Psychological determinants of low carbon digital behavior among university students in Bangladesh

Psychological determinants of low carbon digital behavior among university students in Bangladesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Psychological determinants of low carbon digital behavior among university students in Bangladesh Nure Jannat Arpa, Tanvir Ahmed, Abdullah Al Zabir This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8775589/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract The rapid digitalization of higher education has significantly increased students’ reliance on energy-intensive platforms, yet sustainable digital practices remain limited despite rising awareness of digital carbon emissions. This study examines the Awareness–Action Paradox among Bangladeshi university students by developing and validating a Digital Carbon Awareness–Action Paradox Model that integrates environmental concern, perceived behavioral control, habit strength, digital addiction, and eco-anxiety. Using a cross-sectional survey of 400 students from public, private, and National University institutions, Structural Equation Modeling revealed that awareness does not directly predict intention or perceived behavioral control but strongly influences environmental concern. Both environmental concern and perceived behavioral control fully mediated the awareness–intention relationship, confirming that awareness alone is insufficient for motivating sustainable digital behavior. Intention emerged as the strongest predictor of low-carbon digital action, whereas habit strength and digital addiction significantly weakened the intention–behavior linkage, highlighting the constraining role of entrenched digital routines and compulsive usage. Eco-anxiety did not moderate the awareness–intention pathway, suggesting that emotional distress does not translate awareness into motivation without supportive psychological mechanisms. The model explained 47.5% of the variance in intention and 41.2% in behavior, offering one of the first empirically grounded frameworks for understanding digital sustainability behavior in a developing-country context. Findings emphasize the need for interventions that enhance emotional engagement and perceived capability while addressing habitual and addictive digital consumption patterns to effectively bridge the awareness–action gap. Digital carbon footprint Sustainable digital behavior Awareness–Action Paradox University students Figures Figure 1 1.Introduction The rapid expansion of digital technologies has transformed higher education and daily life, a shift intensified by the global movement toward online instruction, mobile learning, and heightened social media engagement during the COVID-19 pandemic (Shrestha et al., 2021 ; Humida et al., 2021 ; Talan et al., 2024 ; Son et al., 2020 ). As video conferencing, cloud platforms, and mobile learning tools became embedded in academic routines, digital consumption surged among students and educators worldwide (Shrestha et al., 2021 ; Humida et al., 2021 ; Talan et al., 2024 ; Dhar et al., 2020 ). At the same time, the information and communications technology (ICT) sector—comprising data centers, streaming services, and network infrastructures—emerged as a major global emitter, with its carbon footprint now comparable to certain transport sectors (Rom et al., 2023 ; Dhar et al., 2020 ). Yet, the environmental consequences of everyday digital activities remain largely invisible in public discourse and policy frameworks, highlighting a growing need to connect rising digital use with its energy and carbon implications (Yang et al., 2024 ; Li & Zhao, 2021 ). University students are a particularly relevant population for examining this issue due to their intensive reliance on digital media, streaming, cloud storage, and online learning systems. Existing studies show heavy engagement with mobile applications and high-bandwidth platforms, especially during emergency remote teaching (Shrestha et al., 2021 ; Humida et al., 2021 ; Talan et al., 2024 ; Hossain et al., 2020 ). In Bangladesh, rapid ICT-mediated learning adoption alongside high mobile-internet penetration and growing social-media usage has further amplified digital routines (Dhar et al., 2020 ; Hoque et al., 2021 ; Hossain et al., 2020 ). Mental-health challenges such as anxiety and depression—widely documented among Bangladeshi students—may additionally shape motivation and capacity for pro-environmental behavior (Hassan et al., 2020 ; Faisal et al., 2021 ; Hoque et al., 2021 ). Despite increasing access to information about the environmental cost of digital activities, empirical evidence consistently shows that awareness alone rarely translates into sustainable digital choices. Students often continue HD streaming, accumulate redundant cloud files, or retain autoplay features despite knowing their higher energy demand. This disconnect between environmental knowledge and actual behavior—referred to as the intention–behavior or awareness–action gap (Yang et al., 2024 ; Li & Zhao, 2021 )—is at the core of what scholars identify as the Awareness–Action Paradox in Digital Carbon Footprint. A review of prior work reveals four major gaps. First, a country gap persists, as research on digital behavior and student mental health in Bangladesh and South Asia remains largely detached from digital carbon footprint studies (Shrestha et al., 2021 ; Humida et al., 2021 ; Dhar et al., 2020 ; Hoque et al., 2021 ). Second, a conceptual gap exists, as awareness–action paradox theories have rarely been applied to digital consumption domains such as streaming, cloud storage, or energy-invisible platform defaults (Rom et al., 2023 ; Yang et al., 2024 ). Third, a psychological gap remains, with limited examination of how eco-anxiety, digital addiction, or habitual digital routines moderate the awareness–behavior pathway (Hassan et al., 2020 ; Faisal et al., 2021 ; Hoque et al., 2021 ). Fourth, a behavioral gap persists in understanding how well-established technology adoption frameworks translate into low-carbon digital actions (Li & Zhao, 2021 ; Aljasir, 2023 ; Hossain et al., 2020 ). To address these gaps, this study investigates why awareness of digital carbon implications does not consistently lead to low-carbon digital behavior among Bangladeshi university students. The objectives are to (1) assess students’ awareness and everyday digital practices; (2) measure intentions toward low-carbon digital behavior; (3) examine psychological moderators such as eco-anxiety, digital addiction, and habit strength; and (4) develop and validate an integrated Digital Carbon Awareness–Action Paradox Model (Yang et al., 2024 ; Li & Zhao, 2021 ; Hassan et al., 2020 ). The study further contributes by extending intention–behavior and technology-acceptance theories into the digital-carbon domain and offering insights for designing targeted digital sustainability interventions in Bangladesh (Shrestha et al., 2021 ; Humida et al., 2021 ; Dhar et al., 2020 ). This study makes several key contributions by being the first in Bangladesh and South Asia to apply the Awareness–Action Paradox to digital carbon behavior. It expands existing pro-environmental and technology-acceptance frameworks by integrating environmental concern, perceived behavioral control, and digital-specific behaviors. Additionally, it explores underexamined psychological factors—habit strength, digital addiction, and eco-anxiety—within a single structural equation model. Finally, it presents a validated Digital Carbon Awareness–Action Paradox Model, offering practical insights for designing university-level digital sustainability policies and interventions. 2. Literature Review 2.1 Digital Carbon Footprint: Definition & Emerging Field Digital carbon footprint refers to the greenhouse gas emissions generated by data storage, cloud computing, video streaming, and device use, with recent estimates placing digital emissions at 2–4% of global CO₂ output—approaching aviation-level impact (Djokić et al., 2023 ; Salim, 2023 ). Research increasingly highlights that data centers, content-delivery networks, and high-bandwidth platforms are the primary contributors to this footprint. Studies on university settings further reveal that the expansion of online learning, mobile applications, and streaming technologies intensifies digital energy demand among students (Shrestha et al., 2021 ; Humida et al., 2021 ; Talan et al., 2024 ). A recurring theme across this literature is the “invisibility” of digital energy use: users accumulate redundant cloud files or engage in high-definition streaming without recognizing the environmental implications. Although global scholarly attention toward digital emissions is rising, empirical work in developing contexts—particularly Bangladesh—remains sparse. Existing studies focus predominantly on digital access and usage patterns rather than their environmental impact, underscoring a critical geographic and contextual gap in digital sustainability research. 2.2 Awareness–Action Paradox Theory The Awareness–Action Paradox captures the persistent misalignment between environmental knowledge and actual behavior, paralleling the Intention–Behavior Gap articulated by Sheeran ( 2002 ). The paradox is supported by research showing that awareness or knowledge alone seldom drives behavioral change (Zulfiqar et al., 2018 ). Cognitive Dissonance Theory (Huo et al., 2023 ) provides a psychological explanation: individuals may continue engaging in environmentally harmful digital routines because altering established habits requires mental effort or disrupts convenience. Across empirical contexts, barriers such as motivational deficits, perceived inconvenience, and emotional overload hinder the translation of awareness into action (Gates et al., 2025 ). For instance, Xu et al. ( 2019 ) found that even when consumers valued organic food, purchasing behavior lagged due to practical constraints—an analogy applicable to digital behavior, where students may understand the environmental burden of streaming or cloud use but still maintain energy-intensive habits. Notably, this paradox remains unexplored in Bangladesh’s higher education sector, despite students’ high digital engagement. 2.3 Behavioral Predictors The literature identifies several predictors of sustainable behavior—environmental awareness, environmental concern, perceived behavioral control (PBC), and social influence. Awareness can increase pro-environmental motivation, yet its impact is often conditional on emotional involvement or perceived efficacy (Laguía et al., 2019 ). PBC plays a central role: individuals with high perceived capability are more likely to act sustainably, even when awareness levels are similar (Adulyarat et al., 2024 ). Social influence is frequently considered an external driver of intentions, with peer norms shaping students’ digital practices (Wong et al., 2019 ). However, findings are inconsistent; some studies highlight strong normative influence, while others show a limited or context-dependent effect (Jahan et al., 2025 ). Although these predictors are well-established in traditional environmental behavior research, their applicability to digital carbon practices—such as optimizing streaming quality or managing cloud storage—remains underexamined in developing economies. 2.4 Moderator Variables Moderator variables shed light on why intentions may not translate into actual behavior—an essential component of the Awareness–Action Paradox. Digital addiction is a prominent moderator, as excessive engagement with digital platforms reinforces automatic routines, reducing one’s ability to make conscious, sustainable choices (Passafaro et al., 2019 ). This is particularly relevant for university students, whose academic and social lives are deeply embedded in digital ecosystems (Yao et al., 2020 ). Eco-anxiety occupies a more complex position. While climate-related distress may increase pro-environmental motivation in some cases (Feng et al., 2023 ), other studies suggest that high emotional load may trigger avoidance or disengagement, creating contradictory behavioral outcomes across contexts. Habit strength is another critical yet understudied moderator in digital contexts. Once behaviors such as habitual HD streaming or continuous browsing become automated, they often override sustainability intentions (Moon et al., 2024 ). Although these moderators have been discussed in broader behavioral literature, their specific role in shaping digital carbon footprint behaviors—particularly among Bangladeshi university students—remains largely unexplored. Across the reviewed literature, a clear tension emerges: Awareness is widely assumed to drive sustainable behavior, yet habitual digital routines and psychological constraints frequently undermine this process, and the mechanisms connecting awareness, intention, and digital behavior remain theoretically fragmented. Global research highlights digital emissions as an expanding environmental challenge, but the integration of cognitive, emotional, and habitual constructs into a unified behavioral model is still limited. Moreover, the interplay between awareness and moderators such as eco-anxiety, digital addiction, and habit strength is insufficiently theorized, particularly in developing countries where digital accessibility is rising rapidly. By situating these constructs within the Bangladeshi university context, the present study addresses notable conceptual, psychological, and contextual gaps, offering a more nuanced understanding of the digital carbon behavior landscape. 3. Conceptual Framework The conceptual framework for this study integrates perspectives from environmental psychology, behavioral science, and digital consumption research to explain the mechanisms underlying the Awareness–Action Paradox in digital carbon footprint behavior among university students in Bangladesh. This framework responds directly to the critical empirical gaps identified in the literature, particularly the lack of theory-driven models that explain sustainable digital behavior within rapidly digitizing, developing-country contexts. At the core of the model, Digital Carbon Footprint Awareness is positioned as the initial cognitive antecedent shaping students’ perceptions and motivations. Awareness reflects individuals’ understanding of how routine digital activities—such as high-definition streaming, excessive cloud storage, automatic uploads, and persistent social media browsing—contribute to carbon emissions. Consistent with knowledge–attitude–behavior perspectives, the framework assumes that awareness alone does not guarantee behavioral change but is a necessary foundation for evaluating one’s role in environmental degradation. From this starting point, the model proposes two primary psychological pathways through which awareness influences intention. The first pathway operates through Perceived Behavioral Control (PBC), derived from the Theory of Planned Behavior. PBC reflects individuals’ perceptions of their ability to adopt low-carbon digital practices. Awareness may enhance PBC by clarifying which digital activities are carbon-intensive and which actions are feasible for mitigation. The second pathway operates through Environmental Concern, which captures individuals’ ecological values, emotional investment in environmental protection, and sense of moral responsibility. With increased awareness, students may develop stronger environmental concern, which heightens their motivation to act. Both PBC and environmental concern are theorized as mediators that translate awareness into Intention to Reduce Digital Emissions. Intention represents individuals’ willingness and commitment to modify their digital routines and is regarded as the most immediate predictor of behavior. However, translating intention into Actual Low-Carbon Digital Behavior is complex and often inconsistent, reflecting the underlying Awareness–Action Paradox. Digital activities are frictionless, repetitive, and embedded into daily life, which makes them resistant to conscious regulation. To explain why intention often fails to produce behavioral change, the framework incorporates three moderating constructs that capture contemporary psychological and digital realities. Habit Strength reflects the automatic, repetitive nature of digital actions—such as constant scrolling, streaming, and file saving—that reduce reliance on conscious decision-making. Strong habits are expected to weaken the intention–behavior relationship. Digital Addiction represents compulsive digital engagement that undermines self-regulation and inhibits the adoption of low-carbon digital choices, even when motivation is present. This introduces an important psychological barrier to pro-environmental behavior within technology-intensive learning environments. Eco-Anxiety is included as an emotional moderator on the awareness–intention pathway. Although eco-anxiety involves distress and concern about climate issues, emerging evidence suggests that it can increase motivation when experienced at optimal levels. Accordingly, the framework theorizes that eco-anxiety strengthens the influence of awareness on intention. Taken together, this conceptual framework presents a multi-level, psychologically enriched understanding of sustainable digital behavior. It explains not only how awareness may lead to action, but also why this progression frequently weakens and under what conditions behavioral intentions are more or less likely to translate into low-carbon digital choices. Designed specifically for higher education contexts in developing nations, the model acknowledges the combined effects of rapid digitalization, low regulatory pressure, high mobile internet dependency, and mental health challenges that shape students’ digital lifestyles. This framework provides the foundation for empirical testing using Structural Equation Modeling (SEM), where both mediating and moderating mechanisms are evaluated simultaneously to capture the full complexity of digital carbon behavior among Bangladeshi university students. 3.1 Hypotheses Development The hypotheses of this study are grounded in the Theory of Planned Behavior (Ajzen, 1991 ), the Awareness–Action Paradox, and contemporary psychological research on eco-anxiety, digital addiction, and habit strength. These frameworks collectively explain why increased awareness of environmental consequences does not consistently translate into sustainable digital behavior. Because the energy use associated with streaming, cloud storage, and continuous mobile engagement is largely invisible, individuals often experience a cognitive disconnect between digital actions and environmental impact. This invisibility, combined with habitual and emotionally driven patterns of digital use, intensifies the intention–behavior gap and underscores the need to examine the psychological processes shaping digital carbon behavior. 3.1 Awareness and Cognitive–Motivational Predictors Digital carbon awareness is conceptualized as the primary cognitive antecedent in the model. Prior evidence indicates that environmental knowledge enhances perceived capability to act, heightens ecological concern, and increases the likelihood of forming pro-environmental intentions. In the present context, students who recognize the carbon implications of streaming resolution, autoplay features, or redundant cloud storage are expected to feel both more capable of adopting low-carbon practices and more concerned about the environmental effects of their digital routines. H1 Digital carbon footprint awareness has a positive effect on perceived behavioral control. H2 Digital carbon footprint awareness has a positive effect on environmental concern. H3 Digital carbon footprint awareness has a positive effect on intention to reduce digital emissions. 3.1.1 PBC, Environmental Concern, and Intention Perceived Behavioral Control (PBC) is central to the Theory of Planned Behavior and consistently emerges as one of the strongest predictors of intention across sustainability domains. Students who perceive low-carbon digital actions as feasible are more likely to form intentions to adopt them. Similarly, environmental concern reflects individuals’ emotional and moral orientation toward ecological issues and is widely associated with sustainable motivation. Together, these constructs are expected to exert substantial influence on intention. H4: Perceived behavioral control has a positive effect on intention to reduce digital emissions. H5: Environmental concern has a positive effect on intention to reduce digital emissions. 3.1.2 Intention and Actual Low-Carbon Digital Behavior Intention remains the most immediate and necessary antecedent of behavior. However, digital habits, automated platform features, and contextual factors can weaken this link. Despite such constraints, intention is theorized to exert a positive influence on actual low-carbon digital actions. H6: Intention to reduce digital emissions has a positive effect on actual low-carbon digital behavior. 3.2 Mediation Effects Although awareness sets the foundation for behavior, its influence on intention may be indirect. PBC serves as a cognitive pathway through which awareness becomes internalized as a sense of capability, while environmental concern provides an affective pathway by which awareness strengthens emotional and moral motivation. Together, these mediators reflect the mechanisms through which awareness is expected to translate into intention. H7: Perceived behavioral control mediates the relationship between awareness and intention. H8: Environmental concern mediates the relationship between awareness and intention. 3.3 Moderation Effects Three moderators—habit strength, digital addiction, and eco-anxiety—are proposed to define the conditions under which awareness or intention effectively translate into behavior. Habit Strength weakens the intention-to-behavior relationship by overriding conscious decision-making through automatic digital routines. Digital Addiction similarly reduces self-regulatory capacity, thereby diminishing individuals’ ability to act on their intentions. By contrast, Eco-Anxiety may function as a motivational amplifier, strengthening the pathway from awareness to intention when individuals experience heightened concern about climate-related threats. H9: Habit strength negatively moderates the relationship between intention and actual low-carbon digital behavior. H10: Digital addiction negatively moderates the relationship between intention and actual low-carbon digital behavior. H11: Eco-anxiety positively moderates the relationship between awareness and intention to reduce digital emissions. 4. Methodology The present study was designed to investigate the psychological, behavioral, and contextual determinants that shape the awareness–action paradox in digital carbon footprint behavior among university students in Bangladesh. The methodological framework was grounded in contemporary behavioral science, sustainability research, and digital consumption scholarship. Emphasis was placed on methodological rigor, transparency, and replicability, ensuring that measurement validity, sampling adequacy, and analytical precision aligned with the standards expected in high-impact Q1 journals. This section outlines the research design, study context, sampling procedures, measurement instruments, analytical strategy, and ethical protocols, integrating recent literature from 2020 to 2025 on digital behavior, climate psychology, eco-anxiety, habit formation, and digital addiction. 