Digital marketing’s targets: cyberloafing and impulsive buying
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Faculty of Economics and business-fast/” title=”13 Tips to Grow Your Business Fast”>Business, Universitas Riau Kepulauan, Batam, Indonesia
Abstract
In the present era, digitalization has become a primary tool that reshapes how individuals search for information, interact with content, and make consumption decisions. While many studies highlight the advantages of digital marketing, its potential downsides and behavioral risks remain under-discussed. This study examines the associations among digital marketing exposure (DM), cyberloafing (CB), and impulsive buying (IB), including an indirect pathway via a mediating variable. Using a cross-sectional survey conducted from June to November 2025, 375 students responded and 332 valid questionnaires (88.53%) were analyzed. Data were processed using Microsoft Excel and SmartPLS (PLS-SEM with bootstrapping). The results indicate positive and statistically significant associations between DM and CB (β = 0.777, p < 0.001), DM and IB (β = 0.222, p < 0.001), and CB and IB (β = 0.752, p < 0.001). The indirect path (DM, CB, IB) is also positive (β = 0.584, p < 0.001), suggesting that CB statistically mediates the association between DM and IB. Given the cross-sectional design, these findings should be interpreted as correlational rather than causal. The study underscores the importance of digital literacy, responsible marketing practices, and educational guidelines to mitigate counterproductive online behavior and unplanned purchasing.
1 Introduction
The study of consumer and digital behavior in highly connected environments has intensified. However, particularly among young adults in emerging digital markets, the relationship between exposure to digital marketing stimuli, off-task online activity, and impulsive purchasing remains understudied. This study examines the associations among digital marketing exposure (DM), cyberloafing-like off-task internet use (CB) and impulsive buying (IB) in the context of university students in Batam, Indonesia, drawing on relevant theoretical perspectives to explain the proposed links.
University students represent a salient group because they are intensive users of social media and e-commerce, frequently encounter personalized advertising, and often multitask online during study time. In this manuscript, cyberloafing is treated as non-task internet use during time allocated for academic activities (lectures or studying). While the term originates from workplace research, the core behavioral concept—off-task online activity during an obligated primary task—can be examined in an educational setting. Consequently, workplace-oriented claims are avoided and the scope of inference is bounded to the student context.
Significant digital advancements have transformed marketing into an interactive platform between producers and consumers (Busca and Bertrandias, 2020; Dwivedi et al., 2020). In this setting, digital marketing increasingly relies on personalization, data-driven content, and salient visual cues that can heighten attention and emotional engagement, which prior research has linked to stronger impulsive-buying tendencies (Iyer et al., 2020; Lee et al., 2023; Verhagen and van Dolen, 2011).
Prior empirical studies suggest that digital environments through accessibility, promotional incentives, and social influence cues are associated with more frequent unplanned purchases and reduced deliberation in buying decisions (Lee et al., 2023; Tibrani et al., 2024; Verhagen and van Dolen, 2011). These patterns motivate closer examination of how marketing exposure relates to consumers’ everyday online behavior and purchasing tendencies (Lim, 2002; Liu et al., 2025).
Digital environments can intensify affective reactions that may bypass analytical reasoning, especially when users are continuously exposed to persuasive cues (Amos et al., 2014). At the same time, always-on connectivity can facilitate off-task browsing and short online breaks during primary tasks. Consequently, the proliferation of digital technology has resulted in dual adverse effects manifested as impulsive buying and cyberloafing (J-Ho et al., 2017; Sheikh et al., 2015). This phenomenon highlights the need for a critical understanding of the relationship between digital marketing and its implications in universities, reflecting new challenges in the study of consumer behavior and management in the digital age. Hence, these factors encourage us to study DM, CB, and IB within a single model, while treating the estimated connections as relationships given the study’s design.
Cyberloafing is commonly defined as non-task internet use during time that is expected to be devoted to a primary activity. In workplace literature, it is discussed as personal internet use during work hours (Lim, 2002; Lim and Chen, 2012; Lim and Teo, 2005). In an educational setting, an analogous pattern occurs when students browse social media, entertainment, or online shopping platforms during lectures or study sessions, potentially reducing task focus and extending completion time.
Impulsive buying and cyberloafing-like off-task browsing can be understood as interconnected behaviors in digitally saturated environments (Askew et al., 2014; Koay and Soh, 2018). Guided primarily by a self-regulation and stimulus–mechanism–outcome logic, we conceptualize digital marketing exposure (DM) as a salient stimulus that is plausibly associated with attentional diversion and more frequent off-task browsing during a primary task (CB). In turn, such off-task browsing can increase exposure to purchase cues and reduce deliberation, which is consistent with higher impulsive buying tendencies (IB). Accordingly, this study tests whether DM is statistically associated with CB and IB, and whether CB statistically mediates the association between DM and IB within the estimated model.
