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Article

The Moderating Effect of ‘Generation’ on the Relations between Source Credibility of Social Media Contents, Hotel Brand Image, and Purchase Intention

by
Hye-Ryeong Shin
1 and
Jeong-Gil Choi
2,*
1
Smart Tourism Education Platform, Kyung Hee University, Seoul 02447, Korea
2
Hotel Management Department, Kyung Hee University, Seoul 02447, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(16), 9471; https://0-doi-org.brum.beds.ac.uk/10.3390/su13169471
Submission received: 4 August 2021 / Revised: 18 August 2021 / Accepted: 20 August 2021 / Published: 23 August 2021
(This article belongs to the Special Issue Sustainable Brand Strategies in Social Media in Hospitality and Tourism)
Browse Figure
Versions Notes

Abstract

:
This is a timely study that simultaneously considers the issues of source credibility of social media contents and generational differences. The study aims to explore the influence of ‘generation’ on perceived source credibility, and its effect on the relation between source credibility, hotel brand image, and purchase intention in cases where the content providers are general users (UGCs) and hotel marketers (MGCs), respectively. Using an independent samples t-test (278 people sampled), the differences in source credibility between generations were tested and multi-group analysis was conducted to verify the moderating effect of generation. Significant differences appeared in trustworthiness between the generations. Millennials are sharper in observation than the generations born earlier in recognizing the source credibility of social media contents. The moderating effect of generation is noticeable only in the impact of the UGCs’ expertise on hotel brand image, indicating Millennials are affected by the expertise of UGCs more strongly than the earlier generations are. The findings offer insight into better strategizing of social media communication for hotel marketers, utilizing social media and targeting Millennials. A further contribution of the study is that it reveals the relations between variables and effects according to different content providers (UGCs and MGCs).

1. Introduction

Recently, as consumers have been increasingly exploring relevant information on social media before purchasing goods or services, and reflecting the information in their brand evaluation and purchasing behavior [1], the role of social media has become prominent in the branding and marketing sectors. That is, given that social media contents have positive impacts on perceived brand image and the purchasing decisions of consumers [2,3,4,5], marketers in the hotel industry, where digital marketing has emerged, are faced with an existential need to understand social media and communicate with consumers through online platforms [6,7].
Brand-related contents on social media are divided into two forms, according to their sources: user-generated-contents (UGCs) and marketer-generated-contents (MGCs). For hotels, whose basic products include intangible components, UGCs can be an excellent interface with consumers in that UGCs contain a sense of realism and kinship in reproducing the experience of the users (consumers), which are cues for shaping the hotel’s brand image and consumers’ intention to purchase [8,9]. For marketers, both MGCs and UGCs are equally crucial as consumers are still exposed to both types randomly and simultaneously [10]. Nevertheless, the majority of relevant studies have focused on UGCs only and concluded that consumers tend to consider them more credible than MGCs [9,11,12,13].
Moreover, the appearance of Millennials as a new and powerful consumer group has been another huge issue for the hotel industry [14,15]. Millennials, the most ‘tech-savvy’ generation, rely much more on social media than earlier generations and perceive UGCs as more ‘trustworthy’ and ‘important’ than MGCs [16,17,18]. In this respect, the dissimilar media and technological literacy between generations can influence the subsequent effects caused by the sources.
To date, studies reflecting these contextual conditions have been conducted in fragmentary ways. Although understanding the influences of both UGCs and MGCs is crucial for hotel marketers to allocate their limited resources, an effort to explore the two juxtaposed information sources under equal conditions in the hotel sector has thus far not been made. Earlier studies tended to overlook their relative credibility and the effects they exert on brand image and purchase intention, not to mention how the generation gap differentiates Millennials from earlier generations in terms of their tendency to react to social media contents.
Thus, the main objective of this study is to explore the influence of ‘generation’ on source credibility and its effect on the relations between source credibility, hotel brand image, and purchase intention, in the social media context. The study has been conducted along two dimensions. First, whether significant differences in source credibility of social media contents does in fact exist between two generations (Millennials and the earlier generations). Second, the moderating effect of generation, which can be caused by a potential discrepancy between two generations regarding the source credibility of social media contents, hotel brand image, and purchase intention are explored with empirical data, respectively, in the cases of UGCs and MGCs. The findings help fill the theoretical gap with a clearer understanding and provide managerial insights for hotel marketing practitioners to perform more effective communication and branding through social media.

2. Conceptual Background

2.1. Social Media Contents and Source Credibility

The development of information and communication technology, the high internet penetration rate, and the popularization of mobile devices have resulted in a whole new context for communication [9,19,20]. These changes have contributed to the proliferation of social media.
The term ‘social media’ is lexically defined as “websites and applications that enable users to create and share content or to participate in social networking” [21]. They are credited for their greater capacity in terms of virality, credibility, and popularity as search tools for information, than traditional media such as TV, radio, or magazines [13,22,23]. In particular, one of the most salient roles of social media contents today is to be wellsprings where consumers search for information, including other consumers’ opinions [24,25].
Social media is exceptional in that it has reset the direction of communication between brands and their users. Brand-related-contents on social media, which can be produced by anyone and shared boundlessly, are divided into two forms, according to their sources: UGCs and MGCs. UGCs refer to the contents created and distributed by common internet users, including electronic-word-of-mouth (eWOM) [26]. UGCs on social media are considered to be more empathetic, up-to-date, non-commercial, neutral, authentic and, most of all, more credible than the contents made by professional marketers or companies [11,12,13,27,28]. That is, consumers who gather information through social media accept UGCs as information from a very homogenous group of people who have experienced the products, or as an independent third party who is free from any commercial agenda. On the other hand, the MGCs are ‘authoritative’ contents created and posted by marketers and present marketing-related information [29]. MGCs on social media not only have a positive and significant impact on customer behavior [10], but simply trying to promote the company on social media can produce a corporate-friendly image for both targeted and general consumers [30].
Meanwhile, trust in information is essential for consumers to utilize that information, particularly in an online environment [31]. Hence, the reliability of online information has been studied as the antecedent of the consequent consumer behavior [24,25]. According to source credibility theory, the persuasiveness of information depends on the credibility of the source, and its leverage can lead to a positive attitude or behavior on the part of the receivers [32,33]. The source credibility mainly consists of the sender’s ‘expertise’, ‘trustworthiness’, and ‘attractiveness’ [33,34,35,36]. However, if the source is a physically unidentified online user, attractiveness is a less applicable descriptor, as it indicates the physical appearance, personality, or social status of the source [33]. Expertise indicates a source’s ‘capability’ to make valid claims, and trustworthiness refers to the extent to which a source is ‘motivated’ or willing to convey valid arguments without prejudice [35]. Willemsen et al. [37] have found that self-proclaimed experts’ reviews of television sets or accommodations led people who read them to perceive higher ‘expertise’ but less ‘trustworthiness’ than laypersons’ reviews. Reviews by experts were recognized to contain more ‘knowledge’ but less ‘trustworthiness’, while other users’ reviews were perceived as more trustworthy (ibid).
Previous studies examining the impact of source credibility on consumer response have suggested that a more reliable source would elicit a more positive attitude and behavior response from consumers [33,38]. Chakraborty and Bhat [24] found positive causal relations between credible online reviews (UGCs) on Facebook and brand image of consumer electronics. Arif [11] also noted the positive relationship between eWOM and repurchase intention in the airline industry. However, numerous studies have covered the influence of UGCs on branding in certain fields, such as automobiles [3], apparel [39], cities [40], restaurants [41], fashion brands [42], food companies [43], and airlines [11], but not many have investigated UGCs’ influence on the hotel industry. Moreover, the credibility of MGCs has received relatively scant attention, causing an imbalance between studies on UGCs and MGCs [10].

