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Article

Determinants of Solar Photovoltaic Adoption Intention among Households: A Meta-Analysis

by
Wenjie Li
1,
Jiaolan Zhu
2,
Yongchang Li
1,
Yaning Li
1 and
Zhikun Ding
1,3,4,*
1
Sino-Australia Joint Research Center in BIM and Smart Construction, Shenzhen University, Shenzhen 518060, China
2
School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China
3
Key Laboratory for Resilient Infrastructures of Coastal Cities (Shenzhen University), Ministry of Education, Shenzhen 518061, China
4
Shenzhen Key Laboratory of Green, Efficient and Intelligent Construction of Underground Metro Station, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(18), 8204; https://doi.org/10.3390/su16188204
Submission received: 7 August 2024 / Revised: 16 September 2024 / Accepted: 18 September 2024 / Published: 20 September 2024
Browse Figures
Versions Notes

Abstract

:
In recent years, research on the intention to adopt solar photovoltaic technology has yielded rich results. However, controversy still exists regarding the key antecedents of households’ intention to adopt solar photovoltaic technologies. To clarify the critical factors influencing the intention to adopt solar photovoltaic technology and potential moderating variables, this study utilized meta-analysis to perform a quantitative literature analysis on 29 empirical articles. It identified eight key influencing factors and tested the moderating effects of two variables: sample size and research area. The results show that “Attitude” and “Government Incentive” are moderately correlated with the intention to adopt. “Social Influence”, “Product Knowledge”, “Effort Expectancy”, “Perceived Cost-benefit”, “Performance Expectancy”, and “Perceived Behavioral Control” are weakly correlated with the adoption intention. The study also found that using the sample size and research area as moderating variables can partly reveal differences between various studies. Overall, the findings of this study offer theoretical guidance for subsequent in-depth studies and support for the practical promotion of solar photovoltaic technology.

1. Introduction

Vigorously developing renewable energy plays a vital role in promoting pollution reduction and low-carbon energy transition. Solar photovoltaics, as one of the important renewable energy sources, has been growing its installed power generation capacity in recent years, and has huge development potential. In China, for example, as shown in Figure 1, the jump from 30 MW in 2009 to over 600 GW in 2023 is enough to see its rapid development [1]. According to the installed size and layout, solar photovoltaic systems can be divided into two categories: centralized and distributed photovoltaic. Centralized photovoltaic systems are larger and are usually built in large solar power stations; on the contrary, distributed photovoltaic systems are more decentralized and are commonly found on the rooftops, facades, and other locations of urban and rural buildings. To promote the development of distributed photovoltaics, in June 2021, the Chinese government launched an effective intervention—the County-wide Photovoltaic Program [2]. More than 600 counties have conducted installation pilots. In the long run, the development of distributed photovoltaic systems will become a priority for countries around the world.
With technological advances, photovoltaic panels are becoming more and more efficient at generating electricity, and costs are gradually decreasing. However, the adoption rate of photovoltaic technology remains low, which is a key factor hindering its development. Especially in the post-subsidy era, the reduction of financial incentives further reduces the adoption rate of distributed photovoltaic systems, and how to effectively incentive solar photovoltaic adoption has become an important issue. Figure 2 shows the number of publications related to the willingness to adopt solar photovoltaic technology in Web of Science from 2015 to 2024; it can be seen that the number of publications is increasing year by year. Existing studies have basically analyzed the factors influencing the willingness to adopt solar photovoltaic technology around five dimensions: technology, economy, policy, society, and individuals [3,4,5,6,7,8,9,10]. Despite the abundance of empirical research, there is a wide range of factors to be covered and there are opposing findings on the correlation between the same influencing factors and the willingness to adopt [11,12]. The question of what relationship exists between the various influencing factors and adoption intentions has become an urgent problem.
Meta-analysis, as a method of comprehensive analysis of literature documents, uses advanced statistical techniques to integrate effect values obtained from different empirical studies under a specific topic to quantitatively analyze different subcategories of studies. It has gained wide recognition for mining the common effects of the same independent and dependent variables across studies by calculating the average effect of existing empirical studies [13,14,15,16]. For instance, Schulte et al. [17] identified the determinants of solar adoption using a meta-analytical structural equation modeling approach. Ghosh et al. [18] evaluated the influence of environmental factors on household solar photovoltaic pro-environmental behavioral intentions based on meta-analysis. Their results not only provide useful insights into the promotion of solar photovoltaics but serve as a reference for subsequent studies. Notably, compared with meta-analytical structural equation modeling, meta-analysis is more advantageous in terms of applicability, sample size, robustness of the results, etc. In addition, there has been a proliferation of studies on the willingness to adopt solar photovoltaic technologies in the past two years, so it is necessary to further sort out the key influencing factors to provide guidance for designing nudges to spur solar panel adoption.
This study aims to use the research method of meta-analysis, combined with the existing empirical research on the adoption intention towards solar photovoltaic technology, to quantitatively verify and analyze the influencing factors of solar photovoltaic technology adoption intention and then to develop a universal model of the influencing factors of solar photovoltaic technology adoption intention by exploring the overall correlation between the influencing factors and the adoption intention. This study contributes to providing ideas for the subsequent in-depth research and theoretical support for the promotion of solar photovoltaic technology.