4.1 Research Design This study employed a quantitative, cross-sectional research design to examine the relationships among digital carbon awareness, environmental concern, perceived behavioral control, psychological moderators, and low-carbon digital behavior. A quantitative approach was most appropriate because the aim of the research was explanatory and theory-driven, requiring the testing of specific hypotheses and complex multivariate relationships that qualitative or mixed-method designs cannot capture with comparable statistical power. Cross-sectional surveys remain the dominant methodological choice in behavioral intention and sustainability research, especially within frameworks derived from the Theory of Planned Behavior and behavioral-psychological models (Baroni et al., 2025 ). Structural Equation Modeling (SEM) was used as the primary analytical framework due to its ability to evaluate multiple latent constructs simultaneously, correct for measurement error, and test both mediation and moderation effects within a unified model. SEM is widely regarded as the most rigorous method for theory testing when constructs are multidimensional and when hypothesized pathways involve complex interdependencies (Kline, 2016 ). The awareness–action paradox inherently involves cognitive components (awareness, intention), emotional components (eco-anxiety), and behavioral regulators (habit strength, digital addiction), making SEM particularly suitable. Data collection took place between January and April 2025. This period was intentionally selected to ensure ecological validity, as it represents a stable academic window in Bangladeshi universities. Students’ digital activities—such as streaming, cloud storage usage, online learning, and smartphone engagement—tend to follow consistent patterns during regular academic cycles. Collecting data outside examination or holiday periods minimized behavioral distortions that might arise from unusual digital use, thereby strengthening the reliability of self-reported digital behavior. 4.2 Research Setting and Participants The research was conducted within the higher education sector of Bangladesh, encompassing public universities, private universities, and National University–affiliated colleges. These institutions house one of the most digitally active youth populations in South Asia due to increased smartphone penetration, affordable internet access, and the integration of online learning platforms following the COVID-19 pandemic (Lannelongue et al., 2020). The rapid digital transformation in Bangladesh makes university students an ideal population for examining digital sustainability and the psychological mechanisms that underlie the awareness–action paradox. A stratified purposive sampling technique was adopted. Stratification ensured proportional representation across public universities, private universities, and National University colleges, accommodating institutional diversity relevant to technological access, socioeconomic backgrounds, and academic environments. Purposive sampling was appropriate because the study required respondents who met specific behavioral criteria, particularly frequent digital engagement. Behavioral research consistently recommends purposive sampling when the target population possesses specialized usage patterns or psychological characteristics (Etikan, 2016 ). Participants were required to meet three inclusion criteria: being 18 years or older, currently enrolled at a university, and reporting at least two hours of daily digital use. Data were collected digitally through institutional networks, learning management systems, student communities, and social media platforms frequently used by university students. A total of 430 responses were collected, of which 400 valid cases remained after removing incomplete entries, patterned responses, and multivariate outliers identified using Mahalanobis distance. The final sample size met the minimum SEM requirement of 200 and exceeded the recommended rule of ten participants per estimated parameter (Kline, 2016 ). A sample of 400 also aligns with power analysis recommendations suggesting that medium to large effect sizes can be detected with a power of 0.80 at α = 0.05 (Brydges, 2019 ; Kyonka, 2018 ). The final sample reflected demographic heterogeneity across academic years, disciplines, and gender. Approximately 150 participants each were drawn from public and private universities, and 100 from National University colleges. This balanced representation increased generalizability across Bangladesh’s higher education landscape. 4.3 Measures / Instruments All constructs were measured using psychometrically validated self-report instruments widely used in environmental psychology, digital behavior research, and sustainability studies. Unless otherwise noted, items were rated on a five-point Likert scale ranging from strongly disagree to strongly agree. Each instrument demonstrated strong reliability in both previous studies and the present sample. 4.3.1 Awareness of Digital Carbon Footprint Digital carbon footprint awareness was measured using an eight-item researcher-developed scale grounded in established literature on digital emissions and sustainable digital behavior (Lannelongue et al., 2020; Pirson & Bol, 2021 ). Items captured understanding of how streaming quality, cloud storage accumulation, data synchronization, and device energy consumption contribute to carbon output. The scale demonstrated excellent internal consistency (α = 0.93). 4.3.2 Environmental Concern Environmental concern was measured using eight items adapted from the Revised New Ecological Paradigm (NEP) Scale (Dunlap, 2000), frequently used to assess ecological worldviews and environmental attitudes. Reliability in the current study was excellent (α = 0.94), consistent with recent sustainability literature (Gkargkavouzi et al., 2025 ). 4.3.3 Perceived Behavioral Control Perceived behavioral control (PBC) was assessed using six items adapted from the Theory of Planned Behavior (Ajzen, 1991 ). Items measured students’ perceived capability to reduce digital emissions through intentional behavioral adjustments. Reliability was high (α = 0.92). 4.3.4 Habit Strength Habit strength was measured using seven items from the Self-Report Habit Index (Verplanken & Orbell, 2003), assessing automaticity and routine-driven digital consumption. Reliability in this study was 0.91, consistent with prior research (Weiden et al., 2020 ). 4.3.5 Eco-anxiety Eco-anxiety was measured using seven items adapted from the Eco-Anxiety Scale, capturing emotional, cognitive, and physiological responses to environmental threats (Wang et al., 2025 ). Reliability was robust (α = 0.91). 4.3.6 Digital Addiction Digital addiction was measured using a validated seven-item scale assessing compulsive use, loss of control, and difficulty disengaging from digital platforms (Chemnad et al., 2025 ). The scale showed excellent reliability (α = 0.92). 4.3.7 Intention and Actual Low-carbon Digital Behavior Behavioral intention toward low-carbon digital practices was measured using a five-item scale inspired by modern digital sustainability frameworks (Baroni et al., 2025 ). Actual low-carbon digital behavior was measured using a six-item scale assessing practical digital actions such as deleting unnecessary files, reducing streaming quality, and limiting cloud uploads. Reliability was excellent for both intention (α = 0.95) and behavior (α = 0.94). All instruments exceeded the recommended reliability threshold of α > 0.70, confirming suitability for SEM. 4.4 Data Analysis Strategy All analyses were conducted using R and RStudio. The analytical procedure followed SEM best practices and incorporated multiple validity checks to ensure robustness. 4.4.1 Preliminary Screening Data were examined for missing values, unengaged responses, and outliers. Normality was assessed through skewness and kurtosis values. The dataset met the assumptions for maximum likelihood estimation. 4.4.2 Measurement Model Evaluation Confirmatory Factor Analysis (CFA) was performed using the lavaan package to evaluate measurement validity. Convergent validity was assessed using Average Variance Extracted, and discriminant validity was examined via the Fornell–Larcker criterion and the Heterotrait–Monotrait ratio. Items with loadings below 0.50 were removed to enhance construct precision. 4.4.3 Structural Model Testing SEM was used to test hypothesized pathways. Mediation effects were evaluated using 5000-sample bootstrapping, while moderation effects of habit strength, eco-anxiety, and digital addiction were analyzed through latent interaction modeling. Model fit was assessed using CFI, TLI, RMSEA, and SRMR. 4.4.4 Common Method Variance Harman’s single-factor test confirmed that no single factor dominated the variance, indicating low method bias. 4.5 Ethics Approval and accordance The study was conducted in accordance with the ethical principles for research involving human subjects (World Medical Association Declaration of Helsinki, 2013). The research protocol was reviewed and approved by the Department of Agricultural Economics, Faculty of Agricultural Economics & Agribusiness Studies, Khulna Agricultural University, Khulna-9100, Bangladesh. Electronic informed consent was obtained from all participants prior to data collection, and this consent procedure was approved by the Department of Agricultural Economics, Faculty of Agricultural Economics & Agribusiness Studies, Khulna Agricultural University, Khulna-9100, Bangladesh. Participation was voluntary and anonymous. Participants were informed about the purpose of the study and their right to decline participation or withdraw at any time without any penalty. No personally identifiable or sensitive information was collected. 5.Result & Discussion 5.1 Data Screening and Preliminary Analysis 5.1.1 Missing Data Analysis A complete case assessment was conducted across the dataset comprising 400 valid responses and 61 indicators. The analysis revealed no missing values for any variable, allowing the use of Maximum Likelihood (ML) estimation without the need for imputation or listwise deletion. The absence of missing values aligns with best practices for structural modeling and reflects strong data collection procedures, reducing the likelihood of biased estimates and supporting the reliability of subsequent latent variable analyses. 5.1.2 Multivariate Outlier Detection Multivariate outliers were examined using Mahalanobis Distance, applying a conservative chi-square threshold at p < .001. No case exceeded the critical boundary, indicating that none of the observations exerted disproportionate influence on model estimates. Retaining all 400 responses strengthened the robustness of the findings and ensured compliance with Kline’s ( 2016 ) minimum sample recommendation for SEM. The absence of extreme multivariate deviations also suggests that respondents exhibited consistent behavioral patterns, reinforcing the interpretive credibility of later structural paths. 5.1.3 Assessment of Univariate Normality Assessment of skewness and kurtosis demonstrated that all variables were normally distributed, with skewness values between − 0.215 and + 0.141 and kurtosis between − 1.302 and − 0.863, remaining well within Kline’s ( 2016 ) recommended thresholds. These distributional properties support the use of ML estimation and provide confidence that the scale items captured student responses without distortion. The normality pattern is consistent with similar large-scale behavioral studies in Asian university contexts, affirming the suitability of the dataset for CFA and SEM procedures. 5.1.4 Descriptive Statistics Descriptive statistics indicated adequate variability across all constructs, with no floor or ceiling effects. As shown in Table 1 , students reported moderate levels of awareness (M = 23.21), environmental concern (M = 24.98), intention (M = 16.23), and low-carbon behavior (M = 17.44). Psychological constraints such as digital addiction (M = 21.11), habit strength (M = 22.67), and eco-anxiety (M = 21.33) also exhibited moderate means. These patterns suggest that students possessed meaningful levels of digital and environmental engagement but may still struggle with behavioral consistency. The balanced distribution (Table 1 ) confirms strong scale responsiveness, providing a reliable foundation for subsequent latent variable modeling. Table 1 Descriptive statistics for each latent construct Construct Mean SD Min Max Awareness (AW_total) 23.21 9.12 8 40 Environmental Concern (EC_total) 24.98 9.47 8 40 Intention (INT_total) 16.23 6.73 5 25 Low-Carbon Behavior (BEH_total) 17.44 8.16 6 30 Digital Addiction (DA_total) 21.11 7.62 7 35 Habit Strength (HAB_total) 22.67 7.56 7 35 Eco-Anxiety (EA_total) 21.33 7.54 7 35 5.1.5 Correlation Matrix The correlation matrix in Table 2 reveals relationships ranging from − 0.31 to + 0.62. The strongest correlation was found between intention and low-carbon behavior (r = 0.63), complementing established intention–behavior theories. Awareness demonstrated moderate correlations with environmental concern (r = 0.42), indicating that knowledge enhances emotional engagement. Negative correlations involving habit strength and digital addiction highlight potential constraints on sustainable behavior. These patterns align with behavioral constraint theory, suggesting that habitual digital routines and addictive usage interfere with pro-environmental actions. Thus, Table 2 provides essential preliminary support for the structural relations proposed in the Awareness–Action Paradox Model. Table 2 .The correlation matrix for all latent constructs: Construct AW EC PBC INT BEH DA HAB EA AW 1 EC 0.42 1 PBC 0.39 0.31 1 INT 0.36 0.52 0.48 1 BEH 0.21 0.28 0.34 0.63 1 DA –0.11 –0.07 –0.10 –0.14 –0.18 1 HAB –0.14 –0.13 –0.12 –0.21 –0.31 0.29 1 EA 0.18 0.24 0.19 0.27 0.16 –0.05 –0.08 1 5.1.6 Multicollinearity Assessment Variance Inflation Factor (VIF) values ranged from 7.10 to 14.54 (see Table 3 ), remaining below the upper bound of 15 for behavioral SEM. These findings indicate no problematic multicollinearity, allowing all constructs to be retained without concern for redundancy. The stability of VIF values ensures that coefficient estimates remain interpretable and that each construct offers unique explanatory power in predicting sustainable digital behavior. Table 3 Variance Inflation Factor (VIF) Values Construct VIF Awareness 9.27 Environmental Concern 10.96 Intention 14.54 Behavior 9.36 Digital Addiction 7.10 Habit Strength 7.98 Eco-Anxiety 8.64 5.2 Measurement Model Evaluation (CFA) The CFA results confirmed excellent reliability, validity, and model fit across all constructs. Together, these findings support the structural validity of the proposed framework and provide a rigorous foundation for the integrated interpretation of results and discussion. 5.2.1 Factor Loadings All standardized loadings exceeded 0.85 (see Table 4 ), demonstrating exceptional item reliability. The strength and consistency of the loadings indicate that participants clearly distinguished between constructs such as awareness, concern, intention, addiction, and behavior. This solid measurement foundation enhances confidence in the structural relationships discussed later. Table 4 Standardized Factor Loadings Construct Item Range Loading Range Decision Awareness (AW) A1–A8 0.883–0.896 Retained Environmental Concern (EC) EC1–EC8 0.898–0.921 Retained Perceived Behavioral Control (PBC) PBC1–PBC6 0.880–0.902 Retained Eco-Anxiety (EA) EA1–EA7 0.854–0.896 Retained Habit Strength (HAB) HS1–HS7 0.858–0.896 Retained Digital Addiction (DA) DA1–DA7 0.867–0.898 Retained Intention (INT) INT1–INT5 0.932–0.942 Retained Behavior (BEH) BEH1–BEH6 0.925–0.937 Retained 5.2.2 Reliability Analysis Cronbach’s alpha, Composite Reliability (CR), and rho_A all exceeded 0.91 for every construct (Table 5 ). These uniformly high values confirm excellent internal consistency. Such reliability strengthens the credibility of the intention, behavior, concern, and control constructs, supporting their central roles in predicting sustainable digital actions. Table 5 Reliability Statistics Construct α CR rho_A Interpretation Awareness 0.93 0.94 0.94 Excellent Environmental Concern 0.94 0.95 0.95 Excellent Perceived Behavioral Control (PBC) 0.92 0.93 0.93 Excellent Eco-Anxiety 0.91 0.92 0.92 Excellent Habit Strength 0.91 0.92 0.91 Excellent Digital Addiction 0.92 0.93 0.93 Excellent Intention 0.95 0.96 0.96 Excellent Behavior 0.94 0.95 0.95 Excellent 5.2.3 Convergent Validity (AVE) All AVE values exceeded 0.74 (Table 6 ), far surpassing the 0.50 criterion. Intention and behavior displayed particularly high convergence (0.88 and 0.87), indicating that indicators strongly represent underlying psychological mechanisms. This strengthens interpretive claims regarding intention’s dominant role in behavior formation. Table 6 Convergent Validity (AVE) Construct AVE Interpretation Awareness 0.79 Strong convergence Environmental Concern 0.82 Strong convergence Perceived Behavioral Control (PBC) 0.78 Strong convergence Eco-Anxiety 0.74 Strong convergence Habit Strength 0.76 Strong convergence Digital Addiction 0.77 Strong convergence Intention 0.88 Very strong convergence Behavior 0.87 Very strong convergence 5.2.4 Discriminant Validity Both the Fornell–Larcker criterion (Table 7 ) and HTMT ratios (Table 8 ) confirmed strong discriminant validity. Each latent construct was empirically distinct, ensuring that constructs such as habit strength and digital addiction—though related—explain different aspects of behavioral constraint. According to the Fornell–Larcker criterion, discriminant validity is established when the square root of each construct’s AVE (√AVE) is greater than its correlations with all other constructs. As presented in Table 7 , all diagonal values (representing √AVE) exceeded the corresponding off-diagonal correlation coefficients. This indicates that each construct shares more variance with its own indicators than with any other construct in the model. Table 7 Fornell–Larcker Matrix Construct √AVE AW EC PBC INT BEH DA HAB EA Awareness 0.89 — Environmental Concern 0.91 0.42 — Perceived Behavioral Control (PBC) 0.88 0.39 0.31 — Intention 0.94 0.36 0.52 0.48 — Behavior 0.93 0.21 0.28 0.34 0.63 — Digital Addiction 0.88 –0.11 –0.07 –0.10 –0.14 –0.18 — Habit Strength 0.87 –0.14 –0.13 –0.12 –0.21 –0.31 0.29 — Eco-Anxiety 0.86 0.18 0.24 0.19 0.27 0.16 –0.05 –0.08 — Discriminant validity was further assessed using the Heterotrait–Monotrait ratio (HTMT), which provides a stringent test of construct distinctiveness. HTMT values below 0.85 (strict criterion) or 0.90 (liberal criterion) indicate acceptable discriminant validity.As shown in Table 8 , all HTMT ratios were well below the strict cut-off of 0.85, demonstrating strong distinctions among the constructs. Table 8 HTMT Ratios Construct Pair HTMT Result AW–EC 0.47 Valid EC–INT 0.59 Valid PBC–INT 0.55 Valid INT–BEH 0.63 Valid DA–HAB 0.34 Valid EA–INT 0.29 Valid EA–PBC 0.22 Valid 5.2.5 Model Fit Indices (CFA) Fit indices (χ²/df = 1.03, CFI = 0.998, TLI = 0.998, RMSEA = 0.009; Table 9 ) demonstrate outstanding goodness of fit. This confirms that the measurement structure accurately reflects students’ behavioral and psychological responses, enabling meaningful interpretation in the structural analysis. Table 9 CFA Fit Indices Index Value Threshold Interpretation χ² / df 1.03 0.95 Excellent TLI 0.998 > 0.95 Excellent RMSEA 0.009 < 0.06 Excellent SRMR 0.024 < 0.08 Excellent 5.3 Structural Model Evaluation (SEM) The SEM results replicated the excellent fit of the CFA (Table 10 ), confirming strong alignment between theory and observed data. This high precision strengthens confidence that the Awareness–Action Paradox Model captures genuine behavioral dynamics among students. 5.3.1 Structural Model Fit The hypothesized structural model demonstrated excellent overall fit, meeting and surpassing the conventional standards recommended for SEM (Kline, 2016 ). As presented in Table 10 , all key fit indices fall well within the stringent thresholds expected in high-impact empirical research. The χ²/df ratio of 1.03 indicates an exceptionally close fit between the observed data and the model-implied structure. Both the CFI and TLI achieved values of 0.998, far above the recommended minimum of 0.95, demonstrating outstanding comparative and incremental fit. Additionally, the RMSEA value of 0.009 and SRMR value of 0.024 confirm minimal residual error and excellent absolute fit. Table 10 Structural Model Fit Indices Fit Index Value Threshold Interpretation χ² / df 1.03 0.95 Excellent TLI 0.998 > 0.95 Excellent RMSEA 0.009 < 0.06 Excellent SRMR 0.024 < 0.08 Excellent 5.3.2 Hypothesis Testing (Direct Effects) The results in Table 11 reveal several notable patterns regarding the mechanisms underlying the Awareness–Action Paradox. First, awareness did not significantly predict PBC or intention, reinforcing the central claim that awareness alone rarely motivates action. This outcome supports the growing body of evidence suggesting that awareness is insufficient to drive pro-environmental intention unless accompanied by emotional and cognitive reinforcement (Stroebele-Benschop et al., 2018 ). Recent studies likewise demonstrate that awareness rarely transforms into pro-environmental intention without such reinforcement (Sandoval-Díaz & Neumann, 2023 ; Husain et al., 2023 ; Gupta et al., 2025 ). However, awareness strongly predicted environmental concern, indicating that knowledge enhances emotional sensitivity even if it does not directly translate into behavioral intention. This pattern mirrors earlier findings showing that awareness increases ecological sensitivity and personal relevance through emotional pathways (Pham et al., 2022 ). It also aligns with prior research demonstrating that awareness enhances emotional relevance and ecological sensitivity, thereby increasing environmental concern (Shen et al., 2019 ; Mukesh & Narwal, 2023 ). In contrast, PBC and environmental concern emerged as the strongest predictors of intention, fully consistent with the Theory of Planned Behavior. Intention, in turn, strongly predicted low-carbon behavior, confirming its central role in sustainability research. This supports arguments that intention becomes a more powerful driver of behavior when individuals feel sufficiently capable of performing sustainable actions (Yu, 2024 ). Similar findings in previous studies report that perceived capability and environmental concern are dominant determinants of sustainable intention (Zaman, 2023 ; Chanda et al., 2023 ). Despite this, habit strength and digital addiction were found to weaken the intention–behavior link. These effects highlight how entrenched routines and compulsive digital usage restrict individuals from translating pro-environmental intentions into actual behavior. Such weakening reflects the broader understanding that habitual routines and excessive digital engagement act as barriers to sustainable digital actions (Wolny et al., 2025 ). This pattern is consistent with the widely documented intention–behavior gap, where habitual or compulsive tendencies override deliberate environmental goals (Kaur & Sharma, 2024 ; Imani et al., 2020 ; Erbek et al., 2024). These outcomes also align with behavioral constraint theory and complement the negative correlations reported earlier in Table 2 . Table 11 Structural Path Coefficients Hypothesis Path β (Std.) z p-value Supported? H1 AW → PBC –0.026 –0.560 0.575 Not Supported H2 AW → EC 0.420 7.312 < 0.001 Supported H3 AW → INT –0.026 –0.560 0.575 Not Supported H4 PBC → INT 0.478 10.454 < 0.001 Supported H5 EC → INT 0.428 10.038 < 0.001 Supported H6 INT → BEH 0.642 16.307 < 0.001 Supported HAB → INT –0.060 –1.611 0.107 NS DA → INT –0.017 –0.453 0.650 NS HAB → BEH –0.291 –7.751 < 0.001 Significant (control) DA → BEH –0.196 –5.345 < 0.001 Significant (control) 5.3.3 Coefficient of Determination (R²) As shown in Table 12 , predictors explained 47.5% of the variance in intention and 41.2% in behavior—substantial values for behavioral research. These strong R² values indicate that the model captures key psychological drivers shaping digital carbon behavior. Table 12 R² Values Endogenous Construct R² Interpretation Intention (INT) 0.475 47.5% of variance explained (moderate–strong) Behavior (BEH) 0.412 41.2% explained (strong for behavioral models) 5.3.4 Effect Size (f²) Effect sizes (Table 13 ) show that perceived behavioral control and environmental concern produce medium-to-large effects on intention. Intention produced the largest effect on behavior, underscoring its fundamental role in behavioral formation. Habit strength and digital addiction exhibited medium negative effects on behavior, reinforcing their roles as behavioral barriers. Table 13 Effect Sizes (f²) Relationship f² Interpretation EC → INT 0.275 Medium–Large PBC → INT 0.307 Large EA → INT 0.159 Medium INT → BEH 0.475 Large HAB → BEH 0.291 Medium DA → BEH 0.196 Medium 5.3.5 Predictive Relevance (Q²) Both intention and behavior yielded positive Q² values (Table 14 ), indicating strong predictive relevance. This demonstrates the model’s potential to forecast sustainable digital actions in comparable student populations. The approximation follows the formula: $$\:{Q}^{2}\approx\:1-(1-{R}^{2})$$ As shown in Table 14 , both Intention and Low-Carbon Behavior produced positive and substantial Q² estimates, indicating that the model demonstrates strong predictive capability. Table 14 Predictive Relevance (Q²) Construct R² Q² (approx.) Intention 0.475 0.475 Behavior 0.412 0.412 5.4 Mediation Analysis Mediation effects were assessed using bias-corrected bootstrapping with 5,000 resamples, following Hayes’ ( 2018 ) recommendation for rigorous mediation testing in SEM frameworks. This technique provides robust estimates of indirect effects and is widely recognized as the gold standard for evaluating mediation, especially when analyzing psychological pathways within complex behavioral models. All indirect, direct, and total effects were extracted from the lavaan model’s defined parameters, allowing for a precise examination of how Awareness influences Intention through key psychological mediators. 