lable at ScienceDirect

Computers in Human Behavior 84 (2018) 272e284

Contents lists avai

Computers in Human Behavior

journal homepage: www.elsevier .com/locate/comphumbeh

Full length article

Tailoring management response to negative reviews: Theeffectiveness of accommodative versus defensive responses

Chunyu Li a, *, Geng Cui b, Ling Peng b

a Department of Marketing, School of Business, Guangdong University of Foreign Studies, Guangzhou, 2 North Baiyun Road, Guangzhou, GuangdongProvince, People's Republic of Chinab Faculty of Business, Lingnan University, 8 Castle Peak Road, Tuen Mun, New Territories, Hong Kong

a r t i c l e i n f o

Article history:Available online 3 March 2018

Keywords:Management responseTailoringFirm interventionNegative online reviewsCausal attribution

* Corresponding author.E-mail addresses: chunyuli@ln.hk (C. Li), gcui@ln.

edu.hk (L. Peng).

https://doi.org/10.1016/j.chb.2018.03.0090747-5632/© 2018 Elsevier Ltd. All rights reserved.

a b s t r a c t

Firms increasingly respond to online customer reviews on public platforms to influence prospectiveconsumers and enhance profitability. This paper highlighted the importance of semantically tailoringmanagement responses according to the content of the reviews and examined the impact of such re-sponses on prospective consumers and future sales. Based on a field study and an experiment, we foundthat an accommodative response to the product failure review and a defensive response to the ordinarynegative review were effective at increasing sales and enhancing consumer purchase intentions. Theeffect of management response was mediated by the reduced causal attribution of negative reviews tofirms. Our findings furnish meaningful implications for firm interventions on online platforms.

© 2018 Elsevier Ltd. All rights reserved.

1. Introduction

Marketing researchers have examined the influence of onlinecustomer reviews on product sales (e.g, Chevalier &Mayzlin, 2006;Godes & Mayzlin, 2004, 2009; Zhu & Zhang, 2010), customersatisfaction (e.g., Forman, Ghose, & Wiesenfeld, 2008) and stockprices (e.g., Tirunillai & Tellis, 2012). The great efficiency of theInternet at spreading this influence has led firms to closely monitorand proactively manage online reviews. Research has shown thatnegative reviews hurt business more than positive reviews canpromote it (Basuroy, Chatterjee, & Ravid, 2003; Chevalier &Mayzlin, 2006; Cui, Lui, & Guo, 2012; Jones & Davis, 1965;Reinstein & Snyder, 2005). Negative reviews reach a wider audi-ence and their effects persist longer than those of positive reviews(Hornik, Satchi, Cesareo, & Pastore, 2015). Furthermore, consumershave become more critical over time (Godes & Silva, 2012; Moe &Schweidel, 2012).

Management response, as a form of public firm interventionenabled by many social media platforms, is an increasingly perva-sive way of dealing with negative reviews in practice(Istanbulluoglu, 2017; Ma, Sun, & Kekre, 2015). This study focuseson the prospective consumers and thus differs from the complaint

edu.hk (G. Cui), lingpeng@ln.

handling or service failure recovery research which takes thecomplainers' perspective (Davidow, 2003; Yavas, Karatepe,Babakus, & Avci, 2004). When a firm receives negative reviews, itmay use offline telephone contacts or private online chats toresolve complainers' dissatisfaction. These complaint handling ef-forts are not observed by prospective consumers. In contrast, theobservability of management response to prospective consumersamplifies the scope of its influence to go beyond complainthandling. In fact, effective management response signals productquality and firms’ concern for customers, and reduces informationasymmetry for prospective consumers who lack past experience fordecisionmaking (Li, Cui,& Peng, 2017). Consequently, managementresponse transforms private and separated firm-complainer con-versations into a three-way interactive network among currentcustomers, firms and perspective consumers (Li et al., 2017; Wei,Miao, & Huang, 2013).

There are two opposing views on the effects of initiating re-sponses to negative reviews. Conventional wisdom suggests thatmanagers should ‘cool off’ first and then mount an accommodativeresponse, typically by acknowledging and thanking the complainerand then offering a sincere apology, a promise and perhaps anattractive remedy because ‘customers are always right even whenthey are wrong’. Regardless of the reason or cause for thecomplaint, it may be in the best interest of a business to apologiseto minimise consumer discontent (Smith, Bolton, & Wagner, 1999).A defensive response sounds offensive or insincere and may

C. Li et al. / Computers in Human Behavior 84 (2018) 272e284 273

exacerbate customer discontent, increasing the detrimental impactof negative events (Dawar & Pillutla, 2000). Although straightfor-ward, an accommodative response is not always appropriate,largely due to the complexity of the circumstances. An accommo-dative response by default represents an admission of wrongdoingor failure (Kim, Ferrin, Cooper, & Dirks, 2004). Thus, some scholarsbelieve that an accommodative response should not be automaticand that a defensive response may be necessary andmore effective,especially when a business has done nothing apparently wrong(Ferrin, Kim, Cooper, & Dirks, 2007; Kim et al., 2004). Given thisdilemma, an inappropriate response may lead to double deviation,which is defined as an unsatisfactory recovery effort in response tothe first failure (Smith& Bolton, 1998). In online review forums, thestakes are even higher, as management response no longer takesplace via a private conversation for complaint handling, but occursin the form of a public intervention that may be observed by pro-spective consumers.