2. Literature Review

The adoption of solar photovoltaic technology is a multidimensional and complex process, influenced by technological, economic, policy, social, and individual factors. Cognitive-behavioral theories, as the golden key to exploring such issues, have been favored by many scholars. In response to the diversity of data and the variety of research questions, different data collection and analysis methods have been incorporated into the research too. A comprehensive review conducted by [19] provided a critical analysis of residents’ adoption behavior towards solar photovoltaic technology from three aspects: theories, methods, and approaches. This section is based on this noteworthy finding, aiming to review and summarize previous studies on the adoption intention towards solar photovoltaic technology, elaborating from three aspects: theoretical basis, influencing factors, and data collection and analysis methods.

2.1. Theories

Cognitive-behavioral theories, including the Theory of Planned Behavior (TPB) by [11,20], Diffusion of Innovation (DOI) by [21], Social Learning Theory (SLT) by [12], and Technology Adoption Model (TAM) by [22], have been instrumental in assessing the dynamics behind households’ decisions to adopt solar photovoltaic systems. These theoretical frameworks collectively shed light on the intricate dynamics involving individual perceptions, social influences, and technological factors, all of which play crucial roles in shaping the intention of households to embrace solar photovoltaic technology.
At the heart of TPB is the notion that an individual’s behavior is directly influenced by their intention to perform that behavior, which, in turn, is shaped by their attitudes, subjective norms, and perceived behavioral control [23]. This theory underscores the importance of personal attitudes towards solar photovoltaic technology, the influence of social norms, and the perceived ease or difficulty of adopting such technologies [24,25]. Similarly, DOI provides insights into how innovations such as solar photovoltaics spread within a community. This theory underscores the significance of early adopters and the characteristics of innovations, including relative advantage, compatibility, complexity, trialability, and observability, in shaping the adoption rates [26]. SLT, on the other hand, highlights the importance of observational learning and imitation in the adoption process. It suggests that seeing others successfully implement and benefit from solar photovoltaics can encourage individuals to adopt the technology themselves [12]. TAM focuses specifically on the technological aspects, suggesting that the perceived usefulness and ease of use of solar photovoltaic technology are key determinants of its adoption [24].

2.2. Factors

In terms of technology, the performance of photovoltaic panels, including their power generation efficiency, durability, and safety, directly relates to the reliability and benefits of the technology [27]. Rezaei and Ghofranfarid [22] pointed out that the advantage of technology has a significant impact on the willingness to adopt. Economic factors play a decisive role in the adoption process of solar photovoltaic technology. Initial investment costs, operation and maintenance expenses, and the economic returns from the investment are key considerations for potential users [28,29,30]. Policy factors play a crucial role in promoting the adoption of solar photovoltaic technology. Government policies, including legislation, regulations, subsidies, tax incentives, and other motivational measures, establish the foundational framework for the promotion of solar photovoltaic technology [31,32,33,34]. In particular, measures such as feed-in tariff policies and renewable energy quota systems provide a stable and favorable market environment for photovoltaic electricity generation [35,36]. Social factors, including social influence [7,37], peer effect [6,38], environmental awareness [28,39], public knowledge [37,40], also significantly impact the adoption of solar photovoltaic technology. Individual factors, such as values, personal economic situation, and risk preferences, determine an individual’s attitude and behavior towards the adoption of solar photovoltaic technology [28,34,41,42]. Moreover, an individual’s acceptance of technology and willingness to invest in environmental protection, as well as their openness to new technologies, are key personal factors that influence adoption decisions [22,43].

