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Although it is a relatively recent conceptualization of malevolent personality, the dark triad (DT) has been widely researched and shown to be responsible for increases in physical violence, controlling behavior, short-term mating preferences and poor relationship quality. This study aims to investigate whether DT traits predict acceptance toward intimate partner violence (IPV) in the general population, addressing a gap in the literature regarding predictors of harmful attitudes toward romantic relationships.

In total, 150 adults aged 18–74 (76% women) completed two self-report questionnaires: the short DT and the IPV attitude scale-revised.

A series of hierarchical multiple regression analyses were conducted, with gender as predictor in the first models and DT traits added as predictors in second models. The results showed that male participants were more accepting of IPV than female participants. Over and above the contribution of gender, psychopathy and Machiavellianism positively predicted overall IPV acceptance, but narcissism did not. Psychopathy and Machiavellianism positively predicted acceptance of psychological abuse, and psychopathy positively predicted acceptance of controlling behaviors. Narcissism did not predict any facet of IPV acceptance.

To the best of the authors’ knowledge, as the first study to explore the roles of DT traits in acceptance of IPV behaviors, the results contribute to the understanding of how these traits may predispose individuals to harmful intimate partner behaviors. These findings can inform IPV prevention efforts to aid in the early identification of individuals who hold maladaptive beliefs surrounding romantic relationships.

This study aims to delve into the perspectives of educators on integrating ChatGPT, an AI language model into management education. In the current research, educators were asked to talk as widely as possible about the perceived benefits, limitations of ChatGPT in management education and strategies to improve ChatGPT for management education. Also, shedding light on what motivates or inhibits them to use ChatGPT in management education in the Indian context.

Interpretative phenomenological analysis commonly uses purposive sampling. In this research, the purpose is to delve into educators’ perspectives on ChatGPT in management education. The data was collected from the universities offering management education in Uttarakhand, India. The final sample size for the study was constrained to 57 educators, reflecting the point of theoretical saturation in data collection.

The present study involved educators discussing the various advantages of using ChatGPT in the context of management education. When educators were interviewed, their responses were categorized into nine distinct sub-themes related to the benefits of ChatGPT in management education. Similarly, when educators were asked to provide their insights on the limitations of using ChatGPT in management education, their responses were grouped into six sub-themes that emerged during the interviews. Furthermore, in the process of interviewing educators about potential strategies to enhance ChatGPT for management education, their feedback was organized into seven sub-themes, reflecting the various approaches suggested by the educators.

In the qualitative study, perceptions and experiences of educators at a certain period are captured. It would be necessary to conduct longitudinal research to comprehend how perceptions and experiences might change over time. The study’s exclusive focus on management education may not adequately reflect the experiences and viewpoints of educators in another discipline. The findings may not be generalizable and applicable to other educational disciplines.

The research has helped in identifying the strengths and limitations of ChatGPT as perceived by educators for management education. Understanding educators’ perceptions and experiences with ChatGPT provided valuable insight into how the tool is being used in real-world educational settings. These insights can guide higher education institutions, policymakers and ChatGPT service providers in refining and improving the ChatGPT tool to better align with the specific needs of management educators.

Amid the rising interest in ChatGPT’s educational applications, a research gap exists in exploring educators’ perspectives on AI tools like ChatGPT. While some studies have addressed its role in fields like medical, engineering, legal education and natural sciences, the context of management education remains underexplored. This study focuses on educators’ experiences with ChatGPT in transforming management education, aiming to reveal its benefits, limitations and factors influencing adoption. As research in this area is limited, educators’ insights can guide higher education institutions, ChatGPT providers and policymakers in effectively implementing ChatGPT in Indian management education.

Exhibiting an unprecedented rate of advancement, technology’s progression over the past two decades has regrettably led to a disturbing increase in the distribution of child sexual abuse materials (CSAM) online. Compounded by the emergence of an underground cryptocurrency market, which serves as a primary distribution channel for these materials, the investigation and sanctioning of CSAM present a complex and unique set of challenges. The purpose of this study is to accurately diagnose the CSAM sentencing landscape and build a more comprehensive, evidence-based legal framework in penology.

The study collected and analyzed case details regarding CSAM sanctions in a database sourced from the US Department of Justice for 2020. Various factors were analyzed such as the victim’s age, offender typology and previous conviction, accompanied by an analysis of how these factors affect the sentence length.

The study found that the hierarchical agency-level interactions give insight into resource allocation prioritization, as well as confirming a close relationship between prior conviction history and sentence length, with the victim’s age inversely related to sentence length. Leveraging data-driven insights, the study paves the way for more targeted and effective sanctions, ultimately contributing to the broader goal of safeguarding children from online sexual exploitation.

The paper provides a critical analysis of the complex landscape surrounding CSAM distribution and judicial sentencing. By examining case details and leveraging data-driven insights, it offers valuable contributions to understanding the interplay between various factors such as victim age, offender typology and prior convictions on sentencing outcomes. This comprehensive approach not only sheds light on the dynamics of CSAM sanctions but also lays the groundwork for evidence-based legal frameworks in penology. Its originality lies in its nuanced examination of hierarchical agency interactions and its potential to inform more targeted interventions for safeguarding children from online exploitation.