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Abstract

The awareness of green innovation practices (GIP) is trending globally. It is still unclear how guests’ return intentions support GIP. Based on the theory of planned behavior (TPB) and innovation theory, this study explored the influence of GIP on guests’ return intention through the mediating role of green hotel image, guests’ positive mood, and guests’ satisfaction. Data was collected from 1,058 hotel guests and analyzed by utilizing PLS-SEM. The findings confirmed all the hypotheses, except water-saving didn’t affect guests’ positive mood, and energy-saving didn’t affect guests’ satisfaction, thus green hotel image, guest satisfaction, and guests’ positive mood mediated the relationships between GIP and guests’ return intentions. This study enriches GIP and customer behavioral intentions literature in the hospitality industry, hotel managers focus on GIP to improve guests’ satisfaction, and positive moods to enhance guests’ return intentions to the hotels, the results provide some suggestions for future research and managers of the hospitality sector with practical recommendations.

JEL codes: 03; M0

Plain language summary

With the awareness of green innovation practices (GIP) trending global. It is still unclear how GIPs are supported by guests’ return intentions. This study attempts to explore the influence of GIP on guests’ return intention through the mediating role of green hotel image, guest’s positive mood, and guest satisfaction. The findings revealed that GIP significantly directly and indirectly impacts green hotel image, guest satisfaction, and guest’s positive mood, thus establishing mediation, and guests’ return intentions. Non-significant results for water-saving suggest that guests’ positive mood and energy-saving practices suggest that guest satisfaction is not affected by supporting green hotel-based GIP practices.

Keywords

green image green innovation practices green hotels return intention

Introduction

As a result of climate change, the government, organizations, and consumers now have a high awareness of environmental concerns (Jones et al., 2016; Merli et al., 2019; Nhan Dan Journal, 2023). In the global climate risk index, Vietnam was one of the countries densely damaged by climate change and natural disasters from 1997 to 2016. Vietnam was ranked fifth and eighth in 2018 (Thien Nhien Journal, 2022). The tourism and hospitality industry are not at the margins of these issues environment, it must take responsibility for contributing to environmental problems and raising climate change. In recent years, the tourism industry has increased rapidly in Vietnam, it contributed 9.2% of the GDP for developing economies, and it welcomed over 18 million international tourists, an increase of 16.2% compared to 2018 (VNT, 2022), whereas Indonesia welcomed 16.1 million visitors, to compare with neighboring countries. Vietnam was ranked fourth in Southeast Asia, after Thailand, Malaysia, and Singapore (VNT, 2022). Moreover, the rapid development of the tourism industry resulted in the overconsumption of natural resources and disrupted the balance of the ecological environment, the tourism industry of Vietnam was one of the least sustainable economies with environmental pollution, outdated infrastructure, and limited tourism services (VNT, 2019). Specifically, Ho Chi Minh (HCM) City is in the south of Vietnam, has heavy pollution, and was ranked ninth out of 96 major cities in the world (Nguoi Lao Dong Journal, 2022), it was a main reason to decrease a large number of tourists return to HCM City in Vietnam, the rate of tourists who first times visited Vietnam accounted for 90%, and their return intentions visited a tourist destination in Vietnam accounted for 6% (Vietnamnet, 2014). Mai et al. (2019) reported that the rate of international tourists who first times visited HCM City accounted for 59.1%, and second times accounted for 17.1%, and “Never return” is quite high (Nhan Dan Journal, 2023), how to improve this situation?, the Government of Vietnam has been proactive in many activities to minimize the impact of climate change by implementing sustainable development goals, and encourages all organizations to implement GIP (Nhan Dan Journal, 2023) it reduced carbon emissions and greenhouse gas emissions, GIP saved resources and contributed to the improvement in a sustainable environment (Nhan Dan Journal, 2023). In the United Nations’ 26th Conference on Climate Change (COP26), the Government of Vietnam committed to reducing methane emissions by 2030, and net zero emissions by 2050 (Nhan Dan Journal, 2023), and the Government promoted the organization’s use of clean energy transition, save energy and water, build a process to manage waste management, apply new technology to develop clean technology and sustainable development and utilize renewable resources (Ministry of Industry and Trade of the Socialist Republic of VietNam, 2022). However, the success of implementing GIP depends on the capability of the organization and the level of consumer green behavior.

To achieve sustainable tourism, the hospitality industry faces high pressure from the series of regulations from the Government of Vietnam, such as encouraging organizations to use renewable energy sources and apply GIP, organizations achieve these criteria then get a brand named “Green Lotus” issued from the Government of Vietnam (VNAT- Vietnam National Administration of Tourism, 2015), organizations apply GIP to reduce the influence of environmental concerns (Suárez-Cebador et al., 2018), GIP prevents pollution and reduces environmental issues, GIP brings out new systems, eco-friendly products, and waste processes (Su et al., 2020), GIP can improve the values of economics and competitive advantages for organizations (Merli et al., 2019; Ning et al., 2023; S. Wang et al., 2022; Wei & Sun, 2021).

In this context, the hotels have implemented many programs to apply GIP, it mitigates low-carbon development, protects the natural environment, and develops a sustainable business. However, when the hotels apply GIP, whether the tourists’ intentions to return to the green hotels, is critical to gain a better understanding of guests’ intentions to return to green hotels. Many studies have done a study to explore green behavior when organizations apply “going green,” and “green practices” in the hospitality and tourism industry. For example, Nimri et al. (2017) reported that the hotels implementing GIP elicited consumers’ decisions to purchase eco-friendly services and patronize green hotels. Prayag et al. (2017) used different factors to understand green behavior in their research model and the results showed that tourists’ emotional experiences, perceived overall image, and satisfaction increase tourist’s intention to recommend a place to others. In China, J. Wang et al. (2018a) conducted a study to understand tourists’ intention to patronize eco-friendly hotels, the results pointed out that the perceived effectiveness and environmental concern had a positive impact on tourist attitudes and GL. In Turkey, Yarimoglu and Gunay (2020) explored the customers’ perspectives toward green innovations in green hotels, the findings showed that subjective norms, customers’ attitudes, GIP, and green hotel image have a significant influence on GL toward a green hotel. While the impressive array of this research has examined the relationship between green initiatives green image, and customer attitudes (Chung, 2020); green initiatives have a positive effect on customer loyalty, the findings showed that tourists repeatedly purchase green products, enhancing tourists’ return a destination, satisfied with their stay in a green hotel in the Western countries (Merli et al., 2019; Yarimoglu & Gunay, 2020) who explored green behavior with green practices, but they used different factors of green practices that elicited green behavior of tourists influences in various aspects. However, in developing countries, such as HCM City, Vietnam, little research explores the direct and indirect effects of GIP factors and expanding the TPB on green hotel image (HI), guests’ positive mood (GM), guests’ satisfaction (GS), and guests’ return intention (GL), the scholars suggested extending more predictive factors into the TPB model to explain the green behavior of customers’ intentions (Ajzen, 1991; Yarimoglu & Gunay, 2020). Therefore, it needs a study to understand customer behavior intentions by connecting hotel image, guests’ positive mood and satisfaction to the TPB model to expand our knowledge on how GIP affects customer behavior intentions. The purpose of this study was to fill research gaps by investigating (1) what factors of GIP directly affect HI, GM, and GS, (2) Do HI, GM, and GS directly affect GL. (3) the role of HI, GM, and GS mediate the relationship between factors of GIP and GL.

Following, this paper begins with the TPB and DIT are the lens of this research model, a review of the literature of GIP, GL HI, GM, and GS. Then, the research methodology is described. The findings are interpreted and can be a guideline for managers of green hotels in HCM City, Vietnam. Lastly, some implications for hotel managers to build a strategy to enhance HI, to understand GM, efforts made toward GS, and motivate tourists’ intentions to return to green hotels in HCM City, Vietnam, further research suggestions and limitations were clarified.