2.3. Methods

When exploring the willingness to adopt solar photovoltaic technology, the choice of research method is particularly critical as it directly affects the depth and breadth of the study. In existing research, questionnaires, interviews, and case studies are the most commonly used methods for data collection. These approaches allow researchers to deeply understand the views and attitudes of individuals or groups towards the adoption of solar photovoltaic technology, thereby uncovering the complex psychological and social dynamics behind the willingness to adopt.
Questionnaires are widely used due to their efficiency and broad coverage, capable of quickly collecting a large amount of data suitable for quantitative analysis. Interviews, on the other hand, focus more on qualitative research, obtaining detailed information and in-depth views from respondents through deep conversations, which helps to understand the reasons and motivations behind the data. Case studies provide a comprehensive understanding of the adoption process concerning solar photovoltaic technology by deeply analyzing specific individuals’, families’, or communities’ actual cases, revealing the dynamics and details in specific environments [44].
With the diversity of data and research questions, researchers have adopted a variety of different data analysis methods. Methods based on Agent-Based Modeling (ABM) can simulate the impact of individual or group behavior on the adoption of solar photovoltaic technology, thereby analyzing the impact results under different policies or market conditions. For example, Zhang et al. [45] explored the impact of policies on adoption of solar photovoltaic technology based on ABM. Methods based on System Dynamics (SD) focus on the behavior of the entire system and feedback loops, suitable for studying the interaction of factors in complex systems [46]. Structural Equation Modeling (SEM) is a powerful statistical technique that allows for the simultaneous consideration of relationships among multiple variables. It is suitable for validating theoretical models and conducting path analysis. Overall, through these diversified research methods and analytical tools, researchers can explore the motivations behind the adoption of solar photovoltaic technology from different angles and levels, providing scientific evidence and strategic recommendations for policy making and market promotion.
Figure 3 summarizes the above content in the form of a tree. The soil represents the factors influencing the willingness to adopt solar photovoltaic technology, the trunk represents the theoretical basis, and the water and fertilizer represent data collection and analysis methods, respectively.
Through the analysis of the existing literature, it is evident that current research is abundant, with a significant increase in volume especially in recent years. Furthermore, there are numerous factors that affect the willingness to adopt solar photovoltaic technology. Employing meta-analysis to identify the key influencing factors is crucial. This is significant for both future research and the promotion of solar photovoltaic technology, and it represents the objective of this study.

3. Research Method

3.1. Meta-Analysis

The meta-analysis method was first introduced by the British psychologist Glass in 1976. It is a technique that involves a comprehensive literature search for existing statistical results for recalculation and statistical analysis. The goal is to integrate analysis outcomes to address unresolved questions in research findings [47]. The essence of meta-analysis is quantitative analysis. The calculation of effect sizes allows for the integration of disparate research conclusions. Methods such as meta-regression analysis, heterogeneity testing, and moderator effect testing can identify the sources of variation between studies and accurately quantify the impact of these differences on research outcomes [48]. Additionally, compared to other literature review methods, meta-analysis pays attention to the impact of sample size in original studies on the results and analyzes the correlation of samples in original publications to obtain more precise outcomes. It has been widely applied in numerous disciplines, including medicine, psychology, and education [49,50]. In this study, CMA V3.7 meta-analysis software was used to calculate effect sizes, test heterogeneity, check for publication bias, and test overall effects on several factors affecting the willingness to adopt solar photovoltaic technology. Ultimately, through moderator effect analysis, the study explores the variability of research conclusions across different sample sizes and survey regions.

3.2. Literature Search and Selection

This research primarily sourced its data from databases such as Scopus, Web of Science (WoS), and ScienceDirect. Table 1 shows the query sets used during the search. The query was conducted in January 2024. It identified 3345 publications in total. By reviewing the titles, abstracts, and full texts of the retrieved literature, a large number of studies unrelated to ‘willingness to adopt solar photovoltaic technology’ were identified. Further screening was conducted to select articles suitable for meta-analysis, with the selection criteria being:
(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).
After the literature screening process (as shown in Figure 4), a total of 29 articles were identified as suitable for meta-analysis; detailed information can be found in Appendix A Table A1.

3.3. Literature Coding and Processing

Each of the relevant articles selected through screening was coded individually. The coding included basic information (title, authors, year of publication, and journal) as well as the research subjects, sample sizes, and correlation coefficients. Articles that conducted empirical research using different sample data were considered as multiple distinct studies for inclusion in the analysis. To ensure coding consistency and avoid errors from manual coding, two graduate students coded the literature according to a predefined coding framework.
Based on the characteristics of the sample literature, the sample size and survey region (urban/rural) were coded as moderating variables. (1) Sample size: Research indicates that studies with smaller sample sizes tend to report larger effect sizes and may be prone to exaggerated effects [51]. Using the median sample size (n = 464) as a threshold, studies with a sample size less than 464 were coded as ‘A’, and those with a sample size of 464 or more were coded as ‘B’. (2) Survey region: The survey region, as reported in the literature, was divided into two subgroups: rural and urban. If the survey area was rural, it was coded as ‘A’; if it was urban, it was coded as ‘B’.
When the sample literature reports the correlation coefficient between the variable and the dependent variable, the effect size is the corresponding correlation coefficient. When the sample literature reports the regression coefficient between the variable and the dependent variable, the effect size is the standardized regression coefficient. When the literature only reports the path significance t-value, the effect size is calculated as shown in Equation (1) [52,53]; r represents the effect size, t denotes the significance of the path, and df stands for degrees of freedom.
r = t 2 t 2 + d f
This study included a total of 29 pieces of literature, with a combined sample size of 25,388. During the coding process, 63 differently named influencing factors were identified as independent variables. However, because some differently named influencing factors have the same meaning, before analyzing the data, a comparative analysis of the specific connotations of each variable in different studies and the corresponding items in the data collection scales was conducted. Variables with similar meanings were unified under the name of the most frequently occurring variable (for example, social influence, informational social influence, and normative social influence were merged under the most frequently occurring term, “social influence”). As a result, the study ultimately identified 35 independent variables with different meanings. Due to space limitations, only the variables with three or more effect sizes are shown in the text, and the coding summary information is presented in Table 2. To facilitate subsequent moderation effect analyses and to investigate the primary factors influencing the adoption willingness of solar photovoltaic technology, this study will include independent variables that appeared more than five times in the subsequent meta-analysis. The specific variables and their explanations are as follows:
Social Influence, which refers to the direct or indirect influence of individuals or groups on someone’s thoughts, emotions, attitudes, or behaviors [54,55].
Attitude, which refers to an individual’s evaluation of or perspective on performing a specific behavior [23].
Effort Expectancy, which refers to an individual’s expectation of the effort required to use a new technology or system [56].
Perceived Cost–benefit, which refers to the subjective assessment of the costs and benefits associated with taking a certain action or making a decision by an individual or organization [37].
Product Knowledge, which refers to the understanding of a product’s characteristics, functions, advantages, and methods of use [57].
Perceived Behavioral Control, which refers to an individual’s subjective assessment of their capability to perform a specific behavior [23].
Performance Expectancy, which refers to the user’s expectation that using a certain technology will enhance their work performance or task efficiency [56].
Government Incentive, which refers to the rewards or subsidies provided by the government to encourage specific behaviors or activities [34].