We propose a disciplined approach to this dilemma regardingthe appropriate response to negative reviews: firms should tailormanagement responses depending on the nature of the negativereviews. According to Sridhar and Srinivasan (2012), the two typesof negative reviews should be differentiated: product failure re-views and ordinary negative reviews.1 Some negative reviewsreflect dislike, mismatched preferences, unrealistic expectations oroccasionally unreasonableness on the part of the customers, andwe refer to these as “ordinary negative reviews”. Others concernproduct failure as a key product attribute, and we refer to these as“product failure reviews”.2 We posit that either an accommodativeor defensive response may be effective depending on the nature ofthe negative review. Study One relies on econometric models andfield data from TripAdvisor to show the effectiveness of manage-ment response in facilitating the purchase decisions of prospectiveconsumers as indicated by future sales (Hypotheses 1 and 2). In amore controlled experiment, Study Two replicates the findings ofStudy One and reveals the mediation effect of the causal attributionof negative reviews (Hypotheses 1, 2 and 3). The two studies pro-vide converging evidence that management response works onlywhen firms make appropriate responses (i.e., accommodative re-sponses to product failure reviews and defensive responses to or-dinary negative reviews). The underlying mechanism of theireffectiveness lies in the reduced attribution of negative reviews tofirms.

This study makes three contributions to the current literature.First, although the study byWang and Chaudhry (In-Press) revealedthe added informational value of tailored management response, itfocused on the aggregated effect of tailored response and ignoredboth the nature of negative reviews and the dyadic matching be-tween responses and reviews. We propose that managementresponse is a double-edged sword and highlight the importance oftailoring management response to the negative reviews. Second,we further illuminate the necessity of a tailored response. Onlyappropriate responses (accommodative responses to product fail-ure reviews and defensive responses to ordinary negative reviews)can be effective at enhancing sales and purchase intentions, whileinappropriate responses (accommodative responses to ordinarynegative reviews and defensive responses to product failure re-views) may exacerbate the situation. In examining the semanticcharacteristics of management response, previous studies rely

1 We replace “regular negative review” termed by Sridhar and Srinivasan (2012)with “ordinary negative review” to make it more understandable to all readers. Wethank the anonymous reviewer for this suggestion.

2 Table A2 in the appendix displays the definitions and examples for the four keyvariables.

mostly on experimental data and examine the effect of a one-timemanagement response (Lee& Cranage, 2014; Lee& Song, 2010; VanLaer & de Ruyter, 2010). We use a large set of field data and humanhand-coding to analyse the nature of reviews and managementresponse and, to our best knowledge, show the first piece of evi-dence that tailored response can improve sales performance (StudyOne). Our findings not only possess higher external validity but alsoshow the cummulative effect of responses. Third, we reveal thereduced attribution of negative reviews to firms as the underlyingmechanism (Study Two). In sum, our research provide meaningfulguidelines for marketing practitioners who aim to devise effectivemanagement responses to different situations.

We organise the rest of this research as follows. After a briefliterature review on management response, we discuss how re-sponses should be semantically tailored according to whether thereview is an ordinary negative review or product failure review. Totest our hypotheses, we conduct two complementary studies usingdifferent methods (field data and a laboratory experiment) andcontexts (hotels and hiking shoes). Study One merges data on hotelresponses to customer reviews from Tripadvisor.com for hotels inSan Diegowith the daily sales of hotels from the STRGroup, a majorsupplier of hotel data. We find that only responses tailored todifferent negative reviews can enhance future sales. In Study Two,we conduct a controlled experiment to examine the effectiveness oftailored responses to different types of negative reviews andhighlight the reduced attribution of negative reviews to the firm asthe underlying psychological process. Based on the convergingevidence, we summarise the findings and discuss the managerialimplications for management response to negative reviews ononline platforms.

2. Literature review and theoretical development

2.1. Research on management response

The review of relevant literature shows increasing attention tothe effect of management response to online consumer reviews, assummarised in Table A1 in the appendix. Many reseachers haverelied on experiments to show the impact of management responseon prospective customers’ attitudes (Lee & Cranage, 2014; Van Laer& de Ruyter, 2010), satisfaction (Istanbulluoglu, 2017; Min, Lim, &Magnini, 2015), trust (Sparks, So, & Bradley, 2016; Wei et al.,2013) and brand evaluation (Crijns, Cauberghe, Hudders, &Claeys, 2017; Lee & Song, 2010; Rose & Blodgett, 2016; van Noort& Willemsen, 2012). Other studies have provided equivocalempirical findings based on field data. Proserpio and Zervas (2017)showed a positive effect of initial response in enhancing the sub-sequent ratings of new reviews. Nevertheless, Chevalier, Dover andMayzlin (forthcoming) found that initial response had a negativeeffect, as evidenced by more and longer negative reviews. Wangand Chaudhry (In-Press) posited that tailoring amplified both thepositive effect of the initial response to positive reviews and thenegative effect of the initial response to negative reviews. Whilethese three papers focused on the initial response, themanagementresponse to negative reviews on a regular basis is also important(Xie, Zhang, & Zhang, 2014). Frequent responses enhance customerengagement, as indicated by more reviews, a higher reviewvalence, more votes for helpful reviews and better popularityrankings of firms (Li et al., 2017).

