Enhancing Green Electronic Word-of-Mouth in the Saudi Tourism Industry: An Integration of the Ability, Motivation, and Opportunity and Planned Behaviour Theories

Abstract

Green electronic word-of-mouth (GeWOM) is a type of online communication that focuses on eco-friendly products and services. Understanding the determinants of GeWOM can help businesses develop effective strategies for promoting their environmentally friendly products and services. An integration of two main theoretical frameworks was used to analyse the determinants of GeWOM: the Ability, Motivation, and Opportunity (AMO) theory and the Theory of Planned Behaviour (TPB). The current study aims to examine the determinants of green electronic word-of-mouth (GeWOM) through the lens of both AMO and TPB theories. The study further examines the mediating role of tourists’ green purchase intentions in the above-mentioned relationships. The data were collected from a sample of 625 tourists in Saudi Arabia using a structured questionnaire and analysed using PLS-SEM. The results showed a significant positive influence of green AMO and green attitude on green purchase intention. Green ability and green attitude have a direct positive influence on GeWOM and indirect influence through green purchase intention. Furthermore, green motivation and opportunity failed to have a significant direct influence on GeWOM; however, they have indirect influence through green purchase intention. The findings have implications for tourism administrators and scholars, particularly in Saudi Arabia, about green tourism development.

1. Introduction

The growing number of individuals interested in green products and services made organisations pays more attention to the provision of green products and services to meet their customers’ needs and environmental concerns [1,2,3,4,5]. Hence, tourism scholars and administrators have become more concerned about the increasing demand by tourists for green products and services to contribute to environmental sustainability [6,7,8,9,10]. This included understanding individuals’ green purchase intentions and behaviours, as well as their green electronic word-of-mouth (GeWOM) [6]. GeWOM refers to online recommendations for green products, services, or organisations [11]. With the rise of the internet, especially social media sites, electronic word-of-mouth (EWOM) has become an increasingly important factor in shaping tourists’ green purchasing decisions [11,12].

The ability, motivation, and opportunity (AMO) theory [13] suggests that individuals’ green purchase intentions are influenced by three key factors: ability, motivation, and opportunity. Research has shown that the green AMO framework plays an important role in determining individuals’ green purchase intentions and behaviours [12,13], which ultimately affect GeWOM [12,14]. In the tourism context, green ability refers to a tourist’s perceived capability to engage in green behaviour, such as their access to information and resources about green products and services in tourism, their personal financial situation, and their level of environmental knowledge [15]. Motivation refers to a tourist’s inclination or drive to act in an environmentally responsible manner, such as their level of environmental concern, and their personal values and beliefs [15]. Opportunity is the third dimension of AMO theory, which refers to situational conditions, e.g., time constraints, that affect tourist behavioural intention [15]. Hence, it was argued that opportunity is similar to perceived behavioural control in the Theory of Planned Behaviour (TPB) [15]. The TPB assumes that there are three determinants of behavioural intention: attitude towards behaviour, subjective norms, and perceived behavioural control [16]. Attitude towards behaviour refers to individuals’ assessment of expected behaviour, either positively or negatively [17]. The current research is concerned only with the green attitude of tourists as one of the antecedents of their behavioural intention and their word-of-mouth. This research examines the direct effect of green attitude on GeWOM, as well as the indirect effect through green purchase intention.

The Kingdom of Saudi Arabia (KSA) is historically known as a destination for billions of Muslims worldwide because of the holy mosques in Mecca and Madinah. Hence, religious tourism has become the most common type of tourism in the KSA for hundreds of years [18]. However, the government of the KSA has recently promoted other types of tourism, such as recreation, business, sport, and entertainment, in response to the Saudi Vision 2030 [19]. For this purpose, the government has introduced a new tourist visa to facilitate the visits of tourists to the KSA [20]. The green tourism industry in the KSA is a major part of the Saudi Green Initiative, which was launched recently by the Crown Prince of the KSA, Mohamed Bin Salman. The Saudi Green Initiative aims to ensure environmental sustainability in the Kingdom. The Crown Prince of the KSA stressed the collaboration between different sectors and individuals to ensure the success of the initiative, which contributes to quality of life and sustainable development goals (SDGs).

In relation to the digital economy of the KSA, the government has made effort to make the government one of the top digital governments and economies worldwide. Hence, the country ranked ninth in the Digital Capabilities Index and number three among G20 countries [16]. The KSA has the fastest 5G internet in the world [21]. The digitalization of the country continued to include the KSA tourism industry. The government introduced an electronic tourist visa that can be applied for in 5 min to provide quick and prompt green service for potential tourists, initiated in September 2019. The government is keen to provide quality electronic services for the newly developed tourism industry to ensure tourist satisfaction and a quality experience that contributes to the destination image and stimulates positive word-of-mouth [20].

