The Role of Post-Stay Evaluation on Ewom and Hotel Revisit Intention among Gen Y

Abstract

Electronic word-of-mouth (eWOM) has become an important source for customers and hoteliers, and likewise, the interactivity of Web 2.0 has allowed customers to write and share online reviews. Our study aims to examine: (1) the influence of post-stay evaluation factors toward eWOM, (2) the relationship between eWOM and hotel revisit intention, and (3) the influence of country differences as a moderating factor. A total of 872 usable responses were collected from three emerging countries. Our results suggest that post-stay evaluation was the key predictor of positive eWOM, and later transferred greater hotel revisit intention among Gen Y. In terms of country level, all countries depicted a positive relationship for all paths, and the influence of the respondents’ country moderated for some relationship in this study. Our findings also shed further light on the understanding of the generational cohort theory across emerging countries, especially in the hospitality context.

Keywords

post-stay evaluation eWOM hotel revisit intention generational cohort theory emerging countries

Introduction

Web 2.0 enables tourists to communicate, share experiences and seek opinions by the interaction via digital platforms, such as Facebook, Instagram, Twitter, WeChat, and TripAdvisor, facilitating the diffusion of electronic word-of-mouth (eWOM; N. L. Chan & Guillet, 2011; Cheung, Pires, Rosenberger, & De Oliveira, 2020). Indeed, tourists increasingly rely on referrals created by online users in their decision-making processes (Cheung, Pires, & Rosenberger, 2020). Given its importance, hoteliers substantially invest their effort in driving tourists’ postpurchase perceptions, aiming to engage with tourists and subsequently strengthen their positive referral intention (Gardiner et al., 2014; S. Lee, 2018; Viglia et al., 2016).

In recent years, there has been a growing amount of literature examining the impact of postpurchase evaluation factors on tourists’ behavioral intention, given its importance in driving positive referrals and revisit intention of experienced tourists (Chi, 2012; Milman & Pizam, 1995; Sampol, 1996). Recent literature has revealed that postpurchase feelings (e.g., satisfaction, product experience, loyalty) contributed to positive referrals (Amed, 2019), and revisit intention (Ali et al., 2019). Applied to the hotel industry, tourists’ post-stay evaluation, including service experiences, satisfaction, and commitment, play a substantial role in driving positive eWOM and revisit intention (Berezan et al., 2015; X. Xu, 2019). Indeed, post-stay evaluation is especially important in driving Generation Y (Gen Y) tourists’ behavioral intention, since they are more influenced by information and experiences shared by online users (Book et al., 2018). As such, this justifies the empirical study of Gen Y’s post-stay evaluation using generational cohort theory (GCT) in the hotel context.

GCT can be defined as a group of individuals born in the same period, living in the same place and having experienced the same external events (e.g., historical, social, political, and technological) during their coming of age (i.e., late adolescence and early adulthood; Mannheim, 1952; Schewe & Noble, 2000). Arguably, each member has similar profiles (e.g., characteristics, behaviors, and attitudes) within a generational cohort, which varies from other generational cohorts (Motta et al., 2002), thereby justifying the scholarly interest of GCT in the tourism context. Previous studies discovered GCT is useful to understand tourist decision making (Gardiner et al., 2014; Gardiner, King, et al., 2013; Gardiner & Kwek, 2016), motivation (Chung et al., 2016), and technology acceptance (Bravo et al., 2019; Luna-Cortés, 2018; Sox et al., 2016).

To the best of our knowledge, except for Mulvey et al.’s (2020) study, most previous studies in tourism adopted GCT to study tourists’ behaviors in single countries, while cross-countries comparisons in the area of GCT and tourist behaviors were limited (Bravo et al., 2019; Chung et al., 2016; Gardiner & Kwek, 2016; Luna-Cortés, 2018). Indeed, understanding the relationship between post-stay evaluation and eWOM in the hotelier industry of Gen Y among different countries is scant. Moreover, it could be argued that Gen Y in each country experienced different external events during their coming of age and external events may have occurred at different levels (e.g., national and international levels; Mannheim, 1952; Noble & Schewe, 2003; Ryder, 1965; Schuman & Scott, 1989). At the international level, more than one country will experience and be affected by external events. For example, the Coronavirus (Covid-19) pandemic in 2020 has affected more than one country and could share a similar generational cohort effect, such as the “new norm” and social distancing.

On the other hand, countries may have different national-level external events since only people living in that particular country experienced events such as Independence Day, Racial Riots, and the “one-child” policy in China. Furthermore, the generational cohort effect may differ between countries since individuals experience different types of external events, are in different locations, and belong to different cultures (Schewe et al., 2013). Besides, those individuals who directly experienced the external events during their coming of age will have an impact on the formation of generational cohort profiles (Noble & Schewe, 2003). Although Mulvey et al. (2020) called for empirical research to study traveling and sharing trends of Gen Y, understanding of Gen Y’s post-stay evaluation and behavioral intention in emerging countries, it is yet to be explored.

