Electronic Gambling Engagement and Psychosocial Outcomes Among University Students in Ibadan, Nigeria: A Theory of Planned Behavior Perspective

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This study applied the Theory of Planned Behavior to investigate the sociocognitive drivers and psychosocial impacts of electronic gambling among university students. Methods : A descriptive cross-sectional design was employed. Data were collected from 403 undergraduate and postgraduate students at the University of Ibadan using stratified random sampling and a structured questionnaire. Analyses included descriptive statistics, scale reliability assessment, and binary logistic regression. Results : The lifetime prevalence of electronic gambling was 49.6%. Attitudes regarding financial utility, subjective norms involving peer approval, and perceived behavioral control were all significant predictors of gambling engagement (p < .001). Economic hardship was the strongest predictor in the model (OR = 3.06). Among participants who gambled, 50.9% reported increased stress and anxiety, 48.6% reported financial strain, and 42.6% reported negative academic impacts. Conclusions : Gambling among students is driven by economic necessity framed within sociocognitive pathways. Findings advocate for theory-informed interventions that address financial stressors and correct misperceptions about gambling. Electronic Gambling Theory of Planned Behavior University Students Economic Hardship Nigeria Behavioral Addiction Introduction The advent of digital technologies has expanded access to gambling worldwide, shifting participation toward online platforms including sports betting, poker, and casino games [ 1 ]. In Nigeria, electronic gambling has grown rapidly among youth, driven by smartphone availability and aggressive marketing [ 2 , 3 ]. Prevalence studies indicate high engagement rates among Nigerian university students, often exceeding 40% to 50% [ 4 – 6 ]. University students are a vulnerable group due to transitional life stages, peer influences, and socioeconomic pressures such as unemployment and limited income [ 7 , 8 ]. Many view gambling as a potential supplementary income source amid financial constraints [ 9 , 10 ]. However, gambling is associated with adverse outcomes, including psychological distress such as anxiety, depression and suicide ideation, debt, and academic disruption [ 11 – 14 ]. The Theory of Planned Behavior [ 15 ] serves as the guiding theoretical framework for this study, offering a lens to examine the associations between sociocognitive drivers and electronic gambling engagement. Rather than strictly testing the theory's causal validity, this study applies the framework to characterize the specific motivations of Nigerian university students. Within this context, Attitudes are conceptualized not merely as recreational preferences, but often as cognitive evaluations of gambling as a viable financial strategy or investment amidst economic constraints. Subjective Norms capture the perceived social pressure arising from the normalization of betting in student hostels and the ubiquity of peer engagement. Perceived Behavioral Control encompasses the ease of access provided by smartphones and the illusion of control, where students overestimate their skill in sports betting to influence outcomes. Furthermore, this framework provides a baseline for understanding psychosocial outcomes; when gambling is driven by a perceived need for financial control (Attitude/PBC), the subsequent financial losses and academic disruption constitute a dissonance that manifests as significant stress and anxiety [ 16 – 18 ]. In low-resource settings like Nigeria, economic hardship may amplify these constructs, framing gambling as a rational financial strategy [ 3 , 9 ]. Few Nigerian studies have used the Theory of Planned Behavior systematically, particularly in university contexts. To address these gaps, this study was guided by the following specific objectives: To determine the prevalence and patterns of electronic gambling among university students in Ibadan. To examine the influence of Theory of Planned Behavior constructs (attitudes, subjective norms, and perceived behavioral control) and economic hardship on gambling engagement. To assess the self-reported psychosocial and academic consequences associated with gambling behavior in this population. Methods Study Design and Setting A descriptive cross-sectional survey design was adopted for this study. This design was selected to allow for the efficient determination of gambling prevalence and to examine the cross-sectional associations between Theory of Planned Behavior constructs and gambling behavior at a single point in time. The study was conducted at the University of Ibadan, a premier federal institution in Southwestern Nigeria, between March and August 2025. The university comprises a diverse population of students from various socio-cultural backgrounds, making it a suitable setting for investigating youth behaviors. Participants and Sampling Procedure The study population comprised all registered undergraduate and postgraduate students. A multi-stage sampling technique was employed to ensure a representative sample. Stratification: First, the population was stratified into three distinct academic categories: Regular Undergraduate, Distance Learning Centre (DLC) Undergraduate, and Postgraduate students. Proportional Allocation: Second, to ensure that the sample accurately reflected the university's demographic distribution, participants were selected from each stratum based on probability proportional to size (PPS). For instance, strata with larger student populations contributed a higher percentage of participants to the final sample. Random Selection: Finally, a simple random sampling method was used within each stratum to select individual participants, minimizing selection bias. Inclusion and Exclusion Criteria Inclusion criteria were defined as: (1) being a currently registered student of the University of Ibadan; and (2) providing informed consent to participate. Students were excluded if they were absent during the data collection period or if they returned incomplete questionnaires. Sample Size and Ethical Considerations The minimum sample size was determined using Cochran’s formula for infinite populations, assuming a 50% prevalence rate (to maximize sample size), a 95% confidence level, and a 5% margin of error. Adjusting for a 10% non-response rate, the final target sample was 403 students. Ethical approval was obtained from the University of Ibadan Ethics Committee (UI/EC/23/0185, 12 March 2025). All participants were informed of the study's purpose, their right to withdraw, and the confidentiality of their data before providing informed consent. Trained research assistants administered the structured questionnaire at various campus locations after obtaining informed consent. Measures and Instrumentation Data were collected using a structured questionnaire adapted from established measures in the literature. The full questionnaire developed for this study is available as Supplementary File 1. The instrument was divided into five sections: Section A: Socio-demographics : This section utilized multiple-choice and open-ended questions to assess background characteristics, including age, gender, level of study (undergraduate/postgraduate), employment status, and monthly income/allowance. Section B: Gambling Behavior : Participants