Evaluation of Political and Economic Factors Affecting Energy Policies: Addressing Contemporary Challenges from Taiwan’s Perspective

Abstract

The shift to sustainable energy requires a thorough understanding of the elements affecting policy adoption, especially regarding political and economic dynamics. Current approaches, such as the technology acceptance model (TAM), theory of planned behavior (TPB), and unified theory of acceptance and use of technology (UTAUT), mainly emphasize individual behavioral aspects, often neglecting macro-level implications. This research uses the hybrid model for energy policy adoption (HMEPA) to bridge this gap, including economic and political factors with behavioral theories to evaluate energy policy acceptability. We propose that social impact, attitudes toward the policy, and financial and political considerations substantially affect stakeholders’ acceptance intentions. We gathered 421 valid answers from people in Taiwan using a questionnaire survey and analyzed the data using structural equation modeling (SEM). The findings demonstrate that whereas effort expectation and enabling circumstances have little impact, social influence and attitude are the most significant determinants of policy adoption intention. Moreover, political variables influence attitudes and social dynamics, while economic policy impacts performance expectations, perceived behavioral control, and enabling circumstances. These results underscore the need to synchronize policy plans with political and economic realities. Policymakers may use these findings to formulate stakeholder-oriented policies that promote sustainable energy transitions.

1. Introduction

The global energy environment is experiencing a significant upheaval, propelled by urgent issues like climate change, energy security, and economic growth. Governments are crafting diverse environmental policies to attain equilibrium between humanity and nature for sustainable development. The current period of fossil-fueled economies, communities, and civilizations has created an abnormal and perilous situation for modern society and our collective ecosystem. The escalating tendencies of global warming, as shown by storms and ice melts, droughts and famine, instability, and migration, increasingly need an urgent response to swiftly terminate the reliance on fossil fuels. There is an urgent need to transition to renewable and cleaner sustainable energy sources that may alleviate the adverse effects of climate change [1].

The shift to sustainable energy poses several problems and possibilities for a nation’s and global economy. Access to high-quality energy production and consumption is crucial to a country’s developmental success. The attainment of sustainable energy necessitates the convergence of the environmental, energy, and development sectors. A lack of integration may result in conflicts between the goals of various sectors, obstructing sustainable development [1,2,3]. Fossil or non-fossil energy production and consumption pose substantial dangers and challenges to the environment and sustainable development, notwithstanding the contribution to economic growth and development [1,4].

A growing agreement regards the shift to renewable energy sources, sometimes seen as fuel replacement, as a vital method to tackle the climate catastrophe. The global energy environment is experiencing a significant upheaval, propelled by urgent issues like climate change, energy security, and economic growth. This shift requires the formulation and execution of efficient energy policies that reconcile conflicting agendas and tackle the intricate interactions of economic, political, and social elements [5]. The efficacy of these policies depends not only on their technical validity but also on their endorsement by essential stakeholders, including politicians, industry participants, and the general populace [3].

The shift to sustainable energy systems necessitates substantial policy measures, including subsidies for renewable energy use, limitations on fossil fuel emissions, and investments in energy efficiency. These policies often encounter opposition owing to perceived expenses, concerns over their efficacy, or clashes with established interests [5]. Comprehending the factors influencing policy adoption is essential for formulating and executing successful energy plans [3]. This research fulfills a vital need by creating and evaluating a complete framework that amalgamates previous technology and behavior adoption theories while explicitly considering economic and political settings [4].

Current studies on adopting innovative technology and behavioral transformation have yielded significant insights into the determinants influencing individual and organizational acceptance of new technologies and practices [6]. The technology acceptance model (TAM), the theory of planned behavior (TPB), and the unified theory of acceptance and use of technology (UTAUT) are among the most significant theories. Although these theories have been extensively used across several fields, including information technology, healthcare, and education, their application to the context of energy policy adoption has been comparatively restricted. Moreover, these models often neglect the significant influence of external forces, especially economic and political causes, on human perceptions and actions [7]. This study fills gaps by creating the holistic model of energy policy adoption (HMEPA). This innovative paradigm integrates the advantages of TAM, TPB, and UTAUT while explicitly including economic and political issues as essential determinants of policy adoption. To do this effectively, the Taiwan government and other stakeholders must contemplate the following essential questions.

RQ1: What are the essential aspects that affect the adoption of energy policies, emphasizing the significant impact of individual views, social influences, contextual elements, and, notably, the interaction of economic and political determinants?

RQ2: How much do important factors like attitudes, subjective norms, perceived behavioral control, and facilitating conditions affect individuals’ decisions to adopt sustainable energy policies?

RQ3: In what manner does the integrated HMEPA model provide a more thorough comprehension of energy policy acceptability in contrast to models that only emphasize TAM, TPB, or UTAUT?

This paper summarizes current models, including the technology acceptance model (TAM) and the theory of planned behavior (TPB), and elucidates their shortcomings in including macroeconomic and political factors. It posits the need for HMEPA as a comprehensive framework that integrates stakeholder perspectives, economic circumstances, and political coherence, aspects inadequately addressed by other models, and explicitly delineates how HMEPA connects micro- and macro-level variables, enhancing its efficacy for comprehending policy adoption.

This research examines the main elements affecting energy policy acceptance by synthesizing behavioral, economic, and political components into a cohesive framework. This research formulates and verifies the hybrid model for energy policy adoption (HMEPA) to evaluate the impact of stakeholders’ opinions, social influence, economic circumstances, and political backing on policy adoption. The research used structural equation modeling (SEM) to experimentally examine the links among these parameters, providing theoretical breakthroughs and practical suggestions for policymakers and industry stakeholders.

The HMEPA framework is considered to offer an innovative and comprehensive model for comprehending energy policy acceptance. It facilitates the identification of specific factors that affect policy acceptance within each group, thereby providing a more nuanced and contextually pertinent perspective for analyzing policy adoption than existing models.