2.2. Hotel Brand Image and Purchase Intention

In the marketing literature, ‘brand image’ means the ‘subjective perception’ of a consumer about a brand [44] and ‘purchase intention’ is defined as a consumer’s ‘likelihood’ of purchasing a particular product or service [45]. Not only does a brand image serve to distinguish the brand from competitors, it also helps consumers to realize their need and desire for the brand [44], triggering consequent behaviors, including purchase intention [46].
Before the proliferation of the internet, the brand image of a product was compared to the influence of the product’s price in the marketing field [47]. However, in social media marketing, brand is said to be the only thing that is sold [48]. For hotels, brand image counts more than in other industries [49]. A strong hotel brand image positively influences the purchase intention of consumers and consequently improves the sales [50,51,52]. Further, social media contents, particularly UGCs, can provide vivid cues for shaping perceived brand image and intention to purchase hotel products, as the nature of the service industry permits determination of whether a product is satisfactory only after the actual experience [8,9].
Although most hotels already aim to maximize their sales by harnessing social media platforms [31], the relevant studies fail to enumerate the specific factors that affect consequent consumer behaviors. In terms of social media marketing in the hotel context, there have been studies on the effect of marketers’ prompt responses on satisfaction and retention of customers [53], or effectiveness by message type (Facebook) [54]. Lin and Goh [55] tried to find a positive relation between ‘favorableness’ of MGCs and hotel sales, but the data did not support a relationship.
In this regard, examining the positive causal relations of the source credibility of social media contents (both of UGCs and MGCs), brand image, and purchase intention within the hotel sector is new and offers insight into the currently fragmentary literature on the subject.

2.3. Generation Gap: Millennials vs. the Earlier Generations

A generation refers to a cohort that shares collective and distinct features on analogous experiences [56]. Additionally, each generation possesses distinctive attitudes and behavioral patterns [57]. Identified as a powerful consumer group, ‘Millennials’ roughly indicates the generational cohort including people born early 1980s to 2000 [58,59,60]. However, for the purpose of this study, a restrictive definition of ‘Millennials’, born between 1981 and 1996, is employed to compare them with the generations born earlier [60]. Because most Millennials are economically active, their economic capacity is expected to account for a significant portion of total consumption [61]. In this respect, hotels, as in other industries, consider this generation as a lucrative consumer target group to help them retain their sustainable competency and rejuvenate their brand [14]. Previous research has characterized Millennials as objective, consumption-oriented, social-network-motivated, self-expressive, and ‘tech-savvy’ [17,58,62,63]. Among these characteristics of Millennials, the most noticeable and relevant feature for the current study is the social-network-motivated and tech-savvy aspects.
Norum [63] stated that Millennials are ‘the first’ high-tech generational cohort, and Tanyel et al. [64] reported them as the first generation using digital media more than traditional forms. It implies that they differ crucially from earlier generations in their information technology literacy [17,37,61]. Further, in terms of social media usage, Millennials are superior in terms of their level of understanding, facility, and familiarity [18,62,65]. These characteristics distinguish Millennials’ patterns of behavior from those of the older generational groups, particularly concerning online brand communication. Towner and Munoz [18] found that millennials rely much more on online sources, such as websites and social media platforms, than the earlier generations, who appear to prefer hard-copy newspapers. Ana and Istudor [16] explored the role of social media and UGCs on Millennials and found that they perceived UGCs as more ‘trustworthy’ and ‘important’ than traditional media when planning travel.
Based on the above, this study postulates that generation is the key factor when recognizing and differentiating the perceived source credibility of MGCs and UGCs. This study addresses the following research questions:
RQ1. 
Is there a significant difference in the perceived source credibility of social media content between generations?
Further, we posit that generation has a moderating effect on the relations between source credibility, brand image, and purchase intention:
RQ2-1. 
Does the generation to which a consumer belongs moderate the relations between source credibility of UGCs, brand image, and purchase intention?
RQ2-2. 
Does the generation to which a consumer belongs moderate the relations between source credibility of MGCs, brand image, and purchase intention?
Thus, based on the above, the proposed research model for Research Question 2 is shown below in Figure 1.

3. Method

3.1. Context: The Hotel Industry in South Korea and Social Media

In South Korea, the number of hotels in the capital Seoul in 2019 has tripled since 2011 [66]. According to the ‘Monthly Trend report of the Tourism/Hotel Industry’ conducted by the Korea Hotel Association on 200 hotels to brief the Ministry of Culture, Sports, and Tourism on the topic, the room occupancy rate decreased from 61.7% in January 2020, before the coronavirus diseases 2019 (COVID-19), to 44.4% in February and 22.7% in March [67]. Despite the critical influence of COVID-19, which has caused the first-quarter performance of major hotels in 2020 to be sluggish in South Korea, the prospects of the hotel industry are not hopeless [67]. Instead, it was published in the media that the year 2020’s Golden Week, 30 April to 5 May, was stable in terms of COVID-19 spread, and domestic consumption of luxury hotels increased compared to February and March as it became impossible to travel abroad due to the spread of the epidemic [68,69].
Meanwhile, out of the total number of internet users in South Korea, 80.6 percent use social network services, which is one term to refer to social media [70]. Due to the growth trend of the hotel industry, active usage of social media, and a fast sign of recovery from the impact of the pandemic, South Korea provides an appropriate context for the current study.

3.2. Measurement

The survey instrument began with one screening question inquiring whether the respondents had been exposed to social media contents. Based on previous studies, the source credibility of social media contents [71,72] consists of two sub-indicators: trustworthiness (4 items) and expertise (5 items). Each question required the respondents to answer twice, assuming they have been exposed to social media contents from two different information providers: respectively, a brand marketer (MGCs) and a general user (UGCs). Hotel brand image, with six items [52,73,74], and purchase intention, with six items [75,76,77], were also measured. These parts of the questionnaire first reminded respondents of a hotel brand they had experienced or liked, and then asked them to answer subsequent questions with that hotel brand in mind. Again, the respondents were asked to give answers according to the two different information providers. All of the statements in the survey were based on a 5-point Likert scale (5 = strongly agree, 4 = agree, 3 = neutral, 2 = disagree, 1 = strongly disagree).

3.3. Pilot Test and Data Collection

Prior to the actual survey, a pilot test was undertaken by a hotel management professor, graduate students, and social media marketers. Then, the actual survey was conducted for 10 days, from 27 April to 6 May 2020. As the subjects of this study were extracted from a purposive sampling method, a screening question was asked at the beginning of the survey with the sample contents images of Facebook, YouTube, and Instagram: “Have you ever come into contact with contents from social media such as the following examples? (If you haven’t, please stop answering the questionnaire)”. A link to the self-administered online survey was distributed in consideration of age, which was a requisite element to distinguish which generation the respondent belonged to. Among the collected questionnaires, 278 copies were used for the final analysis. Sixteen questionnaires, which were found to be inconsistent, unresponsive, or problematic through the data cleaning process using SPSS, were disregarded.
The demographic characteristics of the final sample are as shown in Table 1. The sample consisted of 95 males (34.2%) and 183 females (65.8%), and the respondents’ generation groups are relatively even (Millennials = 51.1%, earlier generations = 48.9%). The majority of respondents reported they had college degrees (66.2%), and ‘office workers’ (39.6%) account for the largest share of the occupation category, followed by ‘professionals’ (17.6%) and ‘housewives’ (14.4%).

