The Effects of Reviewer Expertise on Future Reputation,Popularity,and Financial Performance of Hotels: Insights from Data-Analytics

Abstract

Although the impacts of reviews provided by reviewers with expertise are well documented, the literature lacks empirical research on how such reviews are longitudinally linked to performance indicators and whether management responses to such reviews lead to differential impacts on these indicators. This study investigates the effects of reviewer expertise on hotels’ online reputation, online popularity, and financial performance. Using a large data set of management responses and online reviews from 730 hotels over 26 quarters, matched with quarterly financial performance data, the authors found that the effects of average ratings and the number of reviews on hotels’ subsequent financial performance are attenuated when reviewer expertise increases. The study also demonstrates that business benefits are enhanced if hotels target reviewers of higher expertise when providing management responses to online reviews. Results suggest that when managing electronic word-of-mouth, practitioners should take strategic approaches that leverage the influence of reviewer expertise.

Keywords

management response online reviews reviewer expertise hotel performance data analytics

Online reviews have a strong impact on how consumers evaluate tourism and hospitality services (Browning, So, & Sparks, 2013). However, unlike traditional word-of-mouth communication, where source credibility and trustworthiness are fundamental to consumers’ acceptance of a message (McGinnies & Ward, 1980), in electronic word-of-mouth (e-WOM) consumers cannot readily assess the credibility of a communication source (Litvin, Goldsmith, & Pan, 2008). Reviews are often written by anonymous sources (Dellarocas, 2003), making the evaluation of the source’s credibility particularly difficult (D.-H. Park & Lee, 2009).

At the prepurchase evaluation stage, potential consumers lack firsthand experience with the service providers and tend to rely on others’ evaluations as a basis for making purchase decisions (Flanagin & Metzger, 2013). In addition, potential consumers seldom have access to all relevant information for making a decision regarding a product, and instead must use whatever cues are available to evaluate the information’s credibility (Sparks, So, & Bradley, 2016). Owing to the anonymous nature of the online environment, consumers use the reviewers’ prior activities to assess the credibility and quality of information (King, Racherla, & Bush, 2014). Therefore, lacking prior consumption experiences, consumers evaluate a hotel’s trustworthiness on the basis of intangible informational cues or signals (Urban, Sultan, & Qualls, 2000). In addition to using online reviews (Browning et al., 2013; K. L. Xie, So, & Wang, 2017), potential consumers employ two other informational cues: (a) expertise of the reviewer (Filieri, 2015) and (b) responses to reviews posted by management (K. L. Xie, So, & Wang, 2017; Sparks et al., 2016).

The powerful impacts of opinions from reviewers with expertise are well documented (e.g., Vermeulen & Seegers, 2009; Zhang, Zhang, & Yang, 2016; Zhao, Wang, Guo, & Law, 2015). Prior research has established that, in general, experienced people with higher level of expertise are more persuasive than amateur people (Petty, Cacioppo, & Goldman, 1981). Furthermore, recommendations of reviewers with expertise influence consumers strongly because consumers may perceive these reviewers as better informed and more reliable (Senecal & Nantel, 2004), thus influencing the impact of the review (Vermeulen & Seegers, 2009). In an online environment, readers are unable to verify a reviewer’s social background and level of product knowledge, leading consumers to equate reviewer expertise with a designation conferred by many third-party review platforms such as TripAdvisor and Yelp (Z. Liu & Park, 2015).

While previous research has contributed significantly to the current understanding of the impact of reviewer expertise, a review of the literature has identified two critical knowledge gaps. First, past studies have largely focused on the influences on customer perceptions such as consideration of the hotel (Vermeulen & Seegers, 2009), usefulness of the review (S. Park & Nicolau, 2015), attitudes toward adoption of user-generated contents for travel planning (Ayeh, Au, & Law, 2013), and booking intentions toward hotels (Zhao et al., 2015). A question of significant practical relevance remains unanswered: How do online reviews provided by reviewers with expertise influence aggregate online marketing performance indicators such as reputation and popularity of hotel firms? Second, prior research does not extend the potential effects of reviews provided by reviewers with expertise to future hotel financial performance—a significant oversight, because hotel firms can adopt management practices to encourage reviews from reviewers with a higher level of expertise. Therefore, to assess how reviewer expertise contributes to hotels’ subsequent online reputation (aggregate ratings of online reviews) and popularity (volume of online reviews) as well as financial performance, we propose the following research questions:

Research Question 1: Does an increase in reviewer expertise influence online reputation and popularity of hotels perceived by consumers?

Research Question 2: Does an increase in reviewer expertise influence financial performance of hotels?

