Fraud in sports is too often studied from a singular perspective, focussing only on one of its manifestations, and from the perspective of a few selected actors, resulting in a biased view of its actual magnitude. In this study, we adopted a broader perspective, and examined the perceptions of a range of internal sports actors regarding six types of fraud in sports. These are three types of occupational fraud: (1) corruption, (2) asset misappropriation, and (3) financial statement fraud; and three types of sports fraud: (1) direct interference in the natural course of sports competitions, (2) the modification of an athlete's identity or personal information, and (3) modifications of playing surfaces, equipment, athlete physiology, or sporting venues that are non-compliant with criminal laws or sporting rules. We collected data using a cross-sectional survey in a sample of n = 1680 respondents from 61 sports federations. Our results showed that occupational fraud was more prevalent than sports fraud according to our respondents' perceptions. Additionally, they revealed that clubs' board members were most often linked with the commission of occupational fraud, and athletes and coaches to the commission of sports fraud. Moreover, sports fraud was less often witnessed in multi-sports federations and non-competitive federations. Lastly, we found that coaches had more lenient moral attitudes towards fraud in sports in general, and board members and other administrative staff casted a more severe moral judgment on it. This study carries concrete implications for prevention initiatives against fraud in the sports ecosystem.

This study applies the crime heartbeat methodology to empirically examine the temporal concentration of crime in a European city (Ghent, Belgium) in relation to police-registered data collected for five specific crime types (aggressive theft, residential burglary, assault and battery incidents, bicycle theft, and car theft) spanning a 12-year period (2007–2018). The temporal concentration of these crime types is quantified by computing the crime heartbeats for each crime type and deriving the Gini index from these heartbeats. In addition to a city-level analysis, this study also examines the temporal concentration of different crime types at the microgeographic level, focusing on grid cells of 200 by 200 m. The results demonstrate that the temporal concentration of crime remains a stable signature across different crime types over the 12-year period. Residential burglaries and bicycle thefts exhibit more homogeneous patterns between Monday and Friday, with two prominent peaks occurring during nighttime and midday. Car theft displays the highest temporal concentration, with a higher intensity during nighttime hours. Furthermore, substantial variations in temporal concentration were identified between hot and hottest spots for each crime type, indicating that crimes rarely coincide temporally across microgeographic units. This study's findings further highlights the importance of understanding the temporal distribution of (microgeographic) crime patterns as well how fine-grained spatiotemporal crime analysis can inform future crime prevention initiatives.

This study empirically compares multiple eXplainable Artificial Intelligence (XAI) techniques to interpret short-term (weekly) machine learning-based burglary predictions at the micro-place level in Ghent, Belgium. While previous research predominantly relies on SHAP to interpret spatiotemporal crime predictions, this is the first study to systematically evaluate SHAP alongside other XAI techniques, offering both global and local model interpretability within the context of crime prediction. Using data from 2014 to 2018 on residential burglary, repeat and near-repeat victimization, environmental features, socio-demographic indicators, and seasonal effects, we trained an XGBoost model with 76 features to predict weekly burglary hot spots. This model serves as a basis for comparing the interpretative power of different XAI techniques. Our results show that built environment and land use characteristics are the most consistent global predictors of burglary risk. However, their influence varies substantially at the local level, revealing the importance of spatial context. While global feature importance rankings are broadly aligned across XAI techniques, local explanations, especially between SHAP and LIME, often diverge. These discrepancies highlight the need for careful method selection when translating predictions into crime prevention strategies. In addition, this study demonstrates that short-term burglary risks are influenced by complex interactions and threshold effects between environmental and social disorganization features. We interpret these findings through the lens of criminological theory, and argue for more integrated approaches that go beyond examining the isolated effects of specific crime predictors. Finally, we call for greater attention to the methodological implications that arise from applying different interpretability techniques, particularly when machine learning model outputs are used to inform crime prevention and policy decisions.

