1. Introduction
In recent years, sustainability has become an essential issue for the hotel industry, with eco-friendly hotels becoming increasingly popular among environmentally conscious travelers [1]. Eco-friendly hotels are lodging facilities that prioritize sustainable practices to limit their negative impact on the environment while still providing high-quality service to guests [2]. Their goal is to operate the lodging facility in an environmentally responsible manner and provide guests with an enjoyable and comfortable experience. One significant factor that impacts the success and sustainability of these hotels is electronic word-of-mouth (eWOM) [3]. eWOM was recognized as any form of positive or negative communication about a brand or service through digital media [4]. It represents the modern-day version of traditional word-of-mouth marketing, significantly changing how customers make purchasing decisions [5]. The hotel industry is highly competitive, and eWOM has become an essential part of the decision-making process for potential customers [6,7]. eWOM can influence consumers’ purchasing decisions, and positive reviews can lead to an increase in bookings and revenue. Further, eWOM facilitates communication between customers and businesses. Customers can provide feedback about their experiences via social media, review sites, and other channels. This feedback can help hotel managers understand the needs of their customers and improve their services accordingly. Second, eWOM can help hotel businesses gain credibility and enhance their brand reputation. Positive reviews can attract new customers, and negative reviews can damage a hotel’s reputation and deter potential customers from making a reservation [6,8].
Perceived quality (PQ), customer satisfaction (CS), and positive emotional experience (PEE) are some of the most influential factors that affect the generation of positive eWOM [9,10,11,12,13]. In the hotel industry context, PQ denotes the customer’s comprehensive evaluation of the level of service quality provided by the hotel. This includes aspects such as the cleanliness of rooms, quality of food, and the level of customer service. When customers hold a perception of high-quality service, they are more likely to generate positive eWOM [9,10]. CS is another key factor that influences the likelihood of generating eWOM. Customers who are satisfied with their experiences are more likely to share these experiences with others, either through social media or review sites. In contrast, dissatisfied customers are more likely to share negative feedback and discourage others from using the hotel’s services [14,15]. Positive emotional experiences (PEE) can further enhance the likelihood of generating eWOM. When customers have positive emotional experiences during their stay, such as feeling delighted, surprised, or excited, they are more likely to remember and share the experience with others [13]. This can, in turn, create a positive image for the hotel and encourage potential customers to try its services.
In the digital marketing era, eWOM has become increasingly important in influencing customer behavioral intentions [16]. The customer’s revisit intention is considered to be one of the most affected variables by eWOM [17,18]. Consumers in recent times are more likely to search for feedback and reviews online before making decisions to revisit a hotel. Reviews on websites such as TripAdvisor have become integral to consumer decision making [19]. Positive reviews serve as recommendations, while negative reviews often serve as warnings. In either case, reviews massively influence the hotel’s revisit intention [18]. The increasing prevalence of eWOM communication has led to a significant amount of research analyzing its impact on consumer behavior, with specific attention given to its direct influence on ITR [17,18].
Although some research has explored the direct connections between PQ, CS, PEE, and eWOM in various settings, only limited empirical investigations have examined the indirect associations among these constructs. Even the previous research that focused on the direct connections between these constructs found mixed results, with some indicating significant direct effects, while others suggested no significant relationship. These inconclusive findings call for additional empirical research to gain greater insights into the PQ–eWOM, CS–eWOM, and PEE–eWOM relationships. Further, studies investigating the impact of eWOM on customer intention to revisit and its role as a mediator in the relationships between perceived quality, positive emotional experience, customer satisfaction, and intention to revisit are relatively limited, especially in the context of eco-friendly hotels in Saudi Arabia. According to the authors’ knowledge, no prior studies have examined the intermediary role played by eWOM in these relationships. Additionally, this is the first study to examine these associations within the eco-friendly hotel industry of a developing nation, such as Saudi Arabia. Due to growing concerns about sustainability and the impact of tourism on the environment in Saudi Arabia in recent years, the growing trend towards eco-tourism and eco-friendly hotels has become increasingly popular. As a result, investigating the attitude and behavior of Saudi individuals toward their intention to revisit eco-friendly hotels is important. Hence, the present study aims to address these gaps by empirically (1) exploring the direct effect of PQ, CS, and PEE on promoting positive eWOM; (2) examining the intermediating role of CS in the relationships between PQ, PEE, and eWOM; (3) investigating the direct effect of eWOM on the customers’ ITR; and (4) exploring the intermediary role of eWOM in the PQ–ITR, CS–ITR, and PEE–ITR relationships in Saudi Arabian five-star eco-friendly hotels.
The research’s findings contribute substantially to the existing literature on eWOM within the eco-friendly hotel industry for several reasons. Firstly, the study empirically examines the most influential factors affecting the spread of positive eWOM. Secondly, to examine the indirect impact of PQ and PEE on eWOM through CS, using the S-O-R model. PQ and PEE serve as stimuli (S), while CS functions as the organism (O), and eWOM is the response (R). Thirdly, to investigate the potential intermediating role of eWOM in the relationships between PQ, PEE, CS, and ITR, where PQ, PEE, and CS represent (S), eWOM utilizes (O), and ITR employs (R). From a practical point of view, investigating the behavior and attitudes of Saudi individuals toward eco-friendly hotels can provide valuable insights into the factors that influence their decision making and preferences. Understanding Saudi individuals’ perceptions of eco-friendly hotels can help hotels tailor their offerings and marketing strategies to better meet the needs and preferences of their target market. Moreover, exploring the behavior and attitudes of Saudi individuals towards eco-friendly hotels can help promote sustainable tourism practices in the region. By raising awareness of the benefits of eco-tourism and encouraging more individuals to choose eco-friendly accommodation options, we can help minimize the negative impact of tourism on the environment and support the development of a more sustainable tourism industry in Saudi Arabia.
2. Literature Review
2.1. eWOM and Sustainable Marketing Development
Sustainable marketing development refers to the integration of environmental, social, and economic concerns in business operations to ensure long-term success [20]. eWOM plays a critical role in sustainable marketing development by facilitating the exchange of information between consumers and businesses [21]. In terms of economic sustainability, eWOM has become a powerful tool in marketing, and its impact on economic sustainability cannot be overstated. With its ability to increase reach and exposure, reduce marketing costs, improve customer engagement, and increase transparency, eWOM has become a critical part of the marketing strategies of businesses of all sizes [3,22]. One of the most significant benefits of eWOM is its ability to increase a business’s reach and exposure. With the rise of social media and review websites, consumers can share their experiences and opinions with a wider audience than ever before. Positive eWOM can lead to increased sales and revenue for businesses, while negative eWOM can have the opposite effect [23,24]. eWOM is also a cost-effective marketing strategy for businesses. eWOM is relatively cheap and can reach a much larger audience. This cost-effectiveness makes eWOM an attractive option for small businesses and startups that may not have the budget for traditional marketing strategies. Further, eWOM also improves customer engagement, which can have a positive impact on economic sustainability [3,22]. By engaging with customers online, businesses can build relationships and increase customer loyalty. These loyal customers are more likely to become repeat customers and recommend the business to others, leading to increased sales and revenue [25].
