Diagnostic accuracy of rapid lateral flow immunoassay tests in Diagnosing HIV Among Key and General Populations: Six Bayesian Meta-Analyses

Introduction

Mental health has become an increasingly urgent public health concern, driven by accelerated urban development, mounting socioeconomic pressures, and the enduring consequences of the SARS-CoV-2 pandemic. Simultaneously, advances in digital technology have transformed the healthcare landscape by facilitating the emergence of digital mental health services (DMHS), which offer scalable, accessible, and relatively affordable alternatives for psychological support (Schueller et al., 2019). Despite their potential to address gaps in conventional mental healthcare delivery, the uptake of these services remains uneven, particularly across emerging economies such as Indonesia. While digital health adoption has attracted growing scholarly attention, empirical research examining the behavioral processes underlying consumer adoption decisions toward DMHS remains limited.

To better understand these behavioral mechanisms, this study adopts the model of goal-directed behavior (MGB) as its primary theoretical foundation. Originally proposed by Perugini and Bagozzi (2001) as an extension of the theory of planned behavior (TPB), the MGB introduces desire as a central motivational construct linking cognitive evaluations—including attitude, subjective norm, and perceived behavioral control—to behavioral intention. By explicitly incorporating motivational and affective dimensions, the model offers a broader explanatory perspective than traditional rational-choice frameworks, making it particularly suitable for investigating technology-related decision-making in emerging service contexts. Although the MGB has demonstrated explanatory power across diverse domains (Thomas-Francois et al., 2023; Poon & Tung, 2022), its application to digital mental health consumption remains relatively underdeveloped, particularly in relation to the psychological and economic factors shaping consumer adoption behavior.

Building on perspectives from behavioral marketing and health economics, this study extends the MGB by integrating two constructs derived from the health belief model (HBM), namely perceived severity and perceived susceptibility. These constructs capture individuals’ perceptions of health-related threats and are introduced to reflect the extent to which perceived mental health risks act as motivational drivers of behavioral intention. The underlying premise is that individuals who recognize the potential personal, social, and economic consequences of unmanaged mental health conditions may demonstrate stronger motivation to seek digital mental health solutions.

Situated within the Indonesian market, this research investigates the behavioral determinants influencing consumers’ intention to purchase DMHS in one of Southeast Asia’s fastest-growing digital economies. Indonesia provides a particularly relevant empirical setting due to its expanding digital infrastructure, rising awareness of mental health issues, and increasing consumer engagement with online services. By combining behavioral theory with economic and technological perspectives, this study seeks to clarify how psychological motivations are translated into market demand for digital health innovation. Accordingly, this study addresses the following research question: how do the core variables of the model of goal-directed behavior, together with perceived severity and perceived susceptibility, influence Indonesian consumers’ intention to adopt digital mental health services, and what implications do these relationships hold for the sustainable development of the digital health sector?

This research contributes to literature in three important ways. First, it advances theoretical understanding by extending the explanatory scope of the MGB through the incorporation of health belief constructs as presented in Figure 1, thereby offering a more comprehensive framework for examining consumer behavior in mental health service adoption. Second, it provides practical insights for digital health providers and industry stakeholders by identifying the psychological mechanisms that influence consumers’ willingness to engage with DMHS. Third, from a broader economic perspective, the study positions DMHS adoption within the context of the knowledge-driven digital economy. By demonstrating how psychological readiness shapes behavioral intention and market participation, the findings offer valuable implications for policymakers and practitioners seeking to foster innovation, investment, and inclusive growth within the digital health ecosystem.

Figure 1. Proposed conceptual framework of the study.

Methods

Results

A total of 462 valid responses were included in the final analysis. The demographic distribution of respondents is presented in Table 1. The respondent profile reflects a predominantly young, digitally engaged, and urban-based sample. Individuals aged between 18 and 35 years represented more than four-fifths of the total participants, which is consistent with evidence suggesting that younger populations demonstrate higher engagement with digital consumption and technology-mediated health behaviors (Pretorius et al., 2019). Female respondents accounted for 68% of the sample, aligning with prior studies indicating that women tend to show greater proactive engagement in seeking mental health information and utilizing preventive health-related services (Demirci et al., 2021). In terms of geographic distribution, 90.9% of participants resided in urban areas, reflecting Indonesia’s concentration of digital infrastructure and online health service exposure within metropolitan centers (IBC Team, 2024). Educational attainment was also relatively high, with 51.9% of respondents having completed tertiary education. This profile suggests sufficient digital literacy to evaluate and navigate digital mental health platforms effectively (van Deursen & van Dijk, 2014).