This phenomenon is significant from marketing and psychological, ethical, and human resource management viewpoints, and it also enhances marketing efficiency and profitability. However, it also facilitates manipulative practices that exploit consumers’ cognitive vulnerabilities, thereby increasing the risk of consumer behavior issues, shopping addiction, and productivity disorientation. At the same time, since the study includes only university students and examines data from a single point, it does not draw conclusions about workplace productivity and instead views the results as specific to the study’s context.
Therefore, this study contributes by (i) testing an integrated model associated among DM, CB, and IB among university students in Batam, (ii) reporting direct and indirect associations using PLS-SEM with bootstrapping, and (iii) providing bounded, context-appropriate implications for digital literacy and online self-regulation. This study employs a quantitative methodology and digital behavior analysis to examine the correlation between cyberloafing and impulsive buying. This study analyses whether both serve as direct, indirect or reciprocal targets within contemporary digital marketing strategies.
2 Literature review and hypothesis development
2.1 Digital marketing theory
Digital marketing has become a prevailing framework in modern marketing discussions, propelled by technological advancements, changes in customer behavior and real-time involvement (Lovelock and Gummesson, 2004; Ravald and Grönroos, 1996). Digital marketing fundamentally involves using digital technologies-such as social media, search engines, email, and mobile applications-to promote items, engage customers, and create value (Chaffey and Ellis-Chadwick, 2019). It’s contrasts with conventional marketing by providing immediacy, personalization and bidirectional contact (Jüttner and Wehrli, 1994; Lings and Greenley, 2005).
The technology acceptance model (TAM) explains user behavior toward digital platforms by emphasizing perceived ease of use and perceived usefulness (Davis, 1989). The theory of planned behavior highlights how attitudes, subjective norms, and perceived behavioral control are related to online purchasing intentions (Ajzen, 1991). Recent work in behavioral economics and digital marketing further discusses how cognitive biases, heuristics, and emotional stimuli shape online decision-making (Liu et al., 2025). Together, these perspectives help explain psychological and behavioral dynamics in digital consumption without implying causal effects from the present cross-sectional evidence.
Digital marketing combines algorithmic personalization and big data analytics to provide tailored content and enhance consumer interaction (Burgess et al., 2017; Burgess and Steenkamp, 2006). The emergence of social media influencers, user-generated content, and real-time marketing has revolutionized consumer-brand interaction, obscuring the distinctions between producers and consumers (Kaplan and Haenlein, 2010; Zheng et al., 2025). This interaction also presents ethical and psychological issues, such as obsessive consumption, digital fatigue, and data privacy breaches (Jeffrey and Hodge, 2007).
Furthermore, efficacy of digital marketing relies not only on technological advancement but also on cultural awareness, content pertinence and timing (Dwivedi et al., 2020). The field is increasingly interdisciplinary, integrating concepts of business, psychology, information systems, and communication studies. Consequently, digital marketing theory and practice are evolving swiftly, presenting opportunities and problems for academics and professionals. As markets increasingly digitize, comprehending the interaction between consumer psychology, digital infrastructure, and marketing strategy is essential for sustainable and ethical digital involvement.
H1: Digital marketing exposure (DM) is positively associated with cyberloafing (CB).
2.2 Impulsive buying
Impulsive buying behavior has received considerable attention in consumer research because of its frequent occurrence in both offline and online markets. Impulsive buying is a spontaneous decision to make a purchase, often influenced by emotions and lacking a thorough cognitive assessment (Rook, 1987). This construct is shaped by personal, situational, and environmental factors; also, it differs from planned purchasing in that it occurs without prior intention and is influenced by external factors such as sales promotions, appealing displays, or emotional states. It is primarily driven by emotions and individual characteristics associated with self-control.
Persuasive interface designs, real-time marketing, and customization algorithms are often discussed as features that can heighten impulse-oriented purchases in digital settings. Chaoyang et al. (2025) and Xiao and Nicholson (2013) review evidence that digital platforms promote instant gratification through stimuli such as flash deals, countdown timers, and scarcity messaging. Also, Sundström et al. (2019) report that visually appealing interfaces and time-sensitive promotions are associated with higher unplanned purchases in online retail contexts. Overall, online settings may facilitate impulsive buying by providing immediate, salient, and frictionless purchasing cues.
The proliferation of digital commerce has expedited the examination of this matter. Digital environments offer distinctive signals, like flash sales, tailored suggestions, and user-generated content that elicits urgency, emotional stimulation, and impulsive acquisitions (Xiao and Nicholson, 2013). Dholakia (2000) posits a substantial correlation between impulsive buying and affective states, suggesting that positive emotions heighten the likelihood of unanticipated purchases. This is strongly associated with the findings of (Liu et al., 2025), which investigate the influence of social media on impulsive behavior by emphasizing its role in magnifying peer influence and generating digital peer pressure via influencers’ likes, shares, and endorsements.