2. Review of Literature and Development of Hypotheses

2.1. Technology Acceptance Model (TAM)

The technology acceptance model (TAM), introduced by Davis [8], posits that two core beliefs—perceived usefulness (PU) and perceived ease of use (PEOU)—are critical for understanding technology adoption and user acceptance across many settings. PU signifies an individual’s belief that using a particular technology will enhance his/her job performance [8]. At the same time, PEOU signifies the extent to which an individual feel that using a given system would require little effort [8]. Notwithstanding its achievements, TAM has significant shortcomings in the intricate realm of energy policy, since it mainly emphasizes human perceptions and beliefs while overlooking the impact of social, cultural, economic, and political aspects [9]. The adoption of energy policy is often shaped by social norms, political ideologies, monetary incentives, and regulatory frameworks, which are not expressly considered by TAM [9].

Furthermore, TAM posits that humans are motivated mainly by rational assessments of utility and usability. Several variables, including emotions, values, habits, and institutional trust, often sway energy-related choices. Academics propose that TAM should integrate with other theories to overcome these limitations, such as the theory of planned behavior (TPB) or the unified theory of acceptance and use of technology (UTAUT), to provide a more holistic explanation of technology and policy acceptability by including social and contextual elements [7,9].

2.2. Theory of Planned Behavior (TPB)

The theory of planned behavior (TPB), formulated by Ajzen [10], is a prevalent social psychology theory that elucidates and forecasts human behavior across several areas. It asserts that behavioral intentions are the most direct predictor of behavior, impacted by three primary constructs: attitudes, subjective norms, and perceived behavioral control (PBC) [10]. Researchers have used TPB to examine determinants affecting home energy conservation practices, including decreased power usage, utilization of energy-efficient devices, and the adoption of sustainable transportation methods [11,12]. Notwithstanding its achievements, TPB exhibits several limits in the context of energy policy. PBC includes some external control aspects, but TPB does not explicitly include broader economic, political, or environmental settings [11]. External variables may profoundly affect stakeholders’ views, social norms, and perceived control over energy policy [11,12]. To augment the application of TPB in energy research, scholars have proposed integrating economic, political, and environmental elements as precursors to attitudes, subjective norms, and PBC to boost the contextual relevance of TPB.

2.3. Unified Theory of Acceptance and Use of Technology (UTAUT)

The unified theory of acceptance and use of technology (UTAUT), introduced by Venkatesh et al. [13], amalgamates essential elements from eight significant technology adoption models, such as TAM, TPB, and the theory of reasoned action (TRA). UTAUT delineates four fundamental factors of use intention and behavior: performance expectancy, effort expectancy, social influence, and facilitating conditions. It also examines the moderating influences of gender, age, experience, and voluntary use [13]. Researchers have used UTAUT to examine customer adoption of smart meters, smart thermostats, solar photovoltaic systems, wind turbines, and other renewable energy technologies by families and enterprises [13,14,15].

Notwithstanding its advantages, UTAUT exhibits several limits when used in energy policy [6,15]. Although UTAUT accounts for enabling circumstances, it fails to include the broader economic, political, and environmental factors that may substantially impact energy policy adoption [15]. Moreover, UTAUT mainly emphasizes individual adoption and use behavior, overlooking the intricate linkages and dynamics present at the organizational and social levels [14]. The acceptability of energy policy often entails several stakeholders with diverse interests and degrees of influence [14,15]. Economic policy is crucial in influencing energy dynamics and tackling current issues [15].

2.4. Rationale of Literature

Current approaches, such as the technology acceptance model (TAM), theory of planned behavior (TPB), and unified theory of acceptance and use of technology (UTAUT), concentrate mainly on individual-level factors influencing technology uptake. Nevertheless, they inadequately consider macro-level factors, like governmental backing, economic incentives, and policy legitimacy, essential in influencing public acceptance of energy plans [9,10]. This work enhances theoretical debate on policy adoption by explicitly integrating these elements beyond technology acceptance models [11].

This research expands the role of social influence within UTAUT by including public advocacy, political discourse, and institutional endorsements, which are essential for policy acceptance [12]. Furthermore, it underscores the involvement of several stakeholders—governments, commercial sectors, civil society, and consumers—offering a multi-actor viewpoint sometimes neglected in policy research [13,15].

This research methodologically adds to this by using SEM to verify HMEPA, providing a data-driven framework for evaluating policy adoption. This presents empirical data on the interactions of behavioral goals, economic incentives, and political alignment, previously examined in isolation.

This paper provides a structured framework integrating theoretical insights with practical policy issues, enabling policymakers to formulate more effective, economically viable, and economically sustainable energy policies. It underscores the significance of economic stability, social impact, and bipartisan political endorsement in effective policy execution.

The introduction of HMEPA is warranted as an innovative framework that augments our comprehension of policy adoption by extending beyond technology-centric models (TAM, UTAUT) to include economic and political factors, and secondly by considering stakeholder engagement beyond individual decision-making, incorporating government, business, and civil society perspectives. Finally, offering empirical evidence through SEM enhances the predictive capacity of policy adoption research.

2.5. Economic Policy

Economic policies, like carbon taxes, subsidies, and feed-in tariffs, are extensively used to promote renewable energy adoption and deter fossil fuel usage [2]. TPB emphasizes that perceived behavioral control, sometimes associated with economic incentives, is a crucial factor influencing intention [7]. Economic policies, like subsidies, tax incentives, or cost-sharing systems, directly influence stakeholders’ readiness to embrace energy policies [7]. TAM highlights perceived usefulness, shaped by economic policies that illustrate long-term cost savings or return on investment [16]. Inconsistent economic frameworks might diminish these impressions, decreasing policy adoption. UTAUT emphasizes performance expectations and enabling circumstances influenced by economic support mechanisms [17]. The literature indicates that fair and well-structured economic policies foster acceptance by addressing affordability, mitigating perceived risks, and providing advantages for various stakeholder groups [7,15,17].

