Should Hotels Respond to Negative Online Reviews?

Abstract

The purpose of this study was to determine whether it is beneficial for service providers, such as hotels and restaurants, to respond to online negative reviews, and (a) whether company reputation is moderated by the number of negative versus positive reviews and (b) whether the underlying issue is attributed to controllable versus uncontrollable factors. To test the hypotheses, a 2 × 2 × 2 quasi-experimental design was utilized. Respondents were asked to imagine that they were planning a trip to New York City, were searching online for a hotel near Times Square, and were provided with several reviews. The results indicate, in general, that company reputation is adversely affected as the number of negative to positive reviews becomes greater. When service failures pertain to controllable factors, management responses can mitigate the adverse effects of negative reviews. When service failures stem from uncontrollable factors, company reputation is not adversely affected, and thus a response from management might not be necessary. A follow-up study examined whether the type of response matters. Findings revealed that an apology with assurance versus an apology with correction action is equally effective.

Keywords

online reviews service failure and recovery reputation management

In today’s digital marketplace, consumers are constantly connected to social media sites such as Twitter, Facebook, Pinterest, Instagram, and YouTube. Using smartphones and other mobile devices, customers post reviews and comments about service providers—such as hotels, resorts, and restaurants—often in “real time.” These firsthand reviews are instantly available to a multitude of people, and have the potential to significantly influence a company’s reputation and consumers’ behavior. Positive reviews and comments create trust (Sparks & Browning, 2011), and thus are highly beneficial for marketers. In the hospitality industry, for example, positive online reviews have been found to increase hotels’ bookings (Torres, Singh, & Robertson-Ring, 2015; Ye, Law, & Gu, 2009) and market share (Duverger, 2013). Negative postings, however, can damage a company’s reputation and adversely affect revenues (Sparks & Browning, 2011; Vermeulen & Seegers, 2009). Due to the rise in social media, marketers are becoming less in control of brand information, with the “balance of power” shifting to consumers (Prahalad & Ramaswamy, 2004). This phenomenon is of particular relevance to service providers, especially those in the hospitality industry whose intangible offerings are difficult to judge beforehand. To assess quality and reduce risk, many consumers utilize online reviews (Kim, Mattila, & Baloglu, 2011; Xiang & Gretzel, 2010). Multiple studies, for example, report that a large percentage of consumers use online reviews when booking hotels (Anderson, 2012; TripAdvisor, 2013; World Travel Market, 2010).

One of the most powerful brand conversations is online consumer reviews (eWOM), and not surprisingly, the number and types of review sites have exploded in recent years. Some of the more popular general review sites are Yelp, Google Plus, Local, and Foursquare. Review sites that are industry-specific include TripAdvisor and VirtualTourist for travel, Angie’s List and HomeAdvisor for home services, Wellness and Doctoroogle for doctors and dentists, DealRater and Edmunds for car dealers, and UrbanSpoon and OpenTable for restaurants. Online reviews are generally considered to be trustworthy (Sher & Lee, 2009; Sparks, Perkins, & Buckley, 2013), coming from fellow consumers with firsthand knowledge regarding a particular product or service and who are voluntarily sharing their opinions and commentary (Y. Chen, Yong, & Zhang, 2012). With an abundance of unbiased and credible reviews readily available, a “new normal” has emerged. Equipped with smartphones and other electronic devices, and easy access to a multitude of social media sites, customers can retrieve online reviews instantaneously before making most of their purchases. For hotels and other hospitality providers, online consumer reviews can be viewed as a valuable resource for managing the firm’s reputation and for continual improvement (Park & Allen, 2013). Recognizing the importance of online reviews, some companies are proactively monitoring online reviews by using free services such as Google Alert; contracting “reputation management” services such as Netvibes.com, Reputation.com, ReviewTrackers.com, and Trackur.com; or establishing in-house monitoring divisions.

Prior research has investigated various factors pertaining to online consumer reviews. Studies indicate that the impact of negative online reviews is more pronounced for services, such as hotels, when compared with tangible goods (Christodoulides, Michaelidou, & Argyriou, 2012). Jiménez and Mendoza (2013) found that online reviews are more credible for search products when detailed information is provided and when the level of reviewer agreement is high.

