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The Influence of General Strain Factors on Cyberbullying Among
University Students in Kenya
Christopher Odhiambo
1*
, Dr. Bonface Ratemo, PhD
2
, Dr. George Musumba, PhD
3
1*
Masters Scholar, Dedan Kimathi University of Technology
2,3
Institute of Criminology, Forensics, and Security Studies, Dedan Kimathi University of Technology
*
Corresponding Author
DOI:
https://doi.org/10.51583/IJLTEMAS.2026.15020000140
Received: 01 March 2026; Accepted: 07 March 2026; Published: 26 March 2026
ABSTRACT
Purpose: This study aimed to examine the influence of general strain factors on cyberbullying among university
students in Kenya, with a focus on emotional distress, coping mechanisms, peer reinforcement, and reduced
empathy.
Methodology: A quantitative research design was employed, using a structured questionnaire to collect data
from student leaders across 72 universities in Kenya. The data were analyzed using Pearson correlation and
regression analysis to explore the relationships between general strain factors and cyberbullying behaviors.
Findings: The study found a moderate positive correlation (r = .492, p < .001) between general strain factors
and cyberbullying. Specific strain factors such as negative emotions and coping mechanisms (r = .453, p = .001),
and peer reinforcement (r = .404, p = .003) were significantly associated with higher cyberbullying severity.
However, reduced empathy showed a non-significant relationship (r = .214, p = .127). Regression analysis
revealed that general strain factors explained 24.2% of the variance in cyberbullying (R² = .242, p < .001),
confirming their significant predictive role.
Unique Contribution to Theory, Practice, and Policy: The study offers a unique contribution to General Strain
Theory (GST) by demonstrating how emotional distress and peer validation processes contribute to
cyberbullying in the Kenyan university context. It also highlights the importance of addressing strain factors
such as stress, isolation, and peer reinforcement in university policies and intervention strategies. The study
suggests the incorporation of mental health programs, stress-management initiatives, and digital ethics training
in universities to mitigate the risks associated with cyberbullying.
Keywords: General Strain Theory, Cyberbullying, University Students, Kenya, Emotional Strain, Coping
Mechanisms, Peer Reinforcement, Digital Ethics, Intervention Strategies.
INTRODUCTION
Background of the Study
Cyberbullying in university settings is a growing global concern, with serious implications for students’
psychological well-being and academic performance (Khine et al., 2020). Research shows that cyberbullying
among university students can lead to a range of negative outcomes, including anxiety, depression, social
isolation, and declining academic achievement (Cassidy et al., 2021). While verbal and relational bullying
remain more common forms of aggression among college students (Lund & Ross, 2016), the rise of digital
communication tools has significantly altered the landscape. Cyberbullying has become a major issue,
particularly in online dating contexts among university students (Martinez-Pecino & Durán, 2016). Estimates
suggest that between 10% and 15% of university students experience cyberbullying, while up to 25% report
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victimization from traditional, non-digital bullying (Lund & Ross, 2016). Beyond mental health consequences,
cyberbullying disrupts social relationships, erodes trust, and in severe cases contributes to emotional distress,
including suicidal thoughts (Cassidy et al., 2017).
This issue is not confined to specific countries; cyberbullying is a global phenomenon with varying rates of
victimization and perpetration across regions. In the United States, Israel, and China, significant efforts have
been made to understand its causes and impacts (Zhu et al., 2021). Risk factors include gender, age, online
behavior, and prior victimization, while protective factors such as empathy, emotional intelligence, and
supportive relationships can mitigate involvement as either perpetrator or victim (Zhu et al., 2021). Despite these
insights, more research is needed to develop standardized tools for evaluating and combating cyberbullying
worldwide.
In Africa, and particularly in Kenya, the rapid adoption of Information and Communication Technology (ICT)
has contributed to an increased prevalence of cyberbullying in university settings. Ndiege et al. (2020) note that
the rise of social media platforms such as Facebook, WhatsApp, and Instagram has fueled this trend, with
Facebook identified as the most popular platform among Kenyan university students (Ogolla et al., 2022).
