The Marketing Effectiveness of Social Media in the Hotel Industry

Abstract

The unprecedented popularity of social media outlets have forced scholars to inquire about their marketing effectiveness, especially in the hotel industry. This study attempted to explore the marketing effectiveness of two different social media sites (Facebook and Twitter) in the hotel industry. Integrating the attitude-toward-the-ad (Aad) model with the concepts of attitude-toward-social-media-page, the study proposed a theoretical model of hotel social media marketing effectiveness. Based on the data collected from an online survey, the goodness of fit of the model implied that the Aad model provides an appropriate theoretical framework to explain the marketing effectiveness of social media in the hotel industry. The results revealed that hotel customers’ social media experiences influence their attitudes-toward-social-media-site, which in turn influences their attitudes-toward-hotel-brand, and that hotel customers’ attitudes-toward-hotel-brand affects their hotel booking intentions and, in turn, intentions to spread electronic word of mouth. The study also indicated that different social media sites demonstrate the same marketing effectiveness, suggesting that hotel managers use the same marketing tactics for Facebook and Twitter marketing.

Keywords

attitude-toward-the-ad attitude-toward-social-media-page eWOM marketing effectiveness social media experience

Introduction

In the past few years, the development of new Internet applications has changed the characteristics of websites toward more participatory, interactive, and user-centric social media. The Merriam-Webster dictionary defines social media as “forms of electronic communication through which users create online communities to share information, ideas, personal messages, and other content” (Merriam-Webster, 2012). These newer versions of social media (Web 2.0 technologies) have become extremely popular on the Internet and are predicted to be future marketing media (Cooke & Buckley, 2008). A recent study by Madden and Zickuhr (2011) reported that 65% of U.S. adult Internet users (50% of all U.S. adults) now use social networking sites, more than double the percentage reported in 2008 (29%) and that 43% of online adults use social networking sites daily. Social media includes applications such as social networking sites, blogs, content communities, forums/bulletin boards, and other interactive applications, which allow users to create and share information (Alarcó-del-Amo, Lorenzo-Romero, & Gómez-Borja, 2011; Constantinides & Fountain, 2008). Among various social media sites, Facebook and Twitter were ranked as the top two most popular sites in terms of website’s traffic (“Top 15,” 2011).

The explosive growth of social media channels has transformed the way many consumers interact with each other and with businesses of all kinds. Ultimately this is changing the way we do business and how businesses attract and retain consumers. A study showed that social media channels were commonly used by U.S. businesses to connect with their customers or prospects than Google (The eMarketer, 2011). Seventy percent (70%) of businesses use Facebook, followed by 46% using Twitter, 37% using LinkedIn, and 25% using YouTube. In an annual study conducted by Stelzner (2010, 2011), Facebook and Twitter were the top two social media tools used by marketers for the past 2 years. In the hotel industry, about 75% of hotels have used social media for marketing purposes (Hotelmarketing.com, 2011). Facebook and Twitter were also found to be the two most useful social media channels for marketing purposes in the hospitality industry (Friebe & Campbell, 2010; Hotelmarketing.com, 2011).

As more hotels are incorporating social media into their integrated marketing communications, the attention on its effectiveness is rising. Although social media is claimed to be effective in improving marketing practices, there has been limited quantitative support to reinforce these claims. According to Stelzner’s (2009, 2010, 2011) annual industry report, how to measure the marketing effectiveness of social media is one of the most important questions marketers need to continue to investigate. However, very few studies have examined the effectiveness of social media promotions in the academic world (Mabry & Porter, 2010). In advertising research, to understand the process through which advertising influences consumer behavior has been a long-standing focus and several major theories have been proposed to study advertising effectiveness (MacKenzie, Lutz, & Belch, 1986). Therefore, the purpose of this study was to explore the marketing effectiveness of social media using advertising theory, namely, attitude toward the ad model and attitude toward the website model. Specifically, this study intended to achieve the following objectives: (a) to develop a hypothesized model of hotel social media marketing effectiveness based on the Attitude-toward-the-ad and the Attitude-toward-the-website models; (b) to test the hypothesized model in the context of the two most popular social media sites: Facebook and Twitter; (c) to compare the marketing effectiveness of two hotel social media pages; and (d) to provide suggestions for the hotel industry in leveraging social media marketing.

Literature Review

Attitude-Toward-the-Ad Model

The studies on the effects of persuasive advertising on attitude formation and change have led to a very important concept in marketing and advertising research: Attitude-toward-the-ad (Aad; Edell & Burke, 1984). The concept of Aad was first introduced by Mitchell and Olson (1981) and Shimp (1981). They found it to be an affective construct referring to individuals’ favorable/unfavorable feelings toward a particular advertisement after ad exposure.

Numerous studies have tested that Aad has a mediating influence on brand attitudes and purchase intentions (Lutz, Mackenzie, & Belch, 1983; MacKenzie et al., 1986; Mitchell & Olson, 1981; Shimp, 1981). The Aad model describes possible sequences of exposure to a persuasive advertisement and generally posits that a recipient of an advertising message develop an Aad, which, in turn, exerts an influence on subsequent measures of advertising effectiveness such as brand attitude and purchase intentions (Lutz et al., 1983). Studies on Aad have proposed four competing Aad models representing different mediating roles of Aad (Lutz et al., 1983; MacKenzie et al., 1986). The four Aad models are based on four alternative hypotheses: affect transfer hypothesis (ATH), dual mediation hypothesis (DMH), reciprocal mediation hypothesis (RMH), and independent influences hypothesis (IIH; Figure 1).