The Stimulus-Organism-Response (S-O-R) model has been used to study impulsive buying, suggesting that environmental cues (ads and interface design) shape internal states (affect and cognition), which are in turn associated with purchase responses (Mehrabian and Russell, 1974). Behavioral economics discusses impulsive buying by examining how cognitive heuristics and biases, including fear of missing out and loss aversion, relate to decision-making under uncertainty.
Recent research emphasizes the ethical and psychological consequences of impulsive buying. Impulsive behavior may result in financial strain, regret and compulsive purchasing tendencies (Verplanken et al., 2005). Analyzing the origins and mechanisms of impulsive buying is essential for formulating efficient marketing strategies and promoting ethical consumer behavior in the digital era.
Impulsive buying yields psychological and cultural ramifications, such as remorse, obsessive buying disorders, and financial hardship. (Verplanken et al., 2005) conducted a study involving university students and identified a correlation between elevated impulse-buying tendencies, detrimental consumption behaviors, and post-buying regret. Roberts and Pirog (2004) found that young consumers who exhibit impulsive buying tendencies also possess elevated credit card debt and financial anxiety. Consequently, whereas impulsive buying advantages marketers, it may yield detrimental psychological and financial repercussions for consumers.
H2: Digital marketing exposure (DM) is positively associated with impulsive buying (IB).
2.3 Cyberloafing theory and practice
Cyberloafing, defined as non-task internet use during an obligated primary activity, has been widely discussed in workplace settings as personal internet use during work hours (Lim, 2002; Lim and Chen, 2012; Lim and Teo, 2005). In this study, the concept is operationalized in an educational context: students’ non-task internet use during lectures or study time. This framing maintains conceptual continuity with the original definition while aligning the construct with the sampled population.
From a self-regulation and time-allocation perspective, off-task online activity may occur when individuals experience attentional fatigue, boredom, or perceived overload, prompting brief disengagement from online activities. Such online disengagement can disrupt task focus and may increase exposure to marketing cues, especially on social media and shopping platforms. People who feel like they have less control over their time or tasks may be more likely to get distracted. On the other hand, structured goal-setting and time-management practices are often talked about as ways to protect against distractions at work and in school (Lepak et al., 2006; Nielsen et al., 2017).
Previous research also shows that when people have many demands and feel stressed, they may turn to digital distractions for a quick escape, leading to more time spent browsing online and less focus on their tasks (Huda, 2020; Tandon et al., 2022). Accordingly, cyberloafing can be interpreted as a behavioral response to situational pressures and self-regulatory constraints, rather than merely an intentional violation. This perspective supports examining CB as a potential mechanism through which digital environments relate to downstream consumer behaviors.
From a social-exchange perspective, earlier workplace studies have emphasized how contextual norms and perceived fairness shape discretionary online behavior (Homans, 1961). In an academic setting, parallel mechanisms may operate through perceived task demands, peer norms, and the salience of online alternatives.
Overall, this stream of research suggests that off-task internet use is shaped by both situational demands and individual self-regulation, which provides a conceptual basis for linking digital marketing exposure to cyberloafing-like behavior and, in turn, to impulsive buying tendencies.
H3: There is a positive correlation between cyberloafing (CB) and impulsive buying (IB).
H4: Cyberloafing (CB) statistically mediates the association between digital marketing exposure (DM) and impulsive buying (IB).
3 Method
3.1 Design
As a free trade zone and strategic hub, Batam, Indonesia, has experienced rapid growth in digital commerce and social media marketing, creating a dense exposure environment for young adults. This context provides an appropriate setting to examine how digital marketing exposure relates to everyday online behavior and purchasing tendencies among university students.
Although cyberloafing is often discussed in workplace research, the behavioral core—off-task internet use during a primary task—also appears in academic settings (browsing social media or online shops during lectures or studying). Consequently, this study concentrates on university students and analyses the results specifically within this demographic, refraining from generalising conclusions about employees or workplace productivity.
In this context, impulsive buying refers to unplanned purchases enabled by easy access to digital platforms, whereas cyberloafing denotes off-task online engagement during academic activities. The study therefore emphasizes digital literacy and self-regulation implications that are proportionate to the single-country, student-based, correlational evidence.
3.2 Research respondent
The research timeline spanned from June to November 2025. 375 respondents completed a questionnaire featuring various statements (Table 1). Measurements utilized a Likert scale ranging from 1 to 5, representing a continuum from strongly disagree to agree strongly. All respondents comprehended stipulations and assurances concerning confidentiality of personal information. University students were selected because they are intensive users of social media and digital shopping platforms and thus represent a theoretically relevant group for examining the DM–CB–IB associations. The study’s external validity is therefore bounded to this population and context.
| Data | Frequency | Total | Percentage (%) |
|---|---|---|---|
| Questionnaire | Valid questionnaire | 332 | 88.53 |
| Invalid questionnaire | 43 | 11.47 | |
| Gender | Male | 171 | 45.60 |
| Female | 204 | 55.40 | |
| Age group | 18–21 yo | 266 | 70.93 |
| 21–24 yo | 109 | 29.07 |
Research respondent.
