Submitted:
22 March 2026
Posted:
23 March 2026
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Abstract
Keywords:
1. Introduction
- How farmers’ perceptions of radio message framing are associated with adaptation intentions, and whether threat appraisal and self-efficacy statistically mediate these relationships
- Whether collective efficacy moderates the observed associations among threat appraisal, self-efficacy, and adaptation intentions
- Which perceived frame exposures (threat-focused, culturally narrativized) show the strongest correlations with adaptive intentions when accounting for psychological factors?
2. Literature Review and Theoretical Framework
2.1. Conceptualising Climate Communication Variables
2.1.1. Radio Message Framing: Strategic Information Architecture
2.1.2. Threat Appraisal: The Cognitive Gateway
2.1.3. Self-Efficacy: The Affective Bridge
2.1.4. Collective Efficacy: The Moderating Factor
2.1.5. Adaptation Intentions: Proximal Behavioural Precursors
2.2. Theoretical Integration: Beyond Fragmented Models
2.2.1. Protection Motivation Theory: Foundational Mechanisms
2.2.2. Extended Parallel Process Model: Cognitive Contingencies
2.2.3. Social Cognitive Theory: Communal Dimensions
2.3. Hypothesis Development and Conceptual Framework
2.3.1. Serial Mediation Pathways
2.3.2. Moderating Role of Collective Efficacy
2.3.3. Direct Effects as Primary Pathways
3. Methods
3.1. Research Design and Rationale
3.2. Sampling
3.3. Data Collection and Measurement Tools
3.4. Analytical Approach
4. Results
4.1. Sample Demographics and Descriptive Statistics
4.2. Measurement Model Assessment
4.3. Structural Model Evaluation and Hypothesis Testing
4.4. Hypothesis Testing Results
5. Discussion
5.1. Theoretical Implications: Reconsidering Fear Appeals in High-Vulnerability Contexts
5.2. The Cultural Frame Paradox: Embedded Authenticity Versus Performative Culture
5.3. The Collective Efficacy Conundrum: Measurement Challenges in Collectivist Contexts
6. Conclusion
6.1. Study Summary and Achievement of Research Objectives
6.2. Theoretical Integration: Toward the Authenticity Primacy Principle
6.3. Implications for Climate Communication Theory and Practice
6.4. Contributions to Sustainable Development Goals
6.5. Methodological Concerns and the Question of Common Method Bias
6.6. Future Research Directions and Methodological Improvements
6.7. Final Reflections on Decolonising Climate Communication
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| Construct | Code | Factor Loadings | CA | rho_a | rho_c | AVE |
|---|---|---|---|---|---|---|
| Adaptation Intention | 0.938 | 0.941 | 0.951 | 0.764 | ||
| AI1 | 0.889 | |||||
| AI2 | 0.832 | |||||
| AI3 | 0.886 | |||||
| AI4 | 0.889 | |||||
| AI5 | 0.906 | |||||
| AI6 | 0.841 | |||||
| Collective Efficacy | 0.92 | 0.921 | 0.94 | 0.759 | ||
| CE1 | 0.828 | |||||
| CE2 | 0.856 | |||||
| CE3 | 0.895 | |||||