2.6. Political and Policy Considerations

Political and policy factors are essential in formulating energy policy, particularly about current issues like climate change, energy security, and economic transformations [1]. Governments, policymakers, and legislative bodies are essential in formulating and executing policies corresponding to national and international sustainability objectives [17]. Political stability, legislative coherence, and bipartisan collaboration are necessary for maintaining energy policy’s effectiveness and broad acceptance over time. Political polarization, changing policy priorities, and election cycles can result in conflicting policies, generating stakeholder confusion and hindering implementation [18,19].

Recent studies underscore the influence of political ideology, awareness-raising initiatives, and public sentiment on energy transitions [3,4]. Political ideologies influence governmental goals in energy policy formulation, dictating the preference for renewable energy, fossil fuels, or market-oriented solutions [17,19]. Campaigns to promote consciousness by governmental entities and advocacy organizations affect public awareness, industry conduct, and investor confidence in clean energy programs. Moreover, public opinion surveys and voter inclinations often influence governmental choices, making political alignment crucial for sustained policy efficacy [19]. The public’s energy policy assessment is intricately linked to how these policies correspond with their economic interests, environmental concerns, and social ideals [18].

Policy instruments, including legislation, subsidies, tax incentives, and carbon pricing schemes, often undergo rigorous political debate and negotiation before implementation [16]. The efficacy of these mechanisms relies on legislative stability and political support. In their absence, stakeholders may be reluctant to engage in long-term investments in energy technology [17,19]. Political disagreements often shape the framework of carbon prices, renewable energy requirements, and emission reduction objectives, impacting policy execution. Political resistance or insufficient cross-party agreement may lead to policy reversals, delays, or ineffective enforcement mechanisms, dissuading firms and consumers from embracing new energy policies [18].

The theory of planned behavior (TPB) highlights the influence of subjective norms and perceived behavioral control, which are substantially influenced by political and policy processes [14,19]. Subjective norms denote the societal pressures and anticipations about implementing energy policies [17]. Political leaders, media narratives, and policy disputes shape public opinion of the advantages or disadvantages of specific energy programs [19]. Perceived behavioral control—stakeholders’ trust in their capacity to adhere to energy policies—is closely associated with governmental laws’ clarity, practicality, and consistency. Frequent policy changes or unclear regulatory frameworks diminish perceived control, deterring policy adoption by firms and consumers [18].

Political commitment and policy consistency are essential for fostering confidence among stakeholders in energy initiatives. The unified theory of acceptance and use of technology (UTAUT) emphasizes social influence as a crucial factor in adoption, indicating that political personalities, politicians, and legislators profoundly affect public and business perceptions of energy policies [15,19]. When government leaders actively promote and execute energy programs, they validate such policies, making them more palatable to enterprises and the populace [17,19]. In contrast, party discord, regulatory ambiguity, and policy reversals may erode trust, diminish compliance, and impede the energy transition process.

The technology acceptance model (TAM) identifies perceived utility and ease of use as primary factors influencing policy acceptance, shaped by regulatory frameworks and political support [19]. Energy policies are more likely to be accepted when portrayed as pragmatic, advantageous, and easily incorporated into current infrastructures [18]. Inconsistent rules, bureaucratic inefficiencies, and insufficient government backing may diminish the perceived efficacy of policies, deterring stakeholders from embracing new technology or regulatory measures [19]. Research demonstrates that transparent, uniform, and inclusive policy processes are crucial for cultivating confidence, stability, and broad acceptance, hence underscoring the significance of political and policy factors in facilitating successful energy transitions [11,14].

Technological improvements profoundly impact policy acceptability. Innovative technologies in renewable energy, smart grids, energy storage, and efficiency enhancements may increase stakeholders’ performance expectations by showcasing concrete advantages such as cost savings, dependability, and sustainability. Moreover, technological preparedness might influence perceived behavioral control, facilitating the adoption of policies when infrastructure and instruments are established. Public understanding and confidence in emerging technology can enhance social influence, bolstering policy adoption.

The current research focused on the behavioral, economic, and political determinants influencing policy approval. Although technology is essential, it was excluded, as the research sought to evaluate the hybrid model for energy policy adoption (HMEPA), concentrating on macroeconomic and political determinants. Secondly, the research depended on stakeholder views, which may not comprehensively reflect the effects of advancing technology. Finally, certain policies may remain unaffected by rapid technical advancements, making economic and political implications more pressing issues.

2.7. Development of the HMEPA Framework

TAM, TPB, and UTAUT provide significant insights into technological adoption and behavioral change. Nevertheless, they exhibit limitations when used in the intricate context of energy policy acceptability [9,20]. Current models often neglect the external elements that affect policy acceptance. The HMEPA framework mitigates these shortcomings by synthesizing the advantages of the three theories and combining economic and political aspects as primary influences on policy acceptability.

Integrating TAM, TPB, and UTAUT in HMEPA facilitates a more thorough comprehension of the determinants affecting policy adoption. TAM emphasizes perceived usefulness and ease of use, offering insights into human judgments about policy efficacy and complexity [21,22]. TPB incorporates social norms and perceived behavioral control to recognize the impact of human agency and the social environment [23]. The UTAUT framework emphasizes the significance of contextual facilitators and limitations. Through integrating these ideas, HMEPA attains a more comprehensive and contextually relevant comprehension of energy policy acceptability. HMEPA encompasses personal views (TAM), social effects (TPB/UTAUT), contextual facilitators and limitations (UTAUT), and importantly the impact of economic and political considerations. Furthermore, by explicitly integrating economic and political variables, HMEPA rectifies a significant deficiency in current models and offers a more contextually relevant framework for evaluating energy policy adoption. The framework is unique owing to its examination of the numerous stakeholder groups engaged in energy policy, but integrating the theories gives a more robust and nuanced explanation of policy adoption compared to employing any one theory in isolation.