responded to multiple-choice items regarding their lifetime history of gambling, frequency of engagement (e.g., daily, weekly), and preferred game types (e.g., sports betting, poker). Section C: Theory of Planned Behavior Constructs : This section assessed the three core TPB predictors using a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). Attitude was measured with 5 items assessing positive evaluations of gambling (Cronbach’s α = .78). Subjective Norms were assessed via 3 items measuring perceived peer pressure (Cronbach’s α = .72). Perceived Behavioral Control was measured using 4 items assessing confidence and the illusion of control (Cronbach’s α = .75). Section D: Economic Hardship Index : A composite variable was derived from items measuring employment status, self-rated income adequacy, and a 3-item financial stress scale. Section E: Psychosocial and Academic Effects : Participants who reported gambling rated their level of agreement with statements regarding adverse outcomes, such as increased anxiety, financial strain, and negative academic impacts. Validity and Reliability : To ensure content validity, the initial draft of the questionnaire was reviewed by experts in Sociology, Psychology, and Public Health to assess item clarity and relevance. Reliability was established using Cronbach’s alpha, with all sub-scales exceeding the recommended threshold of .70. Data Analysis Data were analyzed using IBM SPSS Statistics (Version 27). The analysis was conducted in three stages corresponding to the study objectives: Descriptive Analysis (Objectives 1 & 3) : Descriptive statistics, including frequencies, percentages, and means, were used to summarize socio-demographic characteristics, determine the prevalence and patterns of electronic gambling (Objective 1), and assess the distribution of self-reported psychosocial and academic consequences (Objective 3). Reliability Analysis : Prior to hypothesis testing, the internal consistency of the measurement scales (Attitude, Subjective Norms, PBC) was assessed using Cronbach’s alpha to ensure data quality. Inferential Analysis (Objective 2) : To examine the predictors of gambling behavior, a binary logistic regression was performed. This method was selected because the dependent variable, Gambling Engagement , was categorical (Yes/No). The mean scores for Attitude, Subjective Norms, and Perceived Behavioral Control, alongside the Economic Hardship Index, served as independent variables. The model's goodness-of-fit was evaluated using the Hosmer-Lemeshow test and Nagelkerke R 2 . Statistical significance was set at p < .05. Results Socio-demographic Characteristics The sample consisted of 403 students. The mean age was 26.4 years ( SD = 4.1). As shown in Table 1 , the sample was relatively gender-balanced, with slightly more males (51.9%). A majority (66%) were under 30 years old, and the sample was evenly split between undergraduate and postgraduate levels. Notably, 51.9% reported being employed while studying. Table 1 Socio-demographic Characteristics of Respondents (N = 403) Variable Category Frequency (n) Percentage (%) Gender Male 209 51.9 Female 194 48.1 Age Group 18–23 139 34.5 24–29 127 31.5 30–35 123 30.5 36 and above 14 3.5 Level of Study Undergraduate 203 50.4 Postgraduate 200 49.6 Employment Status Employed 209 51.9 Not Employed 194 48.1 Prevalence and Patterns of Gambling The lifetime prevalence of electronic gambling was 49.6% (n = 200). Among gamblers, the most common forms were poker (29.3%) and sports betting (24.8%). High frequency engagement, defined as daily or weekly participation, was reported by 37.3% of gamblers (see Table 2 ). Table 2 Patterns of Electronic Gambling among Participants (N = 403) Variable Category Frequency (n) Percentage (%) Gambling Engagement Yes 200 49.6 No 203 50.4 Frequency (Gamblers only) Daily 43 21.5 Weekly 52 26.0 Monthly 58 29.0 Rarely 47 23.5 Preferred Game Type Poker 118 29.3 Sports Betting 100 24.8 Slot Machines 93 23.1 Online Casino Games 92 22.8 Predicting Gambling Engagement A binary logistic regression was conducted to predict the likelihood of gambling engagement based on Theory of Planned Behavior constructs and economic hardship. The model was statistically significant, χ 2 (4) = 86.42, p < .001, explaining 32% of the variance in gambling behavior (Nagelkerke R ²). The Hosmer-Lemeshow test indicated a good model fit (χ 2 (8 ) = 7.24, p = .51). As shown in Table 3 , all predictors were significant. Economic Hardship was the strongest predictor ( OR = 3.06), followed by a Positive Attitude toward gambling ( OR = 2.34). Subjective Norms and Perceived Behavioral Control also significantly increased the odds of gambling. Table 3 Logistic Regression Analysis Predicting Engagement in Electronic Gambling (N = 403) Predictor Variable B S.E. Wald χ2 Odds Ratio (OR) 95% C.I. for OR Attitude (Positive) 0.85 0.18 22.31*** 2.34 [1.64, 3.33] Subjective Norms 0.62 0.16 15.02*** 1.86 [1.36, 2.54] Perc. Behav. Control 0.71 0.19 13.97*** 2.03 [1.40, 2.94] Economic Hardship 1.12 0.21 28.45*** 3.06 [2.03, 4.62] Constant -3.45 0.58 35.36*** 0.03 Note. Model χ 2 (4) = 86.42, p < .001. Nagelkerke R^2 = .32. *** p < .001. Self-Reported Effects of Gambling Among the 200 students who gambled, high levels of negative consequences were reported (Table 4 ). Over half (50.9%) agreed that gambling increased their stress and anxiety, and 48.6% reported significant financial strain. Critically, 42.6% believed it negatively affected their academic performance, and nearly half (49.8%) felt unable to stop. Table 4 Self-Reported Psychosocial and Academic Effects among Gamblers (n = 200) Reported Effect Agree/Strongly Agree (%) Neutral (%) Disagree/Strongly Disagree (%) Increased stress and anxiety 50.9 14.4 34.7 Experienced financial strain 48.6 19.9 31.5 Negatively affected academic performance 42.6 20.6 36.8 Felt unable to stop or control gambling 49.8 15.0 35.2 Borrowed money/sold items to fund gambling 38.5 16.3 45.2 Discussion This study applied the Theory of Planned Behavior to investigate electronic gambling among Nigerian university students. The findings revealed a complex interplay between economic desperation and socio-cognitive processes. The primary finding is the powerful validation of the theoretical model in this context. As hypothesized, a positive attitude toward gambling, perceived social approval, and a belief in one's control over gambling outcomes were all significant predictors of engagement. This aligns with core principles of the theory and extends its application to a financially motivated addictive behavior in a low-resource setting [ 15 , 16 , 18 ]. The most compelling result is that Economic Hardship emerged as the strongest predictor of gambling behavior with an odds ratio of 3.06. This finding fundamentally reframes the positive attitude observed among students. It suggests that the motivation is not merely a recreational preference but a financially driven coping attitude. Students evaluate gambling as a viable yet risky strategy for economic survival in a climate of limited opportunities. This aligns with the deprivation theory of gambling which posits that those in lower socioeconomic positions gamble to improve their financial status [ 10 , 2 ]. The high youth unemployment rate in Nigeria likely exacerbates this desperation and pushes students toward high-risk financial behaviors. Subjective norms were also found to be a significant predictor of gambling