Literature Review and Hypothesis Development

Theoretical Foundation

The TPB was originally refined by Ajzen (1985) to predict human behavior, focusing on attitudes, subjective norms, and perceived behavior control, the TPB predicts consumers’ decision-making process, and consumers’ behavioral intentions to make a final decision to return to a hotel (Han, Moon, & Hyun, 2020; Sadiq et al., 2021). In the hospitality industry, many scholars have extended the TPB model (Ajzen, 1991) by adding a new construct for understanding customers’ pro-environment of green services and patronizing their intention to return to green hotels (Khan et al., 2022; Olya et al., 2019). Yarimoglu and Gunay (2020) extended the TPB model by adding environmentally friendly activities and the overall HI to build a GL model, four constructs have a significant and positive impact on GL, except a perceived control was not confirmed. The TPB predicts human behavior such as when an individual touches a positive mood into action, intention behavior reflects how a customer chooses a hotel, and there are attitudes formed by behavioral beliefs that address his/her intention to purchase hotel products based on beliefs about the hotel’s services lead to their satisfaction and have a positive mood about hotel products. Moreover, mood reflects the way people feel in a positive mood or negative mood, a positive mood is expressed by the customers’ feelings “stay at green hotels feel comfortable and relaxed,” feel happy, or feel bad “Services of green hotels are bad” they are in a bad mood, and vice versa (Khan et al., 2022; Olya et al., 2019; Yarimoglu & Gunay, 2020).

The DIT was refined by Rogers (1995), and it is defined as “Ideas, practice, method, or project” through an organization or person to adopt it (Greenacre et al., 2012). Eco-innovation is defined as a new technology or a method to improve and reduce environmental impacts, organizations apply innovative technologies to mitigate pollution and improve environmentally friendly products (Greenacre et al., 2012), using more effective resources than other relevant alternatives. Cabral and Marques (2023) stated that the primary objective of eco-innovation in a hotel is the responsibility for producing eco-friendly services to enhance customers’ intentions and GS. Cabral and Marques (2023) reported that eco-innovation has positively impacted GS toward an eco-friendly hotel. Similarly, Sharma et al. (2020) reported that DIT is a useful theory to predict a perspective of green consumption and a lens to assist managers in the long-term advancement of innovation in the hospitality industry (Rogers, 1995; Sharma et al., 2020).

Guest Return Intention (GL)

The TPB is used to predict customers’ intentions, and return intention is formed by the behavioral intentions and attitudes of customers (Yarimoglu & Gunay, 2020). In the hospitality and tourism literature, many studies pointed out that tourists’ behavioral intention reflects a return to a destination, intention to recommend this destination to other people or introduce a place (e.g., hotel, restaurant, mall, and so on) to family and friends (Bahja & Hancer, 2021; Merli et al., 2019; Nimri et al., 2020; Yarimoglu & Gunay, 2020). Moreover, Cossío-Silva et al. (2019) pointed out that “the intention to return to the destination is considered to be a key component of loyalty.” Many scholars investigated empirical studies to explore consumer behavioral intentions to engage in green behavior, including patronizing eco-friendly products, environmental practices, GIP, willingness to purchase eco-friendly products and services, and intention to stay in a green hotel (Merli et al., 2019); behavioral intentions were reflected (Yarimoglu & Gunay, 2020), tourists’ willingness to choose green hotels in Turkey, purchase green hotel’ services (Nimri et al., 2020), revisit a destination (Bahja & Hancer, 2021).

Green Innovation Practices (GIP)

Innovation is defined as the generation of new ideas and the promotion of creativity, the organizations apply innovation to improve services and products (Khan et al., 2021; Shahzad et al., 2022; Y. Wang et al., 2020). Afridi et al. (2023) viewed creativity as “The development of new ideas or practical solutions for green services, practices, and products or extending their creativity to the environment” (p. 4). GIP is captured in different ways labeled as green practices, green attributes, green initiatives, or green creativity across different studies, which is one way to increase a sustainable environment (Merli et al., 2019; Su et al., 2020). Ning et al. (2023) defined GIP as the improvement of processes or applying a new method to improve products for waste reduction, GIP has become a key method for reducing energy consumption, pollution prevention, and natural resources (Xie et al., 2022), GIP improves sustainable products and reduces the negative effects of the environment by applying energy-saving, water-saving, RR(Chung, 2020; Trang et al., 2019), GIP effectively monitors industrial waste emissions and mitigates the carbon emissions (Lian et al., 2022; Y. Wu et al., 2020).

In the tourism sector, Asadi et al. (2020) emphasized GA that was used in the hotel based on applying alternatively fueled with renewable power and hybrid vehicles (Merli et al., 2019), the hotel built a strategy to use GA with engages in guests toward green behavior, the hotel provides environmental knowledge for their guests such as information of GA (e.g., how they can contribute to reducing the negative environmental imp act; avoid disposable products; publicly declares its specific environmental policies; Han, Ariza-Montes et al., 2020; Legrand et al., 2022). Green design or called “green physical environment design- GD,” can go a step toward pollution prevention, using cleaner technology such as applying a system for sewage treatment, solid-waste management, replacing obsolete technology, reducing energy usage and water, using green materials that are composed of recyclable materials, use green items such as the potted plants, flowers, green walls and trees, green spaces, general design harmonizes with the nature (Trang et al., 2019), utilizes a natural light (Han, Ariza-Montes et al., 2020; Legrand et al., 2022). In China, Peng (2023) revealed that eco-design has a positive effect on GL in sustainable supply chain management, it limits the impact on environmental issues. Lastly, green food focuses on green attributes that are a competitive advantage of the hotels. Green hotels gained their guests’ satisfaction and positive moods through GIP (Trang et al., 2019), the hotels used GIP which means they committed to using organic food that is expressed on the menu to serve their customers, it is a strategic tool to show hotel services and products are environmentally friendly consciousness toward their customers, eco-friendly food focus on fresh and healthy foods; use natural ingredients, prioritize using locally grown food (Prentice et al., 2019; Shapoval et al., 2018), without using fertilizers or pesticides, stored, and processed without adding chemicals, the hotels committed do not use modified genetically food (Shapoval et al., 2018), apply small dish to reduce food waste, and encourages guests to travel more to take food rather than take a lot of food at once (Berezan et al., 2014).

Thus, it is key to understand customer green behavior’s driving forces to increase the chances of implementing GIP successfully. HI is a main factor in building a brand of green hotels. However, limited research to explore the relationship between GIP and HI, GM, and GS. HI is one of the most significant to advertise an organization’s image and strategic tools for expressing friendly product and service consciousness to consumers. Restaurants and hotels have adopted HI increasingly to take advantage of their images by applying CSR (Asadi et al., 2020; Chung, 2020), to create hotels’ images and increase tourists, the hotels apply GIP, which also creates a brand of green hotel image, and increase consumer green behavioral intention (Shahzad et al., 2022). However, there is still little evidence to explore the effect of GA (Merli et al., 2019), GD (Han, Ariza-Montes et al., 2020; Legrand et al., 2022), GF (Berezan et al., 2014; Trang et al., 2019) on HI (Chung, 2020) the hotels provide GF to their services and also apply green design to transform their business in green hotels to enhance GS (Yarimoglu & Gunay, 2020; Yusof et al., 2017), and GM (Kocabulut & Albayrak, 2019; Taheri et al., 2017). Considering the relationship between these constructs, the components of GIP positively affect the future hotel image, guests’ moods, and satisfaction. Therefore, we proposed the following hypotheses:

Hypothesis 1 (H1): Factors of GIP (SE-H1a, SW-H1b, RR-H1c, GF-H1d, GA-H1e, GD-H1f) positively affect HI.

Hypothesis 2 (H2): Factors of GIP (SE-H2a, SW-H2b, RR-H2c, GF-H2d, GA-H2e, GD -H2f) positively affect GM.

Hypothesis 3 (H3): Factors of GIP (SE-H3a, SW-H3b, RR-H3c, GF-H3d, GA-H3e, GD -H3f) positively affect GS.