Research on bystander intervention (BI) during public harassment, that is, intrusive and intimidating (sexual) behaviors in public spaces, remains scarce, creating knowledge gaps regarding its frequency, influencing factors, and impact. This study addresses these gaps by examining BI experiences among an age-diverse sample (16-82 years) living in 10 Belgian municipalities, based on 541 reports collected through an online platform in 2023. Moreover, this study examines BI from two perspectives: receiving help during a specific incident and offering help oneself. BI occurred in only 28% of public harassment incidents with witnesses present, and around half of respondents had ever intervened. Given that prior research on violent conflicts reports much higher intervention rates, this low intervention rate suggests that harassment is perceived as less dangerous and supports the need to raise awareness about its harms. Intervention was more likely when bystanders knew the victim, underlining the potential of fostering a stronger sense of community to encourage intervention. Finally, this study found that victims were significantly more likely to report perceived support when bystanders intervened, highlighting the value of promoting BI.

The present study compares different residential and ambient-like population estimates across two key applications in spatiotemporal crime analysis: (1) the calculation of crime rates, and (2) the prediction of monthly micro-geographic crime risks using machine learning. Using data from Ghent, Belgium across three crime types (i.e., aggressive theft, battery incidents and bicycle theft), we compared traditional administrative residential counts with mobile phone data and various alternative population estimates (i.e., GHS, WorldPop, ENACT and LandScan). Results show that mobile phone counts provided the most robust proxy for the true population-atrisk, demonstrating stronger associations with crime risk and resulting in different crime rates and improved crime prediction performance measures. However, openly accessible alternatives such as GHS and WorldPop redistributed residential estimates performed comparably well, likely due to their incorporation of features that indirectly capture human activity patterns. The findings support crime opportunity theories and highlight that both the nature of population data and its spatiotemporal resolution may substantially influence crime analysis outcomes. For crime prevention, population estimates that directly or indirectly account for human mobility patterns provide avenues to improve crime risk estimation and resource allocation, with freely available alternatives offering cost-effective solutions when fine-grained data are inaccessible.

This study examines the potential of alternative micro-geographic units of analysis compared to the widely used rectangular grid for predicting (monthly) micro-geographic crime risks using a machine learning approach. Specifically, this study compares the prediction performance of machine learning models (XGBoost) in deriving monthly micro-geographic risk predictions for three crime types across rectangular and hexagonal grids of varying resolutions, as well as street segments, using the hit rate, precision, and F1-score as key performance measures. Police-registered data on residential burglary, aggressive theft, and battery (2013–2018), along with environmental and seasonal data on crime predictors were used to train the models and evaluate their performance across different units of analysis and performance measures. Results show that street segments generally achieve higher hit rates compared to grid-based units, but only marginally when compared to high-resolution grids (0.0025 km²). This study thus finds no clear advantage of street segments over small grids in terms of model hit rate. In addition, using street segments and small grids comes at the cost of lower model precision, resulting in more false positive predictions. Grids with resolutions from 0.04 km² to 0.25 km² offer a more balanced performance. Further, no substantial differences were found between rectangular and hexagonal grids, indicating grid shape does not affect prediction performance. Future work should explore how model performance should be defined and operationalised within the context predicting crime risks at specific micro-geographic levels and what the implications are of employing specific micro-geographic units of analysis within the context of crime prevention.

Nonprofit sports clubs are susceptible to fraud, particularly to asset misappropriation and financial statement fraud. Fraud is often committed due to its perceived convenience. Organizational vulnerabilities can create convenience, allowing fraud to occur. This is known as opportunity convenience. Organizational vulnerabilities that increase opportunity convenience can be structural (e.g. organizational capacity, control) or cultural. Yet, the role of these cultural vulnerabilities in mitigating fraud in nonprofit sports clubs is underexplored. This study investigates how ethical culture in nonprofit sports clubs influences the perceived occurrence of fraud (i.e. asset misappropriation and financial statement fraud) and examines the role of perceptions of fraud severity. To this end, a survey was conducted across nonprofit sports clubs in different sports (n = 376). The regression results show that organizational culture is an important determinant of the occurrence of both asset misappropriation and financial statement fraud. However, these relationships are mediated by the perceptions of fraud severity. This study further provides important insights for fraud prevention in nonprofit sports clubs.