A few attempts were undertaken by scholars [6,11,12] to explore the determinants of GeWOM in the tourism industry. A recent study by Huy et al. [12], on improving GeWOM in the Vietnamese context, has called for further research in other countries’ contexts. The same study suggested that further studies should integrate new variables besides the AMO theory for a better understanding of tourists’ GeWOM. The current research attempts to bridge this gap and draws on both AMO [13] and TPB [17] to examine tourists’ green buying intentions and their GeWOM in the Saudi tourism industry. More precisely, the research examines an integrated model, which includes the effect of AMO as well as green attitude toward behaviour on the GeWOM of tourists and the indirect effect through green purchase intention. The research integrates both AMO and TPB to better understand tourists’ green intentions and GeWOM in the Saudi tourism industry, which have not been examined together to the best of the research team’s knowledge. The objectives of the current research are to:

• Examine the effect of green AMO on tourists’ green intentions and on GeWOM in the Saudi tourism industry.
• Examine the effect of green attitude on tourists’ green intentions and on GeWOM in the Saudi tourism industry.
• Test the mediating effect of green intention on the relationship between green AMO, green attitude, and GeWOM in the Saudi tourism industry.
• Establish a conclusion and a set of theoretical and practical implications for stimulating green purchase intention, and hence, the GeWOM for the Saudi tourism industry.

This research contributes to the general body of academic literature by integrating the theory of AMO with TPB, particularly green attitude, to understand tourists’ green intentions and GeWOM in the tourism industry. There is no doubt that GeWOM is important for environmental sustainability, which certainly contributes to sustainable tourism development. A review of the previous literature [11,12] found that individual’s green behaviour was understood through TPB or AMO separately. The current research examines an integrated model for understanding tourists’ green intentions and GeWOM, which enriches previous studies about the antecedents of GeWOM. Therefore, the current research has implications for tourism scholars and administrators in relation to the creation of GeWOM by their tourists using several digital platforms, especially social media sites. This could contribute to the development of the green Saudi tourism industry, especially since tourism development is new in the country. This could be a great opportunity for sustainable tourism development in the KSA, contributing to national initiatives such as the Saudi Green Initiative. Tourism administrators could take advantage of this in the KSA digital economy, as well as it influencing the direction of the government leadership toward greening the country and the region to build a model for the green tourism industry in the kingdom.

2. Conceptual Framework

2.1. The Relationship between Green Ability–Motivation–Opportunity, Green Purchase Intention, and GeWOM

The current research combines AMO theory with TPB to investigate the determinants of tourists’ intentions and behaviours to electronically spread positive words about green products and/or services, especially on social media. As highlighted above, there are limited, if any, studies that integrate the two theories for understanding tourists’ green purchase intentions and behaviours. As underlined earlier, ability refers to individuals’ information and knowledge, which encourage others to engage in certain activities [13,14,22,23], whereas green ability refers to an individual’s information, knowledge, and awareness of environmental concerns [9,24,25]. This knowledge and/or awareness is important for tourists to recognize green information about products or services, affecting their green purchase intentions [26]. The second dimension of AMO is an individual’s motivation, which refers to readiness, desire to process acquired information, and engagement in skills [13]. In the green context, motivation is defined as an individual’s readiness and desire to engage in certain environmental practices [27]. The growing amount of environmental content on social media has affected individuals’ intentions and decisions toward green products and services [12]. The last dimension of AMO is opportunity, which refers to any conditions or constraints, e.g., time and communications between customers online, that affect customers’ processing of acquired information [13]. The greater the ease of the conditions, the higher the purchase intention will be. Hence, green opportunity is defined as “favorable conditions and essential resources that enable individuals to approach environmental issues related information” [6] (p. 550).

The Stimulus–Organism–Response (SOR) theory [28] implies that the three dimensions of AMO are the stimulus of an organism, which is green purchase intention in the current study. The current research assumes that the three dimensions of AMO could directly affect an individual’s green purchase intention. Bigne et al. [15] found that AMO variables are antecedent predictors of travellers’ reactions and responses to different environmental information on social media platforms. When customers have the ability and opportunity to feel motivated about environmental issues, this stimulates their green intentions [29]. Based on these arguments, we suggest the following hypotheses:

H1: 

Green ability positively influences tourist green purchase intention in the tourism industry.

H2: 

Green motivation positively influences tourist green purchase intention in the tourism industry.

H3: 

Green opportunity positively influences tourist green purchase intention in the tourism industry.

Research [29,30] confirmed that individuals are more likely to express positive WOM when they have relevant skills and knowledge (ability), the willingness and desire to get involved (motivation), and better conditions (opportunity). Additionally, individuals with green ability (i.e., with appropriate environmental information, effective technology usage, and digital platforms, including social media) are more likely to share this information with other customers online [12]. While research [31] confirmed that individual motivation predicts their eWOM, recent research [12] demonstrated that green motivation is a determinant of GeWOM. Individuals are more willing to share their experiences online, via their social media sites or other websites, when they read online content about the hotel, product, or service [30]. It was also confirmed that when individuals have green opportunities, e.g., time and access to online reviews, they share positive eWOM [32]. Therefore, we could suggest that:

H4: 

Green ability positively influences tourist GeWOM in the tourism industry.