Figure 1

Four Alternative Attitude-Toward-the-Ad Models

In all four Aad models, ad cognitions and brand cognitions have direct impacts on Aad and attitude toward the brand, respectively. However, different hypotheses posit that Aad has different impacts on attitude toward the brand and purchase intention. The ATH model postulates a direct one-way influence of Aad and brand cognitions on attitude toward the brand (Mitchell & Olson, 1981; Shimp, 1981). The DMH model posits both a direct effect of Aad on attitude toward the brand and an indirect effect through the mediation of brand cognitions (Holbrook, 1978; Lutz & Swasy, 1977). The RMH model is based on the balance theory (Heider, 1946) and asserts a reciprocal causal flow between Aad and attitude toward the brand, that is, these two constructs affect each other. The basic premise of balance theory is that a person has a preference to maintain balance among a set of cognitive components in a system (Heider, 1946). In advertising, the consumer, the ad, and the brand constitute the system (Edell & Burke, 1984). Thus, the ad–brand relationship is always a balanced state, suggesting Aad and attitude toward the brand are highly positively correlated (Edell & Burke, 1984). Finally, the IIH model assumes no causal relationship between Aad and attitude toward the brand while both have direct impacts on purchase intentions (Howard, 1977). Both Mackenzie et al.’s (1986) and Homer’s (1990) studies compared the four competing Aad models using experiment data and demonstrated that the DMH model provides the best fit to the data. That is, Aad has both direct and indirect effects through brand cognitions on attitude toward the brand.

The Aad model has been rarely applied in the hospitality field. Only a study by Miller and Stoica (2004) compared the effects of photograph print ad versus artistic rendition ad for a fictional Caribbean resort island based on the Aad model. The study found that the photograph ad was better than the artistic rendition ad in generating favorable attitude toward the ad, attitude toward the brand, and visit intention. Also, in hospitality research, brand cognition has often been studied as brand equity or brand awareness. Brand equity as customer’s cognitive awareness or mindset of a brand has been demonstrated to influence customers’ purchase intentions and brand choices in the hospitality industry (Cobb-Walgren, Ruble, & Donthu, 1995). Kim, Jin-Sun, and Kim (2008) again stated that brand awareness has a positive effect on midpriced hotel guests’ revisit intention. In this study, the measures of brand awareness were used to measure brand cognition.

Attitude-Toward-the-Website Model

The Aad model has also been extended to explain web advertising effects. Under the web environment, a new construct, attitude-toward-the-website (Aws), is added to be as important as Aad in evaluating advertising effectiveness (Chen & Wells, 1999). Similar to Aad, Aws is defined as web users’ “predispositions to respond favorably or unfavorably to web content in natural exposure situations” (Chen & Wells, 1999, p. 28). The rationale for adding this new construct is that customers’ reactions to the context where an advertisement is presented (the website) are proposed to affect how consumers react to the ad (Bruner & Kumar, 2000). Chen and Wells (1999) developed a reliable and valid scale that measures Aws and concluded that entertainment, informativeness, and organization are three dimensions of Aws. Stevenson, Bruner, and Kumar (2000) proposed that Aws plays an important role in the traditional Aad model, especially the ATH model. Bruner and Kumar (2000) further tested their new model of web advertising effectiveness, which proposed that one’s web experience play an important role along with webpage complexity and interestingness on Aws, which in turn affects Aad, attitude-toward-the-brand, and finally purchase intention. Poh and Adam (2002) incorporated the three dimensions of Aws (Chen & Wells, 1999) with the web advertising effectiveness model proposed by Bruner and Kumar (2000) and developed an integrative Aws model.

In the hospitality field, only a few studies have focused on the concept of Aws. McMillan, Hwang, and Lee (2003) explored determining factors of Aws of hotel websites. Two structural variables (number of features and creative strategy) and two perceptual variables (involvement and perceived interactivity) were tested in terms of their effects on Aws. They found that perceptual variables have greater impacts on Aws than structural variables. Jeong and Choi (2004) examined the potential effects of different picture presentations on hotel websites on Aws and customers’ behavioral intentions. The findings indicated that the picture content and picture realism significantly influence Aws of hotel websites and Aws is a strong predictor of behavioral intentions.

WOM and eWOM

Word-of-mouth (WOM) has been an important concept in the marketing field for decades. Since the early 1950s, researchers have noticed the importance of personal conversation and informal exchange of information among acquaintances in marketing (Arndt, 1967; Whyte, 1954). Arndt (1967) was one of the earliest researchers who defined WOM as oral, person-to-person communication between a communicator and receiver that is perceived as noncommercial message. In the late 1990s, the WOM concept was expanded to be applied in the Internet-based communications and the power of WOM has become stronger using Web 2.0 technologies (Hennig-Thurau & Walsh, 2004). The so-called electronic word-of-mouth (eWOM) is defined as “all informal communications directed at consumers through Internet-based technology related to the usage or characteristics of particular goods and services, or their sellers” (Litvin, Goldsmith, & Pan, 2008, p. 461).

WOM and eWOM can serve as both marketing tools and marketing outcomes (Hennig-Thurau, Gwinner, & Gremler, 2002; Litvin et al., 2008). As marketing tools, WOM and eWOM are powerful in influencing consumers’ attitudes, product evaluation, customer loyalty, and purchase intentions and decisions (Arndt, 1967; Chan & Ngai, 2011; Litvin et al., 2008). Research generally demonstrates that WOM and eWOM are more influential than conventional marketing tools (Day, 1971; Godes & Mayzlin, 2004; Herr, Kardes, & Kim, 1991). On the other hand, WOM and eWOM are also considered as one of the key marketing outcomes, which are as important as customer loyalty (Casaló, Flavián, & Guinalíu, 2010; Hennig-Thurau et al., 2002). Numerous studies have been conducted in an attempt to gain insights into the antecedents and motives of WOM and eWOM (Dichter, 1966; Hennig-Thurau, Gwinner, Walsh, & Gremler, 2004; Sundaram, Mitra, & Webster, 1998).