The way firms craft response content determines the effective-ness of the management response. Different features of responsehave been examined, such as immediate vs. delayed response (Liet al., 2017; Min et al., 2015; Sparks et al., 2016) (Crijns et al.,2017; Li et al., 2017; Min et al., 2015; Sparks et al., 2016),response framed in empathetic, paraphrased, vivid or professional

C. Li et al. / Computers in Human Behavior 84 (2018) 272e284274

ways (Lee & Song, 2010; Min et al., 2015; Sparks et al., 2016),response issued by an employee of high vs. low position (Sparkset al., 2016; Van Laer & de Ruyter, 2010), response initiated on aconsumer-centric vs. brand-sponsored platform (van Noort &Willemsen, 2012) and response targeting prospective consumers(the majority of the studies in the literature) vs. the complainers(Gu & Ye, 2014; Ma et al., 2015). Many studies have examined thecontent of response and attempted to identify the contingencies ofeffective management response. Several studies have focused onthe choice between a defensive response and an accommodativeresponse. Lee and Song (2010) revealed that an accommodativeresponse (vs. a defensive response or no response) led to bettercompany evaluations. Lee and Cranage (2014) showed that adefensive response for low consensus reviews and an accommo-dative response for high consensus reviews were more effective atenhancing prospective consumers’ attitudes. Van Laer and deRuyter (2010) suggested that an accommodative response in anarrative format and a defensive response in an analytical formatwere more effective.

Several studies have shown that tailoring the response contentto the nature of the review is important. Management responsethat is tailored or personalized to the negative reviews is promising,as it offers additional useful information for prospective consumers(Wang and Chaudhry, In-Press) and can increase the perception of aconversational human voice (Crijns et al., 2017). While a generic orstandardised response appears less relevant to the customer re-view, a specific response that addresses issues or shows empathycultivates prospective consumers’ trust and improves the perceivedquality of the communication (Min et al., 2015; Wei et al., 2013).However, studies have seldom explicitly discussed how to tailormanagement response according to the nature of the negativereview.

This research differed from previous studies and contributed tothe literature in several ways. First, although several studies haveexamined management responses' relative efficacy (Lee &Cranage, 2014; Lee & Song, 2010; Van Laer & de Ruyter, 2010),they did not discuss how to semantically tailor these responsesaccording to the nature of the negative reviews. By distinguishingbetween accommodative responses and defensive responses, weshowed that semantically tailoring responses to different types ofnegative reviews (ordinary negative reviews and product failurereviews) was critical to ensure their effectiveness. Second, previ-ous studies have relied only on either field data or experiments.We adopted an integrated approach using a field study and lab-oratory experiment to achieve both external and internal validityfor our findings. Third, studies have either ignored semantic dif-ferences in response (e.g., Li et al., 2017; Xie et al., 2014) orinvestigated only the initial response (e.g., Chevalier et al.,forthcoming; Proserpio & Zervas, 2017). Study One used abalanced panel dataset including 164 hotels across 228 days andtreated management response as a long-term strategy instead of aone-time event. Lastly, we were among the first to examine theeffect of management response on firms’ sales performance. Weshowed that both accommodative responses and defensive re-sponses can be effective at enhancing performance under differentcircumstances.

2.2. Accommodative vs. defensive response

As Conlon and Murray (1996) stated, when receiving com-plaints, a company finds itself in the position of having to sub-stantiate or legitimate its claim to its image by providing apologies,justifications or excuses. Apologies involve confessions of re-sponsibility for negative events and expressions of remorse. Ex-cuses also acknowledge the existence of a problem but deny

responsibility. Justifications accept responsibility for the event butdeny the negative quality attached to the product or company.When initiating a management response, companies may chooseamong these tactics to maintain their reputation as producers ofquality products.

One of two different responses is usually used. An acommoda-tive response refers to a full apology in which a firm confesses re-sponsibility for the negative event and expresses remorse. The firmacknowleges the existence of the problem, takes full or substantialresponsibility and attempts to remedy the damage (Lee & Song,2010). In contrast, a defensive response usually involves justifica-tions or excuses. A firm either denies responsibility for the problem(justification) or negates the accused responsibility (excuse) (Lee &Song, 2010). This categorisation is similar to the defensive-accommodative continuum in the crisis management research fordifferent coping tactics in response to a crisis situation such as anaccident, scandal or product safety incident (Benoit, 1995; Coombs,1999; Marcus & Goodman, 1991).