The ease of internet access and the anonymity it affords have created an environment where cyberbullying
thrives. The United Nations Office on Drugs and Crime (UNODC) highlighted Kenya as one of the countries
with the most active cyberbullying incidents on Twitter in 2020 (Parsitau, 2020). This situation is compounded
by limited regulation and awareness, making Kenya’s online environment fertile ground for both cyberbullying
and other forms of digital violence, including gender-based and political aggression (Parsitau, 2020).
Furthermore, the correlation between frequent online interactions and the likelihood of experiencing
cyberbullying underscores the role of digital behaviors in shaping vulnerability (Giordano et al., 2021; Schultz
et al., 2014).
Despite growing concern, particularly in Kenyan universities, limited research has examined the specific strain
factors contributing to cyberbullying. Strain factors such as academic pressure, peer relationships, social
isolation, and family issues may significantly influence students’ vulnerability (Makori & Agufuna, 2020).
Addressing these challenges requires a deeper understanding of how strain interacts with cyberbullying
dynamics in higher education. This study seeks to fill that gap by identifying and analyzing general strain factors
that contribute to cyberbullying in Kenyan universities, offering insights for prevention and intervention
strategies. By examining the underlying stressors that drive such behavior, universities can develop more
supportive environments that reduce the incidence of cyberbullying and its detrimental effects on students.
Statement of the Problem
Social media has become an essential communication tool for young people, particularly university students,
most of whom use at least one platform regularly. While these digital tools provide significant benefits for
socialization and academic engagement, they have also introduced harmful consequences, especially
cyberbullying. Cyberbullying involves the intentional use of electronic devices to harm or harass others, and it
has emerged as a serious issue in educational settings. Studies indicate that cyberbullying can lead to depression,
low self-esteem, academic difficulties, and even suicidal thoughts, making it a critical concern for university
communities (Adebayo, 2020; Handono, 2019).
Kenya’s higher education sector has experienced substantial growth, with rising numbers of universities and
student enrollments (Mulinge et al., 2017). As more students gain internet access and engage in online activities,
they are increasingly exposed to risks associated with technology, including cyberbullying. The Communication
Authority of Kenya’s National ICT Survey shows that youth aged 20 to 34, an age group typically associated
with university students, are among the highest users of ICT facilities. This group is highly active online, and as
internet usage in Kenya continues to rise, so does the prevalence of cyberbullying, affecting students both
socially and academically.
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Despite widespread social media use, research on the extent and impact of cyberbullying in Kenyan universities
remains limited. Existing studies have often focused narrowly on female youth or specific platforms such as
Facebook, while neglecting others like Twitter, where cyberbullying is also prevalent (Otieno et al., 2022).
Moreover, these studies have not adequately examined the strain factors that contribute to cyberbullying
behavior. Strain factors including academic pressures, social isolation, and challenges in managing online
identities may increase students’ vulnerability to either perpetrating or experiencing cyberbullying.
There is an urgent need to investigate the role of general strain factors in contributing to cyberbullying among
university students in Kenya. Understanding these underlying factors will enable universities, educators, and
parents to design more effective interventions to reduce the impact of cyberbullying. The stress experienced by
victims can lead to significant mental health challenges and negatively affect academic performance. Therefore,
examining strain factors is essential for creating safer, more supportive university environments that reduce the
incidence of cyberbullying and its detrimental effects on students.
LITERATURE REVIEW
Theoretical Literature Review
General Strain Theory (GST)
General Strain Theory (GST), developed by Robert Agnew, provides a framework for understanding how strain
or stress leads individuals to engage in deviant behaviors such as cyberbullying. According to GST, strain occurs
when individuals are unable to achieve their goals, are exposed to negative stimuli, or experience the loss of
positive influences. These strains can generate negative emotions, including frustration, anger, or sadness, which
may increase the likelihood of deviant behaviors as a coping mechanism. In the context of cyberbullying, these
strains often manifest as aggressive or harmful actions toward others as individuals attempt to alleviate their
distress.
Strains can take different forms, including objective strain (e.g., physical harm or poverty), subjective strain
(e.g., personal disappointment such as family conflicts or academic failure), and vicarious strain (e.g., witnessing
others' misfortune).
These strains are typically experienced with varying intensity depending on the individual’s personal
circumstances. In the case of students, the strains they experience whether related to academic pressure, social
exclusion, or family issues can lead to frustration or anger. These emotional responses may then lead to
aggressive actions, such as cyberbullying, as individuals use these negative behaviors to cope with or release
their feelings of stress.