In the hospitality field, the drivers of WOM have also been studied in several different contexts and attitude has been identified as an important attribute of WOM communication (Cheng, Lam, & Hsu, 2006; Leach, Liu, & Winsor, 2008). Leach et al. (2008) proposed a model of conference attendance that included both intention to attend and intention to recommend (WOM). The findings suggested that attitude toward conference is the most important factor affecting both intention to attend a future conference and intention to recommend the conference to others. Thus, we also incorporated intention to spread WOM with hotel booking intention in the hypothesized model.

Hypothesized Model

This study posited that the affect transfer hypothesized (ATH) Aad model can be applied as the theoretical framework to explain the marketing effectiveness of social media for hotels. The ATH model postulates that Aad and brand cognitions both have direct impact on attitude toward the brand, which in turn affects purchase intention (here referred to as hotel booking intention). Since social media are types of websites, the study incorporated the concept of Aws into the ATH model, in which Aws is actually attitude-toward-social-media-page. In addition, this study focused on hotel social media pages as a whole rather than a specific advertising message on the social media page. As a result, the Aad concept in the Aad model was left out in the proposed model. Thus, in the model proposed by the study, hotel customers’ attitudes toward social media pages are proposed to have direct impacts on their attitudes toward hotel brand, which then influences their hotel booking intention. Hotel customers’ social media experience and brand cognition are postulated to affect their attitudes toward social media sites and attitudes toward hotel brand, respectively. Besides, the study added intention of eWOM (in this study, eWOM refers to positive eWOM) as another major marketing outcome of social media that is expected to be determined by both attitudes toward hotel brand and hotel booking intention. Specifically, six directional hypotheses were proposed:

Hypothesis 1: The more positive a customer’s social media experience, the more positive the customer’s attitude toward the social media site.

Hypothesis 2: The more positive a customer’s attitude toward the social media site, the more positive the customer’s attitude toward the hotel brand.

Hypothesis 3: The higher a customer’s cognition of a hotel brand, the more positive the customer’s attitude toward the hotel brand.

Hypothesis 4: The more positive a customer’s attitude toward a hotel brand, the more likely the customer will book this hotel brand.

Hypothesis 5: The more positive a customer’s attitude toward a hotel brand, the more likely the customer will spread positive word-of-mouth about this hotel brand online.

Hypothesis 6: The more likely a customer to book a hotel brand, the more likely the customer to spread positive word-of-mouth about this hotel brand online.

In addition, the study also posited that different social media sites may have different marketing effectiveness:

Hypothesis 7a: The relationships between a customer’s social media experience and attitude-toward-social-media-page are different when the customer uses Facebook or Twitter.

Hypothesis 7b: The relationships between a customer’s attitude-toward-social-media-page and attitude-toward-hotel-brand are different when the customer uses Facebook or Twitter.

Hypothesis 7c: The relationships between a customer’s brand cognition and attitude-toward-hotel-brand are different when the customer uses Facebook or Twitter.

Hypothesis 7d: The relationships between a customer’s attitude-toward-hotel-brand and hotel booking intention are different when the customer uses Facebook or Twitter.

Hypothesis 7e: The relationships between a customer’s attitude-toward-hotel-brand and intention of eWOM are different when the customer uses Facebook or Twitter.

Hypothesis 7f: The relationships between a customer’s hotel booking intention and intention of eWOM are different when the customer uses Facebook or Twitter.

To examine these hypothesized relationships, a path model was proposed (see Figure 2). In the hypothesized model, all aforementioned hypothesized structural relationships were proposed and tested simultaneously. All hypotheses were tested in the context of two social media sites: Facebook and Twitter. These two social media channels were selected because they are the most useful and most used social media tools for marketing purposes (Friebe & Campbell, 2010; Stelzner, 2010, 2011). All factor loadings and effect sizes of the relationships were tested for invariance across the two social media site groups.

Figure 2

Hypothesized Model of Social Media Marketing Effectiveness

Methodology

An online survey was conducted by Qualtrics in November 2011. A convenient sample was selected using a database provided by the Utah-based online research company Qualtrics. Established in 1997, Qualtics panel members now include nearly 4 million individuals within the United States who have already agreed to be contacted for survey participation. An email was sent to all the panel members in search of people who were Facebook users or Twitter users. Qualified participants were invited to take the survey via a link contained in the email. A total number of 408 participants completed the survey within a week of data collection. All participants were assigned to two groups (Facebook or Twitter) randomly, resulting in 204 respondents in each group. The Facebook page or the Twitter page of one major hotel brand was chosen as the stimulus for each group. The conditions for participating in this study were that the respondents had to have a Facebook account to be in the Facebook group or to have a Twitter account to be in the Twitter group. The survey comprised three steps. First, all participants took a presurvey, which consisted of questions on demographics and brand cognition. Second, after finishing the presurvey, the two groups were led to different social media sites. One group was asked to read the Facebook page of the hotel brand. The other group was required to read the Twitter page of the same hotel brand. The participants had as much time to look at the webpages as they needed. Finally, after reading the webpages, all participants were requested to complete a postsurvey, which consisted of questions on social media experience, attitude-toward-social-media-page, attitude-toward-hotel-brand, booking intention, and intention of eWOM.

Instrument

The survey comprised two questionnaires: a presurvey questionnaire and a postsurvey questionnaire. The presurvey questionnaire consisted of two sections. The first section included several demographic questions regarding gender, age, ethnicity, education level, and Internet usage. The second section included questions regarding brand cognition. The respondent’s cognition of the selected hotel brand was measured using multi-item scales. All items were measured by 5-point Likert-type scales anchored by strongly disagree (1) and strongly agree (5).

The postsurvey questionnaire consisted of five sections. The first section asked the respondent’s experience on the hotel social media page he/she just visited. All 14 items were measured by 5-point semantic differential scales anchored by not at all (1) and very much (5). The second and third sections measured the respondent’s attitude toward social media site he/she just visited and attitude toward the hotel brand. Both of them were measured using 5-point semantic differential scales. The fourth and fifth sections asked about the participant’s intentions to book the hotel and to recommend the hotel online in the future. Both of the intentions were measured by 3 items using 5-point Likert-type scales. Table 1 lists all the scale items used to measure each construct and their reference sources.

