A Systematic Review of Longitudinal Studies on Youth Internet Behavior Using the Positive Youth Development Frameworks

ABSTRACT

Background

Positive youth development (PYD) is a strength‐based approach that promotes positive outcomes to support the well‐being of youth. With digital technology becoming central to adolescents' lives, understanding how PYD is related to internet behaviors is crucial. The current study addresses this growing need by systematically reviewing the studies that examined how PYD‐related constructs are associated with youth internet behaviors over time.

Method

This systematic review synthesized findings from 10 longitudinal studies identified from 794 screened records that examined the association between PYD‐related variables and youth internet behaviors. Following PRISMA guidelines, the methodological quality of the studies was assessed using the Joanna Briggs Institute checklist for cohort studies. A narrative synthesis approach was employed to integrate findings.

Result

Findings indicate that high PYD attributes, developmental assets, and school assets were shown to negatively predict problematic internet behaviors, including internet addiction, internet gaming disorder, and cyberbullying victimization, both concurrently and longitudinally. Importantly, these associations were rarely direct. Positive mediators and moderators strengthened the protective effects of PYD, whereas adverse psychological states and contextual risks weakened these associations.

Conclusion

This review highlights that PYD functions mainly as a protective developmental resource operating through indirect and conditional pathways in digital contexts. However, the literature remains heavily focused on risk‐oriented internet outcomes, with limited attention to positive forms of digital engagement. Conceptual inconsistencies in PYD operationalization and a strong geographic concentration of studies further underscore the need for more robust, theoretically integrated, and cross‐cultural longitudinal research.

1. Introduction

Youth play a crucial role in any country's development. It is the collective responsibility of parents, teachers, counselors, and the community to equip them with positive characteristics that help them in their personal and social development, enabling them to contribute meaningfully to society. When discussing youth and their development, positive youth development (PYD) is one of the approaches that comes into the picture for many developmental and positive psychologists (Larson 2000; Lerner et al. 2009). It emerged from the works of comparative psychologists and biologists (Silbereisen and Lerner 2007). Though no single definition captures the entire conceptual scope (Benson et al. 2006), PYD is considered a strength‐based approach, and it emphasizes “manifesting potentialities rather than the supposed incapacities of young people—including those from the most disadvantaged backgrounds and with troubled histories (p. 15)” (Damon 2004).

1.1. Positive Youth Development Frameworks

There has been growing interest among researchers in PYD for the last two decades. Though it is a Western concept, countries have made an attempt to adapt this concept to be relevant to their cultural context (Ataç et al. 2024; Catalano et al. 2002). Most of the research in this area has been conducted in countries such as the U.S., China, and Canada. Researchers vary in their evaluative approach since there is no commonly agreed‐upon definition. Furthermore, various models have been developed to study this concept, focusing generally on youth potential and developmental plasticity (Lerner et al. 2005a).

The common frameworks/models used by scholars to study PYD include the developmental assets framework (Benson et al. 2006), 5Cs/6Cs/7Cs model (Dimitrova et al. 2021; Lerner et al. 2005a), 15 PYD constructs (Catalano et al. 2002), social‐emotional learning (Tolan et al. 2016), and the being perspective (Shek et al. 2019) by emphasizing its roots in deficits view of adolescents through storm and stress notion (Hall 2004), human strengths (Steinberg and Lerner 2004), and the ecological perspective (Bronfenbrenner and Morris 1998) (for more theoretical models check [Lerner et al. 2011]). Due to its evolving definitions and models, researchers have worked extensively to study PYD effectively. What began as a qualitative exploration of its theoretical foundations has now evolved into standardized measurement approaches, with a growing emphasis on applying PYD in practical intervention programs (Qi et al. 2022).

There have been numerous systematic, scoping, and conceptual reviews on PYD over time. Earlier research primarily focused on the role of PYD in sexual and reproductive health (Gavin et al. 2010) and examined the inclusion of various variables, such as social competence (Ma 2012), spirituality (Shek 2012), and self‐efficacy (Tsang et al. 2012), as PYD constructs. More recently, the focus has shifted toward promoting PYD through sports‐based interventions (Bruner et al. 2023), and exploring PYD among marginalized populations (Chowa et al. 2023). To really understand PYD, it's important to look at the environment young people are growing up in (Wiium and Dimitrova 2019). Since today's youth are born into a world surrounded by digital media, it is important to explore how these online spaces shape their development from youth and stakeholder perspectives (Ross and Tolan 2021).

Importantly, PYD models can be understood in two ways: (1) as a framework or approach to youth development that informs programs and policies, and (2) as a set of developmental indicators or outcomes (e.g., confidence, connection, character) that promote healthy adjustment. In this review, we focus on the second usage, specifically examining how measurable PYD constructs are associated with youth behavior over time. This focus is adopted because the majority of existing studies (Gan et al. 2022; Wang et al. 2023) operationalize PYD through quantifiable constructs rather than program‐ or intervention‐driven applications, particularly in the digital context.

1.2. Positive Youth Development and Internet Behavior

Over the years, studies on youth and their digital engagement have provided valuable insights that are beneficial for policy‐making, designing training programs, and delivering therapy (Byrne and Burton 2017; Romero Saletti et al. 2021). While digital tools have shown positive effects (Regmi and Jones 2020) and are increasingly used in therapeutic settings (Datti et al. 2024), researchers from various countries have focused highly on risk factors (Gao et al. 2022) and negative effects of internet usage (Anderson et al. 2017) on diverse samples (Normand et al. 2022). Similarly, despite the need to explore youth development by considering the digital context, limited research has examined how different PYD attributes may relate to distinct forms of internet engagement (Gómez‐Baya et al. 2022), including whether these associations are uniformly protective or contextually differentiated.

From the limited pool, most studies exploring the link between PYD and internet behavior have used qualitative (Watson et al. 2019; Bopp and Vadeboncoeur 2022), quantitative (DeSouza et al. 2022; Gómez‐Baya et al. 2022), or mixed‐method designs (Lee and Suzanne Horsley 2017), with a heavy reliance on cross‐sectional data and limited experimental work (Favini et al. 2024; Vives‐Cases et al. 2019). Furthermore, a large amount of literature on PYD has been accumulated from the “P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes)” project (Shek et al. 2012) and the 4‐H project (Lerner et al. 2005b). While the P.A.T.H.S. project designed “digital positive youth development games” for the prevention of problematic behaviors (Lee et al. 2019), the 4‐H project used longitudinal approaches to examine youth behavior on social networking platforms (Lee and Suzanne Horsley 2017). However, a key limitation is that both projects concluded over a decade ago, in 2012, limiting the applicability of the findings to evolving life on the internet.

Given the evolving nature of both youth development and digital engagement, a major conceptual limitation in the existing literature is the over‐representation of cross‐sectional studies (DeSouza et al. 2022; Gómez‐Baya et al. 2022), which conceptualize PYD‐internet usage associations as static rather than a developmental process. These designs are often criticized for their limitations in establishing temporal ordering or testing developmental mechanisms and providing biased or misleading inferences (Kraemer et al. 2000; Maxwell and Cole 2007). By synthesizing only longitudinal studies from the past decade, this review addresses the conceptual gap by examining how PYD influences youth internet behavior over time, rather than examining the existence of associations at a single point in time.