GST suggests that when individuals experience strain, they may resort to deviant behaviors, like bullying, to
deal with the negative emotions brought about by the strain. Agnew (1992) emphasizes that individuals are more
likely to engage in harmful behavior if they lack the necessary resources to cope with their stress effectively.
For example, a student who is struggling academically or facing rejection from peers may turn to cyberbullying
as a way to express their anger, frustration, or sense of powerlessness. The theory indicates that without healthy
coping mechanisms, these individuals may project their negative emotions onto others through harmful online
behaviors.
In addition to explaining the emergence of bullying behaviors, GST also sheds light on how those who are
subjected to cyberbullying may respond. Victims of cyberbullying experience significant emotional distress,
which creates additional strain in their lives.
This strain may cause them to retaliate or engage in aggressive behaviors as a way to cope with their
victimization. Heatherington and Coyne (2017) note that this reciprocal relationship between strain and deviant
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behavior can exacerbate the cycle of cyberbullying, where both the perpetrators and victims experience
emotional strain that perpetuates the issue.
Furthermore, GST provides an opportunity to explore the broader context of strain, such as the role of academic
pressure, peer relationships, or family dynamics in the lives of university students. Students who face high levels
of strain, such as failing grades, bullying from peers, or family conflict, may become more susceptible to
engaging in cyberbullying as a form of coping.
By focusing on the role of strain in cyberbullying, universities and institutions can identify the root causes of
this behavior and take proactive steps to address the emotional and psychological pressures that contribute to it.
In conclusion, General Strain Theory offers a valuable lens through which to examine the causes of
cyberbullying. It emphasizes the role of stress, negative emotions, and inadequate coping mechanisms in
fostering deviant behavior. By understanding these dynamics, universities can develop interventions that reduce
the impact of strain on students, promote healthier coping strategies, and help create a safer, more supportive
campus environment.
Empirical Literature Review
As the influence of the online domain in daily life has increased, new crime types based on digital technology
have emerged. Psychological stressors such as anxiety and depression are strongly associated with the
perpetration of cyberbullying in university settings. Martínez-Monteagudo et al. (2020) examined the
relationship between aggressors and victims of cyberbullying, as well as the predictive power of emotional
problems and university adaptation.
Their study of 1,282 university students in Spain revealed that both victimization and perpetration were more
likely among individuals with high levels of stress and depression, indicating that poor academic and emotional
adaptation increased the likelihood of cyberbullying. This aligns with Mataga (2022), who found that female
students at the University of Cape Town with frequent social media use and low self-control were particularly
vulnerable to cyberbullying. Similarly, Kemuma (2021) reported that problematic smartphone use at Mount
Kenya University was linked to heightened anxiety and depression, reinforcing the connection between digital
habits and psychological distress.
More recent scholarship has expanded this discussion by examining cyberbullying within broader digital
aggression contexts. Gallegos et al. (2025) identified diverse forms of aggression, risk factors, and educational
responses in digital environments, emphasizing the need for institutional strategies to mitigate cyberbullying
among students. Tabuk and Akbaş (2025) explored the relationship between physical activity habits, aggressive
behavior, and cyberbullying among young adult university students, finding that lifestyle factors can influence
both vulnerability and perpetration. Extending this perspective cross-nationally, El-Ashry et al. (2026)
investigated digital harassment among nursing students in Saudi Arabia and Egypt, highlighting cultural and
contextual factors that shape cyberbullying experiences. Together, these studies underscore that cyberbullying is
not only a psychological and behavioral issue but also a multidimensional phenomenon influenced by lifestyle,
institutional responses, and cultural contexts. Incorporating these recent findings strengthens the scholarly
relevance of the present study and situates it within the evolving discourse on digital aggression in higher
education.
Conceptual Framework
The study adopts General Strain Theory as the guiding framework to explain the determinants of cyberbullying.
This framework posits that strain factors such as negative emotions, reduced empathy, and reinforcement can
increase the likelihood of individuals engaging in cyberbullying. The outcomes are reflected in the frequency,
incidence rate, and platform of bullying behaviors. Figure 2.1 presents this relationship, visually summarizing
how strain factors influence cyberbullying outcomes.
