Research Article - (2025) Volume 20, Issue 4

Designing a Green Marketing Model in the Religious Tourism Industry: A Mixed Method Strategy

Keywords

Green marketing, mixed-methods research, religious tourism, sustainability.

Introduction

The COVID-19 pandemic has affected developed and emerging economies in different ways. For Iraq, the crisis had profound economic impacts, largely due to the sharp decline in oil prices that fund over 90% of the country's budget (Jassim et al., 2023). Such challenges underscore the urgent need to identify alternative sources of revenue to overcome the limitations of an oil-dependent economy. Predicted future global epidemic waves are capable of further restricting economic activity on an international scale, translating to less oil demand and imposing huge risks to Iraq's economic stability (Rahbar & Waheed, 2010). Iraq hence needs to diversify its economy through the integration of more sources of revenue. To do this, more efforts from within will be necessary complemented by leveraging lessons derived from nations that have already conquered comparable situations. Religious tourism has emerged as a most viable alternative, especially when keeping in perspective the fact that the industrial and agricultural sectors require significant time and effort for their development. An objective analysis, coupled with comparisons with countries that have successfully exploited this sector, indicates that Iraq has considerable potential to achieve a leading position in the field of religious tourism. The country is home to numerous religious shrines spread across various cities (Ting et al., 2019). Moreover, amid political unrest that has slowed down the progress and launch of important economic projects, religious tourism can serve as a means to alleviate some of these challenges. Iraq can benefit from the valuable experiences of nations like Iran and Syria, who have succeeded in making religious tourism an effective economic instrument.

The significance of religious tourism in Iraq is highlighted by the fact that it has the potential to generate foreign exchange, thus stabilizing the domestic currency, making it more valuable, and enabling improved exchange rates (Jassim et al., 2023). Religious tourism also assists in the provision of employment opportunities, mitigating unemployment, diversifying income sources, and lessening Iraq's overdependence on oil as the primary source of national income. If strategically developed, Iraq’s religious tourism sector could generate up to $12 billion annually, as religious ceremonies occur throughout the lunar year and are not limited to the months of Muharram and Safar (Eslami et al., 2021). This research operates on the hypothesis that Iraq’s tourism sector remains underdeveloped, and its contribution to the national GDP does not reflect the country’s rich religious and historical heritage. A well-structured, strategic focus on this sector could unlock significant economic potential, with the possibility of generating billions of dollars in annual revenue. The primary objective of this study is to underscore the importance of tourism as a key economic driver in developing countries—many of which lack Iraq’s inherent tourism potential but have nonetheless succeeded in leveraging the industry. Accordingly, the central research question is:

RQ: What constitutes a green marketing model for the religious tourism industry in Iraq, and what are its dimensions, components, and critical relationships?

Literature Review

Theoretical Framework

Green Marketing

Green marketing is branched from the general, conventional discipline of marketing, developing as companies become more aware of the need for environmental sustainability. In the view of Prakash, the interface involving marketing, public policy, and the natural environment is substantial, depicted through various terminologies like environmental marketing, ecological marketing, green marketing, sustainable marketing, and newcomer marketing (Dangelico & Vocalelli, 2017). Peattie and Charter (2012) define newcomer marketing as the promotion of products based on their environmental performance or improvements. Meanwhile, Fuller describes sustainable marketing as "the process of planning, implementing, and controlling the development, pricing, promotion, and distribution of products in a way that satisfies three essential criteria" (Namazi & Khorramdel, 2022).

Polonsky observes that numerous individuals misinterpret green marketing as just selling or advertising goods based on their environmental attributes. Failure achieved in some green marketing initiatives usually stems from companies addressing environmental concerns as surface-level promotion means, not embracing them as central operational tactics. Polonsky also contends that green marketing encompasses a wide range of activities such as product modification, production process modification, packaging modification, and promotional strategy modification. In spite of its increasing significance, the concept of green marketing is still complex to define because of its multidimensionality.

The initial book specifically on this topic, Ecological Marketing, was authored by Henion and Kinnear in 1976. Many books have since then addressed different facets of green marketing (Mashayekh et al., 2021). However, many individuals still equate green marketing with just marketing environmentally friendly products. Terms such as phosphate-free, recyclable, and ozone-friendly are commonly associated with green marketing in consumers' minds, but they are merely indicators of green marketing and do not encompass its entirety.