In this context, emerging countries refer to “low-income, rapid-growth countries using economic liberalization as their primary engine” (Hoskisson et al., 2000, p. 249). Emerging countries are also fast-growing, untapped markets, and contribute to a vast majority of the world’s population as compared with developed countries, such as China, Brazil, and Malaysia (London & Hart, 2004). China and Brazil are the major economies in emerging countries, while Malaysia is the 15th most competitive economy and expected to become a high-income country by 2024 (World Bank, 2019). Importantly, China, Brazil, and Malaysia are considered emerging countries with promising development in tourism (Hoskisson et al., 2000), having a substantial amount of Gen Y tourists who are highly involved in tourism. Moreover, Gen Y members consist of the majority of the population in Brazil and Malaysia (Department of Statistics Malaysia, 2017; IBGE, 2020), and more than 240 million in China (Simmers et al., 2014).

As mentioned earlier, each country’s Gen Y tends to have a unique profile (e.g., characteristics, preferences, values) since they experience different external events. Also, their profile is heterogeneous across various cultures despite the same age group (Mulvey et al., 2020; Schewe et al., 2013). The variation of Gen Y’s profile might also influence their post-stay evaluation from one country to another. Previous studies indicated that post-stay evaluation has an impact, contributed to positive WOM (Amed, 2019) and revisit intention (Ali et al., 2019).

However, there has been a lack of studies empirically testing the relationship between post-stay evaluation as determinant factors influencing positive eWOM and revisit intention of Gen Y among emerging countries. Although Su et al. (2017) conducted a study on the impact of tourists’ perception toward eWOM and revisit intention of tourist attraction sites (i.e., World Heritage Site) in China, their study focused on only on Chinese tourists in general, without a specific age group. As such, in this study, we employ GCT as an underpinning theory to identify Gen Y’s profile and comparing Gen Y in three emerging countries in response to the limitation found in the study by (Su et al., 2017). Similarly, a few studies also urged for cross-cultural studies among Gen Y (Gardiner & Kwek, 2016; Liu et al., 2018). Therefore, the uniqueness of Gen Y’s profile might play a vital role in influencing their post-stay evaluations on eWOM and revisit intention.

Several works of literature posit that demographic variables have an impact on customers’ eWOM behavior (Akinci & Aksoy, 2019; Mladenovic et al., 2019). However, there are a limited number of studies comparing different countries on post-stay evaluation factors, eWOM, and revisit intention, especially in the hotel context. Except for Mulvey et al.’s (2020) study, previous studies focused on a single country (Bolton et al., 2013; Gardiner & Kwek, 2016). Although, more recent literature has highlighted the need to study comparing different countries for better understanding in a tourism context (Cheng et al., 2006; S. Lee, 2018; Leong et al., 2019; Mladenovic et al., 2019). Therefore, our study aims to address the limitation and used the respondents’ generational cohort profile as a moderator. Hence, this study focuses on Gen Y in three emerging countries, namely Brazil, China, and Malaysia.

Based on the above discussion, the objectives of our study are to (1) examine the influence of post-stay evaluation factors (i.e., customer satisfaction, loyalty, and commitment), toward positive eWOM; (2) investigate the relationship of positive eWOM and hotel revisit intention; and (3) investigate the differences in the relationship between constructs mentioned above among Gen Y of the three emerging countries, including China, Brazil, and Malaysia. As such, our study contributes to the hospitality and tourism context by empirically studying the use of the GCT to understand the post-stay evaluation factors that influence Gen Y’s positive eWOM and hotel revisit intention. Moreover, it provides a better understanding of the impact of GCT in shaping Gen Y’s profile across countries and also contributes to GCT knowledge, that provides evidence across three emerging countries. Finally, our study also contributes to identifying which post-stay evaluation factors provide a greater impact on positive eWOM and hotel revisit intention among Gen Y in three emerging countries.

Theoretical Framework

Generational Cohort Theory and eWOM

GCT can be defined as a group of individuals who were born in the same period, having experienced the same external events (historical, social event) during their coming of age (i.e., late adolescence and early adulthood; Mannheim, 1952; Schewe & Noble, 2000). The external events experienced by individuals have a consequential impact on the formation of a generational cohort’s profile (i.e., values, characteristics, attitudes, and behavior; Meredith & Schewe, 1994; Meredith, Schewe, & Karlovich, 2002; Schewe et al., 2000), and it remains unchanged for the duration of their entire life (Schuman & Scott, 1989).

The application and uses of GCT as a segmentation method have been widely adopted in other countries such as the United States, Russia, China, New Zealand, Brazil, Malaysia, and Mexico (Fam et al., 2008; Fernández-Durán, 2016; Meredith, Schewe, Hiam, et al, 2002; Schewe et al., 2013; Sharipudin et al., 2016; Ting & De Run, 2012), and is useful in determining and segmenting consumer profiles (Fam et al., 2008; Gardiner, Grace, et al., 2013).

On the other hand, Gen Y is known as the Digital Native Generation, born in the digital technology era, and are technology savvy compared with previous generations (Prensky, 2001). They devoted more time on the Internet (K. Chan & Fang, 2007; Debevec et al., 2013), and rely on digital technology as a medium of communication (Ellison et al., 2007; Subrahmanyam et al., 2008). A such, it could be the reason why Gen Y customers actively contribute and share their experience on eWOM when they have had a positive postpurchase experience (Bronner & De Hoog, 2011; Hwang & Kim, 2019; Ip et al., 2012; Mladenovic et al., 2019; S. Prasad et al., 2019).

