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While awareness of sexual assaults on college campuses has increased, the majority of efforts to address it are focused on female victims. The relative neglect of male victims may be due in part to problematic rape myths that suggest men cannot be sexually assaulted, especially by women. The purpose of this paper is to compare rates of different types of sexual assault between male and female undergraduates, and explore the relationship between acceptance of traditional rape myths focused on female victims, and rape myths surrounding male victims.

Students at a mid-sized university in Pennsylvania (n=526) answered an online questionnaire about their own experiences of sexual assault since coming to college, as well as their endorsement of male and female rape myths.

While women experienced more sexual assault overall, men were just as likely to have experienced rape (i.e. forced penetration) or attempted rape. Acceptance of male and female rape myths was significantly correlated and men were more likely than women to endorse both. Participants were also more likely to endorse female than male rape myths.

By analyzing sexual assaults in terms of distinct behaviors instead of one composite score, the authors can get a more nuanced picture of how men and women experience assault.

Campus-based efforts to address sexual assault need to be aware that male students also experience assault and that myths surrounding men as victims may impede their ability to access services.

This paper contributes to our knowledge of a relatively understudied topic: undergraduate male victims of sexual assault.

This paper describes the approach to studying leadership within the interpretive paradigm that the author undertook for their doctoral research.

For the study, qualitative data were collected via in-depth interviews and the researcher's systematic reflections. The analytical process consisted of database design and management followed by cyclical interview and cross-interview analyses and integration of the researcher's reflections.

The paper showed that integrating various qualitative methods in pragmatic ways that better align with the research scope, along with a systematic approach to studying complex phenomena—such as leadership—generated richer and more cohesive perspectives.

This paper provides a detailed account of how the qualitative data collection and analysis methods were used, which could be replicated or adapted for future research within the interpretive paradigm.

The paper helps students and junior researchers make sense of qualitative data analysis and interpretation by offering practical tools to tackle the diverse processes of qualitative research.

This paper is based on the Author's doctoral research, recording their experience with engaging in qualitative research.

To determine the differences, as represented by information horizons mapping, in the health information-seeking behavior from a group of participants between March 2019 and April 2020 of the novel coronavirus pandemic.

In March 2019, the author conducted a study on health information-seeking behavior in which 149 participants drew information horizons maps in a health-related context. They also took health and information literacy assessments. This exact study was replicated using the same population in April 2020 to determine the differences in what the participants drew on their maps and how these changes interacted with their health and information literacy, their age and their education.

There is a statistically significant difference in the increased number of sources and the ranked quality of the sources that people used during the pandemic. Participants were much more likely to use credible sources and news sources, especially if they were older, more educated and had higher literacy levels – both health and information. They also relied heavily on social media. The participant group in the pandemic had a much heavier reliance on sources that are often used in a passive encountering way but engaging with them in an active information-seeking manner. The health information-seeking behavior in this study did not adhere to other research that found issue with information overload, avoidance and cyberchondria in response to crisis situations.

This article utilizes information horizons methodology to explore pre- and post-pandemic information-seeking. It is completely unique in this approach.

Recent years have seen an explosion in the study of emotions in organizations, and although emotions play a central role in the job stress process, their role is largely neglected in empirical stressor–strain studies. Our chapter aims to build consensus in the literature by showing that discrete emotions provide a mechanism through which stressors exert their impact on well-being. By examining a larger domain of stressors, emotions, and well-being, we begin to develop and expand upon the nomological network of emotions. In an effort to build on the job demands–resources (JD-R) model, which includes both job demands (i.e., negative stimuli such as time pressure) and resources (i.e., positive stimuli such as autonomy), we include both negative and positive discrete emotions with the expectation that negative emotions will generally be linked to demands and positive emotions will be linked to resources. We also propose that there may be circumstances where demands trigger negative discrete emotions and lead to greater experienced strain, and conversely, where resources arouse positive discrete emotions, which would positively affect well-being. The model in our chapter sheds light on how discrete emotions have different antecedents (i.e., job demands and resources) and outcomes (e.g., satisfaction, burnout, performance), and as such, respond to calls for research on this topic. Our findings will be of particular interest to organizations where employees can be trained to manage their emotions to reduce the strain associated with job stressors.

Technology is transforming teaching in ways that break down classroom walls while improving course quality and capitalizing on educators’ creativity. Rather than using technology in an ad hoc way, technology needs to fit the content and pedagogical style of the teacher.Our chapter builds on the extant literature on the necessary knowledge to integrate content, pedagogy, and technology (TPACK) in the classroom. We propose a comprehensive model that outlines the factors that lead to the development of TPACK, the relationship between TPACK and the use of technology, and outcomes gleaned from technology-enhanced learning.Our proposed model is an important first step to considering the precursors and outcomes of TPACK, which will need to be validated empirically. We extend the TPACK framework by identifying the predictors of TPACK such as teacher self-efficacy, experience with technology, and student factors. We argue that the extent to which educators develop their TPACK and use technology is bound by contextual factors such as organizational culture, resources, and student characteristics. Without considering the extensions that are identified in the Technology Integration Model, the linkages between TPACK and desirable outcomes (e.g., student engagement) are unclear. As a result, our proposed model has implications for educators and institutions alike.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.