Although awareness of digital mental health services was relatively widespread, with 76.0% of respondents indicating familiarity with DMHS, only 50.6% reported previous purchase experience. This discrepancy suggests the existence of a notable awareness-to-adoption gap within the Indonesian market. Furthermore, nearly 60% of respondents reported experiencing at least moderate psychological distress, indicating meaningful latent demand for accessible digital mental health interventions. Overall, the sample characteristics are appropriate for examining behavioral intention toward DMHS adoption, as they reflect the demographic groups most commonly associated with digital health engagement globally, namely younger, urban, and educated consumers (Pretorius et al., 2019; van Deursen & van Dijk, 2014).

Descriptive statistics further indicate moderately positive consumer perceptions toward DMHS. Respondents reported a moderate attitude toward purchasing such services ( $\overline{\mathrm{x}}$ = 3.60), moderate perceived social support for adoption ( $\overline{\mathrm{x}}$ = 3.38), and a relatively favorable perception of behavioral control over service usage ( $\overline{\mathrm{x}}$ = 3.68). Perceived severity of unmanaged mental health conditions was relatively high ( $\overline{\mathrm{x}}$ = 4.11), suggesting strong recognition of the potential consequences of neglecting mental health. Perceived susceptibility was more moderate ( $\overline{\mathrm{x}}$ = 3.41), indicating a balanced assessment of personal vulnerability. Respondents also demonstrated a generally favorable desire to actively manage their mental health ( $\overline{\mathrm{x}}$ = 3.88), while their intention to purchase DMHS was moderate overall ( $\overline{\mathrm{x}}$ = 3.31).

Discussion

The hypothesis testing results presented in Table 6 provide empirical support for five of the eight proposed relationships (p < 0.05, t > 1.96). Overall, the findings suggest that the integrated MGB–HBM framework offers meaningful explanatory power for understanding consumer intention toward digital mental health service (DMHS) adoption in Indonesia. While most motivational pathways were supported, two of the hypothesized direct relationships involving health threat perceptions and one pathway related to perceived behavioral control did not achieve statistical significance. These mixed findings offer important insights into the behavioral dynamics underlying digital mental health adoption.

The results indicate that attitude toward purchasing DMHS positively influenced desire to manage mental health (β = 0.166, p = 0.002, t = 3.102), thereby supporting H1. This finding suggests that favorable evaluations of digital mental health services contribute to stronger motivational readiness to engage in proactive mental health management. This outcome is consistent with prior research emphasizing the central role of attitudinal evaluation in shaping technology adoption behavior (Inegbedion et al., 2016; Schuster & Parkinson, 2022). Individuals who perceive DMHS as useful, beneficial, and appropriate are more likely to develop internal motivation to use these services. Within the context of the model of goal-directed behavior, this relationship highlights how cognitive appraisal serves as an important precursor to motivational activation. Positive shifts in consumers’ perceptions of DMHS are therefore likely to strengthen their desire to pursue digital mental health solutions. Although statistically significant, the structural effect size was relatively modest (f² = 0.025), suggesting that attitude contributes meaningfully but does not represent the dominant predictor of desire. The f-square effect size describes how much influence a variable has in a structural model (Wong, 2019). This indicates that favorable perceptions alone may not be sufficient to generate strong motivational commitment unless supported by complementary emotional or social influences.