Another important informational cue in evaluating a hotel is the management response to the online review. Tourism and hospitality scholars have explicitly urged hospitality firms to respond to reviews (Wei, Miao, & Huang, 2013), and research shows that restaurant managers who respond successfully to online reviews can turn an unsatisfied customer into a loyal one (Pantelidis, 2010). Recent empirical research indicates that providing a response enhanced potential customers’ inferences of trust and concern (Sparks et al., 2016). An emerging line of research explores how to respond to reviews to generate positive consumer evaluations of a hotel (C. H. Lee & Cranage, 2014; Sparks et al., 2016). However, most studies have examined the effects of management responses on consumer outcomes such as trust (Sparks et al., 2016), and on hotel performance in general (K. L. Xie, So, & Wang, 2017; K. L. Xie, Zhang, & Zhang, 2014), without considering the differential magnitude of impacts of reviews by people with different levels of expertise. Given the significant impacts of reviewer expertise suggested in the literature (Z. Liu & Park, 2015; H. Xie, Miao, Kuo, & Lee, 2011; Zhao et al., 2015), we argue that management responses to reviewers with expertise also have a stronger effect on hotel financial performance. On this basis, we propose the third research question:

Research Question 3: Does the effect of management responses to online consumer reviews on the financial performance of hotels increase as the reviewer expertise increases?

To address these research questions, we use large-scale but granular data containing management responses and online consumer reviews on TripAdvisor for hotels in major markets of Texas over 26 quarters, matched with quarterly hotel performance records, for econometric analyses. Drawing on the theory of persuasion as a conceptual framework, our findings demonstrate that the effects of average ratings and the number of reviews on subsequent financial performance of hotels are attenuated when the level of reviewer expertise increases. Furthermore, our study advances current understanding of management responses by empirically showing that targeting reviewers with expertise can magnify the impact of management intervention in online reviews and thus be an effective strategy for increasing hotel performance.

Literature Review

Theoretical Foundation

The conceptual foundation of this research is the well-established theory of persuasion, which holds that beliefs and attitudes can be influenced by perceptions about the message source, including trustworthiness, credibility, and the recipient’s beliefs about the source’s intention to persuade (Petty & Cacioppo, 1981). Conventional wisdom regarding persuasion holds that information from high-credibility sources produces more attitude change than information from low-credibility sources (Eagly, Wood, & Chaiken, 1978). Attribution theory suggests that when source credibility is low, consumers tend to discount the arguments in a message (Eagly & Chaiken, 1975), whereas when source credibility is high, consumers are more inclined to accept the message arguments (Mizerski, Golden, & Kernan, 1979). The elaboration likelihood model of persuasion (Petty & Cacioppo, 1986) also posits that source credibility can inform consumers of how much weight to give the information within a source (Petty & Cacioppo, 1981). Recent research empirically shows that source credibility influences the confidence people have in the thoughts they generate in response to a persuasive message (Brinol, Petty, & Tormala, 2004). Therefore, the theory of persuasion serves as an important foundation for the current investigation.

Hypothesis Development

Positive comments from reviewers with a higher level expertise are thought to be important to engendering hotel reputation. To indicate a reviewer’s level of expertise, many third-party review websites have created a metric to provide such information. For example, TripAdvisor uses a system that shows the different levels of expertise of reviewers and recognizes reviewers who (a) have longer membership on TripAdvisor, (b) earn a higher status badge, and (c) receive more helpful votes. Similarly, Yelp’s users can acquire the “elite” badge if they frequently provide high-quality reviews and actively engage with the community (Filieri, 2015). Consistent with these practices, prior research suggests that increased online review experience (Vermeulen & Seegers, 2009) and badges earned by the reviewers (Schuckert, Liu, & Law, 2016) signal expertise of reviewers. Furthermore, according to the source credibility theory (Ohanian, 1990), when the information sender shows signs of expertise the information may be perceived as more credible. The literature supports that number of helpful votes in part indicates the reviewer expertise thus enhancing the credibility of the review message and the reviewer (Dhanasobhon, Chen, & Smith, 2007). Therefore, on the basis of the literature and the context of TripAdvisor, this study focuses on length of membership, status badge, and the number of helpful votes received as indicators of reviewer expertise.

Empirical research lends support for the effect of positive online review ratings on hotel reputation. Scholars argue that, within the tourism industry, online reputation is derived from reviews (Serra Cantallops & Salvi, 2014) because positive comments can enhance the market reputation of the company. However, the magnitude of the effect may change if the reviews are written by reviewers with expertise. The moderating role of reviewer expertise is evident in the literature. For example, research shows that reviewer expertise strongly influences consumers. A recent study indicates a positive relationship between reviewer expertise and people’s hotel online bookings (Zhao et al., 2015). Furthermore, online information provided by reviewers with higher level of expertise is considered to be more useful and to have more influence on attitudes toward the product and purchase intentions than information provided by amateur reviewers (Lascu, Bearden, & Rose, 1995). Most recently, Zhang et al. (2016) found that as the number of reviews written by reviewers with website-recognized expertise increases, future traveler ratings for the hotel increase. On this basis, we hypothesize the following:

Hypothesis 1 (Null): The positive association between the average rating of online reviews and subsequent hotel reputation will not be stronger as the reviewer expertise increases.

Hypothesis 1 (Alternative): The positive association between the average rating of online reviews and subsequent hotel reputation will be stronger as the reviewer expertise increases.