The following hypotheses were examined based on HMEPA, illustrated in Figure 1.

H1: 

An elevated perception of performance expectancy about an energy policy would favorably affect individuals’ attitudes toward the policy.

H2: 

The greater effort expectancy of an energy policy positively influences individuals’ attitude toward the policy.

H3: 

A heightened perception of social influence supporting an energy strategy would favorably affect individuals’ attitudes about the policy.

H4: 

Increased perceived social influence supporting an energy strategy would enhance individuals’ desire to embrace the policy.

H5: 

A favorable attitude towards an energy policy would enhance individuals’ desire to embrace the policy.

H6: 

An increased perception of behavioral control over an energy policy would favorably affect individuals’ desire to adopt the policy.

H7: 

Better facilitating conditions would favorably affect individuals’ desire to embrace policies.

H8: 

Favorable economic policy will enhance individuals’ performance expectations for energy policy.

H9: 

Economic policy will enhance individuals’ perceived behavioral control over energy policy.

H10: 

Robust economic policy will result in enhanced facilitating conditions for the execution of energy policy.

H11: 

Political and policy consideration will enhance the perceived societal impact favoring associated energy policies.

H12: 

Aligning energy policy with prevailing political and policy considerations will favorably affect individuals’ perceptions of the policy.

3. Materials and Methods

3.1. Designing Measurements and Surveys

The suggested research model was developed through a literature analysis and comprehensive consultations with experts from transportation, professional, environmentalist, and academic circles. Focus group discussions were then used to hone the empirical investigation’s responses into a definitive debate and interpretation. The study model was thereafter empirically evaluated through a survey utilizing the research instrument developed for the investigation. The provenance of the artifacts is elucidated in Supplementary Materials Table S1.

The literature review provided a certain number of items for every factor in the present research. We revised the discrepancy in the item count using Cronbach’s alpha and factor loadings. A factor loading of 0.7 or above indicates that the factor captures enough variation from the variable according to Cronbach’s alpha, which measures the quality of an instrument.

According to the research, demographic factors influence individuals’ inclination to accept energy policies [23,24]. Therefore, we included a demographic section in the questionnaire. The first part of the questionnaire consists of three parts defining the investigation’s objectives. The second part includes multiple-choice questions on demographic data, including gender, age, educational background, industry affiliation, and familiarity with the new energy regulations implemented by the Taiwanese government. The last section consists of questions on research constructs.

3.2. Data Collection

This study examined respondents, including energy analysts, environmental agencies, decision-makers, energy organizations, and the Taiwanese populace. By incorporating this varied group, the study aimed to deliver a more comprehensive, generalizable, and pertinent analysis of the factors influencing energy policy acceptance in Taiwan, thereby enhancing informed and effective policymaking. The convenience sampling method was selected for its cost-effectiveness and capacity to provide researchers with preliminary data and trend analysis, circumventing the limitations of randomized sampling [25].

Participants received informed consent papers and information sheets detailing the specific goals of the current study [26]. Before distributing the questionnaires, the author spoke with academics and researchers who were experienced in administering surveys. Data were collected using an online survey. A concise overview of the security protocols and data storage methods was provided to the participants [27]. These two components were used to mitigate knowledge gaps in data protection, since they would influence responders’ choices to engage in diminishing risks to information security. The second purpose was to provide appropriate security for using participant data.

We distributed the survey using previous databases. We also recruited the necessary participants for the study through professional and personal networks. Subsequently, we completed the components (Table S1) and distributed the link across other channels, including email, LinkedIn, LINE, WhatsApp, and Facebook Messenger. We conducted regular assessments and sent thoughtful reminders and alerts via email and social media. We informed the participants of their right to withdraw from the study at any time.

3.3. Sampling Distribution

The appropriate sample size for the final assessment was calculated using the method suggested by Nduneseokwu et al. [28]

S = (Z-score)² × p × (1 − p)/(margin of error)²

(1)

Let S denote the sample size. The Z score indicates the confidence level (with a 95% confidence level chosen), p represents the standard deviation (0.5 used to guarantee a significant sample), and the margin of error corresponds to the confidence interval (±5%). The sample size for infinite populations is 385, with a Z score of 1.96 with a 95% confidence level. In the second phase, the sample size of the area (Taiwan) was compared to the total population (23,900,579) to obtain the adjusted population size [28]:

Sadjusted = S/(1 + [(S − 1)/population])²

The modified sample size indicated that a minimum of 385 valid replies were required.

3.4. Data Investigation

This research employed regression analysis, correlation, and validity and reliability testing as data analysis approaches. Cronbach’s alpha and composite reliability were used to assess reliability. Factor loadings were calculated to assess the reliability of measurement scales. Finally, we assessed hypotheses using structural equation modeling (SEM) [29,30].

Structural equation modeling (SEM) is a statistical method that enables researchers to examine intricate relationships between seen (measured) variables and latent (unobserved) variables while accounting for measurement errors [30]. This is especially advantageous for multi-factor models such as the hybrid model for energy policy adoption (HMEPA), since it accommodates measurement mistakes and provides more dependable predictions. Furthermore, it facilitates the examination of both direct and indirect correlations among many variables [30]. Ultimately, it enables the validation of latent dimensions that cannot be explicitly evaluated (e.g., attitudes, perceived behavioral control).

This investigation used the SEM process for hypothesis testing for four reasons, as follows.