H5: 

Green motivation positively influences tourist GeWOM in the tourism industry.

H6: 

Green opportunity positively influences tourist GeWOM in the tourism industry.

TPB suggests that behavioural intention is the main antecedent of human behaviour [16,17]. Hence, we could argue that green purchase intention is a predictor of GeWOM. Drawn from the TPB framework, the study of Sobaih and Abdelaziz [33] found that customers’ buying intentions of nutrition-labelled menu items predicts their behavioural intentions to recommend these items to other individuals. The current research draws on AMO, TBP, and SOR to examine the mediating effect of green intention on the relationship between AMO and GeWOM. The SOR theory assumes that green AMO dimensions are stimuli of green intention and GeWOM is a response to the organism of green intention resulting from AMO. The current research is the first attempt to examine the mediating effect of green intention. These relationships are summarized in the conceptual research framework in Figure 1. We suggest that:

H7: 

Green intention positively influences GeWOM in the tourism industry.

H8: 

Green intention mediates the influence of green ability on GeWOM in the tourism industry.

H9: 

Green intention mediates the influence of green motivation on GeWOM in the tourism industry.

H10: 

Green intention mediates the influence of green opportunity on GeWOM in the tourism industry.

2.2. The Relationship between Green Attitude, Green Intention, and GeWOM

According to TPB, attitude toward behaviour is the positive or negative assessment of a specific behaviour [17]. Ellithorpe [34] argued that the effect of attitude on intention and then behaviour could not be fully understood without understanding the role of motivation and opportunity in predicting intention/behaviour. If both motivation and opportunity are present, the prediction of behaviour is determined by a relatively deliberative attitude process. However, if either of them or both are missing, then the predication of behaviour is determined by the relatively spontaneous attitude process. In the green hotel context, individuals tend to choose an eco-friendly hotel or product if they hold positive attitudes towards this hotel or service [12]. The TPB assumes that attitude is an antecedent of behavioural intention and a predictor of actual behaviour. For instance, the study of Sobaih and Abdelaziz [33] found that positive attitudes towards nutrition-labelled menus in fast food operation affect customer buying intentions, and hence their recommendations for others to buy these items. A study of the sustainability of green tourism in Cyprus [35] showed that tourists’ attitudes significantly positively influence their intentions and participation in green tourism. The same study [35] found that tourists’ intentions to participate in the sustainability of green tourism have a significant positive influence on environmentally responsible tourism behaviour. Another recent study in the tourism context [12] found that the green attitude of tourists has a direct and positive impact on their GeWOM. Hence, we argue that:

H11: 

Green attitude positively influences green purchase intention in the tourism industry.

H12: 

Green attitude positively influences GeWOM in the tourism industry.

H13: 

Green intention mediates the influence of green attitude on GeWOM in the tourism industry.

3. Methodology

3.1. Study Measures

The study’s questionnaire was separated into three parts (See in Supplementary Material). The initial section detailed the study’s objectives and included instructions for filling out the questionnaire. In the second section, participants were requested to provide demographic information. The third and final section featured the main questions of the study, which utilized a 5-point Likert scale with “strongly disagree” being represented by 1 and “strongly agree” by 5. GeWOM was constructed using four items as suggested by Rodgers [36] and Liu et al. [37], and the scale was found to have excellent reliability (α = 0.907). To build the green AMO scale, we created a list of variables related to ability (five items, α = 0.952), motivation (three items, α = 0.887), and opportunity (three items, α = 0.816). These items were introduced by Gruen et al. [14] and Christou et al. [38] and revised by Huy et al. [12]. We adopted 6 items (α = 0.934) from previous related studies [39,40,41] to measure purchase intention. Finally, attitude was measured using 4 items (α = 0.876) as suggested by Ajzen [17].

The questionnaire underwent testing by both university academics (10) and professionals (9) to ensure its consistency and simplicity. After receiving feedback, only minor changes were made to improve the questionnaire’s readability. Respondents’ privacy was maintained, and their answers remained confidential. As data were collected primarily through questionnaires, the possibility of common method variance (CMV) arose as a concern [42]. To detect any potential CMV, an exploratory factor analysis (EFA) using Harman’s single factor test analysis was executed. The results revealed that CMV is not a problem, as one item only explained 41% of the variance in the endogenous unobserved variables, which was lower than the 50% recommended by previous research [43].