Green Hotel Image (HI) and GL

The image of an organization is defined as a brand of the organization related to consumers’ memories; consumers’ perception forms an overall image of a firm. Similarly, Hotel image (HI) is a series of consumers’ perceptions related to an image of the environment-friendly of a hotel, it is committed to carrying out sustainable initiatives to reduce the negative environmental consequences and affect the local community (J. Wang et al., 2018b). More recently, Chung (2020) viewed green hotel image (HI) as the aspect of green initiatives, the hotels that run their business by applying GIP to minimize the carbon footprint of the hotel’s operations. In Taiwan, M. H. Wu et al. (2016) stated that HI related to applying GIP by using new technologies, applying cleaner technology to reduce waste and protect the environment, M. H. Wu et al. (2016) pointed out that HI positively affected consumer loyalty toward green hotels, and HI is a key mediation variable. In addition, Pascual-Fernández et al. (2021) also found that HI positively affects GS, HI increases customer satisfaction, HI also enhances tourists’ pro-environmental intention, customers perceived HI and restaurants’ image increase customers’ willingness to purchase more services of a green hotel and restaurant because of environment-friendly aspects of HI (Han, Moon, & Hyun, 2020). GIP increased green cafe image, improved consumers’ environment-friendly attitudes, and formed GM, and it expressed a high GS toward green café (Chung, 2020). In Turkey, Yarimoglu and Gunay (2020) found an overall HI is an antecedent of GL and enhances GS when hotels apply GIP. Furthermore, customers viewed HI as a memorable service, they got it from their tourism experience with a positive mood and amazing image, and guests can participate in GA to protect the environment. HI reflected a powerful and outstanding when it was compared to other hotels because the impression of green hotels has been created in tourists from a green image, and improved GL (Han, Moon, & Hyun, 2020), many studies found a positive relationship between HI and GS (Chung, 2020; Pascual-Fernández et al., 2021). However, previous studies only examined HI plays as an independent variable to explore the direct effect of GS, and GL, it lacks a study in the hospitality field in HCM City, Vietnam, to fill this research gap, so this study tested HI plays as an independent variable, mediating variable, and dependent variable to exploring the effects of HI on GM, GS and GL, which was not examined sufficiently from previous studies,. We supposed the hypothesis.

Hypothesis 4-1 (H4-1): Green hotel image positively affects guest’s positive mood-H4-1a, guest satisfaction-H4-1b, guest’s return intention-H4-1c.

Guest’s Positive Mood (GM) and GL

Positive mood is viewed as the expression of happiness, cheeriness, peacefulness, and warmth state that express the feelings of an individual, whereas negative mood expresses unhappiness, anxiety, guilt, and depression state (Kocabulut & Albayrak, 2019). In the tourism industry, many studies found a positive relationship between positive mood and GS, and behavior intention (Taheri et al., 2017), customer behavioral intention to stay at a green hotel with high positive moods as delighted, happy, relaxed, pleased, excited (Kocabulut & Albayrak, 2019). The GM here acts as a mediating variable, a bridge to connect personal decision-making and organizations, focusing on a memory of the product, and judgment, it also interprets information about a process and social behavior (Chen et al., 2013). However, Afridi et al. (2023) found that guests had a positive on GA, expressed guests’ participation in solving environmental issues, GA made them feel happy, and had high values in social behavior (Taheri et al., 2017). In Singapore, Pornpitakpan et al. (2017) explored the impulse buying behavior has a positive mood that can increase a higher impulse to purchase products, and GM plays a key mediation variable between salespersons’ service quality and impulse buying (Kocabulut & Albayrak, 2019). Some scholars pointed out humans habitually strive for a positive mood and avoid a negative mood, customers would avoid the services in a negative mood, but customers would repeat the service and become loyal to the service with a positive mood, the influence of green creativity on GM in the lodging industry was limited (Afridi et al., 2023), and the results of GM were inconsistent and widely debated by many scholars in other field (Pornpitakpan et al., 2017). As discussed above, it needs more effort to explore the relationship between these factors. Thus, this study proposes the hypothesis.

Hypothesis 4-2 (H4-2): Guest’s positive mood positively affects guest satisfaction-H4-2a, guest’s return intention-H4-2b.

Guest Satisfaction (GS) and GL

Tourists’ satisfaction is defined as an emotional response generated from service experience (Oliver, 1981), GS was defined “as a cognitive process that compares customer experience and its initial reference base” (Merli et al., 2019, p.170). GS reflected guests’ expectations (Yarimoglu & Gunay, 2020). GS focused on happiness in choosing a hotel to stay because of its green image, environmental friendliness, green operations, great feeling when the hotel applies GIP, satisfaction with the green services, and policies, getting experience to do green practices, and met expectations (Yarimoglu & Gunay, 2020; Yusof et al., 2017). In addition, GS is a fundamental factor for the survival of any business, so organizations measure GS through the evaluations of customers. Therefore, it needs more efforts to discover a relationship between GS and GL (Merli et al., 2019; Yusof et al., 2017), GS is the most critical component to enhancing customers and maintaining a competitive business (Han, Ariza-Montes et al., 2020; Ning et al., 2023; S. Wang et al., 2022). Thus, we proposed the hypothesis.

Hypothesis 4-3 (H4-3): Guest satisfaction positively affects guest’s return intention.

Mediating Role of HI, GM, and GS

The GIP used a range of practical changes to respond to customers’ demands. Therefore, when the hotels apply GIP, whether the tourists return to a green hotel, is a critical question to clarify the factors of GIP that drive consumer green behavior, it needs to integrate consumers in understanding the benefits of waste reduction practices and support green services. In China, J. Wang et al. (2018a) have extended the TPB to understand the customer behavioral intention, the empirical findings showed that the perceived effectiveness and the environmental concern had a positive impact on both consumer attitudes and GL. However, environmental concerns did not affect GL in China. Similarly, Yarimoglu and Gunay (2020) explored the customers’ perspectives in the Izmir City context, the findings reported that subjective norms, attitudes, GIP, and HI had a positive significant influence on GL. In a cafe, eco-friendly practices have a positive and significant impact on a green cafe’s image (Chung, 2020). GIP has a significant impact on HI and enhances guest loyalty (J. Wang et al., 2018b), the hotels apply green initiatives to increase customer loyalty and repeat purchase of green services, return a destination, satisfied with their stay in a green hotel (Yarimoglu & Gunay, 2020). Existing hospitality literature tackled a large set of components to measure GL such as destination image, perceived quality, and GS, but it lacked research to explore a relationship between factors of GIP and GL through the mediation of HI, GM, and GS. Pascual-Fernández et al. (2021) reported that perceived HI and GS are the main factors that lead to the organizations applying GIP, which expresses the hotels’ capability and can improve tourist loyalty and build HI. It also lacks a study to explore HI, GM, and GS play as mediating variables to predict the relationship between antecedents of GIP and GL. We consider these constructs can be a critical successful factor in carrying out GIP and understanding deeper consumer green behavior. Therefore, we proposed the following hypotheses:

Hypothesis 5 (H5): HI, GM, and GS mediate the relationship between GIP (SE-H5.1a; H5.2a; H5.3a; H5.4a; H5.5a, SW-H5.1b; H5.2b; H5.3b; H5.4b; H5.5b, RR-H5.1c; H5.2c; H5.3c; H5.4c; H5.5c, GF-H5.1d; H5.2d; H5.3d; H5.4d; H5.5d, GA-H5.1e; H5.2e; H5.3e, H5.4e; H5.5e, GD -H5.1f; H5.2f; H5.3f; H5.4f; H5.5f) GL.

The proposed research model is shown in Figure 1.

Figure 1.

Hypothesized research model.