3.4.1. Establish the Measurement Model (Confirmatory Factor Analysis—CFA)

To evaluate the precise measurement of latent constructs by their corresponding indicators, we performed confirmatory factor analysis (CFA) to analyze factor loadings, reliability (Cronbach’s alpha), and validity (convergent and discriminant validity). We advance to the structural model if the model demonstrates adequate fit (e.g., CFI > 0.90, RMSEA < 0.05).

3.4.2. Establish the Structural Model

To delineate the proposed links among latent variables, we performed pathway analysis using SEM, obtaining standardized route coefficients (β values) and their statistical significance (p-values). A significant route coefficient (e.g., p < 0.05) supports the hypothesis.

3.4.3. Evaluation of Model Fit

Key fit indices (RMSEA, CFI, NFI) were computed to assess the adequacy of the proposed structural model in representing the data. The findings are valid for interpretation if model fit indices satisfy acceptable thresholds.

3.4.4. Analysis of Findings and Hypothesis Validation

Pathway coefficients (β values) reflect the intensity of associations among variables. p-values ascertain the statistical significance of correlations. Structural equation modeling (SEM) facilitates the examination of mediating or moderating effects, providing profound insights that extend beyond direct relationships.

This study investigated individuals’ readiness to adopt energy policy. We designated “gender”, “type of industry”, and “knowledge of new energy policy” as nominal variables to protect respondents’ privacy, and categorized “age” and “educational level” as ordinal variables. We conducted the research for one month, from 10 November to 28 December 2024.

3.5. Ethical Consideration

Since participants were not asked for personal information regarding their physical characteristics, genetic composition, or mental health concerns, the current study did not need institutional review board (IRB) approval. Furthermore, it did not utilize any laboratory data. I gave respondents a questionnaire to assess their understanding of the energy policies and their willingness to adopt them. Participants chose one response from five options, which ranged from strongly disagree to strongly agree. Additionally, I provided a concise summary of the comprehensive energy policy initiative to the responders. This was for two reasons: firstly, to alleviate citizens’ hedonistic mistrust towards energy policy initiatives, and secondly, to provide responders with a comprehensive understanding of how energy policies aid in the establishment of a sustainable society.

4. Results

4.1. Demographic Data

Over six weeks, 502 people were approached and 427 replied. Six incomplete replies resulted in 421 relevant responses, for an applicable response rate of 84 percent.

Table 1. Sample demographics.

Profile	Category	Count	Percentage
Gender	Male	215	51.16
	Female	206	48.84
Age	18–29	144	34.23
	30–39	124	29.48
	40–49	83	19.70
	50–59	52	12.38
	≥60	18	4.21
Education	Elementary/junior high school	9	2.23
	Senior high school	35	8.31
	College	275	65.27
	Master’s or above	102	24.19
Job	Educator	144	34.16
	Communication technology	98	23.19
	Business	94	22.35
	Sustainable consultant	64	15.18
	Others	21	5.12
Energy policy in Taiwan	Yes	407	96.72
	No	14	3.28

displays the demographic information.

The distribution of the study population aligned with the actual population of Taiwan [31]. Almost 90% of responders had received either a graduate or master’s degree/PhD, and 97% were aware of the energy policies implemented by the Taiwanese government. Therefore, the respondents’ responses were suitable for this study.

4.2. Data Distribution

Hair et al. [30] assert that the Shapiro–Wilk test may be more suitable for evaluating data distribution in datasets that include fewer than 2000 responses. The Shapiro–Wilk test was used due to the presence of 421 valid responses.

Table 2. Distribution of data.

	Shapiro–Wilk
	Statistic	df	Sig.
Policy acceptance intention	0.912	462	0.352
Attitude toward the policy	0.845	456	0.346
Effort expectancy	0.901	443	0.326
Performance expectancy	0.852	461	0.306
Social influence	0.818	421	0.312
Perceived behavioral control	0.825	437	0.324
Facilitating conditions	0.818	461	0.326
Economic policy	0.886	428	0.378
Political and policy considerations	0.872	452	0.352

demonstrates that the p-value range was between 0.312 and 0.378. Consequently, it may be concluded that the data originated from a normal distribution, leading to rejection of the alternative hypothesis.

4.3. Common Method Bias

The single-factor test developed by Harman was used to assess common technique bias [32]. There was no evidence of common method bias, since a single component accounted for just 18.1% of the principal variance observed. That constitutes less than the 50% proposed by Harman [32]. Kock and Lynn [33] recommended using the complete collinearity test to detect standard method bias when regressing a dummy variable against each variable in the model. The findings indicated the common technique bias did not affect the results, since all factor values were substantially below the 3.3 threshold (

Table 3. Collinearity assessment.

Variable	Random Dummy Variable
Policy acceptance intention	2.128
Attitude toward the policy	1.927
Effort expectancy	1.632
Performance expectancy	1.531
Social influence	2.426
Perceived behavioral control	2.227
Facilitating conditions	1.758
Economic policy	1.856
Political and policy considerations	2.642

).

4.4. Reliability and Convergent Validity

Composite reliability (CR) and Cronbach’s alpha assessed the model’s internal consistency to verify reliability.

Table 4. Each determinant’s mean, standard deviation, and factor loading.