4. Data Analysis and Results

To examine the responses to the research questions, the Statistical Package of the Social Sciences 22 (SPSS) was used for exploratory factor analysis (EFA), along with an independent samples t-test. Confirmatory factor analysis (CFA), structural equation model (SEM) analysis, and multi-group analysis were carried out using Analysis of Moment Structures 22 (AMOS). The analyses were conducted with the respective data, depending on who the information provider was: a general user or a brand marketer.

4.1. Differences in Source Credibility between Generations

Exploratory factor analysis with varimax rotation was conducted, distinguishing the responses by two different information providers (‘general users’ and ‘brand marketers’). Consequently, two items of trustworthiness (a general user’s (The official brand) account ‘is dependable (tru.1)’ and ‘provides reliable information (tru.4)) with a factor load less than 0.5 were excluded in both cases. If the Kaiser–meyer–olkin (KMO) value was greater than 0.8 and the chi-square value yielded through Bartlett’s Test of Sphericity appeared significant (<0.001), the collected data were regarded as good for conducting EFA [78,79]. In the current study, the KMO values of all the variables in the cases of both MGCs and UGCs exceeded the required threshold of 0.8, ranging from 0.85 to 0.909. The Bartlett’s test measures gave a value of 0.000 for each variable: source credibility, hotel brand image, and purchase intention. Furthermore, their Cronbach’s alpha values were also above the threshold of 0.7, ranging from 0.752 to 0.917, indicating that scale items had appropriate reliability [80].
To answer Research Question 1, an independent samples t-test was employed. The results of the t-test to investigate if there were any significant differences in the perceived source credibility between generations, shown in Table 2, indicated that the members of the earlier generation tended to score all the factors higher than their younger counterparts. The significant differences appeared only in trustworthiness between the generations in cases of both UGCs and the MGCs. The t-values were 2.259 and 6.851 each, and their p-values were less than 0.05, indicating statistical significance. The largest mean difference between generations was the trustworthiness of MGCs (3.25 vs. 2.58). However, no significant differences were found between Millennials and the earlier generation regarding the expertise factors of MGCs (t = 1.856, p > 0.05) and that of UGCs (t = 1.964, p = 0.05), although the earlier generation considered the expertise factor of MGCs and UGCs higher than Millennials did. Hence, it was concluded that there were significant but partial differences between the earlier generation and Millennials with respect to the perceived source credibility of social media contents.
Meanwhile, the Millennials recognized that the trustworthiness of MGCs was lower than ‘3′ (m = 2.58), on average. The gap between the trustworthiness of MGCs and UGCs among Millennials is much wider than that of the earlier generation (0.55 vs. 0.12). Therefore, an additional independent sample t-test was performed to investigate whether there were significant differences in credibility between sources within each respective generation. The results in the Millennials’ case indicated that there were significant differences between the perceived expertise of MGCs and UGCs (MGCs: m = 3.6; UGCs: m = 3.23; t = 4.18 p < 0.000), and between the perceived trustworthiness of MGCs and UGCs (MGCs: m = 3.6; UGCs: m = 3.23; t = −5.22 p < 0.000). On the other hand, in the earlier generation’s case, there was a significant difference only between the perceived expertise of MGCs and UGCs (MGCs: m = 3.78; UGCs: m = 3.4; t = 4.21 p < 0.000). That is, Millennials perceived the expertise and trustworthiness, the two sub-indicators of source credibility of MGCs and UGCs, differently, whereas their counterparts only noticed the difference in the expertise factor of source credibility between MGCs and UGCs.

4.2. Source Credibility, Hotel Brand Image and Purchase Intention

As shown in Table 3, CFA was carried out to evaluate the structural validity of each of the models and to test the validity of the UGCs and MGCs models. The model fit of the UGCs model was χ2 = 263.524 (df = 145), GFI = 0.908, NFI = 0.927, CFI = 0.966, AGFI = 0.88, and RMSEA = 0.054, and that of MGCs model was χ2 = 294.540 (df = 143), GFI = 0.903, NFI = 0.921, CFI = 0.957, AGFI = 0.871, and RMSEA = 0.062. In general, the model is considered fit when the goodness of fit indices of the model are within acceptable ranges; GFI, NFI, CFI > 0.9, AGFI > 0.8, RMSEA < 0.8 [81], indicating a good absolute fit of the models of this study.
To meet the validity of each construct, convergent and discriminant validity methods are used. To assess the convergent validity, three criteria should be examined: composite reliability (CR) scores should be higher than 0.7; average variance extracted (AVE) scores should be greater than 0.5; standardized factor loading should be larger than 0.6 [82]. As listed in Table 3, all constructs of both models meet the CR (>0.7), AVE (>0.5), and standardized factor loading (>0.6) criteria, supporting convergent validity of measures of this research.
To examine the discriminant validity of the measures, the strictest type of test suggested by Fornell and Larker [83] was first used as shown in Table 4. According to this method, if the value of the square root of AVE for each constructor is greater than the correlation value between the paired constructs, the constructs satisfy the criterion [83]. Given the square root of AVE of the hotel brand image of both models are less than the correlation between hotel brand image and purchase intention (0.886 in UGCs model and 0.952 in MGCs model), it has failed to fulfill the criterion of the first method.
Next, the method suggested by Steenkamp and Trijp [84] to assess the discriminant validity was conducted. Constraining the correlation between the hotel brand image variable and the purchase intention variable, which is problematic, the χ2 values of the constrained model with the unconstrained model are compared. In the cases of both models, the differences of the χ2 are greater than the critical value of 3.84, at a 95% significance level (UGCs: Δχ2 = 54.278, Δdf = 1; MGCs: Δχ2 = 27.489, Δdf = 1), indicating the measures have discriminant validity.
Based on the adequate results of CFA, structural equation modeling analyses were performed to examine the relations between the variables before exploring the moderating effect of generation on the relations. Table 5 summarizes the path coefficients for the paths. As presented in Table 5, the two components of source credibility in the cases of both the UGCs and the MGCs models, expertise (UGCs: β = 0.519, p < 0.001; MGCs: β = 0.440, p < 0.001) and trustworthiness (UGCs: β = 0.234, p < 0.01; MGCs: β = 0.328, p < 0.001), have positive and significant effects on hotel brand image. Hotel brand image perceived through UGCs (β = 0.758, p < 0.001) and MGCs (β = 0.838, p < 0.001) are found to have a positive influence on purchase intention. The paths from source credibility to purchase intention, however, appear to be only partly significant for both models. When respondents recalled UGCs, they noticed that only the ‘trustworthiness’ of source credibility (β = 0.136, p < 0.05) was critical in affecting purchase intention. Meanwhile, in the case of MGCs, only ‘expertise’ (β = 0.129, p < 0.05) significantly affected purchase intention.