Methodology

Survey Design

Data was collected through a survey based on the research model that was adapted from previous studies to build a questionnaire. To ensure the content validity of the questionnaire through literature review and experts’ judgment. Before sending a questionnaire to respondents, we conducted a pilot test with the panel consultants including four lecturers from the hospitality industry and seven managers of green hotels. These experts gave feedback on the measurement scales and improved linguistic clarity and accuracy; the results of the feedback decreased superfluous measurements. Then, a questionnaire was modified and finalized by our research group. We sent it directly to the hotels to conduct a semi-structured with 20 hotel guests for the pretest to estimate the time to answer a questionnaire and measure the suitability of the questionnaire to make sure the respondents understand it (Merli et al., 2019), after that, we conducted the minor changes on complex sentence, wording of sentences, phrases to advance clarity, and readability of the measurements. A final version was built. In part one, GIP was represented by GF; GA; GD; SE; SW; and RR. A “5- 5-point Likert-type scale” was used to measure these factors, ranging from 1 to 5 “strongly disagree to strongly agree” (Mai et al., 2022). We used five indicators to gauge GD (Han, Ariza-Montes et al., 2020; Legrand et al., 2022; Merli et al., 2019; Trang et al., 2019), six indicators measure GA (Merli et al., 2019), five indicators gauge GF adapt from the scholars (Berezan et al., 2014; Trang et al., 2019), 17 indicators gauge SE; SW; RRadapt from these scholars (Chung, 2020; Merli et al., 2019). Part two was designed to identify customer behavior, GL was adapted from Yarimoglu and Gunay (2020), Merli et al. (2019), four indicators were used to gauge a HI that was adapted from Chung (2020), Yarimoglu and Gunay (2020). Six indicators were used to gauge GS that were adapted from Yarimoglu and Gunay (2020), Yusof et al. (2017), Merli et al. (2019). Five indicators were used to gauge GM and were adapted from Kocabulut and Albayrak (2019), Taheri et al. (2017). Part three consisted of the characteristics of respondents.

Data Collection and Sample Description

A self-administered questionnaire was conducted from green hotels, data was collected from the hotel guests who stayed at the green hotels in HCM City, Vietnam. The first criteria for choosing the hotel guests, they must be 18 years and above, stayed at a green hotel at least one time, and agreed to participate in this study voluntarily, we used convenience sampling and snowballing sampling techniques to select the hotel guests (Mai et al., 2022; Yusof et al., 2017), because of convenience sampling where subjects are readily available and easy to recruit for the study, and it is in proximity of the researcher and a systematic sampling, it also provides an inexpensive way to reach a large sample, it makes the research more relevant and representative (Hair et al., 2011), whereas, snowball sampling is where a chain referral exists and a fast technique to recruit the target population. A researcher first selects a respondent to collect data then this respondent refers one or more respondents, and, in this chain, everyone refers one or more respondents until the requirements of the researcher are fulfilled (Hair et al., 2010). Before we surveyed, we sent an email to the hotel managers to get an acceptance to survey their guests and explained the purpose of the survey and the value of this research. Finally, hotel managers agreed to support us, so we selected 15 green hotels in HCM City to survey because these hotels are famous and have high ratings from tourists (Booking.com), with the help of hotel managers and staff from 15 green hotels delivered 1,200 questionnaires to their guests from July to December 2022. After 6 months, a total of 1,100 questionnaires were sent back, there were 42 questionnaires were rejected because of missing answers to the questions, and only 1,058 completed questionnaires were used for analysis.

Regarding sample size, this research adopted a rule of thumb from Hair et al. (2013), the minimum sample size is 10 times the greatest total of structural paths directed in PLS-SEM at precise latent constructs (Hair et al., 2013), a conceptual model of this study consists of 10 constructs go with 51 indicators, so the minimum sample size was at least 510 the required samples (51 × 10 = 510 respondents were needed). To ensure the sample size is adequate for getting accurate and running this research model successfully, it’s better to get more respondents to meet the generalization and reliability of data collection (Hair et al., 2011).

As the results of descriptive analysis, the demographic profile of 1,058 respondents showed that in the cohort of 1,058 hotel guests, 537 were male (50.8%), and 521 were female (49.2%). The largest age group from 18 to 20 years with 366 (34.6%), next to the age group from 21 to 30 years with 319 (30.2%), from 31 to 40 years with 278 (26.3%), from 41 to 60 years with 47 (4.4%,), below 18 years with 40 (3.8%), above 60 years with 8 (0.8%). Regarding the participants’ education level, the largest group is 520 (49.1%) were university graduates, 377 were college graduates (35.6%), 59 were post-graduate (5.6%), 57 were high school diplomas (5.4%), 45 were vocational schools (4.3%). Regarding the average monthly income level, with 20 million VND or above (32.2%), between 11 and 12 million VND (28.4%), between 5 and 10 million VND (28.2%), 5 million VND or less (11.2%). For job positions, about 30.6% are categorized as official workers, about 22.5% are business owners, approximately 18.5% are managers/supervisors, about 11.5% are freelancers, about 10.9% are students, about 0.9% are housewives, and 5.0% are others. Regarding the number of times, hotel guests used services at a green hotel, used services three times 285 (26.9%), used services once time 256 (24.2%), two times 238 (22.5%), four times 163 (15.4%), five times 115 (10.9%), and six times or more 1 (0.1%).

Data Analysis

This study employed the “Partial Least Squares Structural Equation Modeling” PLS-SEM to assess the structural equation models and test research hypotheses (Hair et al., 2013). Hair et al. (2011) pointed out that “PLS-SEM is a regression-based approach to minimize the residual variances of the endogenous constructs” (Merli et al., 2019, p. 172), the PLS-SEM was selected due to the exploratory and confirmatory nature of this research and can solve a complex research model by predicting the potential effective relationships among the latent variables (Hair et al., 2017), moreover, it can handle well the reflective factors and “non-normal distributions relatively” and the measures in this study were developed with a 5 Likert Scale, data are the “non-normal data distributions” (Hair et al., 2012). In addition, the PLS-SEM method performed well with the mediating variables analysis, which is used to explore the relationship among these constructs in this current research model, many studies also used this method (Hair et al., 2011). We used SmartPLS version 3.0 software by Ringle et al. (2015) to analyze data in two steps (Hair et al., 2013). Firstly, the measurement items of the outer model were examined to estimate their reliability and validity (Alam et al., 2023; Hair et al., 2013). Secondly, the inner model (structural equation model) is assessed to check if there are any potential relationships among the latent variables (Hair et al., 2019)

Results

Measurement Model Analysis

To estimate the research model with a two-step procedure, the first step was conducted to assess the measurement model that checked that all the investigated factors were appropriately estimated through the indicators. According to Hair et al. (2011), the reliability and validity were established based on factor loading must be larger than 0.50, average variance extracted (AVE) must be at least or larger than 0.50, and composite reliability (CR) is equal to or larger than 0.60 are considered as a satisfaction for the measurement of indicator (Ahmad & Karadas, 2021). Table 2 shows the results of assessing the measurement model. In this study, the indicators’ outer loading for each construct is above the 0.70 threshold presents the measurement of indicator reliability with high satisfaction (Hair et al., 2011), the CR values ranged from 0.873 to 0.929, the CA values ranged from .782 to .909 indicating the model of highly accurate. The convergent validity was assessed by using AVEs, the AVEs threshold values should be at least 0.50 or larger are accepted (Fornell & Larcker, 1981; Khan et al., 2022), all the AVEs exceed 0.5 thresholds, ranging from 0.625 to 0.740 in this study. Therefore, AVE values are suitable with the suggestion of “the rules of thumb for model evaluation” (Hair et al., 2011). Correspondingly, all factors were adequate (see Table 1).

Table 1.

Measurement Model Results.