332 questionnaires (88.53%) were classified as valid and met the established criteria, whereas 43 questionnaires (11.47%) were considered invalid due to incomplete statements (see Table 1). In accordance with the methodology established by Hair et al. (2014), the analysis was conducted in a three-step process: inner model testing, outer model testing, and hypothesis testing, utilizing Microsoft Excel and SmartPLS.
3.3 Variable and indicator
A quantitative, cross-sectional survey gathered empirical data on digital marketing exposure, cyberloafing and impulsive buying (Figure 1). This design enables statistical examination of associations among constructs at a single point in time. To improve response quality and reduce common-method concerns, the study applied procedural remedies (anonymity and confidentiality assurances, clear instructions, and screening for incomplete responses). A systematic approach to sample selection was implemented to support representativeness within the study context.
The conceptual framework is grounded in theoretical research concerning digital marketing, cyberloafing and impulse buying. Key references encompass exposure theory and the persuasive effects of digital marketing on impulsive buying decisions (Grönroos, 2006a; Lim, 2002; Lim and Teo, 2024); the conceptualization-implications of cyberloafing (Askew et al., 2014; Askew and Buckner, 2017); the dynamics among self-regulation, work pressure, and the intensity of non-productive internet use (Lim and Chen, 2012; Mercado et al., 2017; Rook, 1987; Verhagen and van Dolen, 2011). Examining these dimensions is essential to prevent neglecting the hidden risks associated with the increasing digitalization in professional and academic settings.
A questionnaire was developed to assess digital marketing exposure, cyberloafing-like off-task internet use during academic activities, and impulsive buying tendencies among university students. The variables and indicators were assembled through a literature synthesis as presented in Table 2.
| Variable | Indicator | Sources |
|---|---|---|
| Cyberloafing (CB) | CB1-browsing non-study-related websites | (Askew et al., 2014; Lim and Chen, 2012; Mercado et al., 2017) |
| CB2-checking personal emails or social media | ||
| CB3-online shopping during working hours | ||
| CB4-streaming videos or music for entertainment | ||
| CB5-playing online games while at school | ||
| CB6-using work/study time for chatting/messaging for personal reasons | ||
| Impulsive buying (IB) | IB1-often buy things spontaneously | (Amos et al., 2014; Rook and Fisher, 1995; Verhagen and van Dolen, 2011) |
| IB2-buy things according to how I feel at the moment | ||
| IB3-tend to get excited when I see a product I like and buy it immediately | ||
| IB4-find it hard to resist buying items that appeal to me | ||
| IB5-unplanned purchases when I browse online marketplaces | ||
| IB6-frequently experience buyer’s remorse after buying | ||
| Digital marketing (DM) | DM1-often see digital advertisements while browsing social media | (Chaffey and Ellis-Chadwick, 2019; Grönroos, 2006a, 2006b, 2009; Lim, 2002; Lim and Teo, 2024) |
| DM2-receive promotional emails or messages from online stores | ||
| DM3-often get influenced by influencer marketing/content creators | ||
| DM4-personalized ads catch my attention more than traditional ads | ||
| DM5-frequently exposed to limited-time offers or flash sales online | ||
| DM6-online reviews and ratings influence my buying decisions |
Variable and indicator.
3.4 Instrument and analysis
The study used a Likert scale ranging from 1 to 5 to capture participants’ self-reported perceptions and experiences. Verbal consent was obtained, and participation was voluntary. Responses captured reported exposure to digital marketing and participants reported cyberloafing and impulsive buying tendencies, which were analyzed to evaluate the hypothesized associations among constructs.
Following Hair et al. (2014, 2019), the PLS-SEM analysis proceeded in three steps: (i) measurement (outer) model assessment, (ii) structural (inner) model assessment, and (iii) hypothesis testing via bootstrapping. The measurement model evaluation focused on indicator reliability (outer loadings), internal consistency reliability (Cronbach’s alpha and composite reliability), convergent validity (AVE), and discriminant validity using the HTMT criterion. Structural model evaluation included multicollinearity checks (VIF), path coefficients with t-statistics, p-values, confidence intervals, effect sizes (F2), explained variance (R2), and predictive relevance (Q2) (Chin, 1998; Henseler and Sarstedt, 2013; Sardeshmukh and Vandenberg, 2017; Scheines et al., 1999).
Bootstrapping was conducted in SmartPLS with 5,000 subsamples (two-tailed, 95% percentile confidence intervals) to obtain standard errors, t-statistics, and confidence intervals for direct, indirect, and total paths; 95% confidence intervals are reported in the tables. The statistical significance threshold was set at p < 0.05. Following recent consumer-behavior applications that integrate theory and survey-based SEM reporting practices, we also clarify key reporting elements in the tables and text (Zheng et al., 2026).