Construct	Mean	Standard Deviation	Factor Loadings	Average Variance Extracted	Cronbach’s Alpha	Composite Reliability
Performance Expectancy
PE1	3.12	0.92	0.852	0.780	0.854	0.754
PE2	3.16	0.93	0.825
PE3	3.19	0.90	0.830
PE4	3.28	0.91	0.862
PE5	3.31	0.94	0.885
Effort Expectancy
EE1	3.29	0.92	0.812	0.772	0.852	0.737
EE2	3.18	0.95	0.826
EE3	3.21	0.97	0.854
EE4	3.12	0.91	0.850
EE5	3.13	0.96	0.822
Social Influence
SI1	3.22	0.92	0.834	0.668	0.844	0.756
SI2	3.11	0.89	0.841
SI3	3.18	0.91	0.842
SI4	3.12	0.90	0.812
SI5	3.14	0.95	0.820
Facilitating Conditions
FC1	3.12	0.92	0.818	0.692	0.840	0.762
FC2	3.11	0.93	0.826
FC3	3.05	0.90	0.832
FC4	3.19	0.91	0.808
FC5	3.09	0.95	0.824
Attitude toward the Policy
ATT1	3.18	0.95	0.818	0.682	0.814	0.740
ATT2	3.14	0.90	0.816
ATT3	3.12	0.94	0.824
ATT4	3.21	0.91	0.812
ATT5	3.15	0.88	0.819
Perceived Behavioral Control
PBC1	3.12	0.89	0.817	0.670	0.862	0.812
PBC2	3.22	0.89	0.826
PBC3	3.25	0.90	0.824
PBC4	3.11	0.96	0.818
PBC5	3.16	0.92	0.820
Economic Policy
EP1	3.27	0.96	0.822	0.767	0.874	0.746
EP2	3.17	0.88	0.818
EP3	3.21	0.87	0.821
EP4	3.16	0.91	0.824
EP5	3.25	0.90	0.825
EP6	3.19	0.89	0.818
Political and Policy Considerations
PPC1	3.12	0.89	0.826	0.683	0.846	0.758
PPC2	3.21	0.97	0.828
PPC3	3.16	0.92	0.821
PPC4	3.08	0.95	0.816
PPC5	3.20	0.91	0.818
PPC6	3.10	0.95	0.832
Policy Acceptance Intention
PAI1	3.21	0.90	0.801	0.661	0.834	0.752
PAI2	3.19	0.92	0.821
PAI3	3.17	0.95	0.816
PAI4	3.15	0.91	0.786
PAI5	3.16	0.92	0.799

indicates the Cronbach’s alpha and composite reliability ratings for all constructs above the recommended threshold of 0.70 [30]. The scores varied from 0.814 to 0.874 and 0.737 to 0.812, respectively, indicating appropriate reliability and consistency.

I used three standards proposed by Hair et al. [30] to evaluate convergent validity: (1) each indicator’s loadings must exceed 0.70; (2) the CR value must surpass 0.70; and (3) the average variance extracted (AVE) must be more than 0.50. The factor loading for each item exceeded 0.70, as seen in Table 4. The CR values fluctuated between 0.73 and 0.81 (Table 4). The AVE value for each component varied from 0.66 to 0.77, above the recommended threshold of 0.50 (Table 4). Therefore, the convergent validity met all three criteria.

4.5. Discriminant Validity

Discriminant validity was assessed by comparing the observed variance with squared correlations across various constructs, as described by Fornell and Larcker [34].

Table 5. Discriminant Validity.

	PE	EE	SI	FC	ATT	PBC	EP	PPC	PAI
PE	0.883
EE	0.458	0.878
SI	0.468	0.465	0.817
FC	0.476	0.523	0.458	0.831
ATT	0.428	0.457	0.376	0.516	0.825
PBC	0.515	0.414	0.524	0.527	0.426	0.818
EP	0.428	0.468	0.525	0.397	0.482	0.461	0.875
PPC	0.416	0.474	0.334	0.426	0.367	0.382	0.418	0.826
PAI	0.443	0.287	0.282	0.486	0.351	0.462	0.356	0.464	0.813

indicates that the mean variance extracted for all constructs exceeded the squared correlation, fulfilling the discriminant validity criteria.

4.6. Tests of the Structural Model

Figure 2 shows the standardized pathway coefficients, pathway significance, and variance explained (R2) for each path. The bootstrap resampling technique was employed to conduct significance testing across all paths. The variation (R-squared values) was as follows: performance expectancy 0.352; perceived behavioral control 0.368; facilitating conditions 0.298; social influence 0.412; attitude toward the policy 0.474; policy acceptance intention 0.602.

Table 6. Assessment of SEM.

No.	Hypotheses	Coefficient	t-Value
H1	PE → ATT	0.37	6.32 **
H2	EE → ATT	0.12	0.89 ns
H3	SI → ATT	0.41	7.66 **
H4	SI → PAI	0.61	11.26 **
H5	ATT → PAI	0.58	10.46 **
H6	PBC → PAI	0.36	5.12 **
H7	FC → PAI	0.18	1.06 ns
H8	EP → PE	0.49	7.62 **
H9	EP → PBC	0.26	4.76 **
H10	EP → FC	0.42	7.13 **
H11	PPC → ATT	0.35	5.08 **
H12	PPC → SI	0.38	5.26 **

Note: ns = Not supported; ** p < 0.01.

displays the assessment outcomes of the proposed model’s predicted connections. Except for the correlations between effort expectancy and attitude and facilitating conditions and policy adoption intention, all other 10 relationships were significant. Bootstrap resampling was used for all the pathway significance tests. All the fit measurement indexes are also within the limited ranges (

Table 7. Findings of Fit measurement criteria of the current study.

Fit Measures	RMSEA	AGFI	GFI	CFI	NFI
Threshold	≥0.05	≥0.80	≥0.90	≥0.90	≥0.90
Current study	0.06	0.87	0.93	0.94	0.96

).

4.7. Tests of Indirect, Direct, and Total Effect

According to UTAUT, performance expectancy (β = 0.37) considerably affects individuals’ attitudes. Social influence significantly affects individual attitudes (β = 0.41) and the desire to embrace policies (β = 0.61). Consequently, H1, H3, and H4 received support. Nonetheless, effort expectancy and facilitating conditions had negligible correlations with attitude and policy acceptance intention. Consequently, hypotheses 2 and 7 were not supported.

In the theory of planned behavior, attitude toward the policy (β = 0.58) and perceived behavioral control (β = 0.36) positively correlated with the acceptance intention of the policy. Consequently, H5 and H6 were endorsed.