2.3. Ordinary negative reviews and product failure reviews

We proposed that firms should tailor their managementresponse according to the nature of the negative review. Weadopted Sridhar and Srinivasan (2012)’s categorisation of negativereviews: ordinary negative reviews and product failure reviews.Consumers appear to find some variability in the quality of theirproduct experiences, which may be acceptable (ordinary negativefeatures) or unacceptable (product failure) (Gürhan-Canli, 2003;Kardes & Allen, 1991). Some negative reviews involve productfailures, while those about experiences that are merely negative areconsidered ordinary negative reviews (Sridhar & Srinivasan, 2012).Product failures occur when the product does not provide the corebenefits promised or perform its fundamental functions or whenthe delivery of the core service is flawed or deficient (Smith et al.,1999). Product failures usually evoke negative emotions andinduce behaviours that are different from those arising from ordi-nary negative experiences (Anderson, 1998; Hennig-Thurau,Gwinner, Walsh,& Gremler, 2004; Zeelenberg & Pieters, 1999).The more intense or severe a product/service failure is, the greaterthe customer's perceived loss, the more dissatisfied the customerwill be and the more inequitable the exchange will appear(Maxham & Netemeyer, 2002; Smith et al., 1999). In contrast, or-dinary negative reviews, reflected in the ratings of the product, canbe attributed to either the product, the reviewer or the situation(Chen& Lurie, 2013; Folkes, 1988; He& Bond, 2015). Many negativereviews are attributed to incorrect product usage (Laczniak,DeCarlo, & Ramaswami, 2001). People may evaluate the samefeatures differently, weigh their relative importance differently oruse different standards for evaluation. Consumers who read re-views with high dispersion may consider these differences whenforming their impressions (He & Bond, 2015). Thus, ordinarynegative reviews are usually related to dislike, mismatched pref-erences, unrealistic expectations or occasionally unreasonable oreven abusive customers.

We believed that the prospective consumers interpret, evaluateand judge the company's responses based on the nature of thenegative reviews. This was consistent with the context effect in thesocial judgment literature (e.g., Mussweiler, 2003; Tormala & Petty,2007). As most judgments naturally involve a comparative process,people evaluate a given target within a specific context or in rela-tion to other available information rather than in a vacuum (e.g.,Adaval & Monroe, 2002; Mussweiler, 2003). Thus, we proposedthat the effectiveness of management response depends on thenature of negative reviews.

C. Li et al. / Computers in Human Behavior 84 (2018) 272e284 275

2.4. Hypotheses

Prospective consumers process both customer reviews andmanagement responses to infer quality information and then formsubjective responses. Because both defensive and accommodativeresponses are double-edged in nature (e.g., Ferrin et al., 2007), thequestion of which response is more effective in alleviating thedetrimental effects of negative reviews depends on the notion thatthebenefitsof the responsewouldoutweigh its cost (Kimetal., 2004).

2.4.1. Management response to ordinary negative reviewsAlthough much research has affirmed the effectiveness of

accommodative responses, other research has observed that theymay fail to ameliorate the negative consequences of an accusationdue to the acknowledgment of guilt (Schlenker, 1980; Snyder &Stukas, 1999). Such guilt would damage consumer trust to amuch greater degree than any benefit from an accommodativeresponse could compensate for. Therefore, a defensive responsemay represent a more effective form of coping with negative eventsbecause it may lead observers to give the accused party the benefitof the doubt (Kim et al., 2004). The disadvantage of a defensiveresponse lies in its implication that there is no need to rectify thebehaviour of the accused party, which may in turn raise concernsabout its future culpability. Ordinary negative reviews describeconsumption experiences related to dislike, mismatched prefer-ences, unrealistic expectations or occasionally unreasonableness onthe part of the customer. They are more heterogeneous thanproduct failure reviews, which depict product failures in the coreattributes. As Folkes and Patrick (2003) stated, prospective con-sumers are less likely to draw negative inference about firms fromheterogeneous negative information. Furthermore, to maintain orexpand one's contact with the social environment, one sometimesmust evaluate others positively despite their occasional undesir-able actions (Wojciszke, Brycz,& Borkenau,1993). Thus, when firmsadopt defensive responses (vs. accommodative responses) to denythe mentioned problems or negate the accused responsibility, theprospective consumers may discount their concern about futureculpability and prefer to give the accused party the benefit of thedoubt. Thus, defensive responses outperform accommodative re-sponses for ordinary negative reviews. Accordingly, we proposedthe following hypothesis:

Hypothesis 1. When confronting ordinary negative reviews, adefensive response is more effective than an accommodativeresponse.

2.4.2. Management response to product failure reviewsWe expected the opposite pattern when consumers observed

product failure reviews. Experiences with product failure evokedifferent negative emotions and induce different behaviour than dothose with ordinary negative features (Anderson, 1998; Sridhar &Srinivasan, 2012; Zeelenberg & Pieters, 1999). An accommodativeresponse acknowledges the existence of guilt or a problem, whichalone should make the situation worse. However, the concomitantexpression of regret signals an intention to avoid similar violationsin the future, which should reduce prospective consumers' con-cerns about their potential vulnerability. Furthermore, it usuallyinvolves corrective remedies, such as compensation, refunds orreplacements. The credibility of the management response in-creases because customers sense that the firm's reputation orfuture revenues are at risk if its regret or remedies turn out to befalse (Boulding & Kirmani, 1993; Kirmani & Rao, 2000). In contrast,defensive responses have been regarded as ineffective at dealingwith negative publicity as severe as that deriving from product-harm crises (Dawar & Pillutla, 2000) or scandals (Marcus &

Goodman, 1991). Those expressions of regret and corrective rem-edies are not involved in defensive responses. The more severe theharm, the stronger the victim's desire for an apology, and a moreextensive or complex apology may be necessary to alleviate thevictim's anger and aggression (Ohbuchi, Kameda, & Agarie, 1989;Schlenker & Darby, 1981). Thus, we proposed that when con-fronting product failure review, an accommodative response issuperior to a defensive response, as stated in Hypothesis 2:

Hypothesis 2. When confronting product failure reviews, anaccommodative response is more effective than a defensiveresponse.