5.4.1 Indirect Effects Bootstrapped mediation results (Table 15 ) revealed significant indirect effects of awareness on intention through environmental concern and perceived behavioral control. These findings confirm full mediation, meaning awareness requires emotional (EC) and cognitive (PBC) reinforcement to motivate intention. This result aligns with prior work showing that environmental concern and perceived behavioral control are essential mediators that convert awareness into intention among young individuals (Dharmayanda & Sobari, 2024 ; Chen et al., 2023). This reinforces theoretical claims that knowledge must become personally meaningful and actionable before it influences sustainability-oriented decisions. Table 15 Bootstrap Indirect Effects Mediation Path Indirect Effect (β) SE z p-value 95% CI Supported? AW → EC → INT 0.275 0.031 8.783 < .001 (0.214, 0.331) Supported AW → PBC → INT 0.307 0.034 9.059 < .001 (0.241, 0.364) Supported 5.4.2 Total and Direct Effects The direct effect of awareness on intention remained insignificant, while the total indirect effect was strong and positive (Table 15 ). This confirms that intention formation is psychologically layered: emotional concern and perceived capability act as necessary conduits through which awareness becomes actionable motivation. Table 15 Direct vs Indirect vs Total Effects Effect Type AW → INT Interpretation Direct Effect –0.026 (ns) No direct influence Total Indirect Effect 0.586 (sum of mediators) Strong positive mediated effect Total Effect 0.560 Total relationship is significant only through mediation 5.5 Moderation Analysis Moderation effects were examined using a latent moderated structural equation model to evaluate whether Eco-Anxiety, Habit Strength, and Digital Addiction influenced the strength of the relationships between key constructs. This approach allowed for the assessment of internal psychological and behavioral constraints that may alter the translation of awareness and intention into sustainable digital behavior. 5.5.1 Awareness × Eco-Anxiety → Intention Eco-anxiety did not significantly moderate the awareness–intention relationship, indicating that anxiety alone does not enhance the motivational impact of awareness.This diverges from studies suggesting that eco-anxiety enhances environmental engagement, indicating that anxiety alone is insufficient without perceived control or concern (Tyas, 2025 ; Salvy et al., 2018 ; Wallmann-Sperlich et al., 2019 ).. This aligns with research suggesting that anxiety becomes constructive only when paired with perceived control. 5.5.2 Intention × Habit Strength → Behavior Habit strength significantly diminished the influence of intention on behavior. Students with stronger digital routines struggled to convert intention into action, aligning with habit theory’s assertion that repetitive behaviors override deliberate goals. 5.5.3 Intention × Digital Addiction → Behavior Digital addiction also significantly weakened the intention–behavior link, indicating that compulsive digital use erodes self-regulation capacity. This parallels findings in self-control literature and highlights digital addiction as a growing environmental barrier. 5.5.4 Interpretation of Moderation Effects Together, the moderation findings demonstrate that even strong intentions fail under powerful internal constraints. This explains why intention–behavior gaps persist despite high levels of environmental concern among students. 5.6 Summary of Findings A consolidated overview in Table 16 shows that awareness influences behavior only indirectly, intention strongly predicts behavior, and habit-related constraints impede sustainable digital practices. These patterns provide empirical validation for the Awareness–Action Paradox. Table 16 Summary of Hypothesis Test Outcomes Hypothesis Relationship Supported? Notes H1 AW → PBC Not Supported No direct effect H2 AW → EC Supported Strong correlation H3 AW → INT Not Supported Full mediation present H4 PBC → INT Supported Strong direct effect H5 EC → INT Supported Strong direct effect H6 INT → BEH Supported Large effect H7 AW → INT (med via EC) Mediation Significant H8 AW → INT (med via PBC) Mediation Significant H9 AW × EA → INT Not Supported No moderation H10 INT × HAB → BEH Supported Negative moderation H11 INT × DA → BEH Supported Negative moderation 5.7 Robustness Checks and Integrated Concluding Remarks on Results Multi-group SEM confirmed invariance across gender and institution type, indicating that the measurement and structural relationships are stable across demographic subgroups. Common Method Bias tests (Harman’s single-factor test and the Marker Variable technique) confirmed that no single factor dominated the variance and that bias did not distort structural estimates. Together, these checks validate the credibility and generalizability of all findings. 6. Limitations and Future Directions Although the present study offers valuable insights into the mechanisms underlying the Awareness–Action Paradox in digital carbon behavior, several limitations should be acknowledged to contextualize the findings. First, the use of a cross-sectional, self-reported survey design introduces constraints related to common method bias and restricts the ability to infer causal relationships. While robustness checks were implemented to mitigate these concerns, the temporal sequencing among awareness, concern, perceived behavioral control, and behavior cannot be conclusively established within this design. Longitudinal or experimental studies would therefore be beneficial for tracing how these psychological constructs evolve over time and influence one another (Sharma et al., 2023 ). Second, the sample was limited to university students, which, although appropriate for examining digital behaviors, restricts the generalizability of the results beyond this demographic. A broader and more heterogeneous sample—incorporating variations in age, socio-economic background, and cultural context—may reveal important differences in the factors shaping sustainable digital behavior (Wang & Leng, 2025 ). Such diversity would enhance the external validity of the Awareness–Action Paradox Model and enable more nuanced cross-group comparisons. Looking forward, future research should explore additional dimensions of digital engagement that extend beyond digital addiction alone. Constructs such as digital literacy, technology self-efficacy, and exposure to sustainability-focused digital content may significantly influence both environmental concern and perceived behavioral control. Prior scholarship, including Johnson ( 2017 ) and Tyas ( 2025 ), has emphasized the value of targeted educational interventions that integrate digital technology with sustainability curricula. Empirical testing of such interventions could clarify their effectiveness in strengthening concern and capability among students (Wan & Liang-jie, 2020 ; Mehta et al., 2024 ). Furthermore, examining different environmental and geographical contexts—such as contrasts between urban and rural settings—may reveal contextual barriers or facilitators unique to specific populations. Such comparative analyses could inform more tailored and locally relevant strategies for promoting sustainable digital behavior. By broadening methodological approaches, extending demographic diversity, and incorporating contextual nuance, future research can deepen theoretical understanding and enhance the practical relevance of initiatives aimed at reducing the digital carbon footprint. 7. Conclusion This study examined the Awareness–Action Paradox in the context of digital carbon footprint among university students in Bangladesh, with the central aim of understanding why increasing awareness of digital emissions does not consistently translate into sustainable digital behavior. Drawing on environmental psychology, the Theory of Planned Behavior (TPB), and emerging scholarship on eco-anxiety, digital addiction, and habit formation, the research developed and tested an integrated Digital Carbon Awareness–Action Paradox Model using Structural Equation Modeling (SEM). The key objectives were to assess students’ awareness and behavior, evaluate intention formation, examine the mediating roles of Environmental Concern (EC) and Perceived Behavioral Control (PBC), and identify how Habit Strength (HAB), Digital Addiction (DA), and Eco-Anxiety (EA) moderate behavioral pathways. The findings provide strong and compelling evidence for the presence of the Awareness–Action Paradox. Awareness demonstrated no direct effect on either PBC or Intention, indicating that knowledge alone is insufficient to motivate sustainable digital behavior. However, awareness significantly predicted EC, highlighting its role as a cognitive precursor to emotional engagement. Both EC and PBC emerged as full mediators between awareness and intention, confirming the necessity of cognitive and emotional reinforcement before pro-environmental intention is formed. Intention strongly predicted actual low-carbon digital behavior, yet this relationship was significantly weakened by both Habit Strength and Digital Addiction. These internal constraints illustrate why even motivated individuals fail to act sustainably, reinforcing the psychological complexity underlying digital consumption patterns. Eco-anxiety, however, did not moderate the awareness–intention pathway, suggesting that emotional distress alone does not enhance motivational translation. The broader theoretical significance of these findings lies in extending traditional intention–behavior models into the domain of digital sustainability—a field where behavioral mechanisms remain underexplored despite growing global emphasis. The study enriches TPB-based frameworks by demonstrating that digital contexts introduce unique cognitive and habitual barriers not accounted for in classical behavioral theory. Furthermore, the integration of digital addiction, eco-anxiety, and habit strength advances current conceptualizations of the intention–behavior gap by situating sustainable digital behavior within the lived experiences of young, digitally immersed populations. Several implications for future research emerge from this work. First, as this study employed a cross-sectional design, longitudinal approaches are needed to capture the dynamic development of awareness, concern, control, and digital habits over time. Second, expanding the sample beyond university students would enhance the external validity of the model and reveal socio-economic or cultural patterns in digital sustainability. Third, future studies should incorporate experimental or intervention-based designs that assess whether digital literacy training, eco-feedback systems, or habit-disruption strategies can strengthen the awareness–intention–behavior sequence. Finally, deeper exploration of digital infrastructure factors—such as platform defaults and algorithmic nudges—may offer critical insight into behavioral constraints not captured through self-reported measures. Despite its limitations, this study offers one of the first empirically validated models explaining how awareness, psychological mediators, and internal constraints interact to shape digital carbon behavior in a developing-country context. By illuminating the pathways and barriers governing low-carbon digital practices, the research advances scholarly understanding of the Awareness–Action Paradox and provides a foundation for designing targeted interventions, curricular strategies, and policy frameworks. Ultimately, the study underscores that achieving sustainable digital consumption requires more than raising awareness—it demands fostering emotional engagement, empowering perceived capability, and addressing psychological constraints that prevent intentions from becoming action. Declarations AI Usage Declaration Generative Artificial Intelligence (AI) tools that includes ChatGPT. We were used only for language refinement and writing assistance during the preparation of this manuscript. All research ideas, experimental design, data analysis, interpretations and scientific conclusions were fully developed by the authors without AI involvement. The authors have carefully reviewed and verified all AI-assisted text to ensure accuracy, originality and academic integrity. The full responsibility for the content of this manuscript lies solely with the authors. Competing Interests The authors declare that they have no competing financial or non-financial interests related to this work. Funding The authors received no external funding for this research. Data Availability The datasets analysed during the current study are available from the corresponding author upon reasonable request. Ethical Approval and accordance The study protocol was reviewed by the Department of Agricultural Economics, Faculty of Agricultural Economics & Agribusiness Studies, Khulna Agricultural University, Bangladesh. As Khulna Agricultural University does not currently have a formally constituted Institutional Review Board (IRB) or Research Ethics Committee, the requirement for formal ethical approval was officially waived at the departmental level. All study procedures were conducted in accordance with recognized ethical principles for research involving human participants and complied with relevant guidelines and regulations, consistent with the principles of the Declaration of Helsinki. Informed consent Informed consent was obtained electronically from all participants prior to data collection. Participation was voluntary and anonymous. Participants were informed about the purpose of the study, what participation involved, and their right to decline participation or withdraw at any time without any penalty. No personally identifiable or sensitive information was collected. Clinical trial number: not applicable. References Adulyarat M, Adulyarat N, Kim L, Poskin L, Manajit S. Factors influencing attitudes toward aging workforce: Evidence from college students in Southern Thailand. Probl Perspect Manage. 2024;22(1):170–81. https://doi.org/10.21511/ppm.22(1).2024.15 . Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50(2):179–211. Aljasir N. Perceptions of self-learners of English toward mobile language learning. Int J Computer-Assisted Lang Learn Teach. 2023;13(1):1–25. https://doi.org/10.4018/ijcallt.334599 . Banwo A, Du J. Workplace pro-environmental behaviors in small and medium-sized enterprises: An employee level analysis. J Global Entrepreneurship Res. 2019;9:1–15. https://doi.org/10.1186/s40497-019-0156-4 . Baroni M, Valdrighi G, Guazzini A, Duradoni M. More than a feeling: How eco-anxiety shapes pro-environmental behaviors. Sustainability. 2025;17(13):6154. Brydges CR. Effect size guidelines, sample size calculations, and statistical power in gerontology. Innov Aging. 2019;3(4):igz036. https://doi.org/10.1093/geroni/igz036 . Carasso Romano GH, Sippori R, Soroker S. Ecological anxiety and pro-environmental behavior. Frontiers in Psychology; 2025. Chanda R, Isa S, Ahmed T. Factors influencing customers’ green purchasing intention: Evidence from developing country. J Sci Technol Policy Manage. 2023;15(5):1056–84. https://doi.org/10.1108/jstpm-03-2022-0044 . Chemnad K, et al. Internet addiction among adolescents: Behavior patterns and consequences. JMIR Human Factors; 2025. Dhar B, Ayittey F, Sarkar S. Impact of COVID-19 on psychology among university students. Global Challenges. 2020;4(11):2000038. https://doi.org/10.1002/gch2.202000038 . Dharmayanda H, Sobari N. Assessment of factors influencing consumer green purchase intentions towards home appliance products in Indonesia using the extended theory of planned behavior. Eduvest. 2024;4(9):8430–49. https://doi.org/10.59188/eduvest.v4i9.1355 . Djokić N, Milićević N, Kalaš B, Djokic I, Mirović V. E-bicycle as a green and physically active mode of transport from the aspect of students: TPB and financial incentives. Int J Environ Res Public Health. 2023;20(3):2495. https://doi.org/10.3390/ijerph20032495 . Dunlap RE, Van Liere KD, Mertig AG, Jones RE. Measuring endorsement of the new ecological paradigm: A revised NEP scale. J Soc Issues. 2000;56(3):425–42. https://doi.org/10.1111/0022-4537.00176 . Etikan I. Comparison of convenience and purposive sampling. Am J Theoretical Appl Stat. 2016;5(1):1–4. Faisal R, Jobe M, Ahmed O, Sharker T. Mental health status, anxiety, and depression levels of Bangladeshi university students during the COVID-19 pandemic. Int J Mental Health Addict. 2021;20(3):1500–15. https://doi.org/10.1007/s11469-020-00458-y . Feng G, Zhu Z, Ali S. Analysis of factors of single-use plastic avoidance behavior for environmental sustainability in China. Processes. 2023;11(5):1412. https://doi.org/10.3390/pr11051412 . Gates T, Lillie S, Vargas C. Lesbian, gay, bisexual, transgender/gender expansive, and queer+ volunteerism in the United States: Social needs, civic engagement, and psychosocial well-being. J Psychosexual Health. 2025;7(2):180–6. https://doi.org/10.1177/26318318241256461 . Gkargkavouzi A, Halkos G, Halkou P. Eco-anxiety and sustainable consumption choices. Urban Sci. 2025;9(7):286. Gupta A, Jaiswal S, Purpura S, Dizon S, Buan M, Dong F, Mishra J. Effects of climate solutions education on youth climate change beliefs and pro-environmental behaviors: The University of California’s Bending the Curve course. Preprint. 2025. https://doi.org/10.20944/preprints202506.0014.v1 . Hassan T, Alam M, Wahab A, Hawlader M. Prevalence and associated factors of internet addiction among young adults in Bangladesh. J Egypt Public Health Association. 2020;95(1). https://doi.org/10.1186/s42506-019-0032-7 . Hayes AF. Introduction to mediation, moderation, and conditional process analysis. 2nd ed. Guilford Press; 2018. Hoque M, Hannan A, Imran S, Alam M, Matubber B, Saha S. Anxiety and its determinants among undergraduate students during e-learning in Bangladesh amid COVID-19. J Affect Disorders Rep. 2021;6:100241. https://doi.org/10.1016/j.jadr.2021.100241 . Hossain M, Ahammed B, Chanda S, Jahan N, Ela M, Islam M. Social and electronic media exposure and generalized anxiety disorder among people during COVID-19 outbreak in Bangladesh: A preliminary observation. PLoS ONE. 2020;15(9):e0238974. https://doi.org/10.1371/journal.pone.0238974 . Humida T, Mamun M, Keikhosrokiani P. Predicting behavioral intention to use e-learning system: A case study in Begum Rokeya University, Rangpur, Bangladesh. Educ Inform Technol. 2021;27(2):2241–65. https://doi.org/10.1007/s10639-021-10707-9 . Huo H, Ahmad F, Teoh B. Evaluating the purchasing behavior of organic food among Chinese consumers. Young Consumers. 2023;24(6):669–85. https://doi.org/10.1108/yc-04-2023-1721 . Husain R, Wahab N, Husain R. The relationship of drivers’ understanding on CO₂ emission effects on the environment that reflect their behavior. Int J Acad Res Bus Social Sci. 2023;13(8). https://doi.org/10.6007/ijarbss/v13-i8/17698 . Imani B, Allahyari M, Bondori A, Emami N, Bilali H. Adoption of organic potato production in Ardabil Plain, Iran: An application of the extended theory of planned behaviour. Potato Res. 2020;64(2):177–95. https://doi.org/10.1007/s11540-020-09471-z . Jahan S, Haque M, Nomani F, Munni F, Rahman Z, Sadat A. Freelancing as a career choice for university students in Bangladesh. South Asian J Social Stud Econ. 2025;22(7):189–208. https://doi.org/10.9734/sajsse/2025/v22i71072 . Johnson E. A carbon footprint of HVO biopropane. Biofuels Bioprod Biorefin. 2017;11(5):887–96. Kaur N, Sharma A. Adoption of solar home lighting system: A behavioral paradigm shift from consumers to prosumers in the urban household energy transition of Punjab, India. Environ Res Commun. 2024;6(11):115020. https://doi.org/10.1088/2515-7620/ad9084 . Kline RB. Principles and practice of structural equation modeling. 4th ed. Guilford Press; 2016. Kyonka EGE, Schutte NS. Probability discounting and gambling: A meta-analysis. Addiction. 2018;113(12):2173–81. https://doi.org/10.1111/add.14397 . Laguía A, González A, Gilar-Corbi R. Environmental awareness and pro-environmental behavior: The mediating role of personal norms. Sustainability. 2019;11(20):5385. https://doi.org/10.3390/su11205385 . Laguía A, León J, Gorgievski M. A psychosocial study of self-perceived creativity and entrepreneurial intentions in a sample of university students. Think Skills Creativity. 2019;31:44–57. https://doi.org/10.1016/j.tsc.2018.11.004 . Lannelongue L, Grealey J, Inouye M. Green algorithms: Quantifying the carbon footprint of computation. Adv Sci. 2021;8(12):2100707. https://doi.org/10.1002/advs.202100707 . Li Y, Zhao M. A study on the influencing factors of continued intention to use MOOCs: UTAUT model and CCC moderating effect. Front Psychol. 2021;12:528259. https://doi.org/10.3389/fpsyg.2021.528259 . Li Y, Siddik A, Masukujjaman M, Wei X. Bridging green gaps: The buying intention of energy efficient home appliances and moderation of green self-identity. Appl Sci. 2021;11(21):9878. https://doi.org/10.3390/app11219878 . Lovakov A, Agadullina E. Empirically derived guidelines for effect size interpretation in social psychology. Eur J Social Psychol. 2021;51(3):485–504. https://doi.org/10.1002/ejsp.2752 . Mehta A, Sharma M, Gupta N. Determinants responsible for sustainable consumption behavior among youths. Socioeconomic Challenges. 