1.3. Research Question and Objectives

From the conclusions drawn from the discussion so far, with the help of “Population (P), Intervention (I)/Exposure (E), Comparator (C), and Outcome (O)” (PI/ECO) framework (Counsell 1997), the current study developed the following primary review question: What is currently known about the longitudinal (C) association between PYD‐related constructs (E) and internet behavior (O) among youth (P)?

With the help of the above review question, the current study aims to address the following objectives:

• 1.Provide an overview of the PYD theoretical frameworks and measurement tools used by scholars to explain the relationship between PYD and internet use.
• 2.Summarize the effect of PYD models on internet behaviors concurrently and longitudinally.
• 3.Identify key moderating and mediating factors (e.g., self‐control, school assets, life satisfaction) that influence PYD's association with internet behavior.

2. Methods

The current study aims to synthesize the longitudinal studies that examine how PYD constructs relate to youth internet behavior over time. This focus aligns with PYD's concept as a developmental framework that emphasizes individual strengths, adaptability, and contextual change. Longitudinal designs are particularly relevant here, as they capture the dynamic nature of youth development and allow for examining how PYD‐related attributes relate to digital behaviors across time.

Although the number of available studies was limited and geographically concentrated, with all studies conducted in China and eight authored by a single research group, a few studies used the same dataset (Gan et al. 2023a; Gan et al. 2023b; Gan et al. 2023c; Xiang et al. 2022; Xiang et al. 2024a), the 10 included studies provide valuable longitudinal evidence. Each study, with its unique methodological or conceptual insights, has helped us understand the application of PYD in the digital context. While we considered including cross‐sectional studies, we ultimately decided against it to maintain study quality. Many of the available cross‐sectional studies had a limited focus on internet behavior (Bopp and Vadeboncoeur 2022), lacked full‐text access (Lee et al. 2019), were qualitative (Watson et al. 2019), or placed minimal emphasis on the PYD framework (Williford et al. 2021). Therefore, we restricted our review to longitudinal studies only.

Since this study primarily focuses on synthesizing empirical research that addresses the review objectives, rather than conducting a quantitative analysis of study effectiveness (i.e., meta‐analysis), we employed a systematic review approach in this review (Littell et al. 2008). We adhered to the “Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA)” checklist at each stage of the review to ensure that the review was conducted by following the guidelines (Page et al. 2021). The current review was also registered with PROSPERO in September 2024.

2.1. Eligibility Criteria

The eligibility criteria for the current study are designed with the help of “PICO elements for writing review questions,” as highlighted in the Cochrane Handbook (McKenzie et al. 2023). We have undertaken the study selection and the screening process based on the following inclusion and exclusion criteria. The inclusion criteria included:

• Longitudinal studies without restrictions on follow‐up durations—along with the reasons mentioned at the beginning of the methodology section for considering longitudinal studies, we haven't kept any restrictions on follow‐up durations, as this prevents the inclusion of significant findings due to varying time frames, and this also allows us to check if follow‐up duration influences the observed effects;

• Studies that consider both the PYD framework as a guiding theoretical model and any of the positive or negative internet behaviors of youth—here, positive internet behaviors refer to constructive online engagements that promote learning, health, or social participation (e.g., digital education, health tracking, or civic activities), while negative internet behaviors refer to maladaptive or risk‐oriented activities (e.g., cyberbullying, problematic internet use, or excessive gaming). We included both categories to evaluate the effectiveness of PYD factors in promoting positive digital engagement and mitigating online risks. Furthermore, studies were required to explicitly reference a recognized PYD model;

• Studies conducted (different from those published) since 2014—as the internet and youth behavior are the two most unstable aspects that change with time, we considered only those studies that have been conducted over the last decade to ensure the relevance to the current landscape. Furthermore, though the concept of PYD emerged in the early 2000s (Larson 2000) and its application to digital technologies began in 2006 (Umaschi Bers 2006), earlier studies have examined different forms of internet access, platforms, and patterns of use (Lee et al. 2019; Lee and Suzanne Horsley 2017). Including such studies would risk conflating developmentally and technologically distinct contexts;

• Studies with participants aged 10–29 years—although the United Nations (UN) defines youth as those aged 10–24 years and adolescents as those aged 10–19 years, every country has its own definition of youth and adolescents (e.g., India—15–29 years as youth and 10–19 years as adolescents; China—14–35 as youth and 10‐19 years as adolescents; EU—15–29 years as youth and 10–24 years as adolescents). Furthermore, studies conducted using the PYD framework have considered varying age groups (Jiang and Wang 2025 [Mean: 19.12; SD: 1.21]; Marín‐Gutiérrez et al. 2024 [Mean: 14.95, SD: 1.81]; Wang et al. 2023 [Mean: 10.60]). Given these factors, we have decided to consider this broader criterion;

• Studies published in English only—since none of the authors are well‐versed in other languages, we restricted the content to English only.

The exclusion criteria included:

• Apart from longitudinal studies, all the other quantitative, qualitative, pilot, and review study designs—to ensure a focused and methodologically consistent review and decrease complexity due to the mix of study designs, we have only restricted the inclusion criteria to one study design;

• Studies without a PYD model and Internet component—since the main objective of the study is to observe the relationship between PYD and Internet behavior, studies that addressed only one or none of the components were excluded;

• Studies conducted before 2014 and after August 2024—as the formal searching process began in August 2024, we have not included any studies published beyond this month.

• Studies published in languages other than English

We proceeded to the next step of the review process, that is, searching and screening, by considering the inclusion and exclusion criteria mentioned above.

2.2. Search Strategy

An extensive literature search was conducted in August 2024 across six databases to find peer‐reviewed empirical articles. These databases include Scopus, EBSCO Medline, Web of Science, PubMed, and Cochrane. Since grey literature is also crucial in conducting a comprehensive review (Paez 2017), master's and doctoral dissertations published in the Proquest database were also considered. The initial search was conducted in PubMed to identify the MeSH terms for the two main keywords: youth development and the Internet. A comprehensive search strategy (see Supporting Information S1 [Table S1]) was created using MeSH words and other related terms for the keywords. A minor modification to this strategy was made while searching various databases.

Records from each database are stored in Mendeley software (https://www.mendeley.com/) by creating a separate folder for each database for easy reference. The search resulted in 794 documents, comprising 248 articles retrieved from Scopus, 114 from EBSCO Medline, 185 from Web of Science, 132 from PubMed, 20 from Cochrane, 89 from ProQuest (65 doctoral theses and 24 master's dissertations), and six through manual screening. All 794 extracted references were downloaded simultaneously with the.ris extension. SA and AT conducted the database research, and discrepancies encountered during this process were resolved through discussion.

2.3. Screening

During this step, all the records downloaded in the previous step were imported into the Covidence web application (Covidence 2025) to facilitate the screening process. Covidence allows for systematic screening of articles by following the PRISMA 2020 statement (Page et al. 2021). During the first step of this process, 422 duplicates were removed, and the remaining 372 articles were selected for title and abstract screening. During the screening process, 300 articles were removed as they were entirely unrelated to PYD/internet, and 72 articles were selected for full‐text screening. As shown in Figure 1, due to various reasons, 62 articles were removed during the last step, and 10 articles were included in the final review. This complete screening process was carried out by two reviewers (SM and SA), and the inconsistencies were discussed until a consensus was reached.