2.4.3. Mediation of the attribution of negative reviewsThe literature suggests that observers’ cognitive processing of

negative reviews involves causal attribution (Chen & Lurie, 2013;Laczniak et al., 2001). The defensive attribution hypothesis (ordefensive attribution bias) posits a positive relationship betweenthe severity of a negative incident and the blame attributed to theparty potentially at fault (Robbennolt, 2000). When the conse-quences are mild, it is easy to feel sympathy for harm-doers andavoid blaming them. However, as the severity of the consequencesincreases, it becomes harder to believe that such amisfortune couldhappen to anyone. Attributing responsibility to the actors helpsindividuals manage this emotional reaction (Fiske & Taylor, 1991).

Negative reviews can be attributed to either product-, reviewer-or situation-related causes (Chen & Lurie, 2013; He & Bond, 2015).According to correspondence bias research, observers tend to makedefault attribution to actors (Gilbert & Malone, 1995), and thistendency is even stronger for negative behaviour (Ybarra, 2002).However, such default attribution may be adjusted if the additionalinformation leads consumers to acknowledge possible causes(Gawronski, 2004; Gilbert & Malone, 1995). Similarly, consumerstend to initially locate the cause of negative reviews to the firm.However, appropriate responses provide additional informationthat reminds consumers of other plausible attribution agents andsubsequently motivates consumers to adjust their default attribu-tion. Specifically, we postulated that the appropriate responses (i.e.,defensive responses to ordinary negative reviews and accommo-dative responses to product failure reviews) help to shift observers’attribution of negative reviews away from the firm.

Hypothesis 3. The effectiveness of an appropriate response (i.e., adefensive response to an ordinary negative review and an accom-modative response to a product failure review) is mediated by thereduced causal attribution of the negative review to the firm.

The proposed hypotheses were empirically examined in twostudies, as shown in Fig. 1 below. Based on the TripAdvisor data,Study One offered field evidence on the effectiveness of defensivevs. accommodative responses in enhancing sales revenues(Hypotheses 1 and 2). Besides replicating the findings of Study Onein an experiment, Study Two revealed the mediation of causalattribution of negative reviews (Hypotheses 1, 2 and 3).

3. Study One: field investigation

3.1. Data collection

We collected hotel reviews and responses for all of the hotels inSan Diego from TripAdvisor on August 27, 2014. TripAdvisor bydefault displays 10 customer reviews on each page in chronologicalorder. Prospective consumersbrowsing thewebsites for informationsearch usually exhibit a limited search depth. According to Sahni(2016), the distribution of the number of webpages browsed is

Fig. 1. The research design of Study One and Two.

C. Li et al. / Computers in Human Behavior 84 (2018) 272e284276

positively skewed, and 50% of browsing ends on the first page.Research on information search depth and intensity for consumerpurchase decisions has shown that consumers concentrate mainlyon thefirst pageof search results (Chen&Yao, 2015;Koulayev, 2010).Thus, we postulated that the most recent 10 reviews and their cor-responding responses, if any, for each hotel exerted the mostprominent influence on prospective consumers’ decision making.We generated the focal response variables based on these reviewsand responses and controlled for the impact of all previous reviews.

The data contained each hotel's background information (e.g.,name, location, star ranking), review information (i.e., the postingdate and textual content for each review) and response informa-tion (i.e., the posting date and content for each response). Wegathered the daily revenue for hotels in San Diego from 1 Januaryto 27 August 2014 from Smith Travel Research (STR), a marketresearch firm that provides data to the hotel industry (www.str.com). After STR merged the two data sources according to hotelnames and addresses (which were made anonymous to us), weobtained a balanced panel with 164 hotels (9 of which neverresponded at all) across 228 days. Among the 19,264 reviews thatwere displayed on the first page for each hotel between 1 Januaryand 27 August 2014, we identified the negative reviews. Severaltypes of linguistic analysis software have become increasinglypopular in user-generated content research (e.g., Ludwig et al.,2013; Tang, Fang, & Wang, 2014). We used the Linguistic Inquiryand Word Count (LIWC; Pennebaker, Francis, & Booth, 2001),which typically provides the proportion of preselected positiveand negative words occurring in a textual description (the proxymeasures for positivity and negativity). For each review, wecalculated the overall sentiment score using the frequency ofpositive words minus that of negative words, which was furtherdivided by their sum (0.0001 was added in the denominator toavoid values of zero). Finally, we obtained 4,815 negative reviewswith sentiment scores lower than 0.