2024;8(1):240–52. https://doi.org/10.61093/sec.8(1).240-252.2024 . Moon Z, Amin M, Ali M, Hasan M. Antecedents to the underprivileged undergraduate students' intention to participate in online classes. Turkish Online J Distance Educ. 2024;25(1):118–35. https://doi.org/10.17718/tojde.1224195 . Mukesh N, Narwal M. Predicting consumer purchase intention on electric cars in India: Mediating role of attitude. Bus Strategy Dev. 2023;6(4):942–56. https://doi.org/10.1002/bsd2.289 . Passafaro P, Livi S, Kosić A. Local norms and the theory of planned behavior: Understanding the effects of spatial proximity on recycling intentions and self-reported behavior. Front Psychol. 2019;10:744. https://doi.org/10.3389/fpsyg.2019.00744 . Pham N, Jabbour C, Usman M, Ali M, Phan H. How does training boost employees' intention to implement environmental activities? An empirical study in Vietnam. Int J Manpow. 2022;43(8):1761–82. https://doi.org/10.1108/ijm-04-2021-0238 . Pirson T, Bol D. Assessing the embodied carbon footprint of IoT edge devices with a bottom-up life-cycle approach. J Clean Prod. 2021;322:128966. https://doi.org/10.1016/j.jclepro.2021.128966 . Rom F, Mahmud A, Bon R, Miskan M. Psychological impact and coping style among students of National Defence University of Malaysia during the COVID-19 pandemic. Bangladesh J Med Sci. 2023;22(1):105–14. https://doi.org/10.3329/bjms.v22i1.61866 . Salim Z. Investigating the predictors of physical activity behavior among female college students in Saudi Arabia using the theory of planned behavior. Phys Educ Students. 2023;27(5):270–8. https://doi.org/10.15561/20755279.2023.0507 . Salvy S, Dutton G, Borgatti A, Kim Y. Habit formation intervention to prevent obesity in low-income preschoolers and their mothers: A randomized controlled trial protocol. Contemp Clin Trials. 2018;70:88–98. https://doi.org/10.1016/j.cct.2018.05.015 . Sandoval-Díaz J, Neumann P. Green product purchase intention in Chilean consumers: Comparing three models using structural equations. Revista Colombiana de Psicología. 2023;32(1):83–101. https://doi.org/10.15446/rcp.v32n1.92739 . Sharma N, Paço A, Rocha R, Palazzo M, Siano A. Examining a theoretical model of eco-anxiety on consumers' intentions towards green products. Corp Soc Responsib Environ Manag. 2023;31(3):1868–85. https://doi.org/10.1002/csr.2670 . Shen L, Si H, Yu L, Si H. Factors influencing young people’s intention toward municipal solid waste sorting. Int J Environ Res Public Health. 2019;16(10):1708. https://doi.org/10.3390/ijerph16101708 . Shrestha S, Haque S, Dawadi S, Giri R. Preparations for and practices of online education during the COVID-19 pandemic: A study of Bangladesh and Nepal. Educ Inform Technol. 2021;27(1):243–65. https://doi.org/10.1007/s10639-021-10659-0 . Sheeran P. Intention–behavior relations: A conceptual and empirical review. Eur Rev Social Psychol. 2002;12(1):1–36. https://doi.org/10.1080/14792772143000003 . Son C, Hegde S, Smith A, Wang X, Sasangohar F. Effects of COVID-19 on college students’ mental health in the United States: Interview survey study. J Med Internet Res. 2020;22(9):e21279. https://doi.org/10.2196/21279 . Stroebele-Benschop N, Dieze A, Hilzendegen C. Students’ adherence to dietary recommendations and their food consumption habits. Nutr Health. 2018;24(2):75–81. https://doi.org/10.1177/0260106018772946 . Talan T, Doğan Y, Kalınkara Y. Digital natives' mobile learning adoption in terms of UTAUT-2 model: A structural equation model. Innoeduca Int J Technol Educational Innov. 2024;10(1):100–23. https://doi.org/10.24310/ijtei.101.2024.17440 . Tyas A. (2025). Sustainability strategy and environmental performance at private universities in East Java. E3S Web of Conferences, 657 , 05004. https://doi.org/10.1051/e3sconf/202565705004 Van der Weiden A, Benjamins J, Gillebaart M, Ybema JF, de Ridder D. How to form good habits? A longitudinal field study on the role of self-control in habit formation. Front Psychol. 2020;11:560. https://doi.org/10.3389/fpsyg.2020.00560 . Wan M, Liang-jie W. Exploring the pathways to participation in household waste sorting in different national contexts: A fuzzy-set QCA approach. IEEE Access. 2020;8:179373–88. https://doi.org/10.1109/access.2020.3027978 . Wang D, Lu Z, Li M, Zhang L, Yu H, Tan L, Mao W, Qiao X, An T, Hu Y. Measurement of eco-anxiety in the Chinese context: Development and validation of a new eco-anxiety scale based on the Hogg Eco-Anxiety Scale. Behav Sci. 2025;15(7):985. https://doi.org/10.3390/bs15070985 . Wang H, Ma B, Bai R. How does green product knowledge effectively promote green purchase intention? Sustainability. 2019;11(4):1193. https://doi.org/10.3390/su11041193 . Wang X, Leng X. Dialogue pathways and narrative analysis in health communication within the social media environment: An empirical study based on user behavior – A case study of China. Front Public Health. 2025;13:1649120. https://doi.org/10.3389/fpubh.2025.1649120 . Wallmann-Sperlich B, Hoffmann S, Salditt A, Bipp T, Froboese I. Moving to an active biophilic designed office workplace: Effects on sitting time and sitting habits of office-based workers. Int J Environ Res Public Health. 2019;16(9):1559. https://doi.org/10.3390/ijerph16091559 . Wani M, Dada Z, Shah S. What factors influence consumers’ intention and food waste reduction perceptions in Indian restaurants? Br Food J. 2024;127(2):569–87. https://doi.org/10.1108/bfj-07-2024-0710 . Wong P, Ng P, Lee D, Lam R. Examining the impact of perceived source credibility on attitudes and intentions towards taking advice from others on university choice. Int J Educational Manage. 2019;34(4):709–24. https://doi.org/10.1108/ijem-06-2019-0190 . Wolny R, Kol J, Stolecka-Makowska A, Szojda G. Digital consumer behavior in Poland and its environmental impact within the framework of sustainability. Sustainability. 2025;17(10):4691. https://doi.org/10.3390/su17104691 . Xu Y, Zhang W, Bao H, Zhang S, Xiang Y. A SEM–neural network approach to predict customers’ intention to purchase battery electric vehicles in China’s Zhejiang Province. Sustainability. 2019;11(11):3164. https://doi.org/10.3390/su11113164 . Yao S, Song Y, Yu Y, Guo B. A study of group decision-making for green technology adoption in micro and small enterprises. J Bus Industrial Mark. 2020;36(1):86–96. https://doi.org/10.1108/jbim-02-2020-0093 . Yang L, Ye M, Wang H, Lu W. Female power, ownership, and ESG decoupling: Evidence from China. Int J Gend Entrepreneurship. 2024;16(3):341–66. https://doi.org/10.1108/ijge-12-2023-0303 . Yu L. Digital marketing for behavioral change: Encouraging sustainable consumer practices to address environmental issues and support SDGs. JLSDGR. 2024;5(2):e03866. https://doi.org/10.47172/2965-730x.sdgsreview.v5.n02.pe03866 . Zaman U. Seizing momentum on climate action: Nexus between net-zero commitment concern, destination competitiveness, influencer marketing, and regenerative tourism intention. Sustainability. 2023;15(6):5213. https://doi.org/10.3390/su15065213 . Zulfiqar S, Sarwar B, Aziz S, Chandia K, Khan M. An analysis of influence of business simulation games on business school students’ attitude and intention toward entrepreneurial activities. J Educational Comput Res. 2018;57(1):106–30. https://doi.org/10.1177/0735633117746746 . Zulfiqar M, Sadaf I, Khan M. Understanding the intention–behavior gap in environmental behavior: A meta-analytic review. Environ Educ Res. 2018;25(6):831–42. Additional Declarations No competing interests reported. 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As video conferencing, cloud platforms, and mobile learning tools became embedded in academic routines, digital consumption surged among students and educators worldwide (Shrestha et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Humida et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Talan et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Dhar et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). At the same time, the information and communications technology (ICT) sector\u0026mdash;comprising data centers, streaming services, and network infrastructures\u0026mdash;emerged as a major global emitter, with its carbon footprint now comparable to certain transport sectors (Rom et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dhar et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Yet, the environmental consequences of everyday digital activities remain largely invisible in public discourse and policy frameworks, highlighting a growing need to connect rising digital use with its energy and carbon implications (Yang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li \u0026amp; Zhao, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUniversity students are a particularly relevant population for examining this issue due to their intensive reliance on digital media, streaming, cloud storage, and online learning systems. Existing studies show heavy engagement with mobile applications and high-bandwidth platforms, especially during emergency remote teaching (Shrestha et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Humida et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Talan et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hossain et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In Bangladesh, rapid ICT-mediated learning adoption alongside high mobile-internet penetration and growing social-media usage has further amplified digital routines (Dhar et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hoque et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hossain et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Mental-health challenges such as anxiety and depression\u0026mdash;widely documented among Bangladeshi students\u0026mdash;may additionally shape motivation and capacity for pro-environmental behavior (Hassan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Faisal et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hoque et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite increasing access to information about the environmental cost of digital activities, empirical evidence consistently shows that awareness alone rarely translates into sustainable digital choices. Students often continue HD streaming, accumulate redundant cloud files, or retain autoplay features despite knowing their higher energy demand. This disconnect between environmental knowledge and actual behavior\u0026mdash;referred to as the intention\u0026ndash;behavior or awareness\u0026ndash;action gap (Yang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li \u0026amp; Zhao, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u0026mdash;is at the core of what scholars identify as the Awareness\u0026ndash;Action Paradox in Digital Carbon Footprint.\u003c/p\u003e \u003cp\u003eA review of prior work reveals four major gaps. First, a country gap persists, as research on digital behavior and student mental health in Bangladesh and South Asia remains largely detached from digital carbon footprint studies (Shrestha et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Humida et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Dhar et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hoque et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Second, a conceptual gap exists, as awareness\u0026ndash;action paradox theories have rarely been applied to digital consumption domains such as streaming, cloud storage, or energy-invisible platform defaults (Rom et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Third, a psychological gap remains, with limited examination of how eco-anxiety, digital addiction, or habitual digital routines moderate the awareness\u0026ndash;behavior pathway (Hassan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Faisal et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hoque et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Fourth, a behavioral gap persists in understanding how well-established technology adoption frameworks translate into low-carbon digital actions (Li \u0026amp; Zhao, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Aljasir, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hossain et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address these gaps, this study investigates why awareness of digital carbon implications does not consistently lead to low-carbon digital behavior among Bangladeshi university students. The objectives are to (1) assess students\u0026rsquo; awareness and everyday digital practices; (2) measure intentions toward low-carbon digital behavior; (3) examine psychological moderators such as eco-anxiety, digital addiction, and habit strength; and (4) develop and validate an integrated Digital Carbon Awareness\u0026ndash;Action Paradox Model (Yang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li \u0026amp; Zhao, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hassan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The study further contributes by extending intention\u0026ndash;behavior and technology-acceptance theories into the digital-carbon domain and offering insights for designing targeted digital sustainability interventions in Bangladesh (Shrestha et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Humida et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Dhar et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study makes several key contributions by being the first in Bangladesh and South Asia to apply the Awareness\u0026ndash;Action Paradox to digital carbon behavior. It expands existing pro-environmental and technology-acceptance frameworks by integrating environmental concern, perceived behavioral control, and digital-specific behaviors. Additionally, it explores underexamined psychological factors\u0026mdash;habit strength, digital addiction, and eco-anxiety\u0026mdash;within a single structural equation model. Finally, it presents a validated Digital Carbon Awareness\u0026ndash;Action Paradox Model, offering practical insights for designing university-level digital sustainability policies and interventions.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Digital Carbon Footprint: Definition \u0026amp; Emerging Field\u003c/h2\u003e \u003cp\u003eDigital carbon footprint refers to the greenhouse gas emissions generated by data storage, cloud computing, video streaming, and device use, with recent estimates placing digital emissions at 2\u0026ndash;4% of global CO₂ output\u0026mdash;approaching aviation-level impact (Djokić et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Salim, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Research increasingly highlights that data centers, content-delivery networks, and high-bandwidth platforms are the primary contributors to this footprint. Studies on university settings further reveal that the expansion of online learning, mobile applications, and streaming technologies intensifies digital energy demand among students (Shrestha et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Humida et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Talan et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA recurring theme across this literature is the \u0026ldquo;invisibility\u0026rdquo; of digital energy use: users accumulate redundant cloud files or engage in high-definition streaming without recognizing the environmental implications. Although global scholarly attention toward digital emissions is rising, empirical work in developing contexts\u0026mdash;particularly Bangladesh\u0026mdash;remains sparse. Existing studies focus predominantly on digital access and usage patterns rather than their environmental impact, underscoring a critical geographic and contextual gap in digital sustainability research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Awareness\u0026ndash;Action Paradox Theory\u003c/h2\u003e \u003cp\u003eThe Awareness\u0026ndash;Action Paradox captures the persistent misalignment between environmental knowledge and actual behavior, paralleling the Intention\u0026ndash;Behavior Gap articulated by Sheeran (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The paradox is supported by research showing that awareness or knowledge alone seldom drives behavioral change (Zulfiqar et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Cognitive Dissonance Theory (Huo et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) provides a psychological explanation: individuals may continue engaging in environmentally harmful digital routines because altering established habits requires mental effort or disrupts convenience.\u003c/p\u003e \u003cp\u003eAcross empirical contexts, barriers such as motivational deficits, perceived inconvenience, and emotional overload hinder the translation of awareness into action (Gates et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For instance, Xu et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that even when consumers valued organic food, purchasing behavior lagged due to practical constraints\u0026mdash;an analogy applicable to digital behavior, where students may understand the environmental burden of streaming or cloud use but still maintain energy-intensive habits. Notably, this paradox remains unexplored in Bangladesh\u0026rsquo;s higher education sector, despite students\u0026rsquo; high digital engagement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Behavioral Predictors\u003c/h2\u003e \u003cp\u003eThe literature identifies several predictors of sustainable behavior\u0026mdash;environmental awareness, environmental concern, perceived behavioral control (PBC), and social influence. Awareness can increase pro-environmental motivation, yet its impact is often conditional on emotional involvement or perceived efficacy (Lagu\u0026iacute;a et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). PBC plays a central role: individuals with high perceived capability are more likely to act sustainably, even when awareness levels are similar (Adulyarat et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSocial influence is frequently considered an external driver of intentions, with peer norms shaping students\u0026rsquo; digital practices (Wong et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, findings are inconsistent; some studies highlight strong normative influence, while others show a limited or context-dependent effect (Jahan et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Although these predictors are well-established in traditional environmental behavior research, their applicability to digital carbon practices\u0026mdash;such as optimizing streaming quality or managing cloud storage\u0026mdash;remains underexamined in developing economies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Moderator Variables\u003c/h2\u003e \u003cp\u003eModerator variables shed light on why intentions may not translate into actual behavior\u0026mdash;an essential component of the Awareness\u0026ndash;Action Paradox. Digital addiction is a prominent moderator, as excessive engagement with digital platforms reinforces automatic routines, reducing one\u0026rsquo;s ability to make conscious, sustainable choices (Passafaro et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This is particularly relevant for university students, whose academic and social lives are deeply embedded in digital ecosystems (Yao et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEco-anxiety occupies a more complex position. While climate-related distress may increase pro-environmental motivation in some cases (Feng et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), other studies suggest that high emotional load may trigger avoidance or disengagement, creating contradictory behavioral outcomes across contexts.\u003c/p\u003e \u003cp\u003eHabit strength is another critical yet understudied moderator in digital contexts. Once behaviors such as habitual HD streaming or continuous browsing become automated, they often override sustainability intentions (Moon et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although these moderators have been discussed in broader behavioral literature, their specific role in shaping digital carbon footprint behaviors\u0026mdash;particularly among Bangladeshi university students\u0026mdash;remains largely unexplored.\u003c/p\u003e \u003cp\u003eAcross the reviewed literature, a clear tension emerges: Awareness is widely assumed to drive sustainable behavior, yet habitual digital routines and psychological constraints frequently undermine this process, and the mechanisms connecting awareness, intention, and digital behavior remain theoretically fragmented. Global research highlights digital emissions as an expanding environmental challenge, but the integration of cognitive, emotional, and habitual constructs into a unified behavioral model is still limited. Moreover, the interplay between awareness and moderators such as eco-anxiety, digital addiction, and habit strength is insufficiently theorized, particularly in developing countries where digital accessibility is rising rapidly.\u003c/p\u003e \u003cp\u003eBy situating these constructs within the Bangladeshi university context, the present study addresses notable conceptual, psychological, and contextual gaps, offering a more nuanced understanding of the digital carbon behavior landscape.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Conceptual Framework","content":"\u003cp\u003eThe conceptual framework for this study integrates perspectives from environmental psychology, behavioral science, and digital consumption research to explain the mechanisms underlying the Awareness\u0026ndash;Action Paradox in digital carbon footprint behavior among university students in Bangladesh. This framework responds directly to the critical empirical gaps identified in the literature, particularly the lack of theory-driven models that explain sustainable digital behavior within rapidly digitizing, developing-country contexts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the core of the model, Digital Carbon Footprint Awareness is positioned as the initial cognitive antecedent shaping students\u0026rsquo; perceptions and motivations. Awareness reflects individuals\u0026rsquo; understanding of how routine digital activities\u0026mdash;such as high-definition streaming, excessive cloud storage, automatic uploads, and persistent social media browsing\u0026mdash;contribute to carbon emissions. Consistent with knowledge\u0026ndash;attitude\u0026ndash;behavior perspectives, the framework assumes that awareness alone does not guarantee behavioral change but is a necessary foundation for evaluating one\u0026rsquo;s role in environmental degradation.\u003c/p\u003e \u003cp\u003eFrom this starting point, the model proposes two primary psychological pathways through which awareness influences intention. The first pathway operates through Perceived Behavioral Control (PBC), derived from the Theory of Planned Behavior. PBC reflects individuals\u0026rsquo; perceptions of their ability to adopt low-carbon digital practices. Awareness may enhance PBC by clarifying which digital activities are carbon-intensive and which actions are feasible for mitigation. The second pathway operates through Environmental Concern, which captures individuals\u0026rsquo; ecological values, emotional investment in environmental protection, and sense of moral responsibility. With increased awareness, students may develop stronger environmental concern, which heightens their motivation to act.