Another important aspect of online reviews is review volume. Research shows that the volume of online consumer reviews are positively associated with the online popularity of restaurants (Zhang, Ye, Law, & Li, 2010). While online reviews are generated by fellow consumers with different levels of expertise, prior research has assumed equal effects for all review contributors. Although empirical research directly supporting differential effects is lacking, prior studies offer some understanding. For example, reviews by reviewers with low expertise had on average no significant effect on hotel consideration, whereas reviews by reviewers with high expertise, as manipulated through years of reviewing experience, had an overall positive effect (Vermeulen & Seegers, 2009). On this basis, we propose the following hypotheses:

Hypothesis 2 (Null): The positive association between number of online reviews and subsequent hotel popularity will be will not be stronger as the reviewer expertise increases.

Hypothesis 2 (Alternative): The positive association between number of online reviews and subsequent hotel popularity will be stronger as the reviewer expertise increases.

The positive association between online reviews (both average ratings and the number of reviews) and subsequent financial performance of hotels is well supported in the literature. For example, research shows that the volume of online product reviews is positively related to financial outcomes such as movie sales (Duan, Gu, & Whinston, 2008) and box office revenues (Y. Liu, 2006). Similarly, positive reviews lead to an increase in relative sales level (Chevalier & Mayzlin, 2006) and significantly increase hotel bookings (Ye, Law, & Gu, 2009). Further to such direct effect, the literature suggests that reviewer expertise exert stronger influences on consumer attitudes toward the brand or product described. For instance, empirical studies reported that recommendations from reviewers with higher levels of expertise have greater influence on consumers (Senecal & Nantel, 2004). Research also provides empirical support for a positive relationship between reviewer expertise and people’s online hotel bookings (Zhao et al., 2015). On this basis, we propose the following hypotheses:

Hypothesis 3 (Null): The positive association between online reviews (both average ratings and the number of reviews) and subsequent financial performance of hotels will not be stronger as the reviewer expertise increases.

Hypothesis 3 (Alternative): The positive association between online reviews (both average ratings and the number of reviews) and subsequent financial performance of hotels will be stronger as the reviewer expertise increases.

The positive relationship between management response and subsequent financial performance of hotels can be established in prior literature. Empirical research on online complaint behaviors indicates that company responses may help restore the company’s positive image as well as generate positive evaluation of the company (Y. L. Lee & Song, 2010). Providing a response also increases their inferences of trust and concern about the firm (Sparks et al., 2016). Hospitality researchers suggest that management response to negative comments is one of the most salient predictors of hotel performance (Kim, Lim, & Brymer, 2015). However, the literature tends to assume that responses to reviewers with varying levels of expertise have the same impact on potential customers. This assumption may not be consistent with the reviewer expertise literature King et al. (2014) and recent studies that support the significant role of reviewer expertise in determining the message impact (Zhang et al., 2016; Zhao et al., 2015). Building on prior research, we argue that reviewer expertise moderates the effect of management responses on consumer evaluations and subsequently on hotel financial performance.

The conceptual rationale for this argument is based on the WOM marketing literature. For example, in examining the evolution of WOM marketing in online communities, marketing scholars describe that the Network Coproduction Model is most recent and captures the importance of communication via the Internet (Kozinets, De Valck, Wojnicki, & Wilner, 2010). The model holds that it is in the marketers’ interests to identify and attempt to influence the influential, respected, credible, WOM-spreading consumers. In addition, marketers have become interested in directly managing WOM activity through targeted one-to-one seeding and communication programs (Kozinets et al., 2010). This notion is also consistent with the customer engagement literature (So, King, & Sparks; 2014; So, King, Sparks, & Wang; 2016). Extending this notion to the online review settings, responding to reviewers with expertise is thought to have different effects on performance outcomes of managing online reviews. Thus, we propose the following:

Hypothesis 4 (Null): The positive association between the volume of management response and subsequent financial performance of hotels will not be stronger as the reviewer expertise increases.

Hypothesis 4 (Alternative): The positive association between the volume of management response and subsequent financial performance of hotels will be stronger as the reviewer expertise increases.

Methodology

Data and Measures

We developed a software procedure to collect large-scale but granular data of 730 hotels reviewed on TripAdvisor in five major Texas hotel markets between 2005 Quarter 1 and 2011 Quarter 2. Table 1 presents the distribution of sampled hotels by markets. For each hotel, we used automated Python scripts to access and parse HTML and XML pages of TripAdvisor for the following publicly available information: (a) average ratings and number of consumer reviews; (b) number of management responses to consumer reviews; (c) reviewer expertise indicators, including membership with TripAdvisor, reviewer badge, and number of helpful votes received from other consumers; and (d) hotel characteristics such as class, age, size, and number of amenities. We then obtained quarterly revenue records of these hotels provided by the Texas Comptroller Office in their capacity as auditors of state tax collection. The two sources of data were merged at the hotels’ quarterly level. Our unit of analysis in the longitudinal panel data is Hotel–Quarter. Table 2 presents the definitions and summary statistics of the variables. Table 3 shows that the correlation among variables are below 0.8 (Katz, 2006), indicating that the estimation is unlikely to be biased by collinearity of variables.