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Independent Variable Dependent Variable
Figure 2.1: Conceptual Framework
RESEARCH METHODOLOGY
The study adopted a quantitative research design to examine the influence of general strain factors on
cyberbullying in universities. This approach followed a deductive logic, where hypotheses were developed and
tested through data collection. A structured questionnaire was the primary data collection instrument, which was
pilot-tested to ensure reliability and validity. The target population consisted of student leaders from 72
universities in Kenya, including both public and private institutions. These student leaders, such as club
presidents and student council members, were directly involved with student welfare and were well-positioned
to provide insights into the prevalence and impact of cyberbullying. A purposive sampling technique was used
to select a sample of 61 universities, ensuring diverse representation from both public and private sectors (Shah
& Al-Bargi, 2013; Antwi & Hamza, 2015). While this approach enhanced the relevance of the respondents by
deliberately targeting individuals with pertinent expertise, the study acknowledges that the sample size is limited
and may not fully capture the perspectives of all student leaders across Kenyan universities. This limitation is
recognized as a constraint on the generalizability of the findings, though the purposive selection strengthens the
credibility of the insights obtained.
Data collection was conducted by distributing the structured questionnaire either in-person or online, depending
on accessibility. The responses were analyzed using SPSS Version 26, with descriptive statistics (such as
frequencies, percentages, and weighted means) to summarize the data. To assess the relationships between the
independent variable (General Strain Factors) and the dependent variable (cyberbullying), linear regression
analysis was employed. Analysis of Variance (ANOVA) was used to evaluate the overall model's suitability. The
results were presented through tables, charts, and figures to facilitate clear interpretation (Kothari, 2004; Pearse,
2019).
Presentation Of Findings, Analysis, And Interpretation
Response Rate
The questionnaire was distributed to 61 universities in Kenya (35 public and 26 private), with one respondent
from each university. Out of the 61 issued questionnaires, 52 were completed and returned (33 from public and
19 from private universities), resulting in a response rate of 85.25%. According to Mugenda and Mugenda
(2019), a response rate above 70% is considered "very good," which places this study's response rate in the high-
receptivity category for Kenyan universities. The results were presented in Table 4.1.
General Strain Factors
i. Negative emotions and coping
ii. Reduced empathy and consequences
iii. Reinforcement and feedback
Cyberbullying
i. Frequency
ii. Incidence rate
iii. Platform
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Table 4. 1: Response Rate by Category of University
Category of University
Sampled Universities
Responses Received
Response Rate (%)
Public Universities
35
33
94.29%
Private Universities
26
19
73.08%
Total
61
52
85.25%
Demographic Information of Respondents
The demographic profile of the 52 respondents, drawn from university student welfare departments in Kenya,
included gender, age, university type, position, and experience. Of the respondents, 65.4% were male and 34.6%
were female. Age-wise, 57.7% were aged 18-25, with other age groups represented in smaller percentages.
Respondents were primarily from public universities (63.5%) and held positions such as Student Representative
(50%) and Student Affairs Officer (23.1%). Most had less than three years of experience in their roles (76.9%),
suggesting a recent entry into student welfare work. These demographics provide insights into perceptions and
experiences of cyberbullying. The results were summarized in Table 4.2.
Table 4. 2: Demographic Characteristics of Respondents
Type of university respondent is affiliated with
Public
33
63.5
Private
19
36.5
Total
52
100.0
Position held in student welfare department
Student representative
26
50.0
Student affairs officer
12
23.1
Dean of students
5
9.6
Program coordinator
1
1.9
Administrator
1
1.9
Student counselor
4
7.7
Director
1
1.9
Security personnel
1
1.9
None
1
1.9
Total
52
100.0
Number of years respondent has held position in
student welfare department
Less than 1 year
17
32.7
1 - 3 years
23
44.2
4 - 6 years
8
15.4
7 - 10 years
2
3.8
Over 10 years
2
3.8
Total
52
100.0
Descriptive Statistics for General Strain Factors
Respondents identified several general strain factors contributing to cyberbullying in universities. The most
frequently cited was attention-seeking behavior (57.7%), followed by anger and dissatisfaction (53.8%) and
unstable family backgrounds (46.2%).