Despite the increasing academic interest in match-fixing, little is known about the behavioral determinants of this phenomenon. This study applies key theoretical concepts of situational action theory (SAT) to sportspersons' decision-making process when confronted with sports-related match-fixing (SRMF) propositions. Using a factorial survey, amateur football players (n = 661), and tennis players (n = 609) in Flanders (Belgium) were asked to evaluate hypothetical realistic situations containing match-fixing propositions. Our results show that sportspersons' crime propensity, mostly determined by their moral judgment of SRMF and self-control, and their levels of temptation, together with a number of SAT interactions, were the best predictors of SRMF as a form of sports-related rule breaking. We conclude that SAT provides a valuable theoretical framework to study fraud in sports phenomena such as SRMF, and that factorial surveys have great potential to allow researchers to reach beyond the risk factor stage of research, to efficiently inform prevention initiatives.

In this study, we provide a nuanced perspective on a sub-type of match-fixing, called non-betting-related match-fixing (NBMF). Using a cross-sectional survey, we measured the prevalence and motives behind NBMF in Belgian amateur football and tennis. Additionally, we conducted a thorough study of the regulatory documents of the relevant (sports) organizations to examine the sanctionability of different types of NBMF by analyzing their disciplinary regulations surrounding NBMF and, if applicable, their sanctioning guidelines. Furthermore, various instances of NBMF were applied to the definition of competition manipulation of the Council of Europe's Macolin Convention. We conclude that NBMF is not always linked to sporting outcomes and that the gray zone that surrounds it differs between both sports depending on the specificity of their regulations, and the subjectivity with which they are implemented in practice.

Cyber dating abuse (CDA) concerns the use of digital technology to control, monitor, and hurt one's intimate partner. CDA can have profound detrimental outcomes, such as mental health problems. As such, it is important to identify intrapersonal factors that may explain these behaviors. Previous research suggests that one such factor is the personality cluster of Dark Triad traits (DTT), comprising Machiavellianism, narcissism, and psychopathy. Additionally, DTT and CDA perpetration have both been linked to poor self-control ability, but these relationships have not yet been tested together in one model. As such, the present study examines if individuals' poor self-control ability mediates the relationship between the DTT and CDA perpetration. To test these associations, we conducted a survey study among a representative sample of Belgian adults (n = 1,144; Mage = 47.66 years; 51.3% female). Findings from correlation analyses revealed that all three DTT were individually associated with CDA perpetration, such that higher scores on these traits corresponded with more CDA perpetration. Additionally, pathway analyses from structural equation modeling revealed that individuals' poor self-control ability fully explained the relationship between Machiavellianism and narcissism and CDA perpetration, and partially explained the relationship between psychopathy and CDA perpetration. As our findings suggest that self-control plays an instrumental role in explaining why individuals control and monitor their partner via digital technology, prevention and intervention efforts should seek ways to improve individuals' self-control ability in situations that may trigger such harmful interpersonal behaviors, particularly among individuals who exhibit Dark Triad personality traits.

Occupational deviance is one of the most rampant and alarming phenomena in the workplace. It can have negative effects on the individual employee, the organization, the profession, and the larger society. Therefore, it is important to better understand the etiology of such behavior in the hopes of providing solutions to reduce it. One such theory that may provide a conceptual framework to help understand workplace deviance is Agnew's general strain theory. As such, the main goal of this research was to examine the predictive utility of general strain theory in explaining workplace deviance. Using a sample of 336 private bankers in Iran, the findings suggest that the important theoretical concepts of workplace strain, subjective strain, negative emotion, and deviant motivation all predicted workplace misconduct. Consistent with the theory, additional results demonstrated important mediated relationships, such that strain was indirectly related to workplace deviance through negative emotions and deviant motivation. Specific results, policy implications, and study limitations are discussed.

The objective of this panel is to contribute valuable knowledge that can inform future improvements and adaptations of multi-agency structures in varying local environments with robust support from national entities. Key objectives include evaluating the current challenges and limitations faced by these structures in diverse contexts, with a view to gaining insights into effective strategies for overcoming obstacles and enhancing their overall effectiveness.

The chapter examines the vulnerability of critical infrastructure to insider threats by providing an innovative typology of the various types of intentional misconduct that employees can commit. The typology was developed based on an interplay between on the one hand insider threat literature that provided the foundation of the theoretical framework and however publicly available examples of insider threat incidents found in (inter)national media that reassessed and validated the typology. To establish the seven-part insider threat typology, a spin-off version of the who, what, where, when, why, and how (5W1H) methodology is used, answering the following elementary questions: (1) What does the insider want to achieve by committing intentional misconduct? (2) Who suffers or benefits from the insider threat? (3) Why does the insider want to commit intentional misconduct? (4) When does the insider become untrustworthy? (5) How does the insider commit intentional misconduct? (6) How serious is the impact of the insider threat? and (7) How many insiders are involved with the insider threat? We argue that this typology is a valuable starting point in the development of tailor-made approaches to insider threat mitigation. Organizations can build on the conclusions of the typology to prioritize their risk management budget. The typology, therefore, paves the way for a risk-based approach to insider threat mitigation.