Table 1

Measurement Items of the Constructs

Constructs	Measurement Items	Reference
Brand cognition	I have heard of Hilton hotel brand.	Buil, de Chernatony, and Martinez (2008); So and King (2010)
	I have been staying in Hilton hotel.
	I know what Hilton hotel looks like.
	I can recognize Hilton hotel
	When I book a hotel, Hilton hotel comes into my mind.
Experience	Important: unimportant	Choi (2000); Moital, Vaughan, Edwards, and Peres (2009)
	Interesting: boring
	Relevant: irrelevant
	Exciting: unexciting
	Means a lot to me: means nothing
	Appealing: unappealing
	Fascinating: mundane
	Valuable: worthless
	Involving: uninvolving
	Needed: not needed
	Beneficial: prejudicial
	Desirable: undesirable
	Useful: useless
	Essential: dispensable
Attitude-toward-social-media-page	Good: bad	Choi (2000)
	Likable: dislikable
	Favorable: unfavorable
	Enjoyable: unenjoyable
	Pleasant: unpleasant
	Entertaining: not entertaining
	Interesting: uninteresting
	Nice: awful
Attitude-toward-hotel-brand	Positive: negative	Chiang and Jang (2006); Choi (2000)
	Satisfactory: unsatisfactory
	Favorable: unfavorable
	Good: bad
	Likable: dislikable
	Nice: awful
Booking intention	My willingness to book Hilton hotels is very high.	Chiang and Jang (2006)
	The probability that I would consider booking Hilton hotels is very high.
	The likelihood of booking Hilton hotels is very high.
Intention of eWOM	My willingness to recommend Hilton hotels to others on Facebook/Twitter is very high.	Chiang and Jang (2006); Gruen, Osmonbekov, and Czaplewski (2006)
	The probability that I would consider recommending Hilton hotels to others on Facebook/Twitter is very high.
	The likelihood of recommending Hilton hotels to others on Facebook/Twitter is very high.

Note: eWOM = electronic word-of-mouth.

Before data analysis, measurement validity and reliability were first evaluated. Measurement validity of the instrument was evaluated by conducting principal axis factor analysis with oblimin rotation on all of the items. After performing validity analyses, two items in the measures for attitude-toward-social-media-page were deleted to maintain both convergent and discriminant validities. The remaining items were tested for internal consistency by Cronbach’s alpha. The results demonstrated good internal consistency for all measures in both groups (.83-.98; see Table 2). The lowest internal consistency (reliability) is from the brand cognition construct in both groups.

Table 2

Internal Consistency Estimates (α) of Constructs

Construct	Facebook Group	Twitter Group
Brand cognition	.86	.83
Experience	.96	.97
Attitude-toward-social-media-page	.96	.96
Attitude-toward-hotel-brand	.97	.97
Booking intention	.96	.96
Intention of eWOM	.98	.97

Note: eWOM = electronic word-of-mouth.

Data Analysis Procedure

Data were analyzed using multigroup structural equation modeling (SEM). Measurement models were tested first in each group (Facebook or Twitter), followed by invariance tests on factor loadings across two sample groups. The full structural model was tested, followed by invariance tests on structural coefficients between Facebook and Twitter groups. The models were tested using EQS 6.1 software.

The evaluation of model adequacy was based on comparative fit index (CFI), nonnormed fit index (NNFI), standardized root mean square residual (SRMR), and root mean square error of approximation (RMSEA) and its lower and upper confidence interval boundaries. In addition, standardized residuals and the results of Lagrange multiplier tests and Wald tests were inspected along with the theoretical literature of the research area.

Each construct in the model had three indicators. For brand cognition, social media experience, attitude-toward-social-media-page, attitude-toward-hotel-brand constructs, item parceling was conducted to group more than three items into three indicators. Item parceling was used because these constructs (latent variable) had too many items. Using all individual items in the measurement model may harm the overall model fit. Item parceling can lead to a better fitting solution and less bias in estimates of structural parameters when the items are unidimensional (Bandalos, 2002).

Item parceling in this study used the following procedure: First, items belonging to each construct (latent variable) were subjected to exploratory factor analysis to determine if the unidimensional assumptions were met. After finding that the unidimensional assumptions were met, items were grouped into three indicators for each factor based on factor loadings in the exploratory factor analysis conducted in the validity check. Finally, the average score of the items was calculated to represent the subscale scores of each indicator. This procedure was applied for each group.

Linearity, multicollinearity, and singularity assumptions for the SEM analyses were met. The multivariate kurtosis indicated that the data distributions were less than optimal (normalized estimates were 33.40 and 31.97 for the Facebook and the Twitter groups, respectively). However, the data distributions and outlier analysis suggested there was no outlier. Using both the maximum likelihood estimation and the robust methods estimation, multigroup SEMs were run. As the results from both methods were very similar, the results of the maximum likelihood estimation were reported.

Results

Demographic Profile of Respondents

The final sample was composed of 50% (102) males and 50% (102) females in both groups. The demographics of Facebook and Twitter users indicated that there were approximately the same numbers of males and females (Infographic, 2010). The age of the respondents ranged from 18 to 78 years in the Facebook group and from 18 to 72 years in the Twitter group. The average age of the respondents was 43 years in the Facebook group and 41 years in the Twitter group. The biggest age group in Facebook was 18 to 25 years, which is consistent with the Facebook users’ age distribution (Infographic, 2010). The biggest age groups in Twitter were 26 to 34 years and 35 to 44 years, which is also consistent with the Twitter users’ age distribution (Infographic, 2010).