Green marketing, in effect, is a broad concept extending beyond consumer goods to industrial goods and services. The notion entails product development, pricing, promotion, and distribution to reduce environmental impacts (Pride & Ferrell, 1995). Green marketing has been defined by Salmon and Marshall as a marketing approach that enhances environmental sustainability by generating recognizable environmental gains in accordance with the needs of consumers (Salmon et al., 2000). Similarly, the Queensland Government describes green marketing as designing and selling products and services that satisfy customer needs for quality, performance, and price with minimal impacts on the environment.

2.1.2. Tourism Industry

Within the general grouping of service industries, tourism ranks among the fastest-growing industries on an international level. Its significance is underpinned by its critical contribution to economic life and fostering sustainable development in nations. An examination of historical travel patterns indicates that humans have embarked on trips for various reasons throughout centuries, such as trade, education, pilgrimage, religious ceremonies, adventure, and political obligation. The rapid global expansion of tourism, together with its related economic benefits, has attracted great attention from governments and planners, which has led to the development of new policy approaches. The advanced economies were the first to acknowledge the significance of tourism and implemented comprehensive steps to make its growth consistent (Abbasabadi et al., 2023). To comprehend the complexities of the tourism sector to the hilt, it is essential to identify the distinct features of tourism products and services.

As a vital component of the global service economy, tourism differs significantly from traditional production sectors. Six key features distinguish the tourism industry. First, it is inherently coordination-oriented, requiring the integration of various services—such as transportation, accommodation, and recreational activities—into a cohesive final product. Second, tourism needs consumers (tourists) to move to a specific location in order to consume and utilize these services since they cannot be consumed remotely or tested prior to purchase. This makes tourism extremely sensitive to destination experiences and credible information dissemination. Third, tourism is characterized by perishability of its services. Unlike physical goods, tourism products cannot be inventoried for later use; when an opportunity is lost—a room remaining open or a tour seat unfilled—it is gone forever. Tourism products are also intangible and multifaceted, involving several components of service like travel, sightseeing, dining, shopping, and cultural experiences. Fifth, the tourism sector is extremely information-intensive, with travellers making decisions based largely on word of mouth, recommendations, and promotional materials. Finally, the industry is confronted with great fluctuation in demand and sophisticated market behaviours, fuelled by intense competition, shifting economic environments, and fickle consumer attitudes. Successful marketing efforts can create demand, whereas adverse publicity or political unrest can instantly discourage travelers. Moreover, economic cycles within sending countries have a direct impact on the demand for tourism services in host markets.

Due to the inherent complexity, variability, and uncertainty that characterize the tourism industry, there is an imperative to apply systems thinking to understanding tourism as a dynamic and interconnected system. The volatility of tourism is a result of the complex network of interactions among different stakeholders, such as tourists, service providers, governments, and resident communities. Tourism as a social system is highly structurally and behaviourally complex. Structurally, the tourism system consists of a vast number of people, groups, and organizations that engage in complex interactions. Behaviourally, it is formed by the unintended consequences of these interactions over time.

Complexity in tourism can be examined from two primary perspectives. From a quantitative and mathematical standpoint, complexity is understood statistically, reflecting the probability of the system being in a particular state at a given time. From a non-quantitative perspective, complexity is viewed as an emergent property of the system, influenced by five critical factors:

• The number of elements that constitute the system.
• The degree of interaction among these elements.
• The nature of these interactions.
• The characteristics of each element.
• The level of intrinsic organization within the system.

The first two relate to the structural complexity of the system, whereas the subsequent three relate to its behavioral complexity (Anisa, 2023). In tourism, both structural and behavioral complexities matter. Structurally, the industry consists of an array of interconnected components that interact with one another extensively and even boundlessly. Behaviorally, cause-and-effect relationships are usually not direct, and the effects of certain actions can be significantly different on short-term and long-term horizons. Policies applied in a particular part of the system may produce unforeseen or even opposite effects in other parts, underlining the inherent uncertainty associated with tourism dynamics.