Negative reviews, in general, are perceived as more useful (M. Lee, Rodgers, & Kim, 2009) and have greater impact than positive reviews (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001; Sparks & Browning, 2011). Melián-González, Bulchand-Gidumal, and López-Valcárcel (2013) found that as the number of reviews of a particular hotel increased, its ratings became more positive. Other research has found that personal characteristics such as consumer confidence determine how online reviews are perceived (Lee & Ma, 2012), and that women are more likely than men to seek online reviews for risk reduction purposes (Kim et al., 2011).

A key issue for service providers is whether they should respond to negative online reviews. There is little doubt that many consumers take negative reviews into consideration when making purchase decisions. Indeed, a 2011 Online Influence Trend Tracker survey by Cone Communications found that 80% of consumers reverse their purchase decisions based on negative online reviews, and Öğüt and Taş (2012) found that online reviews affect sales in the hotel industry. Although a survey by TripAdvisor found that 57% of consumers are more likely to book a hotel that has responded to online reviews (eMarketer, 2013), Park and Allen (2013) reported that few hotels actively respond to negative reviews. A study by Market Metrix found that only 7% of negative reviews actually get a response from hotels (Bly, 2010).

To date, few studies have assessed the effectiveness of marketers’ responses to online reviews. Recently, Min, Lim, and Magnini (2015) found that including an empathy statement and paraphrasing the complaint improved the ratings of the response. However, our knowledge is limited, and thus, the purpose of this study is to further examine the effectiveness of management responses to online consumer reviews. More specifically, this study examines the effect of hotel management responses to negative online reviews on company reputation and (a) whether the outcome is moderated by the number of negative versus positive reviews, and (b) whether the underlying issue is due to controllable or uncontrollable factors (see Exhibit 1.) A second follow-up study was conducted to determine whether the type of response (an apology with assurance vs. an apology with corrective action) affects company reputation. Given the ubiquity of online consumer reviews, the results of this study should be of interest not only to hotels but also to all hospitality and service providers.

Exhibit 1:

Conceptual Model.

Conceptual Framework and Hypotheses

The conceptual framework for this study is based on research regarding attribution theory, information and persuasion, service recovery, and company reputation. Findings from previous studies provide the bases for the hypotheses.

Online reviews are useful because they help potential customers assess the reputation of a company (Anderson, 2012), and thus reduce risk (Kim et al., 2011; Litvin, Goldsmith, & Pan, 2008; Xiang & Gretzel, 2010). Walsh and Beatty (2007) defined reputation as “the customer’s overall evaluation of a firm based on his or her reactions to the firm’s goods, services, communication activities, interactions with the firm . . . and/or known corporate activities” (p. 129). Two key dimensions of reputation are capability and character (Rindova, Williamson, Petkova, & Sever, 2005). Capability encompasses quality and performance (Milgrom & Roberts, 1986). Management responses can mitigate uncertainty about a firm’s capability, thus reducing perceived risk (M. Chen, Su, & Tsai, 2007). Character is based on a company’s past behavioral tendencies and indicates how a firm will interact with others in the future (Love & Kraatz, 2009). By responding to negative reviews, a firm signals that it cares about its customers and will continue to do so in the future, thus enhancing perceptions of its character. By demonstrating both capability and good character, a firm can enhance its reputation.

Quantity and Valence of Reviews

Two factors that determine the impact of online reviews are the quantity (Chintagunta, Gopinath, & Venkataraman, 2010) and valence of the postings (Blal & Sturman, 2014; Pan & Zhang, 2011). Researchers have found that when a large number of customers have posted reviews, individuals are better able to assess the veracity of the information and are more confident in their judgments (DeMaeyer, 2012; Liu, 2006). The valence of reviews also affects consumers’ attitudes and perceptions (Sparks & Browning, 2011; Vermeulen & Seegers, 2009). Negative information, in general, is more persuasive than either neutral or positive information (Herr, Kardes, & Kim, 1991; Park & Nicolau, 2015), is more credible, and has greater impact than positive reviews (Ahluwalia, 2002). This effect arises because negative information, in general, is scarcer than positive information (Chiou & Cheng, 2003). Scarcity commands attention and motivates consumers to read negative reviews more thoroughly to resolve uncertainty. The result is that people give greater weight to negative information when making judgments (Maheswaran & Meyers-Levy, 1990). The negativity effect becomes greater when the level of reviewer agreement is high (Jiménez & Mendoza, 2013).

