The study aims to investigate the influence of excessive gadget promotion and peer pressure on the gadget usage behavior of young people, as well as to assess the prolonged physical and psychological consequences of digital overdependence.
A quantitative survey utilizing a seven-point Likert scale was conducted on 500 respondents aged 12 to 26 in urban Bangladesh, resulting in the analysis of 366 valid responses through structural equation modeling (SEM).
The SEM analysis revealed that the advertising of gadgets substantially increased screen time and impulsive purchases. Peer pressure strongly predicted gadget overuse. Excessive gadget usage was directly connected with eye strain, sleep disturbance, anxiety and diminished concentration, adversely affecting psychological well-being and health.
The results offer practical recommendations for parents, educators, policymakers and technology developers to reduce hazards via digital literacy instruction, parental involvement, ethical design and enhanced regulation of marketing aimed at young people.
This study enhances the understanding of youth digital well-being by linking marketing influence to health and psychological effects, offering a South Asian viewpoint that benefits educators, policymakers and technology developers.
1. Introduction
In recent years, smartphones, tablets, computers and wearable technologies have revolutionized the communication, learning and socialization of young individuals. Initially regarded as luxuries, these technologies have evolved into requirements, frequently resulting in unforeseen behavioral and health consequences (Xu et al., 2023). Contemporary digital lifestyles are intimately connected to communication, education and entertainment; yet, their excessive utilization—driven by societal pressure and appealing marketing—has aroused significant psychological and physical problems among youths. Influencer endorsements, social media advertising and aggressive gadget marketing encourage impulsive purchasing and sustained screen engagement, perhaps leading to worry or distress upon disconnection (High et al., 2022).
Excessive technology utilization has been associated with many mental and behavioral issues, such as restlessness, irritation and emotional isolation (Atasever et al., 2022). A significant number of young individuals depend on devices for relief from loneliness or stress, hence increasing digital dependency. Peer influence worsens this tendency: the desire for social approval and fear of missing out (FOMO) drive young people to get the latest devices, occasionally beyond their financial capacity (Sandi et al., 2025).
The physical implications are similarly alarming. Extended screen time leads to visual tiredness, poor posture, sleep disturbances and muscle pain, whereas sitting for prolonged periods fosters obesity and lethargy (Genc and Pirincci, 2023). These symptoms are not isolated incidents but signify an escalating health issue among technology-dependent generations.
Recent research indicates that extended gadget use is linked to psychological distress in teenagers, including anxiety, restlessness, decreased attention span, and emotional reliance (Kumar et al., 2024). Many young users experience worry when separated from their devices, annoyance when internet connectivity is interrupted and emotional distress from digital comparisons (Yıldız et al., 2022). Moreover, gadgets are being used to manage stress, increasing screen time and decreasing social interaction (Elhai et al., 2021). These behaviors suggest digital mental exhaustion that may hinder human growth and psychological resilience (Nesi et al., 2022).
Previous studies have thoroughly investigated the utilization of digital technology among youths, emphasizing its psychological and physical health implications (Elhai et al., 2020). Current research recognizes that digital marketing methods, such as social media advertising and influencer endorsements, substantially affect user engagement, while peer pressure and social comparison impact technology adoption (Abu-Taieh et al., 2022; Fatimah, 2025). Nonetheless, these research streams remain predominantly fragmented. Most research considers marketing impact, social dynamics and health outcomes as distinct domains, neglecting their interconnected effects.
Furthermore, empirical studies that simultaneously investigate the impact of external factors, such as gadget promotion and peer influence, on excessive gadget usage – and the subsequent effects of this usage on physical and psychological health – remain scarce (Kumar et al., 2024). This limitation is further heightened by the prevalence of Western-centric research, which limits the relevance of findings to rising economies marked by swift digital adoption and unique socio-cultural dynamics.
This study establishes a comprehensive framework to analyze the synergistic impacts of gadget promotion and peer pressure on gadget usage and its ensuing influence on the physical and psychological well-being of youth in Bangladesh.