Frustrations related to studies (46.2%) and the use of cyberbullying as a coping mechanism (40.4%) were also
notable. Other reported factors included feelings of rejection (36.5%) and stress due to isolation (30.8%). These
results, presented in Table 4.3, suggest that emotional and behavioral strains significantly influence
cyberbullying among university students.
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Table 4.3: Respondents’ Perceptions of General Strain Factors Contributing to Cyberbullying in Kenyan
Universities
General Strain Factors
Responses
Percent of Cases
N
Percent
Unstable family background or poor upbringing contributes
to cyberbullying
24
14.7%
46.2%
Stress due to isolation contributes to cyberbullying
16
9.8%
30.8%
Frustrations related to studies contribute to cyberbullying
24
14.7%
46.2%
Attention-seeking behaviors contribute to cyberbullying
30
18.4%
57.7%
Feelings of rejection contribute to cyberbullying
19
11.7%
36.5%
Anger and dissatisfaction contribute to cyberbullying
28
17.2%
53.8%
Cyberbullying used as a coping mechanism
21
12.9%
40.4%
Competition leadership spaces contribute to cyberbullying
1
0.6%
1.9%
Total
163
100.0%
313.5%
The study found a moderate overall influence of general strain factors on cyberbullying, with a mean score of
3.08 (SD = 0.92). Negative emotions and coping mechanisms were the most influential (M = 3.25, SD = 1.01),
followed by reinforcement and feedback (M = 3.02, SD = 0.92). Reduced empathy and awareness of
consequences had a slightly lower influence (M = 2.96, SD = 0.82). These findings, shown in Table 4.4, highlight
that emotional strain, diminished empathy, and feedback mechanisms contribute to the likelihood of
cyberbullying.
Table 4.4: Descriptive Statistics of General Strain Factor Groups Influencing Cyberbullying in Kenyan
Universities
General Strain Factor Groups
N
Mean
Std. Dev.
Negative emotions and coping mechanisms
52
3.25
1.01
Reduced empathy and awareness of consequences
52
2.96
.82
Reinforcement and feedback
52
3.02
.92
Average Score
52
3.08
0.92
Correlation between General Strain Factors and Cyberbullying
Correlation results (Table 4.5) revealed a moderate, statistically significant positive relationship between general
strain factors and cyberbullying (r = .492, p < .001). This indicates that higher levels of strain are associated with
increased severity of cyberbullying.
Table 4.5: Correlation between General Strain Factors and Cyberbullying
Cyberbullying Index
General Strain Factors
Cyberbullying Index
Pearson Correlation
1
.492
**
Sig. (2-tailed)
.000
N
52
52
General Strain Factors
Pearson Correlation
.492
**
1
Sig. (2-tailed)
.000
N
52
52
**. Correlation is significant at the 0.01 level (2-tailed).
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Further analysis (Table 4.6) showed that negative emotions and coping mechanisms (r = .453, p = .001) and
reinforcement and feedback (r = .404, p = .003) were significantly correlated with cyberbullying severity.
Reduced empathy and awareness of consequences were positively associated but not statistically significant (r
= .214, p = .127).
Table 4.6: Correlation between General Strain Factors Metrics and Cyberbullying
General Strain Metrics
Cyberbullying Index
Negative emotions and coping
mechanisms
Pearson Correlation
.453
**
Sig. (2-tailed)
.001
N
52
Reduced empathy and awareness of
consequences
Pearson Correlation
.214
Sig. (2-tailed)
.127
N
52
Reinforcement and feedback
Pearson Correlation
.404
**
Sig. (2-tailed)
.003
N
52
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Regression results (Table 4.7) confirmed that general strain factors significantly predict cyberbullying,
explaining 24.2% of the variance (R² = .242). The model was statistically significant (F = 15.99, p < .001), with
an unstandardized coefficient (B = .543) indicating that increases in strain factors are associated with greater
cyberbullying severity.
Table 4.7: Linear Regression Results for General Strain Factors
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
1
.492
a
.242
.227
.66136
ANOVA
a
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
6.993
1
6.993
15.988
.000
b
Residual
21.870
50
.437
Total
28.863
51
Coefficients
a
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
1.444
.428
3.373
.001
General Strain Factors
.543
.136
.492
3.998
.000
a. Dependent Variable: Cyberbullying Index
b. Predictors: (Constant), General Strain Factors
Discussion of Findings on the Influence of General Strain Factors on Cyberbullying
The study aimed to assess the influence of general strain factors on cyberbullying in Kenyan universities. The
results indicated a moderate, statistically significant positive correlation (r = .492, p < .001) between general
strain factors and cyberbullying. This finding directly supports General Strain Theory (Agnew, 1992; Agnew &
Cullen, 2017), which posits that strain arising from negative stimuli or the failure to achieve valued goals
generates negative emotions that may lead to deviant coping behaviors. In this case, cyberbullying emerges as a
maladaptive coping mechanism for students experiencing strain.