More than three fourths of the respondents were White/Caucasian in both the Facebook group (77.9%) and the Twitter group (75.9%). These numbers were similar to the results of a survey conducted by Pew Research Center that 78% of Facebook users and 71% of Twitter users were White (Hampton & Goulet, 2011). The education level of the respondents was high. The demographics revealed that 79.9% of the respondents in the Facebook group and 82.8% in the Twitter group had at least some college education. In addition, 39.2% of the respondents in the Facebook group and 47.5% in the Twitter group had at least a bachelor’s degree. These numbers were also similar to the education distribution of Facebook and Twitter users (Hampton & Goulet, 2011).

The respondents reported a long history of Internet usage and social media usage. All the respondents in the Facebook group had used the Internet for at least 6 months, and all the respondents in Twitter group had used the Internet for at least 1 year. Finally, 95.6% of the respondents in both groups had used the Internet for at least 4 years. The data revealed that 84.8% of the respondents in the Facebook group and 94.6% in the Twitter group had used social media websites for at least 1 year.

Measurement Model

The measurement model specified six factors: brand cognition, social media experience, attitude-toward-social-media-page, attitude-toward-hotel-brand, booking intention, and intention of eWOM. To test the model in each group (Facebook and Twitter), indicators were constrained to load only on the factor it was designated to measure. The residual terms for all indicators were fixed to be uncorrelated. No equality constraints on the factor loadings were imposed and the factor covariances were free to be estimated.

Goodness-of-fit indices indicated that the measurement model fit the data well in both groups: χ²(120, N = 204) = 237.72, p < .001, CFI = .98, NNFI = .97, SRMR = .03, RMSEA = .07 (CI = 0.06, 0.08) for the Facebook group, and χ²(120, N = 204) = 193.36, p < .001, CFI = .99, NNFI = .98, SRMR = .03, RMSEA = .05 (CI = 0.04, 0.07) for the Twitter group. All factor loadings of the indicators were statistically significant, ps < .001, ranging from .80 to .98 for the Facebook group and from .72 to .99 for the Twitter group.

Next, whether all factor loadings were invariant between the two groups was tested. The parameters to be tested were constrained to be equal across the two social media groups. Results of Lagrange multiplier tests of multivariate statistics and univariate increment indicated that all factor loadings were invariant. Standardized factor loadings are presented for the Facebook and Twitter groups in Table 3.

Table 3

Standardized Factor Loadings and Variances (R²), After Equality Constraints Were Tested

Indicator	Brand Cognition	Experience	Attitude-Toward-Social-Media-Page	Attitude-Toward-Hotel-Brand	Booking Intention	Intention of eWOM	R ²
BC1	.86 (.86)						.74 (.74)
BC2	.79 (.73)						.62 (.53)
BC3	.96 (.94)						.92 (.89)
EP1		.95 (.96)					.90 (.93)
EP2		.88 (.89)					.77 (.80)
EP3		.96 (.96)					.92 (.92)
AM1			.95 (.96)				.90 (.91)
AM2			.95 (.96)				.90 (.92)
AM3			.93 (.93)				.87 (.87)
AB1				.95 (.97)			.90 (.93)
AB2				.98 (.96)			.95 (.92)
AB3				.96 (.96)			.93 (.92)
BI1					.94 (.92)		.87 (.84)
BI2					.97 (.97)		.94 (.94)
BI3					.95 (.94)		.89 (.88)
WOM1						.94 (.93)	.89 (.86)
WOM2						.98 (.99)	.96 (.97)
WOM3						.97 (.95)	.94 (.91)

Note: eWOM = electronic word-of-mouth. Values in parentheses are loadings for Facebook group. Values not in parentheses are loadings for Twitter group. All ps < .001. All indicators are indicators after item parceling of the measurements.

Variances (R²) of the indicators were accounted for by their corresponding constructs, which ranged from .62 to .96 for the Facebook group and from .53 to .97 for the Twitter group. The two smallest explained variances were both from the indicators of brand cognition for both groups (.62 and .74 for the Facebook group; .53 and .74 for the Twitter group).

The correlations among factors in the measurement model are presented in Table 4. In the Facebook group, correlation coefficients ranged from .50 to .94, all ps < .001. The highest correlation was the relationship between attitude-toward-social-media-page and attitude-toward-hotel-brand. In the Twitter group, correlation coefficients ranged from .36 to .92, all ps < .001. The highest correlation was also found to be the relationship between attitude-toward-social-media-page and attitude-toward-hotel-brand.

Table 4

Correlation Between Constructs

Construct (Factor)	1	2	3	4	5	6
1. Brand cognition	—	.48*	.36*	.37*	.68*	.57*
2. Experience	.56*	—	.85*	.75*	.58*	.54*
3. Attitude-toward-social-media-page	.50*	.83*	—	.92*	.51*	.56*
4. Attitude-toward-hotel-brand	.50*	.80*	.94*	—	.54*	.59*
5. Booking intention	.61*	.64*	.56*	.56*	—	.87*
6. Intention of eWOM	.53*	.67*	.60*	.61*	.84*	—

Note: Values below the diagonal are correlation coefficients for Facebook group. Values above the diagonal are correlation coefficients for Twitter group.

p < .001.

Structural Model

To examine the goodness-of-fit of the hypothesized model, the measurement model was respecified by imposing the structure of the model. The equality constraints on factor loadings across grade that were tested plausible remained fixed. Goodness-of-fit indices indicated that the hypothesized model only represented a marginal fit to the data in both groups: χ²(127, N = 204) = 299.52, p < .001, CFI = .97, NNFI = .96, SRMR = .1, RMSEA = .08 (CI = 0.07, 0.09) for the Facebook group, and χ²(127, N = 204) = 288.99, p < .001, CFI = .97, NNFI = .96, SRMR = .1, RMSEA = .08 (CI = .07, .09) for the Twitter group. For both the Facebook and the Twitter groups, the LM test suggested that one parameter that was not specified in the hypothesized model contributed a lot to model fit; that is, brand cognition had a direct effect on hotel booking intention. Given the findings of marginal fit of hypothesized models to both groups, the model was respecified to both groups based on the LM statistics.