2. Literature review
The young generation, often between the ages of 12 and 26, exhibits significant responsiveness to rewarding stimuli, social acceptance and curiosity. This developmental phase signifies a transition from parental authority to increased peer influence and identity development (Zanolie et al., 2022). Adolescent decision-making is influenced by their search for acceptability, belonging and self-expression, which are intensified in digital contexts (Ohannessian and Vannucci, 2020). These behavioral tendencies establish a solid basis for understanding the increased digital dependency among youth.
Gadgets possess significant psychological and social attraction for youngsters, fueled by immediate satisfaction, connectivity, entertainment and emotional comfort. Smartphones offer instant access to social engagement and approval via likes, messages and comments, which activate dopamine-driven reward systems (Rozgonjuk et al., 2020). For several young people, gadgets are essential for sustaining friendships, engaging in online communities and solidifying social identity (Montag et al., 2021). Digital platforms like YouTube, online gaming and social media provide an immersive getaway and relieve stress from academic and emotional challenges (Elhai et al., 2021). The constant connectedness increases the fear of exclusion, resulting in sustained device engagement among young users.
2.1 Influence of gadget promotion on gadget usage
Digital marketing significantly affects device usage trends among youth by continuous exposure and engaging content. Continuous exposure to digital advertisements promotes prolonged and frequently excessive device usage. Marsh et al. (2024) assert that focused marketing methods significantly enhance product appeal among youngsters through the utilization of persuasive and visually captivating material. Zhang et al. (2022) similarly discovered that customized and repetitive marketing across several platforms enhances device attachment and diminishes users' capacity to disengage.
Social media worsens this phenomenon by integrating advertisements with peer-generated content and hot subjects, thus boosting social pressure to maintain digital engagement. Zheng et al. (2021) assert that continuous exposure to influencer-driven promotions cultivates behavioral tendencies of excessive screen engagement and digital dependence. Bozzola et al. (2022) emphasize that this marketing fosters social comparison, provoking feelings of inadequacy and conformity among youths attempting to align with changing trends. Consequently, users may exhibit compulsive checking habits and experience psychological distress, adversely impacting academic performance, social interactions and sleep quality (Surat et al., 2021).
Gadget promotion has a positive effect on increasing youth gadget usage behavior.
2.2 Influence of peer pressure on gadget usage
Peer pressure significantly impacts youth consumer behavior, particularly in relation to digital device usage. Devices like smartphones, smartwatches and gaming consoles have transitioned from functional tools to symbols of social affiliation and status. Many young individuals feel pressured to get the latest devices to conform to peer expectations and avoid social rejection. Conversations regarding emerging technologies frequently exacerbate this conformity, leading to impulsive or unnecessary purchases.
Hoeben et al. (2024) assert that the FOMO and the fear of isolation drive youngsters to obtain the latest technologies to preserve social relevance. Exclusion from digital engagements or communal contacts due to outdated gadgets might diminish self-esteem and social connectivity. Mishra and Maity (2021) observed that gadgets serve as indicators of social status in youth cultures that prioritize innovation and trendiness. Lou et al. (2022) argued that youth view electronics as extensions of their group identity, utilizing them to reinforce social status and relieve feelings of isolation.
Peer pressure has a significant positive effect on gadget usage among the younger generation.
2.3 Physical health consequences of excessive gadget usage
The incorporation of digital technologies into daily life has normalized prolonged device usage among young people; however, increasing evidence indicates its detrimental physical effects (Paakkari et al., 2021). The overuse of smartphones, tablets and similar gadgets is significantly linked to muscular strain, visual discomfort, weariness and sleep disorders (Dyrek et al., 2024). These problems frequently arise from inadequate seating, including poor posture or improper hand positioning. Marks et al. (2023) discovered that users utilizing mobile devices for over four hours daily exhibit increased incidences of neck, wrist and back pain – symptoms indicative of “tech neck” and repetitive strain injuries. Continuous gadget use also increases joint inflammation and finger pain among frequent smartphone users involved in messaging or gaming.