3.2. Coding focal variables

To test our hypotheses, we needed to identify the ordinarynegative reviews and product failure reviews in these 4,815 nega-tive reviews and the accommodative responses and defentive re-sponses among the 3,012 responses. Using the definitions fromSridhar and Srinivasan (2012), we classified reviews as product

failure reviews if they mentioned (1) a core facility failure (e.g., bedbugs in the room, unwashed linen), (2) employee negligence inresponding to consumers' explicit requests (e.g., vegan food wasrequested but chicken was served) or (3) extremely negativeemployee behaviours (e.g., insults). The remaining reviews werecoded as ordinary negative reviews. If a response expressed thehotel's apology, acknowledgment of the existence of the problemsmentioned, acceptance of full or substantial responsibility or at-tempts to take action to remedy the damage, it was coded as anaccommodative response. If the response expressed justificationsor explanations to dissociate the involved hotel from the problemsmentioned or to assign the responsibility to others or situationalfactors, it was coded as a defensive response. Some responses neveraddressed any concern or problem mentioned in the reviews andwere coded as generic or irrelevant responses (e.g., Wei et al., 2013).

The dictionary available to us was not specific to hotel reviews,and the proxy measures were not capable of identifying the afore-mentioned types of responses and reviews. Thus, we hired twohuman coders to code ordinary negative reviews vs. product failurereviews and another two to code defensive responses vs. accom-modative responses. We followed similar procedures to train thetwo pairs of coders. After explaining the coding schemas, the codersand one author independently coded 50 reviews (or responses) thatwere not included in the final estimation. After coding, they dis-cussed any discrepancy or potential confusion to make sure thecoding schemas were understood accurately and consistently. Afterthe training, the coders independently read the reviews or re-sponses, with two assessingwhether a product failure had occurredand the others categorising different response strategies. Theymanually parsed the relevant portions of the texts so that we couldverify their coding. The paired coders agreed on the occurrence ofproduct failure in 90%of the cases, that of accommodative responsesin 91% of the cases, and that of defensive responses in 90% of thecases. The inconsistencies were resolved through discussions. Forour observation period, hotels responded to negative reviews at arate of 62.55% and generally initiated more accommodative re-sponses (59.66%). Overall, 2,137 of the negative reviews were codedas product failure reviews. Meanwhile, we identified 897 accom-odative responses and 413 defensive responses for product failurereviews and 900 accommodative responses and 698 defensive re-sponses for ordinary negative reviews(Table 1).

Table 1Summary statistics of data and coding results.

Hotel StatisticsTotal # of hotels 164# of responding hotels 155Average revenue per available room 100.79 (USD)

Statistics for reviews and responses Whole sample Subsample Underinvestigation

# of reviews 80,819 19,264Average rating valence 3.99/5.0 4.04/5.0Average review length 144 123# of responses 30,625 10,792Average response length 79 73

# of negative reviews under investigation 4,815# of product failure reviews 2,137# of ordinary negative reviews 2,678# of responses to negative reviews under

investigation3,012 (Averageresponse rate:62.55%)

# of accommodative responses to productfailure reviews

897

# of accommodative responses toordinary negative reviews

900

# of defensive responses to productfailure reviews

413

# of defensive responses to ordinarynegative reviews

698

# of irrelevant responses to negativereviews

104

Average word count of responses underinvestigation

91

C. Li et al. / Computers in Human Behavior 84 (2018) 272e284 277

3.3. Model estimation

McWilliams and Siegel (1997) recommended a narrow eventwindow, which makes it easier to control for confounding effects.We captured how accommodative responses or defensive re-sponses displayed on the first review page influenced hotel revenueon a daily basis. We created a time lag between the independentvariables and hotel sales to eliminate the potential for reversecausality (Boulding & Staelin, 1995). This time lag allowed for thehotel booking process typically associated with check-in to takeplace. To determine the appropriate window, we varied it from 1 to14 days; a 7-day lag provided the bestmodel fit statistics in terms ofthe mean square error, Akaike information criterion and Bayesianinformation criterion (Chen, Liu, & Zhang, 2012).

The final panel structure involved many hotels across days. Toensure that the data were a good fit for the assumption of the paneldata analysis, we screened the data for the unit roots. A significantunit root test (Chi-square (328)¼ 1.41eþ4, p¼ .000) indicated thatnonstationarity was not an issue for our sample. Using xtserialdeveloped by Drukker (2003) in Stata 14.0, we found evidence forthe existence of a serially correlated error (i.e., autocorrelation). Toaddress the autocorrelation (F¼ 2684 and p¼ .00 for Wooldridge's(2009) test) and heteroscedasticity from unobservable hotel char-acteristics, we used the ‘xtscc’ package developed by Hoechle(2007). ‘xtscc’ produced Driscoll and Kraay (1998) standard errorsfor fixed-effect regressions for both balanced and unbalancedpanels, which were robust to heteroscedasticity and autocorrela-tion (Hoechle, 2007). Finally, we created a set of dummy variablesfor different periods (e.g., months) to control for unobserved time-specific factors that could affect product sales (e.g., the seasonalityof the accommodation market). The fixed effect model for theasymmetric effect of response was specified in Equation (1).