3.2. Participants and Data Collection

The focus of this article was on individuals who visit Saudi Arabia for tourism purposes during November and December 2022. From the first week of November to the last week of December 2022, the questionnaires were distributed and collected. A specialized organization was hired to gather the necessary information. The study received assistance from a data collection company that specializes in data collection. The researchers were responsible for making sure that the research protocol was followed, including explaining the purpose of the study to the participants and obtaining their informed consent. The company helped with the communication and data collection from the selected respondents. Once the respondents agreed to participate, they were given a questionnaire to complete. 650 tourists were selected at random and given self-administered surveys, which were later collected as suggested by Ibeh et al. [44]. A systematic random sampling technique was employed to collect the required data, which involved selecting tourists visiting different events in Saudi Arabia, such as Riyadh Season, Diriyah Season, and AlUla Moments. With the help of a data collection company, we decided to select every third tourist entering the event from the gate entrance. Once a tourist was selected, we obtained their consent to participate and fill in a questionnaire. At the exit gate, we collected the completed questionnaires that were dropped off by the participants. Out of the 650 surveys, 625 were completely completed, causing a response rate of 96 percent with no missing data. A T-test was used to compare the average scores of participants who responded early versus those who responded late, and the results did not show any significant differences. This suggests that the issue of nonresponse bias did not affect the data.

Out of the 625 valid responses, 54% were females (totalling to 337) and 46% were males (totalling to 288). The majority of respondents, accounting for 70%, were aged between 25 and 40, with 75% having completed their bachelor’s degree. The majority of visitors (60%) were unmarried, and more than half (53%) had not previously visited Saudi Arabia. The largest number of respondents came from Eastern Europe, accounting for 55% of the total. The high proportion of Eastern Europeans (55%) might be due to several reasons. One possible reason is the historical and cultural ties between the KSA and certain Eastern European countries, which can be traced back to the Ottoman Empire that controlled much of Eastern Europe, which may make it easier for people from those countries to obtain visas and travel to the KSA. Additionally, the KSA’s efforts to promote tourism and attract visitors from around the world may also be contributing to the high proportion of Eastern European visitors. Middle Eastern citizens were in the second order at 22%, followed by Western Europeans at 17%, and finally, Africans at 6%, as shown in

Table 1. Participants’ demographic information.

Category	Group (N = 625)	Frequency	%
Gender	Male	288	46
	Female	337	54
Age group	20–24	125	20
	25–34	250	40
	35–40	188	30
	41 and above	62	10
Education	High school certificate	94	15
	Bachelor’s degree	469	75
	Graduate degree	62	10
Region	Eastern Europe	344	55
	Middle East	138	22
	Western Europe	106	17
	Africa	37	6

.

The average scores given by the participants ranged from 3.80 to 4.48, and the amount of variation in their responses is shown by the standard deviation, which ranged from 0.952 to 1.21. This indicates that the answers were not tightly clustered around the average score [45]. The data also show that there is a normal distribution, as neither the skewness nor kurtosis exceeded values of −2 or +2, according to Nunnally’s [46] criteria. Additionally, the VIF (variance inflation factor) value for the study items was less than 0.5, indicating that there is no issue with multicollinearity, as shown in

Table 2. Measurement model factor loadings, α, C.R, AVE, and VIF for multicollinearity.

Dimensions and Related Variables	Load.	α	C.R	AVE	VIF
Green electronic word-of-mouth (GeWOM)		0.907	0.909	0.787
In the coming days, I plan to regularly utilize SM platforms to communicate my own experiences of the eco-friendly destination.	0.947				1.280
I intend to make frequent use of SM platforms to share my own experiences of the green destination in the near future.	0.929				3.672
I will urge other friends to use SM platforms to share their own experiences of the green destination.	0.909				4.078
Through SM platforms, I aim to inspire others to visit environmentally friendly green destinations.	0.750				1.573
Green Ability		0.952	0.954	0.839
I usually have no trouble talking about environmental concerns with others on social media.	0.903				3.513
I have the ability to effectively convey environmental issues through social media platforms.	0.936				4.024
I am adept at handling environmental topics on social media platforms.	0.932				3.381
I view myself as highly proficient in using social media to address environmental issues.	0.912				1.909
I do not require an excessive amount of personal effort or time to locate environment-related content on social media platforms.	0.897				1.136
Green Attitude		0.876	0.884	0.730
Using social media platforms is a beneficial action.	0.804				1.996
Utilizing social media platforms is a prudent action.	0.896				2.828
Making use of social media platforms is an enjoyable action.	0.857				2.168
Using social media platforms is an appealing action.	0.857				2.371
Green Motivation		0.887	0.888	0.818
The environmental concerns discussed on SM platforms are usually pertinent to me.	0.917				4.594
I consistently find the environmental topics being discussed on SM platforms to be engaging.	0.949				4.499
Engaging in conversations about environmental issues on SM platforms invigorates me.	0.844				1.880
Green Opportunity		0.816	0.831	0.726
Social media provides me with access to information pertaining to environmental conservation and enables me to stay informed.	0.882				3.941
Expressing my thoughts and ideas related to the environment is effortless on social media platforms.	0.863				3.730
With my computer, laptop, mobile phone, and internet connection, I can easily access environmental information on social media platforms	0.809				1.319
Green Purchase Intention		0.934	0.937	0.753
It is probable that I will buy the product(s) offered on the website.	0.865				1.957
I am inclined to suggest the website to my acquaintances.	0.888				1.953
If I require a travel package that is available on the website, I am likely to make another purchase from it.	0.810				1.059
I intend to utilize the online channel for purchasing travel items in the future.	0.920				1.862
The website will be my primary preference for purchasing travel items.	0.911				1.619
I will contact the website to obtain more information regarding my future purchase.	0.805				1.393

.