Al-Mohtaseb et al. (2021) noted that 68% of youngsters utilizing screens for more than five hours per day exhibit symptoms of digital eye strain (DES). Blue light emitted by light-emitting diode screens reduces melatonin production and causes eye strain, leading to tiredness and visual fatigue (Clayton and Siderov, 2022). Moreover, nighttime screen viewing increases the likelihood of insomnia and delayed sleep cycles. Furthermore, substituting physical activity with digital interaction leads to weight gain, diminished stamina and creates overall tiredness (Bahadoran et al., 2023). The cumulative consequences suggest that excessive device usage promotes a cycle of physical deterioration in youth.
Excessive gadget use driven by constant promotion affects the physical health of young individuals.
2.4 Gadget usage and psychological well-being
The rapid expansion of digital gadgets has significantly influenced youth lifestyles, especially in Asia, where smartphones and tablets are essential for communication, education and leisure. Increasing evidence associates excessive device usage with heightened stress, anxiety and behavioral disorders (Soares et al., 2023). Reliance on smartphones frequently induces anger and dissatisfaction in the absence of devices, indicating behavioral dependency tendencies.
Emotional volatility is an additional significant issue. Taylor et al. (2024) discovered that frequent comparisons on social media diminish self-esteem and overall life happiness; they further reported that teenagers subjected to academic and peer pressure exhibit irritation, attention difficulties and mental exhaustion due to prolonged screen exposure. While Internet connections facilitate connection, they often lack emotional depth, thereby causing feelings of loneliness and sadness (Ge et al., 2023).
Numerous youths depend on devices to manage emotions, providing short-term comfort while cultivating permanent dependence (Al-Kandari and Al-Sejari, 2020). Constant notifications and information overload diminish concentration and lead to cognitive exhaustion. Upward social comparison and the FOMO increase anxiety and dissatisfaction. Extended screen usage diminishes attention span and affects concentration (Nesi et al., 2022), although online connectedness frequently takes over genuine social interaction (Einav and Margalit, 2022). Ultimately, engaging gadgets as means of coping could hamper emotional regulation and increase stress reactions (Ratan et al., 2022), illustrating that unregulated digital attachment presents significant threats to the mental well-being of young individuals.
Increased gadget promotion and usage heighten psychological distress among youth.
2.5 The mediating role of excessive gadget usage
Excessive gadget use can serve as a primary means by which external factors impact the well-being of young individuals. Previous studies demonstrate that marketing exposure and promotional activities substantially influence young consumers' perceptions of digital gadgets and enhance their engagement with technology (Gu and Duan, 2024). These corporate effects, such as influencer endorsements and targeted digital marketing, frequently promote increased device interaction, potentially resulting in excessive or compulsive gadget usage over time.
Likewise, social influence elements, including peer pressure, significantly impact technology adoption and usage behavior among teenagers (Abu-Taieh et al., 2022). Young individuals frequently feel obligated to utilize the latest digital technologies to preserve social inclusion and prevent the omission of peer interaction. Social factors may amplify the intensity and frequency of gadget usage, thus encouraging technological dependency.
Prior research has indicated that excessive and uncontrollable device usage correlates with various psychological and physical adverse effects, such as anxiety, diminished attention span, sleep disruptions and musculoskeletal pain (Kumar et al., 2024). Excessive technology use serves as a mediating mechanism connecting external environmental stimuli to individual results. Consequently, excessive gadget usage may influence the connection between external factors – such as gadget marketing (firm-related factors) and peer influence (non-firm-related factors) – and the physical and psychological health of youth.
Gadget usage mediates the relationship between the influence of gadget promotion and its effects on physical health.
Gadget usage mediates the relationship between the influence of gadget promotion and psychological well-being.
Gadget usage mediates the relationship between peer pressure and its effects on physical health.
Gadget usage mediates the relationship between peer pressure and psychological well-being.
The conceptual framework of the study is presented in Figure 1.