After being respecified, the two new models represented good fits to data in two groups, respectively: χ²(126, N = 204) = 257.89, p < .001, CFI = .98, NNFI = .97, SRMR = .05, RMSEA = .07 (CI = .06, .08) for the Facebook group, and χ²(126, N = 204) = 210.24, p < .001, CFI = .98, NNFI = .98, SRMR = .03, RMSEA = .06 (CI = .04, .07) for the Twitter group.

We then examined the invariance of structural coefficients between two groups. All structural coefficients were tested to be invariant between groups, ps > .05. We also tested the invariance of factor covariance (brand cognition and social media experience) between the two groups. The factor covariance between brand cognition and social media experience was also found to be invariant across groups, p > .05.

Combining two data sets, the final configural model represented a good model fit. The goodness-of-fit indices for the final model were as follows: χ²(270, N = 408) = 480.69, p < .001, CFI = .98, NNFI = .98, SRMR = .05, RMSEA = .04 (CI = .04, .05). Figure 3 presents the structural part of the hypothesized model. The dashed line represents the nonsignificant path.

Figure 3

The Structural Model of Social Media Experience With Standardized Path Coefficients

The results showed that customers’ experiences on a hotel social media page had a significant effect on their attitudes toward a social media site (β = .84 for both the Facebook and Twitter groups, ps < .001). Customers’ attitudes toward a social media site had a significant effect on their attitudes toward hotel brand (β = .92 and β = .90 for the Facebook and Twitter groups, respectively, ps < .001). Customers’ brand cognition did not have a significant effect on their attitudes toward the hotel brand, p > .05. However, customers’ brand cognition had a significant effect on their intentions to make hotel booking decisions (β = .51 and β = .50 for the Facebook and Twitter groups, respectively, ps < .001). Customers’ attitudes toward hotel brand had significant effects on both their intentions to make hotel booking decisions (β = .30 and β = .36 for the Facebook and Twitter groups, respectively, ps < .001) and their intentions to spread positive word-of-mouth online (β = .17 and β = .20 for the Facebook and Twitter groups, respectively, ps < .001). Finally, customers’ intentions to make hotel booking decisions had a significant effect on their intentions to spread positive word-of-mouth online (β = .76 and β = .75 for the Facebook and Twitter groups, respectively, ps < .001).

The results also revealed a number of significant indirect effects among constructs. Social media experience had a significant indirect effect on attitude-toward-hotel-brand mediated by attitude-toward-social-media-page (β = .78 and β = .75 for the Facebook and Twitter groups, respectively, ps < .001). It also had a significant indirect effect on booking intention mediated by attitude-toward-social-media-page and attitude-toward-hotel-brand (β =.24 and β = .27 for the Facebook and Twitter groups, respectively, ps < .001) and on intention of eWOM mediated by attitude-toward-social-media-page, attitude-toward-hotel-brand, and booking intention (β = .31 and β = .35 for the Facebook and Twitter groups, respectively, ps < .001). Attitude-toward-social-media-page had a significant indirect effect on booking intention mediated by attitude-toward-hotel-brand (β = .28 and β = .32 for the Facebook and Twitter groups, respectively, ps < .001) and on intention of eWOM mediated by attitude-toward-hotel-brand and booking intention (β = .37 and β = .42 for the Facebook and Twitter groups, respectively, ps < .001). Brand cognition had a significant indirect effect on intention of eWOM mediated by booking intention (β = .40 and β = .39 for the Facebook and Twitter groups, respectively, ps < .001). Attitude-toward-hotel-brand had a significant indirect effect on intention of eWOM mediated by booking intention (β = .23 and β = .27 for the Facebook and Twitter groups, respectively, ps < .001).

Variances (R²) in attitude-toward-social-media-page accounted for by social media experience were .71 for the Facebook group and .70 for the Twitter group. Variances (R²) in attitude-toward-hotel-brand explained by attitude-toward-social-media-page and brand cognition were .92 for the Facebook group and .90 for the Twitter group. Variances (R²) in booking intention accounted for by attitude-toward-hotel-brand, social media experience, and brand cognition were .50 for the Facebook group and .52 for the Twitter group. Variances (R²) in intention of eWOM attributed by attitude-toward-hotel-brand and booking intention were .75 for the Facebook group and .77 for the Twitter group. The correlations between brand cognition and social media experience were significant for both the Facbook group (r = .57) and the Twitter group (r = .48), ps < .001 (Figure 2).

Discussion and Implications

This study attempted to combine the Aad model with the concepts of attitude-toward-social-media-page and eWOM to measure the marketing effectiveness of social media in the hotel industry. Based on the Aad model, the study proposed an integrative model to relate hotel customers’ social media experiences and brand cognitions with their attitudes-toward-social-media-site, attitudes-toward-hotel-brand, booking intentions, and intentions of eWOM. An online survey was conducted by Qualtrics to test the hypothesized model in two different social media contexts and the model was tested to fit the data in both the Facebook and Twitter groups. Hypotheses 1, 2, 4, 5, and 6 were supported by the SEM results. That is, a positive hotel social media experience will lead to positive attitude-toward-social-media-page and positive attitude-toward-hotel-brand; thus, a hotel customer’s intention to book this hotel room and to recommend this hotel brand online will increase. However, Hypothesis 3 was refuted, suggesting that a hotel customer’s brand cognition does not affect his/her attitude-toward-hotel-brand. Hypotheses 7a, 7b, 7c, 7d, 7e, and 7f were all refuted by the invariance test of path coefficients, suggesting that all hypothesized relationships in the model were not statistically different between Facebook and Twitter. Besides, there was one new significant relationship revealed by the SEM results, which was not proposed in the model. It was the relationship between brand cognition and hotel booking intention. The relationship was not statistically different between the Facebook and Twitter groups.