4. Results and Analysis

4.1. Heterogeneity Test

Meta-analyses require heterogeneity tests on the data from included studies to assess the consistency among individual studies, thereby determining the appropriate analytical model based on the test results. In this article, the Q-test method was employed for heterogeneity testing [58]. A non-significant Q-test result suggests an absence of heterogeneity, indicating that a fixed-effects model should be used for analysis. Conversely, a significant Q-test result indicates the presence of heterogeneity, warranting the use of a random-effects model. The random-effects model accounts for both within-study and between-study variations and can estimate the average effect size. It also prevents underestimation of the weight in small-sample studies and overestimation in large-sample studies, leading to wider confidence intervals and, consequently, more conservative and reliable conclusions [59]. As described in Table 3, except for “Performance Expectancy”, all variables’ Q-values reached significance levels (p < 0.05), indicating heterogeneity among the effect sizes. Additionally, except for “Performance Expectancy” and “Effort Expectancy”, all variables’ I2 values exceeded 75%. According to the criteria set by Higgins et al. [60]. I2 < 25% indicates strong homogeneity, I2 around 50% indicates moderate heterogeneity, and I2 > 75% suggests strong heterogeneity. Thus, the studies included in this article exhibit considerable heterogeneity, validating the rationale behind choosing the random-effects model for analysis. This also supports the necessity of conducting subsequent moderator analysis to identify moderating variables that may account for the heterogeneity observed in the study outcomes.

4.2. Publication Bias

Publication bias refers to the phenomenon where studies with statistically significant results are more likely to be accepted and published by journals, which is a critical factor affecting the reliability of research findings. Assessing for publication bias is an indispensable part of meta-analysis [61]. In this study, an initial subjective visual assessment of publication bias for each variable was conducted based on funnel plots. This was followed by an objective quantitative evaluation using Egger’s test and the Fail-Safe N test to determine the presence of publication bias among the variables. Figure 5 displays the funnel plots for publication bias related to all variables, the lines in the funnel plot represent the distribution of the standard error of the effect estimates for each study included in the meta-analysis. The relationship between the lines and the points in the funnel plot is that each point represents a study, with the position along the x-axis indicating the effect size and the position along the y-axis indicating the standard error of the effect size. Overall, there is no significant publication bias present.
The results from the Egger linear regression analysis reveal that the regression outcome for “Perceived Behavioral Control” is significant (p < 0.05), indicating the presence of publication bias in studies related to its correlation with the intention to adopt. The Fail-Safe N is often used to assess the extent to which publication bias influences the results of the main effect tests. The ratio of the Fail-Safe N to 5k + 10 (where k is the number of independent samples included for a given variable) being larger suggests that the impact of publication bias on the meta-analysis results is more limited [61]. Calculations based on the data in Table 4 show that the Fail-Safe N for “Perceived Behavioral Control” is much larger than 5k + 10, suggesting that, despite the presence of publication bias, its impact on the meta-analysis results is very limited, making it less likely that the analysis results are influenced by publication bias.

4.3. Main Effect Test

The effect size analysis investigates the impact of various variables on the intention to adopt solar photovoltaic technology, with the results presented in Table 3. Initially, it is observed that all variables, except for “Perceived Cost-benefit” and “Perceived Behavioral Control”, pass the significance test (p < 0.05). Furthermore, all variables are positively correlated with the intention to adopt solar photovoltaic technology. Additionally, according to the criteria proposed by Cohen for determining the strength of correlation relationships, a value of r between 0.00 and 0.09 indicates a negligible correlation, r values in the range of 0.10 to 0.29 suggest a weak correlation, r values between 0.30 and 0.49 denote a moderate correlation, and r values from 0.50 to 1.00 signify a strong correlation [62]. As indicated in Table 4, “Attitude” (r = 0.437) and “Government Incentive” (r = 0.391) are moderately correlated with the adoption intention; “Social Influence” (r = 0.282), “Product Knowledge” (r = 0.216), “Effort Expectancy” (r = 0.181), “Perceived Cost-benefit” (r = 0.166), “Performance Expectancy” (r = 0.151), and “Perceived Behavioral Control” (r = 0.124) are weakly correlated with the intention to adopt.