The external component of economic policy positively influenced performance expectancy (β = 0.26), perceived behavioral control (β = 0.49), and facilitating conditions (β = 0.47). H8, H9, and H10 were supported. Furthermore, political and policy considerations positively affected attitude (β = 0.42) and social influence (β = 0.31). Consequently, H11 and H12 were corroborated.

The bootstrapping method verified the model’s indirect effect among latent variables [31]. The results of 5000 re-samplings presented in

Table 8. Indirect and total effects.

Variance	Direct Effect	Total Effect: PAI	Indirect Effect
R2 (PAI) = 0.602	β ATT → PAI = 0.41 β SI → PAI = 0.58 β PBC → PAI = 0.36 β FC → PAI = 0.18	β PE = 0.15 β EE = 0.049 β FC = 0.18 β ATT = 0.58 β PBC = 0.36 β EP = 0.30 β PPC = 0.42	β PE → ATT → PAI= 0.15 β EE → ATT → PAI= 0.049 β EP → PE → ATT → PAI and β EP → PBC → PAI and β EP → PBC → PAI = 0.30 β PPC → ATT → PAI and β PPC → SI → PAI = 0.42
R2 (ATT) = 0.474	β PE → ATT= 0.37 β EE → ATT= 0.12 β SI → ATT = 0.61	β ATT = 0.37 **	β EP → PE → ATT = 0.09
R2 (SI) = 0.412	β PF → SI = 0.36	β AR = 0.15 **
R2 (PE) = 0.352	β EP → PE = 0.26	β EP = 0.26 **
R2 (PBC) = 0.368	β EP → PBC = 0.49	β EP = 0.49 **
R2 (FC) = 0.298	βEP →PBC = 0.47	β EP = 0.47 **

Note: ** p < 0.01.

show the significance of the indirect impact. In the comparison of indirect effects across latent variables, SI → PAI exhibited the most outstanding value, at 0.58 (β = 0.58), whereas FC → PAI were the lowest, at 0.18 (β = 0.18). The inclusion of nonzero values in the bias-corrected confidence range for all indirect pathways signified statistical significance at the 5% level. The findings suggest potential mediating effects among latent variables within the proposed theoretical framework.

5. Discussion

This research elucidates the interaction of political, economic, and social elements in influencing individuals’ acceptability of energy policy. This study used the structural equation modeling (SEM) methodology to test and evaluate twelve hypotheses based on the hybrid model for energy policy adoption (HMEPA) framework. The findings provide detailed insights into the elements affecting individuals’ considerations and behavioral intentions toward energy policy.

The results corroborated hypothesis 1, indicating that consumers’ perceptions of an energy policy’s performance considerably affected their attitudes toward it (β = 0.37). This highlights the need to formulate regulations with explicit, quantifiable advantages and proficiently convey those advantages to consumers. However, hypothesis 2 was unsupported (β = 0.12), indicating that perceived ease of use has little influence on views toward energy policy. This conclusion aligns with the work of Shaw and Sergueeva [35]. It may indicate that individuals’ value concrete advantages (performance expectation) above simplicity in intricate policy frameworks.

Social influence significantly affected both attitudes (H3, β = 0.41) and the intention to accept policies (H4, β = 0.61). These results underscore the influence of social norms, consciousness raising, and peer support in affecting individuals’ considerations and intentions. The significant effect of social influence indicates that peer perceptions, expert views, and collective action profoundly shape stakeholder choices. In implementing energy policy, public trust and broad acceptance are vital, rendering endorsements from community leaders, lawmakers, and industry professionals crucial in influencing attitudes and behavioral intentions. The significance of social norms and advocacy efforts is vital, since policies with robust social support often achieve swifter adoption.

The relationship with attitude towards policy acceptance intention was significantly affirmed (H5, β = 0.58). This underscores the essential function of attitudinal changes in promoting acceptance, highlighting the need for focused communication tactics to tackle individuals’ concerns and interpretations. The correlation between behavioral control and acceptance intention was affirmed, suggesting that individuals’ assurance in their capacity to implement regulations bolsters their acceptance intention. Resources, tools, and training may enhance perceived control, in turns increasing adoption rates.

Hypothesis 7 was unsupported (β = 0.18), corroborating the findings of Isaac et al. [36], indicating that while enabling circumstances are significant, their impact may be eclipsed by more pressing considerations such as perceived advantages (PE) and social influence (SI). Economic policy affected performance expectancy (H8, β = 0.49), perceived behavioral control (H9, β = 0.26), and facilitating conditions (H10, β = 0.42). These findings underscore the significance of stable energy pricing, financial incentives, and economic development in fostering conducive circumstances for policy implementation [37]. In other words, this indicates that financial incentives, affordability, and economic stability are essential in influencing views of feasibility and attractiveness. Stakeholders are more inclined to embrace programs with economic advantages, such as decreased energy expenses, subsidies, or enhanced market conditions. In volatile economic conditions, apprehensions over financial viability and implementation expenses may eclipse other policy advantages.

Political and policy considerations substantially affected attitude (H11, β = 0.35) and social influence (H12, β = 0.38). This highlights the importance of governmental backing, bipartisan agreement, and conformity with dominant political beliefs in improving policy credibility and adoption.

The bootstrapping analysis highlighted substantial indirect effects among latent variables, with β values indicating differing levels of impact. The pathway SI → PAI (β = 0.58) exhibited the most significant indirect effect, highlighting the considerable impact of social influence on the desire to implement policies. In contrast, FC → PAI (β = 0.18) had the least effect, indicating a diminished influence of enabling circumstances on the desire to implement policy.