Consistent with previous research, Sparks and Browning (2011) found that consumers who were searching for a hotel were heavily influenced by negative information. Vermeulen and Seegers (2009), however, noted that a single negative review does little harm to a company’s reputation. The latter finding is interesting and deserves greater attention. Additional research is needed to address this issue and assess boundary conditions. Accordingly, it is hypothesized as follows:

Hypothesis 1 (H1): Company reputation will become less favorable as the number of negative reviews—relative to the number of positive reviews—increases.

Attributions

Whether a negative review is harmful to a company’s reputation depends on the cause and conditions to which it is attributed (Weiner, 2000). Attributions are the causal explanations that individuals use to interpret the environment around them, especially when reacting to important, novel, unexpected, and negative events (Martinko, Harvey, & Douglas, 2007). When determining the cause of a particular event, individuals take into consideration locus of control (internal vs. external), stability (will the problem continue to occur), and controllability (could the problem have been prevented). Individuals also consider the consensus, consistency, and distinctiveness of the behavior or event (Kelley, 1973). Negative reviews from multiple individuals indicate consensus, whereas multiple reviews regarding the same issue indicate consistency. In both situations, doubt is cast on a firm’s capabilities or its character. Negative reviews that pertain to routine events (e.g., such as check-ins and hotel cleanliness) are more damaging, whereas reviews that pertain to unusual circumstances (e.g., such as a weather-related power outage) are distinctive and thus are more likely to be discounted.

In the case of service failures, a key issue is whether or not the company could have prevented the failure (Folkes, 1984). Failure to prevent controllable service-related issues is perceived as a sign of apathy or incompetence (Poon, Hui, & Au, 2004) and results in negative consumer reactions (Choi & Mattila, 2008). The adverse impact of service failures on customer satisfaction and company reputation, however, is mitigated in situations of partial self-blame or ambiguity over who is responsible (Yen, Gwinner, & Su, 2004). When consumers attribute the blame solely to the company, its reputation is likely to be tarnished. However, in situations that are beyond the control of the firm, consumers are less likely to attribute blame for the service failure to the company (Folkes, 1984). For example, a negative review about the noise from an adjacent construction site would not be seen by consumers as a problem that could have been prevented by the hotel. In such a case, negative reviews are likely to be discounted and hence have little or no effect on company reputation. Based on the preceding discussion regarding causal attributions, the following hypothesis is presented:

Hypothesis 2 (H2): Company reputation will be less favorable when negative online reviews are attributed to controllable factors as compared with non-controllable factors.

The Effect of Management Response on Company Reputation

As previously stated, online reviews help consumers assess a company’s reputation by reducing uncertainty about its character, capability, and quality of its products or services (Love & Kraatz, 2009). A company with a favorable reputation has a competitive advantage over its competitors, as communications from firms with a good reputation are received more positively by potential customers (Goldberg & Hartwick, 1990; Yoon, Guffey, & Kijewski, 1993). Customers value associations with a reputable company and oftentimes are willing to pay a premium for its products or services (Shapiro, 1983). Reputations are enduring (Alsop, 2004), and unfavorable reputations are difficult to overcome (Flatt & Kowalczyk, 2011). Research shows that a favorable reputation can mitigate damage when implementing recovery strategies (Dowling, 2004). Moreover, the literature on service recovery (Hart, Heskett, & Sasser, 1990; Mattila, 2004) clearly indicates that companies that respond effectively to customer complaints benefit from increased customer loyalty and greater profitability (Blodgett & Anderson, 2000; Öğüt & Taş, 2012; Tax, Brown, & Chandrashekaran, 1998), and that interactional justice (i.e., the manner in which a company responds to customer complaints) is more important than the level of compensation (Blodgett, Hill, & Tax, 1997; Collie, Bradley, & Sparks, 2002). Because company reputation can be a valuable asset (Walker, 2010), hotels and other service providers should be motivated to respond to online reviews. Consumers form expectations based on their own past experiences, word-of-mouth communications, and a firm’s external marketing communication efforts (Walsh & Beatty, 2007; Zeithaml, Berry, & Parasuraman, 1993). When service failures occur, consumers expect a company to take appropriate actions. Responding to negative reviews can therefore be viewed as an investment that benefits a company in the long run. The benefit is twofold in that it helps to ensure repeat patronage of the customer who posted the review, and it reassures potential customers that these failures will not be repeated. By responding, the company signals that it cares about its customers, and thus projects a trustworthy image. Potential customers may then be more willing to engage in business with the company in the future (Min et al., 2015). Companies that do not respond to negative reviews, however, risk letting consumers draw their own conclusion about the firm. When this is the case, consumers might simply assume that the company is incompetent or does not care about its customers. Based on the aforementioned reasoning, our third hypothesis is as follows:

Hypothesis 3 (H3): Company reputation will be more favorable when management responds to negative online consumer reviews as compared with not responding.