Additionally, eWOM can have a significant impact on environmental sustainability. Through the promotion of sustainable products and practices and the raising of awareness about environmental issues, eWOM can be a powerful tool for promoting environmental sustainability [26]. eWOM can affect environmental sustainability through the promotion of sustainable products and practices. Consumers are becoming more aware of the impact their purchasing decisions have on the environment, and as a result, they are seeking out products and services that are environmentally friendly [19,26]. Through eWOM, individuals can share information about these products and services, spreading awareness and encouraging others to make more sustainable choices. Furthermore, eWOM can also be used to raise awareness about environmental issues and encourage action. Social media platforms can be used to share information and educate others about the impact of climate change and other environmental issues. This can lead to a greater understanding of the importance of environmental sustainability and encourage individuals to take action to reduce their environmental impact [27,28].
Finally, eWOM is essential for promoting social sustainability in the hotel industry. Hotels can encourage and manage positive reviews to create a positive image of their commitment to socially responsible practices and attract socially conscious customers [29]. Moreover, eWOM can encourage positive social behaviors and attitudes among customers, leading them to adopt sustainable practices and influencing others to do the same. By utilizing eWOM effectively, hotels can promote social sustainability and strengthen their business operations [29,30].
2.2. The Link between PQ and eWOM
The rise of the Internet and social media has led to a transformation in the way customers engage with businesses, particularly in terms of sharing their experiences and opinions with others [17]. Businesses have become reliant on online reviews and feedback from their customers as effective ways to inform potential customers about the quality and value of their offerings. With the increasing use of online platforms, customer reviews have become a valuable source of information for potential customers to make informed buying decisions [23,24]. This has led to an increased focus on the concept of eWOM, which refers to the electronic sharing of opinions, recommendations, and experiences about a service or product between individuals [31]. One of the key drivers of the eWOM is perceived quality (PQ). PQ was recognized by Zeithaml [32] as a customer’s assessment of the overall quality of a product or service received. A business with a high PQ is more likely to generate positive eWOM, while a business with a poor PQ is more likely to generate negative eWOM [9,10]. Thus, for businesses that aim to enhance their online reputation and, ultimately, their financial performance, it is essential to understand the influence of PQ on eWOM.
Prior research has indicated that PQ has a considerable impact on eWOM. A study conducted by Samad [33] concluded that the quality of perceived service had a substantial positive impact on eWOM. This highlights the importance of delivering high-quality products to customers, as it leads to higher satisfaction levels and generates positive eWOM [34]. Another study by Barlas et al. [35] as well as Sánchez-González and González-Fernández [36] revealed that customers are more likely to share positive feedback and experiences if they have a high opinion of the business or product in question. People are also more likely to actively search for and engage with eWOM about a product or service if they feel it is of high quality. Moreover, in the context of online shopping, Septiari [37] revealed that website quality substantially impacted eWOM intention. Similarly, Dunn et al. [38] confirmed that PQ significantly positively influenced e-loyalty to online travel intermediaries. From the previous, it could be assumed that:
PQ significantly contributes to the positive effect on eWOM.
2.3. The Link between PEE and eWOM
In the age of digital technology, the hospitality industry has placed importance on the availability of information regarding personal experiences shared by other consumers [39]. In previous studies, emphasis was given to emotional experiences linked to restaurants [40], adventure tourism [41], festivals [42], and theme parks [43], but research concerning hotel services is particularly limited. According to Serra-Cantallops et al. [13], in the hotel context, PEE can be described as service encounters that go beyond customer expectations and are flawlessly executed. This could include customized, innovative, and unique experiences that are not commonly found in an average four-star hotel, as well as surprising and unexpectedly high levels of service that differentiate from the norm. More specifically, PEE refers to emotionally engaging offerings in hotel service encounters that generate strong positive emotions and memories. These experiences exceed expectations and are different from the average in terms of global offering, entertainment, pleasure, stimulation, excitement, surprise, curiosity, passion, romance, and trust generation, among others.
Earlier studies demonstrated that the emotional customer experience is a fundamental determinant of post-consumption behaviors. For instance, Bigné et al. [43] and Yuksel and Yuksel [44] found that positive emotions experienced during service encounters serve as a precursor to satisfaction and behavioral intentions. While the tourism industry recognizes the significance of examining customers’ emotions, satisfaction, and behavioral intentions, the link between these three concepts has received significant support. More specifically, several studies affirm that positive customer experiences and emotions are positively linked to recommending positive WOM [45,46,47]. Serra-Cantallops et al. [13] examined the role that PEE played in the generation of eWOM in a sample of 878 customers from the UK and Germany and revealed that PEE significantly positively affected eWOM generation among the investigated participants. They indicated that it may be possible for hotels to generate a great deal of positive eWOM by providing PEE to their guests. Another study in the hotel industry context revealed that providing customers with enjoyable experiences during their time using a product or service can lead to more favorable customer reviews on various eWOM channels [48]. As a result, it could be assumed that by providing customers with PEE, hotels can improve their eWOM reputation. Hence, we suggest the hypothesis that follows:
PEE significantly contributes to the positive effect on eWOM.
2.4. The Link between CS and eWOM
CS is described as the measurement of how well a company’s products or services meet or surpass the expectations of its customers [49]. Due to the growing presence and influence of the Internet and social media, the hospitality industry now places a higher emphasis on delivering exceptional customer satisfaction [50]. As customers have access to more information and options, providing superior customer satisfaction is crucial for businesses to thrive in a highly competitive market and gain positive online reviews and referrals [11,25,50]. In an ever-evolving landscape where customers have the power to influence others to purchase a particular service or product through eWOM, CS is a vital element in the success of businesses [12,15].