FIGURE 1.

PRISMA flow diagram.

2.4. Data Extraction

As shown in Table 1, information gathered from the final 10 articles selected during the screening process has been categorized into five sections (for a complete extraction table, see Supporting Information S2 [Table S3]). The identifying information section provides details on the study's publication year, journal, and country, helping to assess research trends in this domain. The study design and data collection section help to understand the common methodologies used while studying this population. The data analysis section captures key aspects that could help us assess the overall quality of each study. The findings section highlights the primary and secondary results, while the conclusion section sheds light on existing gaps and potential areas for further exploration. Two reviewers (AT and RS) carried out the data extraction process under the third reviewer's (RD) supervision.

TABLE 1.

Data extraction.

Study identification	Study design & Data collection	Data analysis & management	Key variables & findings	Conclusions
Title Authors, Year of Publication Study Conducted Year Journal Country	Study Setting Sample Size Gender Age Inclusion and Exclusion Criteria Follow‐up Duration Measurement Tools	Theoretical Framework Statistical Analysis Ethics & Informed Consent Attrition Rates and Missing Data	Independent Variables Dependent Variables Mediating/Moderating Variables Primary Findings Secondary Findings	Limitations Future Directions Implications Quality Appraisal

2.5. Quality Assessment

The quality assessment of the 10 studies included in this systematic review was conducted using the “Joanna Briggs Institute's (JBI) Critical Appraisal Checklist for Cohort Studies (Moola et al. 2024).” This checklist enables reviewers to evaluate the internal validity, reliability, and risk of bias in longitudinal studies using 11 questions, with possible answers being “yes,” “no,” “unclear,” and “not applicable.” Two reviewers (NM and RD) independently assessed the studies, with a third reviewer (RS) resolving any disagreements that arose. To determine the overall risk of bias (i.e., the likelihood that methodological limitations within a study could distort its findings or interpretations), we adopted a scoring method aligned with the “Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach,” which has been used in previous reviews (Melo et al. 2018). Studies were classified as follows:

• There is a low risk of bias if more than 70% of responses were “yes” or “not applicable.”

• There is a moderate risk of bias if 50%–69% of responses fall into these categories.

• There is a high risk of bias if fewer than 50% of responses were “yes” or “not applicable.”

We have considered “not applicable” as a positive response in our assessment, as the checklist, similar to many other existing checklists (such as Newcastle–Ottawa Scale [NOS] and the Critical Appraisal Skills Programme [CASP] checklist), is designed for cohort‐ or intervention‐based longitudinal studies, and not all longitudinal studies meet these specific criteria.

2.6. Data Synthesis

Data synthesis is the process of summarizing and categorizing the data extracted from individual studies that address the review questions. While data can be synthesized quantitatively and qualitatively, this review adopts a qualitative narrative synthesis of results and a descriptive summary of numerical findings without conducting an in‐depth statistical analysis. The review follows the “elements in narrative synthesis” outlined by Popay et al. (2006) to guide this process. This process involves (i) understanding the role of theory in evidence synthesis, (ii) developing an initial description of results and identifying the influencing factors, (iii) exploring the relationship within and between the studies, and (iv) assessing the quality of included studies.

Using the above‐mentioned approach, we outline how the PYD models have been applied to adolescent internet behavior by discussing theories and their emphasis in the study. Next, we synthesize findings by identifying psychological factors influencing online behaviors and their association with PYD. We then explore relationships within and between studies, analyzing patterns, differences, and the role of mediating and moderating variables in shaping these dynamics. Finally, we assess the quality of included studies to ensure credibility, highlight research gaps, and identify areas requiring further exploration. MS, RD, and NM synthesized the findings.

3. Results

In this section, we provide a comprehensive account of observations from all the included studies in the review, using the narrative synthesis approach. Starting with an overview of the study's significant characteristics, we will present the observations made while examining the review questions, using tables and images for clarity.

3.1. Characteristics of Included Studies

All the studies included in the review were conducted in China, with eight studies conducted in Hubei province. Among the included studies, eight of the 10 studies were authored by Xiong Gan, followed by Xin Jin with seven, Guo‐Xing Xiang with six, Ke‐Nan Qin with four, and Yan‐Hong Zhang, Cong‐Shu Zhu, and Min Li with two studies each. When looking into the internet behaviors studied, eight studies focused on internet gaming disorder, two studies focused on cyberbullying victimization, and two studies focused on internet addiction, indicating the positive side of internet usage (similar to methods mentioned in Umaschi Bers 2006) is underexplored during the last decade using longitudinal methods.

As shown in Table 2, most of the included studies were published between 2020 and 2022, with follow‐up durations ranging from a minimum of 5 months to a maximum of 1 year. Attrition rates were higher (more than 10%) in studies with a 1‐year gap or those conducted over multiple follow‐ups (2–3 times). However, the authors reported that these attrition rates did not introduce bias and had no significant influence on observed results, ensuring the methodological soundness and validity of the studies despite participant dropout. While not all studies provided comprehensive details on their sample sizes, the available data indicate that at the start of each study, the sample sizes ranged from a minimum of 789 to a maximum of 3010 participants, with a dropout rate ranging from 3% to 30% of the initial sample.

TABLE 2.

Methodological characteristics of included studies.