engagement. This highlights the potent role of peer culture and digital socialization in normalizing betting behaviors [ 4 ]. The proliferation of betting shops and mobile apps creates an environment where gambling is viewed as a socially acceptable activity among students. The predictive value of Perceived Behavioral Control (PBC) warrants particular clinical attention. In gambling contexts, PBC often reflects an 'illusion of control' which is a well-documented cognitive distortion where individuals overestimate their skill or ability to influence chance-determined outcomes [ 19 ]. This finding is critical for two reasons. First, it provides a mechanistic link between the TPB and the development of gambling disorder: the mistaken belief in control may reinforce continued engagement despite losses, facilitating a transition from motivated behavior to compulsion [ 20 ]. Second, it suggests that prevention programs targeting attitudes and norms alone may be insufficient. Interventions must also directly challenge this cognitive distortion through psychoeducation about randomness and the house edge, which could reduce the reinforcing value of early 'wins' and mitigate the descent into problem gambling. Despite being driven by economic improvement motives, participants reported severe negative consequences. The high rates of self-reported stress and anxiety at 50.9% and financial strain at 48.6% create a poverty trap paradox where a behavior adopted to alleviate financial difficulty actively worsens it. Students engage in gambling to solve liquidity problems but end up with exacerbated financial deficits and heightened psychological distress. This supports recent literature on the psychosocial costs of gambling in African contexts [ 7 , 13 ]. The finding that 49.8% felt unable to stop signals a significant loss of behavioral control. This suggests that for a substantial subset of this population, the behavior has transcended a rational economic strategy and moved into the realm of a behavioral addiction. This transition from voluntary participation to compulsion underscores the urgent need for clinical and policy interventions. Limitations and Future Research While this study offers valuable insights, several limitations warrant consideration and guide future research directions. First, the cross-sectional design inherently restricts causal inference. Although the Theory of Planned Behavior posits a directional relationship from cognitions to behavior, our data represent a single snapshot. We cannot definitively establish whether economic hardship and positive gambling attitudes precede engagement or if, conversely, escalating gambling losses intensify perceived financial strain and rationalize continued participation through post-hoc cognitive adjustments, such as the chasing of losses reinforcing the attitude that a win is necessary to recover. Future longitudinal or experimental studies are needed to delineate these temporal and causal pathways, particularly the transition from motivated gambling to loss of control. Second, our reliance on self-reported measures introduces potential biases. Social desirability may lead to underreporting of gambling frequency or associated harms, while cognitive biases inherent in problem gambling, such as memory distortion or the minimization of losses, may affect accuracy. The measure of academic impact was subjective; future work would be strengthened by correlating gambling behaviors with objective academic metrics, for example semester GPA, course withdrawals, or library engagement data, to quantify the educational cost more precisely. Third, the sample, drawn from a single premier university, may affect generalizability. Students at the University of Ibadan, while diverse, may not fully represent the experiences of those at private institutions, polytechnics, or universities in regions with different socioeconomic or cultural profiles regarding gambling. Additionally, our operationalization of the TPB, while reliable, was not exhaustive. Future research could incorporate behavioral assays or implicit association tests to complement self-reported perceived behavioral control and disentangle genuine confidence from cognitive distortion. Finally, the study focused on individual-level predictors. A valuable extension would be to integrate multi-level modeling that accounts for environmental factors, such as density of betting shops around campus, exposure to targeted social media advertising algorithms, or faculty attitudes, to provide a more holistic ecological understanding of the risk environment. Conclusion and Implications This study concludes that electronic gambling among University of Ibadan students is a prevalent behavior best understood through the integrated lens of economic strain and the Theory of Planned Behavior. Students' attitudes, perceptions of social norms, and sense of control are all shaped by underlying financial pressure. This leads to engagement in a behavior that paradoxically exacerbates the very hardship it aims to solve and carries significant addictive potential. The findings translate into a clear imperative for multi-tiered, theory-informed interventions that address both the socioeconomic roots and the sustaining cognitive architecture of gambling among students. For clinical and campus health practice, we propose two key actions. First, university counseling and health services must move beyond a focus solely on substance use. Brief, validated screening tools for disordered gambling, such as the Brief Problem Gambling Screen, should be integrated into routine health assessments. A positive screen should trigger a nuanced intervention that acknowledges the economic motivation, thereby avoiding stigmatizing language of irrationality, while providing motivational interviewing to explore the financial distress and cognitive distortions, like illusion of control and gambling as investment, that maintain the behavior. Second, given the high rates of reported stress, anxiety, and loss of control, universities should develop referral pathways and dedicated support groups for behavioral addictions. These groups should be co-facilitated by a clinician and a financial counselor to address the intertwined psychological and economic sequelae concurrently. For prevention and health promotion policy, a reframing of public messaging is required. National and institutional awareness campaigns must move beyond generic warnings. Instead, they should target the specific Theory of Planned Behavior constructs validated here. Campaigns should aim to reshape attitudes by publicizing data on actual long-term net losses and contrasting them with proven micro-savings or entrepreneurship outcomes. They should employ social norms correction strategies that accurately disclose the percentage of students who do not gamble regularly or who have experienced harm, thereby countering the pluralistic ignorance that everyone is participating and winning. Furthermore, content should educate on the mathematical randomness of betting outcomes and the house edge, directly challenging the illusion of skill. Critically, as economic hardship is the strongest predictor, policy must address the opportunity vacuum. Universities, in partnership with government and the private sector, should scale up verified, accessible income-generation opportunities such as work-study programs, competitive micro-grants for student entrepreneurship, and paid skills-development