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Indian voters’ attitude and behavior toward a political brand for producing green products: a mediation model

Abstract

This study explores the attitude–behavior relationship among voters for political party brands that use a sustainable or green marketing approach. A structural equation modeling is used to analyze data collected from 1771 respondents who are registered Indian voters in this case. A positive attitude toward a political brand is compared to intentions to vote for the same brand. According to the findings, political brands create a more environmentally friendly product in the form of green actions because doing so encourages consumers to think favorably of the political brand, which results in them voting for it. As a result, authors are able to identify a positive attitude–behavior relationship for political brands among voters.

Introduction

Global environmental problems have aggravated, and buying green products has become fashionable. People increasingly wear green [91], eat green [101], use green products and sometimes even vote for environment-friendly political parties [28] whenever possible. The consumption of these environmentally friendly and sustainable products or services has emerged as a solution to various environmental issues. Businesses are interested in this apparent rise in ethical consumption because it provides opportunities to expand into a new market. However, Freestone and McGoldrick [34] feel that business organizations should not be the only ones to grab the opportunity; political party brands should also keep their fingers on the pulse when such ethical concerns are voiced by consumers (here, voters). Indeed, through strategic policy marketing, parties must continuously strive to meet their target voter markets’ emerging wants and needs [7].

As of now, consumers of these green products, or we can say environment-friendly products, are few because, while almost all of us have green attitudes, we do not always act in favor of environmentally friendly consumption [52, 65, 82, 96, 104, 105]. This is commonly referred to as “the attitude–behavior gap” or “words-deeds inconsistency” and was coined by Fishbein and Ajzen [2] in a theory developed by them known as the Theory of Reasoned Action. Why it happens and when it happens is still poorly understood [19]; therefore, researchers have called for more research into this weak relationship to bridge the gap known as the “ethical purchasing gap”.

Also, there is substantial scientific evidence that this gap exists in the political context among voters from different countries on environmental issues, i.e., they have favorable attitudes toward saving the environment but do not take necessary actions on a personal level [11, 98]. Bravo and Farjam [17] studied the attitude–behavior gap of citizens in the environmental context for policymakers, as well as the discrepancies between citizens’ self-reported and actual behavior. They demonstrate that despite voters’ commitment to ecological consumerism, voters rarely show environmentally friendly behavior when taking action on personal behalf. This behavior can be described as having a positive attitude toward the environment but exhibiting negative behavior, i.e., not taking sufficient steps to protect the environment.

On the other side, some studies have studied a well-established relationship between the two variables (attitude and behavior) in the category of voters. They generally agree that “greener” voters support “greener” political parties [68]. It implies that those committed to preserving the environment support political parties with more environmentally friendly development objectives. Therefore, we can say that there can be three types of relationships between variables: attitude and behavior. The first one is a positive attitude converting into positive behavior. Second, a positive attitude but still a negative behavior. Third, a negative attitude and a negative behavior. By taking cues from this, the authors of this article investigate the relationship between voters’ attitudes toward a “political brand” engaged in green actions and voters’ behavior in the form of party choice toward the same political brand, which has not been studied yet. This paper aims to add nuance to the literature by adding to the green and political marketing literature. It investigates the impact of the production of green products by political parties on voter attitudes and behavior in the form of “voting” for a political party identified as a “political brand”.

This article is in the Indian context, with examples of mainstream Indian political party brands, such as the Bharatiya Janata Party, Congress and the Aam Aadmi Party, continuously competing with each other on the environmental front. The main reason for choosing mainstream parties is that when studying the effect of environmental attitudes on vote choice in multi-party systems, scholars generally focus on the success of green parties (for instance, [20, 71, 85]). However, mainstream parties should not be excluded from such studies because they are important in articulating governments’ environmental policies [68]. As a result, in this study, we include mainstream or traditional political parties as examples of brands and not green parties. Another reason for not considering green parties is that no green parties exist in India [57]. Election manifestoes of these recent election brands were studied, and their green initiatives were identified as green products.

Firstly, the article briefly overviews the three major environmental crises in India and the policies framed by political brands to tackle the problem. It then provides answers to questions raised in the mind of readers, such as (a) Why have the authors focused this study on Indian political parties and voters? (b) Why has a green initiative been identified as a green product of political brands? After answering these questions, we proceed with the literature section, in which, firstly, the promotion and communication of these green actions through political manifestoes and mass communication media are discussed. Attention then shifts to why studying attitude is essential to predict a consumer’s behavior, here voters. Next, the research methodology is explained before the study results are presented. Finally, the discussion and conclusion section has been described.

India’s environmental quality and related environmental initiatives

River Ganga is among India’s holiest rivers, revered by thousands of Indians. It has socio-cultural and economic importance because it contributes to various economic and livelihood activities for the people who live in the basin [51]. It also has one of the world’s most complex river basins for two reasons. One, due to the large number of people who live near its basin, and second, because of the pressure on its water resources. Over the last few decades, human activities have caused significant changes in the aquatic environments of this river. Because of this increased human civilization and reliance on the river, drinking and other uses of its water have become dangerous [72].

During the Indian general election of 2014, the Bharatiya Janata Party came up with a solution for the above-stated problem by promising the Indian population to clean their holy river, Ganga, if they won [41]. On May 13, 2014, Namami Gange, a flagship program, was launched by the central government led by the BJP, incorporating various components such as municipal sewage treatment, industrial discharge management, enforcing river regulatory zones and wetland restoration. The United Nations (U.N.) also identified this project as one of the top ten world restoration flagships to revitalize the natural world in 2022 (PIB [74]). This example of a green initiative by a political brand applies to the whole country as it was promoted for a national-level election. At the same time, state parties also announced green actions through their manifestoes during the state-level elections. Name of some of these political brands involved in environment-related games are Congress, Aam Aadmi Party, Communist Party of India (Marxist)—CPI(M), Communist Party of India (CPI), Samajwadi Party, All India Trinamool Congress (AITC) and Shiv Sena. All these brands have presented and discussed environmental problems in their respective election manifestos [41].

Another flagship program of the Bharatiya Janata Party was the “Swachh Bharat Abhiyan”. On October 2, 2014, the Government of India, led by the BJP, launched the “Swachh Bharat Abhiyan” (also known as “The Clean India Campaign”), a massive project worth 62,000 crore Indian Rupees (INR), intending to make India clean by October 2, 2019, on the 150th birthday of Mahatma Gandhi. The project’s goal was to eliminate the unhealthy conditions caused by open defecation and littering. In addition to building toilets, the campaign aimed to raise awareness about the importance of hygiene, and efforts are being made to change people’s attitudes toward public defecation [3].

Now, we proceed with an example of an environmental problem in Delhi and nearby areas. This city is located in one of India’s most polluted industrial regions, the Gurgaon-Delhi-Meerut industrial region [70]. Due to this reason and adjoining states’ stubble burning, Delhi became a “gas chamber” in 2016 [97]. It was listed as one of the world’s most polluted cities [55, 93]. During the same year, to tackle this problem, the state political party took a green initiative, the Aam Aadmi Party. Their flagship program, the Odd–Even rule, was launched to combat air pollution in the national capital and nearby areas. This rule was a driving scheme implemented on a trial basis in Delhi city, requiring vehicles with odd and even registration numbers to run on alternate days beginning January 1, 2016 [93]. This is not the only initiative Aam Aadmi Party took; the other examples are that they have developed the largest network of e-rickshaws in the country—promoting clean last-mile connectivity by giving subsidies of RS 30,000; imposed heavy fines for constructing during peak pollution season; increased green cover by 1100 hectares; introduced an E-vehicle policy; planted three lakh tree samplings [1]. Now, we proceed with a section that answers why the authors have chosen India as a place of study.