3.3. Data Analysis Methods

Our study employed the PLS-SEM method, which is a nonparametric method that relies on the variance in unobserved latent factors that cannot directly be observable [47]. We used the SmartPLS v.4 software program to analyse the data. PLS-SEM is a widely accepted approach in management science and is known for producing reliable results [48]. Smart PLS-SEM is commonly used to investigate correlations among multiple variables [49]. The PLS-SEM method is a practical option when the primary aim of the study is to predict dependent variables rather than validate a previously established theoretical model [49]. In this study, PLS-SEM is an appropriate method as it examines the determinants of green electronic word-of-mouth (GeWOM) through the lens of both the AMO and TPB theories, with the mediating role of tourists’ green purchase intentions in the above-mentioned relationships. PLS is also preferred due to its wider applicability across sample sizes, lesser data restrictions, and greater effectiveness compared to other statistical methods [48]. Additionally, the use of PLS-SEM enables more reflective items per factor to be included in the analysis. We followed the guidance of Leguina [50] and evaluated the theoretical model in two stages: the first stage was to confirm the convergent and discriminant validity, and the second stage was to test the hypotheses.

4. The Study Results

4.1. Outer Model Evaluation

After taking into consideration the suggestions of Hair et al. [49] and Kline [51], several standards (indicators) were utilized to check the validity (convergent and discriminant) and reliability of the proposed research model. These standards include the CR “composite reliability” score, “internal consistency reliability” (a) score, “convergent validity” indicator, and “discriminant validity” indicator.

Convergent and Discriminant Validity Evaluation

We tested the scale convergent validity using different criteria such as composite reliability, loadings, and AVE. Table 2 shows that all the scales used, including GeWOM, Green AMO, green attitude, and green purchase intention, had high composite reliability (C.R) and AVE values (GeWOM: C.R = 0.909, AVE = 0.787; green ability: C.R = 0.954, AVE = 0.839; green motivation: C.R = 0.888, AVE = 0.818; green opportunity: C.R = 0.831, AVE = 0.726; attitude: C.R = 0.884, AVE = 0.730; green purchase intention: C.R = 0.937, AVE = 0.735). These values are more than the required threshold values of 0.7 for C.R and 0.5 for AVE, indicating a high level of internal reliability between the scale items. Furthermore, each single factor had SFL values of over 0.70, suggesting that the employed research constructs have adequate convergent validity.

To ensure discriminant validity, our study checked the cross-loadings, Fornell–Larcker [52], and heterotrait–monotrait ratios. The results shown in

Table 3. Cross-loadings.

	GeWOM	G_Ability	G_Attitude	G_Motivation	G_Opportunity	G_Purchase Intention
Ablty_1	0.504	0.903	0.379	0.461	0.368	0.524
Ablty_2	0.453	0.936	0.386	0.513	0.337	0.472
Ablty_3	0.444	0.932	0.381	0.499	0.330	0.464
Ablty_4	0.522	0.912	0.403	0.487	0.375	0.532
Ablty_5	0.473	0.897	0.377	0.457	0.340	0.513
Attitde_1	0.448	0.377	0.804	0.370	0.439	0.453
Attitde_2	0.558	0.337	0.896	0.342	0.383	0.521
Attitde_3	0.547	0.388	0.857	0.406	0.394	0.572
Attitde_4	0.478	0.337	0.857	0.344	0.396	0.466
GeWOM_1	0.947	0.460	0.566	0.519	0.434	0.679
GeWOM_2	0.929	0.448	0.536	0.514	0.418	0.679
GeWOM_3	0.909	0.462	0.558	0.480	0.445	0.696
GeWOM_4	0.750	0.492	0.455	0.507	0.394	0.658
Intntion_1	0.639	0.472	0.544	0.587	0.434	0.865
Intntion_2	0.652	0.488	0.537	0.585	0.436	0.888
Intntion_3	0.642	0.434	0.455	0.529	0.375	0.810
Intntion_4	0.718	0.532	0.523	0.596	0.465	0.920
Intntion_5	0.693	0.509	0.560	0.650	0.432	0.911
Intntion_6	0.641	0.416	0.460	0.540	0.351	0.805
Motvn_1	0.527	0.462	0.388	0.917	0.367	0.574
Motvn_2	0.528	0.490	0.380	0.949	0.408	0.623
Motvn_3	0.490	0.476	0.392	0.844	0.328	0.621
Oportnty_1	0.340	0.321	0.336	0.302	0.882	0.332
Oportnty_2	0.354	0.305	0.305	0.296	0.863	0.370
Oportnty_3	0.487	0.342	0.508	0.409	0.809	0.485

indicate that each single latent variable’s outer-loadings were more than its cross-loadings. Furthermore,

Table 4. Measures of scale discriminant validity.

