algorithms. The authors achieved a high classification F1 score of 83\%. The work in this paper will hopefully aid more accurate classification of radical content.

In this paper, the authors have collected and analyzed thousands of tweets advocating and promoting ISIS. The authors have identified many common markers and keywords characteristic of ISIS rhetoric. Moreover, the authors have applied text processing and AI machine learning techniques to classify the tweets into one of three categories: terror-related, non-terror political chatter and news and unrelated data-polluting tweets.

This study aims to explore the impact of psychosocial entitlement on workplace deviance, particularly in contexts marked by increased job autonomy. Additionally, this study delves into the organizational factors, including perceived support and justice, which play a crucial role in this dynamic.

Applying social exchange theory (SET), this study contends that fostering a fair and supportive workplace can deter entitled employees from workplace deviance. This study used time-lagged, multi-source data to analyse the interplay between psychological entitlement and workplace deviance in the presence of job autonomy and to assess the influence of perceived organizational justice and support. This study’s analysis uses SmartPLS for partial least square-structural equation modelling.

The study’s results indicate an elevated sense of entitlement among employees working autonomously and a heightened propensity for deviant behaviour when psychological entitlement increases. Yet, the data revealed moderating effects of perceived organizational support on the relationship between psychological entitlement and workplace deviance. A post hoc analysis found full mediation effects by psychological entitlement on the relationship between perceived organizational justice and workplace deviance.

To enhance organizational dynamics, management should prioritize promoting employee perceptions of organizational justice and support through impartial human resource policies, consistent policy implementation, initiatives such as virtual learning, improved mental health benefits and measurement tools for feedback on justice and support measures.

An essential theoretical contribution of this research resides in its extension beyond the conventional application of SET, traditionally associated with reciprocity in the workplace. This study showcases its effectiveness in elucidating the impact of psychosocial factors on reciprocity in organizational dynamics.

This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.

The purpose of this paper is to investigate investor herding behaviour and the effect of presidential elections on investor herding behaviour in African stock markets at the sector level.

The study segregates listed firms into financial, consumer goods, consumer services and basic materials sectors and uses the cross-sectional absolute deviation approach as a metric of detecting herding in each of the sectors. The authors extend the model to tease out the effect of presidential elections on investor herding behaviour.

The study reveals that sectoral differences are fundamental to the evolution of herding. Herding is prominent in a financial services sector dominated by banks. The phenomenon also prevails in markets with smaller consumer goods and services sectors. A post-presidential election effect on investor herding is found for the consumer goods and services sectors of Ghana and a pre-presidential election effect is documented in Nigeria's consumer services sector. The authors conclude that post-presidential election effect is as a result of political connections whilst a pre-presidential election effect is attributable to political business cycles.

The study is based on four African countries due to data constraints. Nonetheless, the study is the first in Africa to the best of the authors' knowledge, and the results are very solid and have a lot of practical and policy implications.

The study has implications for investors as it guides investment behaviour in pre- and post-presidential election periods.

Past studies on investor herding behaviour in African stock markets have largely concentrated on the aggregate market. Knowledge on sectoral differences in investor herding is almost non-existent for African stock markets. Furthermore, premised on the fact that stock markets react to presidential elections, there is no known study that have attempted to examine the effect of presidential elections on investor herding behaviour. This paper contributes to the literature by providing evidence on sectoral differences in investor herding behaviour and the effect of presidential elections on sectoral herding behaviour.