Why India, its political parties and voters?

With a population of around 140 crores, India is the world’s largest democracy, surpassing China’s population in 2023 (Hertog [45]). Conducting an election becomes a challenging and colossal task here. It has been claimed that holding general elections in India is equivalent to keeping them in Europe, the USA, Canada and Australia combined. Statistically, the number of voters in India is in the millions. Out of these 140 crores, around 94 crore people (large target market) are registered voters in the country (Ganguli [36]). The number of polling places across the country totals approximately 900,000 (9 lakhs), and about five million people are needed to manage these polling booths, comprising election officials and an additional two million security personnel. All this comes at a huge cost to the government and the candidate running for election [94]. Also, Pich and Dean [75] have asserted that this field is still growing, making voting behavior significant to be studied from both a theoretical and practical point of view. So, it is essential to learn How? When? Why? and where? Do political consumers make their political purchases? All these reasons made the authors realize the need to study Indian voters’ behavior.

Why have green initiatives been taken as green products?

A party, candidates, public image and electoral programs constitute a political product in the political marketing literature [14]. A political party’s environment-friendly initiative will be termed a product from the respective political brand, which in our case is the Swachh Bharat Abhiyan, the clean Ganga mission and the odd–even rule.

We also know that a “green product” protects the environment without depleting resources or endangering it [4]. So, we can say that environment-friendly initiatives are green products of political parties [106], also making it a reason for writing green products and not green initiatives in this article.

Literature review

Impact of the election manifesto and mass media on promoting green products

The manifesto

Party manifestos are considered essential documents by academicians worldwide because they clearly outline a party’s ideology and strategy. During an election season, manifestos outline a political party’s promises that provide a helpful entry into understanding that party’s intentions ex ante, upon which voters base their decisions [50]. As a result, these documents are critical tools for understanding the party’s official position on various policy issues, public concerns and the party’s overall long-term agenda for developing a nation if elected to power. As new needs arise, new promises are added to these manifestos of political brands. Studies examining this connection between public preferences and party policies have attracted much attention in the past [79]. The environment has found its place in these mass communication tools. The parties’ priorities have evolved in response to shifting national and international conditions. It was not until the 1980s that manifestos mentioned the environment. Since then, environmental and sustainable issues have gained importance. In earlier days, “urban” issues received little attention because India was predominately rural. However, as more voters live in urban clusters due to increased urbanization, urban issues such as the environment now receive more attention [100].

An analysis of Indian party manifestos reveals that almost all major political parties in the country, such as Indian National Congress (INC), Bharatiya Janata Party (BJP), Aam Aadmi Party (AAP), Communist Party of India (Marxist)-CPI(M), Communist Party of India (CPI), Samajwadi Party, All India Trinamool Congress (AITC) and Shiv Sena, have mentioned something about safeguarding the environment and have positioned themselves as environment-friendly brands [41, 50, 100].

Mass media

Improvements in communications media have allowed consumer groups to operate simultaneously over international borders, relaying quick and up-to-date information concerning unethical business behavior [19, 34]. Easy availability of information has made the public feel that they can influence the working of the governments and business houses through their actions [69, 84]. Also, media shapes and influences people’s attitudes and understanding of environmental issues [73]. As a result, the media has to play an important role in setting the agenda for public concern and also political campaigning [38, 43, 58].

Through mass media communication, media outlets can convey public messages to the government and the government’s actions and proposals to the public [102]. When there is a public outcry, governments get to know through press media [40]. The study of media’s role in politics has primarily focused on how media influence people’s perceptions of the world [59, 61]. Significantly less research has been conducted to investigate the effects of the media on policymaking. The consensus is that the media-policy nexus is anything but straightforward. The majority of scholars also agree that, even though media may not directly influence policy decisions of the governments, they may still impact how public issues are perceived [27]. Press media has a strong influence on the voting behavior of the citizens as media dependency has increased “in times of change, conflict, uncertainty, and insecurity” [62],Widaya Mohamed [103].

Attitude and behavior (vote) for a “greener” political brand

The relationship between attitudes and behavior can be thought of as a tool that aids both practitioners and researchers in forecasting consumer behavior [107]. An attitude can be defined as a persistent set of beliefs about an object that predisposes people to act in a certain way toward the thing [5]. As attitudes significantly impact a person’s decision-making and behavior [54], the first step toward increasing sustainability, specifically encouraging positive green product purchase behavior, can be seen as having a positive attitude toward green products [78]. A significant relationship exists between people’s attitudes toward the environment and their corresponding behaviors [46]. Numerous literary works demonstrate this connection between environmental attitudes and action. For the USA, for instance, Kahn [49] and Baldassare and Katz [8] demonstrated how environmental concern affects both consumer decisions (e.g., buying hybrid cars, taking the bus to work and using less gasoline) and environmental practices (decreasing their driving, recycling and conserving water). Vaske and Donnelly [98] mapped voting intentions for wildlife preservation in Colorado, USA, to show how value orientations were fully mediated by attitudes affecting behavioral intentions. According to Butler and Francis’s [18] study, environmental attitudes can even be used to explain why women choose certain types of clothing. Numerous additional studies show a strong connection between environmental attitudes and various types of environmental practices in different countries and for different populations [15, 21, 29, 47, 60, 86]. Even voting for parties that offer solutions to environmental problems is frequently viewed as a form of environmental action. Numerous academicians have also noted that voter behavior in states like California, Colorado, New Jersey, Belgium, New Zealand, Australia and Germany may be significantly influenced by voters’ environmental attitudes and concerns [16, 33, 37, 39, 80, 81].

Voters appear to respond to how governments handle relief efforts after natural disasters. After landslides in Colombia [35], Hurricane Sandy in the USA [99] and the 2002 Elbe flooding in Germany [10], voters rewarded the political parties for their relief work. Even though the ruling party performed well in local elections, academics occasionally could not find evidence that wildfires in Spain affected the party’s national vote share [77]. In other cases, citizens respond to natural disasters by criticizing political parties in government for their lack of helpful behavior. Examples of Indian citizens going against the governments are also many, such as the Chipko Andolan of 1973 [32], the Bengal peasant revolt of 1859–1863 [13] and the Narmada Bachao Andolan [63]. These developments suggest that voters choose political parties that meet their expectations for leadership during dramatic events [6]. The electoral choice or vote has been highlighted in this study as an illustration of environmentally conscious behavior, or, as we might say, behavior that is supportive of a greener political brand.

Methodology

Research design (framework with hypothesis)

The development of a research framework and a thorough analysis of pertinent literature marked the beginning of the study. Next, in order to evaluate the relationships between the independent and dependent variables, research hypotheses were developed. This study’s empirical research is based on quantitative data that were analyzed with SPSS and Smart PLS software.