The diagram illustrates the conceptual framework for gadget usage and its effects. It shows four main components: Gadget Promotion and Influence, Peer Pressure, Gadget Usage, Physical Effect, and Psychological Well-being. Gadget Promotion and Influence and Peer Pressure both have arrows pointing towards Gadget Usage, indicating they influence it. Gadget Usage has arrows pointing towards both Physical Effect and Psychological Well-being, suggesting it impacts these factors. The relationships are labeled with H1, H2, H3, and H4, indicating hypotheses or pathways of influence.The conceptual framework. Source: Authors
The diagram illustrates the conceptual framework for gadget usage and its effects. It shows four main components: Gadget Promotion and Influence, Peer Pressure, Gadget Usage, Physical Effect, and Psychological Well-being. Gadget Promotion and Influence and Peer Pressure both have arrows pointing towards Gadget Usage, indicating they influence it. Gadget Usage has arrows pointing towards both Physical Effect and Psychological Well-being, suggesting it impacts these factors. The relationships are labeled with H1, H2, H3, and H4, indicating hypotheses or pathways of influence.The conceptual framework. Source: Authors
3. Methods
3.1 Research design
This research used a quantitative, cross-sectional survey to investigate the impact of gadget promotion and peer pressure on gadget usage and the corresponding physical and psychological health outcomes among youth in Bangladesh. The quantitative method provided an objective assessment of the relationships among latent variables within a hypothesis-testing framework. Purposive and quota sampling ensured that participants tended to be gadget users while preserving demographic balance across gender, age and education (Maier et al., 2023).
3.2 Measurement tool
A seven-point Likert scale was employed to gather data through a structured survey. To verify content validity, reliability and clarity, all measurement scales were pretested and adapted from validated instruments. Pilot research was conducted to verify the internal consistency of all constructs, ensuring measurement reliability before full-scale data collection.
3.3 Sample and data collection
The research focused on individuals aged 12–26, encompassing students from schools, colleges and universities in metropolitan and semi-urban regions, as they exhibit significant engagement with digital devices and are particularly vulnerable to gadget promotion and peer pressure (Montag et al., 2021). A non-probability purposive and quota sampling method was utilized: purposive sampling ensured that respondents were active gadget users capable of providing valuable insights, while quota sampling preserved demographic balance concerning gender, age and education (Maier et al., 2023). Of the 500 circulated questionnaires, 366 valid responses were received (see Table 1), above the specified limits for multivariate analyses and structural equation modeling (SEM), hence providing enough statistical power and reliability (Hair et al., 2021). Participants received information regarding the study's objectives, confidentiality measures and voluntary participation. Parental consent was obtained for participants under 18, in accordance with ethical research standards (Coyne, 2009).
Sample characteristics
| Variable | Category | Percentage (%) | Number of responses (n) |
|---|---|---|---|
| Gender | Male | 57.0% | 209 |
| Female | 43.0% | 157 | |
| Age group | 12–16 years | 16.0% | 57 |
| 17–21 years | 31.0% | 115 | |
| 22–26 years | 53.0% | 194 | |
| Education | School | 8.0% | 28 |
| High school | 27.0% | 99 | |
| Undergraduate | 47.0% | 173 | |
| Postgraduate | 13.0% | 46 | |
| Other | 5.0% | 20 | |
| Frequency of gadget usage | Smartphone | 81.0% | 296 |
| Tablet | 5.0% | 17 | |
| Laptop | 12.0% | 45 | |
| PS4/PS5 | 1.0% | 4 | |
| Personal computer | 1.0% | 3 | |
| Primary device used | Less than 2 h/day | 11.0% | 38 |
| 2–4 h/day | 34.0% | 125 | |
| 5–7 h/day | 31.0% | 114 | |
| More than 7 h/day | 24.0% | 89 |
| Variable | Category | Percentage (%) | Number of responses (n) |
|---|---|---|---|
| Gender | Male | 57.0% | 209 |
| Female | 43.0% | 157 | |
| Age group | 12–16 years | 16.0% | 57 |
| 17–21 years | 31.0% | 115 | |
| 22–26 years | 53.0% | 194 | |
| Education | School | 8.0% | 28 |
| High school | 27.0% | 99 | |
| Undergraduate | 47.0% | 173 | |
| Postgraduate | 13.0% | 46 | |
| Other | 5.0% | 20 | |
| Frequency of gadget usage | Smartphone | 81.0% | 296 |
| Tablet | 5.0% | 17 | |
| Laptop | 12.0% | 45 | |
| PS4/PS5 | 1.0% | 4 | |
| Personal computer | 1.0% | 3 | |
| Primary device used | Less than 2 h/day | 11.0% | 38 |
| 2–4 h/day | 34.0% | 125 | |
| 5–7 h/day | 31.0% | 114 | |
| More than 7 h/day | 24.0% | 89 |
3.4 Data analysis
In order to investigate mediation effects and evaluate relationships among variables, SEM was used. SEM is appropriate for complex mediation models, allows for measurement error and enables the simultaneous evaluation of multiple dependent and independent variables (Hair et al., 2021). Data were examined for outliers, normality and missing values. The construct validity and reliability were confirmed through confirmatory factor analysis (CFA), and the mediation effects were tested using bootstrapping.