| CE4 | 0.876 | |||||
| CE5 | 0.898 | |||||
| Cultural Frame | 0.873 | 0.875 | 0.899 | 0.692 | ||
| CF1 | 0.781 | |||||
| CF2 | 0.968 | |||||
| CF3 | 0.776 | |||||
| CF4 | 0.788 | |||||
| Self-Efficacy | 0.905 | 0.91 | 0.927 | 0.678 | ||
| SE1 | 0.801 | |||||
| SE2 | 0.859 | |||||
| SE3 | 0.733 | |||||
| SE4 | 0.859 | |||||
| SE5 | 0.827 | |||||
| SE6 | 0.856 | |||||
| Threat Appraisal | 0.933 | 0.934 | 0.949 | 0.789 | ||
| TA2 | 0.874 | |||||
| TA3 | 0.919 | |||||
| TA4 | 0.904 | |||||
| TA5 | 0.898 | |||||
| TA6 | 0.844 | |||||
| Threat Frame | 0.941 | 0.941 | 0.958 | 0.85 | ||
| TF1 | 0.92 | |||||
| TF2 | 0.919 | |||||
| TF3 | 0.926 | |||||
| TF4 | 0.923 |
| Panel A: HTMT | ||||||
|---|---|---|---|---|---|---|
| Variable | AI | CE | CF | SE | TA | TF |
| AI | ||||||
| CE | 0.485 | |||||
| CF | 0.035 | 0.044 | ||||
| SE | 0.417 | 0.592 | 0.041 | |||
| TA | 0.594 | 0.422 | 0.063 | 0.437 | ||
| TF | 0.576 | 0.487 | 0.034 | 0.45 | 0.649 | |
| Panel B: Fornell-Larcker | ||||||
| Variable | AI | CE | CF | SE | TA | TF |
| AI | 0.874 | |||||
| CE | 0.421 | 0.871 | ||||
| CF | -0.021 | 0.002 | 0.832 | |||
| SE | 0.369 | 0.523 | 0.014 | 0.824 | ||
| TA | 0.534 | 0.393 | -0.067 | 0.404 | 0.888 | |
| TF | 0.518 | 0.446 | -0.027 | 0.411 | 0.602 | 0.922 |
| Fit Measure | Saturated Model | Estimated Model | Threshold |
|---|---|---|---|
| SRMR | 0.000 | 0.065 | < 0.080 |
| d_ULS | 0.734 | 1.245 | Lower is better |
| d_G | 0.648 | 0.712 | Lower is better |
| Chi-square | 1268.634 | 1298.425 | Lower is better |
| NFI | 1.000 | 0.954 | > 0.900 |
| Variable | R-square | R-square adjusted |
|---|---|---|
| AI | 0.565 | 0.563 |
| SE | 0.262 | 0.258 |
| TA | 0.502 | 0.494 |
| Panel A: Direct Path Coefficients | |||||||
|---|---|---|---|---|---|---|---|
| Hypothesized Path | β | M | SD | t-value | p-value | Hypothesis | Decision |
| CE → AI | -0.058 | -0.057 | 0.044 | 1.309 | 0.191 | - | - |
| CE → TA | -0.105 | -0.105 | 0.075 | 1.401 | 0.161 | - | - |
| CF → AI | 0.004 | 0.004 | 0.022 | 0.197 | 0.844 | H1c | Not supported |
| CF → SE | 0.028 | 0.037 | 0.058 | 0.484 | 0.628 | H1c | Not supported |
| CF → TA | -0.05 | -0.039 | 0.049 | 1.033 | 0.302 | H1c | Not supported |
| SE → AI | 0.172 | 0.172 | 0.046 | 3.762 | <0.001*** | - | - |
| SE → TA | 0.141 | 0.141 | 0.078 | 1.816 | 0.069 | - | - |
| TA → AI | 0.081 | 0.081 | 0.034 | 2.411 | 0.016* | H1a | Supported |
| TF → AI | 0.612 | 0.61 | 0.036 | 22.264 | <0.001*** | H3 | Supported |
| TF → SE | 0.512 | 0.512 | 0.047 | 10.885 | <0.001*** | H1b | Supported |
| TF → TA | 0.685 | 0.685 | 0.045 | 15.221 | <0.001*** | - | - |
| CE × SE → TA | -0.018 | -0.017 | 0.04 | 0.444 | 0.657 | H2a | Not supported |
| CE × CF → AI | 0.006 | 0.004 | 0.02 | 0.278 | 0.781 | - | - |
| CE × TF → AI | 0.02 | 0.02 | 0.018 | 1.1 | 0.271 | - | - |
| Panel B: Total Indirect Effects | |||||||
| Hypothesized Path | β | M | SD | t-value | p-value | Hypothesis | Decision |