Constructs & items	Factor loadings	CA	rho_A	CR	AVE
Green innovation practices (GIP): Energy saving (SE)		.886	0.890	0.913	0.637
SE₁	0.811
SE₂	0.809
SE₃	0.820
SE₄	0.722
SE₅	0.806
SE₆	0.816
Water saving (SW)		.852	0.865	0.893	0.625
SW₁	0.784
SW₂	0.773
SW₃	0.766
SW₄	0.801
SW₅	0.826
Recycling & reusing (RR)− 6 items		.897	0.898	0.921	0.660
RR₁	0.811
RR₂	0.811
RR₃	0.822
RR₄	0.823
RR₅	0.820
RR₆	0.788
Food focus green attributes (GF)		.879	0.883	0.912	0.674
GF₁	0.827
GF₂	0.825
GF₃	0.822
GF₄	0.820
GF₅	0.811
Green activities (GA)		.902	0.902	0.924	0.670
GA₁	0.832
GA₂	0.817
GA₃	0.816
GA₄	0.817
GA₅	0.807
GA₆	0.823
Green physical environment design (GD)		.887	0.889	0.917	0.689
GD₁	0.800
GD₂	0.835
GD₃	0.837
GD₄	0.824
GD₅	0.854
Green hotel image (HI)		.883	0.883	0.919	0.740
HI₁	0.853
HI₂	0.866
HI₃	0.874
HI₄	0.848
Guest satisfaction (GS)		.909	0.909	0.929	0.687
GS₁	0.826
GS₂	0.834
GS₃	0.823
GS₄	0.828
GS₅	0.838
GS₆	0.824
Guest’s positive mood (GM)		.898	0.899	0.925	0.711
GM₁	0.847
GM₂	0.842
GM₃	0.832
GM₄	0.839
GM₅	0.856
Guest’s Return Intention (GL)		.782	0.785	0.873	0.696
GL₁	0.828
GL₂	0.849
GL₃	0.826

Next, the discriminant validity was evaluated with the square root of the individual construct AVE (Hair et al., 2011), the criterion of Fornell and Larcker (1981) proposed that an indicator’s loading must be larger than all its cross-loading, so it established a discriminant validity (Ahmad & Karadas, 2021; Hair et al., 2011), all of the reflective constructs are adequate discriminant validity, the latent components with a value of 0.50 or higher that were established the discriminant validity (Hair et al., 2011; Mai et al., 2022). In addition, Henseler et al. (2015) revealed that the HTMT ratio should be smaller than 0.90 to achieve the discriminant validity for each construct (Alam et al., 2023; Mamun et al., 2023), the results of this study are appropriate for both Fornell and Larcker (1981) criterion and the HTMT ratios of Henseler et al. (2015; see Tables 2 and 3). Therefore, the results fulfilled the discriminant validity.

Table 2.

Discriminant Validity Applying Fornell-Larcker Criteria.

Constructs	Mean	SD	SE	GF	GA	HI	GD	GM	GS	GL	RR	SW
SE	3.770	1.044	0.798
GF	3.839	1.057	0.416	0.821
GA	3.847	1.043	0.449	0.534	0.819
HI	3.771	1.082	0.491	0.541	0.560	0.860
GD	3.799	1.061	0.437	0.460	0.473	0.477	0.830
GM	3.808	1.050	0.429	0.480	0.535	0.545	0.442	0.843
GS	3.807	1.053	0.452	0.497	0.579	0.536	0.490	0.501	0.829
GL	3.840	1.019	0.422	0.485	0.542	0.533	0.458	0.584	0.542	0.834
RR	3.881	1.021	0.468	0.487	0.556	0.527	0.500	0.519	0.582	0.517	0.813
SW	3.682	1.082	0.381	0.357	0.393	0.411	0.393	0.376	0.426	0.357	0.451	0.790

Table 3.

Discriminant Validity for Heterotrait-Monotrait Ratio (HTMT).

Constructs	SE	GF	GA	HI	GD	GM	GS	GL	RR
SE
GF	0.471
GA	0.501	0.598
HI	0.551	0.611	0.627
GD	0.490	0.520	0.527	0.537
GM	0.477	0.536	0.594	0.611	0.494
GS	0.502	0.553	0.639	0.598	0.545	0.554
GL	0.504	0.581	0.645	0.639	0.547	0.693	0.642
RR	0.524	0.548	0.617	0.592	0.560	0.576	0.644	0.614
SW	0.430	0.410	0.438	0.464	0.438	0.416	0.475	0.431	0.504

Structural Model Analysis

The structural equation model was assessed, and the main threshold to assess the structural model is “the path coefficient significance level, the coefficient of determination, and cross-validated redundancy” (Hair et al., 2011; Merli et al., 2019, p. 175). R square ranged at .25; .50, and .75 were assessed as a weak accuracy, a moderate accuracy, and substantial thresholds (Hair et al., 2011), cross-validated redundancy (Q²) is obtained through a blindfolding procedure to access “the predictive relevance of the exogenous constructs on endogenous constructs” (Alam et al., 2023; Merli et al., 2019, p. 175). The values below 0 ensure the model’s predictive relevance and Q² values are higher than 0, 0.25, and 0.50, which was depicted as a small, medium, and large predictive relevance of the PLS-path model (Hair et al., 2011). Table 4 displays R square and Q².

Table 4.

Assessment of Q² and R².

Constructs	R ²	Q ²
Green hotel image	.472	0.346
Guest positive mood	.430	0.301
Guest satisfaction	.495	0.336
Customer’s return intention	.451	0.309

Hypothesis Testing

The Hypothesis testing was performed through a bootstrapping process to determine the path coefficients’ significance with 2000 subsamples by using the SmartPLS version 3.0 software (Ringle et al., 2015). Table 5 shows the results of the path analysis, and must be supported at $ρ < 0.05; t > 1, 96$ (Alam et al., 2023; Hair et al., 2017). The result for the first prediction the hypothesis H1 examined the relationship between factors of GIP and HI (SE with β = .164; ρ = .000; SW with β = .086; ρ = .008; RR with β = .137; ρ = .001; GF with β = .214; ρ = .000; GA with β = .213; ρ = .000; GD with β = .104; ρ = .004) are supported, therefore, the H1(a-f) were confirmed. Furthermore, hypothesis H2 examined the relationship between factors of GIP and GM in five out of six factors of GIP (SE with β = .073; ρ = .024; RR with β = .160; ρ = .000; GF with β = .109; ρ = .005; GA with β = .184; ρ = .000; GD with β = .075; ρ = .039) are supported, therefore, hypothesis H2(a; c; d; e; f) were accepted, however, except H2b (SW with β = .050; ρ = .113 > .05) saving water was not positive effect GM, so H2b was rejected. Similarly, hypothesis H3 examined the relationship between factors of GIP and GS, five out of six factors of GIP have positively related to GS (SW with β = .082; ρ = .007; RR with β = .211; ρ = .000; GF with β = .087; ρ = .023; GA with β = .203; ρ = .000; GD with β = .102; ρ = .005), therefore the H3(b; c; d; e; f)) were confirmed, only except H3a (SE with β = .063; ρ = .057 > .05) saving energy was not positive related to GS, so H3a was rejected. Additionally, hypothesis H4 was tested, and H4-1 examined the relationship between HI, GM, GS, and GL. HI and GM (with β = .206; ρ = .000), GS (with β = .108; ρ = .009), and GL (with β = .208; ρ = .000) were supported, so hypotheses H4-1(a-c) were accepted. In addition, the H4-2 has tested the relationship between GM, GS, and GL, GM and GS (with β = .080; ρ = .031), GM and GL (with β = .340; ρ = .000) were supported, so H4-2(a-b) were confirmed. Finally, hypothesis H4-3 was examined, and the results found that GS was positively related to GL (with β = .260; ρ = .000), therefore, hypothesis H4-3 is supported, and all hypotheses were confirmed.

Table 5.

Hypothesis Testing.