4. Result
4.1 Assessment of measurement model
All criteria for validity and reliability of the measurement model were satisfied. Factor loadings mostly exceeded 0.70, thereby confirming item reliability (Hair et al., 2021). Composite reliability varied between 0.85 and 0.90, while average variance extracted (AVE) values exceeded 0.50, thereby confirming convergent validity. Discriminant validity was confirmed through the Fornell and Larcker (1981) criterion, where the square roots of AVE exceeded the inter-construct correlations.
The CFA exhibited a satisfactory model fit, with the following statistics: χ2/df = 1.908, comparative fit index = 0.935, Tucker–Lewis index = 0.922, incremental fit index = 0.936 and root mean square error of approximation = 0.050. Goodness of fit index (0.874) and adjusted goodness of fit index (0.853) are slightly below the 0.90 threshold; however, they are considered acceptable for complex models (Hair et al., 2021). The findings support the reliability and validity of the constructs (Table 2).
Testing reliability and validity
| AVE | Composite reliability | Cronbach's alpha | GPI | PP | GU | PE | PW | |
|---|---|---|---|---|---|---|---|---|
| GPI | 0.56 | 0.88 | 0.89 | 0.747 | ||||
| PP | 0.54 | 0.89 | 0.89 | 0.146 | 0.732 | |||
| GU | 0.53 | 0.85 | 0.85 | 0.369 | 0.683 | 0.729 | ||
| PE | 0.53 | 0.90 | 0.90 | 0.461 | 0.499 | 0.616 | 0.729 | |
| PW | 0.54 | 0.88 | 0.88 | 0.455 | 0.598 | 0.678 | 0.721 | 0.738 |
| AVE | Composite reliability | Cronbach's alpha | GPI | PP | GU | PE | PW | |
|---|---|---|---|---|---|---|---|---|
| GPI | 0.56 | 0.88 | 0.89 | 0.747 | ||||
| PP | 0.54 | 0.89 | 0.89 | 0.146 | 0.732 | |||
| GU | 0.53 | 0.85 | 0.85 | 0.369 | 0.683 | 0.729 | ||
| PE | 0.53 | 0.90 | 0.90 | 0.461 | 0.499 | 0.616 | 0.729 | |
| PW | 0.54 | 0.88 | 0.88 | 0.455 | 0.598 | 0.678 | 0.721 | 0.738 |
Note(s): Fornell–Larcker: the italic values on the diagonal are the square root of the AVEs, and the values on off diagonal are the correlation between the constructs
4.2 Assessment of the structural model
The research utilizing SEM validated that all proposed hypotheses were statistically supported (Tables 3 and 4), demonstrating significant correlations among the constructs examined.
