Determinants of Solar Photovoltaic Adoption Intention among Households: A Meta-Analysis
Abstract
:1. Introduction
2. Literature Review
2.1. Theories
2.2. Factors
2.3. Methods
3. Research Method
3.1. Meta-Analysis
3.2. Literature Search and Selection
- (1)
- The research question must pertain to the factors influencing the willingness to adopt solar photovoltaic technology, with the article identifying at least one influencing factor;
- (2)
- The study must be empirical, excluding theoretical research, review papers, and similar literature;
- (3)
- The research data must be complete, clearly reporting the sample size, correlation coefficients, or statistics that can be converted into correlation coefficients (such as t-values for path significance).
3.3. Literature Coding and Processing
4. Results and Analysis
4.1. Heterogeneity Test
4.2. Publication Bias
4.3. Main Effect Test
4.4. Moderator Effect Tests
5. Discussion
5.1. Factors and Strength
5.2. Moderator Effect Analysis
5.3. Limitations and Future Scope
5.4. Policy Implications
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
No. | Year | Title | Journal | Region | Reference |
---|---|---|---|---|---|
1 | 2024 | A behavioral study on villagers’ adoption intention and carbon neutrality toward rooftop solar photovoltaic systems in India | International Journal of Energy Sector Management | India | [64] |
2 | 2018 | Cognition process and influencing factors of rural residents’ adoption willingness for solar PV poverty alleviation projects: Evidence from a mixed methodology in rural China | Energy | China | [37] |
3 | 2020 | Consumer attitude and purchase intention toward rooftop photovoltaic installation: The roles of personal trait, psychological benefit, and government incentives | Energy & Environment | China | [34] |
4 | 2022 | Determinants of Adoption and the Type of Solar PV Technology Adopted in Rural Pakistan | Frontiers in Environmental Science | Pakistan | [65] |
5 | 2021 | Determining Factors Affecting Customer Intention to Use Rooftop Solar Photovoltaics in Indonesia | Sustainability | Indonesia | [66] |
6 | 2023 | Determining the Factors Affecting Solar Energy Utilization in Saudi Housing: A Case Study in Makkah | Energies | Arabia | [41] |
7 | 2020 | Determining the Influencing Factors in the Adoption of Solar Photovoltaic Technology in Pakistan: A Decomposed Technology Acceptance Model Approach | Economies | Pakistan | [24] |
8 | 2021 | Do Perceived Risk, Perception of Self-Efficacy, and Openness to Technology Matter for Solar PV Adoption? An Application of the Extended Theory of Planned Behavior | Energies | Pakistan | [43] |
9 | 2022 | Do village leaders’ engagement, social interaction and financial incentive affect residents’ solar PV adoption? An empirical study in rural China? | International Journal of Energy Sector Management | China | [67] |
10 | 2021 | Factors Affecting the Adoption of Photovoltaic Systems in Rural Areas of Poland | Energies | Poland | [68] |
11 | 2023 | Factors Hindering Solar Photovoltaic System Implementation in Buildings and Infrastructure Projects: Analysis through a Multiple Linear Regression Model and Rule-Based Decision Support System | Buildings | Pakistan | [42] |