2.2. Empirical background

Rahimi et al. (2023) investigated the relationship between interactive marketing and tourism marketing, with a particular focus on the mediating role of the green marketing mix. The study’s statistical population consisted of managers, experts, and businesses operating within the tourism service sector of Ardabil Province. Given the presence of 326 hotels, guesthouses, hotel apartments, and ecotourism accommodations, a random sample of 206 participants was selected from a total population of 446, utilizing the Cochran formula for sampling. Data collection was conducted using three standardized questionnaires, and both descriptive and inferential statistical analyses were applied. Hypothesis testing employed confirmatory factor analysis and structural equation modeling, with SPSS and LISREL software used for data processing. The study’s findings confirmed a significant relationship between interactive marketing and its various dimensions—communication, preferential behavior, personalization, rewards, purchase intention, and commitment to communication—and tourism marketing. Additionally, a substantial association was found between the green marketing mix and tourism marketing in Ardabil, as well as between interactive marketing and the green marketing mix.

In a different study, Tabash et al. (2023) examined the relationship between religious tourism and several economic and environmental factors by analyzing empirical data covering 35 years (1986–2020). Ordinary least squares (OLS) models and fully modified dynamic OLS techniques were used for regression analysis. The findings of the study indicated that religious tourism has a direct impact on environmental degradation in Saudi Arabia. Ongoing pilgrimage results in increased economic activities, thus high demand for industrial goods, which has a negative impact on environmental quality. The study also established a negative influence of religious tourism on financial growth, foreign investment, and renewable energy consumption. In contrast, it showed a positive relationship between the use of fossil fuels, economic growth, and increased CO₂ emissions.

Chandran and Bhattacharya (2023) considered the hospitality industry best practices as strategic drivers of environmental sustainability and green marketing. The study aimed to evaluate how green marketing can serve a growing role for the hotel industry for sustainable practice. The study aimed to specifically look into the benefits and impediments to the adoption of green initiatives and their incorporation in the larger marketing strategy. The data is collected from the primary and secondary sources. Primary data of hotel employees from a small sample survey were collected in 20 hotels in India. In conclusion, it was highlighted that hotels with green practices had a competitive edge regarding reputation and customer preferences. However, it found that, in spite of recognizing the long-term worth for their green initiative, most of the hotels lack a systematic approach toward their environmental initiatives.

The broader theme of green practices and pollution reduction has been studied quite extensively across disciplines. Many studies were developed in regard to sustainability and environmental impacts in tourism, widely regarded as the best alternative for oil-based economies. Still, research specifically directed at green marketing in tourism is limited and largely absent for the context of religious tourism. Nevertheless, considering media and economic globalissues of religious tourism, particularly in areas like Iraq, very little systematic inquiry has attended to how the principles of green marketing may find application within this sector. At this point, it seems so far, this remains unexplored. This study is aimed at filling the gap by determining the role and potential contribution of green marketing to the religious tourism industry of Iraq.

Methodology

This section of the research addresses the general methodology, statistical population, and other patterns of research implementation.

3.1. Design

Combining qualitative and quantitative approaches was necessary for the study's purpose of developing an exhaustive green marketing model for the religious tourism sector. In the qualitative phase, the Delphi method was utilized in obtaining expert opinions. The quantitative phase collected data using a descriptive-survey approach, targeting religious tourists.

In the qualitative phase, the aspects and variables of the green marketing model were first identified through an extensive review of theoretical foundations and relevant literature. Subsequently, a Delphi questionnaire was prepared based on these initial findings and forwarded to a panel of experts for evaluation. The experts were asked to score each criterion based on its elevance and importance. The evaluations of the experts were subjected to valid reliability tests. Indicators selected were subjected to further rounds of the Delphi process, where it ensured the high degree of consensus among the experts.

3.2. Participants

The qualitative phase of this study focused on experts and senior managers within the tourism sector, while the quantitative phase targeted religious tourists in Iraq. For the qualitative component, a sample of 14 experts and senior managers specializing in tourism and marketing was selected to participate in the Delphi questionnaire. This sample size was determined using Cochran’s formula, ensuring that the selected participants provided a representative and reliable basis for gathering expert insights.