Interactions

To more thoroughly examine the impact of management responses on company reputation, this study will also test for all possible two-way interactions. Based on previous research indicating that a single negative review oftentimes does little harm to a company’s reputation (Vermeulen & Seegers, 2009), we hypothesize the following:

Hypothesis 4 (H4): Management responses will have a greater impact on company reputation as the number of negative reviews—relative to the number of positive reviews—increases.

Given that studies indicate that negative reviews attributed to uncontrollable factors are unlikely to affect company reputation, that problems attributed to controllable factors can be damaging (Choi & Mattila, 2008), and that consumers are more confident in their judgments when a large number of customers have posted reviews (Liu, 2006), we hypothesize the following:

Hypothesis 5 (H5): Controllability will have a greater impact on company reputation as the proportion of negative reviews that are attributed to controllable factors increases.

Hypothesis 6 (H6): Management responses will have a greater impact on company reputation when negative online reviews are attributed to controllable factors.

Method

Research Design

To test the hypotheses, this study utilized an experimental design. To manipulate the independent variables, respondents were asked to imagine that they were planning a trip to New York City and were searching online for a hotel near Times Square. They were then presented with information about a relatively small boutique hotel in midtown Manhattan (see Appendix A) and provided with reviews posted on an online travel site. After reviewing the information from the hotel’s website and the online reviews, some of which included responses from the hotel’s management, respondents were asked to complete a questionnaire (see Appendix B for examples of reviews and management responses).

To ensure realism, the reviews were adapted from TripAdvisor (based on hotels in New York City) and were modified slightly to ensure that the valence of reviews, attribution of blame (i.e., controllability), and management responses were properly manipulated. The negative reviews pertained to some of the most common complaints of hotel guests (i.e., front desk staff, check-in, noise, cleanliness, bathroom, room size, Internet, parking, billing, restaurant) as identified by Levy, Duan, and Boo (2013). To lessen the possibility of confounds, fictitious usernames were used and little other information was provided, as the presence of personal identifying information has been found to affect the perceived credibility of online reviews (Xiea, Miaob, Kuoc, & Leec, 2011).

Respondents were randomly assigned to one of eight scenarios in a 2 (number of negative to positive reviews) × 2 (controllable vs. non-controllable issues) × 2 (management response vs. no response) between-subjects, full-factorial design. In all cases, subjects were presented with five reviews: Half of the subject pool was provided with one negative and four positive reviews, whereas the other half was presented with two negative and three positive reviews. To have a baseline for comparison purposes, a control group of respondents was presented with all positive reviews. In half of the eight scenarios, negative reviews pertained to factors that were not under the control of management (small rooms, poor views), and in the other scenarios, negative reviews stemmed from issues over which management had control (e.g., poor service, wireless Internet did not work). Finally, in half of the scenarios, management posted responses to the negative reviews, whereas in the other scenarios, management did not respond. All responses from management included an apology and assurance that management will do all it can to make the customer’s next visit more satisfying (those that pertained to non-controllable factors also tactfully pointed out that fact).

Dependent Variable

The dependent variable, company reputation, is an attitudinal type of measure, reflecting subjects’ overall evaluation of the hotel (Walsh & Beatty, 2007). It was measured with eight items (e.g., this hotel cares about its guests, the staff at this hotel is top notch, I would enjoy staying at this hotel, I would recommend this hotel), using a 1 to 7 Likert-type scale. The scale exhibited high reliability, with a Cronbach’s alpha of .89.