12 | 2022 | Factors influencing purchase intention of solar photovoltaic technology: An extended perspective of technology readiness index and theory of planned behaviour | Cleaner and Responsible Consumption | Pakistan | [40] |
13 | 2021 | Factors influencing the residence’s intention to adopt solar photovoltaic technology: a case study from Klang Valley, Malaysia | Clean Energy | Malaysia | [69] |
14 | 2024 | From intentions to actions: unveiling the socio-psychological drivers of solar home system adoption in developing nations | Architectural Engineering and Design Management | Pakistan | [70] |
15 | 2021 | How does satisfaction of solar PV users enhance their trust in the power grid?—Evidence from PPAPs in rural China | Energy, Sustainability and Society | China | [71] |
16 | 2020 | Investigating nonusers’ behavioural intention towards solar photovoltaic technology in Malaysia: The role of knowledge transmission and price value | Energy Policy | Malaysia | [7] |
17 | 2022 | Investigating the Determinants of the Adoption of Solar Photovoltaic Systems—Citizen’s Perspectives of Two Developing Countries | Sustainability | Somalia and Pakistan | [25] |
18 | 2023 | Modeling behavioral factors influencing farmers’ willingness to adopt rooftop solar photovoltaic: Empirical evidence from rural China | Journal of cleaner production | China | [28] |
19 | 2019 | New trends in solar: A comparative study assessing the attitudes towards the adoption of rooftop PV | Energy Policy | United states | [72] |
20 | 2018 | Predicting intention to adopt solar technology in Canada: The role of knowledge, public engagement, and visibility | Energy Policy | Canada | [57] |
21 | 2023 | Predicting Residential Photovoltaic Adoption Intention of Potential Prosumers in Thailand: A Theory of Planned Behavior Model | Energies | Thailand | [11] |
22 | 2022 | Purchase intention of Indian customers: a study on solar PV technology | International Journal of Energy Sector Management | India | [73] |
23 | 2022 | National goals or sense of community? Exploring the social-psychological influence of household solar energy adoption in rural China | Energy Research & Social Science | Pakistan | [74] |
24 | 2024 | Socio-environmental factors and solar housing system adoption: moderating effect of attitude | Innovative Infrastructure Solutions | Pakistan | [75] |
25 | 2021 | Solar photovoltaic as a means to sustainable energy consumption in Malaysia: the role of knowledge and price value | Energy Sources, Part B: Economics, Planning, and Policy | Malaysia | [76] |
26 | 2024 | Sustainable energy development through non-residential rooftop solar photovoltaic adoption: Empirical evidence from India | Sustainable Development | India | [26] |
27 | 2022 | The role of financial inclusion in adoption of solar photovoltaic systems: A case of Uganda | Renewable Energy | Uganda | [10] |
28 | 2022 | Understanding the Factors Influencing Consumers’ Intention toward Shifting to Solar Energy Technology for Residential Use in Saudi Arabia Using the Technology Acceptance Model | Sustainability | Saudi Arabia | [39] |