3.3. Data Collection Procedure

In order to answer the research questions, the required data were gathered by the application of a systematic questionnaire. The reliability of the questionnaire had been tested in advance with the Cronbach's alpha score being 0.7, marking an acceptable point of internal consistency. In addition, the questionnaire's construct validity had also been tested via receiving feedback from the supervising professor as well as from Iraq's and Iran's tourism and marketing managers. Face and content validity checks were employed to make sure that the questionnaire addressed the constructs meaningfully. Through the feedback obtained, the validity of the questionnaire was guaranteed, further maintaining its adequacy for data collection. In conducting the literature review relevant to the green marketing model within the religious tourism context, a wide range of authoritative scientific databases and online sources was utilized. These comprised Emerald, ProQuest, Springer, Science Direct, the Scientific Information Database (SID), the Comprehensive Humanities Portal, the National Journals Database, and the Noor Specialized Journals Database. These resources established a solid theoretical basis for the research, thereby guaranteeing that the study was founded on current academic knowledge and industry practice.

3.4. Data Analysis Method

Relevant qualitative data for this research were gathered via participatory interviews with professionals and stakeholders in the tourism industry. The gathered data will be subjected to qualitative coding analysis to reveal patterns, themes, and insights in line with the research objectives. In order to achieve this purpose, qualitative data analysis software such as ATLAS.ti or MAXQDA will be utilized to further enhance the systematic organization and interpretation of the data. During the quantitative phase, various tests of statistics will be performed in order to examine the inter-relationship among the variables. Initially, the Kolmogorov-Smirnov test will be applied for testing the normality of data distribution. This will be followed by computation of correlation coefficients of the significant research variables in order to investigate any relationship. Quantitative data analysis will entail the use of both descriptive and inferential statistical procedures so that there is a complete understanding of the dataset. In order to investigate the structural relationships between variables more closely, structural equation modeling (SEM) will be applied, with the assistance of specialized software for analyzing and interpreting results accordingly.

Results and findings

4.1. Analysis of Qualitative Data

Descriptive statistical methods were used to summarize the overall characteristics of the respondents, such as variables of gender, age, and education level. Frequencies and percentages corresponding to these demographic features are outlined in Table 1. The table gives a comprehensive description of the participants' features, offering an understanding of the study sample composition. In particular, the gender distribution analysis among the respondents, as derived from the data collected through the administration of the questionnaire, is outlined in the following table.

As illustrated in Table 1, 67% of the respondents were male, while 33% were female. Additionally, the data collected from the questionnaire provide a detailed breakdown of respondents based on their education levels. The corresponding frequencies and percentages for each educational category are presented in Table 2.

As presented in Table 2, 33% of the respondents held a bachelor’s degree, 42% possessed a master’s degree, and 25% had attained a doctorate. Furthermore, the questionnaire data provide insights into the age distribution of the respondents. The corresponding frequencies and percentages based on age groups are detailed in Table 3.

As illustrated in Table 3, 8% of the respondents were between the ages of 31 and 35, while 17% were aged 36 to 40. Another 17% were aged 41 to 45 years. 33% formed the largest group, which was between the ages of 46 to 50 years, and another 25% were aged 51 years and above.

After the expert interviews, and in accordance with the research questions and methods of data collection, the present research utilized thematic analysis for analyzing the qualitative data gathered. The transcripts of the interviews were read completely and coded, which resulted in the uncovering and extraction of numerous concepts that were pertinent to the research aims. They were then categorized into conceptual categories, where each category reflected unique, independent thoughts. The ideas which were classified were then assembled into overarching themes, which are discussed in detail.

Given the study's focus on developing a green marketing model within Iraq's religious tourism industry, the concepts derived from the initial coding of the twelve interview transcripts are presented below. These extracted themes, categorized across three primary sections, are distinctly outlined and explained to provide a comprehensive understanding of the findings. A systematic study of the interviews was made, and concepts directly related to the research objectives were extracted and each assigned a particular code for better clarity and organization.

In the present study, each code is considered a separate entity. The coding system has a definite structure: the first digit on the left designates the interview number, the rightmost last digit specifies the sequence of the code within the respective interview, and the English alphabets (ID) inserted in between are used as an identifier for convenience.

The following section presents the key concepts derived from the initial coding of the twelve interviews. These concepts form the foundation for the design of the green marketing model in the religious tourism industry, with a specific focus on Iraq as a case study.

Initial codes were taken from the interview data based on the examination of the statements and viewpoints of the participants. Final codes were determined by combining the codes that were frequently highlighted by all interviewees and those that the researcher thought were important. The interviewees’ responses to questions regarding the causal factors influencing green marketing within the religious tourism industry provided valuable insights, which contributed to the identification of key themes Table 4.