4.4. Moderator Effect Tests

The heterogeneity test results indicate significant variability across studies included in the meta-analysis, as evident from the basic sample data in Table 2, which shows substantial differences in sample sizes among the studies. This variation in sample size is one contributing factor to the observed heterogeneity. Additionally, the research settings differ (rural/urban), which is another significant factor contributing to heterogeneity. Therefore, it is necessary to conduct moderator effect tests on the aforementioned factors to scientifically and reasonably explain the reasons for the high heterogeneity.
As described in Section 3.2, the sample literature was grouped into subgroups for analysis, and the results of the moderator effect tests for sample size and research setting are presented in Table 5 and Table 6, respectively. From these tables, it is evident that sample size significantly affects the relationship between “Attitude”, “Product Knowledge”, and “Performance Expectancy” and the intention to adopt. Moreover, studies with larger sample sizes show stronger correlations between “Attitude”, “Effort Expectancy”, “Perceived Cost-benefit”, and the intention to adopt compared to studies with smaller sample sizes. The research setting significantly influences the relationship between “Perceived Cost-benefit”, “Perceived Behavioral Control”, and “Performance Expectancy” and the intention to adopt. Studies conducted in rural areas exhibit stronger correlations between “Perceived Cost-benefit”, “Perceived Behavioral Control”, and the intention to adopt compared to those conducted in urban areas.

5. Discussion

5.1. Factors and Strength

This study conducted a meta-analysis on 29 selected research papers and found that the variables included in this study are all positively correlated with the intention to adopt solar photovoltaic technology. Among them, “Attitude” and “Government Incentive” have the strongest correlation with adoption intention, followed by “Social Influence”, “Product Knowledge”, “Effort Expectancy”, “Perceived Cost-benefit”, “Performance Expectancy”, and “Perceived Behavioral Control”. The correlation coefficients between these factors and the intention to adopt are shown in Figure 6.
The meta-analysis results suggest that “Attitude” is the most critical factor affecting residents’ intention to adopt solar photovoltaic technology. This finding is in line with the Theory of Planned Behavior and is consistent with the results of most studies. For example, Liu et al. [12] pointed out that attitude (r = 0.43) exerts the biggest impact on intention, with a correlation coefficient close to the overall effect size of the meta-analysis (r = 0.437). Additionally, “Government Incentive” represents another significant aspect, which is consistent with the findings of [10,34]. This indicates that in promoting solar photovoltaic technology, it is essential, on one hand, to design effective behavioral intervention measures, and on the other hand, to leverage the driving role of government incentives.
Furthermore, compared with previous meta-analysis studies, there are some consistent results. For example, Gimpel et al. [63] identified “Attitude” and “Performance Expectancy” as the primary determinants influence individuals’ decision to adopt smart energy technology. Schulte et al. [17] found that “perceived benefits” is an important factor for residents to adopt solar photovoltaic technology. “Attitude”, “Performance Expectancy” and “Perceived Cost-benefit” are all significant factors influencing households’ adoption intention in this study, which further reflects the robustness of the meta-analysis results. At the same time, we also identified some other important variables that have not been mentioned in previous meta-analysis studies like “Government Incentive”, “Social Influence”, etc. These findings have important implications for enriching theoretical understanding.

5.2. Moderator Effect Analysis

Subgroup analysis reveals that differences in sample size and research settings can partially explain the heterogeneity in the relationship between certain independent variables and the intention to adopt across different studies. For instance, Sun et al. [34] reported a high correlation (r = 0.737) between “Government Incentive” and adoption intention, whereas Liu et al. [12] found the correlation between the two to be not as significant (r = 0.19). This discrepancy may be attributed to the difference in sample sizes between the two studies, with the former having a sample size of 300 and the latter of 10,127. It was discovered that “Government Incentive” has a more significant impact on adoption intention in studies with smaller sample sizes, indicating that sample size indeed has a moderating effect and can explain the inconsistency between these two results. In future research, it is important to consider the potential impact of moderating variables and to design research plans accordingly.

