Response Strategy

Abstract

Online reviews have transformed consumer behavior in information searching and sharing. Their growing popularity has enabled new differentiation strategies for lodging operators. More subtly, online review systems have forced hotel managers to compete through the effective use of information systems that they have not created or purchased. Therefore, managers in the tourism industry must adapt to the widespread use of external systems, incorporate them in their strategy and evaluate their effects.

This study focuses on the impact of managements’ quantity and quality of usage of online review systems. Our findings show that managerial response quantity positively impacts hotels’ competitive performance. Moreover, responses have a stronger positive impact when they address extreme reviews. We evaluate four response strategies and find significant performance differences among them. Our finding demonstrates the importance of proficiency in external information systems use because per- formance differs by “how” the system is used e not only “how much.”

Introduction

Travel is one of the most expensive items purchased regularly by households, and it represents a significant proportion of in- dividual’s annual budget. Travel budgets also represent a significant expense for many corporations. Tourism and travel products (e.g., hotel rooms) are experience goods (Nelson, 1970) where customers must purchase and utilize the service to ascertain its quality. That is, unlike search goods which the customers have an opportunity to evaluate before purchasing, hotel accommodation has always been impossible to “test” a priori. Experience goods are therefore char- acterized by a disproportionate importance of reputation which is used as a proxy for gauging quality prior to consumption (Nelson, 1970). The importance of reputation, as conveyed for example by brands, is widely recognized in the hotel industry and it has been one of the historic drivers of industry consolidation (Prasad & Dev, 2000).

While the nature of travel services as experience goods has not changed, the continuing evolution of Information Technology (IT), and the widespread adoption of the Internet, contributed to vir- tualizing the information gathering process (Overby, 2008). For example, the Internet has enabled the virtualization of tourism information search with dramatic changes in consumers’ behavior (Buhalis & Law, 2008) and the strategic balance of power between operators and intermediaries. The success of alternative accommodation arrangements, such as AirBnB, is arguably enabled by the digitalization of trust and reputation enabled by the IT. Social media and opinion platforms today are mainstream communica- tions media in the tourism industry (Schmidt, Cantallops, & dos Santos, 2008). Having virtualized the information search process, IT shifted the source of hotel information from traditional in- termediaries and operators, to two categories of internet-based entities: (1) online travel agencies (e.g., Expedia) and, (2) online review specialists (e.g., TripAdvisor, Oyster). Online reviews are evaluative statements, written by actual or potential customers, available to end user and institutions via the Internet (Stauss, 2000). They represent a critical information resource enabling prospective hotel guests to leverage the experience of other trav- elers in their selection process (Levy, Duan & Boo, 2013).

Recent industry data indicates that about 53% of travelers would not make a reservation until they read hotel online reviews and 77% of prospective guests report reading reviews before they choose a hotel either “always” or “usually” (TripAdvisor, 2014). The academic literature shows that positive online hotel reviews enhance cus- tomers’ trust  in  the  hotel  (Sparks  & Browning, 2011)  resulting  in improved financial performance (O€ g˘üt & Onur Tas, 2012).

One critical, yet understudied, characteristic of modern online review systems is the ability of operators to respond to guest re- views. This feature of online review platforms enables managers to resolve customers’ complaints. More importantly, it allows hotel operators to join the conversation and engage potential customers in a long-term discussion about their products or services by responding publicly to online comments (Park & Allen, 2013). Thus, online review systems are socio-technical artefacts (Silver & Markus, 2013) that mediate the interaction between the firm and its customers. The ability to respond affords the hotel managers an opportunity to further enhance the hotel performance by better utilizing this online communication channel. Yet, despite the po- tential value offered by response features, there is little research focusing on response management strategies in the hotel industry (Abramova, Shavanova, Fuhrer, Krasnova, & Buxmann, 2015). In this paper, we focus on the question of how profit-maximizing hotel operators should respond to online reviews.