The study's findings indicate that the contextual factors impacting green marketing in the religious tourism industry fall into three key categories: Marketing Mix Tools, Green Humanitarian Activities, and Participation in Green Economic Activities and Responsibilities. Interviewees answered questions that covered all parts of the paradigm model to help explain green marketing in religious tourism. Their opinions were influenced by their personal experiences, professional knowledge, and individual perspectives, which all helped create a well-rounded view of the topic. The first codes came from carefully looking at what the participants said. Final codes were made by merging codes that the researcher thought were important and those that came up frequently in multiple interviews. The answers from the respondents, especially regarding the contextual aspects of green marketing in religious tourism, played a key role in identifying these codes Table 5.

The study's findings enable the overlapping elements influencing green marketing in the religious tourism sector to be classified into two main categories: overuse of natural resources and activities, as well as a lack of regulations to protect tourists' health and environmental interests. When participants were asked about the model's various parts, they gave detailed answers. They shared some great insights on the opportunities and challenges of green marketing in religious tourism, using their personal experiences and professional knowledge. After a thorough examination, initial codes were generated from participant answers to identify recurring concepts and themes. The codes that each participant consistently highlighted were combined with those the researcher deemed important to assign final codes. Crucial information that aided in the definition of these codes came from participants' assessments of the overlapping conditions. The final set of codes pertaining to the overlapping components of green marketing in religious tourism is shown in the following Table 6.

Based on the study findings, the main phenomenon of green marketing in the religious tourism sector is classified into five main areas: green marketing strategy, the formation of a green committee for environmental conservation, the development of sustainable marketing, strategic planning and evaluation, and attention to tourists' needs related to green services. Participants in the interviews gave thorough responses to questions covering every aspect of the model. In order to demonstrate the function and significance of green marketing in the context of religious tourism, they expressed their viewpoints, which were derived from their individual visions, professional experiences, and personal experiences. To find important themes and recurrent ideas, preliminary codes were retrieved from the statements and viewpoints of the participants through a methodical examination. Codes that were frequently highlighted by several participants and those that the researcher thought were significant were combined and accepted as final codes. Participants' perspectives, especially with relation to the primary green marketing phenomenon in religious tourism, played a crucial role in shaping the study findings Table 7.

The study's findings led to the classification of green marketing outcome factors in the religious tourism sector into four main areas: Quality in Green Service Innovation, Development of Social Sustainability, Optimal Performance and Efficiency in Green Marketing, and Social and Environmental Sustainability Factors. During the interviews, participants shared detailed answers about each part of the paradigm model. They talked about how green marketing impacts religious tourism, drawing from their own experiences, work backgrounds, and unique viewpoints. To spot common themes and ideas, we pulled out some initial codes from what the participants said. The final codes came together by mixing codes that were often mentioned by different interviewees and those that the researcher found significant. These findings were heavily shaped by what the respondents explained, particularly regarding the results of green marketing in religious tourism Table 8.

In the open coding phase, we break down the data into different topics to spot the main ideas and themes. This means taking qualitative data apart and tagging them to create initial categories. After open coding, we move on to axial coding, where we connect these categories and their subcategories, focusing on their features and dimensions. To look at how these categories, relate to each other, the researcher uses the paradigm model, which was suggested by Strauss and Corbin in 1998 for analyzing qualitative data. This model helps organize and make sense of the data by showing how different parts interact.

The paradigm model includes several key elements: causal conditions, the phenomenon, contextual conditions, intervening conditions, actions/reactions, and outcomes. Causal conditions are the events or situations that lead to the phenomenon being studied. The phenomenon itself is the main event or occurrence we're looking into, influenced by a series of actions and reactions. Contextual conditions are the broader environmental or situational factors that affect or limit the phenomenon. On the other hand, intervening conditions are specific variables that change how causal conditions impact the phenomenon. Actions and reactions are the strategies or processes used to deal with or respond to the phenomenon. Lastly, outcomes are the results or consequences that come from these actions and reactions. This organized way of looking at things helps us understand the relationships and dynamics within the data.

During axial coding, the researcher uses tools like questioning techniques and constant comparative analysis to refine and connect the categories identified during open coding. This helps create meaningful relationships between categories and subcategories, fitting them into the paradigm model. As we work through open and axial coding, a model starts to take shape, showing how the identified categories are interconnected. Once we’ve developed these relationships enough, we use selective coding to bring the categories and subcategories together into a cohesive emerging theory.