internships. This provides a legitimate alternative to the fast money promise of gambling. For future research, this study underscores the necessity of context-driven models. Subsequent investigations should develop and test interventions directly targeting the economic coping attitude via behavioral economics experiments, for instance evaluating the impact of guaranteed small-income streams on gambling propensity. Research should also investigate the neurocognitive correlates of perceived behavioral control in this population using tasks that measure reward prediction error and impulsivity, thereby bridging sociocognitive models with neuroscience. Furthermore, qualitative studies are needed to deeply explore the narratives and decision-making processes of students who gamble for survival versus those who do not, to identify potential protective factors and resilience strategies within the same high-risk environment. These integrated implications advocate for a paradigm shift: from viewing student gambling primarily as a leisure-time moral failing to treating it as a symptom of broader socioeconomic distress mediated by predictable cognitive pathways, thereby demanding equally sophisticated, compassionate, and multifaceted solutions. Declarations Ethics approval and consent to participate: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the University of Ibadan Ethics Committee (Reference Number: UI/EC/23/0185, Date: 12 March 2025). Informed consent was obtained from all individual participants included in the study. Consent for publication: Not applicable. Competing interests: The authors are volunteer members of GamblePause Africa Initiative, a non-profit organisation that provides gambling-harm prevention education. They receive no salary or honoraria from the initiative. The authors declare that they have no other competing interests. Funding: This research received no external funding. The authors volunteered their time and used personal resources for data collection. Author Contribution TSF and COO conceived the study, designed the questionnaire, collected data, performed the analyses, drafted the manuscript, and approved the final version. Acknowledgement The authors thank the undergraduate and postgraduate students of the University of Ibadan who gave their time, and the field assistants who helped with questionnaire distribution. Data Availability The anonymised dataset and the questionnaire are available from the corresponding author on reasonable request, subject to the conditions of the ethics approval. References Hing N, Smith M, Rockloff M, et al. How structural changes in online gambling are shaping the contemporary experiences and behaviours of online gamblers: an interview study. BMC Public Health. 2022;22:1620. 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The temporal relationship between gambling-related beliefs and gambling behaviour: a prospective study using the theory of planned behaviour. Int Gambl Stud. 2017;17(3):508–19. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 07 Jan, 2026 Editor assigned by journal 07 Jan, 2026 Editor invited by journal 06 Jan, 2026 Submission checks completed at journal 05 Jan, 2026 First submitted to journal 05 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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10:59:56","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":20320,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8436822/v1/cb150feb31e3fdc90e9f6e4f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eElectronic Gambling Engagement and Psychosocial Outcomes Among University Students in Ibadan, Nigeria: A Theory of Planned Behavior Perspective\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe advent of digital technologies has expanded access to gambling worldwide, shifting participation toward online platforms including sports betting, poker, and casino games [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In Nigeria, electronic gambling has grown rapidly among youth, driven by smartphone availability and aggressive marketing [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Prevalence studies indicate high engagement rates among Nigerian university students, often exceeding 40% to 50% [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUniversity students are a vulnerable group due to transitional life stages, peer influences, and socioeconomic pressures such as unemployment and limited income [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Many view gambling as a potential supplementary income source amid financial constraints [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, gambling is associated with adverse outcomes, including psychological distress such as anxiety, depression and suicide ideation, debt, and academic disruption [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Theory of Planned Behavior [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] serves as the guiding theoretical framework for this study, offering a lens to examine the associations between sociocognitive drivers and electronic gambling engagement. Rather than strictly testing the theory's causal validity, this study applies the framework to characterize the specific motivations of Nigerian university students. Within this context, Attitudes are conceptualized not merely as recreational preferences, but often as cognitive evaluations of gambling as a viable financial strategy or investment amidst economic constraints. Subjective Norms capture the perceived social pressure arising from the normalization of betting in student hostels and the ubiquity of peer engagement. Perceived Behavioral Control encompasses the ease of access provided by smartphones and the illusion of control, where students overestimate their skill in sports betting to influence outcomes. Furthermore, this framework provides a baseline for understanding psychosocial outcomes; when gambling is driven by a perceived need for financial control (Attitude/PBC), the subsequent financial losses and academic disruption constitute a dissonance that manifests as significant stress and anxiety [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn low-resource settings like Nigeria, economic hardship may amplify these constructs, framing gambling as a rational financial strategy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Few Nigerian studies have used the Theory of Planned Behavior systematically, particularly in university contexts. To address these gaps, this study was guided by the following specific objectives:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo determine the prevalence and patterns of electronic gambling among university students in Ibadan.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo examine the influence of Theory of Planned Behavior constructs (attitudes, subjective norms, and perceived behavioral control) and economic hardship on gambling engagement.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo assess the self-reported psychosocial and academic consequences associated with gambling behavior in this population.