Author and Year	Study conducted year	Follow‐up duration	Sample size	Gender	Age	Attrition rates and dropoutsa
Dou and Shek (2021)	2016, 2017	One year	3010 in Wave 1 (1362 Grade 7 students; 1648 Grade 8 students) 2648 in Wave 2 (1305 Grade 8; 1343 Grade 9 students)	57.1% boys (1513), 41.9% girls (1109), and 1.0% did not report their gender (26)	Mean age ‐ 13.12 years (SD = 0.81)	The attrition rate was 4.19% for Grade 7 and 18.51% for Grade 8, with 362 students dropping out between Wave 1 and Wave 2. No significant differences were found between dropouts and non‐dropouts. Although dropouts scored higher on some PYD subscales, the small effect sizes (Cohen's d = 0.16–0.36) suggest minimal attrition bias.
Wang et al. (2023)	December 2019, June 2020	6 months	7985 (Wave 1) Wave 2 details are not mentioned	51.65% boys (4124)	Mean age ‐ 10.60 years (SD = 2.18)	Not mentioned
Gan et al. (2023a)	November 2020, May 2021	6 months	995 in Wave 1 962 in Wave 2 Final sample: 956	45.71% boys (437)	Mean age ‐ 16.03 years (SD = 0.75)	33 students dropped off from Wave 1 to Wave 2, and 39 were removed from Wave 2 only for various reasons, resulting in an overall attrition rate of 3.92%.
Gan et al. (2023b)	November 2020, May 2021, November 2021	2 times ‐ 6 months follow‐up	802 in Wave 1 746 in Wave 2 719 in Wave 3	45.2% boys (325 in 719 adolescents)	14–18 years old (Mean Age ‐ 15.95 years, SD = 0.76)	Attrition rates and missing data were not explicitly reported; however, interpretations from available data suggest participant drop rates of 56% (W1‐W2), 27% (W2‐W3), and 83% (W1‐W3). While attrition did not significantly affect the study's primary variables, higher depression scores among dropouts suggest a potential bias.
Gan et al. (2023c)	November 2020, May 2021, November 2021	2 times ‐ 6 months follow‐up	995 in Wave 1 No mention of the Wave 2 sample 719 in Wave 3	b45.2% boys (325 in 719 adolescents)	14–18 years old (Mean Age ‐ 15.95 years, SD = 0.76)	A total of 276 participants dropped out between Wave 1 and Wave 3. However, chi‐square tests suggested that attrition did not introduce bias.
Xiang et al. (2024a)	October 2020, April 2021	6 months	1023 in Wave 1 993 in Wave 2	b49.4% boys (505) in Wave 1 b49.6% boys (493) in Wave 2	Mean age ‐ 13.16 years (SD = 0.86)	Thirty participants dropped out, resulting in an attrition rate of 2.93%. Chi‐square and t‐tests indicated no attrition bias.
Xiang et al. (2022)	October 2020, April 2021	6 months	1023 in Wave 1 993 in Wave 2	b49.4% boys (505) in Wave 1 b49.6% boys (493) in Wave 2	Mean age ‐ 13.16 years (SD = 0.86)	Thirty participants dropped out, resulting in an attrition rate of 2.93%. Chi‐square and t‐tests indicated no attrition bias.
Gan et al. (2022)	September 2021, January 2022	5 months	796 in Wave 1 768 in Wave 2	b53.8% boys (428) and b46.2% girls (368) in Wave 1, Wave 2 details were not given	Wave 1: Mean Age ‐ 13.91 years (SD = 2.01) Wave 2 details were not given	Twenty‐eight students dropped out, with an attrition rate of 3.52%. Chi‐square and t‐tests showed no significant differences between the final sample and dropouts, indicating no attrition bias.
Xiang et al. (2024b)	September 2021, January 2022	5 months	789 in Wave 1 738 in Wave 2	b53% boys (418) in Wave 1; no information on Wave 2	Wave 1: Mean age ‐ 14.00 years (SD = 2.05) Wave 2 details were not given	Fifty‐one students dropped out. Chi‐square and t‐tests indicated no significant difference between the final and dropped samples, indicating that the findings would not be biased due to attrition.
Qin and Gan (2023)	September 2021, January 2022	5 months	822 in Wave 1 742 in Wave 2	54.62% boys (449) and 45.38% girls (373) in Wave 1. 53.23% boys (395) and 46.77% girls (347) in Wave 2	12–18 years Final sample: Mean Age ‐ 13.88 years (SD = 1.99)	Eighty students dropped out, resulting in an attrition rate of 9.73%. Chi‐square and t‐tests indicated no significant difference between final and dropped samples, indicating that findings would not be biased due to attrition.

^a Dropout rates are mentioned for all the studies that have not mentioned their attrition rates.

^b Converted to percentage by calculating $\frac{\text{number of males}(\text{and}/\text{or})\text{females}}{\text{total number of participants in the respective wave}}$.

Furthermore, though most of the studies were skewed toward the boys' sample, the percentage difference is minimal, indicating that most included studies strived to maintain an equal gender ratio. Apart from Dou and Shek (2021), no study has included genders apart from males and females. The age of individuals who participated in the study ranged between 12 and 18 years, with a mean age of 13.98 years and a standard deviation of 1.3 years. It is worth noting that most of these studies placed limited emphasis on sociodemographic factors, often treating them primarily as control variables rather than exploring their direct influence. Though this review aimed to look at the past decade's research, we have observed that longitudinal studies on this topic have become prominent since 2016. The presence of study protocols for ongoing research suggests continued and growing interest in this area (Karim et al. 2021).

3.2. Quality Appraisal

Figure 2 presents the quality appraisal of all included studies. Among the 10 studies, nine demonstrated a low risk of bias (ranging from 72.7% to 90.9%), while one study fell into the category of medium bias (63.6%). In all the studies, exposure‐related questions were deemed not applicable, as none of these studies had exposures or exposed and unexposed groups. A common limitation across all the studies is that none have convincingly argued that their study timeframe is sufficient to generalize the findings. Each study acknowledged this as a limitation. Additionally, seven studies underreported reasons for participant dropout and had attrition rates exceeding 5%, while two did not provide details on confounding variables. Despite these weaknesses, all studies exhibited strengths in sample recruitment, conducting attrition bias analysis, and employing robust statistical techniques for data analysis.

FIGURE 2.

Quality appraisal using JBI's critical appraisal tool for cohort studies.

3.3. Theoretical Frameworks and Measurement Tools

The studies included in this review have incorporated various theories to explore the relationship between variables under investigation (see Supporting Information S1 [Table S2]). The variables studied, along with PYD, are internet gaming disorder (IGD), internet addiction, bullying and its forms, depression, life satisfaction, self‐control, and intentional self‐regulation. In this section, we discuss PYD and internet‐related theories and measurement tools, which address the first review objective.

3.3.1. Positive Youth Development

Positive youth development has been studied using the PYD attributes, developmental assets, and school assets. Most authors (Dou and Shek 2021; Wang et al. 2023; Gan et al. 2023a, 2023b, 2023c) have explored PYD attributes using Catalano's 15‐attribute model (Catalano et al. 2002). On the other hand, Gan et al. (2023c) have also incorporated Lerner's PYD theory (Lerner 2009) to explain the attribute's background and context. Furthermore, researchers have applied the Mindsponge theory (Vuong 2023), ecological systems theory (Bronfenbrenner and Morris 1998), and other theories to understand the relationship between PYD attributes and problematic behaviors, such as IGD, depression, and cyberbullying victimization (CBV). All the studies that examined PYD attributes measured them using the Chinese Positive Youth Development Scale (CPYDS) (original version by Radloff 1977 [Shek et al. 2007]).

Developmental assets, a measure of PYD, have been explored by researchers (Xiang et al. 2024a; Xiang et al. 2022) using the developmental assets framework (Benson 2006) and Lewin's behavior theory (Lewin 2005). Furthermore, researchers (Xiang et al. 2024a) have applied bioecological systems theory (Bronfenbrenner and Morris 2007) to examine the relationship between developmental assets and IGD. These studies have used the developmental assets profile (DAP) (Scales 2011) to measure the developmental assets (overall, internal, and external assets). School assets, another PYD measure, have often been considered by researchers (Gan et al. 2022; Xiang et al. 2024b; Qin and Gan 2023) as a derivative of developmental assets framework (Benson 2006) and were measured using the school assets subscale (SAS) from the Chinese version of the developmental assets profile (Chang et al. 2020).