Hypothesis-testing summary
| NO | Hypothesis | Beta | T | p-value | Decision |
|---|---|---|---|---|---|
| H1 | GPI → GU | 0.362 | 7.057 | 0.000 | Significant |
| H2 | PP → GU | 0.665 | 10.006 | 0.000 | Significant |
| H3 | GU → PE | 0.720 | 10.552 | 0.000 | Significant |
| H4 | GU → PW | 0.783 | 9.548 | 0.000 | Significant |
| NO | Hypothesis | Beta | T | p-value | Decision |
|---|---|---|---|---|---|
| GPI → GU | 0.362 | 7.057 | 0.000 | Significant | |
| PP → GU | 0.665 | 10.006 | 0.000 | Significant | |
| GU → PE | 0.720 | 10.552 | 0.000 | Significant | |
| GU → PW | 0.783 | 9.548 | 0.000 | Significant |
Note(s): p < 0.001, GPI - gadget promotion and influence, PP - peer pressure, GU - gadget usage, PE - physical effect, PW - psychological well-being
The mediating effects
| No | Indirect path | Indirect effect (β) | Significance level (p) | Decision |
|---|---|---|---|---|
| H5 | PP → GU → PE | 0.263 | <0.005 | Supported |
| H6 | PP → GU → PW | 0.265 | <0.005 | Supported |
| H7 | GPI → GU → PE | 0.113 | <0.005 | Supported |
| H8 | GPI → GU → PW | 0.113 | <0.005 | Supported |
| No | Indirect path | Indirect effect (β) | Significance level (p) | Decision |
|---|---|---|---|---|
| PP → GU → PE | 0.263 | <0.005 | Supported | |
| PP → GU → PW | 0.265 | <0.005 | Supported | |
| GPI → GU → PE | 0.113 | <0.005 | Supported | |
| GPI → GU → PW | 0.113 | <0.005 | Supported |
Note(s): GPI - gadget promotion and influence, PP - peer pressure, GU - gadget usage, PE - physical effect and PW - psychological well-being
The results indicate a significant positive relationship between gadget promotion and influence and gadget usage (H1), (T = 7.057, β = 0.362, p < 0.000). Despite the low effect size, it highlights the influence of digital marketing on consuming behaviors, especially among a generation sensitive to online trends and commercialized social media material.
Peer pressure had a more significant impact on gadget usage (H2), indicating that young people are more influenced by their peers than by advertisements (T = 10.006, β = 0.665, p < 0.000). Gadgets often serve as social artifacts that signify status, belonging and identity, prompting young individuals to embrace new technologies or activities to avoid exclusion or criticism.
A significant correlation was identified between gadget usage and physical. Effects (H3), confirming prior concerns over the physical consequences of excessive screen exposure (T = 10.552, β = 0.720, p = 0.000). With 31% of individuals utilizing devices for over five hours daily, this correlation emphasizes the need to regulate screen time and inform consumers regarding digital health.
Lastly, the most significant correlation was identified between gadget usage and psychological well-being (H4), (T = 9.548, β = 0.783, p < 0.000). Young individuals may more pursue emotional solace or diversion through devices, leading to behavioral addiction and diminished resilience to psychological stress.
The mediating effect of gadget usage has been analyzed through the bootstrapping method. The findings demonstrate that gadget usage significantly mediates the association between peer pressure and physical effects (H5) (β = 0.263, p < 0.005), as well as between peer pressure and psychological well-being (H6) (β = 0.265, p < 0.005). Likewise, gadget usage mediates the correlation between the influence of gadget promotion and physical effects (H7) (β = 0.113, p < 0.005), as well as between gadget promotion influence and psychological well-being (H8) (β = 0.113, p < 0.005). All indirect effects are statistically significant, hence supporting the mediation hypotheses and confirming that gadget usage serves as a crucial behavioral channel connecting promotional and social factors to health-related outcomes in youth.
Figure 2 visually demonstrates the structural assessment of the study's results.