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cstrong\u003eStudy Design and Setting\u003c/strong\u003e \u003cp\u003eA descriptive cross-sectional survey design was adopted for this study. This design was selected to allow for the efficient determination of gambling prevalence and to examine the cross-sectional associations between Theory of Planned Behavior constructs and gambling behavior at a single point in time. The study was conducted at the University of Ibadan, a premier federal institution in Southwestern Nigeria, between March and August 2025. The university comprises a diverse population of students from various socio-cultural backgrounds, making it a suitable setting for investigating youth behaviors.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eParticipants and Sampling Procedure The study population comprised all registered undergraduate and postgraduate students. A multi-stage sampling technique was employed to ensure a representative sample.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStratification: First, the population was stratified into three distinct academic categories: Regular Undergraduate, Distance Learning Centre (DLC) Undergraduate, and Postgraduate students.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eProportional Allocation: Second, to ensure that the sample accurately reflected the university's demographic distribution, participants were selected from each stratum based on probability proportional to size (PPS). For instance, strata with larger student populations contributed a higher percentage of participants to the final sample.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRandom Selection: Finally, a simple random sampling method was used within each stratum to select individual participants, minimizing selection bias.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eInclusion and Exclusion Criteria Inclusion criteria were defined as: (1) being a currently registered student of the University of Ibadan; and (2) providing informed consent to participate. Students were excluded if they were absent during the data collection period or if they returned incomplete questionnaires.\u003c/p\u003e \u003cp\u003eSample Size and Ethical Considerations The minimum sample size was determined using Cochran\u0026rsquo;s formula for infinite populations, assuming a 50% prevalence rate (to maximize sample size), a 95% confidence level, and a 5% margin of error. Adjusting for a 10% non-response rate, the final target sample was 403 students. Ethical approval was obtained from the University of Ibadan Ethics Committee (UI/EC/23/0185, 12 March 2025). All participants were informed of the study's purpose, their right to withdraw, and the confidentiality of their data before providing informed consent. Trained research assistants administered the structured questionnaire at various campus locations after obtaining informed consent.\u003c/p\u003e \u003cp\u003eMeasures and Instrumentation\u003c/p\u003e \u003cp\u003eData were collected using a structured questionnaire adapted from established measures in the literature. The full questionnaire developed for this study is available as Supplementary File 1. The instrument was divided into five sections:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSection A: Socio-demographics\u003c/b\u003e: This section utilized multiple-choice and open-ended questions to assess background characteristics, including age, gender, level of study (undergraduate/postgraduate), employment status, and monthly income/allowance.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSection B: Gambling Behavior\u003c/b\u003e: Participants responded to multiple-choice items regarding their lifetime history of gambling, frequency of engagement (e.g., daily, weekly), and preferred game types (e.g., sports betting, poker).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSection C: Theory of Planned Behavior Constructs\u003c/b\u003e: This section assessed the three core TPB predictors using a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree).\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eAttitude\u003c/em\u003e was measured with 5 items assessing positive evaluations of gambling (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.78).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eSubjective Norms\u003c/em\u003e were assessed via 3 items measuring perceived peer pressure (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.72).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ePerceived Behavioral Control\u003c/em\u003e was measured using 4 items assessing confidence and the illusion of control (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.75).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSection D: Economic Hardship Index\u003c/b\u003e: A composite variable was derived from items measuring employment status, self-rated income adequacy, and a 3-item financial stress scale.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSection E: Psychosocial and Academic Effects\u003c/b\u003e: Participants who reported gambling rated their level of agreement with statements regarding adverse outcomes, such as increased anxiety, financial strain, and negative academic impacts.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eValidity and Reliability\u003c/b\u003e: To ensure content validity, the initial draft of the questionnaire was reviewed by experts in Sociology, Psychology, and Public Health to assess item clarity and relevance. Reliability was established using Cronbach\u0026rsquo;s alpha, with all sub-scales exceeding the recommended threshold of .70.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using IBM SPSS Statistics (Version 27). The analysis was conducted in three stages corresponding to the study objectives:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDescriptive Analysis (Objectives 1 \u0026amp; 3)\u003c/b\u003e: Descriptive statistics, including frequencies, percentages, and means, were used to summarize socio-demographic characteristics, determine the prevalence and patterns of electronic gambling (Objective 1), and assess the distribution of self-reported psychosocial and academic consequences (Objective 3).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eReliability Analysis\u003c/b\u003e: Prior to hypothesis testing, the internal consistency of the measurement scales (Attitude, Subjective Norms, PBC) was assessed using Cronbach\u0026rsquo;s alpha to ensure data quality.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInferential Analysis (Objective 2)\u003c/b\u003e: To examine the predictors of gambling behavior, a binary logistic regression was performed. This method was selected because the dependent variable, \u003cem\u003eGambling Engagement\u003c/em\u003e, was categorical (Yes/No). The mean scores for Attitude, Subjective Norms, and Perceived Behavioral Control, alongside the Economic Hardship Index, served as independent variables. The model's goodness-of-fit was evaluated using the Hosmer-Lemeshow test and Nagelkerke \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eStatistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSocio-demographic Characteristics\u003c/p\u003e \u003cp\u003eThe sample consisted of 403 students. The mean age was 26.4 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.1). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the sample was relatively gender-balanced, with slightly more males (51.9%). A majority (66%) were under 30 years old, and the sample was evenly split between undergraduate and postgraduate levels. Notably, 51.9% reported being employed while studying.