Big data policing: The implications of digitalization and datafication for the police function and police work In recent years, our society has become increasingly digitized, leading to increased datafication. The rise of computers, the internet, mobile devices, social media, artificial intelligence (AI), and related technologies has transformed various aspects of our daily lives, the way we work, and the way we recreate. This increasing digitization and datafication have also significantly impacted the police function and police work. As a result, there has been a growing emphasis on ‘big data policing’ as police departments increasingly rely on big data and related applications to steer both operational (e.g., public order enforcement and investigative research) and strategic (e.g., allocation of police resources) processes. This contribution delves into the implications of digitization and datafication for the police function and police work, with a particular focus on big data policing. On one hand, we clearly define and conceptualize big data policing, providing examples of its practical applications in today’s society. On the other hand, we examine the broad implications of big data policing for both the police function and police work. It is clear that big data policing contributes to further fragmentation of the police function by involving actors from various levels of the criminal justice system. Additionally, it introduces significant changes to socio-technological police practices, such as the extent of proactive police work and the impact on the discretion of police officers. The contribution concludes with a brief reflection on how these changes can be appreciated and considers the potential impact of the European AI Act on both the police and other stakeholders who utilize AI in their work. This legislation promises to have substantial consequences for all those directly or indirectly involved in AI-powered police practices.

The COVID-19 pandemic has precipitated quarantines in many urban settings, and rules have been enforced to ensure that citizens are complying with health-related mandates. However, anecdotal and empirical evidence confirm the prevalence of policy transgressions. Non-compliance with COVID-19 mandates can have severe consequences for individual health, societal fear, and the global economy. Thus, it is important to better understand the etiology of such misbehavior in the hopes of ensuring policy adherence. Using Agnew’s social concern theory as a conceptual framework, this study investigates quarantine-related misbehavior in the urban context of Rasht, Iran. Survey data of 393 university students indicate that social concern theory can explain quarantine-related misbehavior. Specific findings, implications, limitations, and directions for future research are discussed.

Academic dishonesty has recently attracted the attention of scholars worldwide in the aftermath of severe breaches of academic rules of conduct. As today's students are tomorrow's leading academics and professionals, it continues to be of importance to understand the basic explanatory mechanisms of academic dishonesty. The present cross-cultural inquiry explores academic dishonesty through the lens of situational action theory (SAT). Specifically, this paper explores the connection between criminogenic propensity and criminogenic exposure, and its impact on academic misconduct. We analyze self-reported data from a random sample of 378 Iranian students. Multivariate regression results, examining both direct and interactional relationships, demonstrate that the key propensity-environment hypotheses of SAT are generally supported as morality, self-control, and perceived deterrence all play a role in explaining academic misconduct. Specific findings, study limitations, and directions for future research are discussed.

As romantic relationships in young adulthood (18–25 years) are frequently characterized by experimentation and risk-taking, this could make young adults particularly vulnerable to experience sexual harassment by a dating or committed partner. This study examines young adults’ victimization and perpetration experiences of online and in-person sexual harassment with their dating or committed partner, and explores the role of the Dark Triad personality traits. We conducted a cross-sectional survey among 458 young adults, 371 of whom were in a romantic relationship ( M age = 20.80, SD age = 1.51, 25.6% men). Our findings revealed that all measured sexual harassment experiences were significantly more prevalent among young adults in dating relationships compared to those in committed relationships. Furthermore, in both relationship types, all online and in-person experiences of sexual harassment were significantly linked, indicating that these harmful experiences occur across contexts. Additionally, all victimization and perpetration experiences were significantly linked in both relationship types, meaning that some young adults were both victims and perpetrators of these behaviors. Lastly, we found that sexual harassment was linked to narcissism in dating relationships, but to Machiavellianism in committed relationships, indicating that different strategies may explain these behaviors.