4.3. Moderating Effect of Generation

As this study considers the generation gap to be a crucial moderator when it comes to people’s perception and reaction to social media contents, a multi-group analysis using a structural equation was conducted to verify the significance of the path coefficients for two groups, according to their generation. Before performing multi-group analyses, an adequate measurement basis should be prepared [85]. To guarantee an overall configural invariance of the measure across groups, measurement equivalence should be achieved by comparing and assessing the difference in fit between two models: an unconstrained model that allows all measurement weights to vary, and a constrained model that fixes the factor loadings between the latent and observable variables across the groups. In the case of the MGCs model, all of the results revealed insignificant differences between unconstrained and constrained models, yielding measurement weights as χ2 = 15.625 (df = 15, p > 0.05), structural covariances as χ2 = 22.859 (df = 25, p > 0.05), and measurement residuals as χ2 = 56.449 (df = 44, p > 0.05), implying that this measure is fully invariant across the groups (null H: there is no difference across the groups). The MGCs model, however, failed to show a significant difference between the groups (Δχ2 = 4.866, p > 0.1), obviating a need for further analysis. Hence, the answer for RQ2-2 is that the generation to which a consumer belongs does not moderate the relations between the source credibility of UGCs, brand image, and purchase intention.
Next, in the case of the UGCs model, the initial analysis for measurement equivalence presented invalid results. Subsequently, having removed four manifest variables whose loadings were lower than 0.7, the remaining constructs and manifest variables were examined across both cohorts again [86]. The following test reveals that the baseline model and the model that constrained structural covariances (χ2 = 31.15, df = 21, p > 0.05) are invariant, partially supporting the null hypothesis. With established metric invariance, the revised structural model (χ2 = 138.248(df = 82), GFI = 0.937, NFI = 0.952, CFI = 0.980, AGFI = 0.908, RMSEA = 0.050) calculates the chi-square difference between two groups for the multi-group analysis with the UGCs model. The result (Δχ2 = 13.391, p < 0.05) verifies the significant difference in path coefficients across the two generation groups. The moderating effect was examined by the t-test results of beta values of regression coefficients of each corresponding path. If the resultant value does not fall between −1.96 and +1.96, it means the difference between the paths is statistically significant. A comparison of each corresponding path coefficients, specifically the results shown in Table 6, shows that, in the sample of Millennials, the positive relationship of the path between expertise and hotel brand image is more intense (0.298 vs. 0.764), and only this difference between the two groups is statistically significant (t = 3.036). The path coefficients between trustworthiness and hotel brand image of each group are significantly different (t = −2.319), but the standard coefficients of the path for Millennials do not appear to be significant. In turn, the answer for RQ2-1 is that the generation to which a consumer belongs moderates the relations between the source credibility of UGCs and hotel brand image.

5. Discussion and Conclusions

The results of this study help one to understand the influence of generation on the source credibility of social media contents, and its moderating effect on the relations between source credibility, hotel brand image, and purchase intention.
Firstly, the results show that there are significant differences only in trustworthiness, one of the components of source credibility, of the UGCs and the MGCs between the generations. The earlier generation values the trustworthiness of MGCs and UGCs significantly higher than Millennials do. Interestingly, however, there is no significant difference in the earlier generation’s trustworthiness between MGCs and UGCs (Δ = 0.12; t = −1.27; p > 0.1). That is, the earlier generation evaluates the trustworthiness of social media contents higher in general than Millennials, but does not discriminate particularly according to the sources. Conversely, Millennials recognize significant differences in the expertise and trustworthiness of social media contents according to their sources. This tech-savvy generation values the expertise of MGCs higher than that of UGCs, and the trustworthiness of UGCs higher than that of MGCs. While previous studies have reported the effect of either MGCs or UGCs only, the comparative preference between MGCs and UGCs, or the partiality of Millennials toward social media or UGCs, compared to traditional media, this study offers a comprehensive understanding that reflects the sources and generations. It is plausible to conclude that Millennials, with their superior level of understanding, facility, and familiarity with social media, can more keenly discern differences in the credibility of social media contents according to the sources.
Second, the source credibility of social media content is important regardless of the source. However, the components of source credibility, expertise, and trustworthiness have positive impacts on hotel brand image, but impacts purchase intention only selectively according to the sources. When the information source was assumed to be a general user, it was considered to be more trustworthy, and motivated the respondents to visit the hotel. On the other hand, when the source was an official of a hotel brand, the higher level of expertise encouraged purchase intention among the respondents. That is, consumers seem to be more sensitive to accept information when considering purchasing, having different expectations depending on the sources of the social media contents.
Third, this study finds that the moderating effect of generation on the relation between the credibility of social media contents and hotel brand image appears when the source is a general user. Perception of the higher expertise of UGCs has a significant impact on hotel brand image, and this influence is greater when the recipients are Millennials. This suggests that Millennials, who are exposed to hotel brand-related UGCs through social media, can be more strongly affected by the expertise aspect of contents than the older generations and will therefore hold a favorable image towards the hotel brand. This result supports the research of Willemsen et al. [37], Lee, M. et al. [61], and Burgess-Wilkerson et al. [17] in finding that Millennials react differently to social media contents compared to their elders. Unexpectedly, however, a person’s generation does not appear to moderate the relationship when the source is a marketer. That is, consumers who have experienced social media contents react similarly towards MGCs, regardless of which generation they represent.
The current research not only affirms the differences in source credibility of social media contents according to generation, but also validates information sources and generation as clues that cause apparent differences in the relations between source credibility, hotel brand image, and purchase intention with empirical data. Particularly, considering the issues of source credibility and generation, it is worth noting that these research subjects have been considered in fragmentary ways to date. Thus, the current study provides a new insight into the existing literature.
The findings of this study offer practical implications for hotel brand marketers utilizing social media as their branding devices. First of all, it is crucial to establish the credibility of MGCs as it is key to enhancing a hotel’s brand image. However, when consumers consider actually purchasing, they tend to rely on the expertise aspect of MGCs. In this regard, it would be meaningful to check the information in their social media contents carefully before posting them. Additionally, the results of the UGCs’ case showed that, while expertise and trustworthiness boosted hotel brand image, only the trustworthiness aspect enhanced purchase intention. The predominant reason why consumers trust UGCs is that they consider UGCs as products of a homogenous group. Hence, marketers should find how to encourage consumers, particularly existing customers, who may share and post hotel-related contents on their social media accounts. To do so, marketers could arrange online promotions to go viral among customers with their personal social media accounts, or offline promotions to prompt existing customers to post hotel-related contents. For example, planning an online event that offers hotel points as a reward for sharing MGCs (e.g., retweeting a Twitter post), or creating some tempting photo spots inside and outside of the hotel to attract customers to take pictures to share on social media can be strategies to reinforce the quality and quantity of UGCs, and to promote consumption.
Generational differences in the social media context are meaningful for marketers. When it comes to the generation gap, no discrepancy in the impact caused by MGCs between Millennials and earlier generations has been discovered. Thus, marketers can focus on an integrated scheme for MGCs. However, when the source was a general user, the different influences on credibility between the two generations were evident. Millennials seem to count more on the expertise of UGCs to build a hotel brand image than does the older generation. As mentioned, Millennials are sharper than the earlier generation in recognizing the differences of the credibility of social media contents according to their sources. If the target customer segment is Millennials, the hotel employees in charge of digital marketing should understand social media fully so as not to fall behind, and they should be aware of what UGCs contain and pay heed to promoting UGCs to disseminate more accurate information. It would be desirable to interview the generation or gather their opinions online to allow the hotel to work out sound strategies to compete effectively in the hotel marketplace.