Table 1

Description of Sampled Hotels Across Five Major Texas Hotel Markets

City	Number of Hotels	Average Class^a	Average Age	Average Size	Average Number of Amenities
Austin	117	2.25	17.00	146.15	6.83
Dallas	100	2.61	20.32	200.50	7.98
Fort Worth	61	2.35	15.43	130.38	6.87
Houston	237	2.24	13.79	136.77	6.69
San Antonio	215	2.25	14.64	127.53	6.85

Hotel class is designated by TripAdvisor using “Crowns,” with the value of 5 for a luxury hotel, 4 for an above average hotel with some outstanding features and a broad range of services, 3 for a full-service hotel, 2 for a midmarket economy hotel, and 1 for a budget traveler hotel.

Table 2

Variable Definition and Summary Statistics

	Variable	Description	Mean	SD	Median	Skewness^a	Minimum	Maximum
Hotel Performance	Revenue	Hotel revenues in dollars in a given quarter	1,350,000	1,760,000	695,495	3.10	15,205	17,300,000
Reviewer Expertise	Membership	Average years of reviewers’ TripAdvisor membership in a given quarter	1.13	1.23	1	1.37	0	8
	Badge	Average level of reviewer expertise in a given quarter, with the value of 1 for “Reviewer,” 2 for “Senior Reviewer,” 3 for “Contributor,” 4 for “Senior Contributor,” and 5 for “Top Contributor”	2.15	1.11	2	0.77	1	5
	NumVotes	Number of helpful votes reviewers received from other users in a given quarter	22.09	38.68	9	4.76	0	645
Consumer Reviews	AveRatings	Average ratings of reviews for hotel quality in a given quarter, with the value of 5 for “Excellent,” 4 for “Very Good,” 3 for “Average,” 2 for “Poor,” and 1 for “Terrible.” It is a measure of online reputation of hotels.	3.48	1.19	3.86	−0.67	1	5
Consumer Reviews	NumReviews	Number of reviews in a given quarter. It is a measure of online popularity of hotels.	3.67	6.62	2	10.92	1	189
Management Responses	NumResponses	Number of manager responses in a given quarter	0.49	2.13	0	9.49	0	52
	Variable	Description	Mean	SD	Median	Skewness^a	Minimum	Maximum
Hotel Characteristics	Class	Hotel class segmentation designated by TripAdvisor using “Crowns,” with the value of 5 for a luxury hotel, 4 for an above average hotel with some outstanding features and a broad range of services, 3 for a full-service hotel, 2 for a midmarket economy hotel, and 1 for a budget traveler hotel	2.72	1.05	3	−0.99	1	5
	Age	Number of years since the hotel is open in a given quarter	17.43	12.65	13	0.73	0	63
	Size	Number of guest rooms in a given quarter	189.41	184.91	132	3.98	5.00	1,840
	NumAmenities	Number of internal amenities such as indoor swimming pool, free high-speed Internet, fitness center, wheelchair access, and pets allowed	7.90	2.54	8	−1.04	0.00	13.00

We checked the normality of the variables through skewness. A logarithm transformation is needed when the data are excessively skewed positively or negatively (Greene, 2012). Therefore, we take log transformations of some highly skewed variables (i.e., logNumReviews, logNumVotes, logSize, logRevenue, and logNumResponses) to normalize the data in our regression analysis for effective estimation.

Table 3

Pearson Correlation of Variables

	1	2	3	4	5	6	7	8	9	10
1. AveRatings	1
2. NumReviews	0.14	1
3. NumResponses	0.03	0.62	1
4. Badge	0.15	0.03	−0.01	1
5. NumVotes	0.10	0.01	0.01	0.19	1
6. Membership	0.10	0.13	0.10	0.12	0.31	1
7. Class	0.26	0.40	0.32	0.16	0.22	0.11	1
8. Age	−0.17	0.13	0.20	−0.05	0.03	−0.05	0.09	1
9. Size	0.05	0.41	0.35	0.14	0.23	0.08	0.54	0.33	1
10. NumAmenities	0.27	0.25	0.15	0.13	0.14	0.12	0.62	−0.01	0.38	1

Figures 1 to 3 show management responses by reviewer expertise indicators, including length of membership, reviewer badge, and number of helpful votes received. Surprisingly, managers tend to respond overwhelmingly to reviewers with less expertise. Specifically, 86.74% of management responses were to reviewers with 0 to 2 years of membership; about 80.68% of management responses were to reviewers who were not yet “contributors”; and only 19.32% of responses went to reviewers with badges of “contributor” and above. Most management responses (94.61%) were to reviewers who received fewer than 30 votes from other peer consumers for the helpfulness of their reviews. The results imply that most hotel managers might not yet have targeted reviewers with higher levels of expertise.

Figure 1

Distribution of Management Responses by Years of Reviewer Membership

Figure 2

Distribution of Management Responses by Reviewer Badge

Figure 3

Distribution of Management Responses by Number of Helpful Votes Received by Reviewers

Model Specification

The goal of our empirical estimation is twofold. First, we examine the moderation effects of reviewer expertise on subsequent online reputation (i.e., aggregate average ratings of reviews) and popularity of hotels (i.e., aggregate number of reviews). Second, we investigate the impact of reviewer expertise on hotel financial performance through its moderation effect with online reviews and management responses. We use a blend of linear regression models to estimate such effects while controlling for hotel characteristics.