Similarly, the uses of eWOM for seeking and sharing information using online platforms (e.g., SNSs, online review sites) have been used widely in the tourism field. Customers tend to rely more on consumer-generated-content as compared with the owner’s information, especially for Gen Y. Previous studies found that Gen Y customers are highly dependent on eWOM platforms for information on products and services (Gardiner et al., 2014; Weibel et al., 2008). Moreover, they actively participate in eWOM (Hwang & Kim, 2019; S. Lee, 2018), and are willing to share a post-stay experience (Bronner & De Hoog, 2011; Ip et al., 2012) as compared with previous generations. It also allows customers to write their comments and opinions and share first-hand experiences, whether it is positive or negative, via online platforms (Cheung et al., 2021). Therefore, our study focuses on Gen Y customers’ postpurchase evaluation toward eWOM and hotel revisit intention.

Customer Satisfaction and eWOM

Customer satisfaction is one of the determinant predictors for positive behavioral outcomes such as eWOM and revisit intention among consumers (Amed, 2019; Su et al., 2017; Zeithaml et al., 1996). Previous studies established the relationship between customer satisfaction influence and their recommendation through eWOM (C. Xu et al., 2015) and also the tendency to disseminate positive eWOM (Wardi et al., 2018; Yu et al., 2017). If customers are satisfied with hotel services, they are more likely to express their positive emotions and views via online reviews (M. Lee et al., 2017), and are more willing to engage with positive eWOM than unsatisfied customers (Akinci & Aksoy, 2019; Berezina et al., 2016).

Studies also assert customer satisfaction as a determinant predictor for positive eWOM. Gen Y customers will actively engage in positive eWOM by sharing their post-stay experience if they are satisfied with the hotels’ services (Harrington et al., 2012). Therefore, we hypothesize that

Hypothesis 1₀: Customer satisfaction will not have a positive influence on eWOM across all countries (i.e., Brazil, China, and Malaysia).

Hypothesis 1_A: Customer satisfaction will have a positive influence on eWOM across all countries (i.e., Brazil, China, and Malaysia).

Customer Loyalty and eWOM

Consumer loyalty is defined as customers’ favorable attitude toward an organization’s products and services (Chi, 2012; Lau et al., 2019), later leading to positive outcomes such as higher purchase intention, repeat purchase behavior, willingness to pay more, and positive WOM (Evanschitzky et al., 2012; Roy et al., 2014). Previous studies demonstrated that customer loyalty affects the willingness to recommend to others (Srinivasan et al., 2002). Moreover, a meta-analytic study asserted customer loyalty as a determinant predictor for positive WOM (De Matos & Rossi, 2008).

Nevertheless, recent studies in the hospitality context conclude that customer loyalty has a positive impact on eWOM (Ali et al., 2019; Kandampully et al., 2015; Lo et al., 2017; S. Prasad et al., 2019). Moreover, the positive post-stay evaluation, like customer loyalty, will lead to customer engagement on positive eWOM among Gen Y. Therefore, based on the literature, we anticipate that Gen Y customers who have loyalty to a hotel would have a greater likelihood to share positive eWOM. Therefore, we hypothesize:

Hypothesis 2₀: Customer loyalty will not have a positive influence on eWOM across all countries (i.e., Brazil, China, and Malaysia).

Hypothesis 2_A: Customer loyalty will have a positive influence on eWOM across all countries (i.e., Brazil, China, and Malaysia).

Customer Commitments Between WOM

Commitment appears like a buyer–seller relationship agreement when both parties are committed to building and sustaining their relationship (Dwyer et al., 1987). Other scholars, however, define customer commitment as an exchange relationship that both parties feel is important for them to remain committed to in their relationship (Akbari et al., 2016). As such, the quality of the relationship between customers and a brand has a positive impact on positive eWOM, while high customer commitment toward a brand has the greatest impact on positive eWOM (De Matos & Rossi, 2008; Karjaluoto et al., 2016). Besides, the duration of commitment between parties plays a pivotal role for customers to hold a positive eWOM with a brand (Akbari et al., 2016; Fierro et al., 2014). Therefore, based on the above discussion, we predict that Gen Y customers who have a higher commitment will have a greater positive eWOM. Accordingly, we hypothesize:

Hypothesis 3₀: Customer commitment will not have a positive influence on eWOM across all countries (i.e., Brazil, China, and Malaysia).

Hypothesis 3_A: Customer commitment will have a positive influence on eWOM across all countries (i.e., Brazil, China, and Malaysia).

E-WOM and Hotel Revisit Intention

As discussed earlier, customers’ post-stay evaluation (e.g., customer satisfaction, loyalty, and commitment) has a greater impact on sharing positive eWOM. Previous literature has found that positive eWOM influences customer purchase intention (Jeong & Koo, 2015; Zhao et al., 2015), and travel destination (Hernández-Méndez et al., 2015). Moreover, customers are more likely to use positive reviews as a future travel reference (Filieri et al., 2015). That said, customers who hold a positive review are more likely to increase booking intentions and revisit intention than negative reviews, especially for Gen Y customers (Gardiner et al., 2014; K. Prasad et al., 2014; Tsao et al., 2015). Based on the discussion above, we expect that customers with positive eWOM will have a greater likelihood of revisit intention. Hence, we propose the following hypotesis:

Hypothesis 4₀: The positive eWOM will not influence the degree of hotel revisit intention across all countries (i.e., Brazil, China, and Malaysia).

Hypothesis 4_A: The positive eWOM will influence the degree of hotel revisit intention across all countries (i.e., Brazil, China, and Malaysia).