The open coding phase wraps up with identifying key concepts and categories. At this point, the researcher needs to clarify how these elements relate based on the actual data collected. This clarification happens during axial coding, where we revisit previously fragmented data to find deeper connections and patterns. The final result of this coding process is the paradigm model, which organizes the data into causal conditions, the phenomenon, context, intervening conditions, mechanisms, and outcomes. From analyzing interview data and expert opinions, we’ve pinpointed the causal factors that affect green marketing in religious tourism. These factors are outlined in the research findings, giving a detailed view of what contributes to developing and implementing green marketing strategies in the religious tourism sector.

4.3. Quantitative Data Analysis

Given the adoption of a sequential exploratory mixed-method approach in this study, the first phase focused on developing a green marketing model for the religious tourism industry. In the second phase, quantitative data were collected using a survey method to evaluate and assess the status and effectiveness of the proposed model.

4.3.1. Kolmogorov-Smirnov (K-S) Test

Before testing the hypotheses, it is crucial to verify the normality of the distribution of the research variables. If the significance level obtained from the normality test exceeds the threshold of 0.05, it indicates that the data are normally distributed. The results of the normality tests for the study variables are presented in Table 9.

As presented in Table 26, the significance level for all variables is less than the threshold of 0.05. Consequently, the null hypothesis of normality is rejected, indicating that the variables in the model do not follow a normal distribution. Given this outcome, non-parametric statistical methods were employed for data analysis, and SMART PLS software was used to facilitate the process.

4.3.2. Sample Size Adequacy Test (KMO)

Before proceeding with data analysis, it is essential to verify the adequacy of the sample size. One of the standard methods for assessing sample adequacy is the calculation of the Kaiser-Meyer-Olkin (KMO) index, developed by Kaiser, Meyer, and Olkin. The KMO index, represented by the KMO symbol, evaluates the suitability of the data for factor analysis. A KMO value above 0.7 is considered optimal, indicating sufficient sampling adequacy. However, values between 0.5 and 0.7 are also deemed acceptable, though with some caution. The results of the KMO test are presented in the table below Table 10.

Following the KMO test, it is essential to verify the homogeneity of variances and check for multicollinearity among the research variables—both of which are critical assumptions for conducting structural equation modeling (SEM). The homogeneity of variances is assessed using Levene’s Test. In this test, the significance level is examined to determine whether the variances are equal across groups. If the Sig value exceeds 0.05, this indicates that the variances are homogeneous, thus meeting one of the main assumptions of SEM Table 11.

The table above shows that the variances of the two groups are homogeneous, as indicated by the Sig value, which is greater than 0.05.

4.3.3. Measurement of the Proposed Model

According to established research guidelines, a reflective measurement model is considered homogeneous if the absolute value of the factor loading for each observable variable associated with a latent variable is at least 0.4 and statistically significant at the 95% confidence level. To evaluate this, the factor loadings were thoroughly examined. Some researchers, such as Holland (1999), recommend removing observable reflective variables from the measurement model if their factor loadings are less than 0.4, especially if this improves the composite reliability of the model. According to the study's findings, all observable variables presented factor loadings with absolute values greater than 0.7, indicating substantial correlations with the corresponding latent variables. This result demonstrates that the measurement model used in this study is robust and meets all validity requirements. The detailed results are presented in Table 12.

4.3.4. Cronbach's Alpha and Composite Reliability

The results of Cronbach's alpha, frequently used to assess internal consistency or reliability between observable variables in a reflective measurement model, exceeded the suggested cutoff value of 0.7, thus demonstrating an acceptable level of reliability. However, it should be noted that Cronbach's alpha assumes that each observable variable is assigned an identical weight, with their relative importance being homogeneous within the measurement model. The Composite Reliability (CR) index, developed by Werts et al. (1974), is used to circumvent this restriction. Composite Reliability provides a more complex and precise measure of reliability than Cronbach's alpha, as it uses the factor loadings of each item in its calculation. Therefore, the Composite Reliability index provides higher and more precise reliability coefficients, allowing for a more thorough examination of the internal consistency of the measurement model Table 13.