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic Characteristics of Respondents (N\u0026thinsp;=\u0026thinsp;403)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUndergraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePrevalence and Patterns of Gambling\u003c/p\u003e \u003cp\u003eThe lifetime prevalence of electronic gambling was 49.6% (n\u0026thinsp;=\u0026thinsp;200). Among gamblers, the most common forms were poker (29.3%) and sports betting (24.8%). High frequency engagement, defined as daily or weekly participation, was reported by 37.3% of gamblers (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatterns of Electronic Gambling among Participants (N\u0026thinsp;=\u0026thinsp;403)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGambling Engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency (Gamblers only)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreferred Game Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSports Betting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlot Machines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnline Casino Games\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePredicting Gambling Engagement\u003c/p\u003e \u003cp\u003eA binary logistic regression was conducted to predict the likelihood of gambling engagement based on Theory of Planned Behavior constructs and economic hardship. The model was statistically significant, χ\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(4)\u0026thinsp;=\u0026thinsp;86.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, explaining 32% of the variance in gambling behavior (Nagelkerke \u003cem\u003eR\u003c/em\u003e\u0026sup2;). The Hosmer-Lemeshow test indicated a good model fit (χ\u003csup\u003e2\u003c/sup\u003e(8\u003cem\u003e)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.51).\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, all predictors were significant. Economic Hardship was the strongest predictor (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.06), followed by a Positive Attitude toward gambling (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.34). Subjective Norms and Perceived Behavioral Control also significantly increased the odds of gambling.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic Regression Analysis Predicting Engagement in Electronic Gambling (N\u0026thinsp;=\u0026thinsp;403)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald χ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% C.I. for OR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude (Positive)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.31***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[1.64, 3.33]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjective Norms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.02***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[1.36, 2.54]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerc. Behav. Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.97***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[1.40, 2.94]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic Hardship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.45***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[2.03, 4.62]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.36***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e Model χ\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e(4)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;86.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. Nagelkerke \u003cem\u003eR^2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.32. *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSelf-Reported Effects of Gambling\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the 200 students who gambled, high levels of negative consequences were reported (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Over half (50.9%) agreed that gambling increased their stress and anxiety, and 48.6% reported significant financial strain. Critically, 42.6% believed it negatively affected their academic performance, and nearly half (49.8%) felt unable to stop.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelf-Reported Psychosocial and Academic Effects among Gamblers (n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReported Effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree/Strongly Agree (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeutral (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDisagree/Strongly Disagree (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncreased stress and anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperienced financial strain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegatively affected academic performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFelt unable to stop or control gambling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorrowed money/sold items to fund gambling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study applied the Theory of Planned Behavior to investigate electronic gambling among Nigerian university students. The findings revealed a complex interplay between economic desperation and socio-cognitive processes. The primary finding is the powerful validation of the theoretical model in this context. As hypothesized, a positive attitude toward gambling, perceived social approval, and a belief in one's control over gambling outcomes were all significant predictors of engagement. This aligns with core principles of the theory and extends its application to a financially motivated addictive behavior in a low-resource setting [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe most compelling result is that Economic Hardship emerged as the strongest predictor of gambling behavior with an odds ratio of 3.06. This finding fundamentally reframes the positive attitude observed among students. It suggests that the motivation is not merely a recreational preference but a financially driven coping attitude. Students evaluate gambling as a viable yet risky strategy for economic survival in a climate of limited opportunities. This aligns with the deprivation theory of gambling which posits that those in lower socioeconomic positions gamble to improve their financial status [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The high youth unemployment rate in Nigeria likely exacerbates this desperation and pushes students toward high-risk financial behaviors.\u003c/p\u003e \u003cp\u003eSubjective norms were also found to be a significant predictor of gambling engagement. This highlights the potent role of peer culture and digital socialization in normalizing betting behaviors [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The proliferation of betting shops and mobile apps creates an environment where gambling is viewed as a socially acceptable activity among students. The predictive value of Perceived Behavioral Control (PBC) warrants particular clinical attention. In gambling contexts, PBC often reflects an 'illusion of control' which is a well-documented cognitive distortion where individuals overestimate their skill or ability to influence chance-determined outcomes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This finding is critical for two reasons. First, it provides a mechanistic link between the TPB and the development of gambling disorder: the mistaken belief in control may reinforce continued engagement despite losses, facilitating a transition from motivated behavior to compulsion [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Second, it suggests that prevention programs targeting attitudes and norms alone may be insufficient. Interventions must also directly challenge this cognitive distortion through psychoeducation about randomness and the house edge, which could reduce the reinforcing value of early 'wins' and mitigate the descent into problem gambling.