For each hotel i, its online reputation and popularity affected by reviewer expertise in Quarter t are modeled as

\begin{array}{l} A v e R a t i n g s_{i t} = α_{1} + β_{1 a} A v e R a t i n g s_{i t - 1} + β_{1 b} E x p e r t i s e_{i t - 1} \\ + β_{1 c} A v e R a t i n g s_{i t - 1} \times E x p e r t i s e_{i t - 1} \\ + γ_{1} H T C_{i t} + δ_{1} H C_{i} + ε_{i t} \end{array}

\begin{array}{l} l o g N u m R e v i e w s_{i t} = α_{2} + β_{2 a} l o g N u m R e v i e w s_{i t - 1} + β_{2 b} E x p e r t i s e_{i t - 1} \\ + β_{2 c} l o g N u m R e v i e w s_{i t - 1} \\ \times E x p e r t i s e_{i t - 1} + γ_{2} H T C_{i t} + δ_{2} H C_{i} + μ_{i t} \end{array}

where Expertise represents the candidate measures for reviewer expertise, including Membership_it-₁, Badge_it-₁, and logNumVotes_it-₁. Using multiple candidate measures of reviewer expertise allows us to identify the relative effects of these reviewer expertise measures in each model and serves as a robustness check. HTC_it is a vector of time-variant hotel characteristics controls such as Age_it and logSize_it, whereas HC_i is a vector of time-invariant hotel characteristics controls including Class_i and NumAmenities_i. ε_it and μ_it are random error terms. The focuses of our estimation are the coefficients β_1c and β_2c, which capture the effects of reviewer expertise on subsequent online reputation and popularity of hotels through moderating average ratings and volume of reviews by prior reviewers in Equations (1) and (2), respectively.

Similarly, for each hotel i, its financial performance (i.e., quarterly revenue) is influenced by online reviews and management responses and moderated by reviewer expertise in Quarter t:

\begin{array}{l} l o g R e v e n u e_{i t} = α_{3} + β_{3 a} A v e R a t i n g s_{i t - 1} + β_{3 b} l o g N u m R e v i e w s_{i t - 1} \\ + β_{3 c} l o g N u m R e s p o n s e s_{i t - 1} + β_{3 d} E x p e r t i s e_{i t - 1} \\ + β_{3 e} A v e R a t i n g s_{i t - 1} \times E x p e r t i s e_{i t - 1} \\ + β_{3 f} l o g N u m R e v i e w s_{i t - 1} \times E x p e r t i s e_{i t - 1} \\ + β_{3 j} l o g N u m R e s p o n s e s_{i t - 1} \times E x p e r t i s e_{i t - 1} \\ + γ_{3} H T C_{i t} + δ_{3} H C_{i} + e_{i t} \end{array}

where we use annotations similar to Equations (1) and (2). e_it is the error term. The focuses of our estimation are the coefficients β_3e, β_3f, and β_3j, which capture the effects of reviewer expertise on hotel performance through moderation with online reviewers (average ratings and number of reviews) and management responses, respectively.

Results and Discussion

We model the subsequent hotel reputation, hotel popularity, and hotel performance as a function of online reviews and management responses, as well as several reviewer expertise indicators (i.e., length of membership, reviewer badge, number of helpful votes received), yet control for relevant hotel characteristics such as hotel age, size, and class. We employ a blend of econometrics models with fixed effects to estimate the hypothesized effects. The fixed effects estimation controls for time-invariant unobserved heterogeneity of individual entities (hotels in our case) without estimating them (Greene, 2012). To reduce heteroscedasticity concerns, we leverage robust standard errors clustered at the hotel level (Wooldridge, 2010). We execute the analyses using STATA Version 14, a statistical software widely used for econometrics analysis (Muenchen, 2012). Table 4 presents the estimation results for Equations (1) and (2). Table 5 shows the estimation results for Equation (3). For each equation we test three models, each using a candidate measure of reviewer expertise, including Membership, Badge, and logNumVotes.

Table 4

Moderation Effects of Reviewer Expertise on Online Reputation and Popularity of Hotels

	AveRatings				logNumReviews
	(1)	(2)	(3)		(4)	(5)	(6)
AveRatings_t- ₁	0.256*** (.000)	0.247*** (.000)	0.256*** (.000)	logNumReviews_t- ₁	0.375*** (.000)	0.501*** (.000)	0.280*** (.000)
Membership_t- ₁	0.053 (.118)			Membership_t- ₁	0.033*** (.000)
Badge_t- ₁		0.079** (.031)		Badge_t- ₁		0.006 (.602)
logNumVotes_t- ₁			0.010 (.740)	logNumVotes_t- ₁			0.006 (.479)
AveRatings_t- ₁ * Membership_t- ₁	0.010*** (.001)			logNumReviews_t- ₁ * *Membership_t- ₁	0.020** (.017)
AveRatings_t- ₁ * Badge_t- ₁		0.006** (.042)		logNumReviews_t- ₁ * Badge_t- ₁		0.071*** (.000)
AveRatings_t- ₁ * logNumVotes_t- ₁			0.004 (.138)	logNumReviews_t- ₁ * logNumVotes_t- ₁			0.032*** (.000)
Age_t	−0.010*** (.000)	−0.010*** (.000)	−0.010***(.000)	Age_t	0.003*** (.000)	0.003*** (.000)	0.004*** (.000)
logSize_t	−0.091*** (.000)	−0.095*** (.000)	−0.096*** (.000)	logSize_t	0.186*** (.000)	0.207*** (.000)	0.177***(.000)
Class_t	—	—	—	Class_t	—	—	—
NumAmenities_t	—	—	—		—	—	—
Constant	2.245*** (.000)	2.265*** (.000)	2.320*** (.000)	Constant	−0.842*** (.000)	−0.909*** (.000)	−0.784*** (.000)
Observations	6,368	7,021	5,735	Observations	6,948	6,432	6,209
R ²	0.189	0.189	0.174	R ²	0.308	0.321	0.301