The study synthesized the literature on sustainability practices by political brands and the relationship between the attitude and behavior of consumers to better influence voters’ behavior toward political party brands. Very little is known about voters’ attitudes and behaviors toward a political brand for the green actions they take, even though numerous studies have been conducted to explore the mechanism of the attitude–behavior link. The research framework presented here was inspired by the work of Papp [68], who looked into the relationship between environmental attitudes and the “greenness” of party preference, with the effect of exposure to environmental problems being moderated. The proposed framework is shown in Fig. 1.

Fig. 1
figure 1

Source: The authors

Proposed framework.

Environmental harm may be lessened due to political party brands’ green initiatives or promises. In doing so, a political brand may develop in voters a perception of a “green” brand, which encourages consumers to have a positive attitude toward the brand and may lead to positive behavior, operationalized in this case as voter choice. We provide a conceptual framework that hypothesizes that the government’s sustainability programs establish a positive attitude (for the brand), leading to favorable behavior (vote) from the voter. The positive attitude of consumers serves as a bridge between government green actions and favorable behavior (in this study, voter choice). This study aims to expose governments or political brands to customers’ reactions, which shape voter choice. Therefore, three variables were used in this study: green product, voter attitude and voter behavior. The definitions of the variables are given in Table 1. Also, the hypotheses developed for the study are:

Table 1 Variables used for study

H1

The government’s green initiatives (green products) foster a favorable attitude toward that party.

H2

Voters who have a positive attitude toward a political party’s brand for their environment-friendly policies prefer to support that party by giving them a vote.

H3

The government’s green actions (green products) lead to positive behavior from the voters.

H4

A positive attitude mediates the relationship between the government’s green actions (green products) and voter behavior (voting).

Development and measurement of the constructs

Respondents responded to questions such as “In my opinion as a voter, environment-friendly policies save the environment” on a scale of 1–5 (where 1 = “strongly disagree”, and 5 = “strongly agree”). This study adapted Suhaily and Darmoyo’s [95] work to develop questions for the variable green product. Their study was used for three items, renamed GP_1, GP_2 and GP_3. Six items from Chen et al.’s [24] work were used for the voter attitude variable, renamed as VA_1, VA_2, VA_3, VA_4, VA_5 and VA_6, and three items from Chen et al. [25] and one from Chan’s (1999) study [22], respectively, were adapted to study the voter behavior variable; the items were renamed as VB_1, VB_2, VB_3 and VB_4. A summary of the sources used to develop the construct is provided in Table 2. All the questions were slightly modified to fit the goal of the study.

Table 2 Construct source from prior literature

Study sample characteristics

Registered Indian voters are the population of interest for this study. The respondent’s gender, age, annual earnings and level of education were among the demographics used in the study. Male respondents made up 53% of the sample as a whole, while female respondents made up 47%. The population participating in this study is evenly distributed by gender, with a mere 6% difference. 35% of the respondents are between 18 and 25 years old. 24% are in the 26 and 35 years of age group. 25% are between 36 and 50 years of age, and 16% are above 50 years of age.

Of the sample, only 12% of respondents reported earning between zero and three lakhs per year, while 24% reported earning between three and six lakhs per year. 36% of respondents said they make between six and nine lakhs yearly, and 28% said they make more than nine lakhs.

Lastly, most respondents classified themselves as senior secondary school pass-outs, comprising 57% of the sample. People with graduation and above degree are 32%, people who studied till secondary level are 8%, and the rest 3% have no education.

Data collection strategy, population, sampling and sample size

The study was conducted from January 15, 2023, to August 27, 2023. A questionnaire was created to carry out the study. Data were collected through online and offline surveys. The survey was divided into two sections, the first of which requests demographic data (name, age, marital status and the name of the party they voted for in the last election). Thirteen questions made up the final section, which measured the variables. Data were gathered from respondents using Google Forms and by circulating hard copies of the questionnaire (in Hindi and English). The need to translate the questions from English to Hindi arose due to the fact that English is not a first language in India, making the questionnaire more accessible to respondents. Respondents in this study are registered voters of the country; approached citizens were asked prior if they were registered voters or not; if they were, they could proceed with filling out the questionnaire, and if not, they could decline to fill out the survey. For Google Forms, the snowball sampling method was used to collect the data (forwarded on personal and group contacts). In total, 184 wholly filled Google forms were returned, and the convenience sampling method was used to get the questionnaire filled out physically. There were many reasons for using this sampling technique, such as the non-availability of contact numbers of registered voters in the country on the election commission website. Although physical addresses are available, reaching each respondent physically would have cost a lot, limited time to conduct research without funds and a large population of India living in rural areas made random selection impossible. Respondents were unknown to the researcher and were approached when visiting public places such as Delhi’s marketplaces, outside of Delhi’s shopping markets, outside universities, and also when using public transport in NCR. A total of 1587 complete responses were obtained from the physical distribution of the questionnaire. Therefore, the total sample size was 1771 respondents.

There are many reasons for going with a small sample size of 1771 from a population of 140 crore for analysis, including limited funding for a comprehensive study, a short time frame and inadequate connectivity to rural areas. Statistically also, if we calculate the sample size with the help of the formula developed by WG Cochran [26], which is \(={z}^{2}pq/{e}^{2}\); where n is the sample size, z is the chosen critical value corresponding to the desired confidence level, p denotes the estimated proportion of a trait within the population, q = 1 − p, and e represents the desired level of precision. All these values used were p = 0.5, q = (1–0.5) = 0.5, e = 0.05 and z = 1.96. So, the sample size comes out to be 385. The minimum number of respondents required was 385; however, data were collected from 1771 respondents, more than four times the required number.

A thorough screening was conducted as part of the data analysis setup. Values that were missing were identified. A few participants did not fully complete the questionnaire and the Google form. Their responses were removed from the entire dataset because they were hard to get back in touch with. Additionally, the questions in the Excel file were renamed GP_1, GP_2, GP_3, VB_4 and so on for easy analysis. The data were also free of outliers because it was measured using the Likert scale. All potential statistical errors, including multicollinearity, correlation, missing values and common method variance, were checked for in the data.

Ethical considerations

The investigators ensured ethical standards and formal clearance procedures were followed. Researchers prioritized consent, privacy and voluntary participation. Participants were fully informed and could leave the survey at any time. No personal identifiers such as names, residential address, phone numbers were not collected, therefore, reducing the risk of data loss or theft.

Analysis and results

As noted in the preceding section, all the constructs considered in the questionnaire survey were derived from prior investigations. Analysis was done using two software, namely SPSS 29 and Smart PLS 4. Tables related to descriptive statistics, eigenvalues and total variance explained were derived using SPSS. Whereas output of the measurement model, internal consistency, convergent validity, composite reliability, average variance extracted, Fornell–Larcker criterion, HTMT ratio, VIF values, model fitness and direct and indirect effect relationship were obtained using Smart PLS 4 software. Beginning with the SPSS results, the descriptive statistics of the sample are below.

Table 3 provides descriptive statistics (minimum, maximum, mean and standard deviation). For green products, voter attitude, voter behavior and competitive advantage, respondents gave average scores of 4.6220, 4.2801, 3.8634 and 4.1830, respectively. Each variable’s minimum and maximum values indicate that the data are standard. In the same order, as previously explained, the standard deviation for the data about the variables is 0.58458, 0.77301, 0.93124 and 0.65899.