6. Limitations and Future Research Direction

The current study has limitations and suggests a need for further research. First, as the sample is mainly composed of respondents from South Korea by the purposive sampling method, which could impede representativeness of the samples, the findings may not be generalizable. In particular, the high usage rate of social media of the population of South Korea may differentiate the results from those of other socio-cultural contexts. Therefore, future research could replicate the study in other contexts, with a sample size sufficient to increase the level of representativeness. Secondly, due to the geographic propinquity, South Korea is one of the nations in which COVID-19 occurred immediately [87]. Despite the domestic status due to the pandemic being reported as stable in the middle of 2020, when the survey for the current study was conducted [68,69], things will change, as the crisis caused by COVID-19 is not quiescent. Hence, further research on the subject from a reactive perspective, for example, the changed perception of MGCs and UGCs on the effect of the increased risk aversion tendency stemming from the pandemic [88], would add value to the topic. Additionally, research examining the antecedents or triggers of the credibility of UGCs and MGCs, respectively, would fill the gap and broaden the understanding of consumers, because the existing research suggests that source credibility is an antecedent of the persuasiveness of eWOM messages [89] or website trust [90]. Third, the present study suffers from the limitation incurred by insufficient resources, in that it employed an online survey method that requested the respondents to recollect from memory social medial contents or hotel brands. It will be meaningful to conduct an experimental study with visual aids to improve the control and help foster the respondents’ recollection.