5.3. Limitations and Future Scope

This article employs meta-analysis methods to systematically review the key factors influencing solar photovoltaic technology, offering a more comprehensive perspective than previous meta-analyses that focused on a specific aspect. In addition, this paper is highly timely, highlighting a summary of recent publications. However, this study has the following limitations: First, similar to most meta-analyses, this research only analyzed papers published in English-language journals, potentially leading to the omission of significant findings published in non-English literature. Second, photovoltaic policies vary across different countries, and many regions have started encouraging enterprises to collectively undertake photovoltaic projects, which can influence the promotion of photovoltaic; this factor was not considered in this paper. Third, the study only considered sample size and research area as moderating variables; in reality, the proportion of males to females, different countries’ economic levels, and climatic conditions could also be potential moderating variables.
Future research could expand the scope of the literature search, monitor the latest policies, and consider as many moderating variables as possible to enhance understanding of the relationships between various variables and the intention to adopt solar photovoltaic technology. Moreover, previous studies on the intention to adopt solar photovoltaic technology have primarily focused on residents’ perceptions, employing questionnaires as the main method of data collection. Subsequent research could incorporate scenario experiments and use interactive devices such as eye trackers and EEG to obtain more objective experimental data. By combining subjective and objective methods from a psychological perspective, the deeper reasons affecting residents’ adoption intentions can be explored.

5.4. Policy Implications

Renewable energy technologies, such as solar photovoltaics, play a pivotal role in mitigating global climate change. This study identifies key factors that influence the adoption of solar photovoltaic technology among residents, which can assist governmental departments in policy making. For instance, appropriate economic subsidies, public education on photovoltaic technology, and promotion of green and eco-friendly awareness can all contribute to enhancing residents’ willingness to adopt either directly or indirectly, thereby accelerating the promotion of the technology.
Furthermore, it is noteworthy to observe that the preponderance of the empirical studies identified in our search are derived from developing countries. From this, we can at least draw two conclusions: Firstly, the majority of developing countries are earnestly engaged in the pursuit of energy greening and transitioning, emerging as pivotal contributors to the mitigation of global climate change. Secondly, the implementation of renewable energy technologies in developing countries faces significant challenges. It is essential for a concerted effort among governmental bodies, societal sectors, and academic institutions to synergize their initiatives and ensure the longevity and stability of this endeavor.

6. Conclusions

This study employs meta-analysis to investigate the factors influencing residents’ willingness to adopt solar photovoltaic technology, identifying eight key determinants and analyzing their effect size and impact. It establishes clear relationships between these variables and the willingness to adopt solar photovoltaic technology, providing a unified conclusion for previous studies that had similar variables but divergent outcomes. The findings reveal positive correlations between “Attitude”, “Government Incentive”, “Social Influence”, “Product Knowledge”, “Effort Expectancy”, “Perceived Cost-benefit”, “Performance Expectancy”, “Perceived Behavioral Control”, and the willingness to adopt, with “Attitude” and “Government Incentive” having a more significant impact. Subgroup tests indicate that sample size and survey region, as moderating variables, can partly explain the heterogeneity between factors and adoption willingness. The results of this paper offer a reference for more detailed analyses in future quantitative studies and hold significant theoretical relevance for developing a universal model of factors affecting residents’ willingness to adopt solar photovoltaic technology. Furthermore, it provides theoretical guidance and practical reference for the further promotion of solar photovoltaic technology.

Author Contributions

Conceptualization, W.L. and Z.D.; Methodology, W.L.; Software, W.L. and Y.L. (Yongchang Li); Data curation, W.L. and Y.L. (Yongchang Li); Writing—original draft preparation, W.L.; Writing—review and editing, J.Z., Y.L. (Yaning Li) and Z.D.; Funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was conducted with the support of the Shenzhen Natural Science Fund (the Stable Support Plan Program No. 20220810160221001).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