Given the single-method bias using a full-collinearity perspectiveh are below the commonly used 3.3 screening threshold, suggesting that common method bias is unlikely to be a dominant concern (Kock, 2015). Procedural remedies (voluntary participation, confidentiality assurances and screening for incomplete responses) were applied
4 Finding
4.1 Measurement (outer model)
Measurement evaluation establishes the reliability and validity of the constructs that underpin the empirical analysis. The structural model estimates the relationships among constructs through path coefficients, including direct and indirect associations. PLS-SEM allows simultaneous estimation of the measurement model and structural paths, and bootstrapping is used to assess statistical significance. However, because the data are cross-sectional, the estimated paths should be interpreted as associations rather than definitive causal effects.
Fifteen indicators met the adopted loading criterion, while three indicators (CB2, CB5, and IB5) were removed because their outer loadings did not reach the minimum threshold (0.60). This threshold was applied to maintain adequate indicator reliability while preserving construct coverage. After indicator refinement, the retained indicators were used for the subsequent reliability/validity checks and structural model estimation.
Table 3 reports outer loadings, Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE). The results indicate satisfactory internal consistency and convergent validity for each construct according to commonly used thresholds (α and CR ≥ 0.70; AVE ≥ 0.50), supporting the adequacy of the measurement model for the structural analysis.
| Variable | Item-indicator | Outer loading | Cronbach’s alpha | Composite reliability (CR) | Average variance extracted (AVE) |
|---|---|---|---|---|---|
| CB | 0.739 | 0.776 | 0.656 | ||
| CB1 | 0.862 | ||||
| CB3 | 0.857 | ||||
| CB4 | 0.700 | ||||
| DM | 0.877 | 0.891 | 0.619 | ||
| DM1 | 0.808 | ||||
| DM2 | 0.845 | ||||
| DM3 | 0.771 | ||||
| DM4 | 0.780 | ||||
| DM5 | 0.816 | ||||
| DM6 | 0.693 | ||||
| IB | 0.873 | 0.883 | 0.671 | ||
| IB1 | 0.863 | ||||
| IB2 | 0.895 | ||||
| IB3 | 0.887 | ||||
| IB4 | 0.633 | ||||
| IB6 | 0.790 |
Measurement result.
Evaluating discriminant validity in the reflective measurement model involves assessing variables against their associated indicators. It was performed by analyzing the results of cross-loading, Fornell-Larcker, and HTMT.
Cross-loading is first established by the measurement item (indicator) that exhibits a stronger correlation with the variable it assesses and a weaker correlation with other variables. The findings indicate that all measurement items (CB, DM, IB) exhibit a stronger correlation with their corresponding measuring variables and a weaker correlation with other variables (Table 4).
| Variable | CB | DM | IB |
|---|---|---|---|
| Cyberloafing (CB) | 0.777 | 0.694 | |
| Digital marketing (DM) | 0.777 | 0.706 | |
| Impulsive buying (IB) | 0.694 | 0.706 |
HTMT dicriminant validity evaluation.
The Fornell-Larcker test assesses discriminant validity at the variable level, necessitating that the square root of the construct’s AVE exceeds the correlation among the constructs. Table 4 illustrates that the square root of the AVE for each construct must be greater than its correlation coefficient with other constructs. This suggests that the measurement model demonstrates strong discriminant validity.
The Heterotrait-Monotrait Ratio (HTMT) assesses discriminant validity at the variable level by analyzing the average correlation of indicators both across different variables and within the same variable. The Heterotrait-Monotrait Ratio (HTMT) between two constructs must not surpass 0.85. If the dimensions are comparable, the HTMT may be adjusted to 0.90. The values presented denote an HTMT below 0.85. This demonstrates that the research measurement model exhibits strong discriminant validity (Table 4).
Discriminant validity evaluates the degree to which constructs in a study differ from one another. This evaluation is crucial in path analysis utilizing Partial Least Squares-Structural Equation Modeling (PLS-SEM). Discriminant validity was supported because the HTMT values between constructs were below commonly used cutoffs (0.85–0.90) indicating adequate distinctiveness among DM, CB, and IB.
4.2 Structural (inner model)
The structural model evaluates the relationships among constructs through path coefficients, their statistical significance, direct and indirect paths, effect sizes (F2), and the role of the mediating variable within the estimated model.
A multicollinearity test using the variance inflation factor (VIF) was previously conducted, yielding a value of less than 5. This test aimed to assess the correlation between constructs within the model. If the value is less than 5, the model is deemed valid; multicollinearity among constructs is indicated if the value exceeds 5. Consequently, it was necessary to recalculate each indicator utilized in the study.