Practicing managers have long intuited that partaking in the conversation is important. An early industry study by TripAdvisor shows that responding to online reviews improves customers’ likelihood of recommending a hotel by more than 20% (Barsky & Frame, 2009). However, the number and the  context  of  a response may increase or decrease hotels’ performance thus, hotel managers need a well-defined strategy on when and how to respond. Surprisingly, there is a lack of research that rigorously and empirically classifies and evaluates response management strate- gies (Liu, Schuckert, & Law, 2015). We pursue the question by conceptualizing online reviews platforms as socio-technical arte- facts that virtualize the communication process between the cus- tomers and the hotels. We categorize and analyze firms’ online review response strategies in terms of the quantity and quality of online review system use. Our results extend previous work on the effect of review valence and review quantity on hotel performance. More importantly, ours is the first study to measure the competitive performance effect of managerial responses to online reviews. Our contribution is in the empirical demonstration that hotels benefit from both the quantity and quality of online review systems use. Specifically, we find that those hotels that embrace externally developed online review systems to respond to customer com- ments perform better than their competitors, and this effect is stronger when the hotel uses the system to address negative comments. Finally, we also demonstrate the competitive effect of four different classes of response strategies and their implications for hotel managers.

The paper is organized as follows. In the next section, we introduce our theoretical framework and discuss previous litera- ture on online reviews and management responsiveness. We then introduce the context, methods and data used in our work. We conclude by reporting and discussing our findings.

Theoretical framework

Online reviews

 The context of this study is the lodging industry, in which travelers make decisions based on their own past experience with the hotel or the brand. Increasingly, over the last decade, travelers leverage online reviews and experiences shared by other travelers over the Internet. The literature shows that online consumer re- views have a significant influence on travel information search and product sales (Duan, Gu, & Whinston, 2008; Mauri & Minazzi, 2013; Xiang & Gretzel, 2010). For example, online hotel reviews increase customers’ awareness of the hotels and enhance their consideration in the customers’ mind (Vermeulen & Seegers, 2009). Further, positive online hotel reviews can enhance customers’ trust (Sparks & Browning, 2011) and, as a consequence, increase the hotel’s financial performance (O€ g˘üt & Onur Tas, 2012). High review scores convey: both product quality and social validation (Cialdini, 2000). The literature has reached consensus on the finding that higher review scores positively affect demand for hotel and, consequently, increase sales and revenue (e.g., Chevalier & Mayzlin, 2006; Phillips, Barnes, Zigan, & Schegg, 2017; Sparks & Browning, 2011), while negative reviews are known to impact customers’ at- titudes negatively (e.g. Lee, Park, & Han, 2008). As customers become more discerning, they use online reviews to better specify their service requirements and uncover the best value propositions in the market. As a result, it is common for people to read com- ments about other’s experiences to reduce uncertainly before they make a purchase (Archak, Ghose, & Ipeirotis, 2011; Zheng, Agarwal, & Lucas, 2011). Prior research has also established that the total number of reviews a product or service receives leads to higher sales and improved brand reputation (e.g., Amblee & Bui, 2011). While not the focus of our work, we seek to establish that the same relationships hold in our context. Thus, we hypothesize:

H1a. Cumulative review scores are positively related to the firm’s competitive performance.

H1b.   The total number of the online reviews positively impacts the firm’s competitive performance.

Firm responsiveness

 An online review system is an IT-enabled customer service system (Lui & Piccoli, 2016) that, because of the reach capability of information technology (Overby, 2008), has the characteristics of a broadcast communication medium. The firm can utilize such a communication channel to collect intelligence and to respond to consumers’ comments. Managerial response is one of the func- tionalities of the online review systems used for the support of customer relationship, reputation and brand management (van Noort & Willemsen, 2001; Baka 2016). We define managerial response as an answer posted on behalf of a tourism operator by its employees, addressing a specific review contributed by a guest. Traditionally, customers interact with a few frontline employees during the service encounter, and typically develop an overall im- age of the emotions that members of a given organization will display (Sutton & Rafaeli, 1988). Given that managerial response is publicly available online and will be viewed by potential customers, readers of online reviews can now form a similar perception of the firm’s customer orientation strategies without physically interact- ing with employees. They do so by reading management responses rather than interacting first hand with the staff. In other words, while hotel services remain largely an experience product, pro- spective guests can vicariously “test it” by reading other guest comments and managerial responses. A positive link exists be- tween a service-oriented business strategy and company perfor- mance. For example, managers respond to negative reviews, in some situations, to reassure customers that the experience described in the negative reviews is unlikely to be repeated (Chevalier, Dover, & Mayzlin, 2016). Another notable study shows that managerial response to negative reviews is more profitable for hotels than answering to positive reviews (Anderson & Han, 2016). In the absence of much academic literature there is some evidence of the importance of managerial responses from consulting firms. For example, analyzing a survey with 12,225 global consumers, PhoCusWright (2013) reported that over half of the respondents are more likely to book a hotel that displays managerial response compared to a hotel that does not. However, these works do not explain how or why managerial responses produce their effects or how hotel operators can maximize their positive influence on performance.