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Specifically, negative emotions and coping mechanisms (r = .453, p = .001) were strongly associated with
cyberbullying severity, reinforcing GST’s emphasis on emotional strain as a driver of deviant behavior.
Reinforcement and feedback (r = .404, p = .003) also showed significant associations, highlighting the role of
peer validation and social rewards in sustaining cyberbullying. This aligns with GST’s assertion that deviant
behaviors are more likely when individuals perceive reinforcement or lack effective coping resources. In
contrast, reduced empathy and awareness of consequences showed a weaker, non-significant relationship (r =
.214, p = .127), suggesting that empathy may be culturally mediated. This nuance reflects GST’s recognition
that strain responses vary across contexts, and it resonates with El-Ashry et al. (2026), who found cultural
differences in empathy and harassment among nursing students in Saudi Arabia and Egypt.
Regression analysis further confirmed the predictive role of strain, with general strain factors explaining 24.2%
of the variance in cyberbullying (R² = .242). This reinforces GST’s explanatory power while extending its
application to the Kenyan university context. The findings are consistent with Martínez-Monteagudo et al. (2020)
and Mataga (2022), who linked stress, depression, and social media use to increased vulnerability to
cyberbullying. However, the prominence of reinforcement and feedback in this study underscores that
cyberbullying is not only an emotional response but also a socially sustained behavior. This nuance enriches
GST by showing that external validation mechanisms can amplify strain-induced deviance in digital
environments.
Overall, the results demonstrate that General Strain Theory provides a robust framework for understanding
cyberbullying in higher education. By situating the findings within both local and global scholarship, the study
highlights the importance of addressing emotional strain, peer reinforcement, and contextual factors in designing
interventions to reduce cyberbullying among university students.
SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS
Summary of the Findings on General Strain Factors and Cyberbullying
The study assessed the extent to which general strain factors influence cyberbullying in Kenyan universities.
Findings indicated that strain factors significantly predict cyberbullying. Correlation analysis revealed a
moderate positive relationship (r = .492, p < .001), demonstrating that students experiencing heightened
emotional distress are more likely to engage in cyberbullying. Negative emotions and coping mechanisms (r =
.453, p = .001) and reinforcement through peer validation (r = .404, p = .003) were both significantly associated
with cyberbullying severity, while reduced empathy showed a weaker, non-significant link (r = .214, p = .127).
Regression analysis confirmed these results, explaining 24.2% of the variance in cyberbullying (R² = .242, F =
15.99, p < .001), with general strain factors emerging as significant predictors (β = .492, p < .001). These findings
highlight that while emotional strain fuels aggressive online behavior, peer approval mechanisms sustain it.
Conclusion
The study concludes that general strain factors significantly influence cyberbullying in Kenyan universities.
Correlation results indicated a moderate positive relationship, while regression analysis confirmed that strain
factors explain 24.2% of the variance in cyberbullying. Negative emotions and coping, along with reinforcement
and feedback, were key drivers, showing that psychological stressors and peer validation heighten cyberbullying
severity. Reduced empathy, however, was not significant. These findings affirm that strain is a critical predictor
of cyberbullying behavior in higher education contexts.
Recommendations
Since general strain factors significantly predicted cyberbullying, universities should integrate mental health and
stress-management programs into student support services. Counseling initiatives should emphasize adaptive
coping strategies to reduce reliance on cyber aggression. Peer mentorship programs can encourage positive
reinforcement and discourage harmful validation-seeking. Digital ethics training should highlight the impact of
online behaviors, while policies should limit algorithms that reward toxic content. Anonymous reporting
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mechanisms can help identify at-risk students early. By combining psychosocial support with digital
accountability, institutions can address both emotional strain and reinforcement dynamics, thereby fostering a
healthier online culture.
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