Author Contributions

Conceptualization, H.-R.S.; methodology, H.-R.S.; writing—original draft preparation, H.-R.S.; writing—review and editing, H.-R.S. and J.-G.C.; supervision, J.-G.C. Both authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data will be made available on request from the authors.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ro, D.; Kim, S.; Baek, H. The influence of eWOM on drama ratings. Korean J. Broadcasting Telecommun. Stud. 2020, 34, 70–113. [Google Scholar]
  2. Alsubagh, H. The Impact of Social Networks on Consumers’ Behaviors. Int. J. Bus. Soc. Sci. 2015, 6, 209–216. [Google Scholar]
  3. Jalilvand, M.R.; Samiei, N. The effect of electronic word of mouth on brand image and purchase intention. Mark. Intell. Plan. 2012, 30, 460–476. [Google Scholar] [CrossRef]
  4. Mir, I.; Rehman, K. Factors affecting consumer attitudes and intentions toward user-generated product content on YouTube. Manag. Mark. 2013, 8, 637–654. [Google Scholar]
  5. Rani, A.; Shivaprasad, H. Determinants of Electronic Word of Mouth Persuasiveness a Conceptual Model and Research Propositions. J. Contemp. Manag. Res. 2018, 12, 1–16. [Google Scholar]
  6. Espino-Rodríguez, T.F.; Díaz, M.R. Make or buy in the hotel marketing department: Transaction costs, financial and relational performance. Tour. Manag. Stud. 2017, 13, 7–17. [Google Scholar] [CrossRef] [Green Version]
  7. Slootweg, E.; Rowson, B. My generation: A review of marketing strategies on different age groups. Res. Hosp. Manag. 2018, 8, 85–92. [Google Scholar] [CrossRef]
  8. Barreda, A.A.; Bilgihan, A.; Nusair, K.; Okumus, F. Online branding: Development of hotel branding through interactivity theory. Tour. Manag. 2016, 57, 180–192. [Google Scholar] [CrossRef]
  9. Litvin, S.W.; Goldsmith, R.E.; Pan, B. Electronic word-of-mouth in hospitality and tourism management. Tour. Manag. 2008, 29, 458–468. [Google Scholar] [CrossRef]
  10. Kumar, A.; Bezawada, R.; Rishika, R.; Janakiraman, R.; Kannan, P. From Social to Sale: The Effects of Firm-Generated Content in Social Media on Customer Behavior. J. Mark. 2016, 80, 7–25. [Google Scholar] [CrossRef] [Green Version]
  11. Arif, M.E. The Influence of Electronic Word Of Mouth (EWOM), Brand Image, and Price on Re-Purchase Intention of Airline Customers. J. Appl. Manaj. 2019, 17, 345–356. [Google Scholar] [CrossRef]
  12. Barreda, A.A.; Bilgihan, A. An analysis of user-generated content for hotel experiences. J. Hosp. Tour. Technol. 2013, 4, 263–280. [Google Scholar] [CrossRef]
  13. Bruhn, M.; Schoenmueller, V.; Schäfer, D. Are social media replacing traditional media in terms of brand equity creation? Manag. Res. Rev. 2012, 35, 770–790. [Google Scholar] [CrossRef] [Green Version]
  14. Bang, J. Our Goal Is to Provide the Best Hotel Service for the Millennial Generation. Available online: http://news.hankyung.com/article/2018040859391 (accessed on 1 December 2019).
  15. Đaković, J.; Njeguš, A.; Milović, N. System Issues and Requirements of Hotel Marketing Management. Available online: http://portal.sinteza.singidunum.ac.rs/Media/files/2016/533-537.pdf (accessed on 5 December 2020).
  16. Ana, M.I.; Istudor, L.G. The Role of Social Media and User-Generated-Content in Millennials’ Travel Behavior. Manag. Dyn. Knowl. Econ. 2019, 7, 87–104. [Google Scholar] [CrossRef]
  17. Burgess-Wilkerson, B.; Hamilton, C.; Garrison, C.; Robbins, K. Preparing Millennials as Digital Citizens and Socially and Environmentally Responsible Business Professionals in a Socially Irresponsible Climate. In Proceedings of the 83rd Annual Conference of the Association for Business Communication, Miami, FL, USA, 24–27 October 2018; Available online: https://arxiv.org/ftp/arxiv/papers/1901/1901.06609.pdf (accessed on 20 February 2020).
  18. Towner, T.; Munoz, C. Boomers versus Millennials: Online Media Influence on Media Performance and Candidate Evaluations. Soc. Sci. 2016, 5, 56. [Google Scholar] [CrossRef] [Green Version]
  19. Björlin-Delmar, A.; Jönsson, G. How User-Generated Content Can Be Used to Reveal the Brand Identity and Image Gap. Available online: https://gupea.ub.gu.se/bitstream/2077/39448/1/gupea_2077_39448_1.pdf (accessed on 22 April 2020).
  20. Dev, C.S.; Buschman, J.D.; Bowen, J.T. Hospitality Marketing: A Retrospective Analysis (1960–2010) and Predictions (2010–2020). Cornell Hosp. Q. 2010, 51, 459–469. [Google Scholar] [CrossRef]
  21. Oxford English Dictionary Online. Available online: https://www.lexico.com/en/definition/social_media (accessed on 13 November 2019).
  22. Keller, K.L. Building strong brands in a modern marketing communications environment. J. Mark. Commun. Integr. Mark. Commun. 2009, 15, 139–155. [Google Scholar] [CrossRef]
  23. Wathen, N.C.; Burkell, J. Believe it or not: Factors influencing credibility on the web. J. Am. Soc. Inf. Sci. Technol. 2002, 53, 134–144. [Google Scholar] [CrossRef] [Green Version]
  24. Chakraborty, U.; Bhat, S. Credibility of online reviews and its impact on brand image. Manag. Res. Rev. 2018, 41, 148–164. [Google Scholar] [CrossRef]
  25. Farzin, M.; Fattahi, M. eWOM through social networking sites and impact on purchase intention and brand image in Iran. J. Adv. Manag. Res. 2018, 15, 161–183. [Google Scholar] [CrossRef]
  26. Yang, L. More Is Less: Only Moderate Polarized Online Product Reviews can Affect Sales. Int. J. Bus. Manag. 2018, 13, 192. [Google Scholar] [CrossRef] [Green Version]
  27. Jalilvand, M.R.; Ebrahimi, A.; Samiei, N. Electronic Word of Mouth Effects on Tourists’ Attitudes toward Islamic Destinations and Travel Intention: An Empirical Study in Iran. Soc. Behav. Sci. 2013, 81, 484–489. [Google Scholar] [CrossRef] [Green Version]
  28. Lee, J.; Lee, S.; Hansen, S. Source Credibility in Consumer-Generated Advertising in Youtube: The Moderating Role of Personality. Curr. Psychol. 2017, 36, 849–860. [Google Scholar] [CrossRef]
  29. Wan, F.; Ren, F. The Effect of Firm Marketing Content on Product Sales: Evidence from a Mobile Social Media Platform. J. Electron. Commer. Res. 2017, 18, 288–302. [Google Scholar]
  30. Naylor, R.W.; Lamberton, C.P.; West, P.M. Beyond the “like” button: The impact of mere virtual presence on brand evaluations and purchase intentions in social media settings. J. Mark. 2012, 76, 105–120. [Google Scholar] [CrossRef] [Green Version]
  31. Kang, J.E. The effects of e-WOM information on trust, purchase intention, and e-WOM intention. Int. J. Tour. Hosp. Res. 2018, 32, 187–199. [Google Scholar]
  32. Chiang, C.; Jang, S. The effects of perceived price and brand image on value and purchase intention: Leisure travelers’ attitudes toward online hotel booking. J. Hosp. Leis. Mark. 2006, 15, 49–69. [Google Scholar] [CrossRef]
  33. Chu, S.; Kamal, S. The Effect of Perceived Blogger Credibility and Argument Quality on Message Elaboration and Brand Attitudes: An Exploratory Study. J. Interact. Advert. 2008, 8, 26–37. [Google Scholar] [CrossRef]
  34. Hovland, C.I.; Irving, L.J.; Harold, H.K. Communication and Persuasion; Yale University Press: New Haven, CT, USA, 1953. [Google Scholar]
  35. McCracken, G. Who Is the Celebrity Endorser? Cultural Foundations of the Endorsement Process. J. Consum. Res. 1989, 16, 310–321. [Google Scholar] [CrossRef]
  36. Ohanian, R. The Imapct of Celebrity Spokespersons’ Perceived Image on Consumers’ Intention to Purchas. J. Advert. Res. 1991, 31, 46. [Google Scholar]
  37. Willemsen, L.; Neijens, P.; Bronner, F. The ironic effect of source identification on the perceived credibility of online product reviewers. J. Comput. Mediat. Commun. 2012, 18, 16–31. [Google Scholar] [CrossRef] [Green Version]
  38. Ayeh, J.K. Travelers’ acceptance of consumer-generated media: An integrated model of technology acceptance and source credibility theories. Comput. Hum. Behav. 2015, 48, 173–180. [Google Scholar] [CrossRef]
  39. Goh, K.; Heng, C.S.; Lin, Z. Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content. Inf. Syst. Res. 2013, 24, 88–107. [Google Scholar] [CrossRef]
  40. Anholt, S. Competitive Identity: The New Brand Management for Nations, Cities and Regions; Palgrave Macmillan: Hounds mills, UK, 2007. [Google Scholar]