3.3.2. Internet Behavior

The most explored internet behavior was Internet Gaming Disorder (IGD). IGD has been studied using various theoretical lenses, but it has been measured consistently using the Chinese version of the Internet Gaming Disorder questionnaire – adapted from Gentile (2009) (Yu et al. 2017)—across all eight studies (Gan et al. 2023a, 2023b, 2023c; Xiang et al. 2024a, 2022; Gan et al. 2022; Xiang et al. 2024b; Qin and Gan 2023). In addition to the IGD‐related theories mentioned in the previous section, researchers (Gan et al. 2023c) have examined the association between IGD and CBV using problem behavior theory (Jessor 1987).

IGD has also been widely studied in relation to school assets and is frequently considered a potential mediating variable. Researchers (Xiang et al. 2024b) have applied general strain theory (Agnew and White 1992) to check IGD's relation with school assets, while social‐control theory (Hirschi 2017) was used to examine IGD association with PYD attributes (Gan et al. 2023a) and also its role as a mediator for school assets and bullying (Gan et al. 2022). Furthermore, the selection, optimization, and compensation (SOC) model (Baltes 1997) has provided the theoretical basis for exploring IGD's serial mediation with intentional self‐regulation (ISR) on school assets and bullying (Gan et al. 2022).

Internet addiction, another prominently explored internet behavior, has been measured using the Internet Addiction Scale (Young 2009). Its association with PYD attributes has been examined by researchers (Dou and Shek 2021; Wang et al. 2023) through a combination of theoretical frameworks, including self‐determination theory (Deci and Ryan 2000), cognitive‐behavioral model (Davis 2001), identity theory (Stets and Burke 2000), and social‐control theory (Hirschi 2017). Although cyberbullying victimization has been less extensively studied, researchers have explored its relationship with PYD attributes, along with the theories mentioned above, by employing the comprehensive theoretical model of problem behavior (Guoli and Qi 2011) to examine CBV's association with PYD attributes (Gan et al. 2023b). CBV is typically measured using the E‐Bullying scale (Lam and Li 2013).

Although not explicitly discussed in this review, it is essential to note that researchers have also examined the association between PYD and internet behavior through various mediating variables guided by their respective theoretical frameworks. Furthermore, all the scales used in the included studies demonstrated strong Cronbach's alpha values, indicating high internal consistency. Additionally, the authors conducted advanced statistical analyses, including confirmatory factor analysis (CFA), common method bias analysis, model fit assessments, and reliability tests, to validate the strength of the models and ensure the reliability of the measures used.

3.4. Association Between Positive Youth Development and Internet Behavior

This section highlights the key findings from all the studies, as shown in Table 3, specifically focusing on PYD and internet behavior to address this review's second and third objectives. While the studies also report PYD and internet behavior's relationship with other variables, we haven't explored those findings in this review (presented unedited “findings” in Supporting Information S2 [Table S3]).

TABLE 3.

Relationship between positive youth development, internet behavior, and its mediating and moderating factors.

Author	PYD Factor	Internet behavior	Mediator/Moderator	Concurrent finding	Longitudinal finding	Mediator/Moderator finding
Dou and Shek (2021)	PYD attributes	Internet Addiction	Life satisfaction at W2 ‐ Mediator	All the PYD attributes at both waves were associated with lower levels of IA.	All PYD attributes at W1 predicted a reduction in IA at W2. Even after controlling for baseline IA, all the PYD attributes still had a protective effect, though very small over time.	Life satisfaction significantly mediated the relationship between all PYD dimensions, total PYD attributes, and IA.
Wang et al. (2023)	PYD attributes	Internet Addiction	Not Applicable	All the PYD attributes predicted lower IA levels at both waves, with general PYD showing the strongest effect.	All PYD attributes at W1 predicted lower internet addiction at W2, with general PYD having the strongest protective effect. Furthermore, higher total PYD at W1 reduced internet addiction at W2, while higher internet addiction at W1 led to lower total PYD (TPYD) at W2.	Not Applicable
Gan et al. (2023a)	PYD attributes	Internet Gaming Disorder (IGD)	Not Applicable	TPYD predicted lower IGD at both waves, even after controlling for gender, grade, and baseline IGD levels	TPYD at W1 negatively predicted IGD at W2.	Not Applicable
Gan et al. (2023b)	PYD attributes	Cyberbullying Victimization (CBV) and Internet Gaming Disorder	Internet Gaming Disorder – Mediator Depression – Moderator	Not discussed	[Interpreted from Mediation findings] TPYD at W1 negatively predicted IGD at W2 and CBV at W3	IGD at W2 significantly mediated the relationship between total PYD (TPYD) at W1 and CBV at W3. Depression moderated the relationship between TPYD and IGD.
Gan et al. (2023c)	PYD attributes	Cyberbullying Victimization and Internet Gaming Disorder	Depression, Cyberbullying Victimization, and Internet Gaming Disorder – Sequential Mediation	Not discussed	TPYD at W1 was linked to reduced CBV and IGD at W3, with small effect sizes	Depression at W2 mediated TPYD at W1 and IGD at W3. T3 IGD mediated T1 TPYD and T3 CBV. T3 CBV mediated T1 TPYD and T3 IGD. T2 Depression and T3 IGD together mediated the effect of T1 TPYD on T3 CBV.
Xiang et al. (2024a)	Developmental Assets (Internal and External)	Internet Gaming Disorder	Internal Assets – Mediator	W1 total developmental assets (DA) negatively predicted W1 IGD after controlling for age and gender. (linear relationship, not a quadratic relation)	W1 total DA negatively predicted W2 IGD (controlled for W1 IGD, Age, and gender). (linear relationship, not a quadratic relation)	The relationship between W1 external assets and W2 IGD is fully mediated by W2 internal assets.
Xiang et al. (2022)	Developmental Assets (Overall)	Internet Gaming Disorder	Self‐Control – Mediator Gender – Moderator	Not discussed	[Interpreted from Mediation findings] W1 cumulative DA negatively predicts the W2 IGD.	The relation between W1 cumulative DA and W2 IGD is partially mediated by W1 self‐control. No moderating effect of gender was observed for the mediation model.
Gan et al. (2022)	School Assets	Internet Gaming Disorder	Intentional Self‐Regulation (ISR) and Internet Gaming Disorder – Mediator	Not discussed	[Interpreted from Mediation findings] W1 school assets predicted lower W2 IGD	W1 school assets increased W2 ISR, which then reduced W2 IGD, ultimately decreasing bullying at W2. (serial multiple mediation model)
Xiang et al. (2024b)	School Resources (SR)	Internet Gaming Disorder	Self‐Control (SC), IGD, School Bullying (SB), and School Victimization (SV) – Mediator	School resources at W1 and W2 negatively predicted W1 and W2 IGD	W1 school resources negatively predicted W2 IGD	W1 SC mediated the relation between W1 SR and W2 SB, SV, and IGD. W2 SV mediated the relation between W1 SR, W2 IGD, and W2 SB. W2 IGD mediated the relation between W1 SR, W2 SB, and W2 SV. W1 SC and W2 SB sequentially mediated the relation between W1 SR, W2 SV, and W2 IGD. W1 SC and W2 IGD chain mediated the relation between W1 SR, W2 SV, and W2 SB.
Qin and Gan (2023)	School Assets (SA)	Internet Gaming Disorder	Self‐Control (SC) – Mediator Intentional Self‐Regulation (ISR) – Moderator	Not discussed	[Interpreted from Mediation findings] W1 SA negatively predicted W2 IGD	W1 SC mediated the relation between W1 SA and W2 IGD. High W1 ISR strengthened the positive impact of W1 school assets on W1 self‐control, reducing both W2 traditional bullying and W2 IGD. When ISR was low, these protective effects were not significant,

Note: Components of PYD attributes are cognitive behavioral competence, prosocial attribute, positive identity, and general PYD; W1: Wave 1; W2: Wave 2; W3: Wave 3.