Subjects

The sample included both traditional (full-time) and nontraditional (part-time) students at several universities in the United States. A total of 255 usable responses were collected. The sample was evenly split between males and females with 55% between the ages of 18 and 23, 25% between ages 24 and 29, and approximately 20% who were 30 years of age or older. Given that Gretzel and Yoo (2008) found that younger consumers (18-25) are significantly more likely to use online reviews to plan a trip, and that millennials (those aged 18 to 34) are now the largest age group in the United States (Fry, 2015), the sample is appropriate for this study. Approximately 45% of the respondents were Caucasian, 22% were African American, 11% were Asian American, and 6% were Hispanic. Approximately 75% reported more than 1 year of full-time work experience. Seventy-seven percent of the respondents had booked a hotel online 1 to 3 times within the past 2 years, and, of those, more than 50% had done so more than once. Furthermore, 91% had made an online purchase within the past year. Overall, the sample consists of a fairly broad set of individuals who frequently book hotels and make other purchases online.

Results

Manipulation and Confound Checks

To assess the reliability and validity of the independent variables, manipulation and confound checks were first conducted (Perdue & Summers, 1986). To do so, a control group (in which all five reviews were positive) was employed to establish a baseline for company reputation. A one-way ANOVA revealed that company reputation in both “number of negative to positive reviews” conditions was significantly different from that of the control group. As expected, company reputation in the control group (M = 5.93) was more favorable than in the “1 negative/4 positive” (M = 5.68) and “2 negative/3 positive” conditions (M = 5.39). To ensure that controllability was properly manipulated, subjects were also asked whether the hotel had any control over the size of each room, the view from each room, and customer service. Subjects indicated that the hotel had little control over the size of the room (M = 2.85) or the view from the room (M = 2.19), but it had much control over customer service (M = 6.31). To rule out other confounds, subjects were also asked to rate the quality of the reviews. Findings revealed that the perceived quality of the online reviews was not affected by hotel management responses or whether the underlying issue giving rise to the negative review was controllable (vs. not controllable). Together, these findings provide confidence as to the reliability and validity of subsequent findings.

Hypotheses Testing

The hypotheses were tested via a 2 × 2 × 2 full-factorial ANOVA. Cell sizes ranged from 31 to 35. To test H1, H2, and H3, the main effects of negative/positive reviews, controllability, and management responses were examined. To test H4, H5, and H6, the appropriate interactions were examined (see Exhibit 2 for mean values of company reputation across each of the various conditions, and Exhibit 3 for statistical results).

Exhibit 2:

Study 1: Cell Means for Company Reputation.

Negative/Positive Reviews × Controllability		No Response	Management Response
2 negative/3 positive reviews	Controllable	4.83 (n = 31)	5.22 (n = 31)
2 negative/3 positive reviews	Not controllable	5.77 (n = 31)	5.72 (n = 34)
1 negative/4 positive reviews	Controllable	5.08 (n = 31)	5.82 (n = 34)
1 negative/4 positive reviews	Not controllable	6.02 (n = 31)	5.80 (n = 32)

Exhibit 3:

Study 1: ANOVA.

Source	SS	df	MS	F	p	η_p²
Corrected model	39.70^a	7	5.67	8.04	.000	.186
Intercept	7,789.38	1	7,789.38	11,040.82	.000	.978
Proportion of negative/positive reviews	5.45	1	5.45	7.72	.006	.030
Controllability attributions	21.98	1	21.98	31.15	.000	.112
Management response	2.95	1	2.96	4.18	.042	.017
Management Response × Negative/Positive Reviews	0.12	1	0.12	0.16	.686	.006
Negative/Positive Reviews × Controllability	1.07	1	1.07	1.51	.220	.001
Controllability × Management Response	7.70	1	7.70	10.91	.001	.042
Control × Response × Negative/Positive	1.07	1	1.07	1.51	.220	.006
Error	174.26	247	0.71	—	—	—
Total	8033.95	255	—	—	—	—
Corrected total	213.96	254	—	—	—	—

Note. ANOVA = analysis of variance; SS = sum of squares; MS = mean square

R² = .186 (Adjusted R² = .162).

As hypothesized in H1, as the proportion of negative to positive reviews increased (i.e., from one out of five, to two out of five), company reputation became less favorable (M = 5.68 vs. 5.39), F(1, 247) = 7.72, p = .006; η_p² = .03, small effect. H2 was also supported, as company reputation was less favorable when negative reviews pertained to controllable factors (M = 5.25 when controllable vs. 5.82 when not controllable), F(1, 247) = 31.15, p = .000; η_p² = .11, medium effect. Contrary to Vermeulen and Seegers (2009), this finding indicates that a single negative review (i.e., that is attributed to a controllable factor) can indeed be harmful. As hypothesized in H3, the effect of management response was significant—company reputation was more favorable when management responded to negative reviews (M = 5.65 with response vs. 5.42 with no response), F(1, 247) = 4.18, p = .042; η_p² = .017, small effect.