The findings indicated the absence of multicollinearity. The values for each variable ranged from 1.324 to 3.015, indicating robust or unbiased PLS-SEM parameters. The VIF values were 1.000 for DM to IB, and 2.528 for CB to IB and DM to IB. The results demonstrate that collinearity among model variables was absent in the estimated path coefficients of the structural model.
The subsequent section presents findings derived from the bootstrapping procedure (Table 5). The analysis reports direct paths, indirect paths, and total effects (path coefficients), including standard deviation, t-statistics, 95% confidence intervals, p-values, and effect size (F2). Following common reporting practice, statistical significance is evaluated using p < 0.05 (Hair et al., 2014). Effect-size thresholds for F2 are 0.02 (small), 0.15 (medium), and 0.35 (large) (Chin, 1998; Cohen, 1998).
| Item | Original sample (O) | Sample mean (M) | Standard deviation (STDEV) | T statistics (|O/STDEV|) | Path coefficient confidence interval (95%) | p-values | F-square | |
|---|---|---|---|---|---|---|---|---|
| 2.5% | 97.5% | |||||||
| Direct effect | ||||||||
| DM → CB | 0.777 | 0.779 | 0.020 | 39.434 | 0.685 | 0.815 | 0.000 | 1.528 |
| DM → IB | 0.222 | 0.222 | 0.037 | 6.060 | 0.737 | 0.815 | 0.000 | 0.153 |
| CB → IB | 0.752 | 0.751 | 0.033 | 22.705 | 0.151 | 0.295 | 0.000 | 1.763 |
| Total effect | ||||||||
| CB → IB | 0.752 | 0.751 | 0.033 | 22.705 | 0.685 | 0.815 | 0.000 | |
| DM → CB | 0.777 | 0.779 | 0.020 | 39.434 | 0.737 | 0.815 | 0.000 | |
| DM → IB | 0.806 | 0.807 | 0.021 | 39.000 | 0.764 | 0.845 | 0.000 | |
| Indirect effect | ||||||||
| DM → CB → IB | 0.584 | 0.585 | 0.027 | 21.278 | 0.530 | 0.639 | 0.000 |
Direct and indirect path result.
Bootstrapping settings (5,000 subsamples; two-tailed; 95% percentile confidence intervals) are used to report uncertainty around direct, indirect, and total paths. These bootstrapped paths should be interpreted as associations rather than definitive causal effects given the cross-sectional design.
The path from DM to CB is positive and statistically significant (β = 0.777, t = 39.434, p < 0.001), indicating that higher reported digital marketing exposure is associated with higher cyberloafing. The effect size is large (F2 = 1.528). The 95% confidence interval does not include zero.
The path from DM to IB is positive and statistically significant (β = 0.222, t = 6.060, p < 0.001). The effect size is in the small-to-medium range (F2 = 0.153). The 95% confidence interval does not include zero.
The path from CB to IB is positive and statistically significant (β = 0.752, t = 22.705, p < 0.001), suggesting that higher cyberloafing is associated with higher impulsive buying. The effect size is large (F2 = 1.763). The 95% confidence interval does not include zero.
Overall, the estimated paths are positive and statistically significant, supporting the proposed model at the level of associations. Substantively, the results indicate that greater reported digital marketing exposure co-occurs with higher cyberloafing and higher impulsive buying, and that cyberloafing is also positively associated with impulsive buying.
The indirect path (DM, CB, IB) is positive and statistically significant (β = 0.584, t = 21.278, p < 0.001). This result suggests that CB statistically mediates the association between DM and IB in the estimated model. Given the cross-sectional design, this mediation should be interpreted as an indirect association consistent with the proposed theory, not as definitive causal mediation.
Taken together, the findings suggest that in a highly digital environment, stronger exposure to marketing stimuli may be accompanied by more frequent non-task internet use and a greater tendency toward unplanned purchasing. These patterns warrant careful attention to digital well-being and self-regulatory supports in educational and organizational settings.
Busca and Bertrandias (2020) identified an increase in digital marketing fragmentation, which is influencing various aspects of life and providing enhanced accessibility. It can logically address increasingly complex needs (Lovelock and Gummesson, 2004). Also Liu et al. (2025) was observed that relationship between economics and behavior is intricate and complex to disentangle, particularly due to the dynamic characteristics of digital marketing. The advantages of digital marketing rapidly impact an individual’s cognitive, heuristic, and stimulus factors in decision-making, cyberloafing, and impulsive buying decisions (Chaffey and Ellis-Chadwick, 2019; Doost and Zhang, 2024).