	Fornell–Larcker Values						HTMT
	A	B	C	D	E	F	A	B	C	D	E	F
A. GeWOM	0.887
B. Green Ability	0.525	0.916					0.564
C. Green Attitude	0.598	0.421	0.854				0.667	0.461
D. Green Motivation	0.570	0.527	0.428	0.904			0.637	0.574	0.485
E. Green Opportunity	0.478	0.384	0.469	0.407	0.852		0.536	0.427	0.534	0.462
F. Green Purchase Intention	0.766	0.549	0.593	0.671	0.480	0.868	0.834	0.579	0.650	0.736	0.529

shows that the diagonal AVE scores were greater than the intervariable correlations. These results showed that the employed measures do not have any expected theoretical relationships with other measures. Additionally, the HTMT scores were less than 0.90 as discussed by Leguina [50], as per the reference values given in Table 4. We then conducted a structural model to test the study’s hypotheses.

4.2. Inner Model Hypotheses Testing

In order to assess the model’s capacity to justify and predict the variance in endogenous latent unobserved variables triggered by exogenous latent unobserved variables, SmartPLS v.4 was used to assess the inner model. To determine the goodness-of-fit (GoF), the formula suggested by Chin [53] was used. This involved “calculating the square root of the average of R² multiplied by the average of all AVE values”, which resulted in a value of 0.69, indicating a large GoF according to Wetzels et al. [48]. According to this, the R² score for endogenous latent unobserved variables should be no less than 0.10 for the model to be confirmed as having good GoF. In this study, GeWOM had an R² value of 0.635, and purchase intention had an R² value of 0.595, both exceeding the recommended value and confirming that the model matches the empirical data.

The Stone–Geisser Q2 statistics displayed a score of 0.511 for GeWOM and a score of 0.587 for green purchase intention, both higher than zero and confirming acceptable findings [47]. Furthermore, the SRMR score had to be below 0.08, and the NFI score had to be beyond 0.90 to safeguard a GoF to the data [49]. The SRMR score was 0.047, and the NFI score was 0.929, both exceeding the recommended threshold scores and confirming a good fit. Furthermore, the f² scores, which compute the variances in R² when an exogenous variable is eliminated, were estimated. The results exposed the following f² scores for the four exogenous unobserved variables’ impacts on GeWOM (green ability, f² = 0.017; green motivation, f² = 0.004; green opportunity, f² = 0.012; and attitude, f² = 0.055). This implies that any exogenous unobserved variables, when eliminated from the proposed model, will only have a small effect (<0.15) on the main suggested model, as argued by Cohen [54].

After assessing that the fit was satisfactory, a bootstrapping process with 5000 replications was conducted using SmartPLS v.4 to calculate the path coefficient and t-value for the proposed relationships, as can be seen in

Table 5. Hypotheses findings.

	Hypotheses	Beta (β)	(t-Value)	p Values	Results
H1	Green Ability -> Green Purchase Intention	0.165	4.287	0.000	Accepted
H2	Green Motivation -> Green Purchase Intention	0.413	9.523	0.000	Accepted
H3	Green Opportunity -> Green Purchase Intention	0.111	2.879	0.004	Accepted
H4	Green Ability -> GeWOM	0.100	2.527	0.012	Accepted
H5	Green Motivation -> GeWOM	0.051	1.080	0.280	Not Acceped
H6	Green Opportunity -> GeWOM	0.078	1.930	0.054	Not Acceped
H7	Green Attitude -> Green Purchase Intention	0.295	6.257	0.000	Accepted
H8	Green Attitude -> GeWOM	0.183	4.465	0.000	Accepted
H9	Green Purchase Intention -> GeWOM	0.531	8.973	0.000	Accepted
Specific indirect effects
Green Ability -> Green Purchase Intention -> GeWOM		0.088	3.690	0.000	Accepted
Green Motivation -> Green Purchase Intention -> GeWOM		0.219	6.495	0.000	Accepted
Green Opportunity -> Green Purchase Intention -> GeWOM		0.059	2.574	0.010	Accepted
Green Attitude -> Green Purchase Intention -> GeWOM		0.157	5.444	0.000	Accepted

and Figure 2. This research paper suggested and tested nine direct and four indirect hypotheses. The results of the PLS-SEM analysis indicated that green purchase intention was positively and significantly impacted by green ability (β = 0.165, t-value = 4.287, p < 0.001), green motivation (β = 0.413, t-value = 9.523, p < 0.001), and green opportunity (β = 0.111, t-value = 2.879, p < 0.001), thus confirming hypotheses H1, H2, and H3. In a similar fashion, GeWOM was positively and significantly affected by green ability (β = 0.10, t-value = 2.527, p < 0.001) and green attitude (β = 0.183, t-value = 4.465, p < 0.001), therefore supporting hypotheses H4 and H8.