\u003c/p\u003e \u003cp\u003eDespite being driven by economic improvement motives, participants reported severe negative consequences. The high rates of self-reported stress and anxiety at 50.9% and financial strain at 48.6% create a poverty trap paradox where a behavior adopted to alleviate financial difficulty actively worsens it. Students engage in gambling to solve liquidity problems but end up with exacerbated financial deficits and heightened psychological distress. This supports recent literature on the psychosocial costs of gambling in African contexts [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe finding that 49.8% felt unable to stop signals a significant loss of behavioral control. This suggests that for a substantial subset of this population, the behavior has transcended a rational economic strategy and moved into the realm of a behavioral addiction. This transition from voluntary participation to compulsion underscores the urgent need for clinical and policy interventions.\u003c/p\u003e \u003cp\u003eLimitations and Future Research\u003c/p\u003e \u003cp\u003eWhile this study offers valuable insights, several limitations warrant consideration and guide future research directions. First, the cross-sectional design inherently restricts causal inference. Although the Theory of Planned Behavior posits a directional relationship from cognitions to behavior, our data represent a single snapshot. We cannot definitively establish whether economic hardship and positive gambling attitudes precede engagement or if, conversely, escalating gambling losses intensify perceived financial strain and rationalize continued participation through post-hoc cognitive adjustments, such as the chasing of losses reinforcing the attitude that a win is necessary to recover. Future longitudinal or experimental studies are needed to delineate these temporal and causal pathways, particularly the transition from motivated gambling to loss of control.\u003c/p\u003e \u003cp\u003eSecond, our reliance on self-reported measures introduces potential biases. Social desirability may lead to underreporting of gambling frequency or associated harms, while cognitive biases inherent in problem gambling, such as memory distortion or the minimization of losses, may affect accuracy. The measure of academic impact was subjective; future work would be strengthened by correlating gambling behaviors with objective academic metrics, for example semester GPA, course withdrawals, or library engagement data, to quantify the educational cost more precisely.\u003c/p\u003e \u003cp\u003eThird, the sample, drawn from a single premier university, may affect generalizability. Students at the University of Ibadan, while diverse, may not fully represent the experiences of those at private institutions, polytechnics, or universities in regions with different socioeconomic or cultural profiles regarding gambling. Additionally, our operationalization of the TPB, while reliable, was not exhaustive. Future research could incorporate behavioral assays or implicit association tests to complement self-reported perceived behavioral control and disentangle genuine confidence from cognitive distortion.\u003c/p\u003e \u003cp\u003eFinally, the study focused on individual-level predictors. A valuable extension would be to integrate multi-level modeling that accounts for environmental factors, such as density of betting shops around campus, exposure to targeted social media advertising algorithms, or faculty attitudes, to provide a more holistic ecological understanding of the risk environment.\u003c/p\u003e"},{"header":"Conclusion and Implications","content":"\u003cp\u003eThis study concludes that electronic gambling among University of Ibadan students is a prevalent behavior best understood through the integrated lens of economic strain and the Theory of Planned Behavior. Students' attitudes, perceptions of social norms, and sense of control are all shaped by underlying financial pressure. This leads to engagement in a behavior that paradoxically exacerbates the very hardship it aims to solve and carries significant addictive potential. The findings translate into a clear imperative for multi-tiered, theory-informed interventions that address both the socioeconomic roots and the sustaining cognitive architecture of gambling among students.\u003c/p\u003e \u003cp\u003eFor clinical and campus health practice, we propose two key actions. First, university counseling and health services must move beyond a focus solely on substance use. Brief, validated screening tools for disordered gambling, such as the Brief Problem Gambling Screen, should be integrated into routine health assessments. A positive screen should trigger a nuanced intervention that acknowledges the economic motivation, thereby avoiding stigmatizing language of irrationality, while providing motivational interviewing to explore the financial distress and cognitive distortions, like illusion of control and gambling as investment, that maintain the behavior. Second, given the high rates of reported stress, anxiety, and loss of control, universities should develop referral pathways and dedicated support groups for behavioral addictions. These groups should be co-facilitated by a clinician and a financial counselor to address the intertwined psychological and economic sequelae concurrently.\u003c/p\u003e \u003cp\u003eFor prevention and health promotion policy, a reframing of public messaging is required. National and institutional awareness campaigns must move beyond generic warnings. Instead, they should target the specific Theory of Planned Behavior constructs validated here. Campaigns should aim to reshape attitudes by publicizing data on actual long-term net losses and contrasting them with proven micro-savings or entrepreneurship outcomes. They should employ social norms correction strategies that accurately disclose the percentage of students who do not gamble regularly or who have experienced harm, thereby countering the pluralistic ignorance that everyone is participating and winning. Furthermore, content should educate on the mathematical randomness of betting outcomes and the house edge, directly challenging the illusion of skill. Critically, as economic hardship is the strongest predictor, policy must address the opportunity vacuum. Universities, in partnership with government and the private sector, should scale up verified, accessible income-generation opportunities such as work-study programs, competitive micro-grants for student entrepreneurship, and paid skills-development internships. This provides a legitimate alternative to the fast money promise of gambling.\u003c/p\u003e \u003cp\u003eFor future research, this study underscores the necessity of context-driven models. Subsequent investigations should develop and test interventions directly targeting the economic coping attitude via behavioral economics experiments, for instance evaluating the impact of guaranteed small-income streams on gambling propensity. Research should also investigate the neurocognitive correlates of perceived behavioral control in this population using tasks that measure reward prediction error and impulsivity, thereby bridging sociocognitive models with neuroscience. Furthermore, qualitative studies are needed to deeply explore the narratives and decision-making processes of students who gamble for survival versus those who do not, to identify potential protective factors and resilience strategies within the same high-risk environment.