Table 6 presents the model’s explanatory and predictive indicators (R2, Q2, and GoF). R2 indicates the proportion of variance in an endogenous construct explained by its predictors. The results show that DM explains 60.3% of the variance in CB (R2 = 0.603; moderate), and DM together with CB explains 87.3% of the variance in IB (R2 = 0.873; strong). These values reflect explanatory power within the estimated model and should not be interpreted as causal effects.
| Items | R2 | Q2 | F2 | Effect | Goodness of fit (GoF) | SRMR |
|---|---|---|---|---|---|---|
| Cyberloafing (CB) | 0.603 | 0.600 | 0.764 | Strong | 0.691 | 0.081 accept |
| Impulsive buying (IB) | 0.873 | 0.647 | 0.958 | Strong |
Final assessment.
The Q2 test elucidates the predictive significance of alterations in exogenous variables and forecasts outcomes related to endogenous variables. According to the provisions established by Hair et al. (2014, 2019) a threshold value exceeding 0 indicates predictive relevance and adheres to practical guidelines. The criteria are greater than 0 for low, greater than 0.25 for moderate, and greater than 0.50 for high. The findings demonstrate that each variable exhibits significant predictive value and measurement capability, with values of 0.600 and 0.647, respectively.
The research model’s goodness of fit (GoF) value was calculated. The objective is to elucidate the comprehensive assessment and the degree to which the statistical model aligns with the observed data. The thresholds are 0.1 for low, 0.25 for moderate, and 0.36 for high. The calculation results indicate a GoF value of 0.691, categorizing it as high. The empirical data utilized in the study demonstrate a high level of fit for both the measurement and structural models.
The standardized root mean square residual (SRMR) value indicates model fit, with acceptable values ranging from 0.08 to 0.10. According to the empirical data, the SRMR results of 0.081 signifies a strong fit, elucidating the relationship between variables and indicators within the research model. Figure 2 presents the research model’s findings.
5 Discussion
The rapid advancement of digital marketing and personalization has resulted in an appealing but distracting online environment (Grönroos, 2009). Digital marketing patterns have evolved from passive promotions to actively targeting users via behavioral targeting algorithms, thereby indirectly enhancing the potential for cyberloafing (Grönroos, 2006a, 2006b). This danger signal necessitates critical analysis to emphasize the adverse effects associated with personal distraction in digital consumption (Li et al., 2021; Sin et al., 2025), as it diminishes concentration on work and study, serving as a significant pathway for impulsive buying (Chaoyang et al., 2025; Husnain et al., 2019; Tibrani et al., 2024).
The results suggest that in a highly personalized digital environment, students who report greater exposure to digital marketing cues also report higher levels of off-task browsing during academic activities and higher impulsive buying tendencies. This pattern is consistent with the view that attention-capturing marketing stimuli and frictionless purchasing features can co-occur with reduced deliberation and increased online distraction.
Importantly, because the evidence is correlational and based on a student sample, these findings should be interpreted as context-specific associations rather than causal effects or workplace-level outcomes. The estimated model nonetheless provides a coherent account of how marketing exposure and off-task browsing may cluster with unplanned purchasing in everyday digital life.
In practice, the implications are best framed in terms of digital literacy and self-regulation support in educational settings. Universities and instructors can encourage healthy online habits during lectures and study time (attention-management strategies, clear expectations about device use, and supportive digital well-being initiatives) without implying punitive workplace monitoring.
From the perspective of responsible marketing practice, the results also motivate reflection on how aggressive personalization, scarcity cues, and persuasive interface designs may interact with users’ self-control constraints. However, broader policy conclusions are outside the empirical scope of this study and should be evaluated using designs and samples directly suited to policy-level inference.
Overall, the study adds evidence from an emerging-market context by testing an integrated DM–CB–IB model among university students. The findings complement prior literature by highlighting the role of cyberloafing-like off-task browsing as a plausible mechanism linking marketing exposure to impulsive buying tendencies, while maintaining appropriate caution regarding causality and generalization.
Moreover, continuous engagement with the internet, social media, and interactions with digital marketing enhances psychological needs, particularly in burnout and stress (Cho and Song, 2021). External pressures result in personal conclusions, including a tendency to seek identity and psychological compensation (cyberloafing) through consumer behavior, which can lead to impulsive actions (Chaoyang et al., 2025; Lee et al., 2023). This phenomenon suggests a prevalent explanation digital marketing may serve as a key factor with institutional foundations, influencing digital behavior and consumption patterns. It is essential to examine this matter carefully and thoroughly analyze it from the viewpoints of human resource management, organizations, institutions, and government policy.
The indirect pathway becomes particularly relevant in contexts with limited empirical coverage from developing settings such as Batam, Indonesia. The estimated indirect path (DM, CB, IB) is positive (β = 0.584, p < 0.001), indicating that CB statistically mediates the association between DM and IB within the model. Across the structural paths, the coefficients are positive (β ranging from 0.222 to 0.777), suggesting that higher reported DM exposure co-occurs with higher CB and IB, and that higher CB is associated with higher IB. As noted, these patterns are correlational and temporally indistinguishable in cross-sectional data.