Country Differences

GCT suggests that generational cohort profiles (e.g., characteristics, behaviors, and values) are homogeneous among the same generational cohort members; however, external events experienced by individuals will probably distinguish their generational cohort profiles even though they share a similar context culture (e.g., Eastern, collectivist culture), and religion (Usunier, 1996). Gen Y tends to experience the events in different countries and cultures that contribute to different generational cohort effect (Schewe et al., 2013) since the external events may occur at national and international levels (Mannheim, 1952; Noble & Schewe, 2003; Ryder, 1965; Schuman & Scott, 1989). The generational cohort effect is aroused when Gen Y experiences the external events personally during their coming of age, thus having an impact on the formation of Gen Y profiles (e.g., characteristics, behaviors, and values; Noble & Schewe, 2003).

Previous studies discussed generational cohort profiles based on the external events experienced by Gen Y’s in China, Brazil, and Malaysia. Arguably, Gen Y in each country had experienced different external events that could influence their profiles despite sharing the emergence of the internet and the digital technology era. For instance, Gen Y in China represents individuals who were born after the 1980s. This generation experienced the “One-Child Policy” imposed by the Chinese government to control exponential population growth at that time (Yang & Lau, 2015; Yi et al., 2010). Additionally, this generation experienced economic growth prosperity, and families had more money to spend than with previous generations (Gardiner & Kwek, 2016; Yi et al., 2010). Hence, the policy had a significant impact on shaping Gen Y’s profile as compared with other countries.

On the other hand, the government crisis, President Collor’s impeachment, currency changed to “Real” are the external events experienced by Gen Y in Brazil (Schewe & Meredith, 2004). Besides, they also suffered high unemployment rates, privatization, and uncertainty toward the country’s future (Motta & Schewe, 2008). Based on the external events, these are the generational cohort profile for Gen Y in Brazil, such as self-sufficiency, consumerism, an attempt to recover ethical and moral values, and self-protection (Motta et al., 2002; Motta & Schewe, 2008; Schewe & Meredith, 2004).

In Malaysia, Gen Y comprises individuals who were born between 1980 and 1999, with their coming of age starting in 1997 until the present. Besides the advent of the internet and digital media, these are the external events for Malaysian Gen Y such as the Asian financial crisis, expelling of the Malaysian Deputy Prime Minister, Reform movement, a series of Coalition for Clean and Fair Elections (BERSIH) demonstration, 1Malaysia concept, and the 2013 General Election (Hamayotsu, 2010; Ping & Yean, 2007; Sharipudin et al., 2016; Teik, 2002). These events consequently shaped Gen Y’s values, such as being information savvy, transparent, integrity, more concerned about democracy and human rights, ethnic tolerance, and accepting diversity.

Although China, Brazil, and Malaysia are emerging countries with collectivistic culture, there are significant differences in external events among the three countries. Thus, Gen Y tourists in different countries might have a unique profile given the unique socialization processes. Thus, it is logical to posit that there are differences in how post-stay experience influence eWOM and hotel revisit intention among China, Brazil, and Malaysia even though they are of the same age group (i.e., Gen Y), justifying the following hypotheses:

Hypothesis 5a₀: The influence of customer satisfaction toward eWOM will not differ across all countries (i.e., Brazil, China, and Malaysia).

Hypothesis 5a_A: The influence of customer satisfaction toward eWOM will differ across all countries (i.e., Brazil, China, and Malaysia).

Hypothesis 5b₀: The influence of customer loyalty toward eWOM will not differ across all countries (i.e., Brazil, China, and Malaysia).

Hypothesis 5b_A: The influence of customer loyalty toward eWOM will differ across all countries (i.e., Brazil, China, and Malaysia).

Hypothesis 5c₀: The influence of customer commitment toward eWOM will not differ across all countries (i.e., Brazil, China, and Malaysia).

Hypothesis 5c_A: The influence of customer commitment toward eWOM will differ across all countries (i.e., Brazil, China, and Malaysia).

Hypothesis 5d₀: The relationship between eWOM and hotel revisit intention will not differ across all countries (i.e., Brazil, China, and Malaysia).

Hypothesis 5d_A: The relationship between eWOM and hotel revisit intention will differ across all countries (i.e., Brazil, China, and Malaysia).

Method

Measures

Questionnaire surveys were used for data collection in this study. We used a self-administered, online survey in English, Malay, Portuguese, and Simplified Chinese following the back translation process (Brislin, 1970; Sinaiko & Brislin, 1973) to collect the data from three emerging countries (i.e., Malaysia, Brazil, and China). As discussed earlier, emerging countries comprise of the majority of the world’s population, and fast-growing economies as compared with developed countries (London & Hart, 2004), such as China, Brazil, and Malaysia, who are recognized as high-growth emerging countries (Hoskisson et al., 2000).

We distributed self-administrated online surveys hosted on Qualtrics, an online-survey platform to collect data from individuals who had experiences in booking hotels. The respondents were asked to state three hotels that they had stayed in over the past 12 months and to answer the survey based on the overall perceptions of the three hotels, which was regarded as useful stimuli in helping respondents to formulate perceptions about their post-stay experiences toward the hotels. The respondents were required to respond to all questions based on the hotels that they stated earlier (e.g., Based on your hotels stated earlier, please indicate the extent to which you agree or disagree). These questions were useful to provide respondents with an idea about their post-stay experience toward the hotels. The questionnaire’s measurement items were adapted from previous studies using a 7-point Likert-type scale (1 = strongly disagree, 7 = strongly agree). In particular, to measure post-stay experience, we adapted customer satisfaction items from Oliver (1980; α = .82), Zeithaml et al. (1996; α = .93) for customer loyalty, and Beatty et al. (1988; α = .75) for customer commitment. Word-of-mouth (WOM) items were adapted from Brown et al. (2005; α = .95), and Coyle and Thorson (2001; α = .83) to measure hotel revisit intention.