On the other hand, green motivation (β = 0.051, t-value = 1.080, p = 0.280) and green opportunity (β = 0.078, t-value = 1.930, p = 0.54) did not have a positive and significant effect on GeWOM, hence H5 and H6 were not supported. The results also showed that green attitude had a positive and significant effect on green purchase intention (β = 0.295, t-value = 6.257, p < 0.001), thus confirming H7. Additionally, green purchase intention was found to have a positive and significant impact on GeWOM (β = 0.531, t-value = 8.973, p < 0.001), thus confirming H9.

The results also showed evidence for the mediating effects of green purchase intention on the tested relationships. As can be seen in Table 5, green purchase intention had a positive and significant influence on the relationship between green ability and GeWOM (β = 0.088, t-value = 3.690, p < 0.001), green motivation and GeWOM (β = 0.219, t-value = 6.495, p < 0.001), green opportunity and GeWOM (β = 0.059, t-value = 2.574, p < 0.001), and green attitude and GeWOM (β = 0.157, t-value = 5.44, p < 0.001).

5. Discussions

The current research draws on AMO and TPB to investigate the determinants of GeWOM in the Saudi Tourism Industry. The research examines an integrated model, which combines the three dimensions of green AMO theory, i.e., green ability, motivation and opportunity, alongside green attitude for understanding tourists’ green purchase intentions as well as their GeWOM. This research examined nine direct and four indirect relationships, totalling thirteen research hypotheses. With regard to the direct relationships, the results of the PLS-SEM showed tourists’ green purchase intentions were positively and significantly influenced by the three dimensions of AMO: green ability, green motivation, and green opportunity. This means that when tourists are aware of environmental concerns, able to handle environmental topics on social media, have access to environmental information on social media, are engaged on environmental topics on social media, and express their thoughts and ideas related to the environment on social media, they are more likely to have green purchase intentions. Hence, they can use different websites to purchase their products and services. Websites have become their primary preference for purchasing travel items and their future purchases. In conclusion, green AMO has the ability to shape the green purchase intentions of tourists. This supports previous research [12], that the spread of environmental content on social media and individuals’ engagement in such information stimulates their green purchase intentions. The results support the work of Bigne et al. [15], who also revealed that the constructs of AMO are predictors of travellers’ reactions and responses to different environmental information on social media platforms. It also confirms that tourists’ ability, motivation, and opportunity are their green buying intentions [29].

Supporting the work of Huy et al. [12], the current research found that tourist GeWOM was directly, positively, and significantly affected by green ability. This means that when tourist have green ability, they are more likely to share their experience on social media about the eco-friendly product, service, or organisation, such as a hotel. Those who hold green ability inspire others to visit environmentally friendly green destinations. Furthermore, tourists who hold a green attitude and see social medial usage, particularly for environmental issues, as a beneficial, prudent, enjoyable, and appealing action, exhibit GeWOM. In other words, tourists with a green attitude share positive experiences with others about products, services, or destinations. Unlike Huy et al. [12], the current research found that green motivation and opportunity failed to have a direct, positive, and significant impact on GeWOM. This could be because tourism is new in Saudi Arabia; hence, tourists’ green motivation and opportunity did not directly influence sharing their experience with others because some tourists are on their first visit to this new destination. These findings do not support previous research [31] that an individual’s motivation predicts their eWOM or [12] that green motivation is a determinant of GeWOM. Additionally, the results do not support the significant positive effect of green opportunity on eWOM [32].

The results support the assumption made by the TPB framework [16,17], that green attitude has a positive and significant effect on green purchase intention. Additionally, green purchase intention was found to have a positive and significant impact on GeWOM. The TPB framework implies that attitude is an antecedent of behavioural intention, which is a predictor of actual behaviour. This also supports the SOR theory, that green purchase intention is an organism of green attitude, while GeWOM is a response to the green intention of tourists. This finding aligns with previous research [12], which found that tourists tend to choose eco-friendly hotels if they hold a positive attitude toward such hotels. It also supports a recent study [32] that tourists’ attitudes have a significant effect on their intention to participate in the sustainability of green tourism, which has a significant positive influence on environmentally responsible tourism behaviour.

For the first time, to the best of the researchers’ knowledge, the results confirmed the mediating effects of green purchase intention on the relationship between green ability and GeWOM, green motivation and GeWOM, green opportunity and GeWOM, and green attitude and GeWOM. These findings imply that a tourist’s green purchase intention can play a role in turning insignificant direct relationships, such as the effect of green motivation and opportunity on GeWOM, to a significant relationship through the influence of green purchase intention. Additionally, the results confirm the SOR framework that green AMO and attitude are stimuli of green purchase intention, which is an organism of GeWOM that acts as a response to green purchase intention.