The relationship between subjective norm and desire was also statistically significant (β = 0.122, p = 0.013, t = 2.482), supporting H2. This finding underscores the importance of social influence in shaping digital mental health behavior. Encouragement and approval from family members, peers, or other significant social actors appear to reinforce individuals’ internal motivation to manage their mental health. The result aligns with evidence reported by Pretorius et al. (2019), which demonstrates that social endorsement increases online help-seeking behavior among younger adults. Mental health decision-making often occurs within broader interpersonal contexts, where support systems can reduce uncertainty and legitimize help-seeking behavior. Supportive familial and interpersonal relationships are essential for sustaining mental well-being (Fusar-Poli et al., 2020). In collectivist social environments such as Indonesia, the influence of close relational networks may be particularly salient. However, similar to attitude, the effect size was relatively small (f² = 0.015), indicating that while subjective norm contributes to motivational formation, its explanatory influence remains limited when considered independently.

In contrast, perceived behavioral control did not significantly influence desire (β = 0.058, p = 0.246, t = 1.159), resulting in the rejection of H3. This result diverges from traditional MGB expectations, which propose that greater perceived control should strengthen motivational states (Ajzen, 1991; Perugini & Bagozzi, 2001). The lack of significance here may indicate that confidence and resources alone are insufficient to generate emotional motivation for mental health management. This finding is the same as that found by Schuster et al. (2017) that PBC did not influence desire. Several explanations may account for this non-significant relationship. First, the sample consisted predominantly of young, digitally experienced, and highly educated respondents, which may have reduced variability in perceived control. When most individuals already feel capable of accessing digital services, perceived control may lose explanatory significance (van Deursen & van Dijk, 2014). Second, prior technology adoption studies suggest that control-related predictors tend to diminish in importance among experienced users, as familiarity reduces perceived barriers (Venkatesh et al., 2012). Third, mental-health help-seeking is heavily influenced by emotional factors and stigma, which can suppress motivational desire even when users feel practically capable of using DMHS (Rickwood et al., 2005). This aligns with the notion that mental health–related behaviors often depend more on emotional and social reinforcement than on perceived capability (Rosenstock, 1974). This interpretation is consistent with mental health adoption research suggesting that emotional readiness and psychological safety often outweigh functional competence in predicting engagement with digital mental health tools (Torous et al., 2018). Taken together, these findings indicate that in digitally mature consumer segments, practical capability may represent a baseline condition rather than a motivating force.

The results further reveal that perceived severity significantly influenced desire to manage mental health (β = 0.138, p = 0.006, t = 2.737), supporting H4, but did not significantly affect behavioral intention to purchase DMHS (β = −0.006, p = 0.899), leading to the rejection of H5. This pattern suggests that recognizing the seriousness of mental health consequences increases psychological motivation for self-management but does not directly translate into purchasing intention. This finding is consistent with core assumptions of the health belief model, which posits that perceived seriousness functions as an internal motivational trigger for protective action (Rosenstock, 1974; Becker, 1974). Consumers who recognize the potential consequences of unmanaged mental health—such as impaired productivity, deteriorating relationships, or reduced life satisfaction—appear more inclined to desire preventive action. This result is also consistent with prior digital health studies showing that perceived severity enhances motivational readiness (Dou et al., 2017; Wei et al., 2021). However, the relatively small effect size (f² = 0.016) indicates that severity alone exerts limited structural influence. More importantly, its lack of direct impact on purchase intention suggests that awareness of risk is insufficient to produce market commitment. This disconnect may reflect practical concerns such as uncertainty regarding service effectiveness, privacy concerns, or reluctance to pay for mental health support despite acknowledging its importance.

A similar pattern emerged for perceived susceptibility, which significantly influenced desire (β = 0.156, p = 0.004, t = 2.908), supporting H6, but did not significantly affect behavioral intention (β = 0.044, p = 0.418, t = 0.810), resulting in rejection of H7. These findings indicate that personal perceptions of vulnerability stimulate psychological readiness to engage in self-care but are insufficient to directly motivate purchase behavior. This is consistent with the health belief model’s proposition that perceived susceptibility serves as an early motivational trigger for preventive cognition (Champion & Skinner, 2008; Rosenstock, 1974). In digital mental health contexts, awareness of personal vulnerability may heighten concern and encourage contemplation of intervention. Consistent with prior studies, individuals who perceived higher health vulnerability were more willing to adopt mobile health technologies (Dou et al., 2017; C.C. & Prathap, 2020). The effect size, however, remained modest (f² = 0.022), suggesting that susceptibility acts primarily as an initial psychological catalyst rather than a decisive behavioral determinant. The absence of a direct relationship with behavioral intention reinforces the notion that risk perception alone cannot fully explain digital service adoption.