Table 3 Descriptive statistics.

Table 4 demonstrates the eigenvalues and total variance explained. The extraction method of factor analysis used in this study is principal component analysis. Before extraction, thirteen linear components are identified within the dataset. After extraction and rotation, there are three distinct linear components within the dataset for the eigenvalue > 1. The three factors are extracted, accounting for 71.471% of the total variance. It is suggested that the proportion of the total variance explained by the retained factors should be at least 50% [92]. Therefore, the need to conduct Harman’s single-factor test did not arise. The result shows that 71.471% of the common variance shared by thirteen variables can be accounted for by three factors. This initial solution suggests that the final solution will extract not more than three factors. The first component has explained 45.26% of the total variance with an eigenvalue of 5.88. The second component has explained a 17.658% variance with an eigenvalue of 2.295. The third component has explained an 8.547% variance with an eigenvalue of 1.111.

Table 4 Eigenvalues (E.V.) and total variance explained.

Further, SmartPls 4 software was used in the study because it has a small sample size and uses latent variables, so PLS-SEM works well. Furthermore, the assumption of data distribution in PLS-SEM is more flexible than in CB-SEM. With SEM, marketers can visually analyze the relationships between relevant variables in order to allocate resources more effectively and provide better customer service [56]. Therefore, in this article, authors have used SEM to provide political marketers with a better understanding of the various marketing strategies they use. The examination estimated two kinds of validity: convergent and discriminant validity. Convergent validity is typically discovered in a measurement model by investigating the outer loadings, average variance extracted (AVE) and composite reliability (C.R.). To establish convergent validity, the loadings must be greater than 0.5, and the C.R. and AVE must be greater than 0.7 and 0.5, respectively. The discriminant validity is examined by the heterotrait–monotrait (HTMT) ratio and the method of the Fornell–Larcker criterion. The output of the measurement model is given in Fig. 2 and Tables 5, 6 and 7.

Fig. 2
figure 2

Structural model assessment. Source: The authors

Table 5 Internal consistency, convergent validity, composite reliability and average variance extracted (AVE).
Table 6 Fornell–Larcker criterion.
Table 7 Heterotrait–monotrait (HTMT) ratio.

Table 5 shows that the loadings of all items are greater than 0.6, the C.R. values for all variables are greater than 0.7, and the AVE values are greater than 0.5, as recommended by [76]. As a result, this study establishes the validity of convergence.

Before presenting the findings, the researcher must also establish discriminant validity. This is done to ensure that the latent constructs being used to assess the causal relationships being investigated are, in fact, distinct from one another. For this, the Fornell–Larcker criterion and HTMT criterion have been used.

The Fornell–Larcker criterion compares the correlation of latent constructs with the square root of the average variance extracted (AVE). Instead of the variance of other latent constructs, a latent construct should be able to explain the variance of its indicator better. As a result, the correlations with other latent constructs should be smaller than the square root of each construct’s AVE. This measurement model’s discriminant validity is acceptable as the correlations with other latent constructs are smaller than the square root of each construct's AVE, highlighted using bold style and asterisk, and support the discriminant validity between the constructs (Table 6).

The heterotrait–monotrait ratio also demonstrates that this study established discriminant validity because all HTMT ratio values are less than 0.90 (referring to Table 7). In this study, both the validity measures (convergent and discriminant validity) were established.

Multicollinearity occurs when two or more independent variables in a data frame highly correlate in a regression model. This means that one independent variable can be predicted from another in a regression model [30]. When VIF values are more than 5 to 10, the problem of multicollinearity arises [53]. The VIF range of the predictor variables was between 1.628 and 3.609, as shown in Table 8, which is within an acceptable range. Therefore, in this study, the problem of multicollinearity did not exist.

Table 9 explains the SRMR and NFI criteria that are used for checking model fitness. The difference between the observed correlation and the model-implied correlation matrix is the SRMR. It enables the evaluation of the average magnitude of the differences between the observed and expected correlations as an absolute criterion for model fit. Meanwhile, NFI calculates the suggested model’s Chi2 value and assesses it against a significant reference point. In the model, the SRMR value is 0.065, which is less than the threshold limit of 0.08 [48], and the NFI value is 0.805, which is greater than the required minimum value of 0.8 [12]. As both the values are within the acceptable limit, the model is fit for analysis.

Table 8 VIF score of indicators.
Table 9 Assessments of model fitness.

Structural model evaluation

To test the significance of the path coefficients, the bootstrapping technique was used [90]. A bootstrapping method with 5,000 resamples was used to examine the t-values. Figure 2 and Tables 10 and 11 show the results of the structural model.

Table 10 Structural model assessment direct effect.
Table 11 Structural model assessment indirect effect (mediation effects).

The results of the direct effects are shown in Table 10. The results showed that G.P. (green product of political party) has a significant and positive effect on V.A. (voter attitude) with beta value = 0.692, t = 13.460 and p = 0.000, and V.A. (voter attitude) also has a significant and positive effect on V.B. (voter behavior) with beta value = 0.855, t = 18.130 and p = 0.000. However, G.P. does not have a significant relationship with V.B. where beta value = − 0.061, t = 1.040 and p = 0.299. Hence, H1 and H3 are supported, but H2 is not.

Additionally, in Fig. 2 outer model results were displayed due to which each indicator linked to a variable has a p value of 0.000, indicating a significant correlation between the indicators and the variable. On the other hand, the negative coefficient value of -0.061 between the green product and voter behavior indicates an insignificant connection between the two.

Table 11 shows the results of V.A.’s mediating role in the relationship between G.P. and V.B. VA significantly mediates the relationship, according to the findings. G.P. with V.B. (beta = 0.591, t = 9.036 and p = 0.000). Hence, H4 is supported. It also demonstrates that G.A. plays an important mediating role in the relationship. It is an example of full mediation, where the mediating variable leads to zero direct effect between the independent and outcome variables.

Discussion

The study’s findings suggest that green products (β = 0.692) significantly improve Indian voters’ perceptions of the political brand that takes green initiative. To summarize, G.P. favorably influences voter attitudes (H1: G.P.— > V.A., β = 0.692, p-value = 0.000). This indicates that when people learn that a political party actively produces both conventional and green products, they are willing to vote for that political brand. This outcome is consistent with earlier research in the green marketing area, which has tested the impact of green marketing activities of business houses on consumers’ attitudes [4, 64, 66, 88].

Moreover, voter attitude (β = 0.885) predicts the strong likelihood that Indian voters will support a political brand that creates environmentally friendly goods. This finding indicates that V.A. and V.B. have a positively significant relationship (H3: V.A.— > V.B., β = 0.855, p-value = 0.000). Voters’ positive views of political brands undoubtedly influence their decision to support them. Therefore, to draw in voters, a brand must cultivate a positive self-image that will eventually translate into actual behavior represented in the form of votes. As a result, it can be claimed that a positive attitude is essential to convert a prospective customer into an actual customer [9, 42, 87, 108].