Hypotheses	Path relationship	$β - values$	T–statistics	$ρ -$ value	Results
H_1a	SE → HI	.164	5.204	.000	Supported
H_1b	SW → HI	.086	2.650	.008	Supported
H_1c	RR → HI	.137	3.353	.001	Supported
H_1d	GF → HI	.214	5.937	.000	Supported
H_1e	GA → HI	.213	5.715	.000	Supported
H_1f	GD → HI	.104	2.910	.004	Supported
H_2a	SE → GM	.073	2.254	.024	Supported
H_2b	SW → GM	.050	1.583	.113	Rejected
H_2c	RR → GM	.160	3.813	.000	Supported
H_2d	GF → GM	.109	2.798	.005	Supported
H_2e	GA → GM	.184	4.632	.000	Supported
H_2f	GD → GM	.075	2.061	.039	Supported
H_3a	SE → GS	.063	1.907	.057	Rejected
H_3b	SW → GS	.082	2.678	.007	Supported
H_3c	RR → GS	.211	5.775	.000	Supported
H_3d	GF → GS	.087	2.278	.023	Supported
H_3e	GA → GS	.203	5.032	.000	Supported
H_3f	GD → GS	.102	2.796	.005	Supported
H_4-1a	HI → GM	.206	5.144	.000	Supported
H_4-1b	HI → GS	.108	2.601	.009	Supported
H_4-1c	HI → GL	.208	5.873	.000	Supported
H_4-2a	GM → GS	.080	2.162	.031	Supported
H_4-2b	GM → GL	.340	10.063	.000	Supported
H_4-3	GS → GL	.260	7.129	.000	Supported

Testing the Mediation Effect

For the mediation, HI, GM, and GS mediate the relationship between factors of GIP and GL (Hypothesis 5), we utilized Hayess (2013) process analysis of mediation with formulate: “Direct effect of X on Y = c’”; “Indirect effect of X on Y through M_i = a_i × b_i”; and “Indirect effect of X on Y through M₁ and M₂ in serial = a₁ × d₂₁ × b₂” (Hayes, 2013; Merli et al., 2019). Moreover, this study used a bootstrapping procedure with 2000 subsamples with a 95% confidence interval (Hair et al., 2011; Y. Wang et al., 2020), through a bootstrap analysis, the mediation effects are tested, which inspected the indirect impact of an independent construct on a dependent construct via a mediation construct (Y. Wang et al., 2020).

First, HI (H5.1a-f) mediates the relationship between GIP factors and GL, and the results showed that HI positively mediated (SE with β = .034; ρ = .000; SW with β = .018; ρ = .013; RR with β = .028; ρ = .004; GF with β = .045; ρ = .000; GA with β = .044; ρ = .000; GD with β = .022; ρ = .011) between GIP factors and GL, so hypothesis H5-1(a-f) was supported completely (see Table 6). Second, GM (H5-2a-f) mediates the relationship between GIP factors and GL, five out of six factors of GIP have an indirect effect on GL through GM (SE with β = .025; ρ = .029; RR with β = .054; ρ = .001; GF with β = .037; ρ = .009; GA with β = .063; ρ = .000; GD with β = .026; ρ = .047), H5-2(a; c; d; e; f) are supported, except SV was not positively indirect effect on GL through GM, so H5-2b (SW with β = .017; ρ = .117 > .05) was rejected, therefore, GM partial mediated between GIP factors and GL (see Table 6).

Table 6.

Result of Mediating Effect.

Hypotheses	Path relationship	$β$	T values	p Values	Results
H_5-1a	SE → HI → GL	.034	3.842	.000	Supported
H_5-1b	SW → HI → GL	.018	2.477	.013	Supported
H_5-1c	RR → HI → GL	.028	2.846	.004	Supported
H_5-1d	GF → HI → GL	.045	4.255	.000	Supported
H_5-1e	GA → HI → GL	.044	3.838	.000	Supported
H_5-1f	GD → HI → GL	.022	2.542	.011	Supported
H_5-2a	SE → GM → GL	.025	2.188	.029	Supported
H_5-2b	SW → GM → GL	.017	1.568	.117	Rejected
H_5-2c	RR → GM → GL	.054	3.456	.001	Supported
H_5-2d	GF → GM → GL	.037	2.608	.009	Supported
H_5-2e	GA → GM → GL	.063	4.112	.000	Supported
H_5-2f	GD → GM → GL	.026	1.991	.047	Supported
H_5-3a	SE → GS → GL	.016	1.853	.064	Rejected
H_5-3b	SW → GS → GL	.021	2.561	.011	Supported
H_5-3c	RR → GS → GL	.055	4.342	.000	Supported
H_5-3d	GF → GS → GL	.023	2.129	.033	Supported
H_5-3e	GA → GS → GL	.053	3.785	.000	Supported
H_5-3f	GD → GS → GL	.026	2.509	.012	Supported
H_5-4a	SE → HI → GM → GL	.012	3.405	.001	Supported
H_5-4b	SW → HI → GM → GL	.006	2.238	.025	Supported
H_5-4c	RR → HI → GM → GL	.010	2.694	.007	Supported
H_5-4d	GF →HI → GM → GL	.015	3.843	.000	Supported
H_5-4e	GA → HI → GM → GL	.015	3.770	.000	Supported
H_5-4f	GD → HI → GM → GL	.007	2.514	.012	Supported
H_5-5a	SE → HI → GS → GL	.005	2.192	.028	Supported
H_5-5b	SW → HI → GS → GL	.002	1.711	.087	Rejected
H_5-5c	RR → HI → GS → GL	.004	1.978	.048	Supported
H_5-5d	GF → HI → GS → GL	.006	2.216	.027	Supported
H_5-5e	GA → HI → GS → GL	.006	2.299	.022	Supported
H_5-5f	GD → HI → GS → GL	.003	1.798	.072	Rejected

Third, GS (H5-3a-f) mediates the relationship between GIP factors and GL, five out of six factors of GIP have a positively indirect effect on GL (SW with β = .021; ρ = .011; RR with β = .055; ρ = .000; GF with β = .023; ρ = .033; GA with β = .053; ρ = .000; GD with β = .026; ρ = .012), H5-3(b; c; d; e; f) were supported, except saving energy was not positively indirect effect on GL through GS (SE with β = .016; ρ = .064 > .05), so H5-3a was rejected, therefore, the findings reported that GS partial mediated between GIP factors and GL (see Table 6).

Four, H5-4(a-f) both HI and GM mediate the relationship between GIP factors and GL, the results showed positive significance in all proposed hypotheses, so both HI and GM fully mediated between GIP factors and GL, hypothesis H5-4(a-f) were supported completely (see Table 6).

Finally, test hypothesis H5-5(a-f), both HI and GS mediate the relationship between GIP factors and GL, four out of six factors of GIP have an indirect effect on GL, H5-5(a; c; d; e) were confirmed, however, H5-5a and H5-5f non-significant, therefore both HI and GS partial mediated betweenGIP factors and GL (see Table 6).

Figure 2 below presents PLS bootstrapping results of a structural model.

Figure 2.

PLS bootstrapping results of a structural model.

Discussion

Discussion of Findings

This research aimed to advance the application of the TPB model to predict guests’ green behavior, it examined the relationship between GIP factors and HI, GM, and GS. Furthermore, it also investigated how HI, GM, and GS are mediated in the relationship between GIP and GL toward green hotels in HCM City, Vietnam.

Firstly, to answer what factors of GIP directly affect HI, GM, and GS? As proposed, all six factors of GIP have a positive impact on HI (H1) in HCM City, the findings were in line with previous studies (Asadi et al., 2020; Chung, 2020; J. Wang et al., 2018b). These findings indicated that HI increases if the hotels simultaneously apply GIP and increase green hotel image attributes that are mainly focused on being amazing and positive, fostering sustainable business models, be more powerful and outstanding than other hotels based on environmental factors (Han, Ariza-Montes et al., 2020; Yarimoglu & Gunay, 2020). Furthermore, the study predicted factors of GIP positively affect GM (hypothesis H2) five out of six antecedents of GIP directly affected GM, the results were in line with previous studies (Kocabulut & Albayrak, 2019; Taheri et al., 2017), if the guest perceives the hotels as its’ socially responsible for protecting the natural environment, their positive moods toward the green hotel’s increases, and their intention to return a green hotel (Berezan et al., 2014; Chung, 2020; Fawehinmi et al., 2020; Legrand et al., 2022; Merli et al., 2019; Trang et al., 2019), however, SW of GIP is no significant impact on GM, in contrast, Taheri et al. (2017) revealed that customer participation in saving water to implement GIP, which creates customer’s values and enhances their positive mood for a willingness to purchase green products. Thus, the study predicted that factors of GIP directly affect GS (hypothesis H3) five out of six antecedents of GIP affect GS, these results agreed with previous studies (Cabral & Marques, 2023; Merli et al., 2019; Yarimoglu & Gunay, 2020). It indicates that hotels with the ability to simultaneously adopt GIP can enhance GS (Berezan et al., 2014; Chung, 2020; Legrand et al., 2022; Merli et al., 2019; Trang et al., 2019; Yusof et al., 2017). However, the SE of GIP is no significant impact on GS, this finding is rather disappointing and is inconsistent with previous studies (Ning et al., 2023; Shahzad et al., 2022; S. Wang et al., 2022). The plausible element of these different results may be the customers have not evaluated a high awareness of SE in GIP, the customers may think that the application of innovative technology to save energy that is the hotel’s investment and the hotel’s duty.