29 | 2023 | Visual observation or oral communication? The effect of social learning on solar photovoltaic adoption intention in rural China | Energy Research & Social Science | China | [12] |
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Query Set | Meaning |
---|---|
Topic = (“Solar photovoltaic*” OR “Solar PV” OR “solar home system”) | Keywords related to solar photovoltaics |
Topic = (“intention” OR “adoption” OR “willingness to adopt” OR “attitude” OR “motives” OR “awareness” OR “knowledge” OR “perception” OR “tendency”) | Keywords related to adoption intention |
Factors | k | n | rmin | rmax |
---|---|---|---|---|
Social Influence | 13 | 7462 | −0.17 | 0.705 |
Attitude | 11 | 16,286 | 0.047 | 0.8 |
Effort Expectancy | 10 | 2725 | 0.027 | 0.289 |
Perceived Cost-benefit | 9 | 14,850 | −0.28 | 0.564 |
Product Knowledge | 7 | 6070 | 0.129 | 0.356 |
Perceived Behavioral Control | 6 | 12,103 | −0.18 | 0.396 |
Performance Expectancy | 5 | 2007 | 0.085 | 0.235 |
Government Incentive | 5 | 11,922 | 0.184 | 0.737 |
Subjective Norms | 4 | 11,482 | 0.12 | 0.566 |
Environmental Concern | 4 | 2384 | −0.36 | 0.298 |
Promotional Strategies | 3 | 1331 | 0.178 | 0.437 |
Innovativeness | 3 | 1581 | −0.3 | 0.56 |
Facilitating Conditions | 3 | 992 | 0.224 | 0.604 |
Variable | k | n | r | Random Effects Model | Heterogeneity | |||||
---|---|---|---|---|---|---|---|---|---|---|
95% Interval | Test of Null (2-Tail) | |||||||||
Lower Limit | Upper Limit | Z-Value | p-Value | Q-Value | p-Value | I-Squared | ||||
Social Influence | 13 | 7462 | 0.282 | 0.159 | 0.397 | 4.376 | 0 | 378.916 | 0 | 96.833 |
Attitude | 11 | 16,286 | 0.437 | 0.285 | 0.567 | 5.244 | 0 | 826.356 | 0 | 98.79 |
Effort Expectancy | 10 | 2725 | 0.181 | 0.129 | 0.233 | 6.65 | 0 | 17.97 | 0.036 | 49.917 |
Perceived Cost-benefit | 9 | 14,850 | 0.166 | −0.029 | 0.349 | 1.667 | 0.096 | 686.088 | 0 | 98.834 |
Product Knowledge | 7 | 6070 | 0.216 | 0.152 | 0.278 | 6.507 | 0 | 34.556 | 0 | 82.637 |
Perceived Behavioral Control | 6 | 12,103 | 0.124 | −0.121 | 0.355 | 0.989 | 0.323 | 329.328 | 0 | 98.482 |
Performance Expectancy | 5 | 2007 | 0.151 | 0.093 | 0.207 | 5.064 | 0 | 6.232 | 0.182 | 35.815 |
Government Incentive | 5 | 11,922 | 0.391 | 0.195 | 0.556 | 3.758 | 0 | 192.189 | 0 | 97.919 |
Variable | k | n | Egger Regression | Fail-Sae N | |
---|---|---|---|---|---|
Intercept | p-Value | ||||
Social Influence | 13 | 7462 | 7.57914 | 0.10386 | 1678 |
Attitude | 11 | 16,286 | 1.27903 | 0.39556 | 5826 |
Effort Expectancy | 10 | 2725 | −3.50898 | 0.13574 | 222 |
Perceived Cost-benefit | 9 | 14,850 | −7.98005 | 0.05648 | 1345 |
Product Knowledge | 7 | 6070 | 3.05924 | 0.12127 | 409 |
Perceived Behavioral Control | 6 | 12,103 | −8.34126 | 0.03667 | 541 |
Performance Expectancy | 5 | 2007 | 2.91581 | 0.05849 | 51 |
Government Incentive | 5 | 11,922 | 6.95026 | 0.07854 | 840 |
Variables | Adjustment Variables | k | r | 95% Interval | Test of Null (2-Tail) | Q-Group Inter | df(Q) | p-Value | ||
---|---|---|---|---|---|---|---|---|---|---|
Lower Limit | Upper Limit | Z-Value | p-Value | |||||||
Social Influence | A | 7 | 0.363 | 0.196 | 0.509 | 4.107 | 0.000 | 2.265 | 1 | 0.132 |
B | 6 | 0.187 | 0.019 | 0.345 | 2.184 | 0.029 | ||||
Attitude | A | 4 | 0.283 | 0.229 | 0.335 | 9.900 | 0.000 | 4.704 | 1 | 0.03 |