The partial least squares structural equation modeling (PLS-SEM) approach was used in performing the data analysis, using a 5,000-bootstrap procedure in SmartPLS v3.2.8 (Ringle et al., 2015). PLS-SEM is an appropriate approach to test the hypotheses of this study, given its advantages. First, PLS-SEM is an appropriate approach to study with prediction-oriented goals and complex model structures. Second, PLS-SEM is better than covariance-based SEM for theory testing, and third, PLS-SEM is suitable for studies having a number of constructs (Haenlein & Kaplan, 2004; Hair et al., 2017). Thus, we followed recent studies in tourism marketing (e.g., Gao et al., 2017; Ting et al., 2019) in using PLS-SEM to perform the data analysis.

Results

Respondent Profile

A total of 872 usable responses were collected in Malaysia (39%), Brazil (36%), and China (24.5%), in which the majority of respondents were female (59.6%). All respondents were aged between 18 and 25 years. Regarding the experiences and preference in booking hotels, 54.6% of respondents had more than 1 year experience in booking hotels, while 29.8% of respondents had more than 2 years’ experience in booking hotels. In terms of the preferred hotel booking platforms, the majority of respondents preferred Trivago (29.7%), followed by Booking.com (29.1%), Expedia (10.6%), and Agoda (9.2%), while the remainder consisted of Hotels.com (7.8%), Trip.com (7.2%), Traveloka (3.1%), and others (3.1%). Overall, the respondents were young, being conceptualized as young consumers (Gen Y) with experience in hotel booking and online platforms that fit the present study.

Measurement Model Results

Data analysis was undertaken in two stages: (1) the assessment of reliability and validity of the measurement (outer) model, and (2) the assessment of the structural (inner) model, examining the path coefficients, p values, R² values, f² values, and Q² values. We assessed the reliability of the latent constructs by assessing the individual item loadings, Cronbach’s alpha, and composite reliability (Hair et al., 2017). As presented in Table 1, the results confirmed that Cronbach’s alpha and composite reliability of each construct exceeded .757, indicating a good level of internal consistency (Nunnally & Bernstein, 1994). Additionally, the loading of each item was greater than .722, and as such, all loadings were highly significant (p < .001).

Table 1

Measurement Model Results

Construct	Loading	t Value	α	Composite Reliability
Customer satisfaction			.92	.94
I am satisfied with my decision to book the hotel.	.82	46.20
My choice to book this hotel was a wise one.	.83	49.14
I feel good about my decision concerning the hotel booking.	.90	95.33
I think that I did the right thing when I decided to book the hotel.	.89	82.37
I am happy when I decided to book the hotel.	.90	86.13
Customer loyalty			.89	.92
I will say positive things about the hotel to other people.	.85	66.91
I will recommend the hotel to someone who seeks my advice.	.87	68.07
I will encourage friends and relatives to book the hotel.	.87	62.67
I will consider the hotel as my first choice to stay.	.84	55.07
I will visit to the hotel in the next few years.	.72	27.27
Customer commitment			.76	.89
I will consider myself to be highly loyal to my preferred hotel.	.90	64.33
When another hotel is on promotion, I still commit to my preferred hotel.	.89	78.02
WOM intention			.88	.91
I mentioned to others that I booked the hotel.	.77	38.88
I spoke positively about hotel employee(s) to others.	.85	70.30
I recommended the hotel to family members.	.86	65.77
I spoke positively of the hotel to others.	.88	77.17
I recommended the hotel to close personal friends.	.72	26.79
Revisit intention			.87	.92
It is very likely that I will book the hotel again.	.89	86.83
I will book the hotel the next time I need an accommodation.	.92	107.54
I will definitely book the hotel in the future.	.87	71.51

The convergent validity of the model was assessed using the average variance extracted (AVE), in which the scores of all constructs were greater than the recommended 0.50 threshold (see Table 2), thus satisfying the AVE criterion (Hair et al., 2017). Discriminant validity was assessed using the Fornell and Larcker (1981) criterion. The square roots of the AVEs for the latent constructs were larger than the corresponding latent-variable correlations; hence, discriminant validity was achieved (Hair et al., 2017). We also verified the discriminant validity using the Heterotrait and Monotrait (HTMT) ratio (Henseler et al., 2015), where the values of the HTMT ratio of all constructs in the research model were smaller than the threshold value of 0.90 (Henseler et al., 2015), further confirming discriminant validity (see Table 3).

Table 2

Discriminant Validity of Measurement Model: Fornell–Larcker Criterion

	COMM	INT	LOY	SAT	eWOM	AVE
COMM	.897					0.805
INT	.476	.892				0.796
LOY	.378	.717	.829			0.688
SAT	.26	.616	.794	.87		0.758
eWOM	.405	.667	.763	.662	.818	0.669

Note: Diagonals represent the square root of the AVE, while the off diagonals represent the correlations. COMM = customer commitment; INT = revisit intention; LOY = customer loyalty; SAT = customer satisfaction; eWOM = electronic word-of-mouth; AVE = average variance extracted.