In terms of the impact of CS on eWOM, numerous studies have revealed a substantial positive impact of CS on eWOM. For instance, in the setting of the hotel industry, Zhou et al. [15] emphasized that customers who are satisfied with their accommodations are more likely to recommend them to others and are more likely to focus on intangibles such as staff and service than those who are dissatisfied. In addition, Pang [14] in his study on 408 WeChat users illustrated that eWOM engagement was significantly affected by customer satisfaction. More precisely, their research highlighted that satisfied customers are more likely to participate in eWOM and share their positive experiences with others regarding the goods and services they received, while dissatisfied ones are more inclined to post negative reviews that may dissuade others from using the service. Similarly, in the context of digital platform services, Ruiz-Alba et al. [12] showed that higher levels of CS led to more positive eWOM. In addition to its direct effect, they also explored the moderating effect of perceived technological innovativeness in this relationship and found that CS and eWOM are more closely connected when technological innovation is perceived as high. Further, in timeshare and peer-to-peer accommodation contexts, Redditt et al. [11] revealed that CS substantially influenced customers’ eWOM intention. Hence, firms should focus on providing high levels of CS to encourage positive eWOM and ultimately increase customer acquisition and retention. Accordingly, we could assume that:
CS significantly contributes to the positive effect on eWOM.
2.5. The Intermediating Role of CS in PQ–eWOM and PEE–eWOM Relationships
PQ, PEE, and CS are important predictors that drive eWOM in the digital age [9,10,11,12,13]. Previous studies revealed that PQ significantly contributed to eWOM engagement [9,10]. In addition, numerous studies have recognized PQ as the key predictor of enhancing CS [51,52,53]. For instance, Pedraja Iglesias and Jesus Yagüe Guillén [54] concluded that customers who perceived their product or service as of higher quality were more likely to experience higher levels of satisfaction than those who perceived it as of lower quality. This finding can be seen in a wide variety of subsequent studies, such as Joung et al.’s [55] study of the influence of PQ on CS and retention within the food service industry. They concluded that customers with higher perceptions of their product were more likely to be satisfied and loyal.
However, the indirect relationship between PQ and eWOM through CS is still limited, particularly in the hotel industry context. Some scholars demonstrated that CS acted as a mediating factor between PQ and eWOM. In the UAE banking industry context, Al-Hawari [56] concluded that e-quality significantly enhanced e-satisfaction, which, in turn, affected customer e-loyalty, namely positive recommendations to colleagues, encouraging friends to use online banking services, and encouraging them to recommend the bank to others. Further, in the green marketing context, Hashish et al. [57] examined the mediating role of green satisfaction (GS) in the relationship between green perceived quality (GPQ) and customer green behavioral intentions. Results of the study demonstrated that GS partially mediated the link between GPQ and customer green behavioral intentions, including positive WOM. In addition, the empirical investigation carried out by Saleem et al. [58] on a sample of 789 online Chinese shoppers aimed to examine the role of CS in the relationship between website quality and eWOM and revealed that CS has a partial mediating effect in the nexus between WQ and eWOM. Similarly, in the Indian banking context, Kaura et al. [59] demonstrated that CS acts as a partial mediating factor in the relationship between service quality and customer loyalty (i.e., saying positive things about this bank to other people and recommending the bank to others).
To demonstrate the intermediary role of CS in the link between SQ and eWOM, the S-O-R model was utilized. The S-O-R is a psychological model developed to explain how stimuli interact with individual characteristics and influence behavior [60]. The model emphasizes that an individual’s behavior is not just a result of external stimuli but is also influenced by biological, psychological, and cognitive processes [61]. This model is crucial to understanding how people respond to stimuli and how their internal processes shape their behavior. In the S-O-R model, stimuli (S) are external events or situations that elicit a response or reaction from an individual [60]. However, the internal processes and structures of organisms, represented by O, play a crucial role in mediating the relationship between external stimuli and the individual’s subsequent responses, actions, and reactions. Meanwhile, responses represent consumers’ final decisions, which can be either approachable or avoidable [62].
According to the S-O-R model, if a consumer regards a product or service as having exceptional quality (S), it is probable they will experience satisfaction with their purchase (O) [33,63]. This satisfaction (O) could increase the likelihood of them recommending the product or service to others through favorable eWOM (R). On the other hand, if a consumer perceives the product or service to be of low quality, they are less satisfied with their purchase and may choose to provide negative feedback through negative eWOM [11,14]. Hence, we could suggest that:
PQ significantly contributes to enhancing CS.
CS has a significant intermediary role in the PQ-positive eWOM relationship.
Similarly, with regard to PEE, the impact of PEE on customer satisfaction is becoming increasingly recognized in the realm of marketing and customer service [64]. Positive emotions play an essential role in establishing customer relationships, driving customer loyalty and satisfaction, and thus enabling businesses to achieve success [65]. By creating PEE for customers, hotels can foster a stronger customer-business relationship that can enhance customer satisfaction and loyalty [64,66]. In addition, PEE significantly contributed to positive eWOM, suggesting that the higher the perceived PEE, the greater the engagement in positive eWOM [34]. They also concluded that CS was drastically influenced by PEE. From these findings and according to the S-O-R model, we could suggest that when a consumer perceives a high level of PEE (S), they are more likely to be satisfied with their experience (O), which ultimately may lead to a positive eWOM (R). Hence, we could suggest that:
PEE significantly contributes to enhancing CS.
CS has a significant intermediary role in the PEE-positive eWOM relationship.
2.6. eWOM and Intention to Revisit (ITR)
ITR refers to a consumer’s inclination or predisposition to return to a business or service in the future [67]. This intention is particularly relevant in the context of customer loyalty. Consumers’ intention to revisit is influenced by a range of factors such as product or service quality, customer satisfaction, brand equity, perceived attractiveness, and positive service experience [68,69,70]. One key factor affecting customers’ revisit intention is eWOM [17,18]. The eWOM has become an important aspect of online communication, particularly in the domains of e-commerce, social media, and consumer reviews [18,71]. This type of communication significantly impacts a consumer’s intentions and behavior. One of the vital intentions of eWOM is to create a positive image and promote the revisit intention [18]. Recently, customers’ revisit intention has been heavily influenced by eWOM, as it has emerged as a crucial source of information for consumers during the purchasing process [16,17,18,67]. Previous research revealed that positive eWOM has a substantial impact on the ITR. For example, in their empirical investigation adopted on a sample of tourists from Japan and Indonesia, Setiawan et al. [72] proved that eWOM significantly contributed to promoting tourists’ intention to revisit. Similarly, post-COVID-19 pandemic, Azhar et al. [73] confirmed the significant positive effect of social media eWOM on tourists’ revisit intention. Furthermore, Uslu and Karabulut [74] concluded that tourists’ ITR was significantly affected by the intention to spread eWOM. Based on the previous results, it could be mentioned that favorable eWOM has the potential to boost the reputation of a business and increase the likelihood that customers will revisit it [67,75]. In contrast, negative eWOM can deter customers from revisiting the business and harm its reputation [16,76]. As a result, it is proposed that:
eWOM significantly contributes to the positive effect on ITR.