3.4.1. Concurrent Findings

Before discussing the findings, it is essential to note that five of the 10 studies (Gan et al. 2023b; Gan et al. 2023c; Xiang et al. 2022; Gan et al. 2022; Qin and Gan 2023) did not report within‐wave (concurrent) findings, reflecting differences in study objectives rather than a study design. Specifically, studies that examined the role of mediating and moderating variables did not discuss concurrent findings, even indirectly. Among the remaining five studies, two have studied internet addiction with PYD attributes, while the other three examined IGD in relation to PYD attributes, developmental assets, and school resources, respectively.

Studies on internet addiction (Dou and Shek 2021; Wang et al. 2023) have observed that all PYD attributes, including cognitive‐behavioral competence, prosocial attributes, positive identity, and general PYD, were associated with lower levels of internet addiction during both waves. Among these attributes, higher general PYD was shown to strongly predict lower internet addiction levels (Wang et al. 2023). Researchers have also observed that stronger PYD traits are linked to lower IGD. Higher total PYD (TPYD) predicted less IGD at both waves, even after accounting for gender, grade, and wave 1 IGD levels (during wave 2) (Gan et al. 2023a). Similarly, having more developmental assets at the first wave was associated with lower IGD in a linear pattern, with no evidence of a quadratic relationship, even when age and gender were controlled (Xiang et al. 2024a). School resources also played a protective role, with more resources at both waves predicting lower IGD at the same time points (Xiang et al. 2024b).

3.4.2. Longitudinal Findings

Like the concurrent findings, four of the 10 studies (Gan et al. 2023b; Xiang et al. 2022; Gan et al. 2022; Qin and Gan 2023) did not explicitly report longitudinal findings. However, depending on the mediation results, we have tried to gather longitudinal results. Longitudinal findings from internet addiction studies (Dou and Shek 2021; Wang et al. 2023) revealed that stronger PYD attributes at Wave 1 were associated with a reduction in internet addiction at Wave 2. Even after accounting for baseline internet addiction, these attributes still had a minimal but positive protective effect over time (Dou and Shek 2021). Among them, general PYD was the strongest protective factor. Interestingly, the relationship is bidirectional, that is, higher PYD at Wave 1 led to lower internet addiction at Wave 2. In contrast, higher internet addiction at Wave 1 contributed to a decline in overall PYD at Wave 2 (Wang et al. 2023).

Researchers have observed that stronger PYD attributes were shown to predict lower IGD and CBV levels longitudinally. Three of the studies (Gan et al. 2023a, 2023b, 2023c) indicated that higher TPYD at Wave 1 was linked to lower IGD at Wave 2/3, and two of the studies (Gan et al. 2023b, 2023c) observed that TPYD at Wave 1 also predicted lower CBV at Wave 3, though the effect size was small. When looking at developmental assets' relation with IGD, higher total developmental assets at Wave 1 were associated with lower IGD at Wave 2 (Xiang et al. 2022), even after controlling for baseline IGD, age, and gender. This relationship followed a linear pattern with no evidence of a quadratic effect (Xiang et al. 2024a), suggesting that higher developmental assets were consistently protective rather than exhibiting threshold or curvilinear effects. Lastly, school resources/assets—another PYD measure—also played a protective role, with higher school resources/assets at Wave 1 predicting lower IGD at Wave 2 (Xiang et al. 2024b; Qin and Gan 2023).

3.4.3. Mediators and Moderators

While examining the role of mediators and moderators in the relationship between PYD and internet behaviors, we found that two of the 10 studies (Wang et al. 2023; Gan et al. 2023a) did not include any mediator or moderator variables. Among the remaining eight, most focused on mediation, with three (Gan et al. 2023c; Gan et al. 2022; Xiang et al. 2024b) explicitly conducting sequential (or serial) mediation analyses. Across these studies, five explored positive mediators such as life satisfaction (Dou and Shek 2021), internal assets (Xiang et al. 2024a), self‐control (Xiang et al. 2022; Qin and Gan 2023), and intentional self‐regulation (ISR) (Gan et al. 2022), while four examined negative mediators, including IGD (Gan et al. 2023a, 2023b, 2023c), CBV, depression (Gan et al. 2023c), school bullying (SB), and school victimization (SV) (Xiang et al. 2024b). Only three studies (Gan et al. 2023b; Xiang et al. 2022; Qin and Gan 2023) incorporated moderator variables, but only two demonstrated significant effects.

Researchers (Dou and Shek 2021) have observed that the positive factor, life satisfaction, was found to be a significant mediator in the relationship between all PYD dimensions, TPYD, and internet addiction, suggesting that higher PYD contributes to greater life satisfaction, reducing internet addiction. Upon examining the mediators in the relationship between PYD attributes and cyberbullying victimization (CBV), researchers found that W2 IGD significantly mediated the link between W1 TPYD and W3 CBV (Gan et al. 2023b, 2023c). Additionally, depression at Wave 2 mediated the relationship between TPYD at Wave 1 and IGD at Wave 2. Furthermore, CBV at Wave 2 mediated the relationship between TPYD at Wave 1 and IGD at Wave 2. Notably, depression at Wave 2 and IGD at Wave 2 together mediated the effect of TPYD at Wave 1 on CBV at Wave 2 (Gan et al. 2023c), emphasizing the intertwined roles of psychological distress and internet behaviors in cyberbullying victimization.

Studies have identified positive factors mediating the relationship between developmental assets and IGD. Internal assets at Wave 2 fully mediated the relationship between external assets at Wave 1 and IGD at Wave 2, highlighting the crucial role of internal strengths in mitigating IGD (Xiang et al. 2024a). Additionally, self‐control at Wave 1 partially mediated the link between cumulative developmental assets at Wave 1 and IGD at Wave 2, suggesting that higher self‐regulation can reinforce the protective effects of developmental assets against IGD (Xiang et al. 2022).

When examining the mediators between school assets/resources and IGD, researchers found that W1 self‐control played a key role in mediating the relationship between W1 school assets/resources and W2 IGD (Xiang et al. 2024b; Qin and Gan 2023), as well as W2 school bullying and W2 school victimization (Xiang et al. 2024b). Additionally, W2 IGD, along with W2 ISR (Gan et al. 2022) and W1 self‐control (Xiang et al. 2024b), formed a chain mediation effect linking W1 school assets/resources to W2 bullying and W2 victimization.