A table that includes information on the 29 empirical studies used in this meta-analysis, featuring details such as paper titles, publication years, journals of publication, and regions.
Table A1. Information table of the 29 empirical studies used in the meta-analysis.
Table A1. Information table of the 29 empirical studies used in the meta-analysis.
No.YearTitleJournalRegionReference
12024A behavioral study on villagers’ adoption intention and carbon neutrality toward rooftop solar photovoltaic systems in IndiaInternational Journal of Energy Sector ManagementIndia[64]
22018Cognition process and influencing factors of rural residents’ adoption willingness for solar PV poverty alleviation projects: Evidence from a mixed methodology in rural ChinaEnergyChina[37]
32020Consumer attitude and purchase intention toward rooftop photovoltaic installation: The roles of personal trait, psychological benefit, and government incentivesEnergy & EnvironmentChina[34]
42022Determinants of Adoption and the Type of Solar PV Technology Adopted in Rural PakistanFrontiers in Environmental SciencePakistan[65]
52021Determining Factors Affecting Customer Intention to Use Rooftop Solar Photovoltaics in IndonesiaSustainabilityIndonesia[66]
62023Determining the Factors Affecting Solar Energy Utilization in Saudi Housing: A Case Study in MakkahEnergiesArabia[41]
72020Determining the Influencing Factors in the Adoption of Solar Photovoltaic Technology in Pakistan: A Decomposed Technology Acceptance Model ApproachEconomiesPakistan[24]
82021Do Perceived Risk, Perception of Self-Efficacy, and Openness to Technology Matter for Solar PV Adoption? An Application of the Extended Theory of Planned BehaviorEnergiesPakistan[43]
92022Do village leaders’ engagement, social interaction and financial incentive affect residents’ solar PV adoption? An empirical study in rural China?International Journal of Energy Sector ManagementChina[67]
102021Factors Affecting the Adoption of Photovoltaic Systems in Rural Areas of PolandEnergiesPoland[68]
112023Factors Hindering Solar Photovoltaic System Implementation in Buildings and Infrastructure Projects: Analysis through a Multiple Linear Regression Model and Rule-Based Decision Support SystemBuildingsPakistan[42]
122022Factors influencing purchase intention of solar photovoltaic technology: An extended perspective of technology readiness index and theory of planned behaviourCleaner and Responsible ConsumptionPakistan[40]
132021Factors influencing the residence’s intention to adopt solar photovoltaic technology: a case study from Klang Valley, MalaysiaClean EnergyMalaysia[69]
142024From intentions to actions: unveiling the socio-psychological drivers of solar home system adoption in developing nationsArchitectural Engineering and Design ManagementPakistan[70]
152021How does satisfaction of solar PV users enhance their trust in the power grid?—Evidence from PPAPs in rural ChinaEnergy, Sustainability and SocietyChina[71]
162020Investigating nonusers’ behavioural intention towards solar photovoltaic technology in Malaysia: The role of knowledge transmission and price valueEnergy PolicyMalaysia[7]
172022Investigating the Determinants of the Adoption of Solar Photovoltaic Systems—Citizen’s Perspectives of Two Developing CountriesSustainabilitySomalia and Pakistan[25]
182023Modeling behavioral factors influencing farmers’ willingness to adopt rooftop solar photovoltaic: Empirical evidence from rural ChinaJournal of cleaner productionChina[28]
192019New trends in solar: A comparative study assessing the attitudes towards the adoption of rooftop PVEnergy PolicyUnited states[72]
202018Predicting intention to adopt solar technology in Canada: The role of knowledge, public engagement, and visibilityEnergy PolicyCanada[57]
212023Predicting Residential Photovoltaic Adoption Intention of Potential Prosumers in Thailand: A Theory of Planned Behavior ModelEnergiesThailand[11]
222022Purchase intention of Indian customers: a study on solar PV technologyInternational Journal of Energy Sector ManagementIndia[73]
232022National goals or sense of community? Exploring the social-psychological influence of household solar energy adoption in rural ChinaEnergy Research & Social SciencePakistan[74]
242024Socio-environmental factors and solar housing system adoption: moderating effect of attitudeInnovative Infrastructure SolutionsPakistan[75]
252021Solar photovoltaic as a means to sustainable energy consumption in Malaysia: the role of knowledge and price valueEnergy Sources, Part B: Economics, Planning, and PolicyMalaysia[76]
262024Sustainable energy development through non-residential rooftop solar photovoltaic adoption: Empirical evidence from IndiaSustainable DevelopmentIndia[26]
272022The role of financial inclusion in adoption of solar photovoltaic systems: A case of UgandaRenewable EnergyUganda[10]
282022Understanding the Factors Influencing Consumers’ Intention toward Shifting to Solar Energy Technology for Residential Use in Saudi Arabia Using the Technology Acceptance ModelSustainabilitySaudi Arabia[39]
292023Visual observation or oral communication? The effect of social learning on solar photovoltaic adoption intention in rural ChinaEnergy Research & Social ScienceChina[12]