Table 3

Discriminant Validity of Measurement Model: HTMT Ratio

	COMM	INT	LOY	SAT
COMM
INT	.59
LOY	.47	.818
SAT	.311	.684	.876
eWOM	.498	.761	.859	.736

Note: COMM = customer commitment; INT = revisit intention; LOY = customer loyalty; Sat = customer satisfaction.

Last, we assessed the model fit by determining the standardized root mean square residual, which was 0.05, less than the recommended criterion benchmark of 0.08 (Shmueli et al., 2019), revealing that the model had satisfied the requirements for goodness-of-fit.

Inner Model Results

The hypotheses were tested by using the bootstrapping procedure with a resampling of 5,000 to examine the significance of the path coefficients among the constructs (Hair et al., 2017). Using a two-tailed test, a hypothesis was accepted when the t value was larger than the critical value (i.e., t ≥ 1.96, p ≤ .05). As presented in Table 4, and Figure 1, the relationship between customer loyalty (LOY) → eWOM was strongest (β = .575, p =.000), followed by customer satisfaction (β = .168, p = .000), and customer commitment (β = .144, p = .000); thus, null Hypotheses 1₀, 2₀, and 3₀, were rejected, accepting the alternative Hypotheses 1_A, 2_A, and 3_A. Additionally, eWOM intention had a strong, positive, and significant impact on Gen Y’s hotel revisit intention (β = .667, p = .000); thus, null Hypotheses H4₀ was rejected and the alternative Hypotheses 4_A was accepted.

Table 4

PLS-SEM Analysis of Research Model

Relationship	Path Coefficient	t Value	p Value	f ²	R ²	Q ²
eWOM
SAT → eWOM	.168	4.09	.000	.026	.608	.381
LOY → eWOM	.575	14.05	.000	.285
COMM → eWOM	.144	5.87	.000	.045
INT
eWOM → INT	.667	25.67	.000	N/A^a	.444	.333

Note: PLS-SEM = partial least squares structural equation modeling;COMM = customer commitment; INT = revisit intention; LOY = customer loyalty; SAT = customer satisfaction; eWOM = electronic word-of-mouth.

N/A: Effect size (f²) is not required to be reported for the path with only one independent variable.

Figure 1

Research Model

The coefficient of determination, R² values were used to evaluate the explanatory power of the conceptual model. The R² values for eWOM intention (R² = .608) and revisit intention (R² = .444), suggested that the research model explained a meaningful amount of variation in the endogenous variables. The R² values in the research model exceeded .35, being higher than the recommended criterion benchmark of .10 (Chin, 1998), thus indicating that the exogenous constructs accurately explained the endogenous constructs in the research model. Besides, we found that the effect size of LOY → eWOM (f² = .285) was the strongest (Cohen, 1988) as compared with the other factors.

Moreover, the blindfolding procedure was used to check the predictive relevance (Q²) of the research model (see Table 4). The checking of Q² is useful because it helped us to use PLS-SEM to explore the prediction results of the model. The values of Q² in each model were greater than zero, revealing that the exogenous latent variables were good enough to predict endogenous variables and thereby confirming that the predictive relevance of the model was acceptable (Hair et al., 2017).

PLS-multigroup analysis (MGA) was also used to compare the path relationship between Brazil, China, and Malaysia respondents. To identify the significant difference of the models across countries, we followed the recommendations by Hair et al. (2017), which stated that there are significant differences between groups when p values are smaller than .05 or greater than .95. Our results showed that customer satisfaction →WOM and LOY → WOM were not statistically different across countries (see Table 5). Thus, null Hypotheses 5a₀ and 5b₀ were not rejected, and alternative Hypotheses 5a_A and 5b_A were not accepted.

Table 5

Comparison of Malaysia, Brazil, and China Models

Paths	Malaysia	Brazil	China
SAT → eWOM	.193	.183	.147
LOY → eWOM	.488	.595	.481
COMM → eWOM	.209	.125^b	.261^b
eWOM → INT	.598^a	.756^a	.592^b

Note: COMM = customer commitment; INT = revisit intention; LOY = customer loyalty; SAT = customer satisfaction; eWOM = electronic word-of-mouth.

Significant difference in the strength of path between Malaysia and Brazil. ^bSignificant difference in the strength of path between Brazil and China.

Meanwhile, the relationship between customer commitment →WOM showed significant differences only for Brazil and China respondents (mean difference = 0.136; p = .982). Hence, null Hypotheses 5c₀ was accepted, and alternative Hypotheses 5c_A was not accepted. Finally, the relationship between WOM→ revisit intention found significant differences between Malaysia and Brazil respondents (mean difference = 0.158; p = .999), and Brazil and China (mean difference = 0.164; p = .004). However, there was no significant difference between Malaysia and China respondents. Thus, the null Hypothesis 5d₀ was not rejected, and alternative Hypotheses 5d_A was not accepted.