Additionally, the findings indicated that the green product was not positively correlated with voters’ behavior to vote toward the brand; there was no statistically significant relationship between green product and voter behavior (H2: G.P.— > V.B., β = − 0.061, p-value = 0.299). The inference is that Indian voters’ intentions to vote for a brand are not directly affected by a political brand’s green action. This is highly unexpected, but how could it have happened? This result conflicts with earlier studies [67, 68, 89]. Consequently, it can be explained that in an emerging country like India, traditional political products are plenty more than green political products, or rural areas are still not as polluted as urban areas, so voters do not pay much attention to urban issues such as pollution.

Furthermore, a significant indirect relationship was observed between green products and voter behavior (H4: G.P.—> V.A.—> V.B., β = 0.591, p = 0.000); voter attitude was a mediating variable that examined the link between green products and voter behavior [78, 83]. This can also be explained as changing voter behavior; political marketers should first focus on changing potential voters’ attitudes. They should provide evidence of the actual work related to the promises made to the electoral, which will help generate a positive attitude that precedes positive behavior.

The authors of this study were unable to identify the attitude–behavior gap that is typically present for goods or services marketed as green, which can be explained through an example of electric vehicles, although most consumers have a positive attitude toward electric vehicles because they are more environmentally friendly; still, less number of people buy them. Absenteeism of the attitude–behavior gap in this study was statistically established using hypothesis 3, which showed a significant relationship between voter attitude and behavior, indicating that voters’ attitudes do translate into actual behavior.

Conclusion

Political marketing is still in its infancy and has not received much attention from scholars recently, particularly in India. Thus, by carrying out this study, the authors have substantially contributed to the academic field. This study exemplifies the effective application of interdisciplinary research by combining two separate marketing domains: political and green marketing. In order to obtain a deeper understanding of the thoughts of political consumers, this study integrated environmental studies, marketing and political science in a multidisciplinary manner. Parties can modify their electoral strategies to target environmentally conscious voters by emphasizing eco-friendly projects and sustainable methods. They can make the best use of their financial resources by paying closer attention to political consumers.

Findings

The study looked at how voters’ attitudes and behaviors were affected by the creation of green products by political brands. The findings of this study indicated that creating environmentally friendly products would positively influence voters’ attitudes and change their behaviors toward the brand. All of this results from India’s younger generation having a high literacy rate, which makes them more conscious of environmental issues and are likely to take protective measures. Additionally, the recent pandemic has increased awareness of this issue across all age groups, compelling governments and corporations to take proactive measures that are environmentally friendly.

However, the result also revealed a negative direct relationship between green products and voters’ behavior. Through this, political parties should understand that, although enacting green policies influences voters’ opinions, people will only back a political brand if they witness real results; in other words, political brands should focus on fulfilling their electoral pledges.

Limitations and future scope

This study was limited to the Indian population; therefore, it is impossible to extrapolate the findings to other nations. It is hoped that more cities, countries and people of all ages will be included. Ultimately, the study was limited to analyzing the self-stated voting intentions of the participants; therefore, future research should concentrate on evaluating the actual voter behavior and contrasting it with the stated one.

Availability of data and materials

Data will be made available on reasonable request.

Abbreviations

AVE:

Average variance extracted

C.R.:

Composite reliability

STD:

Standard deviation

V.A:

Voter attitude

V.B:

Voter behavior

G.P:

Green product

t :

T value

p :

p value

H1:

Hypothesis 1

H2:

Hypothesis 2

H3:

Hypothesis 3

H4:

Hypothesis 4

GP_1:

Statement 1 of variable green product

GP_2:

Statement 2 of variable green product

GP_3:

Statement 3 of variable green product

VA_1:

Statement 1 of variable voter attitude

VA_2:

Statement 2 of variable voter attitude

VA_3:

Statement 3 of variable voter attitude

VA_4:

Statement 4 of variable voter attitude

VA_5:

Statement 5 of variable voter attitude

VA_6:

Statement 6 of variable voter attitude

VB_1:

Statement 1 of variable voter behavior

VB_2:

Statement 2 of variable voter behavior

VB_3:

Statement 3 of variable voter behavior

VB_4:

Statement 4 of variable voter behavior

VIF values:

Variance inflation factor

HTMT:

Heterotrait–monotrait ratio of correlations

References

  1. Aam Aadmi Party (2019) Nine measures Arvind Kejriwal’s government has taken to reduce air pollution in Delhi by 25%. https://aamaadmiparty.org/nine-measures-arvind-kejriwals-government-has-taken-to-reduce-air-pollution-in-delhi-by-25/

  2. Ajzen I (1975) Belief, attitude, intention and behaviour: An introduction to theory and research

  3. Anand IM, Gaur SS (2019) Consequences of consumers’ emotional responses to government’s green initiatives: insights from a scenario-based experimental study. Manag Environ Qual Int J 30(1):243–259. https://doi.org/10.1108/MEQ-02-2018-0045

    Article  Google Scholar 

  4. Ansu-Mensah P (2021) Green product awareness effect on green purchase intentions of university students’: an emerging market’s perspective. Future Bus J. https://doi.org/10.1186/s43093-021-00094-5

    Article  Google Scholar 

  5. Argyriou E, Melewar TC (2011) Consumer attitudes revisited: a review of attitude theory in marketing research. Int J Manag Rev 13(4):431–451. https://doi.org/10.1111/j.1468-2370.2011.00299.x

    Article  Google Scholar 

  6. Baccini L, Leemann L (2021) Do natural disasters help the environment? How voters respond and what that means. Polit Sci Res Methods 9(3):468–484. https://doi.org/10.1017/psrm.2020.25

    Article  Google Scholar 

  7. Baines PR, Harris P, Lewis BR (2002) The political marketing planning process: improving image and message in strategic target areas. Mark Intell Plan 20(1):6–14. https://doi.org/10.1108/02634500210414710

    Article  Google Scholar 

  8. Baldassare M, Katz C (1992) The personal threat of environmental problems as predictor of environmental practices. Environ Behav 24(5):602–616. https://doi.org/10.1177/0013916592245002

    Article  Google Scholar 

  9. Baldinger AL, Rubinson J (1996) Brand loyalty: the link between attitude and behavior. J Advert Res 36:22–34

    Google Scholar 

  10. Bechtel MM, Hainmueller J (2011) How lasting is voter gratitude? An analysis of the short- and long-term electoral returns to beneficial policy. Am J Polit Sci 55(4):852–868. https://doi.org/10.1111/j.1540-5907.2011.00533.x

    Article  Google Scholar 

  11. Bell D, Gray T, Haggett C (2005) The “social gap” in wind farm siting decisions: explanations and policy responses. Environ Polit 14(4):460–477. https://doi.org/10.1080/09644010500175833

    Article  Google Scholar 

  12. Bentler PM, Bonett DG (1980) Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull 88(3):588–606. https://doi.org/10.1037/0033-2909.88.3.588

    Article  Google Scholar 

  13. Bhattacharya S (1977) The indigo revolt of Bengal. Soc Sci 5(12):13–23. https://doi.org/10.2307/3516809

    Article  Google Scholar 

  14. Bigi A (2017) Political marketing: understanding and managing stance and brand positioning. KTH Royal Institute of Technology

    Google Scholar 

  15. Blake DE (2001) Contextual effects on environmental attitudes and behavior. Environ Behav 33(5):708–725. https://doi.org/10.1177/00139160121973205