Early academic research in this area suggests that managerial responses positively impact subsequent review rating and review volume, especially in the case of unsatisfied customers (Gu & Ye, 2014). More importantly, archival research using TripAdvisor data shows that providing timely and lengthy responses to reviews enhances the hotels’ future financial performance (Xie, So, & Wang, 2017) but there is an inverted U relationship between response percentage and revenue (Anderson & Han, 2016). From this work, it follows that hotels should devise explicit strategies for managerial response to customer reviews.

We adopt the concept of usage quality from the information systems literature. Quality of system usage (i.e., effective use of the system) is crucial to obtain maximum benefits from information systems implementations (Burton-Jones & Grange, 2012). Conversely, ineffective usage leads to resources waste and a decrease chance of reaching the objectives associated with the system’s introduction (Bevan, 1995). We thus theorize that, at the firm-level, managerial responses to online reviews reflect the firm’s underlying capability in using online review systems. In other words, the emergence of social media and user generated content has forced hotel operators to develop the ability to manage the hotel’s reputation online, engage customers, address customers’ concerns, and restore customer satisfaction (Xie et al., 2016). The hotels that are able to develop such capabilities, send a credible signal to potential guests, that the management team is reading and responding to the suggestions and comments of their cus- tomers. It is such a signal that stimulates future reviewing activities and fosters communications between the customers and the hotels (Chevalier et al., 2016; Wang & Chaudhry, 2017). In summary, managerial responses are the manifestation of the operator’s capability to utilize an online review system to implement their service-oriented business strategy.

Quantity of responses

Previous research has established a direct link between systems usage and firm performance (Devaraj & Kohli, 2003). With the emergence of online review systems and their opening of a managerial response channel, firms have the opportunity to use the system to contribute new information about their product or ser- vice. Customers perceive managerial responses as an indicator of the fact that the firm cares about customer service (Lee & Hu, 2005). Thus, the presence of a managerial response conveys an important message of the firm’s customer-orientation strategy and is correlated with greater sales and improved satisfaction of com- plaining customers (Gu & Ye, 2014). Those organizations that recognized the nature of online review systems as broadcast channels and their role in customer decision-making devote organizational resources to its use. As a consequence, managerial response correlates with increased hotel ratings and review volume on TripAdvisor (Xie et al., 2016). We propose that there is a direct link between online review systems use and competitive financial performance.¹

H2. The cumulative percentage of managerial response to online reviews is positively related to the firm’s competitive performance

Quality of responses

While recent academic research has begun to investigate the relationship between managerial response and hotel financial performance, no work to date has investigated the relative effect of different response strategies. In other words, while managerial response in online review systems is a type of digital firm compe- tence, there is no work to date mapping the impact of this com- petency on hotels’ competitive performance. We argue that different response strategies are indicative of different degrees of competence by the hotel in adapting to the emergence of online review systems. Thus, they result in different competitive perfor- mance outcomes. Information systems scholars have empirically investigated the link between quantity of system usage and firm performance. Conversely, the role of quality of system usage has proven elusive (Sabherwal & Jeyaraj, 2015). Burton-Jones and Grange (2012) defined effective use as “using a system in a way that helps attain the goals for using the system” (p.2). This char- acterization provides a general definition, which can be applied to any context and level of analysis. However, it lacks specificity. In the context of online review systems, the quality of use relies on the firms’ capability to utilize the information in the system effectively and produce responses to help attract more customers. Effective information use is defined as “the extent to which information provided by the organization’s information systems is successfully utilized to enable and support its business strategies and value- chain activities” (Kettinger, Zhang, & Chang, 2013, p. 846). Build- ing on these definitions, we describe the quality of review system usage as the extent to which the firm employs the online review system to enable its customer orientation strategy. Quality of usage stems from the firms’ ability to optimize its resource allocation to the managerial response activity.