The findings align with the broader discussion that contemporary digital marketing is increasingly embedded in daily routines and information consumption. Within the student context examined here, higher reported exposure to marketing cues is associated with patterns of online multitasking and unplanned purchasing, highlighting the behavioral salience of personalization and continuous connectivity.
Rather than presenting digital marketing as a deterministic force, this manuscript interprets it as a set of stimuli that may be associated with cognitive and affective responses (heightened arousal, reduced deliberation) that co-occur with specific behavioral tendencies in digitally saturated environments.
These results also intersect with discussions on marketing ethics and digital well-being. Nonetheless, the evidence does not directly evaluate regulatory outcomes; therefore, ethical considerations are discussed as context-appropriate reflections rather than as prescriptive policy conclusions. This process reinforces the connection between digital consumption and heuristic patterns that operate quickly and intuitively in consumer decision-making, particularly when faced with time pressure or narratives of exclusivity (Verplanken and Sato, 2011). Given the single-country student sample, the study does not claim population-level prevalence or national economic impact. Future research using multi-site samples and cross-country designs would be valuable to assess the generality of the DM–CB–IB pattern across demographic groups and institutional contexts.
In summary, the study positions cyberloafing-like off-task browsing as a statistically supported mechanism within the estimated model, adding nuance to how digital marketing exposure relates to impulsive buying tendencies in an educational context.
At the same time, the digital economy and marketing innovation continue to evolve. Continued empirical attention is warranted, especially research that can establish temporal ordering and test boundary conditions (self-control, financial literacy, platform type, and situational demands).
Accordingly, we recommend that future studies replicate the model in employee or workplace samples and use longitudinal or time-lagged designs to strengthen external validity and assess whether similar associations hold in organizational contexts.
6 Conclusion
This study provides evidence of positive associations between digital marketing exposure, cyberloafing, and impulsive buying. Specifically, higher reported digital marketing exposure is associated with more cyberloafing and impulsive buying, and cyberloafing is also positively associated with impulsive buying. The estimated indirect pathway further suggests cyberloafing statistically mediates the association between digital marketing exposure and impulsive buying within the model.
Substantively, these results imply that emotionally engaging and personalized digital marketing environments may co-occur with reduced deliberation and more spontaneous purchasing, particularly when individuals spend time online for non-task purposes. The findings highlight the need to consider digital well-being and self-regulation when interpreting digital consumption behaviors.
From a practical standpoint, organizations and educational institutions may benefit from strengthening digital literacy, clarifying responsible internet-use guidelines, and promoting digital well-being initiatives to reduce counterproductive online behavior and unplanned purchasing. These measures may include clear usage norms, supportive monitoring systems, and interventions that respect privacy while encouraging healthy online habits.
Importantly, because the study relies on cross-sectional self-report data, the results are correlational and do not establish temporal precedence or causal direction. Future research should use long-term studies, gather information from different relationships and factors that might influence them
As a result, the need for digital literacy and algorithmic ethics in regulatory frameworks is pressing. Sectoral policy and management also necessitate adaptive internal policy design, which may involve bolstering digital monitoring, implementing behavioral technology-based therapies, and encouraging digital well-being. To handle digital disruption more methodically and critically, this research brings up new potential for contributions to the literature on organizational behavior, digital ethics, and behavioral marketing.
6.1 Limitations
The cross-sectional design and reliance on self-reported measures may be subject to common method bias and cannot rule out reverse causality or unobserved confounding. Accordingly, the reported mediation should be interpreted as an indirect association rather than causal mediation. Future studies using longitudinal, time-separated, or experimental designs and incorporating objective behavioral indicators are recommended to strengthen causal inference.
Statements
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
The studies involving humans were approved by Universitas Riau Kepulauan (066/KL/R/UNRIKA/VI/2025). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
TT: Writing – original draft, Software, Writing – review & editing, Funding acquisition, Reministration, Methodology, Validation, Conceptualization, Visualization, Supervision. CU: Writing – original draft, Reualization. LH: Investigation, Writing – review & editing, Project administration, Re
Funding
The author(s) declared that financial support was received for this work and/or its publication. This research was supported/funded by the Ministry of Higher Education, Sciences and Technology of the Republic of Indonesia. Grant Number: 005/KP-PFR/LPPM/UNRIKA/VI/2025.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was not used in the creation of this manuscript.
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Summary
cyberloafing behavior, digital business, digital marketing (DM), impulsive buying behavior, management
Tibrani T, Ukhriyawati CF and Hakim L (2026) Digital marketing’s targets: cyberloafing and impulsive buying. Front. Hum. Dyn. 8:1721285. doi: 10.3389/fhumd.2026.1721285
Sufyan Habib, Saudi Electronic University, Saudi Arabia
Fayyaz Hussain Qureshi, Oxford Business College, United Kingdom
Dhani Chaubey, Uttaranchal University, India
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