The support of the hypothesis regarding the relationship between social media experience and attitude-toward-social-media-page suggests that if a hotel customer enjoys the hotel social media page, the customer’s attitude toward the hotel social media page is more positive. This finding is consistent with Bruner and Kumar’s (2000) notion that an individual’s web experience has a positive effect on Aws. Their study also suggested that complexity and interestingness are two important factors affecting individual’s web experience (Bruner & Kumar, 2000). Thus, if a hotel wants to achieve favorable attitudes toward a social media page from its customers, it should try to enhance its customers’ experiences with its social media page. Specifically, a hotel should make its social media page more interesting, appealing, informative, interactive, and consumer centric so the customers can enjoy their social media experiences. Besides, when customers feel the hotel social media page is useful, valuable, and beneficial, their experiences are more likely to be positive.

The support of the hypothesis regarding the relationship between attitude-toward-social-media-page and attitude-toward-hotel-brand suggests that if a hotel customer has a favorable attitude toward the hotel social media page, the customer’s attitude toward the hotel brand is more positive. This finding is consistent with all studies involving the Aws model, which tests that Aws is positively correlated with attitude-toward-the-brand (Bruner & Kumar, 2000; Poh & Adam, 2002; Stevenson et al., 2000). It demonstrates the importance of social media marketing since hotel customers’ attitudes toward hotel social media pages positively affect their attitudes toward the hotel brand. Thus, it is very important to leverage social media pages to impress customers and change their attitudes in becoming more favorable to the brand.

The disapproval of the hypothesis regarding the relationship between brand cognition and attitude-toward-hotel-brand indicates that no matter what a customer knows about a hotel brand, the customer’s attitude toward the hotel brand does not change. This finding is interesting because it is contrary to the proposed relationship in the Aad model that brand cognition is positively related to attitude-toward-the-brand (Mitchell & Olson, 1981; Shimp, 1981). However, in the study, brand cognition is tested to directly affect customers’ hotel booking intention instead of affecting booking intention indirectly through the mediation of attitude-toward-the-brand. This finding indicates that a hotel’s potential customers will book the hotel as long as they are familiar with the hotel brand. Therefore, it implies that hotels need to market their brand name heavily so that the potential customers can strongly identify with the hotel brand. Thus, when they need to book a hotel room, the preconceived hotel brand will influence their booking decision. This finding also suggests another alternative hypothesis of Aad model besides the four Aad models proposed in the literature. In this alternative Aad model, brand cognition directly affects purchase intention, whereas attitude toward the ad indirectly affects purchase intention through the mediation of attitude toward the brand.

The support of the hypothesis regarding the relationship between attitude-toward-hotel-brand and hotel booking intention suggests that if a hotel customer has a favorable attitude toward the hotel brand, the customer is more willing to book this hotel in the future. This finding supports the Aad model and the Aws model, both of which proposed that attitude-toward-the-brand has a positive impact on purchase intention (Bruner & Kumar, 2000; Mitchell & Olson, 1981). Thus, hotels should try to use their social media pages to build a positive and impressive hotel brand image to build their potential customers’ affection for their hotel brand. This suggests that social media marketing should not only be used for promotions but also for brand building purposes.

The support of the hypothesis regarding the relationship between attitude-toward-hotel-brand and intention of (positive) eWOM implies that if a hotel customer has a favorable attitude toward the hotel brand, the customer is more willing to recommend this hotel online to others. Studies on electronic word-of-mouth have identified consumers’ affective elements as factors motivating them to share experiences and recommend products to others (Dichter, 1966; Neelamegham & Jain, 1999; Nyer, 1997). This study extends this finding by the notion that electronic word-of-mouth can also be triggered by customers’ favorable attitude toward the hotel brand. The positive eWOM is another important outcome of social media marketing. Previous studies demonstrate that word-of-mouth and eWOM can change consumers’ attitudes toward the brand and even influence their purchase intentions (Arndt, 1967; Day, 1971; Jones, Aiken, & Boush, 2009). Thus, attitude toward hotel brand, booking intention, and eWOM constitute a positive feedback loop. Therefore, to build and promote hotel brand online is of significance for hotels to stimulate and manage eWOM on social media sites.

The support of the hypothesis regarding the relationship between booking intention and intention of eWOM suggests that if a hotel customer has a stronger intention to book the hotel, the customer is more willing to recommend this hotel online to others. The study also found that the relationship between booking intention and intention of eWOM is much stronger than the relationships between attitude-toward-hotel-brand and booking intention and intention of eWOM. This implies that the two marketing outcomes of social media marketing, booking intention and intention of eWOM, are closely related to or even tied to each other. One positive outcome leads to the others positive outcome, and vice versa. Since in the literature WOM as marketing outcome fell short, this finding supports the statement that both purchase intention and WOM are important marketing outcomes.

There were a lot of significant indirect effects revealed in the study. First, social media experience has an indirect effect on booking intention and the intention of eWOM, strengthening the importance of social media experience in social media marketing. When customers enjoy their social media experience, they tend to have positive attitudes-toward-social-media-site and attitudes-toward-hotel-brand, which, in turn, increases their booking intention and intention to spread positive WOM on social media sites. Second, there are the indirect effects of attitude-toward-social-media-page on booking intention and the intention of eWOM. There also exist indirect effects of brand cognition and attitude-toward-hotel-brand on intention of eWOM. All these indirect effects demonstrate that attitude-toward-social-media-page and attitude-toward-hotel-brand are important mediators of social media marketing effects, which is consistent with Lutz et al.’s (1983) and Bruner and Kumar’s (2000) findings that Aad and Aws are significant mediators of advertising effects.