Note: p value in parentheses.

***

p < .01. **p < .05. *p < .1.

Table 5

Moderation Effects of Reviewer Expertise on Hotel Performance

	logRevenue
	(7)	(8)	(9)
AveRatings_t- ₁	0.028** (.039)	0.013 (.415)	0.046*** (.002)
logNumReviews_t- ₁	0.079*** (.006)	0.094** (.015)	0.029 (.379)
logNumResponses_t- ₁	0.057** (.025)	0.049* (.056)	0.053* (.077)
Membership_t- ₁	0.031 (.478)
Badge_t- ₁		0.012 (.761)
logNumVotes_t- ₁			−0.005 (.885)
AveRating_t- ₁ * Membership_t- ₁	0.001** (.015)
logNumReviews_t- ₁ * Membership_t- ₁	0.026* (.056)
logNumResponses_t- ₁ * Membership_t- ₁	0.010* (.060)
AveRating_t-1 * Badge_t- ₁		0.010** (.032)
logNumReviews_t- ₁ * Badge_t- ₁		0.011 (.509)
logNumResponses_t- ₁ * Badge_t- ₁		0.005* (.072)
AveRating_t- ₁ * logNumVotes_t- ₁			0.003* (.072)
logNumReviews _t- ₁ * logNumVotes_t- ₁			0.045*** (.001)
logNumResponses_t- ₁ * logNumVotes_t- ₁			0.011 (.518)
Age_t	−0.010*** (.000)	−0.010*** (.000)	−0.008*** (.000)
logSize_t	1.020*** (.000)	1.024*** (.000)	0.983*** (.000)
Class_t	0.304*** (.000)	0.305*** (.000)	0.294*** (.000)
NumAmenities_t	0.018*** (.009)	0.016** (.021)	0.020*** (.006)
Constant	7.430*** (.000)	7.402*** (.000)	7.567*** (.000)
Observations	933	940	871
Adjusted R²	0.879	0.880	0.876

Note: p value in parentheses.

***

p < .01. **p < .05. *p < .1.

Moderation Effects of Reviewer Expertise on Online Reputation and Popularity of Hotels

The results of Models 1 to 3, presented in Table 4, show that reviewer expertise indicators in all three models positively moderate the effect of average ratings on subsequent hotel reputation. Specifically, Model 1 shows that as length of membership increases, the positive effects of average ratings on hotel reputation increase (0.010***), a finding consistent with the theory of persuasion (Petty & Cacioppo, 1981, 1986; Petty, Wheeler, & Tormala, 2003), which suggests that credibility of the source provides a basis for the consumer to determine how much weight to give the information within a source (Hovland & Weiss, 1951; Petty & Cacioppo, 1981). We find similar results for reviewer badge, another measure of reviewer expertise, in Model 2. As the reviewer status increases (e.g., from “reviewer” to “senior reviewer”), the effect of average ratings on subsequent hotel reputation is strengthened (0.006**). However, we find an insignificant moderation effect of the number of helpful votes (0.004) in Model 3, indicating that reviewers who receive more helpful votes do not necessarily influence subsequent consumers’ perception of the hotel reputation.

Models 4 to 6 in Table 4 show the estimated moderation effects of reviewer expertise on influencing hotel popularity. As Model 4 shows, the effect of the number of reviews on hotel popularity is positively moderated by the length of membership of previous reviewers (0.020**). That is, as the length of a reviewer’s membership increases, the volume of his/her reviews will likely trigger more reviews from subsequent consumers, magnifying hotel popularity. Model 5 uses a different expertise measure, reviewer badge, and reveals that the positive effect of the volume of reviews on subsequent hotel popularity increases as the reviewer’s badge upgrades to a higher level (0.071***). Positive moderation is also found in Model 6, which uses the number of helpful votes as the measure of reviewer expertise. The results show that as reviewers receive more helpful votes, the effect of their volume of reviews on subsequent hotel popularity increases correspondingly (0.032***). Overall, our results in three models are consistent, lending support to the significance of source credibility in influencing the perceptions of subsequent consumers (Fang, Ye, Kucukusta, & Law, 2016; Senecal & Nantel, 2004; Zhao et al., 2015).

Moderation Effects of Reviewer Expertise on Hotel Performance

Models 7 to 9 in Table 5 present the estimated effects of reviewer expertise on financial performance of hotels through interaction with online reviews and management responses. Again, we use three measures of reviewer expertise in each model.