Discussion

This study aimed to examine the impact of Gen Y’s post-stay evaluation factors on eWOM, and revisit intention across three emerging countries. The results confirmed the findings of prior studies, in particular, our findings confirmed the importance of customer loyalty, customer satisfaction, and customer loyalty in driving positive eWOM, which in turn strengthened Gen Y’s revisit intention, and confirming the findings of prior studies (Akinci & Aksoy, 2019; Berezina et al., 2016). Our findings also indicate that customer commitment, satisfaction, and loyalty play considerable roles in driving positive Gen Y’s eWOM intention across the three countries. Interestingly, our study indicates that customer loyalty had the greatest impact on positive eWOM among Gen Y as compared with other post-stay evaluation factors for all countries. Therefore, we conclude that customer loyalty is the most important predictor of positive eWOM referrals among Gen Y tourists of the three emerging countries.

As discussed earlier, each country’s Gen Y had a unique generational cohort profile that emerged from external events during their coming of age. The MGA results of this study found significant differences in the path relationships among Gen Y in the three countries. In particular, the impact of customer commitment on eWOM was stronger in China than in Brazil. That is to say, Gen Y tourists in China are more committed to disseminating positive eWOM as compared with their respective counterparts, especially Brazilian. One possible reason why Chinese are concerned about commitment is that they embrace Confucian values, which is not the case among the Brazilian or Malaysian respondents.

In Confucian ideology, commitment to maintaining a harmonious society is one of the principles that need to be followed (Badi et al., 2017; Pan et al., 2012). Moreover, Chinese Gen Y practices Guanxi in their lives. For example, salespeople show some commitment to foster a good relationship with their friends (e.g., buying dinner, helping friends, gift-giving; Zhang & Zhang, 2014), and also establish long-term orientation (Yen & Abosag, 2016). Hence, it that very reason why Gen Y in China had the highest customer commitment compared with their counterparts despite being in the same generational cohort.

Also, the MGA results revealed that there are significant differences in the impact of eWOM on revisit intention among the three countries. In contrast, the impact of eWOM on revisit intention is strongest in Brazil, followed by Malaysia and China. In other words, our study shows that Gen Y’s behavior on eWOM differs from one country to another, although Gen Y is labeled as a Digital Native generation and being technology savvy. One possible reason why the Brazilian’s Gen Y have greater hotel revisit intention through positive eWOM is due to the digital inclusion policy initiated by the Brazilian government. Moreover, they sponsored low-income families with LAN-houses to provide internet access, which plays a vital role in shaping Gen Y’s consumption of new media (Horst, 2011).

Interestingly, it seems like the cohort effect influences young Brazilians to increase their consumer empowerment (Motta & Schewe, 2008; Schewe & Meredith, 2004). The feedback or comments received from using the new media mentioned that it had a significant impact on online purchasing among young Brazilians (De Oliveira et al., 2016).

Bolton et al. (2013) urged that factors such as technological, cultural, and political contributed to the variation of Gen Y’s behavior on new media. Here, Brazilians have a greater tendency to rely on eWOM from user-generated content, thereby affecting their behavioral intention as compared with other countries. The positive reviews posted online are considered a stronger predictor for hotel revisit intention, especially among Brazilian. Our findings corroborate with a recent study, where it discovered the differences between new media adoption and sharing among Gen Y in different countries (Mulvey et al., 2020). As such, this study concludes the importance of countries’ differences in the relationship between eWOM and revisit intention of Gen Y tourists (Mulvey et al., 2020; Schewe et al., 2013).

Theoretical and Managerial Contributions

There are several significant theoretical contributions to this study. First, this study contributes to the growing body of knowledge by extending the use of GCT to understand the post-stay evaluation factors toward positive eWOM and revisit intention in the hotelier industry. Although previous studies discovered the importance of demographic factors in driving customers’ eWOM behavior (Akinci & Aksoy, 2019; Mladenovic et al., 2019), the majority of the hospitality and tourism studies applied the theory in a single country context (Bravo et al., 2019; Chung et al., 2016; Gardiner & Kwek, 2016; Luna-Cortés, 2018), thus justifying for cross-country study to explore the antecedents and consequences of eWOM among tourists in the hospitality context (Gardiner, King, et al., 2013; Ip et al., 2012; S. Lee, 2018; Leong et al., 2019; Mladenovic et al., 2019; K. Prasad et al., 2014). As such, our study adds new insight and offers empirical evidence to present the importance of country differences in the relationship between post-stay evaluation and eWOM intention. Further, we argue that the external events of different countries shaped Gen Y’s, and subsequently influenced their perceptions and behaviors, as reflected by their perception on post-stay evaluation, eWOM, and revisit intention despite being in the same generational cohort.

Second, this study provides empirical evidence of how positive eWOM contributes to hotel revisit intention in emerging countries. Previous studies suggested that Gen Y are more active contributors and share their experience on online or via social media platforms (Bronner & De Hoog, 2011; Hwang & Kim, 2019; Ip et al., 2012; Mladenovic et al., 2019; S. Prasad et al., 2019). Our findings indicate that positive eWOM from Gen Y has an impact on high hotel revisit intention. Additionally, our study sheds some light on this subject by providing evidence of Gen Y’s behavior on new media in different emerging countries. Although Gen Y is considered as the Digital Native generation and being familiar with digital technologies. However, the distinction between cohort profiles in different countries contributes to the variation of new media behavior among Gen Y, thus offering some indication for hoteliers regarding the importance of understanding the cohort profile for Gen Y since it contributes to a positive eWOM and hotel revisit intention in emerging countries.