Contrary to H4, the interaction of management responses and the proportion of negative to positive reviews was not significant, F(1, 247) = 0.16, p = .686. Although it was hypothesized that management responses would exert a greater impact on company reputation as the proportion of negative to positive online reviews increased, the beneficial effect of responding to negative reviews (i.e., as compared with no response) was fairly consistent across the 1 negative/4 positive (M = 5.81 vs. 5.55) and 2 negative/3 positive conditions (M = 5.48 vs. 5.30). The data also failed to support H5. Although the adverse impact of controllability attributions on company reputation appeared to be marginally greater in the 2 negative/3 positive condition (M = 5.03 when the problem was controllable vs. 5.74 when not controllable) as compared with the 1 negative/4 positive condition (M = 5.47 vs. 5.90), the interaction was not statistically significant, F(1, 247) = 1.51, p = .220. H6_, however, was supported as the interaction of controllability attributions and management response was statistically significant, F(1, 247) = 10.91, p = .001; η_p² = .04, small effect. When problems were perceived to be controllable, management responses to online reviews resulted in more favorable perceptions of the firm (M = 5.53 when management responded vs. 4.96 with no response), F(1, 125) = 14.19, p = .000. When problems were attributed to non-controllable factors, management responses had no discernable impact (M = 5.76 vs. 5.89), F(1, 126) = .808, p = .371, as company reputation was unaffected by negative online reviews. A one-way ANOVA revealed no significant difference in company reputation between the latter two “non-controllable” negative review(s) conditions and the control group, in which all reviews were positive, F(2, 157) = 0.442, p = .643. This latter finding is good news for hotels, resorts, and other hospitality and service providers.

Follow-Up Study

Given that the main effect of management response on company reputation was significant, a follow-up study was conducted to determine whether the type or content of the response matters. Additional data were collected and combined the relevant data from Study 1 so that the effect of an apology with assurance of future satisfaction (i.e., the type of response utilized in the original study) could be compared with that of an apology with notification of corrective action. According to Coombs (1998), an organization that engages in corrective action signals to consumers that it not only accepts responsibility for the current problem but is also committed to preventing future problems. B. K. Lee (2005) provided evidence that this type of response might be more effective in restoring company reputation than a simple apology. Hence, we hypothesize the following:

Hypothesis 7 (H7): The type of management response (an apology with assurance of future satisfaction versus an apology with corrective action) will have an effect on company reputation.

Using the same set of customer reviews, four additional scenarios were developed in which management—in addition to apologizing—also indicated the type of corrective action it had taken to remedy the problem (see Examples 4 and 5 in Appendix B). To obtain sufficient data, an additional 133 usable surveys were collected using the same collection method and drawing from the respondent pool described in the original study. A 2 × 2 × 2 (Controllability × Number of Negative to Positive Reviews × Type of Management Response) full-factorial ANOVA was used to test the hypotheses.

As in Study 1, as the proportion of negative to positive reviews increased (i.e., from one to two out of five), company reputation became less favorable (M = 5.83 vs. 5.38) F(1, 256) = 17.78, p = .000; η_p² = .065, medium effect. Similarly, company reputation was less favorable when negative reviews pertained to controllable (vs. uncontrollable) factors (M = 5.44 vs. 5.76), F(1, 246) = 8.11, p = .005; η_p² = .031, small effect. Combined with Study 1, these findings clearly demonstrate the adverse impact of negative online reviews particularly those that pertain to controllable factors.

Given that Study 1 demonstrated that company reputation is enhanced when management responds to negative reviews, the main purpose of Study 2 was to determine whether the type of management response matters. Contrary to H7, the main effect of type of management response on company reputation was not significant, F(1, 246) = 0.42, p = .517. The findings revealed no significant difference between an apology with assurance of future satisfaction, versus an apology with corrective action (= 5.65 vs. 5.57), F(1, 246) = 0.42, p = .517. Similarly, the interaction of management response type and the proportion of negative to positive reviews was not significant, F(1, 256) = 1.36, p = .245, nor was the interaction of management response type and controllability, F(1, 256) = 0.44, p = .506. Overall, it appears that an apology with assurance of future satisfaction and an apology with notification of corrective action are equally effective (see Exhibit 4 for cell means and Exhibit 5 for statistical results).