2.7. The Intermediating Role of eWOM in the Link between PQ, PEE, CS, and ITR
In terms of the indirect relationship between PQ, PEE, CS, and ITR through the intermediating role of eWOM, this is still under study, particularly in the hotel industry context. In accordance with the prior conclusions that revealed the significant effect of PQ, PEE, and CS on customer-positive eWOM [13,15,33] and the substantial positive effect of eWOM on ITR [17,67], it could be assumed that eWOM is more likely to play a vital intermediating role in the PQ–ITR, PEE–ITR, and CS–ITR relationships. More specifically, in accordance with the S-O-R model, the higher perceived quality, the more positive emotional experience, and the higher satisfaction (S) are more likely to lead to highly positive eWOM intention (O), which in turn may significantly positively affect customers’ ITR (R). Hence, the following hypotheses should be examined:
PQ–ITR relationship is significantly mediated by eWOM.
PEE–ITR relationship is significantly mediated by eWOM.
CS–ITR relationship is significantly mediated by eWOM.
The study’s theoretical framework is presented in Figure 1.
3. Materials and Methods
3.1. Measures and Instrument Development
In order to examine the relationship between perceived quality (PQ), positive emotional experience (PEE), customer satisfaction (CS), electronic word-of-mouth (eWOM), and intention to revisit (ITR), the researchers in this study utilized a questionnaire survey method. Data collection was conducted through an online survey questionnaire that consisted of six sections. The Section 1 contained questions on participants’ demographic information (i.e., gender, age, level of education), length of stay in the investigated hotels, and their source of information when booking hotels. The succeeding Section 2, Section 3 and Section 4 focused on investigating participants’ perceptions of PQ, PEE, and CS, respectively. The last two Section 5 and Section 6 were specifically designed to evaluate participants’ inclination to engage in positive eWOM and their ITR to the hotels, respectively.
To ensure valid measures for the study, the researchers conducted a review of previous studies and developed a questionnaire consisting of five measurement scales: PQ, PEE, CS, eWOM, and ITR. For measuring perceived quality, a scale composed of five items was adapted from Hashish et al. [57]. A sample of the items is, “The hotel’s product and service quality is regarded as the best benchmark”. To measure PEE, six items were taken and modified from Serra-Cantallops et al. [13]. A sample of these items includes “The hotel was exceptional”. A four-item scale for CS and a five-item scale for eWOM were also adapted from the same study [13]. Samples of the items include “This hotel satisfied your needs.”, and “I have posted positive comments on social networks about this hotel”, respectively. For ITR, a scale with three items was adapted from the studies of Abubakar et al. [17] and Paisri et al. [68]. One of these items is “I intend to revisit this hotel in the near future”. All items were measured on a five-point Likert scale, ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). Appendix A, Table A1, presents the constructs of the study and their related items.
To ensure the accuracy of the questionnaire, it was first developed in English and then translated into Arabic by two skilled professionals in both languages. The Arabic version was then back-translated to English by other experts to compare it with the original English version, and no disparities were found between the updated translated and original English versions. Further, the content of the questionnaire was reviewed by four hospitality scholars who specialize in marketing and consumer behavior, and thirty participants were piloted to assess the questionnaire’s clarity, simplicity, and consistency. Based on feedback from participants and scholars, some of the questionnaire’s wording was modified, and certain statements were changed in sequence and organization. All these steps were taken to ensure the questionnaire’s content validity.
3.2. Data Collection and Sample
The purpose of this study is to examine the influence of customers’ PQ, PEE, and CS on their positive eWOM intention. Further, based on the S-O-R model, we empirically seek to examine the potential intermediating role of CS in the PQ–eWOM and PEE–eWOM relationships. In addition, we explore the intermediating role of eWOM in the relationship between its antecedents (i.e., PQ, PEE, and CS) and the intention to revisit (ITR) among hotel customers in Saudi Arabian five-star eco-friendly hotels. The study’s target population is Saudi individuals who have recently stayed at five-star eco-friendly hotels in Saudi Arabia, particularly in Riyadh City (the capital of Saudi Arabia where most luxury eco-friendly hotels are located), and have followed, commented on, and interacted with these hotels on the TripAdvisor website during the last six months. Based on their availability and willingness to participate, the convenience sampling technique was utilized to select participants for data collection.
Firstly, TripAdvisor, a well-known travel website that offers reviews and information on resorts, hotels, and other travel-related businesses, was utilized to recruit participants for this research [77,78]. The reasons for selecting TripAdvisor are that the website hosts millions of reviews and ratings written by travelers worldwide. It is also a great resource for anyone planning a trip and looking for advice and recommendations from other travelers. The website also offers a variety of travel forums, where users can ask questions and get advice from other travelers. Secondly, participants in the study were selected based on their site interactions and emailed separately via their accounts to ask them to take part in this study. An online survey link was provided to participants with a welcome message that included a comprehensive explanation of the study’s aims. Participants were informed that their involvement was optional and were encouraged to review and resubmit their responses after completion. Data collection took nearly two months (January–February 2023), and a total of 423 valid forms were gathered and analyzed.
Based on Nunnally’s [79] recommendations, the sample size for the study was determined by considering the number of items being examined. Nunnally suggested that a ratio of 1:10 (item: sample) is appropriate. For example, as found in this study, if there are 23 items, then a sample size of 230 respondents would be suitable. In the current study, a sample size of 403 participants was used, which is adequate. This is also consistent with Hair et al.’s [80] recommendation, which illustrated that the minimum sample size needed for PLS-SEM is 155 samples with an expected minimum path coefficient (Pmin) of significance between 0.11 and 0.20 and a significance level of 0.05. Moreover, the sample size is consistent with Boomsma’s [81] recommendation that a minimum of 200 samples be used for structural equation modeling.
The study analyzed data from a total of 423 participants. Regarding their gender, the majority of respondents (69.8%, n = 295) were males and 30.2% (n = 128) were female. Further, most of the participants (63.8%, n = 270) were between the ages of 30 and 40 years old. More than two-thirds of the participants (64.3%, n = 272) had completed a university degree, while 18.5% (n = 78) had pursued higher education and obtained a post-graduate degree. The largest group of participants (71.6%, n = 310) indicated that they stayed in the investigated hotels for more than 3 to 5 days. Finally, most participants (42.8%, n = 181) used review websites such as TripAdvisor, followed by hotel websites (26.2%, n = 111), as their source of information when booking hotels. Based on the sample characteristics, the sample primarily consisted of Saudi individuals who had recently stayed in eco-friendly hotels, who were male, between the ages of 30 and 40 years old, who had completed a university degree, who stayed in the investigated hotels for more than 3 to 5 days, and who used review websites such as TripAdvisor for information when booking hotels.