While examining the moderation effects, depression was shown to moderate the relationship between TPYD and IGD, indicating that higher depression reduced TPYD's protective effect, increasing IGD risk, whereas lower depression strengthened TPYD's role in preventing IGD (Gan et al. 2023b). Another key moderator, W1 ISR, influenced the relationship between W1 school assets and W2 IGD, with this effect being mediated by W1 self‐control and W2 bullying. However, this significant effect was observed only when ISR levels were high (Xiang et al. 2024b). These findings highlight that while PYD can serve as a protective factor against problematic internet behaviors, its effectiveness can be influenced by individual factors like depression and self‐regulation.

4. Discussion

To our knowledge, this review is the first to consolidate evidence on the connection between positive youth development and internet behavior by considering the longitudinal studies published. While numerous systematic and scoping reviews have explored PYD in various contexts (Bruner et al. 2023; Chowa et al. 2023; Gavin et al. 2010), those that have examined PYD's role in internet behavior remain scarce; the current review aimed to cover this major methodological gap present in the literature. Despite being based solely on empirical studies from China, this review offers valuable insights into the importance of adopting culturally relevant PYD frameworks. It highlights PYD's effectiveness in addressing negative internet behaviors and emphasizes how PYD attributes can be further strengthened through positive psychological factors. Figure 3 provides an overview of the variables examined across all 10 studies and their associations with other variables.

FIGURE 3.

General conceptual model. Note: The model presented in this figure includes variables and findings from all the included studies. It provides a general overview of the review study, and the associations depicted should be interpreted in the context of the respective study findings.

4.1. Summary of Findings

The studies reviewed indicated a high degree of conceptual consistency when examining PYD and youth internet behavior, despite differences in terminology and study focus. PYD was commonly theorised using Catalano's 15‐attribute model (Catalano et al. 2002) and Benson's developmental assets framework (Benson 2006), with school assets derived as a context‐specific extension of the latter (Gan et al. 2022). These approaches can be comprehended not as competing theories, but as cultural and context‐specific adaptations of closely aligned, strength‐based frameworks, reflecting differences in emphasis rather than fundamental conceptual differences. This pattern indicates the flexible and evolving nature of PYD frameworks, which allow both selective emphasis across operational forms (i.e., attribute‐based or asset‐based) and theoretical elaboration over time (Benson et al. 2006; Dimitrova et al. 2021).

Across the reviewed studies, internet behaviors were examined using multiple established theories (Jessor 1987; Hirschi 2017; Agnew and White 1992), with frameworks selected specifically based on the outcomes being studied. However, all internet behaviors are problematic in nature, and they are often framed in terms of regulation failure, strain, and maladaptive adjustment. Furthermore, PYD is used alongside established risk‐focused frameworks as a protective or buffering factor within these models, rather than as a core guiding framework.

While examining the measurement tools, PYD and internet behaviors were primarily assessed using culture‐specific and culturally validated tools, such as the CPYDS (Shek et al. 2007), the Chinese version of the IGD questionnaire (Yu et al. 2017), and the E‐Bullying Scale (Lam and Li 2013). This highlights the importance that Chinese researchers place on ensuring cultural relevance and measurement accuracy before applying established constructs. The ongoing research also points to the similar importance being given to the cultural validation of PYD frameworks (see the special issue of PYD [Wiium and Dimitrova 2019]). However, the strong emphasis on risk‐oriented outcomes in the reviewed studies suggests that PYD has been primarily used to examine problematic internet behaviors, with comparatively limited application as a framework for understanding positive digital engagement.

Among the included studies, PYD has shown a consistent relation with internet behaviors, that is, higher levels of PYD have negatively predicted IGD, CBV, and internet addiction concurrently and longitudinally. This reaffirms that PYD models serve as a protective resource against maladaptive outcomes (Benson et al. 2006; Catalano et al. 2002). Despite cross‐sectional research indicating a strong direct association (Yu and Shek 2021), we observed that PYD's influence on internet behavior is largely indirect and conditional, operating through psychological (Gan et al. 2023a; Xiang et al. 2024a) and contextual factors (Gan et al. 2023c; Xiang et al. 2024b) as mediators rather than uniform main effects. Analytical variations in PYD operationalization revealed that dimension‐focused analyses (Dou and Shek 2021; Xiang et al. 2024a) provided greater specificity regarding which developmental strengths were relevant to particular online risks, while overall indices (Gan et al. 2023c; Xiang et al. 2022) showed broader protective behavior. Together, these findings suggest that individual‐specific attributes and their corresponding contexts largely shape the associations between PYD and internet behavior.

Another important observation is that, although three of the 10 studies were conducted during COVID‐19, only one study examined how the COVID‐19 pandemic shaped the association between PYD and internet behavior. The other two studies (Gan et al. 2023c; Xiang et al. 2024a) mentioned COVID‐19 only in their discussion, suggesting that positive attributes act as protective factors during crises, without any empirical examination. However, Wang et al. (2023) observed that, compared to the pre‐pandemic period, adolescents exhibited slightly lower PYD qualities and higher internet addiction levels during COVID‐19, and that pre‐existing PYD strengths buffered increases in internet addiction during the crisis, while higher pre‐pandemic internet addiction predicted subsequent declines in PYD. This observation aligns with COVID‐19 literature (Park et al. 2021), which discusses how stressors and resources can help individuals cope with unexpected life disruptions. However, limited attention to COVID‐19 factors indicates an important conceptual gap that necessitates the need for longitudinal research to explicitly examine how non‐normative contexts alter the associations between PYD and internet behavior over time.

Lastly, we observed that mediation emerged as the dominant analytical approach, indicating that PYD influences adolescent internet behavior primarily through developmental processes rather than direct effects. Mediating factors are clustered around regulatory, affective, and social pathways, including self‐control (Xiang et al. 2022; Xiang et al. 2024b; Qin and Gan 2023), intentional self‐regulation (Gan et al. 2022), life satisfaction (Dou and Shek 2021), depressive symptoms (Gan et al. 2023c), and experiences of bullying or victimization (Xiang et al. 2024b). These findings are consistent with developmental perspectives (Lerner et al. 2011) that view strengths as shaping behavior gradually through ongoing interactions between individuals and their environments (Bronfenbrenner and Morris 2007). The observed chained mediation pathways also suggest reciprocal links between psychological distress and problematic internet behaviors, consistent with transactional views of development (Sameroff 2009).

On the other hand, despite being given less importance, studies utilizing moderation indicated the conditional nature of PYD's protective role. Findings indicate that PYD‐related strengths were less effective under conditions of elevated depression (Gan et al. 2023b) and more effective when adolescents possessed stronger regulatory capacities, such as intentional self‐regulation (Xiang et al. 2024b). These findings align closely with the differential susceptibility framework (Belsky and Pluess 2009), which emphasizes that an individual's resources are most protective when they have sufficient capacity to mobilize them. Overall, these results suggest that PYD serves as a context‐sensitive developmental resource, whose effectiveness varies across individual and environmental conditions.

4.2. Limitations and Future Directions

A major limitation of these studies is their reliance on self‐reported data, resulting in common method bias and potential social desirability effects. This limitation matters because it risks overestimating the protective role of PYD, especially in sensitive behaviors like addiction or cyberbullying. The lack of external evaluations, such as input from parents, teachers, or peers, and the minimal use of tracking data further limit reliability. Future studies should adopt a mixed‐methods approach, combining self‐reports with observational and digital tracking data. Additionally, family dynamics, including parenting styles (Yu and Shek 2021) and parental internet use (Vaala and Bleakley 2015), are often overlooked despite their strong influence on adolescents' online behavior. Incorporating these factors would provide a more comprehensive understanding of the protective effects of PYD.