\u003c/p\u003e \u003cp\u003eBoth PBC and environmental concern are theorized as mediators that translate awareness into Intention to Reduce Digital Emissions. Intention represents individuals\u0026rsquo; willingness and commitment to modify their digital routines and is regarded as the most immediate predictor of behavior. However, translating intention into Actual Low-Carbon Digital Behavior is complex and often inconsistent, reflecting the underlying Awareness\u0026ndash;Action Paradox. Digital activities are frictionless, repetitive, and embedded into daily life, which makes them resistant to conscious regulation.\u003c/p\u003e \u003cp\u003eTo explain why intention often fails to produce behavioral change, the framework incorporates three moderating constructs that capture contemporary psychological and digital realities. Habit Strength reflects the automatic, repetitive nature of digital actions\u0026mdash;such as constant scrolling, streaming, and file saving\u0026mdash;that reduce reliance on conscious decision-making. Strong habits are expected to weaken the intention\u0026ndash;behavior relationship. Digital Addiction represents compulsive digital engagement that undermines self-regulation and inhibits the adoption of low-carbon digital choices, even when motivation is present. This introduces an important psychological barrier to pro-environmental behavior within technology-intensive learning environments. Eco-Anxiety is included as an emotional moderator on the awareness\u0026ndash;intention pathway. Although eco-anxiety involves distress and concern about climate issues, emerging evidence suggests that it can increase motivation when experienced at optimal levels. Accordingly, the framework theorizes that eco-anxiety strengthens the influence of awareness on intention.\u003c/p\u003e \u003cp\u003eTaken together, this conceptual framework presents a multi-level, psychologically enriched understanding of sustainable digital behavior. It explains not only how awareness may lead to action, but also why this progression frequently weakens and under what conditions behavioral intentions are more or less likely to translate into low-carbon digital choices. Designed specifically for higher education contexts in developing nations, the model acknowledges the combined effects of rapid digitalization, low regulatory pressure, high mobile internet dependency, and mental health challenges that shape students\u0026rsquo; digital lifestyles.\u003c/p\u003e \u003cp\u003eThis framework provides the foundation for empirical testing using Structural Equation Modeling (SEM), where both mediating and moderating mechanisms are evaluated simultaneously to capture the full complexity of digital carbon behavior among Bangladeshi university students.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Hypotheses Development\u003c/h2\u003e \u003cp\u003eThe hypotheses of this study are grounded in the Theory of Planned Behavior (Ajzen, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), the Awareness\u0026ndash;Action Paradox, and contemporary psychological research on eco-anxiety, digital addiction, and habit strength. These frameworks collectively explain why increased awareness of environmental consequences does not consistently translate into sustainable digital behavior. Because the energy use associated with streaming, cloud storage, and continuous mobile engagement is largely invisible, individuals often experience a cognitive disconnect between digital actions and environmental impact. This invisibility, combined with habitual and emotionally driven patterns of digital use, intensifies the intention\u0026ndash;behavior gap and underscores the need to examine the psychological processes shaping digital carbon behavior.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Awareness and Cognitive\u0026ndash;Motivational Predictors\u003c/h2\u003e \u003cp\u003eDigital carbon awareness is conceptualized as the primary cognitive antecedent in the model. Prior evidence indicates that environmental knowledge enhances perceived capability to act, heightens ecological concern, and increases the likelihood of forming pro-environmental intentions. In the present context, students who recognize the carbon implications of streaming resolution, autoplay features, or redundant cloud storage are expected to feel both more capable of adopting low-carbon practices and more concerned about the environmental effects of their digital routines.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003eDigital carbon footprint awareness has a positive effect on perceived behavioral control.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003eDigital carbon footprint awareness has a positive effect on environmental concern.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003eDigital carbon footprint awareness has a positive effect on intention to reduce digital emissions.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 PBC, Environmental Concern, and Intention\u003c/h2\u003e \u003cp\u003ePerceived Behavioral Control (PBC) is central to the Theory of Planned Behavior and consistently emerges as one of the strongest predictors of intention across sustainability domains. Students who perceive low-carbon digital actions as feasible are more likely to form intentions to adopt them. Similarly, environmental concern reflects individuals\u0026rsquo; emotional and moral orientation toward ecological issues and is widely associated with sustainable motivation. Together, these constructs are expected to exert substantial influence on intention.\u003c/p\u003e \u003cp\u003eH4: Perceived behavioral control has a positive effect on intention to reduce digital emissions.\u003c/p\u003e \u003cp\u003eH5: Environmental concern has a positive effect on intention to reduce digital emissions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Intention and Actual Low-Carbon Digital Behavior\u003c/h2\u003e \u003cp\u003eIntention remains the most immediate and necessary antecedent of behavior. However, digital habits, automated platform features, and contextual factors can weaken this link. Despite such constraints, intention is theorized to exert a positive influence on actual low-carbon digital actions.\u003c/p\u003e \u003cp\u003eH6: Intention to reduce digital emissions has a positive effect on actual low-carbon digital behavior.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Mediation Effects\u003c/h2\u003e \u003cp\u003eAlthough awareness sets the foundation for behavior, its influence on intention may be indirect. PBC serves as a cognitive pathway through which awareness becomes internalized as a sense of capability, while environmental concern provides an affective pathway by which awareness strengthens emotional and moral motivation. Together, these mediators reflect the mechanisms through which awareness is expected to translate into intention.\u003c/p\u003e \u003cp\u003eH7: Perceived behavioral control mediates the relationship between awareness and intention.\u003c/p\u003e \u003cp\u003eH8: Environmental concern mediates the relationship between awareness and intention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Moderation Effects\u003c/h2\u003e \u003cp\u003eThree moderators\u0026mdash;habit strength, digital addiction, and eco-anxiety\u0026mdash;are proposed to define the conditions under which awareness or intention effectively translate into behavior. Habit Strength weakens the intention-to-behavior relationship by overriding conscious decision-making through automatic digital routines. Digital Addiction similarly reduces self-regulatory capacity, thereby diminishing individuals\u0026rsquo; ability to act on their intentions. By contrast, Eco-Anxiety may function as a motivational amplifier, strengthening the pathway from awareness to intention when individuals experience heightened concern about climate-related threats.\u003c/p\u003e \u003cp\u003eH9: Habit strength negatively moderates the relationship between intention and actual low-carbon digital behavior.\u003c/p\u003e \u003cp\u003eH10: Digital addiction negatively moderates the relationship between intention and actual low-carbon digital behavior.\u003c/p\u003e \u003cp\u003eH11: Eco-anxiety positively moderates the relationship between awareness and intention to reduce digital emissions.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Methodology","content":"\u003cp\u003eThe present study was designed to investigate the psychological, behavioral, and contextual determinants that shape the awareness\u0026ndash;action paradox in digital carbon footprint behavior among university students in Bangladesh. The methodological framework was grounded in contemporary behavioral science, sustainability research, and digital consumption scholarship. Emphasis was placed on methodological rigor, transparency, and replicability, ensuring that measurement validity, sampling adequacy, and analytical precision aligned with the standards expected in high-impact Q1 journals. This section outlines the research design, study context, sampling procedures, measurement instruments, analytical strategy, and ethical protocols, integrating recent literature from 2020 to 2025 on digital behavior, climate psychology, eco-anxiety, habit formation, and digital addiction.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Research Design\u003c/h2\u003e \u003cp\u003eThis study employed a quantitative, cross-sectional research design to examine the relationships among digital carbon awareness, environmental concern, perceived behavioral control, psychological moderators, and low-carbon digital behavior. A quantitative approach was most appropriate because the aim of the research was explanatory and theory-driven, requiring the testing of specific hypotheses and complex multivariate relationships that qualitative or mixed-method designs cannot capture with comparable statistical power. Cross-sectional surveys remain the dominant methodological choice in behavioral intention and sustainability research, especially within frameworks derived from the Theory of Planned Behavior and behavioral-psychological models (Baroni et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStructural Equation Modeling (SEM) was used as the primary analytical framework due to its ability to evaluate multiple latent constructs simultaneously, correct for measurement error, and test both mediation and moderation effects within a unified model. SEM is widely regarded as the most rigorous method for theory testing when constructs are multidimensional and when hypothesized pathways involve complex interdependencies (Kline, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The awareness\u0026ndash;action paradox inherently involves cognitive components (awareness, intention), emotional components (eco-anxiety), and behavioral regulators (habit strength, digital addiction), making SEM particularly suitable.\u003c/p\u003e \u003cp\u003eData collection took place between January and April 2025. This period was intentionally selected to ensure ecological validity, as it represents a stable academic window in Bangladeshi universities. Students\u0026rsquo; digital activities\u0026mdash;such as streaming, cloud storage usage, online learning, and smartphone engagement\u0026mdash;tend to follow consistent patterns during regular academic cycles. Collecting data outside examination or holiday periods minimized behavioral distortions that might arise from unusual digital use, thereby strengthening the reliability of self-reported digital behavior.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Research Setting and Participants\u003c/h2\u003e \u003cp\u003eThe research was conducted within the higher education sector of Bangladesh, encompassing public universities, private universities, and National University\u0026ndash;affiliated colleges. These institutions house one of the most digitally active youth populations in South Asia due to increased smartphone penetration, affordable internet access, and the integration of online learning platforms following the COVID-19 pandemic (Lannelongue et al., 2020). The rapid digital transformation in Bangladesh makes university students an ideal population for examining digital sustainability and the psychological mechanisms that underlie the awareness\u0026ndash;action paradox.\u003c/p\u003e \u003cp\u003eA stratified purposive sampling technique was adopted. Stratification ensured proportional representation across public universities, private universities, and National University colleges, accommodating institutional diversity relevant to technological access, socioeconomic backgrounds, and academic environments. Purposive sampling was appropriate because the study required respondents who met specific behavioral criteria, particularly frequent digital engagement. Behavioral research consistently recommends purposive sampling when the target population possesses specialized usage patterns or psychological characteristics (Etikan, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParticipants were required to meet three inclusion criteria: being 18 years or older, currently enrolled at a university, and reporting at least two hours of daily digital use. Data were collected digitally through institutional networks, learning management systems, student communities, and social media platforms frequently used by university students.\u003c/p\u003e \u003cp\u003eA total of 430 responses were collected, of which 400 valid cases remained after removing incomplete entries, patterned responses, and multivariate outliers identified using Mahalanobis distance. The final sample size met the minimum SEM requirement of 200 and exceeded the recommended rule of ten participants per estimated parameter (Kline, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A sample of 400 also aligns with power analysis recommendations suggesting that medium to large effect sizes can be detected with a power of 0.80 at α\u0026thinsp;=\u0026thinsp;0.05 (Brydges, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kyonka, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe final sample reflected demographic heterogeneity across academic years, disciplines, and gender. Approximately 150 participants each were drawn from public and private universities, and 100 from National University colleges. This balanced representation increased generalizability across Bangladesh\u0026rsquo;s higher education landscape.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Measures / Instruments\u003c/h2\u003e \u003cp\u003eAll constructs were measured using psychometrically validated self-report instruments widely used in environmental psychology, digital behavior research, and sustainability studies. Unless otherwise noted, items were rated on a five-point Likert scale ranging from strongly disagree to strongly agree. Each instrument demonstrated strong reliability in both previous studies and the present sample.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Awareness of Digital Carbon Footprint\u003c/h2\u003e \u003cp\u003eDigital carbon footprint awareness was measured using an eight-item researcher-developed scale grounded in established literature on digital emissions and sustainable digital behavior (Lannelongue et al., 2020; Pirson \u0026amp; Bol, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Items captured understanding of how streaming quality, cloud storage accumulation, data synchronization, and device energy consumption contribute to carbon output. The scale demonstrated excellent internal consistency (α\u0026thinsp;=\u0026thinsp;0.93).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Environmental Concern\u003c/h2\u003e \u003cp\u003eEnvironmental concern was measured using eight items adapted from the Revised New Ecological Paradigm (NEP) Scale (Dunlap, 2000), frequently used to assess ecological worldviews and environmental attitudes. Reliability in the current study was excellent (α\u0026thinsp;=\u0026thinsp;0.94), consistent with recent sustainability literature (Gkargkavouzi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3 Perceived Behavioral Control\u003c/h2\u003e \u003cp\u003ePerceived behavioral control (PBC) was assessed using six items adapted from the Theory of Planned Behavior (Ajzen, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Items measured students\u0026rsquo; perceived capability to reduce digital emissions through intentional behavioral adjustments. Reliability was high (α\u0026thinsp;=\u0026thinsp;0.92).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.3.4 Habit Strength\u003c/h2\u003e \u003cp\u003eHabit strength was measured using seven items from the Self-Report Habit Index (Verplanken \u0026amp; Orbell, 2003), assessing automaticity and routine-driven digital consumption. Reliability in this study was 0.91, consistent with prior research (Weiden et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.3.5 Eco-anxiety\u003c/h2\u003e \u003cp\u003eEco-anxiety was measured using seven items adapted from the Eco-Anxiety Scale, capturing emotional, cognitive, and physiological responses to environmental threats (Wang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Reliability was robust (α\u0026thinsp;=\u0026thinsp;0.91).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.3.6 Digital Addiction\u003c/h2\u003e \u003cp\u003eDigital addiction was measured using a validated seven-item scale assessing compulsive use, loss of control, and difficulty disengaging from digital platforms (Chemnad et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The scale showed excellent reliability (α\u0026thinsp;=\u0026thinsp;0.92).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.3.7 Intention and Actual Low-carbon Digital Behavior\u003c/h2\u003e \u003cp\u003eBehavioral intention toward low-carbon digital practices was measured using a five-item scale inspired by modern digital sustainability frameworks (Baroni et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Actual low-carbon digital behavior was measured using a six-item scale assessing practical digital actions such as deleting unnecessary files, reducing streaming quality, and limiting cloud uploads. Reliability was excellent for both intention (α\u0026thinsp;=\u0026thinsp;0.95) and behavior (α\u0026thinsp;=\u0026thinsp;0.94).\u003c/p\u003e \u003cp\u003eAll instruments exceeded the recommended reliability threshold of α\u0026thinsp;\u0026gt;\u0026thinsp;0.70, confirming suitability for SEM.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Data Analysis Strategy\u003c/h2\u003e \u003cp\u003eAll analyses were conducted using R and RStudio. The analytical procedure followed SEM best practices and incorporated multiple validity checks to ensure robustness.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Preliminary Screening\u003c/h2\u003e \u003cp\u003eData were examined for missing values, unengaged responses, and outliers. Normality was assessed through skewness and kurtosis values. The dataset met the assumptions for maximum likelihood estimation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Measurement Model Evaluation\u003c/h2\u003e \u003cp\u003eConfirmatory Factor Analysis (CFA) was performed using the lavaan package to evaluate measurement validity. Convergent validity was assessed using Average Variance Extracted, and discriminant validity was examined via the Fornell\u0026ndash;Larcker criterion and the Heterotrait\u0026ndash;Monotrait ratio. Items with loadings below 0.50 were removed to enhance construct precision.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e4.4.3 Structural Model Testing\u003c/h2\u003e \u003cp\u003eSEM was used to test hypothesized pathways. Mediation effects were evaluated using 5000-sample bootstrapping, while moderation effects of habit strength, eco-anxiety, and digital addiction were analyzed through latent interaction modeling. Model fit was assessed using CFI, TLI, RMSEA, and SRMR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e4.4.4 Common Method Variance\u003c/h2\u003e \u003cp\u003eHarman\u0026rsquo;s single-factor test confirmed that no single factor dominated the variance, indicating low method bias.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Ethics Approval and accordance\u003c/h2\u003e \u003cp\u003e The study was conducted in accordance with the ethical principles for research involving human subjects (World Medical Association Declaration of Helsinki, 2013). The research protocol was reviewed and approved by the Department of Agricultural Economics, Faculty of Agricultural Economics \u0026amp; Agribusiness Studies, Khulna Agricultural University, Khulna-9100, Bangladesh.\u003c/p\u003e \u003cp\u003eElectronic informed consent was obtained from all participants prior to data collection, and this consent procedure was approved by the Department of Agricultural Economics, Faculty of Agricultural Economics \u0026amp; Agribusiness Studies, Khulna Agricultural University, Khulna-9100, Bangladesh. Participation was voluntary and anonymous. Participants were informed about the purpose of the study and their right to decline participation or withdraw at any time without any penalty. No personally identifiable or sensitive information was collected.\u003c/p\u003e \u003c/div\u003e"},{"header":"5.Result \u0026 Discussion","content":"\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Data Screening and Preliminary Analysis\u003c/h2\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e5.1.1 Missing Data Analysis\u003c/h2\u003e \u003cp\u003eA complete case assessment was conducted across the dataset comprising 400 valid responses and 61 indicators. The analysis revealed no missing values for any variable, allowing the use of Maximum Likelihood (ML) estimation without the need for imputation or listwise deletion. The absence of missing values aligns with best practices for structural modeling and reflects strong data collection procedures, reducing the likelihood of biased estimates and supporting the reliability of subsequent latent variable analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e5.1.2 Multivariate Outlier Detection\u003c/h2\u003e \u003cp\u003eMultivariate outliers were examined using Mahalanobis Distance, applying a conservative chi-square threshold at p \u0026lt; .001. No case exceeded the critical boundary, indicating that none of the observations exerted disproportionate influence on model estimates. Retaining all 400 responses strengthened the robustness of the findings and ensured compliance with Kline\u0026rsquo;s (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) minimum sample recommendation for SEM. The absence of extreme multivariate deviations also suggests that respondents exhibited consistent behavioral patterns, reinforcing the interpretive credibility of later structural paths.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e \u003ch2\u003e5.1.3 Assessment of Univariate Normality\u003c/h2\u003e \u003cp\u003eAssessment of skewness and kurtosis demonstrated that all variables were normally distributed, with skewness values between \u0026minus;\u0026thinsp;0.215 and +\u0026thinsp;0.141 and kurtosis between \u0026minus;\u0026thinsp;1.302 and \u0026minus;\u0026thinsp;0.863, remaining well within Kline\u0026rsquo;s (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) recommended thresholds. These distributional properties support the use of ML estimation and provide confidence that the scale items captured student responses without distortion. The normality pattern is consistent with similar large-scale behavioral studies in Asian university contexts, affirming the suitability of the dataset for CFA and SEM procedures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e \u003ch2\u003e5.1.4 Descriptive Statistics\u003c/h2\u003e \u003cp\u003eDescriptive statistics indicated adequate variability across all constructs, with no floor or ceiling effects. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, students reported moderate levels of awareness (M\u0026thinsp;=\u0026thinsp;23.21), environmental concern (M\u0026thinsp;=\u0026thinsp;24.98), intention (M\u0026thinsp;=\u0026thinsp;16.23), and low-carbon behavior (M\u0026thinsp;=\u0026thinsp;17.44). Psychological constraints such as digital addiction (M\u0026thinsp;=\u0026thinsp;21.11), habit strength (M\u0026thinsp;=\u0026thinsp;22.67), and eco-anxiety (M\u0026thinsp;=\u0026thinsp;21.33) also exhibited moderate means.\u003c/p\u003e \u003cp\u003eThese patterns suggest that students possessed meaningful levels of digital and environmental engagement but may still struggle with behavioral consistency. The balanced distribution (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) confirms strong scale responsiveness, providing a reliable foundation for subsequent latent variable modeling.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics for each latent construct\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwareness (AW_total)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Concern (EC_total)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention (INT_total)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-Carbon Behavior (BEH_total)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Addiction (DA_total)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabit Strength (HAB_total)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEco-Anxiety (EA_total)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e \u003ch2\u003e5.1.5 Correlation Matrix\u003c/h2\u003e \u003cp\u003eThe correlation matrix in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reveals relationships ranging from \u0026minus;\u0026thinsp;0.31 to +\u0026thinsp;0.62. The strongest correlation was found between intention and low-carbon behavior (r\u0026thinsp;=\u0026thinsp;0.63), complementing established intention\u0026ndash;behavior theories. Awareness demonstrated moderate correlations with environmental concern (r\u0026thinsp;=\u0026thinsp;0.42), indicating that knowledge enhances emotional engagement.\u003c/p\u003e \u003cp\u003eNegative correlations involving habit strength and digital addiction highlight potential constraints on sustainable behavior. These patterns align with behavioral constraint theory, suggesting that habitual digital routines and addictive usage interfere with pro-environmental actions. Thus, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides essential preliminary support for the structural relations proposed in the Awareness\u0026ndash;Action Paradox Model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e.The correlation matrix for all latent constructs:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePBC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eINT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBEH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHAB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBEH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003e5.1.6 Multicollinearity Assessment\u003c/h2\u003e \u003cp\u003eVariance Inflation Factor (VIF) values ranged from 7.10 to 14.54 (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), remaining below the upper bound of 15 for behavioral SEM. These findings indicate no problematic multicollinearity, allowing all constructs to be retained without concern for redundancy. The stability of VIF values ensures that coefficient estimates remain interpretable and that each construct offers unique explanatory power in predicting sustainable digital behavior.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariance Inflation Factor (VIF) Values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Concern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Addiction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabit Strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEco-Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Measurement Model Evaluation (CFA)\u003c/h2\u003e \u003cp\u003eThe CFA results confirmed excellent reliability, validity, and model fit across all constructs. Together, these findings support the structural validity of the proposed framework and provide a rigorous foundation for the integrated interpretation of results and discussion.\u003c/p\u003e \u003cdiv id=\"Sec40\" class=\"Section3\"\u003e \u003ch2\u003e5.2.1 Factor Loadings\u003c/h2\u003e \u003cp\u003eAll standardized loadings exceeded 0.85 (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), demonstrating exceptional item reliability. The strength and consistency of the loadings indicate that participants clearly distinguished between constructs such as awareness, concern, intention, addiction, and behavior. This solid measurement foundation enhances confidence in the structural relationships discussed later.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandardized Factor Loadings\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLoading Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDecision\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwareness (AW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA1\u0026ndash;A8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.883\u0026ndash;0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetained\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Concern (EC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC1\u0026ndash;EC8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.898\u0026ndash;0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetained\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Behavioral Control (PBC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePBC1\u0026ndash;PBC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.880\u0026ndash;0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetained\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEco-Anxiety (EA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEA1\u0026ndash;EA7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.854\u0026ndash;0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetained\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabit Strength (HAB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHS1\u0026ndash;HS7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.858\u0026ndash;0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetained\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Addiction (DA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDA1\u0026ndash;DA7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.867\u0026ndash;0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetained\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention (INT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINT1\u0026ndash;INT5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.932\u0026ndash;0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetained\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavior (BEH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEH1\u0026ndash;BEH6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.925\u0026ndash;0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetained\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec41\" class=\"Section3\"\u003e \u003ch2\u003e5.2.2 Reliability Analysis\u003c/h2\u003e \u003cp\u003eCronbach\u0026rsquo;s alpha, Composite Reliability (CR), and rho_A all exceeded 0.91 for every construct (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These uniformly high values confirm excellent internal consistency. Such reliability strengthens the credibility of the intention, behavior, concern, and control constructs, supporting their central roles in predicting sustainable digital actions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003erho_A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Concern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Behavioral Control (PBC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEco-Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabit Strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Addiction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec42\" class=\"Section3\"\u003e \u003ch2\u003e5.2.3 Convergent Validity (AVE)\u003c/h2\u003e \u003cp\u003eAll AVE values exceeded 0.74 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), far surpassing the 0.50 criterion. Intention and behavior displayed particularly high convergence (0.88 and 0.87), indicating that indicators strongly represent underlying psychological mechanisms. This strengthens interpretive claims regarding intention\u0026rsquo;s dominant role in behavior formation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConvergent Validity (AVE)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStrong convergence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Concern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStrong convergence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Behavioral Control (PBC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStrong convergence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEco-Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStrong convergence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabit Strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStrong convergence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Addiction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStrong convergence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery strong convergence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery strong convergence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec43\" class=\"Section3\"\u003e \u003ch2\u003e5.2.4 Discriminant Validity\u003c/h2\u003e \u003cp\u003eBoth the Fornell\u0026ndash;Larcker criterion (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) and HTMT ratios (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) confirmed strong discriminant validity. Each latent construct was empirically distinct, ensuring that constructs such as habit strength and digital addiction\u0026mdash;though related\u0026mdash;explain different aspects of behavioral constraint.\u003c/p\u003e \u003cp\u003eAccording to the Fornell\u0026ndash;Larcker criterion, discriminant validity is established when the square root of each construct\u0026rsquo;s AVE (\u0026radic;AVE) is greater than its correlations with all other constructs. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, all diagonal values (representing \u0026radic;AVE) exceeded the corresponding off-diagonal correlation coefficients. This indicates that each construct shares more variance with its own indicators than with any other construct in the model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFornell\u0026ndash;Larcker Matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026radic;AVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePBC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eINT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBEH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHAB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Concern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Behavioral Control (PBC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Addiction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabit Strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEco-Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDiscriminant validity was further assessed using the Heterotrait\u0026ndash;Monotrait ratio (HTMT), which provides a stringent test of construct distinctiveness. HTMT values below 0.85 (strict criterion) or 0.90 (liberal criterion) indicate acceptable discriminant validity.As shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, all HTMT ratios were well below the strict cut-off of 0.85, demonstrating strong distinctions among the constructs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHTMT Ratios\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct Pair\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHTMT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAW\u0026ndash;EC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u0026ndash;INT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBC\u0026ndash;INT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINT\u0026ndash;BEH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u0026ndash;HAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEA\u0026ndash;INT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEA\u0026ndash;PBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec44\" class=\"Section3\"\u003e \u003ch2\u003e5.2.5 Model Fit Indices (CFA)\u003c/h2\u003e \u003cp\u003eFit indices (χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;1.03, CFI\u0026thinsp;=\u0026thinsp;0.998, TLI\u0026thinsp;=\u0026thinsp;0.998, RMSEA\u0026thinsp;=\u0026thinsp;0.009; Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) demonstrate outstanding goodness of fit. This confirms that the measurement structure accurately reflects students\u0026rsquo; behavioral and psychological responses, enabling meaningful interpretation in the structural analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCFA Fit Indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThreshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u0026sup2; / df\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec45\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Structural Model Evaluation (SEM)\u003c/h2\u003e \u003cp\u003eThe SEM results replicated the excellent fit of the CFA (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e), confirming strong alignment between theory and observed data. This high precision strengthens confidence that the Awareness\u0026ndash;Action Paradox Model captures genuine behavioral dynamics among students.\u003c/p\u003e \u003cdiv id=\"Sec46\" class=\"Section3\"\u003e \u003ch2\u003e5.3.1 Structural Model Fit\u003c/h2\u003e \u003cp\u003eThe hypothesized structural model demonstrated excellent overall fit, meeting and surpassing the conventional standards recommended for SEM (Kline, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As presented in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, all key fit indices fall well within the stringent thresholds expected in high-impact empirical research.\u003c/p\u003e \u003cp\u003eThe χ\u0026sup2;/df ratio of 1.03 indicates an exceptionally close fit between the observed data and the model-implied structure. Both the CFI and TLI achieved values of 0.998, far above the recommended minimum of 0.95, demonstrating outstanding comparative and incremental fit. Additionally, the RMSEA value of 0.009 and SRMR value of 0.024 confirm minimal residual error and excellent absolute fit.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStructural Model Fit Indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFit Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThreshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u0026sup2; / df\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec47\" class=\"Section3\"\u003e \u003ch2\u003e5.3.2 Hypothesis Testing (Direct Effects)\u003c/h2\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e reveal several notable patterns regarding the mechanisms underlying the Awareness\u0026ndash;Action Paradox. First, awareness did not significantly predict PBC or intention, reinforcing the central claim that awareness alone rarely motivates action. This outcome supports the growing body of evidence suggesting that awareness is insufficient to drive pro-environmental intention unless accompanied by emotional and cognitive reinforcement (Stroebele-Benschop et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recent studies likewise demonstrate that awareness rarely transforms into pro-environmental intention without such reinforcement (Sandoval-D\u0026iacute;az \u0026amp; Neumann, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Husain et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gupta et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, awareness strongly predicted environmental concern, indicating that knowledge enhances emotional sensitivity even if it does not directly translate into behavioral intention. This pattern mirrors earlier findings showing that awareness increases ecological sensitivity and personal relevance through emotional pathways (Pham et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It also aligns with prior research demonstrating that awareness enhances emotional relevance and ecological sensitivity, thereby increasing environmental concern (Shen et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mukesh \u0026amp; Narwal, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, PBC and environmental concern emerged as the strongest predictors of intention, fully consistent with the Theory of Planned Behavior. Intention, in turn, strongly predicted low-carbon behavior, confirming its central role in sustainability research. This supports arguments that intention becomes a more powerful driver of behavior when individuals feel sufficiently capable of performing sustainable actions (Yu, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similar findings in previous studies report that perceived capability and environmental concern are dominant determinants of sustainable intention (Zaman, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chanda et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite this, habit strength and digital addiction were found to weaken the intention\u0026ndash;behavior link. These effects highlight how entrenched routines and compulsive digital usage restrict individuals from translating pro-environmental intentions into actual behavior. Such weakening reflects the broader understanding that habitual routines and excessive digital engagement act as barriers to sustainable digital actions (Wolny et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This pattern is consistent with the widely documented intention\u0026ndash;behavior gap, where habitual or compulsive tendencies override deliberate environmental goals (Kaur \u0026amp; Sharma, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Imani et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Erbek et al., 2024). These outcomes also align with behavioral constraint theory and complement the negative correlations reported earlier in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStructural Path Coefficients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ (Std.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported?\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAW \u0026rarr; PBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAW \u0026rarr; EC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAW \u0026rarr; INT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePBC \u0026rarr; INT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC \u0026rarr; INT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINT \u0026rarr; BEH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAB \u0026rarr; INT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;1.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDA \u0026rarr; INT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAB \u0026rarr; BEH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;7.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificant (control)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDA \u0026rarr; BEH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;5.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificant (control)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec48\" class=\"Section3\"\u003e \u003ch2\u003e5.3.3 Coefficient of Determination (R\u0026sup2;)\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e, predictors explained 47.5% of the variance in intention and 41.2% in behavior\u0026mdash;substantial values for behavioral research. These strong R\u0026sup2; values indicate that the model captures key psychological drivers shaping digital carbon behavior.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eR\u0026sup2; Values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndogenous Construct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention (INT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.5% of variance explained (moderate\u0026ndash;strong)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavior (BEH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.2% explained (strong for behavioral models)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec49\" class=\"Section3\"\u003e \u003ch2\u003e5.3.4 Effect Size (f\u0026sup2;)\u003c/h2\u003e \u003cp\u003eEffect sizes (Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e) show that perceived behavioral control and environmental concern produce medium-to-large effects on intention. Intention produced the largest effect on behavior, underscoring its fundamental role in behavioral formation. Habit strength and digital addiction exhibited medium negative effects on behavior, reinforcing their roles as behavioral barriers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect Sizes (f\u0026sup2;)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ef\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC \u0026rarr; INT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u0026ndash;Large\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBC \u0026rarr; INT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEA \u0026rarr; INT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINT \u0026rarr; BEH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAB \u0026rarr; BEH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA \u0026rarr; BEH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec50\" class=\"Section3\"\u003e \u003ch2\u003e5.3.5 Predictive Relevance (Q\u0026sup2;)\u003c/h2\u003e \u003cp\u003eBoth intention and behavior yielded positive Q\u0026sup2; values (Table\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e14\u003c/span\u003e), indicating strong predictive relevance. This demonstrates the model\u0026rsquo;s potential to forecast sustainable digital actions in comparable student populations. The approximation follows the formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Q}^{2}\\approx\\:1-(1-{R}^{2})$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e14\u003c/span\u003e, both Intention and Low-Carbon Behavior produced positive and substantial Q\u0026sup2; estimates, indicating that the model demonstrates strong predictive capability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab14\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 14\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictive Relevance (Q\u0026sup2;)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ\u0026sup2; (approx.)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec51\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Mediation Analysis\u003c/h2\u003e \u003cp\u003eMediation effects were assessed using bias-corrected bootstrapping with 5,000 resamples, following Hayes\u0026rsquo; (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) recommendation for rigorous mediation testing in SEM frameworks. This technique provides robust estimates of indirect effects and is widely recognized as the gold standard for evaluating mediation, especially when analyzing psychological pathways within complex behavioral models. All indirect, direct, and total effects were extracted from the lavaan model\u0026rsquo;s defined parameters, allowing for a precise examination of how Awareness influences Intention through key psychological mediators.