    Article  Google Scholar 

  16. Boonen J, Meeusen C, Quintelier E (2014) The link between social attitudes and voting propensities: attitude-vote consistency among adolescents in Belgium. Elect Stud 36:81–93. https://doi.org/10.1016/j.electstud.2014.07.002

    Article  Google Scholar 

  17. Bravo G, Farjam M (2022) Actions speak louder than words: attitudes, behaviour, and partisan identity in a polarised environmental domain. Energy Res Soc Sci. https://doi.org/10.1016/j.erss.2022.102547

    Article  Google Scholar 

  18. Butler SM, Francis S (1997) The effects of environmental attitudes on apparel purchasing behavior. Cloth Text Res J 15(2):76–85. https://doi.org/10.1177/0887302X9701500202

    Article  Google Scholar 

  19. Carrington MJ, Whitwell GJ (2014) Lost in translation: exploring the ethical consumer intention-behavior gap. J Bus Res 67(1):2759–2767. https://doi.org/10.1016/j.jbusres.2012.09.022

    Article  Google Scholar 

  20. Carroll P, Casswell S, Huakau J, Perry P, Chapman PH (2009) Environmental attitudes, beliefs about social justice and intention to vote green: Lessons for the New Zealand green party? Environ Polit 18(2):257–278. https://doi.org/10.1080/09644010802682635

    Article  Google Scholar 

  21. Casaló LV, Escario JJ (2018) Heterogeneity in the association between environmental attitudes and pro-environmental behavior: a multilevel regression approach. J Clean Prod 175:155–163. https://doi.org/10.1016/j.jclepro.2017.11.237

    Article  Google Scholar 

  22. Chan RYK (1999) Environmental attitudes and behavior of consumers in china: survey findings and implications. J Int Consum Mark 11(4):25–52. https://doi.org/10.1300/J046v11n04_03

  23. Chan RYK (1999) Environmental attitudes and behavior of consumers in china: survey findings and implications. J Int Consum Mark 11(4):25–52. https://doi.org/10.1300/J046v11n04_03

    Article  Google Scholar 

  24. Chen CC, Chen CW, Tung YC (2018) Exploring the consumer behavior of intention to purchase green products in belt and road countries: an empirical analysis. Sustainability. https://doi.org/10.3390/su10030854

    Article  Google Scholar 

  25. Chen YS, Huang AF, Wang TY, Chen YR (2020) Greenwash and green purchase behaviour: the mediation of green brand image and green brand loyalty. Total Qual Manag Bus Excell 31(1–2):194–209. https://doi.org/10.1080/14783363.2018.1426450

    Article  Google Scholar 

  26. Cochran WG (1977) Sampling techniques. Wiley

    Google Scholar 

  27. Cohen BC (2015) Press and foreign policy, vol 221. Princeton University Press

    Google Scholar 

  28. Colvin RM, Jotzo F (2021) Australian voters’ attitudes to climate action and their social-political determinants. PLoS ONE. https://doi.org/10.1371/journal.pone.0248268

    Article  Google Scholar 

  29. Cottrrel SP (2003) Influence of sociodemographics and environmental attitudes on general responsible environmental behavior among recreational boaters. Environ Behav 35(3):347–375. https://doi.org/10.1177/0013916503035003003

    Article  Google Scholar 

  30. Daoud JI (2018) Multicollinearity and regression analysis. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/949/1/012009

    Article  Google Scholar 

  31. DasGupta K, Sarkar S (2022) Linking political brand image and voter perception in India: a political market orientation approach. J Public Aff. https://doi.org/10.1002/pa.2751

    Article  Google Scholar 

  32. Tewari DD (1995) The chipko: the dialectics of economics and environment. Dialect Anthropol 20(2):133–168

    Article  Google Scholar 

  33. Edwards B, Lomax N (2012) “For a richer New Zealand”: environmentalism and the green party in the 2011 New Zealand general election. Environ Polit 21(6):994–1000. https://doi.org/10.1080/09644016.2012.724219

    Article  Google Scholar 

  34. Freestone OM, McGoldrick PJ (2007) Ethical positioning and political marketing: the ethical awareness and concerns of U.K. voters. J Mark Manag 23(7–8):651–673. https://doi.org/10.1362/026725707x229984

    Article  Google Scholar 

  35. Gallego J (2018) Natural disasters and clientelism: the case of floods and landslides in Colombia. Elect Stud 55:73–88. https://doi.org/10.1016/j.electstud.2018.08.001

    Article  Google Scholar 

  36. Ganguli B (2023) India has over 94.50 crore voters as on January 1: Government. The Economic Times. https://economictimes.indiatimes.com/news/india/india-has-over-94-50-crore-voters-as-on-jan-1-government/articleshow/97559222.cms?from=mdr

  37. Gauja A, Jackson S (2016) Australian greens party members and supporters: their profiles and activities. Environ Polit 25(2):359–379. https://doi.org/10.1080/09644016.2015.1104803

    Article  Google Scholar 

  38. Gilardi F, Gessler T, Kubli M, Müller S (2021) Social media and political agenda setting. Polit Commun 39(1):39–60. https://doi.org/10.1080/10584609.2021

    Article  Google Scholar 

  39. Gill JD, Crosby LA, Taylor JR (1986) Ecological concern, attitudes, and social norms in voting behavior. Public Opin Q 50(4):537–554. https://doi.org/10.1086/269002

    Article  Google Scholar 

  40. Grossman E (2022) Media and Policy making in the digital age. Annu Rev Polit Sci 2022(25):443–461. https://doi.org/10.1146/annurev-polisci-051120

    Article  Google Scholar 

  41. Guha A, Joe T, Joe ET (2019) “Environment” in the election manifestos. Econ Pol Wkly 54(9):13–16

    Google Scholar 

  42. Ha H-Y, Janda S, Muthaly SK (2010) A new understanding of satisfaction model in e-re-purchase situation. Eur J Mark 44(7/8):997–1016. https://doi.org/10.1108/03090561011047490

    Article  Google Scholar 

  43. Harris P, Fury D, Lock A (2006) Do political parties and the press influence the public agenda? A content analysis of press coverage of the 2001 U.K. general election. J Polit Mark 5(3):1–28. https://doi.org/10.1300/J199v05n03_01

    Article  Google Scholar 

  44. Hazarika B (2015) Voting behaviour in India and its determinants. IOSR J Humanit Soc Sci 20(10):22–25. https://doi.org/10.9790/0837-201042225

    Article  Google Scholar 

  45. Hertog S, Gerland P, Wilmoth J (2023) India overtakes China as the world’s most populous country. United Nations. https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/undesa_pd_2023_policy-brief-153.pdf

  46. Hines JM, Hungerford HR, Tomera AN (1987) Analysis and synthesis of research on responsible environmental behavior: a meta-analysis. J Environ Educ 18(2):1–8. https://doi.org/10.1080/00958964.1987.9943482

    Article  Google Scholar 

  47. Holbert RL, Kwak N, Shah DV (2003) Environmental concern, patterns of television viewing, and pro-environmental behaviors: integrating models of media consumption and effects. J Broadcast Electron Media 47(2):177–196. https://doi.org/10.1207/s15506878jobem4702_2