Secondly, to apply the TPB model to predict HI, GM, and GS related to GL toward eco-friendly hotels the results found that HI has a positive significance effect on GM, confirmed the measurement scales adapted from Kocabulut and Albayrak (2019), Taheri et al. (2017), then, evidence of a significant positive relationship between HI and GS was identified, the finding agreed with (Merli et al., 2019; Yarimoglu & Gunay, 2020; Yusof et al., 2017), and HI direct affect GL the finding was in line by Yarimoglu and Gunay (2020), Merli et al. (2019), this finding is an unique and contributes the latest evidence in green hotels, because previous studies have not yet conducted an empirical study of these constructs, however, the theoretical has the similar opinion, the evidence of these findings were confirmed measurement scales from previous studies and are in line with (Cossío-Silva et al., 2019; M. H. Wu et al., 2016), the HI lead to guest behavior to be a willingness to put up a higher price for eco-friendly products and services (Chung, 2020), HI and GS already supported by some studies (Pascual-Fernández et al., 2021; Yarimoglu & Gunay, 2020). The results reported that HI directly affected GL agreeing with Yarimoglu and Gunay (2020), Han, Moon, and Hyun (2020). This result indicates that the implementation of marketing for a HI is a critical condition for green hotels, and is a driver of GS, GM, and GL. Moreover, to predict the relationship between GM and GS, and GL, the results revealed that GM directly affected GS, and GM directly affected GL, confirmed by previous studies (White, 2006). The result implies that hotels’ ability to implement GIP promotes the GM and improves successfully in both GS and GL to green hotels providing them with various innovative services and eco-friendly products and improving efficiency and a sustainable environment. Moreover, the result reported GS has a positive impact on GL, it is consistent with Pascual-Fernández et al. (2021), Merli et al. (2019), GS enhances GL toward green hotels.

Finally, to investigate the mediating roles of HI, GM, and GS between factors of GIP and GL toward eco-friendly hotels. The results indicated that HI fully mediates between GIP factors and GL, it indicated that when the hotels establish a HI and it builds a bridge between GIP factors and GL to the hotels, the results were in line with previous studies (Chung, 2020; J. Wang et al., 2018b) who also found HI played an antecedent factor increased GL, and HI is a key mediation variable between green marketing orientation and consumer loyalty. Regarding GM, the results reported that GM partially mediates between GIP factors and GL, and five out of six antecedents of GIP have a positive significance indirect impact on GL through GM, the results were confirmed measurement scales from previous studies (Fawehinmi et al., 2020; Kocabulut & Albayrak, 2019; Mai et al., 2022; Taheri et al., 2017), positive moods build a bridge between GIP factors and GL to the hotels when they perceive that the hotel is highly participating in CSR activities, these findings were supported the measurement scales were adopted from previous studies (Kocabulut & Albayrak, 2019; Pornpitakpan et al., 2017). However, saving water from GIP was not significant, and it did not indirectly affect GL through GM, this occurred because the GM effect between deciding to choose a hotel to stay in is different from the perspective of the owners of the hotel must invest financial to implement GIP, which is not evident in the GL in HCM City, Vietnam. Regarding GS, the results reported that GS partially mediated between factors of GIP and GL, five out of six factors of GIP yielded a positive significant indirect effect on GL through GS, and the results were confirmed by measurement scales from previous studies (Han, Ariza-Montes et al., 2020; Legrand et al., 2022; Merli et al., 2019; Trang et al., 2019; Yarimoglu & Gunay, 2020; Yusof et al., 2017), GS builds a bridge between GIP factors and GL to the hotels when guests perceive that the hotel is highly participating in CSR activities, the findings were in line with previous studies (Merli et al., 2019; Yarimoglu & Gunay, 2020), however, except the SE was not reported as an important predictor to affect GL.

Regarding HI and GM, this is the first time to investigate both HI and GM played as mediators between factors of GIP and GL, the results reported that HI and GM fully mediated between GIP and GL, and the results confirmed the measurement scales adapted from previous studies (Chung, 2020; Kocabulut & Albayrak, 2019; Merli et al., 2019; Taheri et al., 2017; Yarimoglu & Gunay, 2020), it shows that the higher the perception of GM and HI, the higher the GL to the hotel, such GM and HI build a bridge between GIP factors and GL to the hotels when guests perceive that the hotel is highly participating in CSR activities. The findings were unique and have contributed to the latest empirical evidence in the hospitality literature, so the findings hatched consequential evidence connected factors of GIP and GL via both HI and GM to support all hypotheses. Finally, regarding HI and GS, this was the first research to investigate both mediator variables of HI and GS between GIP factors and GL, the results reported that both HI and GM partially mediated between factors of GIP and GL, and the results revealed that four out of six factors of GIP have a positive significant indirect effect on GL through both HI and GS, the results confirmed measurement scales from previous studies (Berezan et al., 2014; Chung, 2020; Merli et al., 2019; Trang et al., 2019; Yarimoglu & Gunay, 2020; Yusof et al., 2017). However, Saving water and GD were insignificant, therefore, these factors were not important to predict the indirect effect on GL through both HI and GS, the findings of this investigation complemented those of earlier studies (Berezan et al., 2014; Chung, 2020; Merli et al., 2019; Trang et al., 2019; Yarimoglu & Gunay, 2020; Yusof et al., 2017) they have not examined this point yet, it led to an incomplete understanding of GIP factors impacting guest behavioral intentions, here, the current study reported that both HI and GS partially mediate between factors of GIP and GL. and guests are more willing to introduce a green hotel to their friends and enhance their return intention to stay at hotels that apply GIP.

Theoretical Contributions

This study made several important theoretical contributions to the current hospitality literature. Firstly, the findings contributed to integrating two theories that play a backbone for the research model, including innovation theory and TPB, it also contributed as a unique research model to explore relationships among factors of GIP and GL through HI, GM, and GS, which were validated and effectively contributed to the existing GIP factors and consumer behavior literature in different fields. This study, therefore, contributes to TPB (Ajzen, 1985) to predict customer behavioral intentions by connecting HI, GM, and GS in green hotels in the HCM City context (Han, Moon, & Hyun, 2020; Sadiq et al., 2021). findings enrich the hospitality literature more generally by strengthening the direct and indirect relationship between GIP factors and GL through HI, GS, and GM, the findings provided evidence that the customer behavioral intentions to return to stay at a green hotel complies with Ajzen’s TPB model (1985). It reflects customers’ attitudes toward green behavior, including GS and GM are considered important predictors to understanding deeper customer behavioral intentions. On the other hand, this study supported evidence of a significant positive relationship between HI and GL (Yarimoglu & Gunay, 2020) who extended the TPB model, perceived behavioral control based on the customer belief the HI, HI significantly affects customer intentions to return to a hotel and purchase green products. Perceived behavioral control reflects the hotels implementing GIP that builds the resource beliefs in customers, they can perform a certain behavior such as they can carry out GA during their stay at green hotels by implementing GIP with hotels, the research model of this study can be applied in different fields such as the marketing field, tourism field, business strategy, environment field. This study is the first comprehensive investigation of applying innovation theory to advance GIP by adding new factors, the novelty of this study integrated six factors of GIP (energy saving; water saving; recycling & reusing; green foods, green activities; and green design; Chung, 2020; Fawehinmi et al., 2020; Han, Ariza-Montes et al., 2020; Legrand et al., 2022; Mai et al., 2022; Merli et al., 2019; Trang et al., 2019). The hotels apply GIP as a new technology innovation to develop sustainable business in the hospitality industry.