The findings of the composite reliability test, which demonstrated that the index values beyond the 0.7 threshold, confirmed the validity of the measurement items. To ascertain the degree to which observable variables truly explain their particular latent variables, convergent validity—as defined by Barclay et al. (1995)—was examined concurrently with the reliability test. Convergent validity was evaluated using the average variance extracted (AVE) metric. A value of 0.5 or greater is deemed acceptable when observable factors account for at least 50% of the variance in their particular hidden variables. The results confirmed the convergent validity of the measures used in this investigation by showing that the AVE measures reached or above the necessary threshold, as shown in Table 14.

4.3.5. Fornell-Larcker Criterion Test

According to the Fornell-Larcker criterion, a latent variable demonstrates high discriminant validity when it exhibits greater variance among its own observed variables compared to its correlation with other latent variables. Specifically, the square root of the AVE for each latent variable should exceed its highest correlation with any other latent variable in the model (Fornell & Larcker, 1981). This criterion ensures that each construct is distinct and captures unique aspects of the data, thereby confirming the model's discriminant validity Table 15.

4.3.6. Structural Model Evaluation of the Main Research

A structural model is one in which the relationships between latent (independent) or exogenous and dependent endogenous variables are considered. In fact, the structural model is derived from the relationship between the measurement models existing in the model. After evaluating the reliability and validity of the measurement models, the evaluation of the structural model follows. In Figure 1, the factor loadings related to the research questions, the path coefficients of the research hypotheses, and the values of the determination coefficient of the dependent variables are shown Figure 1.

Figure 1. Research model in the case of standard coefficients.

One of the most important fit measures of the structural model is the coefficient of determination (R²). It is a statistic that provides an indication of how well the independent variable(s) of the model account for the variability in the dependent variable. In structural equation modeling, R² is interpreted in the following way: 0.19 signifies a low degree of explanatory power, 0.33 signifies a moderate degree, and 0.67 signifies a high degree of variance explained. It should be understood that if the latent dependent variable is determined by only a limited number of independent variables, high R² values can still be accepted on the grounds that the explanation is focused with fewer predictors. Conversely, the R² value for independent variables is basically zero as these independent variables are not determined by any other variables in the model Table 16.

The significance of path coefficients has remained a critical criterion for confirming relationships among constructs in structural models. The values shown on paths connecting the latent variables represent the path coefficients and are commonly called the Beta weights (β). Positive path coefficient values imply a direct and positive relationship among the constructs or, in other words, if an increase has occurred in one variable, one can also expect an increase in the other. On the contrary, a negative path coefficient indicates that variables are inversely or negatively related, meaning whenever one variable increases, the other one decreases Table 17.

To examine the significance level of the path coefficients, the t-value for each path must be calculated. If the obtained value is within the considered confidence level, the relationship or hypothesis is confirmed. At significance levels of 90%, 95%, and 99%, the t-values are 1.64, 1.96, and 2.58, respectively. The results are presented in Figure 2, Table 18.

Figure 2. Research model in a meaningful state.

The results indicate that the t-values for all variables exceed 1.96, meaning that all path coefficients are statistically significant at the %95 confidence level.

Structural equation models that utilize a variance-based approach, such as those analyzed using Smart PLS, do not rely on a single, comprehensive index to evaluate the model in its entirety. To address this, the GOF index is employed, as it simultaneously considers both the structural model and the measurement model. The GOF index provides an overall assessment of the model's quality by evaluating how well the data fit both the measurement indicators and the hypothesized relationships between constructs. GOF is manually calculated using the average R² and the average AVE as follows:

Where AVE represents the Average Variance Extracted, and R² is the Coefficient of Determination. The recommended thresholds for GOF values, as proposed by Tenenhaus et al. (2009), are: 0.01 (Weak), 0.25 (Moderate), and 0.36 (Strong) Table 19.

Since the calculated GOF value (0.691) exceeds the recommended threshold of 0.36, the model is considered strong. This confirms the adequate overall model fit.

Discussion and Conclusion

This study, aimed at identifying and analyzing the factors influencing green marketing in the religious tourism industry, has yielded findings that illuminate various dimensions of this phenomenon through a scientific and systematic approach. The results revealed that the causal factors affecting green marketing fall into three main categories: ecological transformations, the status of the green economy, and advancements in technology and marketing.Ecological transformations include indicators such as biological issues, environmental concerns, and patterns of energy consumption. The status of the green economy relates to financial conditions and green investment, while technological and marketing developments encompass innovations in technology and the modernization of marketing tools.