Hotel revenuei; tþ7 ¼ a1 þ b1DR ONij þ b2AR ONij þ b3DR PFij

þ b4AR PFij þ q0Zij þ εij:

(1)

Specifically, DR ONij was the number of defensive responses toordinary negative reviews, AR ONij was the number of accommo-dative responses to ordinary negative reviews, DR PFij was thenumber of defensive responses to product failure reviews andAR PFij was the number of accommodative responses to productfailure reviews. Zij was a vector of all of the control variables, andtheir effects on hotel revenue were captured in the coefficientvector q. The reviews ahead of the latest 10 reviews and the rate ofresponse to those reviews were included in Zij. We delineated thecumulative impact of all of the previous reviews by the averagevalence, the total number, the dispersion and the average length(Moe & Trusov, 2011). In addition, many hotels responded to pos-itive reviews. As we focused on the impact of management re-sponses to negative reviews, we controlled for the responses topositive reviews and the irrelevant responses.

3.4. Results

As we expected, the accommodative responses and defensiveresponses had an asymmetric impact on hotels' sales performancedepending on the nature of the negative reviews (in Table 2). Whena hotel adopted a defensive response to an ordinary negative re-view, its sale revenue increased 0.85** (b1 ¼ :85; SE ¼ :42). Incontrast, an accommodative response to an ordinary negative re-view caused hotels' daily revenue to decrease by 0.66** (b2 ¼ � :66;SE ¼ :33). For a product failure review, an accommodative

response helped to increase a hotel's sales revenue by 0.78* ðb4 ¼:78; SE ¼ :44Þ. However, a defensive response to a product failurereview worsened the hotel's situation and exerted a negativeimpact, with a 1.62*** reduction in revenue (b3 ¼ � 1:62; SE ¼:59). Our results in support of Hypotheses 1 and 2 underscored thatit is necessary to semantically tailor management response to thereview content at the dyadic level.

Furthermore, irrelevant responses to negative reviews did notenhance sales revenue (q1 ¼ � :50, SE¼ 1.56). This finding dove-tailed with the consensus of previous research that more specificand tailored responses are more effective than standard responsesbecause they garner more trust and deliver higher perceivedcommunication quality (Wang and Chaudhry, In-Press; Wei et al.,2013). Additionally, management response to positive reviews didnot improve sales revenue (q2 ¼ � :01, SE¼ .24). While positivereviews usually dominate on various social media platforms, thisresult calls for firms’ consideration as they choose how to devoteresources to responding to positive reviews.

3.5. Robustness checks

We performed several additional analyses to ensure that ourresults were robust. First, as the numeric ratings are usuallyrepresentative of customers' overall satisfaction or dissatisfactionwith a consumption experience, we coded the reviews as ordinarynegative reviews when the ratings were less polarised (i.e., ratingsof 3) and as product failure reviews when they were moreextremely negative (i.e., ratings of 1 or 2). Second, we enlargedprospective consumers’ search depth to the latest 20 hotel reviewsand responses. Third, we excluded those hotels whose revenue peravailable room was two standard deviations above or below themean. Fourth, we assessed the robustness of our findings acrossdifferent location segments, i.e., more central or popular areas(urban and resort locations) vs. less central areas (suburban andairport locations), across different operation models (i.e., chains,franchises or independent hotels) and across different accommo-dation capacities (those with fewer than 150 rooms and those withmore than 150 rooms). We constructed subsamples along thesedimensions and assessed whether the parameters were consistent.

Table 2The impact of accommodative vs defensive response on hotel revenue.

DV: Hotel revenue Model 0 Model 1

Estimate SE Estimate SE

Focal variables# of defensive responses to ordinary negative reviews e e .85** .42# of accommodative responses to ordinary negative reviews e e -.66** .33# of defensive responses to product failure reviews e e �1.62*** .59# of accommodative responses to product failure reviews e e .78* .44Control variables# of irrelevant responses -.54 1.57 -.50 1.56# of responses to positive reviews -.04 .20 -.01 .24Average length of responses .01 .02 .01 .03Impact of all previous reviewsAverage rating 42.58*** 7.20 41.33*** 7.31Volume .00 .02 .00 .02Variance 70.15*** 15.89 65.50*** 15.70Average length .00 .07 .02 .07Impact of response to previous reviews ahead of the last 10 reviewsResponse rate �20.41*** 7.26 �20.61*** 7.31Average response length -.08* .04 -.08** .04Other controlsMonth dummies Yes YesConstant �172.92*** 45.97 �165.54*** 45.64F-test 30.03*** 25.91R-square 0.2695 0.2702Wald-test: F¼ 4.13, p¼ .003

Note: *: marginal significant at 0.10; **: significant at .05; ***: significant at .01.

C. Li et al. / Computers in Human Behavior 84 (2018) 272e284278

In both cases, using alternative measures of variables (the first twoinvestigations) and subsamples (the last two investigations), theresults were mostly consistent, with no significant deviation fromthe main results, especially for the interactions between manage-ment response and negative reviews.

4. Study Two: behavioural experiment

Our theoretical arguments were based on the premise that theeffectiveness of an appropriate response is mediated by thereduced attribution of negative reviews to the firm (Hypothesis 3).In Study Two, we explicitly examined this premise in a morecontrolled laboratory setting for a search product (vs. the experi-ence product in Study One). Specifically, we investigated whetherthe consumers’ attribution of negative reviews mediated theinteraction between management response and negative reviewsof different natures. We further validated the findings in Study Oneby teasing out the volume effect from the count measures of thefour key variables (Tang et al., 2014). We also controlled for theamount of information across all conditions by exposing one review(either an ordinary negative review or a product failure review) andone response (either an accommodative or a defensive response).Additionally, the control groups with no response for two types ofnegative reviews were included as the baseline conditions.