Individuals may recognize vulnerability while simultaneously postponing action due to cost concerns, uncertainty, social stigma, or low confidence in digital therapeutic effectiveness. An interesting descriptive insight may help contextualize this result: 65.6% of respondents identified themselves as introverts. This characteristic may influence how threat perceptions translate into behavioral action. Introverted individuals may exhibit stronger internal awareness of psychological distress while remaining hesitant to engage in formal intervention pathways, including paid digital services. Different personalities can have different cognitive and behavioral characteristics (Serenko, 2026). Although this explanation remains speculative and was not directly tested in the structural model, it offers a promising direction for future research examining personality as a potential moderator.

Finally, desire to manage mental health demonstrated the strongest and most statistically robust positive effect on behavioral intention to purchase DMHS (β = 0.179, p = 0.000, t = 3.818), thereby supporting H8. This result strongly reinforces the central proposition of the model of goal-directed behavior, which positions desire as the immediate motivational antecedent of intention (Perugini & Bagozzi, 2001). The finding confirms that while evaluative and threat-based perceptions contribute to motivational formation, behavioral intention emerges primarily when these antecedents are translated into a genuine internal desire to act. This highlights desire as the key psychological mechanism bridging cognition and behavioral commitment in digital mental health adoption. The result is consistent with prior research across digital technology settings, which consistently identifies desire as a critical determinant of behavioral intention (Thomas-Francois et al., 2023; Schuster & Parkinson, 2022). From a practical standpoint, this finding suggests that strategies aimed solely at increasing awareness of mental health risks may be insufficient. Digital mental health providers must instead focus on strengthening motivational desire through emotionally resonant communication, trust-building mechanisms, and value propositions that connect psychological need with actionable service engagement.

Overall, the findings demonstrate that DMHS adoption is shaped less by functional capability and direct threat perception than by the extent to which these factors activate internal motivational desire. This reinforces the relevance of integrating motivational and health belief perspectives when examining consumer behavior in emerging digital health markets.

Conclusions and implications

This study examined consumer intention to adopt digital mental health services (DMHS) in Indonesia through an integrated framework combining the model of goal-directed behavior (MGB) and the health belief model (HBM). By positioning desire to manage mental health as the central motivational mechanism, the study provides empirical evidence regarding the psychological pathways that shape adoption decisions in the digital mental health context. The findings demonstrate that desire functions as the primary motivational channel through which evaluative and health-related perceptions are translated into behavioral intentions. Attitude toward purchasing DMHS and subjective norm were found to significantly enhance desire, indicating that both personal evaluations and social influence play meaningful roles in motivating mental health management through digital means. Similarly, perceived severity and perceived susceptibility significantly influenced desire, suggesting that individuals’ recognition of mental health risks contributes to motivational activation. However, perceived behavioral control did not exert a significant effect, indicating that functional capability or perceived ease of access alone is insufficient to stimulate psychological readiness in this context. Most notably, desire showed a strong positive relationship with behavioral intention, reinforcing its position as the most immediate predictor of DMHS adoption.

The robustness of the proposed framework is further supported by model evaluation indicators. The structural model demonstrated satisfactory explanatory capacity, meaningful predictive relevance, acceptable fit based on SRMR values, strong out-of-sample predictive performance, and generally stable structural relationships. The identification of several nonlinear effects additionally suggests that certain motivational influences may operate in more complex ways than linear models typically assume. Collectively, these results support the suitability of the integrated MGB–HBM framework for explaining consumer adoption behavior toward DMHS within emerging-market settings.

Ethical considerations

This study was approved by the Deputy Vice Chancellor (Research & Innovation), Professor Dr. Sandeep Poddar, on June 23, 2025, by the Research & Innovation Committee of Lincoln University College with the ethics approval number LUC/MKT/IND/SP/007/310. All participants were adults over 18 years of age. All data collected was anonymized to ensure participant privacy and confidentiality, and after explanation, respondents were asked to sign a consent form indicating their willingness to participate.