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Figure 1. Installed power generation capacity in China from 2009 to 2023. Note: The blue line represents the change in ratio. The ratio in Figure 1 represents the proportion of solar-power installed capacity to the total installed capacity.
Figure 1. Installed power generation capacity in China from 2009 to 2023. Note: The blue line represents the change in ratio. The ratio in Figure 1 represents the proportion of solar-power installed capacity to the total installed capacity.
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Figure 2. Number of publications related to the willingness to adopt solar photovoltaic technology in Web of Science from 2015 to 2023 (as of January 2024).
Figure 2. Number of publications related to the willingness to adopt solar photovoltaic technology in Web of Science from 2015 to 2023 (as of January 2024).
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Figure 3. Knowledge map of adoption intention towards solar photovoltaic technology.
Figure 3. Knowledge map of adoption intention towards solar photovoltaic technology.
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Figure 4. Literature screening process.
Figure 4. Literature screening process.
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Figure 5. Funnel plot of published deviation.
Figure 5. Funnel plot of published deviation.
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Figure 6. Influencing factors model of adoption intention towards solar photovoltaic technology. Note: * represents significance level. (* p < 0.05, ** p < 0.01, *** p < 0.001).
Figure 6. Influencing factors model of adoption intention towards solar photovoltaic technology. Note: * represents significance level. (* p < 0.05, ** p < 0.01, *** p < 0.001).
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Table 1. Literature search query set.
Table 1. Literature search query set.
Query SetMeaning
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
Table 2. The coding summary information.
Table 2. The coding summary information.
Factorsknrminrmax
Social Influence137462−0.170.705
Attitude1116,2860.0470.8
Effort Expectancy1027250.0270.289
Perceived Cost-benefit914,850−0.280.564
Product Knowledge760700.1290.356
Perceived Behavioral Control612,103−0.180.396
Performance Expectancy520070.0850.235
Government Incentive511,9220.1840.737
Subjective Norms411,4820.120.566
Environmental Concern42384−0.360.298
Promotional Strategies313310.1780.437
Innovativeness31581−0.30.56
Facilitating Conditions39920.2240.604
Note: “k” represents the number of studies included in the analysis, and “n” represents the total sample size of the k samples.
Table 3. Heterogeneity test results.
Table 3. Heterogeneity test results.
VariableknrRandom Effects ModelHeterogeneity
95% IntervalTest of Null (2-Tail)
Lower LimitUpper LimitZ-Valuep-ValueQ-Valuep-ValueI-Squared
Social Influence1374620.2820.1590.3974.3760378.916096.833
Attitude1116,2860.4370.2850.5675.2440826.356098.79
Effort Expectancy1027250.1810.1290.2336.65017.970.03649.917
Perceived Cost-benefit914,8500.166−0.0290.3491.6670.096686.088098.834
Product Knowledge760700.2160.1520.2786.507034.556082.637
Perceived Behavioral Control612,1030.124−0.1210.3550.9890.323329.328098.482
Performance Expectancy520070.1510.0930.2075.06406.2320.18235.815
Government Incentive511,9220.3910.1950.5563.7580192.189097.919
Table 4. Publication bias results.
Table 4. Publication bias results.
VariableknEgger RegressionFail-Sae N
Interceptp-Value
Social Influence1374627.579140.103861678
Attitude1116,2861.279030.395565826
Effort Expectancy102725−3.508980.13574222
Perceived Cost-benefit914,850−7.980050.056481345
Product Knowledge760703.059240.12127409
Perceived Behavioral Control612,103−8.341260.03667541
Performance Expectancy520072.915810.0584951
Government Incentive511,9226.950260.07854840
Table 5. Analysis results of sample size as a moderating variable.
Table 5. Analysis results of sample size as a moderating variable.
VariablesAdjustment Variableskr95% IntervalTest of Null (2-Tail)Q-Group Inter df(Q)p-Value
Lower LimitUpper LimitZ-Valuep-Value
Social InfluenceA70.363 0.196 0.5094.107 0.000 2.26510.132
B60.187 0.019 0.3452.184 0.029
AttitudeA40.283 0.229 0.3359.900 0.000 4.70410.03
B70.508 0.313 0.6624.640 0.000
Effort ExpectancyA90.173 0.114 0.2315.705 0.000 1.28510.257
B10.234 0.146 0.3185.119 0.000
Perceived Cost-benefitA30.065 −0.189 0.3110.498 0.618 0.70310.402
B60.213 −0.027 0.4301.746 0.081
Product KnowledgeA30.290 0.204 0.3716.391 0.000 4.1310.042
B40.178 0.110 0.2435.111 0.000
Perceived Behavioral ControlA40.126 0.035 0.2142.710 0.007 0.00110.982
B20.119 −0.438 0.6100.398 0.691
Performance ExpectancyA40.184 0.125 0.2416.078 0.000 5.04210.025
B10.085 0.021 0.1492.591 0.010
Government IncentiveA30.458 0.071 0.7252.289 0.022 0.710 10.399
B20.285 0.089 0.4592.820.005
Table 6. Analysis results of research setting as a moderating variable.
Table 6. Analysis results of research setting as a moderating variable.
VariablesAdjustment Variableskr95% IntervalTest of Null (2-Tail)Q-Group Inter df(Q)p-Value
Lower LimitUpper LimitZ-Valuep-Value
Social InfluenceA50.200 0.000 0.385 1.964 0.049 1.12610.289
B80.334 0.173 0.477 3.958 0.000
AttitudeA20.429 0.413 0.445 46.609 0.000 0.01510.903
B90.443 0.206 0.630 3.502 0.000
Effort ExpectancyA30.148 0.028 0.264 2.405 0.016 0.46310.496
B70.194 0.134 0.252 6.232 0.000
Perceived Cost-benefitA40.326 0.141 0.490 3.373 0.001 5.05710.025
B50.032 −0.148 0.210 0.348 0.728
Product KnowledgeA30.195 0.113 0.275 4.611 0.000 0.34210.559
B40.239 0.114 0.357 3.700 0.000
Perceived Behavioral ControlA20.322 0.142 0.481 3.431 0.001 5.33410.021
B40.023 −0.157 0.202 0.253 0.801
Performance ExpectancyA10.085 0.021 0.149 2.591 0.010 5.04210.025
B40.184 0.125 0.241 6.078 0.000
Government IncentiveA20.285 0.089 0.459 2.820 0.005 0.710 10.399
B30.458 0.071 0.725 2.2890.022
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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

AMA Style

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 Style

Li, 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

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