The structural model diagram features several labeled components: GPI, PP, GU, PW, and PE. Each component is connected to specific labeled items: GPI is connected to GPI1, GPI4, GPI3, GPI6, GPI5, and GPI2; PP is connected to PP7, PP6, PP5, PP4, PP3, PP2, and PP1; GU is connected to GU1, GU6, GU7, GU8, and GU9; PW is connected to PW2, PW3, PW5, PW6, PW8, and PW9; PE is connected to PE2, PE4, PE5, PE6, PE7, PE8, PE9, and PE10. Arrows indicate the direction of influence or relationship between these components, with numerical values representing the strength of these connections.Structural model. Source: Authors
The structural model diagram features several labeled components: GPI, PP, GU, PW, and PE. Each component is connected to specific labeled items: GPI is connected to GPI1, GPI4, GPI3, GPI6, GPI5, and GPI2; PP is connected to PP7, PP6, PP5, PP4, PP3, PP2, and PP1; GU is connected to GU1, GU6, GU7, GU8, and GU9; PW is connected to PW2, PW3, PW5, PW6, PW8, and PW9; PE is connected to PE2, PE4, PE5, PE6, PE7, PE8, PE9, and PE10. Arrows indicate the direction of influence or relationship between these components, with numerical values representing the strength of these connections.Structural model. Source: Authors
5. Discussion
5.1 Impact of gadget promotion on gadget usage
The results demonstrate that gadget promotion substantially enhances device utilization among youth. Digital marketing tactics, including influencer endorsements, targeted advertising and algorithm-driven content, significantly influence perceived necessity and behavioral engagement. Prior studies demonstrate that influencer marketing and tailored commercials markedly enhance technology adoption and screen engagement among young customers (Bozzola et al., 2022). Xu et al. (2023) assert that digital marketing serves as a behavioral catalyst that enhances cognitive connection to devices. Likewise, Lou et al. (2022) discovered that influencer credibility strengthens consumer trust and stimulates impulsive digital consumption, especially among young people. Consequently, gadget promotions as strong external stimulants influence persistent usage habits.
5.2 Influence of peer pressure on gadget usage
The results indicate that peer pressure significantly increases gadget usage, emphasizing the influence of social conformity on digital behavior. This aligns with Hoeben et al. (2024), who emphasize that FOMO substantially drives obsessive technology usage. FOMO is significantly correlated with excessive smartphone usage and social media dependence. Abu-Taieh et al. (2022) further illustrate that peer networks enhance digital adoption via normative pressure and social comparison. Consequently, the utilization of gadgets is not solely utilitarian but rather socially built through reinforcement of behaviors prompted by peers.
5.3 Effects of gadget usage on physical health
The findings indicate that excessive gadget usage adversely impacts physical health, namely leading to eye strain, musculoskeletal pain, sleep disruption and diminished physical activity. Prior research consistently indicates that prolonged screen exposure results in “tech neck,” sleep disorders and repetitive strain injuries (Priftis and Panagiotakos, 2023). Al-Mohtaseb et al. (2021) discovered that DES is significantly common among extensive screen users. Moreover, Clayton and Siderov (2022) emphasize that exposure to blue light interferes with circadian cycles, resulting in sleep disturbances. Bahadoran et al. (2023) demonstrated that diminished physical activity resulting from screen dependency causes obesity and weariness. These data validate the aggregate physical strain associated with excessive device usage.
5.4 Influence of gadget usage on psychological well-being
The research indicates that excessive use of devices markedly diminishes psychological well-being, heightening anxiety, emotional fatigue and FOMO. Elhai et al. (2021) discovered a significant correlation between problematic smartphone usage and anxiety, as well as emotional dysregulation. Nesi et al. (2022) emphasize that social media comparison contributes to depression symptoms and diminishes self-esteem. Montag et al. (2021) contend that digital dependency alters reward processing in the brain, hence promoting compulsive behavior. Furthermore, online interactions frequently supplant profound social bonds, increasing loneliness and mental instability. These findings highlight the psychological impact of excessive digital activity.
5.5 The mediating role of gadget usage
The results indicate that gadget usage mediates the connection between external factors (promotion and peer pressure) and health outcomes. This suggests that marketing and social influences do not directly influence well-being but rather function through the intensity of behavioral involvement.