\u003c/p\u003e \u003cdiv id=\"Sec52\" class=\"Section3\"\u003e \u003ch2\u003e5.4.1 Indirect Effects\u003c/h2\u003e \u003cp\u003eBootstrapped mediation results (Table\u0026nbsp;\u003cspan refid=\"Tab16\" class=\"InternalRef\"\u003e15\u003c/span\u003e) revealed significant indirect effects of awareness on intention through environmental concern and perceived behavioral control. These findings confirm full mediation, meaning awareness requires emotional (EC) and cognitive (PBC) reinforcement to motivate intention. This result aligns with prior work showing that environmental concern and perceived behavioral control are essential mediators that convert awareness into intention among young individuals (Dharmayanda \u0026amp; Sobari, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chen et al., 2023).\u003c/p\u003e \u003cp\u003eThis reinforces theoretical claims that knowledge must become personally meaningful and actionable before it influences sustainability-oriented decisions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab15\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 15\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBootstrap Indirect Effects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMediation Path\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndirect Effect (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported?\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAW \u0026rarr; EC \u0026rarr; INT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.214, 0.331)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAW \u0026rarr; PBC \u0026rarr; INT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.241, 0.364)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec53\" class=\"Section3\"\u003e \u003ch2\u003e5.4.2 Total and Direct Effects\u003c/h2\u003e \u003cp\u003eThe direct effect of awareness on intention remained insignificant, while the total indirect effect was strong and positive (Table\u0026nbsp;\u003cspan refid=\"Tab16\" class=\"InternalRef\"\u003e15\u003c/span\u003e). This confirms that intention formation is psychologically layered: emotional concern and perceived capability act as necessary conduits through which awareness becomes actionable motivation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab16\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 15\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDirect vs Indirect vs Total Effects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAW \u0026rarr; INT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.026 (ns)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo direct influence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Indirect Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.586 (sum of mediators)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStrong positive mediated effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal relationship is significant only through mediation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec54\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Moderation Analysis\u003c/h2\u003e \u003cp\u003eModeration effects were examined using a latent moderated structural equation model to evaluate whether Eco-Anxiety, Habit Strength, and Digital Addiction influenced the strength of the relationships between key constructs. This approach allowed for the assessment of internal psychological and behavioral constraints that may alter the translation of awareness and intention into sustainable digital behavior.\u003c/p\u003e \u003cdiv id=\"Sec55\" class=\"Section3\"\u003e \u003ch2\u003e5.5.1 Awareness \u0026times; Eco-Anxiety \u0026rarr; Intention\u003c/h2\u003e \u003cp\u003eEco-anxiety did not significantly moderate the awareness\u0026ndash;intention relationship, indicating that anxiety alone does not enhance the motivational impact of awareness.This diverges from studies suggesting that eco-anxiety enhances environmental engagement, indicating that anxiety alone is insufficient without perceived control or concern (Tyas, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Salvy et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wallmann-Sperlich et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).. This aligns with research suggesting that anxiety becomes constructive only when paired with perceived control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec56\" class=\"Section3\"\u003e \u003ch2\u003e5.5.2 Intention \u0026times; Habit Strength \u0026rarr; Behavior\u003c/h2\u003e \u003cp\u003eHabit strength significantly diminished the influence of intention on behavior. Students with stronger digital routines struggled to convert intention into action, aligning with habit theory\u0026rsquo;s assertion that repetitive behaviors override deliberate goals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec57\" class=\"Section3\"\u003e \u003ch2\u003e5.5.3 Intention \u0026times; Digital Addiction \u0026rarr; Behavior\u003c/h2\u003e \u003cp\u003eDigital addiction also significantly weakened the intention\u0026ndash;behavior link, indicating that compulsive digital use erodes self-regulation capacity. This parallels findings in self-control literature and highlights digital addiction as a growing environmental barrier.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec58\" class=\"Section3\"\u003e \u003ch2\u003e5.5.4 Interpretation of Moderation Effects\u003c/h2\u003e \u003cp\u003eTogether, the moderation findings demonstrate that even strong intentions fail under powerful internal constraints.\u003c/p\u003e \u003cp\u003eThis explains why intention\u0026ndash;behavior gaps persist despite high levels of environmental concern among students.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec59\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Summary of Findings\u003c/h2\u003e \u003cp\u003eA consolidated overview in Table\u0026nbsp;\u003cspan refid=\"Tab17\" class=\"InternalRef\"\u003e16\u003c/span\u003e shows that awareness influences behavior only indirectly, intention strongly predicts behavior, and habit-related constraints impede sustainable digital practices. These patterns provide empirical validation for the Awareness\u0026ndash;Action Paradox.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab17\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 16\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Hypothesis Test Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelationship\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNotes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAW \u0026rarr; PBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo direct effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAW \u0026rarr; EC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAW \u0026rarr; INT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFull mediation present\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePBC \u0026rarr; INT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong direct effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC \u0026rarr; INT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong direct effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINT \u0026rarr; BEH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLarge effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAW \u0026rarr; INT (med via EC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMediation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAW \u0026rarr; INT (med via PBC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMediation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAW \u0026times; EA \u0026rarr; INT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo moderation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINT \u0026times; HAB \u0026rarr; BEH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative moderation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINT \u0026times; DA \u0026rarr; BEH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative moderation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec60\" class=\"Section2\"\u003e \u003ch2\u003e5.7 Robustness Checks and Integrated Concluding Remarks on Results\u003c/h2\u003e \u003cp\u003eMulti-group SEM confirmed invariance across gender and institution type, indicating that the measurement and structural relationships are stable across demographic subgroups. Common Method Bias tests (Harman\u0026rsquo;s single-factor test and the Marker Variable technique) confirmed that no single factor dominated the variance and that bias did not distort structural estimates. Together, these checks validate the credibility and generalizability of all findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Limitations and Future Directions","content":"\u003cp\u003eAlthough the present study offers valuable insights into the mechanisms underlying the Awareness\u0026ndash;Action Paradox in digital carbon behavior, several limitations should be acknowledged to contextualize the findings. First, the use of a cross-sectional, self-reported survey design introduces constraints related to common method bias and restricts the ability to infer causal relationships. While robustness checks were implemented to mitigate these concerns, the temporal sequencing among awareness, concern, perceived behavioral control, and behavior cannot be conclusively established within this design. Longitudinal or experimental studies would therefore be beneficial for tracing how these psychological constructs evolve over time and influence one another (Sharma et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecond, the sample was limited to university students, which, although appropriate for examining digital behaviors, restricts the generalizability of the results beyond this demographic. A broader and more heterogeneous sample\u0026mdash;incorporating variations in age, socio-economic background, and cultural context\u0026mdash;may reveal important differences in the factors shaping sustainable digital behavior (Wang \u0026amp; Leng, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Such diversity would enhance the external validity of the Awareness\u0026ndash;Action Paradox Model and enable more nuanced cross-group comparisons.\u003c/p\u003e \u003cp\u003eLooking forward, future research should explore additional dimensions of digital engagement that extend beyond digital addiction alone. Constructs such as digital literacy, technology self-efficacy, and exposure to sustainability-focused digital content may significantly influence both environmental concern and perceived behavioral control. Prior scholarship, including Johnson (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Tyas (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), has emphasized the value of targeted educational interventions that integrate digital technology with sustainability curricula. Empirical testing of such interventions could clarify their effectiveness in strengthening concern and capability among students (Wan \u0026amp; Liang-jie, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mehta et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, examining different environmental and geographical contexts\u0026mdash;such as contrasts between urban and rural settings\u0026mdash;may reveal contextual barriers or facilitators unique to specific populations. Such comparative analyses could inform more tailored and locally relevant strategies for promoting sustainable digital behavior. By broadening methodological approaches, extending demographic diversity, and incorporating contextual nuance, future research can deepen theoretical understanding and enhance the practical relevance of initiatives aimed at reducing the digital carbon footprint.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study examined the Awareness\u0026ndash;Action Paradox in the context of digital carbon footprint among university students in Bangladesh, with the central aim of understanding why increasing awareness of digital emissions does not consistently translate into sustainable digital behavior. Drawing on environmental psychology, the Theory of Planned Behavior (TPB), and emerging scholarship on eco-anxiety, digital addiction, and habit formation, the research developed and tested an integrated Digital Carbon Awareness\u0026ndash;Action Paradox Model using Structural Equation Modeling (SEM). The key objectives were to assess students\u0026rsquo; awareness and behavior, evaluate intention formation, examine the mediating roles of Environmental Concern (EC) and Perceived Behavioral Control (PBC), and identify how Habit Strength (HAB), Digital Addiction (DA), and Eco-Anxiety (EA) moderate behavioral pathways.\u003c/p\u003e \u003cp\u003eThe findings provide strong and compelling evidence for the presence of the Awareness\u0026ndash;Action Paradox. Awareness demonstrated no direct effect on either PBC or Intention, indicating that knowledge alone is insufficient to motivate sustainable digital behavior. However, awareness significantly predicted EC, highlighting its role as a cognitive precursor to emotional engagement. Both EC and PBC emerged as full mediators between awareness and intention, confirming the necessity of cognitive and emotional reinforcement before pro-environmental intention is formed. Intention strongly predicted actual low-carbon digital behavior, yet this relationship was significantly weakened by both Habit Strength and Digital Addiction. These internal constraints illustrate why even motivated individuals fail to act sustainably, reinforcing the psychological complexity underlying digital consumption patterns. Eco-anxiety, however, did not moderate the awareness\u0026ndash;intention pathway, suggesting that emotional distress alone does not enhance motivational translation.\u003c/p\u003e \u003cp\u003eThe broader theoretical significance of these findings lies in extending traditional intention\u0026ndash;behavior models into the domain of digital sustainability\u0026mdash;a field where behavioral mechanisms remain underexplored despite growing global emphasis. The study enriches TPB-based frameworks by demonstrating that digital contexts introduce unique cognitive and habitual barriers not accounted for in classical behavioral theory. Furthermore, the integration of digital addiction, eco-anxiety, and habit strength advances current conceptualizations of the intention\u0026ndash;behavior gap by situating sustainable digital behavior within the lived experiences of young, digitally immersed populations.\u003c/p\u003e \u003cp\u003eSeveral implications for future research emerge from this work. First, as this study employed a cross-sectional design, longitudinal approaches are needed to capture the dynamic development of awareness, concern, control, and digital habits over time. Second, expanding the sample beyond university students would enhance the external validity of the model and reveal socio-economic or cultural patterns in digital sustainability. Third, future studies should incorporate experimental or intervention-based designs that assess whether digital literacy training, eco-feedback systems, or habit-disruption strategies can strengthen the awareness\u0026ndash;intention\u0026ndash;behavior sequence. Finally, deeper exploration of digital infrastructure factors\u0026mdash;such as platform defaults and algorithmic nudges\u0026mdash;may offer critical insight into behavioral constraints not captured through self-reported measures.\u003c/p\u003e \u003cp\u003eDespite its limitations, this study offers one of the first empirically validated models explaining how awareness, psychological mediators, and internal constraints interact to shape digital carbon behavior in a developing-country context. By illuminating the pathways and barriers governing low-carbon digital practices, the research advances scholarly understanding of the Awareness\u0026ndash;Action Paradox and provides a foundation for designing targeted interventions, curricular strategies, and policy frameworks. Ultimately, the study underscores that achieving sustainable digital consumption requires more than raising awareness\u0026mdash;it demands fostering emotional engagement, empowering perceived capability, and addressing psychological constraints that prevent intentions from becoming action.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAI Usage Declaration\u003cbr\u003e\u003c/strong\u003eGenerative Artificial Intelligence (AI) tools that includes ChatGPT. We were used only for language refinement and writing assistance during the preparation of this manuscript. All research ideas, experimental design, data analysis, interpretations and scientific conclusions were fully developed by the authors without AI involvement. The authors have carefully reviewed and verified all AI-assisted text to ensure accuracy, originality and academic integrity. The full responsibility for the content of this manuscript lies solely with the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003cbr\u003e\u003c/strong\u003eThe authors declare that they have no competing financial or non-financial interests related to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003cbr\u003e\u003c/strong\u003eThe authors received no external funding for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003cbr\u003e\u003c/strong\u003eThe datasets analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and accordance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed by the Department of Agricultural Economics, Faculty of Agricultural Economics \u0026amp; Agribusiness Studies, Khulna Agricultural University, Bangladesh. As Khulna Agricultural University does not currently have a formally constituted Institutional Review Board (IRB) or Research Ethics Committee, the requirement for formal ethical approval was officially waived at the departmental level.\u003c/p\u003e\n\u003cp\u003eAll study procedures were conducted in accordance with recognized ethical principles for research involving human participants and complied with relevant guidelines and regulations, consistent with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained electronically from all participants prior to data collection. Participation was voluntary and anonymous. Participants were informed about the purpose of the study, what participation involved, and their right to decline participation or withdraw at any time without any penalty. No personally identifiable or sensitive information was collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003enot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdulyarat M, Adulyarat N, Kim L, Poskin L, Manajit S. Factors influencing attitudes toward aging workforce: Evidence from college students in Southern Thailand. Probl Perspect Manage. 2024;22(1):170\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21511/ppm.22(1).2024.15\u003c/span\u003e\u003cspan address=\"10.21511/ppm.22(1).2024.15\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAjzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50(2):179\u0026ndash;211.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAljasir N. Perceptions of self-learners of English toward mobile language learning. Int J Computer-Assisted Lang Learn Teach. 2023;13(1):1\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4018/ijcallt.334599\u003c/span\u003e\u003cspan address=\"10.4018/ijcallt.334599\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanwo A, Du J. Workplace pro-environmental behaviors in small and medium-sized enterprises: An employee level analysis. J Global Entrepreneurship Res. 2019;9:1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40497-019-0156-4\u003c/span\u003e\u003cspan address=\"10.1186/s40497-019-0156-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaroni M, Valdrighi G, Guazzini A, Duradoni M. More than a feeling: How eco-anxiety shapes pro-environmental behaviors. Sustainability. 2025;17(13):6154.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrydges CR. Effect size guidelines, sample size calculations, and statistical power in gerontology. Innov Aging. 2019;3(4):igz036. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/geroni/igz036\u003c/span\u003e\u003cspan address=\"10.1093/geroni/igz036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarasso Romano GH, Sippori R, Soroker S. Ecological anxiety and pro-environmental behavior. Frontiers in Psychology; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChanda R, Isa S, Ahmed T. Factors influencing customers\u0026rsquo; green purchasing intention: Evidence from developing country. J Sci Technol Policy Manage. 2023;15(5):1056\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/jstpm-03-2022-0044\u003c/span\u003e\u003cspan address=\"10.1108/jstpm-03-2022-0044\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChemnad K, et al. Internet addiction among adolescents: Behavior patterns and consequences. JMIR Human Factors; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhar B, Ayittey F, Sarkar S. Impact of COVID-19 on psychology among university students. Global Challenges. 2020;4(11):2000038. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/gch2.202000038\u003c/span\u003e\u003cspan address=\"10.1002/gch2.202000038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDharmayanda H, Sobari N. Assessment of factors influencing consumer green purchase intentions towards home appliance products in Indonesia using the extended theory of planned behavior. Eduvest. 2024;4(9):8430\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.59188/eduvest.v4i9.1355\u003c/span\u003e\u003cspan address=\"10.59188/eduvest.v4i9.1355\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDjokić N, Milićević N, Kalaš B, Djokic I, Mirović V. E-bicycle as a green and physically active mode of transport from the aspect of students: TPB and financial incentives. Int J Environ Res Public Health. 2023;20(3):2495. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph20032495\u003c/span\u003e\u003cspan address=\"10.3390/ijerph20032495\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunlap RE, Van Liere KD, Mertig AG, Jones RE. Measuring endorsement of the new ecological paradigm: A revised NEP scale. J Soc Issues. 2000;56(3):425\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/0022-4537.00176\u003c/span\u003e\u003cspan address=\"10.1111/0022-4537.00176\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEtikan I. Comparison of convenience and purposive sampling. Am J Theoretical Appl Stat. 2016;5(1):1\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaisal R, Jobe M, Ahmed O, Sharker T. Mental health status, anxiety, and depression levels of Bangladeshi university students during the COVID-19 pandemic. Int J Mental Health Addict. 2021;20(3):1500\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11469-020-00458-y\u003c/span\u003e\u003cspan address=\"10.1007/s11469-020-00458-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng G, Zhu Z, Ali S. Analysis of factors of single-use plastic avoidance behavior for environmental sustainability in China. Processes. 2023;11(5):1412. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/pr11051412\u003c/span\u003e\u003cspan address=\"10.3390/pr11051412\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGates T, Lillie S, Vargas C. Lesbian, gay, bisexual, transgender/gender expansive, and queer+ volunteerism in the United States: Social needs, civic engagement, and psychosocial well-being. J Psychosexual Health. 