Online review management: Towards a model of Response Customization of Customer's negative online reviews. A study of the 4 and 5 * Hotel Industry in Ireland

The digital era has transformed consumers’ purchasing decision making of hospitality services as scepticism about companies advertising messages (Lee, 2009) has led to a greater reliance on user-generated content. The most... [ view full abstract ]

The digital era has transformed consumers’ purchasing decision making of hospitality services as scepticism about companies advertising messages (Lee, 2009) has led to a greater reliance on user-generated content. The most trusted sources of information are still recommendations from “people I know” (Nielsen, 2015), but the major consulted sources of information have become customer online reviews or eWOM (electronic word of mouth) (Kwok & Xie, 2017). They influence consumers’ decision making prior to and after decisions have been made (Gretzel et al., 2007). According to a report by Thorton (2015), most people using Online Travel Agencies, read online reviews first and then book; only after that, they notice that there is a brand attached. Therefore, it is not surprising that online reviews can influence business performance in the hospitality sector.

When it comes to the hotel industry, Ye et al. (2009) found that a 10% improvement in reviewers’ ratings can increase room sales by 4.4%, and a 10% increase in review variance can decrease room sales by 2.8%. On average, a 1% gain in a guest review score translates to a 1% gain in RevPAR (UNWTO, 2014). Moreover, the importance of customer online reviews has already been recognized across the Irish hotel Industry. In Mayer’s (2015) report, Dublin’s 4-5 star hotels had above-average response rates in comparison with all of the other 47 cities surveyed around the world. According to Wu et al. (2010) increased awareness of TripAdvisor among hotel managers might have contributed to quality improvement in the Irish hotel sector.

Despite the growing importance of online reviews, 85 % of hotels have no guidelines for monitoring and responding to online reviews (Barsky and Frame 2009; Levy et al. 2012; Niu and Fan 2017). And while monitoring and managing online reviews is offered by some consulting firms, response to reviews is still conducted in-house (Sparks and Bradley, 2017). According to Briggs et al. (2007); O’Connor (2008) and Vasquez (2011), the majority of hotels are very passive when it comes to responding to online reviews. Sparks et al. (2013) reported that only 7% of hotels respond to online customer reviews.

One of the reasons why hotels are being so passive in responding to customer reviews could be the fact that generally more positive than negative online customer reviews are published (Sparks et al., 2016), and therefore hotels may feel no need to respond. However, negative online reviews have the potential to damage a company’s reputation (Lackermair et al., 2013) and negatively influence observers opinion. It can be argued that companies should focus on negative online reviews at first and then on positive reviews (Krishna et al., 2014). Litvin and Hoffman (2012) and Levy et al. (2013) focused on the importance of responding to online reviews, particularly negative ones. Being unresponsive could result in a company losing future business (Chan and Guilet, 2011). In addition, the organisational response strategy to negative online reviews could influence observers opinion about the company’s reputation which in turn can lead to increased sales (Lee and Song ,2010). Some academics called companies to track and respond to online customer reviews (Wei et al., 2013). According to Minwoo (2017), hotel management should take immediate action to address reviewers’ distress before they become increasingly disillusioned.

Previous studies in this academic research area looked only at how hotels are responding to negative online reviews. Sparks and Bradely (2017) developed Triple A Typology of responding to customer online reviews through service failure/recovery literature. The first category “acknowledgement” refers to statements of recognition, acceptance and confirmation. The second category “account” refers to explanation, while the third category “action” refers to initiatives taken to address the source of customers complaints. Vasquez and Zhang (2014) investigated the generic structure of hotel responses to negative online reviews published on the TripAdvisor. They found that while responding to online customer reviews, businesses use ten already defined moves in typical sequence order: opening pleasantries, expression of gratitude, apologise for source of trouble, refer to customer reviews, proof of action, acknowledge complaints (feedback), avoidance of reoccurring problems, invitation for a second visit, solicit response and closing pleasantries. Research by the author (2017) investigated how 4-5 Star hotels in Ireland respond to negative online reviews. The generic structure of hotel responses in Ireland confirmed the generic structure already defined by Zhang & Vasquez (2014). All in all, responding to negative online seems to be more automated than personalized. However, Niu and Fan (2017) found that these generic protocols and transcripts can be ineffective and have a negative impact on customers.

Generic responses could reflect negatively even on prospective customers who could be left with the impression that the organisation didn’t even look at the problem. Wei et al. (2013) found that effectiveness of specific management responses was rated higher than that of generic responses. According to Chen and Xie (2008), companies should develop unique strategic responses to negative online customer reviews.

Online review management is a function of online reputation management in which companies practice certain guidelines to understand guest public opinions about their brands (Couzin and Grappone, 2014), and take an active role to attain favourable sentiments and to attract prospective customers (Niu and Fan, 2017).Response customization, defined as part of online review management, is a process which refers to the degree to which organisational responses are tailored to individual reviews (Niu and Fan, 2017). According to Niu and Fan (2017) the goal of an online review management system is 1) to attract prospective customers through response customization, review analytics and integration and show that a business cares about customers and intends to improve services, while 2) the goal of service recovery is to address a customer complaint regarding a perceived service failure to improve customer satisfaction. That being said, responses should be customized in a function of attracting prospective customer. Moreover, Niu and Fan (2017) suggested that those two systems can be complementary in order to improve the overall service quality (i.e. when responding to reviews, customers could be encouraged to contract customer service to seek restitution). So far, they are generic and they address customer complaints. That suggests that a successful online review response is one that is built on the foundation of successful service recovery principles (prompt answer, accommodative responding strategy, conversational human voice, etc.). Is that the form that attracts prospective customers?

So far, there is a disagreement in the literature regarding the definition of negative online reviews. While Vasquez (2011) takes the negative online reviews as computer mediated customer complaints, pointing out that they have characteristics of both spoken and written discourse, Bradley et al. (2015) indicates that negative online reviews are not identical to customer complaints, in the way that they include one or more complaints with negative comments frequently mixed with neutral or even positive comments. Further on, Bradley et al. (2015) stated that they are both issued for somewhat different reasons; complaints to achieve personal restitution, whereas negative online reviews to seek public revenge and or to engage with and inform other customers. For companies to know whether to respond to negative online reviews and how to do that effectively and properly, there is a need for consensus regarding a negative online review definition. As Niu and Fan (2017) already opined, there should be a systematic approach to understand and manage online reviews. This requires a clear definition.

Min et al. (2015) called for more academic research into organisational responses to negative online reviews and Kwok and Xie (2017) call for further research regarding how managers may strategically respond to certain types of consumer reviews. Niu and Fan (2017) call to investigate how online review management is integrated with other management systems inside of organisation so there is a clear gap in the literature and in order to address these issues the Research question of this study is how should hotels respond and customize their responses to negative online reviews?

The research objectives are to a) define and classify negative online reviews, b) gain insight into how organisations respond to negative online reviews c) understand how the process is organised (in house) and d) explore current organisational response customization process and best practices.

The purpose of this research is to develop an integrative response customization framework to negative online reviews that will help 4 & 5 star hotels manage online reviews.

The research methodology: To meet the exploratory research objectives an interpretivist paradigm was chosen. Netnography approach and move analysis of discourse structure was used to analyse qualitative data from TripAdvisor. In the second stage, interviews with industry experts and hotel managers will be undertaken to verify initial version of framework.

Practical implications: The study provides a systematic guideline for online review management practices. The framework could be used as a tool for hotels to respond to negative online review in a way that will serve to attract new customers.

Originality/value: This study contributes to online review management literature and it is the first paper to propose a model of response customization in the function of online review management.