    Article  Google Scholar 

  48. Hu LT, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Model 6(1):1–55. https://doi.org/10.1080/10705519909540118

    Article  Google Scholar 

  49. Kahn ME (2002) Demographic change and the demand for environmental regulation. J Policy Anal Manag 21(1):45–62. https://doi.org/10.1002/pam.1039

    Article  Google Scholar 

  50. Karwa O, Goyal Y, Deo S (2022) Manifestos as a tool for accountability: a content analysis of the 2004–2019 UPA and NDA poll manifestos. In: Observer Research Foundation (no 535). https://www.orfonline.org/research/manifestos-as-a-tool-for-accountability/

  51. Kaushal N, Babu S, Mishra A, Ghosh N, Tare V, Kumar R, Sinha PK, Verma RU (2019) Towards a healthy Ganga-improving river flows through understanding trade offs. Front Environ Sci. https://doi.org/10.3389/fenvs.2019.00083

    Article  Google Scholar 

  52. Kennedy EH, Beckley TM, Mcfarlane BL, Nadeau S (2009) Why we don’t “walk the talk”: understanding the environmental values/behaviour gap in Canada. Source Hum Ecol Rev 16(2):151–160

    Google Scholar 

  53. Kim JH (2019) Multicollinearity and misleading statistical results. Korean J Anesthesiol 72(6):558–569. https://doi.org/10.4097/kja.19087

    Article  Google Scholar 

  54. Kotler P, Armstrong G (1994) Marketing management, analysis, planning, implementation, and control. Prentice-Hall International

    Google Scholar 

  55. Kumari S, Lakhani A, Kumari KM (2020) Covid-19 and air pollution in indian cities: world’s most polluted cities. Aerosol Air Qual Res 20(12):2592–2603. https://doi.org/10.4209/aaqr.2020.05.0262

    Article  Google Scholar 

  56. Kwong K, Wong K (2013) Partial least square structural equation modeling (PLS-SEM) techniques using SmartPLS. Mark Bull 24:1–32

    Google Scholar 

  57. Lahiri AK (2015) Green politics and the Indian middle class. Econ Polit Wkly 50(43):35–42

    Google Scholar 

  58. Langer AI, Gruber JB (2021) Political agenda setting in the hybrid media system: why legacy media still matter a great deal. Int J Press/Polit 26(2):313–340. https://doi.org/10.1177/1940161220925023

    Article  Google Scholar 

  59. Lasswell HD (1971) Propaganda technique in the world war 1. MIT Press

    Google Scholar 

  60. Levine DS, Strube MJ (2012) Environmental attitudes, knowledge, intentions and behaviors among college students. J Soc Psychol 152(3):308–326. https://doi.org/10.1080/00224545.2011.604363

    Article  Google Scholar 

  61. Lippmann W (2017) Public opinion, 1st edn. Routledge. https://doi.org/10.4324/9781315127736

    Book  Google Scholar 

  62. Lowrey W (2004) Media dependency during a large-scale social disruption: the case of September 11. Mass Commun Soc 7(3):339–357. https://doi.org/10.1207/s15327825mcs0703_5

    Article  Google Scholar 

  63. Maiti P (2001) “Till the rivers all run dry”: a human ecological analysis of the Narmada Bachao Andolan. Electron Green J. https://doi.org/10.5070/G311410418

    Article  Google Scholar 

  64. Mehta P, Chahal Singh H (2021) Consumer attitude towards green products: revisiting the profile of green consumers using segmentation approach. Manag Environ Qual 32(5):902–928. https://doi.org/10.1108/MEQ-07-2020-0133

    Article  Google Scholar 

  65. Moraes C, Carrigan M, Szmigin I (2012) The coherence of inconsistencies: attitude-behaviour gaps and new consumption communities. J Mark Manag 28(1–2):103–128. https://doi.org/10.1080/0267257X.2011.615482

    Article  Google Scholar 

  66. Morel M, Kwakye F, Hultén P (2012) Green marketing: consumers’ attitudes towards eco-friendly products and purchase intention in the fast moving consumer goods (FMCG) sector [Umea School of Business]. https://www.diva-portal.org/smash/get/diva2:553342/FULLTEXT01

  67. Ogiemwonyi O, Bin HA (2020) Green product awareness has the potential to promote green consumer behaviour: evidence from Kuala-Lumpur. Israel J Ecol Eval. https://brill.com/view/journals/ijee/67/1-2/article-p39_39.xml

  68. Papp Z (2022) Environmental attitudes, environmental problems and party choice: a large-N comparative study. Polit Geogr. https://doi.org/10.1016/j.polgeo.2022.102652

    Article  Google Scholar 

  69. Park CS, Gil De Zúñiga H (2021) Learning about politics from mass media and social media: moderating roles of press freedom and public service broadcasting in 11 countries. Int J Public Opin Res 33(2):315–335. https://doi.org/10.1093/ijpor/edaa021

    Article  Google Scholar 

  70. Parveen N, Siddiqui L, Sarif MN, Islam MS, Khanam N, Mohibul S (2021) Industries in Delhi: air pollution versus respiratory morbidities. Process Saf Environ Prot 152:495–512. https://doi.org/10.1016/j.psep.2021.06.027

    Article  Google Scholar 

  71. Patulny R, Norris K (2005) Sustaining interest: are green values converting to votes? Int J Environ Workplace Employ 1(3/4):296–309

    Article  Google Scholar 

  72. Paul D (2017) Research on heavy metal pollution of river Ganga: a review. Ann Agrar Sci 15(2):278–286. https://doi.org/10.1016/j.aasci.2017.04.001

    Article  Google Scholar 

  73. Pegu UK (2017) Environment as news: an analysis on the challenges in environmental reporting. AGU Int J Manag Stud Res 5. http://www.aguijmsr.com

  74. PIB Delhi (2022) United Nations recognizes namami gange initiative as one of the top 10 world restoration flagships to revive the natural world. Ministry of Jal Shakti. https://pib.gov.in/PressReleasePage.aspx?PRID=1883661

  75. Pich C, Dean D (2015) Political branding: Sense of identity or identity crisis? An investigation of the transfer potential of the brand identity prism to the U.K. conservative party. J Mark Manag 31(11–12):1353–1378. https://doi.org/10.1080/0267257X.2015.1018307

    Article  Google Scholar 

  76. Ramayah T, Yeap JA, Ahmad NH, Rahman SA (2017) Testing a confirmatory model of facebook usage in SmartPLS using consistent PLS. Int J Bus Innov 3(2):1–14

    Google Scholar 

  77. Ramos R, Sanz C (2020) Backing the incumbent in difficult times: the electoral impact of wildfires. Comp Pol Stud 53(3–4):469–499. https://doi.org/10.1177/0010414019858959

    Article  Google Scholar 

  78. Riskos K, Dekoulou P, Mylonas N, Tsourvakas G (2021) Ecolabels and the attitude–behavior relationship towards green product purchase: a multiple mediation model. Sustainability. https://doi.org/10.3390/su13126867

    Article  Google Scholar 

  79. Romeijn J (2020) Do political parties listen to the(ir) public? Public opinion–party linkage on specific policy issues. Party Polit 26(4):426–436. https://doi.org/10.1177/1354068818787346