The contextual factors were classified into three primary domains:

Green marketing mix tools, including marketing mix components and associated instruments.

Green humanitarian activities, involving environmental actions and green ethics.

Participation in green economic responsibilities, covering aspects such as consumer protection and fulfilling social responsibilities.

Intervening factors were divided into two categories:

Overexploitation of natural resources and environmental costs.

Lack of clear regulations to prevent the misuse of tourists’ environmental concerns.

The core phenomenon of the study-green marketing in the religious tourism industry-was elaborated across five key axes:

Green marketing strategies, including strategic approaches to enhance sustainability.

Formation of green committees to ensure coordination and environmental protection.

Development of sustainable marketing with an emphasis on ecological balance.

Strategic planning and evaluation, involving performance monitoring and transparency.

Addressing tourist needs by aligning green services with their expectations.

The findings were categorized into four key outcome areas:

Social and environmental sustainability, with indicators like justice and equity in resource utilization.

Optimal performance and efficiency of green marketing, emphasizing environmental and economic improvement.

Sustainable social development, achieved through meeting the needs of religious tourists and strengthening social cohesion.

Quality and innovation in green services, measured by ecological credibility and service quality.

The study emphasizes that green marketing can provide an effective platform for achieving sustainability in the religious tourism industry. Furthermore, institutionalizing green strategies and actively involving stakeholders can not only protect the environment but also enhance service quality and foster economic and social development in this field. Attention to supportive legislation, strategic transparency, and targeted investment in green technologies are among the essential requirements for realizing these goals Figure 3.

Figure 3. The final proposed research model.

Suggestion and future research

To provide practical and scholarly recommendations, they should be directly linked to the findings, scientifically sound, and aimed at improving and expanding the research topic. Based on the current study, the following suggestions can be included in the final section.

Develop Green Marketing Policies in the Tourism Industry: Tourism organizations and government bodies can define clear and actionable frameworks to enforce green marketing practices and require companies to adhere to environmental standards. Example: Creating environmental certification labels for hotels and lodging centres.

Training Employees and Managers in Green Marketing: Conduct training programs to familiarize tourism company staff and managers with the concepts of green marketing and the green marketing mix. Example: Specialized workshops conducted by environmental experts.

Financial Support for Green Businesses: Governments and NGOs can support green marketing businesses through funding and low-interest financial facilities. Example: Offering tax incentives to companies using recyclable raw materials.

Study the Long-Term Impacts of Green Marketing: Future research should explore the long-term effects of green marketing on customer and brand loyalty across various industries. Example: A longitudinal study in the green hotel industry

Comparative Analysis of Green Marketing in Different Regions: Researchers can compare green marketing across various geographic areas (e.g., provinces or countries) to identify best localization practices. Example: A comparative study between eco-tourism strategies in different cultural contexts.

Utilizing Modern Technologies in Green Marketing: Future studies should examine the role of technologies like Artificial Intelligence, Internet of Things (IoT), and Blockchain in enhancing green marketing strategies. Example: Using mobile apps to promote eco-friendly travel experiences.

Create Legal Frameworks for Green Marketing: Legislative bodies should establish mandatory regulations to ensure environmental standards in advertising and marketing. Example: Banning misleading green claims in advertisements.

Promote Sustainable Consumption Culture in Tourism: Design awareness campaigns to educate the public on the benefits of sustainable consumption and green tourism. Example: Awareness campaigns to reduce plastic use during travel.

Establishing Green Collaborative Networks: Forming networks of tourism companies with a shared goal of promoting environmental sustainability and green marketing. Example: Collaborating between hotels, travel agencies, and governments to implement joint green initiatives.

Designing Green Tourism Packages: Offering travel packages that include environmentally friendly services (such as staying in green hotels or using electric transportation). Example: Sustainable Hajj packages or nature trips.

Measuring and Reporting Environmental Impacts: Companies can use tools to assess and report their environmental impacts to increase transparency with their customers. Example: Annual sustainability reports that include carbon footprint metrics.

Engaging with Local Communities: Companies can collaborate with local communities to develop sustainable tourism and allocate a portion of their revenue to environmental projects. Example: Funding local conservation projects through tourism revenue sharing.

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