Model 7 shows a significant positive moderation effect of the length of reviewer membership on the performance effect of average ratings (0.001**) and number of reviews (0.026*). While the performance implications of average ratings (Duverger, 2013; Ye, Law, Gu, & Chen, 2011) and number of reviews (Ye et al., 2009) have been widely recognized in the literature, we find that positive impacts are likely to increase when reviews are written by reviewers with longer membership. Furthermore, we find that length of membership also positively moderates the effect of management responses on hotel performance (0.010*). This result extends recent studies about the positive effects of management responses on hotel financial performance (Mattila & Mount, 2003; S.-Y. Park & Allen, 2013; K. L. Xie, So, & Wang, 2017; K. L. Xie et al., 2014), suggesting that offering management responses to reviewers with longer membership will likely strengthen the impact of management response on financial performance.

Model 8 shows a positive moderation effect of reviewer badge in enhancing the associations between financial performance and average rating (0.010**) and management responses (0.005*). On one hand, as the reviewer’s badge upgrades, the effect of the reviewer’s average rating on hotel performance increases because reviewers in higher status have more influence on the purchase decision of subsequent consumers (Zhang et al., 2016). This result occurs because online information given by reviewers with more expertise is perceived to be more useful and has greater influence on attitudes toward the product and purchase intentions (Lascu et al., 1995). On the other hand, responding to reviewers with a higher status would amplify the effect of management responses on hotel performance. A plausible reason for this finding is that customer care, as communicated in management responses, has a stronger impact when managers respond to these experienced and seasoned reviewers with expertise. Consistent with the network coproduction model (Kozinets et al., 2010), the result supports that in the context of managing online reviews, a greater level of benefits can be achieved by influencing the more influential, respected, and credible WOM-spreading consumers.

Model 9 reveals significant moderation effects of the number of helpful votes on average rating (0.003*) and number of reviews (0.045***), but not on management responses (0.011). Although the reviewers who receive more helpful votes enhance the positive effects of consumer reviews on hotel financial performance, responding to those reviewers may not be a useful strategy for managers given the insignificant moderation effect of management responses. Clearly, the moderation effects of reviewer expertise measures may vary, and managers can influence hotel performance by engaging with reviewers with longer membership and higher level status or badge. However, responding to reviewers who receive more helpful votes may not necessarily result in increased performance. Our recommendation is that managers be selective and strategic when responding to reviewers.

Conclusion and Implications

This study investigates how reviewer expertise may influence the online reputation and popularity as well as the financial performance of hotels and how managers can use social persuasion to leverage hotel performance by responding to reviewers with expertise. Our study adds new insights to the literature by theorizing and empirically testing the moderating effect of reviewer expertise on predictors of performance such as average ratings of online reviews (Chevalier & Mayzlin, 2006), number of reviews (Y. Liu, 2006), and management response (Sparks et al., 2016; K. L. Xie, So, & Wang, 2017; K. L. Xie et al., 2014). Insights from this study yield both theoretical and practical implications.

Our results indicate that consumers value length of membership and reviewer badge when using online reviews for decision making. As such, the results suggest that source credibility may play an important role in evaluating the information. Reviewer expertise disclosed online is significantly and positively associated with both reputation and popularity of hotels as endorsed by subsequent consumers. When the expertise of reviewers increases (e.g., longer membership, higher badge status), their opinions not only influence online reputation and popularity of hotels but also hotel performance. Our findings indicate that, when faced with numerous reviews online, consumers more heavily weigh the opinions from reviewers with a higher level of expertise. This result is consistent with the conceptual thinking found in persuasion research, which holds that beliefs and attitudes can be influenced by perceptions about the message source, including trustworthiness and credibility (Petty & Cacioppo, 1981).

Reviewer expertise serves as a convenient and efficient heuristic device on which to base consumers’ decisions, as when source credibility is high, consumers are more inclined to accept the message arguments (Mizerski et al., 1979) and can proactively respond to reviewers with expertise to leverage hotel performance. Responding to these reviewers with longer membership and higher badge status results in increased financial performance of hotels. While scholars and practitioners have been primarily concerned about the value of management responses to hotels (Sparks & Bradley, 2014; Sparks et al., 2016; K. L. Xie, So, & Wang, 2017; K. L. Xie et al., 2014), this study explicitly addresses an important but less researched question of to whom hotel managers should respond when managing online reviews. Overall, this study addresses the business importance of targeting reviewers with higher levels of expertise and responding to their reviews, which not only lead to contagious review actions of subsequent consumers but also potentially increase their purchases toward the hotels.

Theoretical Implications

Several significant theoretical implications of this study warrant discussion. First, previous research on reviews provided by reviewers with expertise have mainly examined the impacts of such reviewers on customer-level variables such as consideration of the hotel, usefulness of the review, attitudes, and booking intentions. Extending the existing literature, this study provides empirical evidence indicating how reviews written by fellow consumers who possess a higher level of expertise, as recognized by TripAdvisor, also affect aggregate online marketing performance indicators such as reputation and popularity of hotel firms. The results clearly demonstrate the power of reviewers with higher expertise in generating positive performance outcomes at the firm level. Furthermore, our study supports the significant role of reviewer expertise in enhancing future hotel financial performance, thus contributing to the extant literature on the impacts of reviewer expertise in hospitality settings.