B | 7 | 0.508 | 0.313 | 0.662 | 4.640 | 0.000 | ||||
Effort Expectancy | A | 9 | 0.173 | 0.114 | 0.231 | 5.705 | 0.000 | 1.285 | 1 | 0.257 |
B | 1 | 0.234 | 0.146 | 0.318 | 5.119 | 0.000 | ||||
Perceived Cost-benefit | A | 3 | 0.065 | −0.189 | 0.311 | 0.498 | 0.618 | 0.703 | 1 | 0.402 |
B | 6 | 0.213 | −0.027 | 0.430 | 1.746 | 0.081 | ||||
Product Knowledge | A | 3 | 0.290 | 0.204 | 0.371 | 6.391 | 0.000 | 4.13 | 1 | 0.042 |
B | 4 | 0.178 | 0.110 | 0.243 | 5.111 | 0.000 | ||||
Perceived Behavioral Control | A | 4 | 0.126 | 0.035 | 0.214 | 2.710 | 0.007 | 0.001 | 1 | 0.982 |
B | 2 | 0.119 | −0.438 | 0.610 | 0.398 | 0.691 | ||||
Performance Expectancy | A | 4 | 0.184 | 0.125 | 0.241 | 6.078 | 0.000 | 5.042 | 1 | 0.025 |
B | 1 | 0.085 | 0.021 | 0.149 | 2.591 | 0.010 | ||||
Government Incentive | A | 3 | 0.458 | 0.071 | 0.725 | 2.289 | 0.022 | 0.710 | 1 | 0.399 |
B | 2 | 0.285 | 0.089 | 0.459 | 2.82 | 0.005 |
Variables | Adjustment Variables | k | r | 95% Interval | Test of Null (2-Tail) | Q-Group Inter | df(Q) | p-Value | ||
---|---|---|---|---|---|---|---|---|---|---|
Lower Limit | Upper Limit | Z-Value | p-Value | |||||||
Social Influence | A | 5 | 0.200 | 0.000 | 0.385 | 1.964 | 0.049 | 1.126 | 1 | 0.289 |
B | 8 | 0.334 | 0.173 | 0.477 | 3.958 | 0.000 | ||||
Attitude | A | 2 | 0.429 | 0.413 | 0.445 | 46.609 | 0.000 | 0.015 | 1 | 0.903 |
B | 9 | 0.443 | 0.206 | 0.630 | 3.502 | 0.000 | ||||
Effort Expectancy | A | 3 | 0.148 | 0.028 | 0.264 | 2.405 | 0.016 | 0.463 | 1 | 0.496 |
B | 7 | 0.194 | 0.134 | 0.252 | 6.232 | 0.000 | ||||
Perceived Cost-benefit | A | 4 | 0.326 | 0.141 | 0.490 | 3.373 | 0.001 | 5.057 | 1 | 0.025 |
B | 5 | 0.032 | −0.148 | 0.210 | 0.348 | 0.728 | ||||
Product Knowledge | A | 3 | 0.195 | 0.113 | 0.275 | 4.611 | 0.000 | 0.342 | 1 | 0.559 |
B | 4 | 0.239 | 0.114 | 0.357 | 3.700 | 0.000 | ||||
Perceived Behavioral Control | A | 2 | 0.322 | 0.142 | 0.481 | 3.431 | 0.001 | 5.334 | 1 | 0.021 |
B | 4 | 0.023 | −0.157 | 0.202 | 0.253 | 0.801 | ||||
Performance Expectancy | A | 1 | 0.085 | 0.021 | 0.149 | 2.591 | 0.010 | 5.042 | 1 | 0.025 |
B | 4 | 0.184 | 0.125 | 0.241 | 6.078 | 0.000 | ||||
Government Incentive | A | 2 | 0.285 | 0.089 | 0.459 | 2.820 | 0.005 | 0.710 | 1 | 0.399 |
B | 3 | 0.458 | 0.071 | 0.725 | 2.289 | 0.022 |
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Li, W.; Zhu, J.; Li, Y.; Li, Y.; Ding, Z. Determinants of Solar Photovoltaic Adoption Intention among Households: A Meta-Analysis. Sustainability 2024, 16, 8204. https://doi.org/10.3390/su16188204
Li W, Zhu J, Li Y, Li Y, Ding Z. Determinants of Solar Photovoltaic Adoption Intention among Households: A Meta-Analysis. Sustainability. 2024; 16(18):8204. https://doi.org/10.3390/su16188204
Chicago/Turabian StyleLi, Wenjie, Jiaolan Zhu, Yongchang Li, Yaning Li, and Zhikun Ding. 2024. "Determinants of Solar Photovoltaic Adoption Intention among Households: A Meta-Analysis" Sustainability 16, no. 18: 8204. https://doi.org/10.3390/su16188204
APA StyleLi, W., Zhu, J., Li, Y., Li, Y., & Ding, Z. (2024). Determinants of Solar Photovoltaic Adoption Intention among Households: A Meta-Analysis. Sustainability, 16(18), 8204. https://doi.org/10.3390/su16188204