This corresponds to behavioral mediation models, indicating that external cues affect outcomes via using behavior (Genc and Pirincci, 2023). Gu and Duan (2024) demonstrate that digital marketing enhances engagement, resulting in behavioral reliance. Abu-Taieh et al. (2022) assert that peer influence indirectly affects psychological and physical consequences by enhancing usage. Consequently, excessive gadget usage serves as a primary behavioral conduit connecting environmental stimuli to well-being results.
6. Implications
6.1 Theoretical implication
This research enhances the theoretical understanding of youth digital behavior by expanding the stimulus–organism–response (S–O–R) paradigm in the context of digital consumption. The results indicate that gadget promotion and peer pressure serve as external factors that markedly enhance gadget usage, which in turn affects both physical and mental health (Kim et al., 2024). This endorses a sequential framework connecting environmental and social determinants to behavioral and health outcomes (Hatuka et al., 2020).
The findings further support social conformity theory by demonstrating that adolescent technology usage is influenced by peer pressure and the desire for social acceptance (Hoeben et al., 2024). Moreover, the mediating function of gadget usage elucidates how external stimuli convert into negative consequences via behavioral involvement. This aligns with previous research suggesting that excessive gadget usage is a significant contributor to psychological distress and reliance (Elhai et al., 2021). Consequently, the study enhances understanding by integrating marketing, social and health viewpoints into a cohesive framework.
6.2 Practical implications and mitigation strategies
This study's findings considerably enhance the broader discussion on youth well-being in the digital era and offer practical insights for various stakeholders:
Parents should diligently supervise and direct their children's online engagements. Implementing screen-time restrictions, encouraging transparent discussions regarding online conduct and advocating for outdoor or family-centered activities might mitigate excessive device usage and its psychological and physical consequences.
Schools and universities need to incorporate digital literacy and responsible technology use into their courses. Educators can raise awareness of the risks associated with misuse and promote co-curricular activities that enhance physical engagement and real-world interaction to mitigate digital dependency.
Regulatory authorities must implement stricter rules on aggressive digital marketing aimed at young individuals. Policies must guarantee transparency, restrict age-inappropriate advertisements and advocate for ethical marketing on social media sites.
Developers and technology designers must incorporate wellness-oriented features, including screen-time notifications and digital well-being reminders. Emphasizing ethical design that prioritizes user well-being over engagement metrics is crucial for reducing digital addiction among young customers.
7. Limitations and future research
The findings of this study are reliable, although they have limitations. The study used self-reported data, which may be influenced by memory recall and social desirability. The study focused on young Bangladeshis, restricting its applicability to other age groups and cultures. The cross-sectional approach limits the causation between gadget promotion, peer pressure and physical and psychological outcomes.
Future studies should use a more diverse and worldwide population to confirm these findings across socio-economic and cultural contexts. Longitudinal research would reveal how digital behavior patterns change over time and affect psychological and physical health. Analyzing digital literacy, parental mediation and personality features may identify protective factors that offset the harmful effects of excessive gadget use. To improve young well-being in high-digital usage conditions, researchers should examine intervention-based studies such as digital detox programs or mindfulness training.
8. Conclusion
This study examined the influence of gadget promotion and peer pressure on gadget usage, along with the consequences of excessive usage on the physical and psychological health of young individuals in Bangladesh. The findings demonstrate that commercial exposure and social influence significantly increase gadget engagement among youth. Furthermore, the results demonstrate that excessive device usage is associated with considerable physical discomfort and psychological suffering. The study identified device usage as a mediating factor via which promotional and social influence impact health-related outcomes.
These findings underscore the growing impact of digital marketing and peer interactions on the technology activities of young individuals. This study contributes to the expanding literature on internet consumption and youth well-being by examining these relationships within the context of a developing nation. The findings highlight the importance of promoting proper device usage and raising awareness about the potential health effects of excessive digital engagement.
The authors gratefully acknowledge Ms Samina Huq for her valuable assistance in data collection, proofreading of the manuscript, and administrative coordination. Her support was instrumental in facilitating the successful completion of this study.

