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"Shoplifting"
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Chasing power
Sixteen-year-old Kayla, whose telekinetic ability allows her to be a master shoplifter with enough money to flee with her mother if her father finds them again, meets Daniel, who has the ability to teleport and needs her help to find and steal an ancient incantation that can help rescue his kidnapped mother--but may lead to great danger for Kayla.
Book
How personality traits, employee incompetence and consumer similarity influence shoplifting behavior
Purpose
This study aims to investigate the mediating role of shopping intention and the moderating roles of employee incompetence and consumer similarity in the relationship between consumers’ personal traits and their shoplifting behaviors
Design/methodology/approach
Drawing on the theory of planned behavior (TPB), the authors develop and test a model that links personality traits to shoplifting intention and behavior. The results from a sample of 507 consumers.
Findings
The results from a sample of 507 consumers show that shoplifting intention mediates the effects of personality traits (materialism, alienation and sensation seeking) on shoplifting behavior. In addition, both employee incompetence and consumer similarity are found to moderate the relationship between shoplifting intention and behavior. The findings offer some useful theoretical and managerial implications.
Originality/value
Drawing on the TPB, the authors investigate how personality traits (i.e. materialism, sensation seeking and consumer alienation) influence shoplifting behavior via shoplifting intention. They find that the effects of materialism, sensation seeking and alienation on shoplifting behavior are mediated by shoplifting intention. More importantly, they also find strong support for the moderating roles of employee incompetence and consumer similarity on the relationship between shoplifting intention and behavior. While employee incompetence enhances the relationship between shoplifting intention and shoplifting behavior, consumer similarity negatively moderates the relationship between shoplifting intention and shoplifting behavior.
Journal Article
Exploring the Immediate Effects of COVID-19 Containment Policies on Crime: an Empirical Analysis of the Short-Term Aftermath in Los Angeles
This work investigates whether and how COVID-19 containment policies had an immediate impact on crime trends in Los Angeles. The analysis is conducted using Bayesian structural time-series and focuses on nine crime categories and on the overall crime count, daily monitored from January 1st 2017 to March 28th 2020. We concentrate on two post-intervention time windows—from March 4th to March 16th and from March 4th to March 28th 2020—to dynamically assess the short-term effects of mild and strict policies. In Los Angeles, overall crime has significantly decreased, as well as robbery, shoplifting, theft, and battery. No significant effect has been detected for vehicle theft, burglary, assault with a deadly weapon, intimate partner assault, and homicide. Results suggest that, in the first weeks after the interventions are put in place, social distancing impacts more directly on instrumental and less serious crimes. Policy implications are also discussed.
Journal Article
Shoplifting Detection Using Hybrid Neural Network CNN-BiLSMT and Development of Benchmark Dataset
Shoplifting poses a significant challenge for shop owners as well as other stakeholders, including law enforcement agencies. In recent years, the task of shoplifting detection has gained the interest of researchers due to video surveillance generating vast quantities of data that cannot be processed in real-time by human staff. In previous studies, different datasets and methods have been developed for the task of shoplifting detection. However, there is a lack of a large benchmark dataset containing different behaviors of shoplifting and standard methods for the task of shoplifting detection. To overcome this limitation, in this study, a large benchmark dataset has been developed, having 900 instances with 450 cases of shoplifting and 450 of non-shoplifting with manual annotation based on five different ways of shoplifting. Moreover, a method for the detection of shoplifting is proposed for evaluating the developed dataset. The dataset is also evaluated with methods as baseline methods, including 2D CNN and 3D CNN. Our proposed method, which is a combination of Inception V3 and BILSTM, outperforms all baseline methods with 81 % accuracy. The developed dataset will be publicly available to foster in various areas related to human activity recognition. These areas encompass the development of systems for detecting behaviors such as robbery, identifying human movements, enhancing safety measures, and detecting instances of theft.
Journal Article