    Article  Google Scholar 

  80. Rüdig W (2012) The perennial success of the German greens. Environ Polit 21(1):108–130. https://doi.org/10.1080/09644016.2011.643371

    Article  Google Scholar 

  81. Rudman LA, McLean MC, Bunzl M (2013) When truth is personally inconvenient, attitudes change: the impact of extreme weather on implicit support for green politicians and explicit climate-change beliefs. Psychol Sci 24(11):2290–2296. https://doi.org/10.1177/0956797613492775

    Article  Google Scholar 

  82. Sadiq M, Adil M, Paul J (2022) Eco-friendly hotel stay and environmental attitude: a value-attitude-behaviour perspective. Int J Hosp Manag. https://doi.org/10.1016/j.ijhm.2021.103094

    Article  Google Scholar 

  83. Sagung AA, Wulandari A, Rahyuda K, Ni N, Kerti Y (2015) The role of customer attitude in mediating knowledge influence towards the purchase intention of green product. JDM 6(2):133–144

    Google Scholar 

  84. Sarkar P (2017) Role of media in strengthening democracy in India. J Adv Res Jour Mass Commun 4(3 & 4):111–115

    Google Scholar 

  85. Schumacher I (2014) An empirical study of the determinants of green party voting. Ecol Econ 105:306–318. https://doi.org/10.1016/j.ecolecon.2014.05.007

    Article  Google Scholar 

  86. Scott D, Willits FK (1994) Environmental attitudes and behavior: a Pennsylvania survey. Environ Behav 26(2):239–260. https://doi.org/10.1177/001391659402600206

    Article  Google Scholar 

  87. Sharma AP (2021) Consumers’ purchase behaviour and green marketing: a synthesis, review and agenda. Int J Consum Stud 45(6):1217–1238. https://doi.org/10.1111/ijcs.12722

    Article  Google Scholar 

  88. Sharma B, Gadenne D (2014) Consumers’ attitudes, green practices, demographic and social influences, and government policies: an empirical investigation of their relationships. J New Bus Ideas Trends 12(2):22–36

    Google Scholar 

  89. Shazia Bukhari S (2011) Green marketing and its impact on consumer behavior. Eur J Bus Manag 3(4):375–383

    Google Scholar 

  90. Shiau WL, Sarstedt M, Hair JF (2019) Internet research using partial least squares structural equation modeling (PLS-SEM). In: Internet research. Emerald Group Publishing Ltd., vol 29, no 3, pp 398–406

  91. Shim S, Kim J, Na Y (2018) An exploratory study on up-cycling as the sustainable clothing life at home. Fashion and Textiles 5(1). https://doi.org/10.1186/s40691-018-0129-1

  92. Shrestha N (2021) Factor analysis as a tool for survey analysis. Am J Appl Math Stat 9(1):4–11. https://doi.org/10.12691/ajams-9-1-2

    Article  Google Scholar 

  93. Singhania K, Girish GP, Nnaemeka Vincent E (2016) Impact of odd-even rationing of vehicular movement in Delhi on air pollution levels. Low Carbon Econ 07(04):151–160. https://doi.org/10.4236/lce.2016.74014

    Article  Google Scholar 

  94. Sircar N (2018) Money in elections. In: Costs of democracy. Oxford University Press, pp 36–73. https://doi.org/10.1093/oso/9780199487271.003.0003

  95. Suhaily L, Darmoyo S (2019) Effect of green product and green advertising to satisfaction and loyalty which mediated by purchase decision. Int J Contemp Appl Res 6(1):44–57

    Google Scholar 

  96. Terlau W, Hirsch D (2015) Sustainable consumption and the attitude-behaviour-gap phenomenon—causes and measurements towards a sustainable development. Int J Food Syst Dyn 6(3):159–174. https://doi.org/10.18461/ijfsd.v6i3.634

    Article  Google Scholar 

  97. Terry JP, Jia G, Boldi R, Khan S (2018) The Delhi’ gas chamber’: smog, air pollution and the health emergency of November 2017. Weather 73(11):348–352. https://doi.org/10.1002/wea.3242

    Article  Google Scholar 

  98. Vaske JJ, Donnelly MP (1999) A value-attitude–behavior model predicting wildland preservation voting intentions. Soc Nat Resour 12(6):523–537. https://doi.org/10.1080/089419299279425

    Article  Google Scholar 

  99. Velez Y, Martin D (2013) Sandy the rainmaker: the electoral impact of a super storm. P.S.: Political Science and Politics 46(2):313–323. https://doi.org/10.1017/S1049096513000139

    Article  Google Scholar 

  100. Verma R (2022) Promises that matter to indian democracy: a study of election manifestos since 1952. https://cprindia.org/briefsreports/promises-that-matter-to-indian-democracy-a-study-of-election-manifestos-since-1952/

  101. Vermeir I, Weijters B, De Houwer J, Geuens M, Slabbinck H, Spruyt A, Van Kerckhove A, Van Lippevelde W, De Steur H, Verbeke W (2020) Environmentally sustainable food consumption: a review and research agenda from a goal-directed perspective. In Frontiers in Ppsychology, vol 11. Frontiers Media SA. https://doi.org/10.3389/fpsyg.2020.01603

  102. Whitten-Woodring J (2009) Watchdog or lapdog? Media freedom, regime type, and government respect for human rights. Int Stud Q 53(3):595–625. https://doi.org/10.1111/j.1468-2478.2009.00548.x

    Article  Google Scholar 

  103. Widaya Mohamed Nawi N, Agil Alsagoff S, Nizam Osman M, Abdullah Z (2020) New media use among youth in malaysia: a media dependency theory perspective. PalArch’s J Archaeol Egypt/Egyptol 17(9):3097–3112

    Google Scholar 

  104. Witek L (2019) Attitude-behaviour gap among polish consumers regarding green purchases. Visegr J Bioecon Sustain Dev 8(1):31–36. https://doi.org/10.2478/vjbsd-2019-0006

    Article  Google Scholar 

  105. Wong V, Turner W, Stoneman P (1996) Marketing strategies and market prospects for environment ally-friendly consumer products 1. Br J Manag 7:263–281

    Article  Google Scholar 

  106. Yadav P, Agarwal B, Mathew J (2023) Green marketing strategy adoption by indian political party brands with special emphasis on marketing mix. Indian J Mark 53(5):57–69. https://doi.org/10.17010/ijom/2023/v53/i5/172727

    Article  Google Scholar 

  107. Yamoah FA, Acquaye A (2019) Unravelling the attitude-behaviour gap paradox for sustainable food consumption: insight from the U.K. apple market. J Clean Prod 217:172–184. https://doi.org/10.1016/j.jclepro.2019.01.094

    Article  Google Scholar 

  108. Zsóka Á, Ásványi K (2022) Transforming students’ behaviour preferences: achievable changes by a sustainability course. Int J Sustain High Educ 24(1):141–159. https://doi.org/10.1108/IJSHE-01-2022-0018

    Article  Google Scholar 

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PY developed the idea and identified the research gap, BA designed the research methodology, and JM did the analysis.

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Yadav, P., Agarwal, B. & Mathew, J. Indian voters’ attitude and behavior toward a political brand for producing green products: a mediation model. Futur Bus J 10, 41 (2024). https://doi.org/10.1186/s43093-024-00327-3

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