Secondly, the findings provided new light on the new role of mediator variables of HI, GM, and GS to analyze customer green behavior and apply GIP, the findings provided evidence that HI, GM, and GS mediate between GIP and GL, the analysis of these constructs undertaken here, it has extended the body of knowledge in GIP and customer behavior intentions in green hotels in HCM City context, the findings successfully addressed the limitations of previous studies to explore factors of GIP on customer green behavior, because previous studies have not analyzed the relationships among these factors both direct and indirect effects.

Finally, the empirical findings in this research have added to the growing body of hospitality literature, the findings provided a new relationship between factors of GIP and HI, GIP factors and GM, GIP factors and GS, and indirect effects of factors of GIP on GL through the roles of mediating variables of HI, GM, and GS in green hotels in HCM City. this makes our research model distinctive, which has not been examined yet in developing countries like Vietnam. As we know many studies conducted in developed countries (Merli et al., 2019; Yarimoglu & Gunay, 2020). Moreover, the empirical findings expanded the body of knowledge to understand of clearly the TPB model by adding HI, GM, and GS to explore both the direct and indirect impact of GIP on GL and provided a deeper insight into new theoretical contributions of DIT and TPB to understand customers’ behavior intentions to support supported the green hotels products in HCM City context.

Managerial Implications

Based on the findings in this study, there are some practical recommendations for managers of the eco-friendly hospitality sector.

First, to increase GL toward green hotels, the managers of hotels should focus on applying GIP factors to build a strategy to meet the tourists’ demand focus on organic food, GA, SE, RR, GD, and SW leading to HI in HCM City. Moreover, the managers of hotels should design green services, and eco-friendly products, and support local suppliers to protect the environment and enhance GM and GS, these elements were the main factors to increase the rate of tourists returning to stay at green hotels. The findings revealed that HI and GM, GS are the main factors to elicit GL toward green hotels, are the utmost important mediators between factors of GIP and GL. Therefore, the managers of green hotels should pay high attention to applying green innovation management, exploiting green innovation technology to reduce costs, and investing finance and resources for developing sustainable hospitality to build a strategy that hotels become a spearhead economy after the COVID-19 pandemic, and it continues to attract the tourist return to use green hotels services.

Another implication is that the HI affects strongly customer return to green hotels, therefore, the managers of the hotel should develop many green activities that reflect the hotel-implemented GIP with social responsibility activities and environmental activities, GIP expresses a successful business of the hotel to build a brand of HI, the hotels donate finance to protect the environment such as grow trees in the forests or support recycle bins at the parks, and invest money to advertise the theme of health to show the benefits of using organic foods, and the hotels should hold a show or conference to talk about protecting the environment and the benefits of fresh air, and carry out GD, the hotels also applied GA to build a HI and improve GM to choose green hospitality services.

Finally, findings reveal that GA and GD are the main elements to boost GS, GM, and HI enhanced to stimulate effectively GL toward green hotels, therefore, managers of hotels should provide valuable information on GA for tourists through indoor posters, outdoor banners, social media (Facebook, Twister), internet, blogs, website, magazines, and TV. In addition, the managers of hotels should use eco-friendly material to décor the hotels and design with openings for ventilation within the building, and interior decoration that uses surface materials that are easy to sanitize. The hotel rooms are designed within an interior privacy atmosphere, but they show a green space perspective in both the green outdoor and green indoor atmospherics of hotels.

Limitations and Future Research

This study has several limitations. Firstly, the data were collected for 6 months in green hotels in HCM City. Therefore, the data may not represent all target samples to explain general perspectives of customer behavior in the Vietnam context. Therefore, the researcher in the future should survey a wider region to collect a larger sample in all cities in Vietnam, large samples are more accurate in the results. Secondly, the aspects and perspectives were adapted from previous studies in existing literature that may be weak in our context, there were limitations on the quantity and depth of data that could be collected. Therefore, the findings reported that water-saving did not affect GM, and energy-saving did not affect GS. Therefore, future research should investigate further these factors to identify whether GS and GM are more important in supporting water-saving and energy-saving. Another limitation, we used only quantitative methods, so it is weak to evaluate the data on one side only, so in the future, the researcher should use qualitative methods or mixed methods to explore insight into this phenomenon for future studies that may provide further theoretical details. Thirdly, the researcher can apply this research model to different fields because this study examines the GIP only in the hospitality sector in HCM City. The expansion of the current findings can apply to leisure, food and beverage restaurants, travel companies, travel agencies, and consumer behavior in health care, and in the supply chain context, it needs to be examined further in future studies before we have conclusions about this research model can be drawn. Finally, in future studies, practitioners can alternatively consider other mediators to build a more comprehensive research model, such as adding a factor of environmental knowledge, eco-concerned activities, innovation ability, and so on, that are already adopted in customers’ daily lives, and prior experience with green hotels.

Conclusion

The present study successfully developed a research mode by integrating TPB and DIT to predict guests’ return intentions toward green hotels. Specifically, it extended TPB and DIT by establishing the relationships among the factors of GIP with green hotel image, guests’ satisfaction, guests’ positive moods, and return intention to stay at green hotels. As there are limited studies in understanding guests’ behavioral intentions toward green hotels, this study responds to recent calls for research, as highlighted by previous scholars with factors of GIP (Fawehinmi et al., 2020; Han, Ariza-Montes et al., 2020; Legrand et al., 2022; Merli et al., 2019; Trang et al., 2019) and guest’ return intentions (Merli et al., 2019; Yarimoglu & Gunay, 2020) through green hotel image (Chung, 2020; Yarimoglu & Gunay, 2020), guests’ satisfaction (Merli et al., 2019; Yarimoglu & Gunay, 2020; Yusof et al., 2017), guests’ positive moods (Kocabulut & Albayrak, 2019; Taheri et al., 2017).

The mediation analyses with green hotel image (Chung, 2020; Yarimoglu & Gunay, 2020), guests’ satisfaction (Merli et al., 2019; Yarimoglu & Gunay, 2020; Yusof et al., 2017), guests’ positive moods (Kocabulut & Albayrak, 2019; Taheri et al., 2017). Most importantly, the current results reveal that the green hotel image is a complete mediation between GIP and guests’ return intentions. Moreover, guests’ satisfaction and guests’ positive moods partially mediate between GIP and guests’ return intentions, which supports some exciting insights for hotel managers and academicians, the findings extended prior research and advanced hospitality literature on tourists’ behavioral intentions, which is an important extension of the extant theory.

In particular, the results have generated substantial evidence of a significant positive relationship between GIP and green hotel image, guest satisfaction, and guests’ positive mood. However, except for the impact of water-saving didn’t affect guests’ positive mood, and the energy-saving didn’t affect guests’ satisfaction. This study enriches GIP and customer behavioral intentions literature in the hospitality industry, the hotel managers focus on GIP to improve guests’ satisfaction, and positive moods, and to enhance guests’ return intentions to the green hotel context.

Footnotes

ORCID iDs

Mai Ngoc Khuong

Nguyen Thi Minh Phuong

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Green Innovation Practices and Guests’ Return Intention: Mediating Roles of Green Image,Guest’s Positive Mood and Satisfaction

Abstract

Plain language summary

Keywords

Introduction

Literature Review and Hypothesis Development

Theoretical Foundation

Guest Return Intention (GL)

Green Innovation Practices (GIP)

Green Hotel Image (HI) and GL

Guest’s Positive Mood (GM) and GL

Guest Satisfaction (GS) and GL

Mediating Role of HI, GM, and GS

Methodology

Survey Design

Data Collection and Sample Description

Data Analysis

Results

Measurement Model Analysis

Structural Model Analysis

Hypothesis Testing

Testing the Mediation Effect

Discussion

Discussion of Findings

Theoretical Contributions

Managerial Implications

Limitations and Future Research

Conclusion

Footnotes

ORCID iDs

Funding

Declaration of Conflicting Interests

Data Availability

References