Exhibit 4:

Study 2: Cell Means for Company Reputation.

Negative/Positive Reviews × Controllability		Apology and Corrective Action	Apology and Assurance of Satisfaction
2 negative/3 positive reviews	Controllable	4.89 (n = 33)	5.22 (n = 31)
2 negative/3 positive reviews	Not controllable	5.65 (n = 35)	5.72 (n = 34)
1 negative/4 positive reviews	Controllable	5.86 (n = 31)	5.82 (n = 34)
1 negative/4 positive reviews	Not controllable	5.87 (n = 34)	5.80 (n = 32)

Exhibit 5:

Study 2: ANOVA.

Source	SS	df	MS	F	p	η_p²
Corrected model	28.99^a	7	4.14	5.21	.000	.125
Intercept	8,273.11	1	8,273.11	10,399.02	.000	.976
Proportion of negative/positive reviews	14.15	1	14.15	17.80	.000	.065
Controllability attributions	6.45	1	6.45	8.11	.005	.031
Type of management response	0.34	1	0.34	0.42	.517	.002
Type of Response × Negative/Positive Reviews	1.08	1	1.08	1.36	.245	.032
Negative/Positive Reviews × Controllability	6.67	1	6.67	8.38	.004	.005
Controllability × Type of Response	0.35	1	0.35	0.44	.506	.002
Control × Negative/Positive × Response Type	0.23	1	0.23	0.29	.591	.001
Error	203.66	256	0.80	—	—	—
Total	8,529.62	264	—	—	—	—
Corrected total	232.66	263	—	—	—	—

Note. ANOVA = analysis of variance; SS = sum of squares; MS = mean square; —denotes empty cell.

R² = .125 (Adjusted R² = .101).

It should be noted that in Study 2, the interaction of controllability and the proportion of negative to positive reviews was significant, F(1, 256) = 8.38, p = .004; η_p² = .032, small effect. In Study 2, the adverse impact of controllability on company reputation in the 2 negative/3 positive condition (M = 5.05 when controllable vs. 5.69 when not controllable) F(1, 131) = 15.32, p = .000, was greater as compared with the 1 negative/4 positive condition (M = 5.83 vs. 5.84) F(1, 129) = .000, p = .983. This finding indicates that management responses to negative online reviews are effective only up to a point (i.e., as the number of reviews that pertain to controllable factors increase, management responses do not fully counterbalance the negative information).

Discussion

The purpose of this study was to determine whether it is beneficial for management to respond to negative online reviews. The results indicate that when service failures pertain to controllable factors, management responses can mitigate the adverse effects of negative reviews and have a favorable impact on company reputation. In these situations, responding to negative reviews—with an apology and assurance of future satisfaction or with an apology and notification of correction action—shows that the company is conscientious and cares about its customers, thus exhibiting good character (Rindova et al., 2005). This finding is consistent with Min et al. (2015), who found that empathetic responses resulted in more favorable hotel ratings, which complements research that indicates that reputation management benefits companies in the long term through increased trust, cooperation, and reciprocity from consumers (Tennie, Frith, & Frith, 2010). It should be noted, however, that management responses do not fully compensate for the harmful effects of negative reviews that pertain to controllable factors. In both Study 1 and Study 2, company reputation became less favorable as the number of negative reviews pertaining to controllable factors increased, although management responded to each negative review. The good news, though, is that even as the number of negative reviews pertaining to controllable factors increased, company reputation was more favorable when management responded to the reviews (as compared with when it did not respond). This latter finding indicates that management responses serve—if not fully, at least partially—to counter-balance the negativity effect (Ahluwalia, 2002), and that it is in a firm’s best interests to respond to negative online reviews, especially those that are due to controllable factors.

Another key finding is that when negative reviews pertain to issues that are not under management’s control, company reputation is not harmed. Even as the proportion of negative reviews stemming from uncontrollable factors increased from “1 negative/4 positive” to “2 negative/3 positive,” company reputation was unaffected. This finding is consistent with attribution theory, and indicates that when the company is not to blame, negative reviews are discounted (Hess, Ganesan, & Klein, 2003; Poon et al., 2004). In these situations, individuals do not seem to hold the company responsible. Given that company reputation is not adversely affected when negative reviews pertain to uncontrollable factors, one might conclude that it is not necessary for management to respond. As a practical matter, though, it might be wise to acknowledge and respond to these reviews, further demonstrating empathy and good character. Doing so could be especially helpful in the case of “high risk-averse” travelers who find negative reviews more useful than positive reviews (Casaló, Flavián, Guinalíu, & Ekinci, 2015), and high-involvement travelers who process information via a central route of persuasion (Filieri & McLeay, 2014).