Finally, our study contributes to the literature by empirically testing the relative importance of various post-stay evaluation factors in a hospitality context. The findings suggest that customer satisfaction, loyalty, and commitment are significant predictors in driving eWOM referrals. Interestingly, our finding indicates that customer loyalty has the highest effect on Gen Y’s positive eWOM intention, followed by customer commitment and satisfaction.

Nevertheless, our findings offer some interesting managerial implications as well. In general, this study provides implications for the hospitality industry, especially hoteliers. As such, hoteliers are advised not to take their customers for granted if targeting global customers. The assumption of “One-size-fits-all” is no longer applicable in this case. The heterogeneity of Gen Y’s profile in different countries indicates that there is a growing need for targeted marketing for specific countries and gives some indication for hoteliers of the importance of identifying and understanding Gen Y’s profile before implementing their marketing strategy given it reflects customers’ post-stay experience, positive eWOM, and their revisit intention.

Specifically, our study suggests that young Chinese customers are more committed to sharing their post-stay experience on eWOM as compared to Brazilian and Malaysian customers. In this case, hoteliers might offer exceptional services that provide favorable post-stay experience among Gen Y, as they will provide a positive review if they are happy with the services, and vice versa, especially for countries that rely heavily on social media (i.e., Brazil). However, it could be argued that a recent report shows that the hotel industry is one of the top spending sectors in tourism (U.S. Travel Association, 2019). Therefore, implementing a marketing strategy tailored to each country’s cohort profile of customers will offer positive post-stay experience, especially when dealing with Gen Y.

Moreover, our findings will assist hoteliers to understand Gen Y customers from emerging countries much better since it provides some evidence of Gen Y in emerging countries, particularly China, Brazil, and Malaysia. This study not only helps the hoteliers from emerging countries, but it also improves the understanding of those from developed countries as well. For instance, in 2018, China and Brazil were the top contributors to US tourism (U.S. Travel Association, 2019). Meanwhile, Asia is predicted to be the world’s largest tourist destination (Su et al., 2017). Therefore, it is beneficial for Hoteliers to predict Gen Y’s behavior on post-stay evaluation, eWOM, and revisit intention and consequently provide ideas to managers on what to expect from them since they are future customers.

Limitation and a Concluding Summary

Similar to other research, this study is subject to several limitations. First, this study only examines the Gen Y sample population in three emerging countries. Therefore, future research is suggested to study other generational cohorts such as Baby Boomers and Gen X and to make comparisons on the cross-countries differences, especially for the differences between developed and emerging countries, as it may offer a better understanding of how GCT works in post-stay evaluation.

Second, surveys are used in this study. As such, respondents might have different expectations and experiences on hotel services, and hence, post-stay evaluation might differ. Unlike an experimental method, researchers can control the study’s environment and any interference from external influences that could affect the results of the study (Cornwell & Maignan, 1998; Guba & Lincoln, 1994; Quester & Thompson, 2001). It is also well-controlled and useful for providing a better understanding of post-stay experience stimuli as compared to a survey method (Cornwell et al., 2005; Quester & Thompson, 2001; Speed & Thompson, 2000). As such, further research is suggested to employ an experimental study to reduce outside influences (e.g., nuisance). In addition, we also urge for future research to compare between preferred and least preferred hotels in the experimental setting for post-stay evaluation.

Additionally, hotel classification and the types of hotels might influence Gen Ys’ decision making. However, our study did not specify the hotel classification, such as the “star” rating, QualityMark Norway, and online guest review sites (e.g., TripAdvisor, Booking, and Expedia). Our study also did not compare the types of hotels; for example, independent vs. chain hotel brand among Gen Y. Therefore, future research should consider exploring whether hotel classification and types of hotels have an impact on Gen Y’s post-stay evaluation.

Finally, this study focuses on post-stay evaluation factors that influence positive eWOM and hotel revisit intention, but does not study the antecedent factors of post-stay evaluation. As such, future research could focus on what are the antecedent factors for post-stay evaluation, especially customer loyalty since it has a significant impact on positive eWOM in our study.

In conclusion, our study employed GCT as an underpinning theory to examine the influence of post-stay evaluation factors toward eWOM, and hotel revisit intention among Gen Y by comparing three emerging countries (i.e., Brazil, China, and Malaysia). Our main findings suggested that all three post-stay evaluations were predictors toward positive eWOM for Gen Y across countries. Interestingly, customer loyalty had the greatest impact on positive eWOM as compared to other factors. Moreover, significant differences were found regarding the impact of eWOM on Gen Y’s hotel revisit intention across countries. As such, this finding can broaden our understanding of the impact of post-stay evaluation factors achieving positive eWOM and hotel revisit intention from emerging countries.

Footnotes

ORCID iD

Mohamad-Noor Salehhuddin Sharipudin

Mohamad-Noor Salehhuddin Sharipudin (corresponding author; mail: mnsaleh@uum.edu.my), is a senior lecturer at the School of Multimedia Technology and Communication, Universiti Utara Malaysia, Malaysia. Man Lai Cheung (manlaiicheung@gmail.com), is an Assistant Professor at the Department of Marketing, The Hang Seng University of Hong Kong. Mauro Jose De Oliveira (mail: maurojornalista@fei.edu), is an associate researcher at the Centro Universitario da FEI, Sao Paulo, Brazil. Andrea Solyom (andrea.solyom@uni-corvinus.hu), is an assistant professor at the Institute of Management in the Corvinus University of Budapest in Hungary.

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