The online review literature has demonstrated the disproportionate impact that negative reviews have on user decision-making. Specifically, there is an inverse relationship between review rating and review diagnosticity, with negative reviews perceived as significantly more helpful by readers (Archak et al., 2011). More- over, negative reviews have a greater effect on customers due to the “negativity bias.” The bias leads customers to pay more attention to negative information than positive inputs (Vaish, Grossmann, & Woodward, 2008). Because they counterbalance the negativity bias, specific management responses to negative online reviews engender more trust and deliver higher perceived communication quality than generic responses (Wei, Miao, & Huang, 2013). It fol- lows that managerial response should have the greatest impact when  it  addresses  negative  online  reviews.  In  other  words,  on average, the positive impact of managerial response on competitive performance is stronger when the review rating is lower. Formally:

H3a. The cumulative review scores moderates the relationship between cumulative percentage of managerial response to online reviews and firm’s competitive performance

Response strategies

One aspect of quality of usage is captured by the prioritization of resource allocation toward negative reviews. However, such conceptualization does not capture the variety of response strate- gies the firm may enact. We posit that the quantity of managerial response impact firm performance (H2), and responses will have the greatest impact when addressing negative responses (H3a). However, there is no one well-defined response strategy and a firm can enact a range of response strategies since: “hotels within the same brand can have completely different response rates and patterns” (Park & Allen, 2013, p. 72). Most research in the field has analyzed guests’ perceptions of the response strategies hotels use to address negative reviews (e.g., Lee & Song, 2010; Lee & Cranage, 2012;  van  Noort  &  Willemsen,  2001;  Trevin~o  &  Castan~o,  2013; Abramova et al., 2015). These works address the effects of a com- bination of the following strategies:

• Confession/Apology strategies: The managers politely recognize and apologize for the situation but do not offer compensation or follow  up  actions  (Trevin~o  &  Castan~o,  2013,;  Abramova  et  al., 2015).
• Changing/Accommodative strategies: The managers politely recognize the situation and explain how they will redress the situation for future occasions. These strategies encompass any form of apology, compensation, and/or corrective action (Lee & Song, 2010;; Trevin~o & Castan~o, 2013).
• Denial/Defensive strategies: The managers deny the existence of the negative experience mentioned in the review, deny re- sponsibility for the negative events, and, sometimes attack the customers who leave the negative reviews. The managers disagree with the negative statements either directly by saying “I do not agree”, “It is not true” or indirectly by providing counterarguments to show that the truth is different from the events described in the negative reviews (Lee & Song, 2010;; Trevin~o & Castan~o, 2013,; Abramova et al., 2015).
• Excuse strategies: The managers introduce uncontrollable cau- ses of the negative event as an explanation to distance them- selves from the responsibility for the incident or to shift the blame to a third party (Abramova et al., 2015; Weiner, 2000).
• No Response strategies: The managers offer no response to the negative comments or take no overt action with the purpose of separating themselves from the negative events by remaining silent in the online review platforms (Lee, 2004).

The findings of this research in laboratory settings, suggest that managerial responses to negative reviews increase customers’ trust toward the firm (Sparks, Kam Fung So, & Bradley, 2016). That an accommodative response strategy to negative reviews has a more positive impact on customers’ evaluation of the company, compared to a defensive response strategy or a no response strat- egy (Lee & Song, 2010). That unsatisfied customers expect accom- modative response from the hotel, when they strongly perceive that the causes of the negative event are controllable by the hotel (Coombs, 1999). This approach can reduce feeling of aggression (Conlon & Murray, 1996), which in turn leads to favorable evalua- tion of product or service providers. More specifically, a recent study of response strategies on Airbnb shows that when customers’ complaints are related to a factor controllable by the firm (e.g., cleanliness), a confession/apology strategy results in higher cus- tomers’ trust toward the firm while an excuse strategy reduces trust (Abramova et al., 2015). On the other hand, when the complaints are beyond the control of the firm, a confession/apology strategy, or an excuse one, positively influences customers’ trust, while a denial strategy yields a negative effect (Abramova et al., 2015). Finally, a no response strategy may risk allowing negative information about the company to stand unchallenged, which in turn may damage the company’s reputation and cause potential reputation damage and consequent business loss in the future (Chan & Guillet, 2011). As these strategies studied in the past mainly concerns negative online reviews, very little research to date examines empirically the managerial response strategies to all of the reviews present on the online review systems. Moreover, no empirical research to date has formally evaluated the impact of different response strategies on the competitive performance of the hotels adopting them.