\u003c/p\u003e \u003cp\u003eThese integrated implications advocate for a paradigm shift: from viewing student gambling primarily as a leisure-time moral failing to treating it as a symptom of broader socioeconomic distress mediated by predictable cognitive pathways, thereby demanding equally sophisticated, compassionate, and multifaceted solutions.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003e This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the University of Ibadan Ethics Committee (Reference Number: UI/EC/23/0185, Date: 12 March 2025). Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors are volunteer members of GamblePause Africa Initiative, a non-profit organisation that provides gambling-harm prevention education. They receive no salary or honoraria from the initiative. The authors declare that they have no other competing interests.\u003c/p\u003e \u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research received no external funding. The authors volunteered their time and used personal resources for data collection.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTSF and COO conceived the study, designed the questionnaire, collected data, performed the analyses, drafted the manuscript, and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank the undergraduate and postgraduate students of the University of Ibadan who gave their time, and the field assistants who helped with questionnaire distribution.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe anonymised dataset and the questionnaire are available from the corresponding author on reasonable request, subject to the conditions of the ethics approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHing N, Smith M, Rockloff M, et al. How structural changes in online gambling are shaping the contemporary experiences and behaviours of online gamblers: an interview study. BMC Public Health. 2022;22:1620.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdebisi TA, Alabi O, Arisukwu O, Asamu F. Gambling in transition: assessing youth narratives of gambling in Nigeria. J Gambl Stud. 2021;37:59\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEde MO, Nwosu KC, Onyedibe MCC, Oneli JO. Predictors of pathological gambling behaviours in parents\u0026rsquo; population in Nigeria. Sci Rep. 2024;14:9197.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyandele O, Popoola O, Obosi AC. Influence of demographic and psychological factors on attitudes toward sport betting among young adults in Southwest Nigeria. J Gambl Stud. 2020;36:343\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaniel FM, Gbuchie MD, Eze CC, Nweke JO, Onyedibe MCC. 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BJPsych Int. 2021;18:61\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelfabbro P, King DL. The evolution of young gambling studies: digital convergence of gaming, gambling and cryptocurrency technologies. Int Gambl Stud. 2023;23:256\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAjzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50:179\u0026ndash;211.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSt-Pierre RA, Derevensky JL, Temcheff CE, Gupta R. Adolescent gambling and problem gambling: examination of an extended theory of planned behaviour. Int Gambl Stud. 2015;15:506\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee H-S. Predicting and understanding undergraduate students\u0026rsquo; intentions to gamble in a casino using an extended model of the theory of reasoned action and the theory of planned behavior. J Gambl Stud. 2013;29(2):315\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin RJ, Usdan S, Nelson S, Umstattd MR, LaPlante D, Perko M, Shaffer H. Using the theory of planned behavior to predict gambling behavior. Psychol Addict Behav. 2010;24(1):89\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLanger EJ. The illusion of control. J Pers Soc Psychol. 1975;32:311\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlack M, Morris M. The temporal relationship between gambling-related beliefs and gambling behaviour: a prospective study using the theory of planned behaviour. Int Gambl Stud. 2017;17(3):508\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Electronic Gambling, Theory of Planned Behavior, University Students, Economic Hardship, Nigeria, Behavioral Addiction","lastPublishedDoi":"10.21203/rs.3.rs-8436822/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8436822/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground and Aims\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e: The proliferation of electronic gambling in Nigeria presents a significant public health concern among youths facing economic adversity. This study applied the Theory of Planned Behavior to investigate the sociocognitive drivers and psychosocial impacts of electronic gambling among university students.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e: A descriptive cross-sectional design was employed. Data were collected from 403 undergraduate and postgraduate students at the University of Ibadan using stratified random sampling and a structured questionnaire. Analyses included descriptive statistics, scale reliability assessment, and binary logistic regression.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e: The lifetime prevalence of electronic gambling was 49.6%. Attitudes regarding financial utility, subjective norms involving peer approval, and perceived behavioral control were all significant predictors of gambling engagement (p \u0026lt; .001). Economic hardship was the strongest predictor in the model (OR = 3.06). Among participants who gambled, 50.9% reported increased stress and anxiety, 48.6% reported financial strain, and 42.6% reported negative academic impacts.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e: Gambling among students is driven by economic necessity framed within sociocognitive pathways. Findings advocate for theory-informed interventions that address financial stressors and correct misperceptions about gambling.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Electronic Gambling Engagement and Psychosocial Outcomes Among University Students in Ibadan, Nigeria: A Theory of Planned Behavior Perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-09 10:59:52","doi":"10.21203/rs.3.rs-8436822/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-01-07T14:29:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-07T14:27:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-06T12:18:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-05T19:42:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-01-05T19:37:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e4dca01d-d762-4e3a-a99e-d2f04316f3e8","owner":[],"postedDate":"January 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T09:40:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-09 10:59:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8436822","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8436822","identity":"rs-8436822","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}