| CE → AI | -0.009 | -0.009 | 0.008 | 1.119 | 0.263 | H2b | Not supported |
| CF → AI | 0.001 | 0.004 | 0.013 | 0.082 | 0.934 | - | - |
| CF → TA | 0.004 | 0.005 | 0.01 | 0.409 | 0.683 | - | - |
| SE → AI | 0.011 | 0.011 | 0.008 | 1.37 | 0.171 | - | - |
| TF → AI | 0.149 | 0.15 | 0.039 | 3.869 | <0.001*** | - | - |
| TF → TA | 0.072 | 0.072 | 0.041 | 1.769 | 0.077 | - | - |
| CE × SE → AI | -0.001 | -0.002 | 0.004 | 0.398 | 0.691 | H2b | Not supported |
| Panel C: Specific Indirect Effects | |||||||
| Hypothesized Path | β | M | SD | t-value | p-value | Hypothesis | Decision |
| CF → SE → TA → AI | 0 | 0 | 0.001 | 0.366 | 0.714 | - | - |
| TF → SE → TA | 0.072 | 0.072 | 0.041 | 1.769 | 0.077 | - | - |
| CE → TA → AI | -0.009 | -0.009 | 0.008 | 1.119 | 0.263 | - | - |
| CF → SE → AI | 0.005 | 0.006 | 0.011 | 0.455 | 0.649 | - | - |
| SE → TA → AI | 0.011 | 0.011 | 0.008 | 1.37 | 0.171 | - | - |
| TF → SE → TA → AI | 0.006 | 0.006 | 0.004 | 1.353 | 0.176 | - | - |
| TF → SE → AI | 0.088 | 0.088 | 0.025 | 3.482 | 0.001*** | ||
| CF → TA → AI | -0.004 | -0.003 | 0.004 | 0.923 | 0.356 | - | - |
| CE × SE → TA → AI | -0.001 | -0.002 | 0.004 | 0.398 | 0.691 | - | - |
| CF → SE → TA | 0.004 | 0.005 | 0.01 | 0.409 | 0.683 | - | - |
| TF → TA → AI | 0.056 | 0.056 | 0.024 | 2.297 | 0.022* | - | - |
| Panel D: Total Effects | |||||||
| Hypothesized Path | β | M | SD | t-value | p-value | ||
| CE → AI | -0.066 | -0.066 | 0.045 | 1.48 | 0.139 | ||
| CE → TA | -0.105 | -0.105 | 0.075 | 1.401 | 0.161 | ||
| CF → AI | 0.005 | 0.008 | 0.027 | 0.198 | 0.843 | ||
| CF → SE | 0.028 | 0.037 | 0.058 | 0.484 | 0.628 | ||
| CF → TA | -0.046 | -0.034 | 0.051 | 0.905 | 0.366 | ||
| SE → AI | 0.183 | 0.183 | 0.047 | 3.913 | <0.001*** | ||
| SE → TA | 0.141 | 0.141 | 0.078 | 1.816 | 0.069 | ||
| TA → AI | 0.081 | 0.081 | 0.034 | 2.411 | 0.016* | ||
| TF → AI | 0.562 | 0.56 | 0.026 | 37.109 | <0.001*** | ||
| TF → SE | 0.512 | 0.512 | 0.047 | 10.885 | <0.001*** | ||
| TF → TA | 0.757 | 0.757 | 0.052 | 14.592 | <0.001*** | ||
| CE × SE → AI | -0.001 | -0.002 | 0.004 | 0.398 | 0.691 | ||
| CE × SE → TA | -0.018 | -0.017 | 0.04 | 0.444 | 0.657 | ||
| CE × CF → AI | 0.006 | 0.004 | 0.02 | 0.278 | 0.781 | ||
| CE × TF → AI | 0.02 | 0.02 | 0.018 | 1.1 | 0.271 | ||
| Panel A: construct cross validated redundancy | |||
|---|---|---|---|
| SSO | SSE | Q² (=1-SSE/SSO) | |
| Adaptation Intention | 2088 | 724.562 | 0.653 |
| Collective-Efficacy | 1740 | 1740 | 0.000 |
| Cultural Frame | 1392 | 1392 | 0.000 |
| Self-Efficacy | 2088 | 1724.329 | 0.174 |
| Threat Appraisal | 1740 | 1061.572 | 0.390 |
| Threat Frame | 1392 | 1392 | 0.000 |
| Panel B: q2 predict | |||
| Q²predict | RMSE | MAE | |
| Adaptation Intention | 0.833 | 0.412 | 0.298 |
| Self-Efficacy | 0.247 | 0.872 | 0.670 |
| Threat Appraisal | 0.473 | 0.731 | 0.557 |
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