Given the paucity of research on this subject we abstract and categorize response strategies empirically. Specifically, we identify the following four managerial response archetypes:

• No response strategy (NRS): the hotel never addresses any of the guests’ online concerns. The NRS is the least costly approach to online review systems usage since the hotel devotes zero re- sources to the effort.
• Strategic customer orientation strategy (SCO): the hotel selec- tively responds to extreme customers’ comments (the online reviews with the lowest and highest evaluations).
• Full response strategy (FRS): the hotel responds indiscriminately to all guest comments in an effort to signal its attention to all customers, regardless of their comments.
• No strategy (NS): the hotel displays no discernible response strategy and managers address customer comments seemingly at random.

When a hotel has a clearly defined managerial response strat- egy, customers can structure their expectation of accommodation experience based on the customer-orientation strategies the hotel implemented. Without a clearly defined pattern of managerial response, it is difficult for prospective guests to create a perception of the hotel and to vicariously test the quality of an experience good (e.g., hotel rooms) before consumption. Therefore, we propose:

H3b. A defined response strategy (SCO, FRS and NRS) has a stronger positive effect on firm’s competitive performance than the no strategy (NS).

The presence of managerial response creates a dynamic and interactive communication between the hotels and the customers. This two-way communication reduces information asymmetry and the problems associated with it for experience goods (Litvin, Goldsmith, & Pan, 2008; Xie, Zhang, & Zhang, 2014). Having devel- oped a response strategy, the operator signals its care for guests and service quality. Previous work shows that this signaling effect leads to improved review valence, number of reviews and hotel ratings (Li, Cui, & Peng, 2017). As a consequence, we expect this approach to engender superior competitive performance of the hotel as compared to a no response strategy. Therefore, we propose:

H3c. The effect of a strategy with different levels of managerial response (SCO and FRS) on firm’s competitive performance is stronger than the effect of a no response strategy (NRS).

Customer reviews display a J-shaped distribution due to pur- chasing bias (i.e., the prospective customers with lower valuations are less likely to purchase the product) and underreporting bias (i.e., the customers with extreme ratings are more likely to write  reviews than the ones with moderate reviews) (Hu, Zhang, & Pavlou, 2009). Rational people react to these  two  biases  by paying more attention to extreme reviews compared to moderate reviews and even more attention to extreme negative reviews (Hu et al., 2009). On the other hand, responding to positive reviews publicly recognizes customers’ supportive comments and creates a positive emotion in the hotel’s online interactions with customers (Dickinger and Lalicic, 2014). A template response that simply shows gratitude to customer online compliments when the cus- tomers write to express their positive feelings about the experience can enhance future customers’ attitudes (Deng & Ravichandran, 2016). It signals that the hotel cares about showing appreciation of customers’ business and experience more than just fighting the negative reviews. In addition, when management provides a personalized response to altruistic positive reviews, customers perceive higher usefulness of the response and are more likely to agree with the compliment to leave a positive comment. As a result, the managerial response will have a positive influence on future review valence (Deng & Ravichandran, 2016). Therefore, focusing on responding to extremes, positive and negative reviews, should yield a higher return. We propose:

H3d. The effect of a strategic customer orientation strategy (SCO) on firm’s competitive performance is stronger than the effect of a full response strategy (FRS).

Methodology

We developed a dataset uniquely suited to test our hypothesis by joining financial data with online reviews and responses for hotels in Taipei, Taiwan listed on TripAdvisor e one of the biggest online hotel reviews providers. In total, there are 588 hotels listed on TripAdvisor in Taipei. However, to avoid confounds and systematic differences between the hotels, we focused the analysis on the 39 properties that met the requirements for international hotels established by the Ministry of Transportation and Communication of Taiwan. For the July 2012 to January 2017 timeframe our dataset includes the hotels’ monthly average room rate, monthly average occupancy percentage, and the total number of employees reported during the month. The choice of the Taipei market was dictated by the fact that it is one of the few markets where the government collects and publicizes monthly hotel performance data e thus enabling rigorous compet- itive performance analyses. To have a complete picture of the effect of past actions on performance, we merged financial performance data with review data from TripAdvisor. We compiled the full set of reviews since hotel responses became possible on the platform until January 31, 2017. The first review in one of the hotels in our sample appeared on June 20, 2004. Collecting this data is necessary to accurately compute, for any point in time during the analysis, his- torical measures such as the total number of reviews (see measures). A total of 27,635 are used in this analysis.