3.3. Data Analysis
In this research, statistical programs such as SPSS v.25 and SmartPLS v.4.0.8.7 were utilized to examine and analyze the collected data. Demographic characteristics, length of stay, and source of information were examined using frequency and percentage calculations. Kolmogorov–Smirnov and Shapiro–Wilk tests were employed to assess the normality of the data distribution. Further, the study used various statistical techniques to ensure the reliability and validity of the construct items, which included CFA and Cronbach’s alpha. Researchers employed CR and AVE to assess convergent validity. Discriminant validity was evaluated using the Fornell–Larcker criterion and HTMT. To assess the predictive accuracy, relevancy, and adequacy of the structural model, R2, Q2predict, and f2 were calculated. Multicollinearity was detected through VIF examination. The study tested its hypotheses using PLS-SEM with the bootstrapping technique.
4. Results
4.1. Common Method Variance (CMV)
Some measures were implemented by researchers to guarantee honest and accurate responses while minimizing the risk of common method bias/variance (CMV). Respondents were assured that their responses were confidential and used only for research purposes. Anonymity was also employed to decrease the potential for any biases [82]. In addition, Harman’s single-factor test was utilized to evaluate if CMV was a factor. The results indicated that only one factor contributed to 41.02% of the variance, implying that CMV did not pose a problem. According to Podsakoff et al. [83], CMV might pose a problem if more than 50% of the variance is due to a single factor, which does not appear here.
4.2. Test of Normality
Understanding the normality of each metric variable is a crucial step in data analysis [80]. To determine whether the collected data followed a normal distribution, researchers used the Kolmogorov–Smirnov and Shapiro–Wilk tests. If the p-value was greater than 0.05, the data were deemed to be normally distributed, whereas if the p-value was less than 0.05, the data were not normally distributed [84,85]. In Table 1, both p-values were less than 0.05, indicating that the collected data did not follow a normal distribution. As a result, the researchers opted to use partial least squares structural equation modeling (PLS-SEM) to analyze the data, as it does not require normally distributed data [86,87].
4.3. Analyses of the Measurement Model
To evaluate the measurement model in this study, several steps were taken. First, outer loadings equal to or above 0.708 were recommended [87], with all factor loadings presented in Table 1 exceeding this threshold and being statistically significant. The internal consistency reliability of each construct was then determined utilizing Cronbach’s alpha and CR, with the values of both ranging from 0.784 to 0.879 and 0.861 to 0.911, respectively, indicating excellent internal consistency reliability. Finally, convergent validity was evaluated using the AVE, with the constructs’ values ranging from 0.580 to 0.745, demonstrating good validity. According to Hair et al. [87], a higher AVE, equal to or greater than 0.50, is recommended.
Additionally, the measurement model’s discriminant validity was tested using two statistical techniques. Firstly, Fornell–Larcker’s [88] criterion was utilized, which requires the square root of AVE for each construct to be higher than its correlation with the other constructs. This can be seen in Table 2, where the square root of AVE for each construct was higher than their correlations to other constructs. Secondly, the HTMT approach proposed by Henseler et al. [89] was used, where values above 0.90 imply that discrimination is not valid. The HTMT values for all latent construct pairs in Table 3 were below 0.90, indicating that the constructs in the study can be reliably distinguished from one another.
4.4. Multicollinearity Statistics
VIF (variance inflation factors) were used to detect the existence of multicollinearity in the model under investigation. According to Hair et al. [81] and Shrestha [84], if the VIF score rises above 5, it signifies the presence of multicollinearity, and remedial measures should be taken to address it. In Table 4, it can be observed that, in all cases, the VIF value is less than 5, implying that there are no significant concerns regarding multicollinearity among the investigated variables in the studied model.
4.5. Assessing the Structural Model Quality
To evaluate the quality of the structural model, various measures, including the model’s predictive accuracy/power using the coefficient of determination (R2), the model’s predictive relevance using Q2predict, and the model’s adequacy using the effect size (f2) of the predictors, were examined. R2 represents how much change in the endogenous construct can be described by one or more independent variables [90]. As a means of expressing their relative strength, R2 can be classified as substantial at 0.75, moderate at 0.50, and weak at 0.25 [91]. Further, in terms of effect size, Cohen [92] demonstrated that F-square refers to the change in R2 when an exogenous variable is removed from the model. Based on Cohen’s [92] suggestions, the effect size (f2) can be categorized as small at 0.02, medium at 0.15, and large at 0.35. In addition, to assess the model’s predictive relevance in SmartPLS 4, the Q2 value is now calculated using the PLSpredict technique, which differs from the previous versions that used blindfolding [93]. The PLSpredict method calculates the Q2predict by comparing the prediction errors of the PLS path model with simple mean predictions. To ensure better predictive relevance, the Q2predict for endogenous constructs should be greater than zero. As mentioned in Table 5, results showed that the R2 values of the endogenous constructs were between 0.381 and 0.675, implying good predictive accuracy. In terms of the effect size of the exogenous constructs, results revealed that the f2 ranged from 0.022 (small) for PQ on CS to 0.616 (high) for eWOM on ITR, suggesting good model adequacy. Lastly, the Q2predict values for endogenous constructs were greater than zero, suggesting the model’s predictive relevance.
4.6. Testing the Study Hypotheses
The study employed the PLS-SEM algorithm and bootstrapping on a subset of 5000 samples to evaluate the path coefficient and establish the significance of correlations among variables. The results of the analysis are presented in both Table 6 and Figure 2.
Concerning the direct paths, the study findings displayed in Table 6 and Figure 2 revealed that there was a significant positive impact of PQ on intention to positive eWOM (represented by β = 0.515, t-value = 9.397, p < 0.001), which lends support to hypothesis 1. Further, results also demonstrated that both PEE and CS significantly contributed to enhancing customers’ eWOM intention, as indicated by the β values of 0.159 and 0.247, respectively. These findings support the acceptance of hypotheses 2 and 3. In terms of the effect of PQ and PEE on CS, results indicated that both significantly positively affected CS (represented by β = 0.196, t-value = 2.474, p < 0.05; β = 0.452, t-value = 5.371, p < 0.001), supporting hypotheses 4 and 6. Similarly, the study results supported H8, which assumed that eWOM significantly positively affects ITR (β = 0.617, t-value = 18.098, p < 0.001).
Regarding the indirect paths, the study examined how PQ and PEE indirectly affect positive eWOM intention through the intermediating role of CS. The results showed that CS significantly mediated the link between PQ-eWOM and PEE-eWOM, indicating support for hypothesis 5 for PQ and hypothesis 7 for PEE. In addition, the results emphasized the pivotal role of eWOM as a significant positive mediator of the link between PQ, PEE, CS, and ITR among the investigated participants. These findings lend substantial support to hypotheses 9, 10, and 11 of this study.