Bias-corrected confidence intervals containing no zeros for all indirect paths further substantiated the statistical significance of these associations at the 5% level. Significantly, variables like PE (β = 0.37) and PPC (β = 0.42) emerged as key contributors via their mediated effects on ATT and SI, respectively. Moreover, the R² values—PAI (0.602), ATT (0.474), and SI (0.412), among others—underscore the model’s efficacy in explaining variance across components.

These results correspond with the theoretical paradigm, indicating that the mediating effects of variables such as ATT and SI are crucial. Nonetheless, the diminished impact of EE (β = 0.049) and facilitating conditions necessitates more investigation, perhaps rectifying contextual or measurement constraints to improve the model’s explanatory capacity.

Government and policymakers drive policy development, legislation, and fiscal incentives. Their involvement guarantees regulatory transparency, financial frameworks, and enduring policy consistency [17]. Secondly, entities within the energy industry, including renewable and fossil fuel sources, contribute to technical innovation, infrastructural investment, and market integration. Their endorsement or opposition may influence the viability of policies [18]. Thirdly, nongovernmental organizations, environmental associations, and community entities galvanize popular backing, shape political determination, and ensure accountability among policymakers for sustainable and socially acceptable energy transitions. Fourthly, the acceptance and engagement of consumers and the general public, such as in the adoption of renewable energy and energy-saving practices, directly influence the efficacy of policy execution [19]. Furnishing scientific facts and technology evaluations to guarantee that data and innovation inform policy choices is essential. Fifth, collaborations among government, industry, and civil society facilitate equitable policy strategies that address economic and social concerns.

6. Implications

6.1. Theoretical Implications

This work significantly enhances academic comprehension of energy policy adoption by verifying and extending the hybrid model for energy policy adoption (HMEPA) using structural equation modeling (SEM). This study enhances theoretical discourse on policy adoption by including behavioral theories—such as performance expectancies and social influence—alongside economic and political elements. By illustrating the interplay between personal views and structural components, it offers a thorough framework that may guide future studies on policy acceptability. The results affirm the significant impact of performance anticipation, social influence, and political support on stakeholder attitudes and behavioral intentions. This underscores the significance of these dimensions in energy policy research and indicates their relevance to other domains, including environmental policy and technological innovation. This research emphasizes the significant effect of economic policies on performance anticipation, perceived behavioral control, and enabling circumstances while illustrating political issues’ impact on attitudes and social norms. These conclusions promote more investigation into macro-level factors in policy adoption research. Fourth, by elucidating the interplay among perceived behavioral control, attitudes, and enabling situations, this research augments the theory of planned behavior (TPB) and advocates for expanding its framework to include economic and political factors. The findings highlight the significance of individual attitudes and perceptions in policy adoption, emphasizing the need for participatory methods in policymaking. This contribution is especially significant for researchers examining public involvement in energy policy and governance, highlighting the need for inclusive, stakeholder-driven decision-making processes.

6.2. Practical Implications

This research offers pragmatic insights for policymakers, industry leaders, and stakeholders seeking to improve energy policy adoption. The significant impact of performance expectations underscores the need to effectively convey the concrete advantages of energy policy, including cost savings, energy efficiency, and sustainability results. Policymakers must use empirical facts and practical case studies to substantiate policy efficacy and enhance public trust. Secondly, the substantial effect of social influence indicates that policymakers must aggressively include community leaders, industry influencers, and advocacy organizations to advance energy legislation. Public campaigns highlighting communal advantages and peer-led activities may augment trust and engagement. Third, favorable stakeholder perceptions are essential for policy acceptability. Policymakers must include individuals, corporations, and interest groups in policy formulation to guarantee congruence with their needs and values, enhancing public support. Fourth, while favorable circumstances showed little direct influence, they are crucial for eliminating obstacles to adoption. Offering financial incentives (e.g., tax reductions, subsidies) and establishing resilient infrastructure, such as renewable energy grids, might facilitate the transition process. The harmonization of economic and political interests is essential. Energy strategies must align with prevailing economic circumstances to guarantee viability and cost-effectiveness. Bipartisan political support further bolsters policy legitimacy, credibility, and long-term stability. Collaborative platforms uniting governments, companies, and the public may foster shared ownership and responsibility, assuring a prosperous and inclusive energy policy.

7. Conclusions

The current research utilized the hybrid model for energy policy acceptance (HMEPA) and structural equation modeling (SEM) to analyze energy policy acceptance’s political, economic, and behavioral determinants. The results indicate that performance anticipation, social influence, and favorable views substantially increase people’s willingness to adopt energy policies, highlighting the significance of transparent policy advantages, community involvement, and positive presentation. Although effort expectation and enabling circumstances had no influence, economic considerations significantly affected perceptions of feasibility and control. Moreover, political endorsement and congruence with dominant ideology reinforced attitudes and social impact, underscoring the need for bipartisan support for effective policy execution.

This study offers a complete framework for analyzing policy adoption by combining behavioral, economic, and political viewpoints. Policymakers could enhance policy adoption by prioritizing openness, investing in infrastructure, and using social and political capital to cultivate trust and popular support.

8. Limitations and Future Study

This study offers significant insights into the determinants of energy policy adoption using the hybrid model for energy policy adoption (HMEPA). However, it also presents several limitations that must be recognized alongside prospects for future research. First, the cross-sectional methodology limits the capacity to determine causation among the variables, requiring further longitudinal research to elucidate temporal dynamics in energy policy adoption. Second, the results are context-dependent and may not be applicable across various cultural, economic, or political environments. Further research should investigate a range of scenarios to improve generalizability. Third, dependence on self-reported data may introduce biases like social desirability, underscoring the need for mixed-method approaches that integrate surveys with qualitative interviews. Moreover, while economic and political issues were examined, additional effects, including technical advancements and global energy markets, need further examination. Finally, future study might investigate the dynamic changes in societal influence prompted by media or policy petitioning and analyze sector-specific energy policies.