While prior research on the relationship between online reviews and sales performance has generally assumed that the primary reason for such linkage is that online reviews provide potential consumers with information about products/services and service providers (Chevalier & Mayzlin, 2006; Ye et al., 2009), this study draws attention to an understudied aspect of reviewer expertise—the business value of increased reputation, popularity, and financial performance of hotels. We particularly highlight the complementary role of the message source (i.e., reviewers with different levels of expertise) and the message itself (i.e., review ratings and number of reviews) as drivers of hotel financial performance. We also suggest that reviewer disclosure of expertise such as membership and status contributes to the monetary gains of the hotel being reviewed. This study thus adds business-driven inquiry to the scholarly work on reviewer expertise.

Building on previous studies that support the significant impacts of reviews provided by reviewers with expertise (Z. Liu & Park, 2015; H. Xie et al., 2011; Zhao et al., 2015), this study offers empirical evidence to suggest that management responses to reviews provided by reviewers with higher levels of expertise also have a stronger effect on hotel financial performance. As such, our study suggests that managers can actually leverage the influence of reviewers with expertise by actively responding to their comments, thereby magnifying the effects on hotel performance. Empirical evidence in this regard is important: It extends the emerging research on the importance of management responses (e.g., Sparks et al., 2016; K. L. Xie et al., 2014; K. L. Xie, So, & Wang, 2017) with unique action plans to enhance the online reputation of hotels.

Practical Implications

Given the extent and salience of social information that reviewers disclose in product reviews (Forman, Ghose, & Wiesenfeld, 2008), expertise of reviewers becomes accessible to consumers who seek truly insightful information online to support their decision making. However, the importance of reviewers’ expertise to hotel businesses and understanding of what strategy managers should employ to use such unique information remain practically less known. This study provides insights into the importance of reviews from reviewers with expertise in influencing hotels’ online reputation, popularity, and performance and how hotel managers can use such information to leverage their business performance.

Our results also show that consumers are responsive to the level of expertise disclosed by reviewers, especially those with longer membership and higher badge status. Our study thus suggests that managers may be able to increase hotels’ performance by encouraging reviewers to reveal more expertise–descriptive information about themselves. Adequate incentives (loyalty points, free drink coupons, etc.) can be offered to encourage seasoned reviewers of the hotel to write reviews, no matter positive or negative. In this way, managers can better identify reviewers with expertise and may selectively focus on addressing their concerns to leverage the online social influence of these reviewers.

Our study additionally highlights the interplay between management responses and reviewer expertise. Our empirical evidence shows the business value of management responses to online reviews by seasoned reviewers. The new insight from this study is that hotel firms should respond to a selective collection of reviews by reviewers with expertise. Our empirical results show that such an approach could be an effective and efficient strategy for increasing business performance. First, we recommend that managers responding to online reviews also consider the expertise of reviewers who write the online reviews, as responding to reviewers with greater expertise enhances the impact of management responses on financial performance. In addition, we advocate that when allocating resources to manage online reviews, managers may want to give priority to reviews by reviewers with expertise. A careful screening of review profile information would help managers target influential fellow reviewers among consumers who write reviews for the business. Finally, hotels can feature well-written examples of management responses to seasoned reviewers in their marketing outlets and promote these example responses both internally and externally. Internally, the example responses can help employees learn the skills of customer engagement, and externally consumers who are exposed to the example management responses can appreciate the customer care they reflect and can follow their peer reviewers with expertise in a positive way.

Limitations and Future Research

The study has some limitations. First, our sample is restricted to five major hotel markets in Texas over a limited period of time. To increase the representativeness of the sample and the generalizability of the estimation results, additional work in other markets is needed. Future research can replicate the estimation of this study using alternative data sources and provide more empirical insights about reviewer expertise in the arena of social media research. Second, although this study focuses on the moderation effects of the expertise of reviewers, it is theoretically plausible to postulate that reviewer expertise may mediate the effects of hotel reputation and popularity on hotel performance. Future studies could explore the multidirectional effects of reviewer expertise. We believe such research would bring useful insights and perspectives to the field of online review research. Finally, we focus on the revenue of individual hotels as one of the dependent variables in this study. Because of the data unavailability, we did not incorporate other hotel performance variables such as RevPAR in estimating the effects of the expertise of reviewers. Future studies are encouraged to replicate our modelling approaches using RevPAR as the performance outcome, thus providing additional meaningful insights and validation into the effects estimated.

Footnotes

Authors’ Note:

The authors contributed equally to this research.

Karen L. Xie, PhD (e-mail: Karen.Xie@du.edu), is an assistant professor of Hospitality Management at the Daniels College of Business, University of Denver, Denver, Colorado.

Kevin Kam Fung So, PhD (e-mail: kevinso@hrsm.sc.edu), is an assistant professor in the Center of Economic Excellence in Tourism and Economic Development, School of Hotel, Restaurant and Tourism Management, University of South Carolina, Columbia.

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