  41. Charo, N.; Sharma, P.; Shaikh, S.; Haseeb, A.; Sufya, M. Determining the Impact of Ewom on Brand Image and Purchase Intention through Adoption of Online Opinions. Int. J. Humanit. Manag. Sci. 2015, 3, 41–46. [Google Scholar]
  42. Yoon, A.; Lee, E.; Lee, H. Consumer Perceptions of Images in Fashion Instagram by Information Providers (Brand vs Consumer). J. Korean Soc. Cloth. Text. 2018, 42, 379–396. [Google Scholar] [CrossRef]
  43. Hamdani, N.A.; Maulani, G. The influence of E-WOM on purchase intentions in local culinary business sector. Int. J. Eng. Technol. 2018, 7, 246–250. [Google Scholar] [CrossRef]
  44. Keller, K.L. Conceptualizing, Measuring, and Managing Customer-Based Brand Equity. J. Mark. 1993, 57, 1–22. [Google Scholar] [CrossRef]
  45. Dodds, W.B.; Monroe, K.B.; Grewal, D. Effects of price, brand, and store information on buyers’ product evaluations. J. Mark. Res. 1991, 28, 307–319. [Google Scholar]
  46. Ryu, K.; Han, H.; Kim, T. The relationships among overall quick-casual restaurant image, perceived value, customer satisfaction, and behavioral intentions. Int. J. Hosp. Manag. 2008, 27, 459–469. [Google Scholar] [CrossRef]
  47. El-Said, O.A. Impact of online reviews on hotel booking intention: The moderating role of brand image, star category, and price. Tour. Manag. Perspect. 2020, 33, 1–12. [Google Scholar] [CrossRef]
  48. Erdoğmuş, İ.E.; Cicek, M. The impact of social media marketing on brand loyalty. Procedia-Soc. Behav. Sci. 2012, 58, 1353–1360. [Google Scholar] [CrossRef] [Green Version]
  49. Cho, Y.; Chung, K. Emotional Marketing of hotel influencing Brand Image and Customer Loyalty. Korean J. Hosp. Tour. 2016, 25, 1–16. [Google Scholar]
  50. Inversini, A.; Masiero, L. Selling rooms online: The use of social media and online travel agents. Int. J. Contemp. Hosp. Manag. 2014, 26, 272–292. [Google Scholar] [CrossRef] [Green Version]
  51. Kandampully, J.; Suhartanto, D. Customer loyalty in the hotel industry: The role of customer satisfaction and image. Int. J. Contemp. Hosp. Manag. 2000, 12, 346–351. [Google Scholar] [CrossRef]
  52. Lien, C.H.; Wen, M.J.; Huang, L.C.; Wu, K.L. Online hotel booking: The effects of brand image, price, trust and value on purchase intentions. Asia Pac. Manag. Rev. 2015, 20, 210–218. [Google Scholar] [CrossRef]
  53. Chan, N.L.; Guillet, B.D. Investigation of social media marketing: How does the hotel industry in Hong Kong perform in marketing on social media websites? J. Travel Tour. Mark. 2011, 28, 345–368. [Google Scholar] [CrossRef]
  54. Leung, X.; Bai, B.; Erdem, M. Hotel social media marketing: A study on message strategy and its effectiveness. J. Hosp. Tour. Technol. 2017, 8, 239–255. [Google Scholar] [CrossRef]
  55. Lin, Z.; Goh, K. Measuring the Business Value of Online Social Media Content for Marketers. In Proceedings of the 32nd International Conference on Information Systems, Shanghai, China, 4–7 December 2011; Available online: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.690.4421&rep=rep1&type=pdf (accessed on 23 June 2020).
  56. Beldona, S.; Nusair, K.; Demicco, F. Online travel purchase behavior of generational cohorts: A longitudinal study. J. Hosp. Mark. Manag. 2009, 18, 406–420. [Google Scholar] [CrossRef]
  57. Moore, M.; Carpenter, J. Intergenerational perceptions of market cues among US apparel consumers. J. Fash. Mark. Manag. 2008, 12, 323–337. [Google Scholar] [CrossRef]
  58. Eastman, J.; Liu, J. The impact of generational cohorts on status consumption: An exploratory look at generational cohort and demographics on status consumption. J. Consum. Mark. 2012, 29, 93–102. [Google Scholar] [CrossRef]
  59. Martin, C.; Turley, L. Malls and consumption motivation: An exploratory examination of older Generation Y consumers. Int. J. Retail Distrib. Manag. 2004, 32, 464–475. [Google Scholar] [CrossRef]
  60. Stack, L. Are You 21 to 37? You Might Be a Millennial. The New York Times. Available online: https://www.nytimes.com/2018/03/01/style/millennials.html (accessed on 10 November 2019).
  61. Lee, M.; Kwahk, J.; Han, S.; Jeong, D.; Park, K.; Kim, J.; Oh, S.; Chae, G. A Survey on the Multi-device Media Usage of Millennials. J. Ergon. Soc. Korea 2017, 36, 645–663. [Google Scholar]
  62. Duffett, R. Facebook advertising’s influence on intention-to-purchase and purchase amongst Millennials. Internet Res. 2015, 25, 498–526. [Google Scholar] [CrossRef]
  63. Norum, P. Examination of Generational Differences in Household Apparel Expenditures. Fam. Consum. Sci. Res. J. 2003, 32, 52–75. [Google Scholar] [CrossRef]
  64. Tanyel, F.; Stuart, E.W.; Griffin, J. Have “Millennials” Embraced Digital Advertising as They Have Embraced Digital Media? J. Promot. Manag. 2013, 19, 652–673. [Google Scholar] [CrossRef]
  65. Fietkiewicz, K.J.; Lins, E.; Baran, K.S.; Stock, W.G. Inter-Generational Comparison of Social Media Use: Investigating the Online Behavior of Different Generational Cohorts. In Proceedings of the 49th Hawaii International Conference on System Sciences, Koloa, HI, USA, 5–8 January 2016; Available online: https://www.phil-fak.uni-duesseldorf.de/fileadmin/Redaktion/Institute/Informationswissenschaft/sarah_hartmann/Lehre/Fietkiewicz_Lins_Baran_Stock_HICSS_2016_289.pdf (accessed on 15 February 2020).
  66. Lim, S. Domestic Hotels, Which Had Been Popping Up, Have Stopped Opening. NewdailyBiz. Available online: http://biz.newdaily.co.kr/site/data/html/2019/05/20/2019052000077.html (accessed on 3 December 2019).
  67. Kim, Y. Five-Star-Hotels with Unprecedented Sales Promotions. Hankyoreh. Available online: https://www.hani.co.kr/arti/economy/consumer/946915.html#csidxe338159258fa13d8c660b0e23f73e86 (accessed on 13 August 2021).
  68. Park, S. Coronavirus-era Luxury Hotel and Untact Service Are Coming Up. FinancialNews. Available online: https://www.fnnews.com/news/202006101115503637 (accessed on 13 August 2021).
  69. Min, S. Coronavirus Vacation “Go to Hotel in Resort Look”. ChosunBiz. Available online: https://biz.chosun.com/site/data/html_dir/2020/06/12/2020061202886.html?utm_source=naver&utm_medium=original&utm_campaign=biz&form=MY01SV&OCID=MY01SV (accessed on 13 August 2021).
  70. Nasmedia. Netizen Profile Research. 2019. Available online: http://www.nasmedia.co.kr/ko/pr/pr_report.do (accessed on 10 November 2019).
  71. Lee, H.T. The Effect of Professional Performance on the Celebrity’s SNS WOM Accept Intention. J. Mark. Stud. 2018, 26, 1–17. [Google Scholar]
  72. Ohanian, R. Construction and Validation of a Scale to Measure Celebrity Endorsers’ Perceived Expertise, Trustworthiness, and Attractiveness. J. Advert. 1990, 19, 39–52. [Google Scholar] [CrossRef]
  73. Keller, K.L. Strategig Brand Management; Prentice Hall: New York, NY, USA, 2003. [Google Scholar]
  74. Kim, T.G.; Bae, M.G. Effect of brand image on customer’s value, satisfaction and intentions of recommendation in hotel industry. Northeast Asia Tour. Res. 2013, 9, 326–339. [Google Scholar]
  75. Han, J.; Seo, J. The Effect of Hotel Corporate Social Responsibility (CSR) on Purchase Intention and Corporate Image: Focused on Five Star Hotel. J. Tour. Leis. Res. 2016, 28, 131–147. [Google Scholar]
  76. Jeon, J. Influence of Hotel Blog Characteristics on Blog User Attitude, Participation Intention and Purchase Intention. J. Hotel Resort 2018, 17, 119–138. [Google Scholar]
  77. Kudeshia, C.; Kumar, A. Social eWOM: Does it affect the brand attitude and purchase intention of brands? Manag. Res. Rev. 2017, 40, 310–330. [Google Scholar] [CrossRef]
  78. Bartlett, M. Tests of significance in factor analysis. Br. J. Psychol. 1950, 3, 77–85. [Google Scholar] [CrossRef]
  79. Kaiser, H. An index of factorial simplicity. Psychometrika 1974, 39, 31–36. [Google Scholar] [CrossRef]
  80. Cronbach, L.J. Coefficient alpha and the internal structure of tests. Psychometrika 1951, 16, 297–334. [Google Scholar] [CrossRef] [Green Version]
  81. Bagozzi, R.P.; Yi, Y. On the evaluation of structural equation models. J. Acad. Mark. Sci. 1988, 16, 74–94. [Google Scholar] [CrossRef]
