The Variation of Prosocial Behavior to Intervene with the Variation of Cultural Norms, Gender, and Situational Factors among University Students | ||||
| مجلة الإرشاد النفسي | ||||
| Volume 82, Issue 1, April 2025, Page 623-670 PDF (1.75 MB) | ||||
| Document Type: المقالة الأصلية | ||||
| DOI: 10.21608/cpc.2025.431039 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Reham Ateya Nasr1; Julia Kovalenko2; Ahmad M. Abd Al-Aziz3; Amany Mohamed Riad Osman Elbary4 | ||||
| 1Lecturer of Educational Psychology Faculty of Education-Ain Shams University Lecturer of Psychology The British University in Egypt (BUE) | ||||
| 2Lecturer of Psychology The British University in Egypt (BUE) | ||||
| 3Psychology Department The British University in Egypt (BUE) | ||||
| 4Associate Professor Department of Educational Psychology-Ain Shams University | ||||
| Abstract | ||||
| Abstract: This mixed-methods study explores the variations in types of intervention behaviors in social support (bystander) actions demonstrated by male and female university students witnessing a socially provocative situation on campus at the British University in Egypt. The research examines the influence of cultural norms in Egyptian society, gender (male/female), and environmental contextual factors on helping behavior, focusing specifically on two settings: a campus cafeteria and a library. The researchers employed two equivalent experimental scenarios, one in the cafeteria and one in the library featuring male and female victims subjected to verbal and physical assaults in the presence of bystanders who were unaware they were part of an experiment. The bystanders' reactions were observed, followed by in-depth individual interviews aimed at capturing qualitative data regarding helping behaviors. For qualitative analyses, the researchers utilized two artificial intelligence applications: Natural Language Understanding (NLU) to analyze the emotions expressed in each interview response, quantifying the intensity of different emotional reactions. Large Language Models (LLMs) to explore complex relationships between words, sentences, and underlying meanings within the bystanders’ responses. This holistic analysis enabled the identification of emotional states, intentions, and motivations underlying the various types of helping behaviors. The combination of these tools yielded highly accurate insights, highlighting key behavioral indicators and the latent reasons behind intervention or lack thereof, supplemented by researchers’ interpretations within theoretical and empirical frameworks. Quantitative analysis was conducted using the Chi-square test (χ²). Key findings revealed gender-based differences in intervention behaviors, both positive and negative, in response to the staged aggression. The level of engagement was significantly influenced by the gender of the bystander, supporting the qualitative findings. Results also showed a marked gender bias, with higher intervention rates when the victim was female aligning with culturally rooted ideals of male chivalry. Conversely, bystanders were less likely to intervene when the victim was male, potentially reflecting societal assumptions about male resilience and a presumed lack of need for assistance. Further analyses revealed nuanced emotional interactions, where both empathy and fear played significant roles in determining whether a bystander would intervene or withdraw. Cultural norms surrounding family honor and reliance on institutions also shaped bystander behavior. The researchers conclude that understanding the emotional, contextual, and cultural dynamics of helping behavior can inform the design of culturally responsive intervention programs that challenge traditional gender roles and promote collective responsibility. The study calls for additional research into the influence of gender and cultural norms on prosocial behavior in educational and community settings. Keywords: Social support behavior, active bystander training, environmental and cultural contextuality, implicit gender bias, cultural stereotypes, AI applications in qualitative analysis. | ||||
| Keywords | ||||
| Social support behavior; active bystander training; environmental and cultural contextuality; implicit gender bias; cultural stereotypes; AI applications in qualitative analysis | ||||
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