Measures

 Cumulative review scores (Cum_AvgR) is the running average, for each review, of all chronological prior rating for the hotel. This represents the aggregated review score of the hotel on TripAdvisor. We then aggregate the cumulative review scores by averaging by month to match with the monthly performance data. The total number of reviews is the review count for the month (TotR). Managerial response capabilities are not a native feature of the TripAdvisor platform. The first managerial response for our sample of hotels appeared on June 28, 2009. Thus, cumulative response percentage (Cum_RespP) is computed by dividing the monthly running total of response number and the monthly running total of the review posted since July 2009.²

We measure competitive performance using the Revenue per Available Room (RevPAR) Index. RevPAR Index is a standard mea- sure of competitive financial performance in the hotel industry, allowing comparison across hotels with different number of rooms and characteristics. It is computed as the product of the occupancy percentage and the average daily room rate. RevPAR Index com- pares an individual property’s RevPAR to its competitive set, thereby creating a standardized RevPAR measure. We create meaningful competitive sets of international hotels by dividing the 39 hotels into 5 equally distributed groups based on average daily room rate (4 groups of 8 hotels and 1 group of 7 hotels). RevPAR Index is thus computed as the hotel’s RevPAR divided by the competitors’ average RevPAR times 100. Therefore, a RevPAR Index that is greater than 100 indicates that the hotel outperforms its competitive set within the comparable room rate group while numbers below 100 indicate relative underperformance. Using RevPAR Index as a competitive performance measure allows use to control for all exogenous influences on hotel performance (e.g., economic performance of the overall market, travel market cycle in each segment, seasonality). However, there is a time lag from the day when customers start searching for the hotel information and read the reviews to the actual staying date (when the hotel receives financial benefits). This lead time can be divided into two phases (1) from search to booking and (2) from booking to hotel stay. Based on the statistics reported by HEBS Digital (a hotel marketing com- pany), on average a traveler takes 24 days to book, after starting a search (Starkov, 2014). We obtained transactional data from July 1, 2012 to August 13, 2015 from one of the 39 hotels under study. The data contains 182,322 reservation records, including reservation dates and arrival dates. On average, customers made a reservation 20.53 days before their arrival. Therefore, we assume that the time lag between a customer reading the hotel reviews and the arrival days is within a month and we lag RevPAR Index by 1 months.

We merge the monthly hotel competitive performance  data with the monthly aggregated cumulative review score and cumu- lative percentage of managerial response. This results in a panel of 2076 hotel-month paired observations. Out of the 39 hotels, 35 hotels have 55 monthly performance and aggregated review data. The other 4 hotels were established after July 2012; thus, they have less than 55 monthly observations (25, 38, 42 and 46 months to be exact). The descriptive Statistics of the variables are presented in Table 1.

We measure response strategy on a monthly basis to capture strategy changes by the firms. We categorize the different strategies based on the pattern of responses exhibited on the online review platforms. A firm that responds to no online reviews falls into the no response strategy (NSR). A firm that selectively responds only positive reviews with a rating of 4 and 5, and/or negative reviews with a rating of 1 and 2, falls into the strategic customer orientation strategy (SCO). A firm that responds to all reviews is assigned to the full response strategy (FRS). The remaining firms, which engage in response activity that does not follow any of the above systematic patterns, represent the no strategy (NS) group.

Controls

We include average review score (Avg_Review), guest to staff ratio (GuestToStaff), average response window (Avg_Window), TripAdvisor star rating (Tripadvisor), and affiliation (Affiliation) as control variables.

Limitations and conclusions

As with any study using an archival research methodology, we acknowledge some limitations. While we observe the correlation between quantity and quality of online review system usage and hotel performance, we cannot establish a conclusive causal relationship. We seek to limit the impact of this limitation by controlling for product quality and tease out the effect of mana- gerial response. In addition, due to the exploratory nature of the study, we categorize the quality of system usage (in terms of managerial response) into four different strategies based on the empirical data and previous literature. While our categories are sensible with respect to practice, there is a need for a theoretical framework for guiding future research. Despite the above limita- tions, we believe our work uncovers an interesting pattern of re- sults that points to the importance of research on quality of online review system use at the property level. Moreover, as one of the first empirical works focused on the competitive effect of mana- gerial response strategies to online reviews, we hope that our effort spurs future research in this important area. As customer service interactions are increasingly mediated by digital technology, the ability to foster high quality system usage by employees will become a critical competitive lever for hospitality operators.