  82. Hair, J.; Black, W.C.; Babin, B.J.; Anderson, R.E.; Tatham, R.L. Multivariate Data Analysis; Prentice Hall: Englewood Cliffs, NJ, USA, 2010. [Google Scholar]
  83. Fornell, C.; Larcker, D. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  84. Steenkamp, J.; Trijp, H. The use of LISREL in validating marketing constructs. Int. J. Res. Mark. 1991, 8, 283–299. [Google Scholar] [CrossRef]
  85. Byrne, B.; Van de Vijver, F. Testing for Measurement and Structural Equivalence in Large-Scale Cross-Cultural Studies: Addressing the Issue of Nonequivalence. Int. J. Test. 2010, 10, 107–132. [Google Scholar] [CrossRef]
  86. Collins, A.; Cronin, J.; Burt, S.; George, R. From store brands to store brandscapes: The emergence of a time and money saving heuristic. Eur. J. Mark. 2015, 49, 894–918. [Google Scholar] [CrossRef]
  87. Chang, M.C.; Baek, J.H.; Park, D. Lessons from South Korea Regarding the Early Stage of the COVID-19 Outbreak. Healthcare 2020, 8, 229. [Google Scholar] [CrossRef]
  88. Im, J.; Kim, J.; Choeh, J.Y. COVID-19, social distancing, and risk-averse actions of hospitality and tourism consumers: A case of South Korea. J. Destin. Mark. Manag. 2021, 20, 100566. [Google Scholar]
  89. Teng, S.; Khong, K.W.; Goh, W.W.; Chong, A. Examining the antecedents of persuasive eWOM messages in social media. Online Inf. Rev. 2014, 38, 746–768. [Google Scholar] [CrossRef]
  90. Filieri, R.; Alguezaui, S.; Mcleay, F. Why do travelers trust TripAdvisor? Antecedents of trust towards consumer-generated media and its influence on recommendation adoption and word of mouth. Tour. Manag. 2015, 51, 174–185. [Google Scholar] [CrossRef] [Green Version]
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Figure 1. Research Model.
Figure 1. Research Model.
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Table
Table 1. Sample characteristics (n = 278).
Table 1. Sample characteristics (n = 278).
VariableClassification% (n = 278)
GenderMale34.2 (n = 95)
Female65.8 (n = 183)
GenerationEarlier Generation48.9 (n = 136)
Millennials51.1 (n = 142)
OccupationOffice worker39.6 (n = 110)
Professional17.6 (n = 49)
Business owner9 (n = 25)
Housewife14.4 (n = 40)
Student5.4 (n = 15)
Others14 (n = 39)
Monthly incomeLess than 1000 USD8.3 (n = 23)
(approximately)1000~less than 3000 USD27.7 (n = 77)
3000~less than 5000 USD39.2 (n = 109)
5000~less than 7000 USD13.3 (n = 37)
More than 7000 USD7.2 (n = 20)
Others4.3 (n = 12)
Level of educationHigh school10.4 (n = 29)
College66.2 (n = 184)
Graduate school23.4 (n = 65)
Others0
NationalityKorean (South)97.5 (n = 271)
others2.5 (n = 7)
Notes: In this study, ‘Millennials’ refers to those born between 1981 and 1996 [60]. ‘Earlier Generation’ refer to those born before 1981.
Table
Table 2. Independent Samples t-test Results.
Table 2. Independent Samples t-test Results.
VariablesEarlier G (n = 136)Millennials (n = 142)tSig.
MeanS.D.MeanS.D.
UGCsExpertise3.40.723.230.711.9640.05
Trustworthiness3.370.863.130.922.2590.025
MGCsExpertise3.770.753.60.781.8560.065
Trustworthiness3.250.762.580.856.8510.000
Table
Table 3. CFA summarized results.
Table 3. CFA summarized results.
Information
Provider		ConstructMeasurement ItemsStandardized Factor LoadingC.R.AVECR
UGCs
(MGCs)		Source CredibilityA general user’s (The hotel brand) account is…ex1. professional0.602 (0.715)-0.587 (0.589)0.876 (0.877)
ex2. skillful at providing information0.796 (0.738)10.042 (11.402)
ex3. suitable for providing information0.797 (0.764)10.048 (11.784)
ex4. full of knowledge0.715 (0.803)9.358 (12.332)
ex5. full of experience0.742 (0.739)9.598 (11.417)
tr2. not hypocritical0.881 (0.897)-0.628 (0.675)0.769 (0.804)
tr3. trustful0.684 (0.71)9.765 (9.565)
Hotel Brand ImageWhen I get information about the hotel provided by a general user’s (by the hotel brand) account on social media, …1. I feel the hotel has a differentiated image0.626 (0.691)-0.651 (0.633)0.917 (0.911)
2. I feel that the hotel has a unique personality0.721 (0.737)13.414 (14.121)
3. I find the hotel interesting0.779 (0.787)10.752 (12.203)
4. I feel good about the hotel0.817 (0.802)11.128 (12.42)
5. I feel that the hotel is special0.841 (0.837)11.359 (12.908)
6. I find the hotel attractive0.875 (0.852)11.665 (13.118)
Hotel Purchase IntentionInformation about the hotel provided by a general user’s (the hotel brand) account on social media,1. made me like the hotel.0.826 (0.83)-0.678 (0.613)0.926 (0.904)
2. made me tell others good things about the hotel I saw0.819 (0.793)16.252 (15.569)
3. increased the willingness to purchase (use) the hotel0.846 (0.823)17.079 (16.445)
4. made me want to use the hotel when I have the chance0.81 (0.776)15.983 (15.052)
5. helps me decide to use hotels.0.743 (0.695)14.122 (12.941)
6. made the hotel more desirable than any other hotel.0.785 (0.786)15.256 (15.383)
Table
Table 4. Results on convergent validity and discriminant validity.
Table 4. Results on convergent validity and discriminant validity.
ConstructsCRFactor LoadingAVE1234
UGCsSource CredibilityExpertise0.8760.602~0.7970.5870.766
Trustworthiness0.7690.684~0.8810.6280.6910.793
Hotel Brand Image0.9170.626~0.8750.6510.680.5920.807
Hotel Purchase Intention0.9260.743~0.8460.6780.6790.6330.8860.823
MGCsSource CredibilityExpertise0.8770.715~0.8300.5890.768
Trustworthiness0.8040.71~0.8970.6750.5820.821
Hotel Brand Image0.9110.691~0.8520.6330.6310.5840.795
Hotel Purchase Intention0.9040.695~0.830.6130.690.620.9520.783
Note: the figures in grey background were the problematic ones that required further verification.
Table
Table 5. Results of SEM of UGCs and MGCs.
Table 5. Results of SEM of UGCs and MGCs.
PathSRWS.E.C.R.
UGCExpertise ⟶ Hotel Brand Image0.519 ***0.1115.049
Trustworthiness ⟶ Hotel Brand Image0.234 **0.0592.573
Expertise ⟶ Hotel Purchase Intention0.0690.0960.994
Trustworthiness ⟶ Hotel Purchase Intention0.136 *0.0542.102
Hotel Brand Image ⟶ Hotel Purchase Intention0.758 ***0.1118.824
MGCExpertise ⟶ Hotel Brand Image0.439 ***0.0855.399
Trustworthiness ⟶ Hotel Brand Image0.329 ***0.0644.059
Expertise ⟶ Hotel Purchase Intention0.135 **0.0632.635
Trustworthiness ⟶ Hotel Purchase Intention0.0510.0461.03
Hotel Brand Image ⟶ Hotel Purchase Intention0.83 ***0.09210.607
UGCs model fit: χ2 = 263.524(df = 145), GFI = 0.908, NFI = 0.927, CFI = 0.966, AGFI = 0.88, RMSEA = 0.054
MGCs model fit: χ2 = 294.540(df = 143), GFI = 0.903, NFI = 0.921, CFI = 0.957, AGFI = 0.871, RMSEA = 0.062
Note: SRW = standardized regression weight/C.R. = critical ratio/*** p < 0.001, ** p < 0.01, * p < 0.05.
Table
Table 6. Results of multi-group analysis (UGCs model).
Table 6. Results of multi-group analysis (UGCs model).
PathsEstimatesC.R.
Earlier GMillennialsEarlier GMillennials
Expertise ⟶ Hotel Brand Image0.298 **0.764 ***2.6154.49
Trustworthiness ⟶ Hotel Brand Image0.461 ***−0.0193.588−0.126
Expertise ⟶ Purchase Intention0.0610.1040.8160.74
Trustworthiness ⟶ Purchase Intention0.10.1511.1891.417
Hotel Brand Image ⟶ Purchase Intention0.805 ***0.39 ***7.5166.545
Note: The figures in grey background were the problematic ones that required further verification. Multi-group analysis for generation with UGCs model: Δχ2 = 13.391, p = 0.02, *** p < 0.001, ** p < 0.01.
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Shin, H.-R.; Choi, J.-G. The Moderating Effect of ‘Generation’ on the Relations between Source Credibility of Social Media Contents, Hotel Brand Image, and Purchase Intention. Sustainability 2021, 13, 9471. https://0-doi-org.brum.beds.ac.uk/10.3390/su13169471

AMA Style

Shin H-R, Choi J-G. The Moderating Effect of ‘Generation’ on the Relations between Source Credibility of Social Media Contents, Hotel Brand Image, and Purchase Intention. Sustainability. 2021; 13(16):9471. https://0-doi-org.brum.beds.ac.uk/10.3390/su13169471

Chicago/Turabian Style

Shin, Hye-Ryeong, and Jeong-Gil Choi. 2021. "The Moderating Effect of ‘Generation’ on the Relations between Source Credibility of Social Media Contents, Hotel Brand Image, and Purchase Intention" Sustainability 13, no. 16: 9471. https://0-doi-org.brum.beds.ac.uk/10.3390/su13169471

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