Bridging the Behavioral Intention Action Gap in Reusable Menstrual Product Adoption through Digital Health Literacy and Protection Motivation Theory

Technical Summary: Bridging the Behavioral Intention–Action Gap in Reusable Menstrual Product Adoption

Problem Statement
Despite the growing popularity of reusable menstrual products (RMPs) such as cups, cloth pads, and period underwear, their actual adoption remains low, particularly in low- and middle-income countries. This discrepancy is characterized by a "behavioral intention–action gap," where favorable environmental and cost-related intentions fail to translate into sustained usage. Existing literature often focuses on awareness, barriers, and sociodemographic factors or applies general frameworks like the Theory of Planned Behavior (TPB) or Health Belief Model (HBM). However, there is a limited application of Protection Motivation Theory (PMT) specifically to RMP adoption, and a notable omission of Digital Health Literacy (DHL) in explaining how digital information access influences the translation of intention into actual behavior. The study addresses the need to understand the cognitive and informational processes that facilitate behavior implementation rather than solely focusing on intention creation.

Methodology
The research employed a quantitative, cross-sectional survey design to test a conceptual framework integrating PMT with Digital Health Literacy.

• Sample: Data were collected via a self-administered online survey distributed through social media and email. The final sample consisted of 186 valid responses from menstruators of reproductive age (18–30 years), with a majority holding postgraduate degrees.
• Sampling Technique: Non-probability convenience sampling was used, deemed appropriate for theory-testing research using Structural Equation Modeling (SEM) where model estimation is prioritized over population generalization.
• Measures: Constructs were operationalized using multi-item reflective scales adapted from validated literature:
  • Threat Appraisal: Perceived Severity and Perceived Vulnerability.
  • Coping Appraisal: Response Efficacy, Self-Efficacy, and Response Cost.
  • Outcomes: Behavioral Intention and Actual Behavior (self-reported adoption).
  • Moderator: Digital Health Literacy (DHL), measured via the eHealth Literacy Scale (eHEALS).
• Analysis: Partial Least Squares Structural Equation Modeling (PLS-SEM) was conducted using SmartPLS (version 4). The analysis followed a two-step approach: assessing the measurement model (reliability, convergent validity, discriminant validity via HTMT) and the structural model (path coefficients, $R^2$, $Q^2$, and bootstrapping with 5,000 resamples).

Key Results
The structural model analysis yielded the following findings regarding the hypothesized relationships:

• Significant Predictors of Behavioral Intention:
  • Perceived Severity ($\beta = 0.290, p < 0.001$): The subjective evaluation of the consequences of health/environmental threats positively influences intention.
  • Perceived Vulnerability ($\beta = 0.271, p < 0.001$): The subjective likelihood of encountering the threat positively influences intention.
  • Response Cost ($\beta = 0.375, p < 0.001$): Perceived obstacles (financial, time, effort) were found to be a robust predictor of intention.
• Non-Significant Predictors:
  • Response Efficacy ($\beta = -0.030, p = 0.857$): Belief in the effectiveness of the RMP did not significantly predict intention.
  • Self-Efficacy ($\beta = 0.071, p = 0.625$): Belief in one's capacity to perform the behavior did not significantly predict intention.
• Intention–Behavior Link:
  • Behavioral Intention to Action ($\beta = 0.345, p < 0.001$): A significant positive relationship was found, indicating that intention is a strong proximal predictor of actual adoption behavior in this context.
• Moderation Effect:
  • Digital Health Literacy (H7): The hypothesized moderating effect of DHL on the relationship between Behavioral Intention and Actual Behavior was not supported ($\beta = -0.252, p = 0.574$). The data suggests DHL does not statistically strengthen the translation of intention to action in this specific sample.
• Model Fit: The model explained 32.8% of the variance in behavioral intention ($R^2$) and 48.0% of the variance in action behavior. Predictive relevance ($Q^2$) was positive for both constructs.

Key Contributions and Claims
The paper claims to make the following contributions to the literature on sustainable health behaviors and PMT:

1. Contextual Application of PMT: It extends PMT to the underexplored domain of reusable menstrual product adoption, demonstrating that in this specific context, threat appraisal variables (severity, vulnerability) and response cost are more influential on intention than coping appraisal variables (efficacy beliefs).
2. Intention–Behavior Gap Analysis: The study empirically validates the strong link between intention and behavior for RMPs, suggesting a narrower gap in this specific domain compared to other health behaviors, while highlighting that practical barriers (cost) remain critical.
3. Role of Digital Health Literacy: The research challenges the assumption that DHL acts as a moderator for the intention–behavior gap. Instead, it posits that in digitally active populations, DHL may function as a foundational skill or antecedent rather than a differentiating moderator, suggesting that high literacy does not necessarily guarantee the translation of intention to action without addressing other barriers.
4. Theoretical Refinement: The findings suggest that in technologically advanced and behaviorally familiar contexts, risk assessment and cost-benefit analysis drive intention more than perceived ability, refining the application of PMT for pro-environmental health behaviors.

Significance
The study offers practical insights for policymakers and health practitioners aiming to promote sustainable menstrual health. It argues that interventions should prioritize:

• Risk Salience: Framing messages around the severity and personal vulnerability to health and environmental threats.
• Cost Reduction: Actively mitigating perceived response costs (financial, psychological, and logistical) rather than relying solely on efficacy messaging.
• Digital Intervention Design: Recognizing that for digitally literate users, platforms should be action-oriented and efficient to avoid cognitive overload, rather than merely providing more information.

The paper concludes that while digital health literacy is vital for accessing information, bridging the intention–action gap in RMP adoption requires a focus on evaluative and motivational factors (threat and cost) rather than assuming digital skills alone will facilitate behavioral change.