5. Discussion and Implications
5.1. Discussion
This empirical investigation was conducted to explore the direct impact of PQ, PEE, and CS on positive eWOM intention among Saudi Arabian hotel customers, as well as PQ and PEE’s influence on CS, and eWOM’s effect on ITR. Additionally, the study aimed to investigate CS’s intermediating role in the relationships between PQ–eWOM and PEE–eWOM, as well as eWOM’s mediating role in the connections linking PQ, PEE, CS, and ITR. Through the hypothesis tests, several significant results emerged, as follows. Firstly, the results of the study emphasized that PQ, PEE, and CS substantially positively influenced the customers’ intention toward positive eWOM. PQ was the most effective predictor, followed by CS and PEE, respectively.
In terms of the PQ–eWOM relationship, the findings of this research support the findings of prior studies carried out by Samad [33], Barlas et al. [35], Sánchez-González and González-Fernández [36], as well as Dunn et al. [38], who illustrated that PQ is the key predictor of promoting positive eWOM. Hence, one could propose that eWOM may be improved as the perceived quality level increases. This implies that customers who perceive high-quality products and services possess a higher likelihood of generating positive eWOM. As online customer reviews and feedback continue to gain importance, businesses that invest in improving their perceived quality are likely to reap the rewards of positive eWOM.
With regard to the CS–eWOM relationship, our findings are consistent with those of previous studies, which revealed that CS significantly positively affected positive eWOM intention [11,12,14,15]. Based on these findings, it could be concluded that highly satisfied customers are more likely to share positive experiences and recommendations with their social network. Contrary to the prior findings, Serra-Cantallops et al. [34] concluded that CS did not significantly affect eWOM. Similarly, in terms of the PEE–eWOM relationship, study findings emphasized the notion that customer intent to spread positive eWOM is strongly connected to PEE [13,34]. In other words, when customers perceive a positive emotional connection with a brand or service, it increases the likelihood that they will engage in eWOM and share their positive experiences with others [34].
Secondly, it is imperative to note that the study’s findings prove that CS is significantly influenced by both the PEE and PQ, respectively. This conclusion corroborates the findings of Bou-Llusar et al. [51], Samudro et al. [52], and Xie and Sun [53], who all found that higher perceived service and product quality were key factors in enhancing CS. Further, the results of this research highlighted the importance of PEE, as providing hotel guests with a positive emotional experience has a considerable impact on their overall satisfaction, a finding that agreed with those that were concluded from earlier studies [34,64,66]. Overall, these findings underscore the significance of creating positive emotional experiences and delivering high-quality products and services to promote customer satisfaction.
Thirdly, positive eWOM has been found to significantly increase customers’ intention to revisit, which aligns with the findings of other scholars who have emphasized the importance of eWOM in determining customer ITR [17,67,75]. This finding offers a more comprehensive understanding of the relationship between eWOM and ITR, indicating that the higher the perceived positivity of eWOM, the greater the intent to revisit.
Fourthly, with regard to indirect associations, the study found that CS plays a significant partial mediating role in the association between PQ, PEE, and intention to engage in positive eWOM. This suggests that customers tend to provide positive eWOM when they experience excellent services and products, coupled with positive emotional experiences. This positively impacts their overall satisfaction, prompting them to share their positive experiences online. In summary, the indirect relationship between study constructs emphasizes the importance of creating positive emotional experiences and delivering high-quality products and services to enhance customer satisfaction and drive positive eWOM. These findings support the results of the previous studies that illustrated the significant partial intermediary role of CS in the nexus between service quality and consumer behavioral intention, including positive WOM [57,58,59].
Furthermore, the study’s findings shed light on the considerable intermediating role of positive eWOM in examining the indirect association between PQ, PEE, CS, and intention to revisit. Specifically, it was found that eWOM played a significant role in mediating the indirect effect of PQ, PEE, and CS on the intention to revisit. These findings suggest that the higher the perceived high product and service quality, the more positive the emotional experience, and satisfaction are key drivers, which significantly contributed to promoting positive eWOM, which, in turn, significantly increased the customers’ revisit intention.
5.2. Theoretical Implications
This study has made significant contributions to the literature on the power of e-WOM and its antecedents in driving customers’ revisit intention in the hotel industry context. The research provided a comprehensive understanding of how PQ, PEE, and CS play a critical role in fostering positive eWOM and increasing customers’ intention to revisit. The study identified PQ as the most predictive variable in promoting positive eWOM, which ultimately affects the increase in intention to revisit more than CS and PEE. Additionally, the research revealed the significant intermediating role of CS in exploring how PEE and PQ positively impacted customers’ intentions to promote positive eWOM. This is the first empirical investigation that explores the indirect effect of PQ and PEE via CS in predicting positive eWOM among hotel customers, particularly in developing countries such as Saudi Arabia. Another novel contribution of this research is the substantial intermediating role of eWOM in the association between PQ, PEE, CS, and ITR. The results extended the use of the S-O-R model in the hotel marketing context. Using this model, PQ, CS, and PEE (S) contributed significantly to promoting eWOM (O), which in turn led to an enhancement in customers’ intentions to revisit (R). Moreover, the study verified the crucial influence of eWOM in forecasting the intention to revisit among hotel clients, reinforcing the notion of the significant impact that positive eWOM can have on driving repeat business for hotels. Finally, this study created and validated a new model that includes PQ, PEE, and CS, along with eWOM and ITR. This has made a significant contribution to the current research on hotel consumer behavior and can serve as a reference point for further investigations in this field.
5.3. Practical Implications
Based on the research findings, the following practical implications should be taken into account by hotel marketers and operators. Firstly, the study showed that PQ, PEE, and CS play a critical role in fostering positive eWOM and increasing customers’ ITR. PQ was the most predictive variable. Hence, hotel operators and marketers should focus consistently on delivering high-quality products and services across all aspects of the guest experience, such as the cleanliness of the rooms, the quality of the amenities, and the responsiveness of the staff. Further, customizing the guest experience to encourage repeat business should also be considered. By offering personalized experiences to customers, hotel operators can encourage loyalty and increase the likelihood of repeat visits. This could include personalized recommendations for local activities and dining options, customized room amenities, or personalized communications throughout the guest’s stay. Secondly, the study also concluded that eWOM has become a highly impactful phenomenon for the hotel industry. Positive eWOM was found to have a significant positive impact on ITR and play a significant role in the relationships between PQ, CS, PEE, and ITR. As a result, hotel managers must develop effective marketing strategies by gaining a better understanding of the specific factors that drive positive eWOM (such as PQ, CS, and PEE). Hotel managers should actively seek out guest reviews and encourage their guests to leave positive feedback and share their positive experiences on social media, review sites, and other platforms. This can be carried out with post-stay surveys, social media campaigns, or other incentivized programs. Not only that, but hotel managers should monitor online reviews regularly and respond promptly to any negative feedback. This will show guests that the hotel values their feedback and is committed to providing an exceptional guest experience. Thirdly, CS significantly mediated the relationships between PEE, PQ, and eWOM, respectively. Accordingly, hotel managers should focus on promoting positive emotional experiences for guests, such as personalized service, loyalty rewards, surprise and delight gestures, and other initiatives that can enhance guest satisfaction levels and elicit positive emotions from their customers. Additionally, investing in training and empowering the hotel staff to deliver high-quality service to guests, which can help enhance perceptions of quality, PEE, and CS levels, leading to positive eWOM, is crucial.