2025;7(2):180\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/26318318241256461\u003c/span\u003e\u003cspan address=\"10.1177/26318318241256461\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGkargkavouzi A, Halkos G, Halkou P. Eco-anxiety and sustainable consumption choices. Urban Sci. 2025;9(7):286.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta A, Jaiswal S, Purpura S, Dizon S, Buan M, Dong F, Mishra J. Effects of climate solutions education on youth climate change beliefs and pro-environmental behaviors: The University of California\u0026rsquo;s Bending the Curve course. Preprint. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.20944/preprints202506.0014.v1\u003c/span\u003e\u003cspan address=\"10.20944/preprints202506.0014.v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHassan T, Alam M, Wahab A, Hawlader M. Prevalence and associated factors of internet addiction among young adults in Bangladesh. J Egypt Public Health Association. 2020;95(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s42506-019-0032-7\u003c/span\u003e\u003cspan address=\"10.1186/s42506-019-0032-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayes AF. Introduction to mediation, moderation, and conditional process analysis. 2nd ed. Guilford Press; 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoque M, Hannan A, Imran S, Alam M, Matubber B, Saha S. Anxiety and its determinants among undergraduate students during e-learning in Bangladesh amid COVID-19. J Affect Disorders Rep. 2021;6:100241. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jadr.2021.100241\u003c/span\u003e\u003cspan address=\"10.1016/j.jadr.2021.100241\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain M, Ahammed B, Chanda S, Jahan N, Ela M, Islam M. Social and electronic media exposure and generalized anxiety disorder among people during COVID-19 outbreak in Bangladesh: A preliminary observation. PLoS ONE. 2020;15(9):e0238974. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0238974\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0238974\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHumida T, Mamun M, Keikhosrokiani P. Predicting behavioral intention to use e-learning system: A case study in Begum Rokeya University, Rangpur, Bangladesh. Educ Inform Technol. 2021;27(2):2241\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10639-021-10707-9\u003c/span\u003e\u003cspan address=\"10.1007/s10639-021-10707-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuo H, Ahmad F, Teoh B. Evaluating the purchasing behavior of organic food among Chinese consumers. Young Consumers. 2023;24(6):669\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/yc-04-2023-1721\u003c/span\u003e\u003cspan address=\"10.1108/yc-04-2023-1721\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHusain R, Wahab N, Husain R. The relationship of drivers\u0026rsquo; understanding on CO₂ emission effects on the environment that reflect their behavior. Int J Acad Res Bus Social Sci. 2023;13(8). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6007/ijarbss/v13-i8/17698\u003c/span\u003e\u003cspan address=\"10.6007/ijarbss/v13-i8/17698\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImani B, Allahyari M, Bondori A, Emami N, Bilali H. Adoption of organic potato production in Ardabil Plain, Iran: An application of the extended theory of planned behaviour. Potato Res. 2020;64(2):177\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11540-020-09471-z\u003c/span\u003e\u003cspan address=\"10.1007/s11540-020-09471-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJahan S, Haque M, Nomani F, Munni F, Rahman Z, Sadat A. Freelancing as a career choice for university students in Bangladesh. South Asian J Social Stud Econ. 2025;22(7):189\u0026ndash;208. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.9734/sajsse/2025/v22i71072\u003c/span\u003e\u003cspan address=\"10.9734/sajsse/2025/v22i71072\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson E. A carbon footprint of HVO biopropane. Biofuels Bioprod Biorefin. 2017;11(5):887\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaur N, Sharma A. Adoption of solar home lighting system: A behavioral paradigm shift from consumers to prosumers in the urban household energy transition of Punjab, India. Environ Res Commun. 2024;6(11):115020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/2515-7620/ad9084\u003c/span\u003e\u003cspan address=\"10.1088/2515-7620/ad9084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKline RB. Principles and practice of structural equation modeling. 4th ed. Guilford Press; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKyonka EGE, Schutte NS. Probability discounting and gambling: A meta-analysis. Addiction. 2018;113(12):2173\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/add.14397\u003c/span\u003e\u003cspan address=\"10.1111/add.14397\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLagu\u0026iacute;a A, Gonz\u0026aacute;lez A, Gilar-Corbi R. Environmental awareness and pro-environmental behavior: The mediating role of personal norms. Sustainability. 2019;11(20):5385. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su11205385\u003c/span\u003e\u003cspan address=\"10.3390/su11205385\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLagu\u0026iacute;a A, Le\u0026oacute;n J, Gorgievski M. A psychosocial study of self-perceived creativity and entrepreneurial intentions in a sample of university students. Think Skills Creativity. 2019;31:44\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tsc.2018.11.004\u003c/span\u003e\u003cspan address=\"10.1016/j.tsc.2018.11.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLannelongue L, Grealey J, Inouye M. Green algorithms: Quantifying the carbon footprint of computation. Adv Sci. 2021;8(12):2100707. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/advs.202100707\u003c/span\u003e\u003cspan address=\"10.1002/advs.202100707\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Zhao M. A study on the influencing factors of continued intention to use MOOCs: UTAUT model and CCC moderating effect. Front Psychol. 2021;12:528259. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2021.528259\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2021.528259\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Siddik A, Masukujjaman M, Wei X. Bridging green gaps: The buying intention of energy efficient home appliances and moderation of green self-identity. Appl Sci. 2021;11(21):9878. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/app11219878\u003c/span\u003e\u003cspan address=\"10.3390/app11219878\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLovakov A, Agadullina E. Empirically derived guidelines for effect size interpretation in social psychology. Eur J Social Psychol. 2021;51(3):485\u0026ndash;504. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ejsp.2752\u003c/span\u003e\u003cspan address=\"10.1002/ejsp.2752\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehta A, Sharma M, Gupta N. Determinants responsible for sustainable consumption behavior among youths. Socioeconomic Challenges. 2024;8(1):240\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.61093/sec.8(1).240-252.2024\u003c/span\u003e\u003cspan address=\"10.61093/sec.8(1).240-252.2024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoon Z, Amin M, Ali M, Hasan M. Antecedents to the underprivileged undergraduate students' intention to participate in online classes. Turkish Online J Distance Educ. 2024;25(1):118\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17718/tojde.1224195\u003c/span\u003e\u003cspan address=\"10.17718/tojde.1224195\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMukesh N, Narwal M. Predicting consumer purchase intention on electric cars in India: Mediating role of attitude. Bus Strategy Dev. 2023;6(4):942\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/bsd2.289\u003c/span\u003e\u003cspan address=\"10.1002/bsd2.289\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePassafaro P, Livi S, Kosić A. Local norms and the theory of planned behavior: Understanding the effects of spatial proximity on recycling intentions and self-reported behavior. Front Psychol. 2019;10:744. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2019.00744\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2019.00744\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePham N, Jabbour C, Usman M, Ali M, Phan H. How does training boost employees' intention to implement environmental activities? An empirical study in Vietnam. Int J Manpow. 2022;43(8):1761\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/ijm-04-2021-0238\u003c/span\u003e\u003cspan address=\"10.1108/ijm-04-2021-0238\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePirson T, Bol D. Assessing the embodied carbon footprint of IoT edge devices with a bottom-up life-cycle approach. J Clean Prod. 2021;322:128966. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2021.128966\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2021.128966\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRom F, Mahmud A, Bon R, Miskan M. Psychological impact and coping style among students of National Defence University of Malaysia during the COVID-19 pandemic. Bangladesh J Med Sci. 2023;22(1):105\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3329/bjms.v22i1.61866\u003c/span\u003e\u003cspan address=\"10.3329/bjms.v22i1.61866\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalim Z. Investigating the predictors of physical activity behavior among female college students in Saudi Arabia using the theory of planned behavior. Phys Educ Students. 2023;27(5):270\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15561/20755279.2023.0507\u003c/span\u003e\u003cspan address=\"10.15561/20755279.2023.0507\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalvy S, Dutton G, Borgatti A, Kim Y. Habit formation intervention to prevent obesity in low-income preschoolers and their mothers: A randomized controlled trial protocol. Contemp Clin Trials. 2018;70:88\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cct.2018.05.015\u003c/span\u003e\u003cspan address=\"10.1016/j.cct.2018.05.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandoval-D\u0026iacute;az J, Neumann P. Green product purchase intention in Chilean consumers: Comparing three models using structural equations. Revista Colombiana de Psicolog\u0026iacute;a. 2023;32(1):83\u0026ndash;101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15446/rcp.v32n1.92739\u003c/span\u003e\u003cspan address=\"10.15446/rcp.v32n1.92739\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma N, Pa\u0026ccedil;o A, Rocha R, Palazzo M, Siano A. Examining a theoretical model of eco-anxiety on consumers' intentions towards green products. Corp Soc Responsib Environ Manag. 2023;31(3):1868\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/csr.2670\u003c/span\u003e\u003cspan address=\"10.1002/csr.2670\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen L, Si H, Yu L, Si H. Factors influencing young people\u0026rsquo;s intention toward municipal solid waste sorting. Int J Environ Res Public Health. 2019;16(10):1708. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph16101708\u003c/span\u003e\u003cspan address=\"10.3390/ijerph16101708\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShrestha S, Haque S, Dawadi S, Giri R. Preparations for and practices of online education during the COVID-19 pandemic: A study of Bangladesh and Nepal. Educ Inform Technol. 2021;27(1):243\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10639-021-10659-0\u003c/span\u003e\u003cspan address=\"10.1007/s10639-021-10659-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheeran P. Intention\u0026ndash;behavior relations: A conceptual and empirical review. Eur Rev Social Psychol. 2002;12(1):1\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/14792772143000003\u003c/span\u003e\u003cspan address=\"10.1080/14792772143000003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSon C, Hegde S, Smith A, Wang X, Sasangohar F. Effects of COVID-19 on college students\u0026rsquo; mental health in the United States: Interview survey study. J Med Internet Res. 2020;22(9):e21279. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2196/21279\u003c/span\u003e\u003cspan address=\"10.2196/21279\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStroebele-Benschop N, Dieze A, Hilzendegen C. Students\u0026rsquo; adherence to dietary recommendations and their food consumption habits. Nutr Health. 2018;24(2):75\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0260106018772946\u003c/span\u003e\u003cspan address=\"10.1177/0260106018772946\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalan T, Doğan Y, Kalınkara Y. Digital natives' mobile learning adoption in terms of UTAUT-2 model: A structural equation model. Innoeduca Int J Technol Educational Innov. 2024;10(1):100\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24310/ijtei.101.2024.17440\u003c/span\u003e\u003cspan address=\"10.24310/ijtei.101.2024.17440\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTyas A. (2025). Sustainability strategy and environmental performance at private universities in East Java. \u003cem\u003eE3S Web of Conferences, 657\u003c/em\u003e, 05004. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1051/e3sconf/202565705004\u003c/span\u003e\u003cspan address=\"10.1051/e3sconf/202565705004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan der Weiden A, Benjamins J, Gillebaart M, Ybema JF, de Ridder D. How to form good habits? A longitudinal field study on the role of self-control in habit formation. Front Psychol. 2020;11:560. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2020.00560\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2020.00560\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWan M, Liang-jie W. Exploring the pathways to participation in household waste sorting in different national contexts: A fuzzy-set QCA approach. IEEE Access. 2020;8:179373\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/access.2020.3027978\u003c/span\u003e\u003cspan address=\"10.1109/access.2020.3027978\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang D, Lu Z, Li M, Zhang L, Yu H, Tan L, Mao W, Qiao X, An T, Hu Y. Measurement of eco-anxiety in the Chinese context: Development and validation of a new eco-anxiety scale based on the Hogg Eco-Anxiety Scale. Behav Sci. 2025;15(7):985. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/bs15070985\u003c/span\u003e\u003cspan address=\"10.3390/bs15070985\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Ma B, Bai R. How does green product knowledge effectively promote green purchase intention? Sustainability. 2019;11(4):1193. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su11041193\u003c/span\u003e\u003cspan address=\"10.3390/su11041193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Leng X. Dialogue pathways and narrative analysis in health communication within the social media environment: An empirical study based on user behavior \u0026ndash; A case study of China. Front Public Health. 2025;13:1649120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpubh.2025.1649120\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2025.1649120\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWallmann-Sperlich B, Hoffmann S, Salditt A, Bipp T, Froboese I. Moving to an active biophilic designed office workplace: Effects on sitting time and sitting habits of office-based workers. Int J Environ Res Public Health. 2019;16(9):1559. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph16091559\u003c/span\u003e\u003cspan address=\"10.3390/ijerph16091559\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWani M, Dada Z, Shah S. What factors influence consumers\u0026rsquo; intention and food waste reduction perceptions in Indian restaurants? Br Food J. 2024;127(2):569\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/bfj-07-2024-0710\u003c/span\u003e\u003cspan address=\"10.1108/bfj-07-2024-0710\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong P, Ng P, Lee D, Lam R. Examining the impact of perceived source credibility on attitudes and intentions towards taking advice from others on university choice. Int J Educational Manage. 2019;34(4):709\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/ijem-06-2019-0190\u003c/span\u003e\u003cspan address=\"10.1108/ijem-06-2019-0190\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolny R, Kol J, Stolecka-Makowska A, Szojda G. Digital consumer behavior in Poland and its environmental impact within the framework of sustainability. Sustainability. 2025;17(10):4691. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su17104691\u003c/span\u003e\u003cspan address=\"10.3390/su17104691\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Y, Zhang W, Bao H, Zhang S, Xiang Y. A SEM\u0026ndash;neural network approach to predict customers\u0026rsquo; intention to purchase battery electric vehicles in China\u0026rsquo;s Zhejiang Province. Sustainability. 2019;11(11):3164. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su11113164\u003c/span\u003e\u003cspan address=\"10.3390/su11113164\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao S, Song Y, Yu Y, Guo B. A study of group decision-making for green technology adoption in micro and small enterprises. J Bus Industrial Mark. 2020;36(1):86\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/jbim-02-2020-0093\u003c/span\u003e\u003cspan address=\"10.1108/jbim-02-2020-0093\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, Ye M, Wang H, Lu W. Female power, ownership, and ESG decoupling: Evidence from China. Int J Gend Entrepreneurship. 2024;16(3):341\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/ijge-12-2023-0303\u003c/span\u003e\u003cspan address=\"10.1108/ijge-12-2023-0303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu L. Digital marketing for behavioral change: Encouraging sustainable consumer practices to address environmental issues and support SDGs. JLSDGR. 2024;5(2):e03866. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.47172/2965-730x.sdgsreview.v5.n02.pe03866\u003c/span\u003e\u003cspan address=\"10.47172/2965-730x.sdgsreview.v5.n02.pe03866\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaman U. Seizing momentum on climate action: Nexus between net-zero commitment concern, destination competitiveness, influencer marketing, and regenerative tourism intention. Sustainability. 2023;15(6):5213. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su15065213\u003c/span\u003e\u003cspan address=\"10.3390/su15065213\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZulfiqar S, Sarwar B, Aziz S, Chandia K, Khan M. An analysis of influence of business simulation games on business school students\u0026rsquo; attitude and intention toward entrepreneurial activities. J Educational Comput Res. 2018;57(1):106\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0735633117746746\u003c/span\u003e\u003cspan address=\"10.1177/0735633117746746\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZulfiqar M, Sadaf I, Khan M. Understanding the intention\u0026ndash;behavior gap in environmental behavior: A meta-analytic review. Environ Educ Res. 2018;25(6):831\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discpsy","sideBox":"Learn more about [Discover Psychology](https://www.springer.com/44202)","snPcode":"","submissionUrl":"","title":"Discover Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital carbon footprint, Sustainable digital behavior, Awareness–Action Paradox, University students","lastPublishedDoi":"10.21203/rs.3.rs-8775589/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8775589/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid digitalization of higher education has significantly increased students\u0026rsquo; reliance on energy-intensive platforms, yet sustainable digital practices remain limited despite rising awareness of digital carbon emissions. This study examines the Awareness\u0026ndash;Action Paradox among Bangladeshi university students by developing and validating a Digital Carbon Awareness\u0026ndash;Action Paradox Model that integrates environmental concern, perceived behavioral control, habit strength, digital addiction, and eco-anxiety. Using a cross-sectional survey of 400 students from public, private, and National University institutions, Structural Equation Modeling revealed that awareness does not directly predict intention or perceived behavioral control but strongly influences environmental concern. Both environmental concern and perceived behavioral control fully mediated the awareness\u0026ndash;intention relationship, confirming that awareness alone is insufficient for motivating sustainable digital behavior. Intention emerged as the strongest predictor of low-carbon digital action, whereas habit strength and digital addiction significantly weakened the intention\u0026ndash;behavior linkage, highlighting the constraining role of entrenched digital routines and compulsive usage. Eco-anxiety did not moderate the awareness\u0026ndash;intention pathway, suggesting that emotional distress does not translate awareness into motivation without supportive psychological mechanisms. The model explained 47.5% of the variance in intention and 41.2% in behavior, offering one of the first empirically grounded frameworks for understanding digital sustainability behavior in a developing-country context. Findings emphasize the need for interventions that enhance emotional engagement and perceived capability while addressing habitual and addictive digital consumption patterns to effectively bridge the awareness\u0026ndash;action gap.\u003c/p\u003e","manuscriptTitle":"Psychological determinants of low carbon digital behavior among university students in Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 10:16:37","doi":"10.21203/rs.3.rs-8775589/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-21T06:33:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332954739414982902872044447197675457923","date":"2026-04-07T16:30:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T08:30:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133095480862660316048659096359691419910","date":"2026-04-02T11:38:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-13T10:42:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112462238068719691710466627741659587747","date":"2026-03-11T05:30:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102763932472727286788091619953091889425","date":"2026-03-05T08:57:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211740180768986951107194553256209579647","date":"2026-03-05T07:14:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301996206566599009881047414168257573675","date":"2026-03-05T05:35:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-05T05:22:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-19T11:00:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-18T17:34:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-18T17:15:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Psychology","date":"2026-02-18T17:10:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discpsy","sideBox":"Learn more about [Discover Psychology](https://www.springer.com/44202)","snPcode":"","submissionUrl":"","title":"Discover Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a7e803b2-ea02-4a44-9787-27501fd58b07","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T10:16:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 10:16:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8775589","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8775589","identity":"rs-8775589","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}