6. Implications of the Research

The result of this research has several theoretical and practical implications. Theoretically, current research results extended our understanding of GeWOM in the tourism industry, especially concerning the determinants of GeWOM. The research confirmed that green AMO and green attitude significantly influence tourists’ green purchase intentions. They also were found to predict tourists’ GeWOM indirectly through green purchase intentions. The current research confirmed the mediating effect of green purchase intentions in the relationship between green AMO, green attitude, and GeWOM in the tourism industry. The results support the MODE model [32] that the effect of a green attitude on GeWOM occurs in the absence of the impact of motivation and opportunity on GeWOM. This means that the prediction of GeWOM is determined by the relatively spontaneous attitude process. This spontaneous attitude process could be because tourist experience in the KSA is new, and tourists did not have enough motivation and opportunity to positively affect their GeWOM.

Practically, administrators in the Saudi tourism industry should pay more attention to environmental content on social media, since it shapes the green AMO of tourists, which affects their green intention and behaviour, such as GeWOM. The content shared on social media has to be easily accessible, enjoyable, and pertinent to social media users. Green social media campaigns are essential to raise the awareness of users and motivate them to engage in environmental conservation action to support environmental sustainability. This would encourage tourists to share their experiences on social media platforms. Such practices would promote green tourism in Saudi Arabia, which is aligned with the Green Saudi Initiative and the Saudi Vision 2030, and contribute to sustainable tourism development.

7. Conclusions, Limitations, and Future Research Opportunities

In recent years, there has been an increasing concern for the environment, and consumers have become more conscious of the impact of their actions on the environment. As a result, the demand for environmentally friendly products has increased, and consumers are actively seeking information about such products before making a purchase decision. One of the most effective sources of information for consumers is Green electronic word-of-mouth (GeWOM). This study aimed to examine the impact of green ability, motivation, opportunity, and attitude on green electronic word-of-mouth (GeWOM) through the mediating role of green purchase intention. Data were collected from 625 individuals who visited Saudi Arabia for tourism purposes. PLS-SEM was employed to analyse the obtained data. The study’s findings revealed that green ability, motivation, opportunity, and attitude positively impacted green purchase intention. Green purchase intention, in turn, had a positive impact on green eWOM. Furthermore, green purchase intention fully mediated the relationships between green motivation and green opportunity, and green eWOM, while green purchase intention partially mediated the impact of green ability and green attitude on green eWOM. The study contributes to the literature on green marketing and provides practical implications for marketers to enhance green eWOM. One possible explanation is that since tourism is a relatively new industry in Saudi Arabia, tourists’ willingness and ability to engage in sustainable tourism practices did not directly and significantly affect their likelihood of sharing their experiences with others. This could be because tourists are visiting this destination for the first time and are not yet fully familiar with the local, sustainable tourism options.

This research has some limitations in relation to the demographic of the respondents as the majority of them (70%) were aged between 25 and 40, which may have an effect on the results; hence, future research could examine the moderating effects of gender and age on the research model. In this research, we employed the three dimensions of the AMO (ability, motivation, and opportunity) and attitude as direct antecedents of intention and behaviour (WoM); further study can use attitude as a mediator and other variables as a moderator (i.e., trust), and the results can be compared with our results to fully understand the interaction between all variables in predicting intention and behaviour.

Additionally, the research only examined the effects of green attitude on green purchase intention and GeWOM, as one construct of TPB; further research could examine the effects of subjective norms and perceived behavioural control. Other future research opportunities can be explored to further understand the impact of green ability, motivation, opportunity, and attitude on green electronic word-of-mouth (eWOM) through the mediating role of green purchase intention. Firstly, future research can focus on examining the moderating effects of demographic factors such as age, gender, education, and income on the relationships between green ability, motivation, opportunity, attitude, green purchase intention, and green eWOM. This will help identify any significant differences in the relationships based on demographic characteristics and provide a more nuanced understanding of the factors influencing green eWOM. Secondly, future research can explore the role of trust and credibility in the relationship between green eWOM and green purchase intention. This will help to understand the extent to which consumers rely on eWOM when making purchase decisions and how the trustworthiness and credibility of the sources of eWOM influences this reliance. Thirdly, future research can investigate the impact of different types of green eWOM (e.g., reviews, ratings, recommendations, comments, and social media posts) on green purchase intention. This will help identify which types of eWOM are more effective in influencing consumer behaviour and provide insights into the types of eWOM companies should focus on when developing their marketing strategies. Finally, future research can examine the impact of green eWOM on other consumer behaviours, such as loyalty, advocacy, and repeat purchase intention. This will help to understand the broader effects of eWOM on consumer behaviour and provide insights into the long-term impact of eWOM on companies’ financial performance.