The follow-up study indicated that an apology with assurance of future satisfaction and an apology with notification of corrective action are equally effective. However, further research is warranted to identify the optimal form of management response. Overall, these findings can help hotels, resorts, and other service providers take a more strategic approach in responding to and utilizing online reviews (Park & Allen, 2013). Responding to negative online reviews in an ongoing and purposeful manner can enhance a hotel’s reputation and increase bookings as many consumers use these when making travel decisions. These findings are particularly important for upper-tier hotels whose sales are heavily influenced by highly positive ratings, as well as for hotels in the mid- and lower-tier segments, in which sales are affected more by the number of online reviews (Blal & Sturman, 2014). By responding to negative reviews, hotels and resorts are likely to increase the number of reviews, which in turn leads to more positive ratings (Melián-González et al., 2013) and higher booking transaction values (Torres et al., 2015).

Limitations and Future Research

Although this study found that it is beneficial for management to respond to negative online reviews, a greater variety of conditions is necessary to flesh out boundary conditions. Future studies, for example, could include a greater number of reviews (both positive and negative) to determine whether the adverse impact of negative reviews on company reputation is linear or curvilinear. Some studies indicate that the effects of positive and negative reviews are curvilinear (Duverger, 2013) or asymmetric (Park & Nicolau, 2015). Additional contexts are also called for as this study was based on a single hotel. Future studies should include a greater cross-section of hotel properties along with other types of service providers. The severity of the problem could also be varied. In the current study, the underlying problems might be considered by some individuals to be only slightly to moderately severe. Future studies might explore situations in which the consumer encountered a more extreme situation or suffered a more substantial loss. And, although the respondent pool was fairly diverse, it would be beneficial to include a greater number of middle-aged adults who did not grow up with the Internet and who perhaps are not as connected to social media as the younger population of this study. Finally, some online review sites do not allow companies to respond to consumer postings, thus limiting the applicability of these findings.

To better disentangle the effects of the independent variables, future studies might include reviews for multiple hotels and ask respondents to indicate which one they would choose. Research might also explore different types of management responses. Although this study found no difference between an “apology with assurance of future satisfaction” and an “apology with corrective action,” it is still plausible that some types of responses are more effective than others. It is also possible that some types of responses are ineffective and perhaps even counterproductive. Another issue is whether management should tactfully point out when the customer is to blame. On the surface, doing so might seem counterproductive; however, perhaps there are situations when such a response is warranted (e.g., a booking mistake is actually the fault of the customer; or in the case of a “deviant” customer). It might also be interesting to examine whether the effectiveness of different responses is moderated by gender. Previous research demonstrates that women play a dominant role in family decision making (Mottiar & Quinn, 2004), and that women rely to a greater extent than men on online reviews to reduce risk when shopping for hotels (Kim et al., 2011); hence, it is important to be particularly responsive to this segment.

Summary

In conclusion, online reviews are pervasive and highly influential in today’s social media age. Many consumers will not make a purchase without first seeking out online reviews. Service providers such as hotels should not ignore negative customer reviews but instead should manage their reputations by effectively responding to negative reviews. Companies today should view such activities as an investment for future patronage. However, not all review sites allow marketers to respond to consumer reviews. Nonetheless, this study demonstrates that hotels and other service providers can benefit by monitoring online reviews and responding if so allowed, especially if the review pertains to an issue that can be controlled by management.

Footnotes

Appendix A

Appendix B

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, or publication of this article.

Funding

The authors received no financial support for the research, authorship, or publication of this article.

Author Biographies

Mei Rose (PhD, University of Mississippi) is Assistant Professor of Marketing in the College of Business and Public Policy at University of Alaska - Anchorage. Her previous research has focused on the effects of interface design on children’s processing of websites.

Jeff G. Blodgett (PhD, Indiana University) is Professor of Marketing at Univesity of Houston-Victoria. His research interests include consumer complaint behavior and the effects of justice on recovery outcomes.

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