The invariance test of path coefficients showed that all the relationships among the constructs are invariant between the Facebook and the Twitter groups. This finding implied that different social media sites share the same marketing mechanism. Thus, hotels can use the same marketing strategy in marketing on different social media sites. The important factors that drive customer’s booking intention and eWOM are not what social media site the hotel uses, but how customers feel about the social media experience and what their attitudes are. Therefore, no matter whether the hotel use Facebook or Twitter as marketing tool, the managers should focus on the social media experience they create for customers and the brand building on social media. This finding makes hotel social media marketing simpler since hotels can employ the same strategy and tactics in using different social media sites. Finally, the correlation coefficients between brand cognition and social media experience were significant in both social media groups. There was no difference in correlation coefficients across the two groups. This finding indicates that hotel customers’ prebeliefs of the hotel brand are positively related to their experiences on hotel social media pages. Assuming a customer has higher brand cognition toward a hotel, the customer’s social media experience is more likely to be positive.

Conclusion and Limitations

The purpose of this study was to propose a theoretical framework to understand the marketing effectiveness of different social media sites in the hotel industry. The Aad model and the concepts of Aws and eWOM have been introduced into the social media context to form the hypothesized model. Results of the study find that hotel customers’ social media experiences influence their attitudes-toward-social-media-site, which in turn influences their attitudes-toward-hotel-brand. Then their attitudes-toward-hotel-brand affect their hotel booking intentions and eWOM, which are two major marketing outcomes of hotel social media marketing. Contrary to the Aad model, hotel customers’ brand cognition does not affect their attitudes-toward-hotel-brand. The goodness of fit of the model to the data further suggests that the Aad model serves as an appropriate theoretical framework to analyze the marketing effectiveness of social media. The other important finding of the study is that different social media sites show the same marketing mechanism in the marketing effectiveness model. Thus, hotel can employ the same marketing strategy and tactics in using different social media sites.

This study makes valuable contributions to both the academia and industry. From a theoretical perspective, the research on the marketing effectiveness of social media is very limited. This study is one of the first attempts to apply the Aad model, a commonly used model in advertising area, into social media context to explore the marketing effectiveness of social media. The study provides empirical evidence to support the use of the Aad model combined with the Aws concept in the social media field. That is, a customer’s attitude toward hotel social media page affects his/her attitude toward hotel brand, which in turn influence his/her hotel booking intention. Besides, the study also suggests another alternative hypothesis of Aad model besides the four commonly used ones in the literature, in which brand cognition directly affects purchase intention. In terms of methodology, this study attempts to identify the marketing effectiveness of different social media sites using multigroup SEM. Since multigroup SEM is rarely used in the hospitality and tourism field, this study extends the literature by introducing a new theory and applying a new method for future research.

In a practical sense, hotel managers can use the findings of this study to better their social media marketing plans. The study identified social media experience as a very important construct in social media marketing, indicating that hotels need to focus on how to create a positive and satisfactory experience for their social media users. As a result, hotels should focus on social media page design to enhance customers’ experiences. Specifically, a hotel should make its social media pages more interesting, appealing, informative, interactive, and consumer centric so the customers can enjoy their social media experiences. The hotel social media pages should also be useful, valuable, and beneficial to customers. Another important factor leading to hotel booking intention is brand cognition. Thus, a hotel should try to enroll more existing customers or even loyal customers as fans on its Facebook page or followers on its Twitter page. Since existing customers are found to have higher brand cognition toward the hotel, they are more likely to be influenced by social media marketing and are more likely to book hotel rooms in the future. Besides, attitude-toward-the-hotel-brand was found as an important mediator for social media marketing to achieve effective outcomes. Thus, hotels should emphasize brand building on their social media pages. Hotel managers should remember that the quintessential function of social media page is not to exchange information, but to enhance brand reputation. The same marketing mechanism across different social media sites let hotel managers simplify their social media marketing strategy and tactics. Managers can employ the same marketing tactics focusing on the major factors in the marketing effectiveness model when using different social media sites. That is, hotels should focus on customers’ social media experience, brand cognition, and attitudes of customers regardless what social media site they are using as the marketing tool.

Limitations and Future Research

As is always the case with any research there are several limitations of this examination that should be noted. One major limitation of this study is the inclusion of data only from two social media sites using one hotel brand as sample, rendering its inability to generalize research findings. Since this study is an exploratory study in the hotel social media marketing area, further work using more hotel brands and more social media sites to test the model proposed in the study would certainly be beneficial to hospitality researchers from better generalizing the results. Also, the measurement of the brand cognition construct should be further refined because of the low internal consistency (reliability) and factor loadings. Besides, the time the participants spent on the specific Facebook page or Twitter page was not controlled. This may have also affected their social media experience and the final results. Another notion of the study was the large range of ages in the study sample. In this study, the impacts of the sample demographics on their attitudes and behavior intentions were not considered in the hypotheses and the model, which might be an area of future study. Finally, the study compared the marketing effectiveness of Facebook and Twitter because these two are the most commonly used social media channels. However, as we all know, Facebook and Twitter have different functions. Facebook marketing focuses on the website, whereas Twitter marketing uses mobile devices. These different functions may affect the comparison results. Future research should address the limitations of this study and explore the application of the Aad model and the Aws model in other contexts in the hospitality and tourism field.

Footnotes

Authors’ Note:

This article was partially supported by the Conrad N. Hilton Foundation. We are also thankful for the partial support by the Caesars Foundation for this research.

Xi Y. Leung (e-mail: xyleung@olemiss.edu) is an assistant professor in the Department of Nutrition and Hospitality Management at the University of Mississippi. Billy Bai (e-mail: Billy.bai@unlv.edu) is an associate professor in the William F. Harrah College of Hotel Administration at University of Nevada, Las Vegas. Kurt A. Stahura (e-mail: kurt.stahura@unlv.edu) is a professor and director of student affairs at University of Nevada, Las Vegas, Singapore campus.

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