6. Limitations of the Study and Further Research
The current study has the following limitations. TripAdvisor, one of the most popular travel websites, was used to select participants, limiting the generalizability of the findings to other populations. Additionally, only five-star eco-friendly hotels in Saudi Arabia were investigated regarding PQ, PEE, CS, eWOM, and ITR. Conducting further research that involves a larger sample size and covers multiple segments of the hospitality industry, including green restaurants, green cruises, and green resorts, would offer more comprehensive insights into the factors that impact customer behavior and decision making. Expanding the research scope in this manner can help researchers gain a more in-depth understanding of the trends, preferences, and experiences of customers across various segments of the hospitality service industry. In addition, the study focused solely on CS as a mediator in the link between PQ, PEE, and eWOM. Forthcoming studies can examine other potential mediating factors to better understand the mechanisms that may affect these relationships. Moreover, eWOM was employed as a mediator to investigate the indirect effect of its antecedents (PQ, PEE, and CS) on the intention to revisit. To understand these relationships more comprehensively, the influence of PQ, PEE, and CS on other behavioral intentions, such as purchase/repurchase intentions and willingness to pay a premium price, via eWOM, should also be explored. Further, the respondents’ characteristics, including their demographics and length of stay, may also moderate the relationship between PQ, PEE, CS, eWOM, and ITR and should be considered in subsequent studies. Moreover, the study only focused on the online questionnaire for data collection. Combining qualitative and quantitative approaches for data collection would yield a more complete understanding of the subject being studied. In addition, after the height of the COVID-19 pandemic, the effect of innovative concepts in the tourism and hospitality industry on sustainable development as well as customers’ behavioral intentions should be considered in further research. Some of these concepts include service robots and untact tourism [94,95,96]. Service robots and untact tourism can contribute to providing a contactless experience for guests, which aligns with the social distancing protocols in place due to the COVID-19 pandemic and contributes to creating a safer environment for both guests and staff. Further, untact tourism can potentially reduce the environmental impact of tourism by minimizing physical travel and promoting virtual experiences.
Conceptualization and supervision, A.H.A. Further, all authors participated equally in methodology, software, validation, formal analysis, data curation, writing—original draft preparation, writing—review and editing, funding acquisition, and visualization. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the Declaration of Helsinki and approved by the deanship of the scientific research ethical committee, King Faisal University (Project number: GRANT3587, date of approval: 1 May 2023).
All subjects who participated in this research provided informed consent.
Contact the corresponding author.
The authors declare no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Study constructs’ validity, reliability, and items’ normal distribution.
| Construct | Item | Outer Loading | α | CR | AVE | Kolmogorov–Smirnov | Shapiro–Wilk | ||
|---|---|---|---|---|---|---|---|---|---|
| Statistic | Sig. | Statistic | Sig. | ||||||
| Perceived quality |
PQ1 | 0.824 *** | 0.879 | 0.911 | 0.674 | 0.274 | 0.000 | 0.864 | 0.000 |
| PQ2 | 0.793 *** | 0.289 | 0.000 | 0.847 | 0.000 | ||||
| PQ3 | 0.879 *** | 0.279 | 0.000 | 0.851 | 0.000 | ||||
| PQ4 | 0.852 *** | 0.267 | 0.000 | 0.853 | 0.000 | ||||
| PQ5 | 0.752 *** | 0.288 | 0.000 | 0.831 | 0.000 | ||||
| Positive emotional experiences |
PEE1 | 0.741 *** | 0.855 | 0.892 | 0.580 | 0.258 | 0.000 | 0.864 | 0.000 |
| PEE2 | 0.770 *** | 0.267 | 0.000 | 0.853 | 0.000 | ||||
| PEE3 | 0.786 *** | 0.288 | 0.000 | 0.826 | 0.000 | ||||
| PEE4 | 0.774 *** | 0.277 | 0.000 | 0.841 | 0.000 | ||||
| PEE5 | 0.761 *** | 0.275 | 0.000 | 0.849 | 0.000 | ||||
| PEE6 | 0.736 *** | 0.269 | 0.000 | 0.877 | 0.000 | ||||
| Customer satisfaction |
CS1 | 0.809 *** | 0.784 | 0.861 | 0.609 | 0.272 | 0.000 | 0.793 | 0.000 |
| CS2 | 0.806 *** | 0.287 | 0.000 | 0.788 | 0.000 | ||||
| CS3 | 0.794 *** | 0.261 | 0.000 | 0.753 | 0.000 | ||||
| CS4 | 0.706 *** | 0.300 | 0.000 | 0.834 | 0.000 | ||||
| eWOM |
eWOM1 | 0.735 *** | 0.800 | 0.867 | 0.567 | 0.258 | 0.000 | 0.869 | 0.000 |
| eWOM2 | 0.786 *** | 0.310 | 0.000 | 0.816 | 0.000 | ||||
| eWOM3 | 0.789 *** | 0.288 | 0.000 | 0.848 | 0.000 | ||||
| eWOM4 | 0.747 *** | 0.257 | 0.000 | 0.839 | 0.000 | ||||
| eWOM5 | 0.705 *** | 0.258 | 0.000 | 0.853 | 0.000 | ||||
| Intention to revisit |
ITR1 | 0.867 *** | 0.830 | 0.898 | 0.745 | 0.274 | 0.000 | 0.864 | 0.000 |
| ITR2 | 0.832 *** | 0.256 | 0.000 | 0.878 | 0.000 | ||||
| ITR3 | 0.889 *** | 0.275 | 0.000 | 0.849 | 0.000 | ||||
Note: *** p < 0.001.