Most research so far has focused on individual and school‐related factors, leaving broader ecological influences underexplored. According to bioecological theory (Bronfenbrenner and Morris 2007), multiple interconnected systems shape adolescent development. Yet, the majority of research has focused on examining PYD within the microsystem (Eyler et al. 2023; Yu and Shek 2021), with a minor focus on the role of community, cultural norms, and digital technologies (Lee et al. 2019; Lerner 2011). Future research should adopt a wider lens, considering how these broader societal factors shape PYD and internet behavior, leading to a more holistic understanding.

This review of the limited number of longitudinal studies, conducted primarily by Chinese researchers, highlights a significant research gap. By 2020, the dominance of Chinese literature on PYD was evident (Qi et al. 2022); however, we observed that no research had been conducted by other countries addressing this significant gap. There is a clear need for more cross‐cultural, experimental, and longitudinal studies to further explore the role of PYD in internet behavior. While we place less emphasis on qualitative studies, given that PYD's role in positive internet use has already been well established (Good 2023; Guo et al. 2024), we did observe a pattern in research methodologies. Specifically, studies on positive internet usage often employed qualitative methods, whereas those on negative internet usage relied more on quantitative approaches. Future researchers should take note of this gap and consider a mixed‐method approach to gain a more comprehensive understanding of the topic.

We observed that research on PYD and internet behavior has been predominantly conducted by the same set of authors over the past few years, often using similar methodological approaches. Notably, all identified studies were conducted in China, which is classified as a non‐WEIRD (Western, Educated, Industrialized, Rich, and Democratic) society (Muthukrishna et al. 2020). While this geographic concentration limits generalizability to Western settings, it provides valuable insight into how PYD constructs operate within a collectivist and rapidly digitalizing society. Future research should extend to diverse cultural contexts to test the cross‐cultural validity and adaptability of PYD frameworks across both WEIRD and non‐WEIRD populations.

Additionally, some researchers have published multiple papers on the same sample at different time points, suggesting that the actual level of research interest in this association may be lower than it appears to be. Furthermore, there is limited reporting of concurrent and longitudinal findings when studies focus on examining the influence of mediating and moderating variables, indicating that researchers should report their study findings comprehensively, even when their objectives differ. The lack of such reporting restricts reviewers from conducting further analyses and synthesizing a more complete understanding of PYD's association with internet behaviors.

Researchers have proposed the potential role of social competence (Ma 2012), spirituality (Shek 2012), self‐efficacy (Tsang et al. 2012), and several other constructs (see The Scientific World Journal, Volume 1, 2012) in PYD through their conceptual reviews. Future studies can consider these variables as mediators, potential developmental assets, or PYD attributes and examine their association with internet behaviors. Furthermore, the internet is an ever‐evolving phenomenon with positive and negative effects. Therefore, researchers must explore the positive (such as civic engagement [Saud 2020], digital learning [Phillips and Killian Lund 2021]) and negative internet behaviors (such as gambling [Marchica et al. 2022], excessive screentime [Muppalla et al. 2023]) and incorporate digital technologies during interventions (Lee 2019) to ensure that technological advancements only promote growth in the youth.

4.3. Implications

Despite a concept originating in Western countries, this review highlights how PYD models are flexible and culturally adaptable for understanding how adolescents interact with the internet. The fact that different theories have been used alongside PYD shows that no single approach fully explains these behaviors. Moving forward, scholars should explore how PYD fits into the broader digital landscape, considering factors like family influence, community norms, and societal expectations. A more ecological perspective would help us see the bigger picture of how young people navigate the online world.

For policymakers and educators, these findings underscore the importance of implementing proactive strategies to promote healthy internet use. Schools can play a key role by integrating PYD principles into both academics and extracurricular activities, helping students develop self‐regulation and digital literacy. Supporting programs that focus on mentorship, sports, the arts, and community service can provide adolescents with a stronger foundation, reducing the likelihood of excessive or problematic internet use. Additionally, early screening for internet addiction and IGD at the start of each school year would allow schools to identify and support at‐risk students before their struggles escalate.

Parents also have an important role to play. Open conversations, setting clear and reasonable digital boundaries, and even participating in online activities together can help adolescents develop better self‐control and a healthier relationship with technology. Teachers and counselors should also prioritize social‐emotional learning, helping students build coping skills for stress and peer pressure. Finally, young people themselves should be encouraged to find balance by exploring offline hobbies, strengthening real‐world connections, and taking ownership of their digital habits. When families, schools, and communities work together, we can create a more supportive environment where adolescents thrive both online and offline.

5. Conclusion

This review brings together evidence on how positive youth development influences internet behavior, showing that PYD acts as a protective factor against cyberbullying victimization, internet addiction, and internet gaming disorder. Across studies, youth with stronger PYD constructs tended to engage in healthier online behaviors in the form of developmental assets, school resources, or PYD attributes. Factors like self‐control, life satisfaction, and school assets further strengthened this protective effect, while challenges like depression, school bullying, and victimization could weaken it. The review also highlights the cultural adaptability of PYD models.

These findings help strengthen the PYD‐based interventions and help young people navigate the digital world more effectively. Schools and communities can play a key role in building PYD attributes like self‐control and intentional self‐regulation to support positive internet use. Future research needs to take a more rigorous approach, with meta‐analyses, experimental studies, longitudinal studies with longer follow‐ups, and a systematic review of existing qualitative research helping to deepen our understanding. It is also necessary to explore how PYD principles can be delivered through digital platforms, such as online mentorship programs, interactive learning, or app‐based interventions. Given the ever‐growing role of technology in young people's lives, understanding how to promote positive digital engagement in their development is more important than ever.

Author Contributions

Maneela Sirisety: conceptualization and study design, screening, data analysis and interpretation, writing – original draft, and reviewing and editing. Ravi Shanker Datti: conceptualization and study design, data analysis and interpretation, writing – original draft, and supervision and project administration. Nidhi Mishra: quality assessment, data analysis and interpretation, writing – original draft, supervision and project administration. Ashutosh Tewari: systematic search, data extraction, writing – original draft, and reviewing and editing. Swathi Akkaraju: systematic search, screening, writing – original draft, and reviewing and editing. Revathi Sampathirao: data extraction, writing – original draft, and reviewing and editing.

Funding

The authors received no specific funding for this work.

Ethics Statement

There are no human participants in this article and informed consent is not required. The authors declare that the Grammarly tool was used to enhance the writing of the manuscript.

Consent

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table 1: Search Strategy. Table 2: Theoretical Frameworks and Measurement Tools.

Table S3: Complete Data Extraction.

Data Availability Statement

The data that support the findings of this study are available in the Supporting material of this article. Data present in Supporting Information S2 are taken directly from the source without any paraphrasing. Therefore, the authors do not claim copyright of the data present in that file.