A Fornell–Larcker test for discriminant validity.
| Construct | CS | ITR | PEE | PQ | eWOM |
|---|---|---|---|---|---|
| 1- Customer satisfaction | 0.780 | ||||
| 2- Intention to revisit | 0.497 | 0.863 | |||
| 3- Positive emotional experience | 0.611 | 0.729 | 0.762 | ||
| 4- Perceived quality | 0.562 | 0.661 | 0.759 | 0.821 | |
| 5- eWOM | 0.633 | 0.617 | 0.726 | 0.747 | 0.753 |
Note: AVE’s square root is represented by the bold diagonal values.
Discriminant validity according to the HTMT.
| Construct | CS | ITR | PEE | PQ | eWOM |
|---|---|---|---|---|---|
| 1- Customer satisfaction | |||||
| 2- Intention to revisit | 0.609 | ||||
| 3- Positive emotional experience | 0.744 | 0.862 | |||
| 4- Perceived quality | 0.667 | 0.763 | 0.864 | ||
| 5- eWOM | 0.789 | 0.736 | 0.857 | 0.879 |
Note: values of HTMT are less than 0.90.
Multicollinearity analysis based on the VIF value.
| Construct | CS | ITR | PEE | PQ | eWOM |
|---|---|---|---|---|---|
| 1- Customer satisfaction | 1.630 | ||||
| 2- Intention to revisit | |||||
| 3- Positive emotional experience | 2.887 | 3.219 | |||
| 4- Perceived quality | 2.887 | 2.949 | |||
| 5- eWOM | 1.000 |
Note: VIF values are lower than 5.0.
Structural model quality.
| Construct | R-Square | Q2predict | f 2 | ||
|---|---|---|---|---|---|
| CS | eWOM | ITR | |||
| CS | 0.386 | 0.373 | 0.115 | ||
| eWOM | 0.675 | 0.619 | 0.616 | ||
| ITR | 0.381 | 0.472 | |||
| PEE | 0.115 | 0.024 | |||
| PQ | 0.022 | 0.276 | |||
Structural parameter estimates.
| Hypothesized Path | Path Coefficient | T Statistics | Confidence Intervals | Result | |
|---|---|---|---|---|---|
| 2.5% | 97.5% | ||||
| Direct Paths | |||||
| H1: PQ→eWOM | 0.515 *** | 9.397 | 0.408 | 0.623 | Accepted |
| H2: PEE→eWOM | 0.159 ** | 2.793 | 0.045 | 0.268 | Accepted |
| H3: CS→eWOM | 0.247 *** | 5.716 | 0.161 | 0.333 | Accepted |
| H4: PQ→CS | 0.196 * | 2.474 | 0.048 | 0.360 | Accepted |
| H6: PEE→CS | 0.452 *** | 5.371 | 0.280 | 0.607 | Accepted |
| H8: eWOM→ITR | 0.617 *** | 18.098 | 0.548 | 0.682 | Accepted |
| Indirect Paths | |||||
| H5: PQ→CS→eWOM | 0.049 * | 2.303 | 0.011 | 0.095 | Accepted |
| H7: PEE→CS→eWOM | 0.112 *** | 3.576 | 0.057 | 0.178 | Accepted |
| H9: PQ→eWOM→ITR | 0.318 *** | 8.819 | 0.252 | 0.391 | Accepted |
| H10: PEE→eWOM→ITR | 0.098 ** | 2.633 | 0.027 | 0.173 | Accepted |
| H11: CS→eWOM→ITR | 0.152 *** | 5.460 | 0.099 | 0.208 | Accepted |
Note: *** p < 0.001, ** p < 0.01, and * p < 0.05.
Appendix A
Study constructs and their related items.
| Construct | Items | Statement |
|---|---|---|
| Perceived quality |
PQ1 | The hotel’s products and services are perceived to be dependable in meeting environmental standards. |
| PQ2 | The hotel’s product and service quality is regarded as the best benchmark. | |
| PQ3 | The products/services of the hotel are durable and eco-friendly. | |
| PQ4 | The hotel’s products/services are environmentally reputable and of professional quality. | |
| PQ5 | The hotel’s environmental image is boosted by the quality of its products/services. | |
| Positive emotional |
PEE1 | The hotel was exceptional. |
| PEE2 | The hotel made me feel like a VIP guest. | |
| PEE3 | The stay has helped me break away from the daily routine. | |
| PEE4 | The stay aroused my emotions. | |
| PEE5 | The stay has been memorable for me. | |
| PEE6 | I encountered positive and unique situations distinct from other hotels. | |
| Customer satisfaction |
CS1 | This hotel satisfied your needs. |
| CS2 | I made a wise choice by choosing this hotel. | |
| CS3 | I enjoyed my stay at this hotel. | |
| CS4 | Generally, I am satisfied with this hotel. | |
| eWOM | eWOM1 | I have shared positive comments on my social pages about this hotel. |
| eWOM2 | I was eager to post on social media that I was staying at this hotel. | |
| eWOM3 | I wrote favorable reviews about this hotel on websites and/or travel review websites. | |
| eWOM4 | I uploaded photos and/or videos on social media about my hotel experience. | |
| eWOM5 | I became a follower of the hotel’s profile on social media. | |
| Intention to revisit |
ITR1 | I intend to revisit this hotel in the near future. |
| ITR2 | I have a strong intention to bring my family and friends to visit this hotel again. | |
| ITR3 | This hotel is the ideal destination for me if I want to enjoy an eco-friendly vacation. |
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; Hassan, Thowayeb H 3
; Maha Abdul-Moniem Mohammed El-Amin 4 ; Adam Basheer Adam Kegour 4 ; Hanem Mostafa Mohamed Alboray 5 ; Amal Salah Darder Mohamed 1 ; Howayda Said Ahmed Mohamed Ali 6 ; Elham Farouq Ali Mohammed 7 1 Accounting Department, Applied College, King Faisal University, Al-Ahsa 31982, Saudi Arabia
2 Social Studies Department, College of Arts, King Faisal University, Al-Ahsa 31982, Saudi Arabia; Hotel Studies Department, Faculty of Tourism and Hotels, Mansoura University, Mansoura 35516, Egypt
3 Social Studies Department, College of Arts, King Faisal University, Al-Ahsa 31982, Saudi Arabia; Tourism Studies Department, Faculty of Tourism and Hotel Management, Helwan University, Cairo 12612, Egypt
4 Department of Education and Psychology, College of Education, King Faisal University, Al-Ahsa 31982, Saudi Arabia
5 Department of Education and Psychology, College of Education, King Faisal University, Al-Ahsa 31982, Saudi Arabia; Mental Health Department, Faculty of Education, Suez Canal University, Ismailia 41522, Egypt
6 Computer Science Department, Applied College, King Faisal University, Al-Ahsa 31982, Saudi Arabia
7 Education Fundamentals Department, Applied College, King Faisal University, Al-Ahsa 31982, Saudi Arabia