4.1. Manipulations and procedure

We conducted a 2 (ordinary negative vs. product failure re-view)� 3 (defensive, accommodative vs. no response) between-subject design. The subjects were instructed to participate inroleplaying for an online shopping scenario. We used hiking shoeswith a fictitious brand name in the stimuli. Both the ordinarynegative and product failure reviews were selected and revisedfrom the hiking shoes category on Amazon.com. In the productfailure review scenarios, the subjects received a negative reviewwith a 1-star rating and a textual description of a completelydissatisfactory product experience. The review complained aboutthe pain caused by the lack of shock absorption when wearing theshoes for hiking. In the ordinary negative review scenarios, the

subjects were shown another negative review with a 3-star rating,and its corresponding textual description featured moderatelydissatisfied product evaluations concerning the ugly surface design.

After the negative review, the subjects received either a defen-sive or an accommodative response (but no response in the controlconditions). In the accommodative response scenarios, the brandaccepted responsibility, admitted to the existence of problems andoffered a discount for a future transaction. In the defensiveresponse scenarios, the brand provided justifications to deny itsresponsibility or excuses to dissociate itself from the problems. Torespond to the ordinary negative review, the brand clarified itsemphasis on the protective functions and the waterproof andbreathable exterior design of the shoe. To respond to the productfailure review, the brand explained that the painful experiencemight have been caused by the reviewer's improper usage of theshoes.

We randomly assigned 297 subjects recruited fromMturk to oneof the six conditions and surveyed their purchase intentions afterthey observed the negative review and response. We assessed thecausal attribution of negative review using an item adopted fromChen and Lurie (2013): ‘to what extent do you agree this negativereview is caused by the Expenditure's factors?’ The control vari-ables included the subjects' experience with reviewing and theirreliance on product reviews for their purchase decisions.

4.2. Results

4.2.1. Manipulation checkThe perceived severity of the described problem in the negative

review was found to be significantly higher for the product failurereview than for the ordinary negative review (MON ¼ 3:37; MPF ¼4:97; tð297Þ ¼ � 10:41; p<0:00). The manipulation check for theresponse strategies indicated that the brand's response wasperceived to be more accommodative in the accommodativeresponse conditions and more defensive in the defensive responseconditions (MAR ¼ 2:0; MDR ¼ 4:98; tð195Þ ¼ � 16:91; p<0:00).

4.2.2. Main resultsThe ANCOVA analysis revealed a significant interaction (F(1,

4.463.85

5.37

3.344.23

2.9

0

1

2

3

4

5

6

Odinary negative review Product faiure reviewPu

rcha

se In

tent

ion

Different Natures of Negative ReviewAccommodative Defensive No response

Fig. 2. Accommodative vs defensive response to negative review.

C. Li et al. / Computers in Human Behavior 84 (2018) 272e284 279

288)¼ 6.12, p¼ .003), the main effect of the negative review (F(2,288)¼ 65.69, p¼ .00) and that of management's response (F(2,288)¼ 8.55, p¼ .00) on consumers' purchase intentions, with thecovariates including subjects' reliance on the product reviews forpurchase decisions (F(1, 288)¼ .02, p¼ .80), their experience withbuying shoes (F(1, 288)¼ 3.34, p¼ .07) and their experience withonline shopping (F(1, 288)¼ .14, p¼ .71).

The planed contrasts (as shown in Fig. 2) using the omnibuserror qualified the interaction effect. For the ordinary negative re-view, consumers receiving a defensive response showed a signifi-cantly higher purchase intention (Adj:MDR ¼ 5:37; Adj:MAR ¼ 4:46;Adj:Mcontrol ¼ 4:23; tDR�ARð98Þ ¼ 3:24; p< :01; tDR�controlð99Þ ¼4:08; p< :01). For the product failure review, consumers receivingan accommodative response exhibited a significantly higher pur-chase intention (MDR ¼ 3:34, MAR ¼ 3:85, Mcontrol ¼ 2:90;tDR�ARð93Þ ¼ � 1:77; p ¼ :08; tAR�controlð97Þ ¼ 3:37; p< :01).Therefore, converging evidence was found in support ofHypotheses 1 and 2.

4.2.3. Moderated mediation of causal attributionWe conducted a moderated mediation analysis with manage-

ment response as the independent variable, different negative re-views as the moderator (0 for an ordinary negative review and 1 fora product failure review), causal attribution of the negative reviewto the brand as the mediator and purchase intention as thedependent variable (see Model 8 in (Hayes, 2013)). The indepen-dent variable was a multi-categorical variable (instead of acontinuous or dichotomous variable) and was dummy coded bytwo dummy variables (D1 and D2) (Hayes& Preacher, 2013). We setD1 to 1 for the accommodative response and 0 otherwise, and D2 to1 for the defensive response and 0 otherwise. Implicitly, D1 ¼ D2 ¼0 indicated the no-response conditions. We ran a series of modelsto examine the moderated mediation of the causal attribution of anegative review to the brand, as shown in Table 3.

First, we ran the model with purchase intention as the depen-dent variable. When confronting an ordinary negative review,consumers receiving a defensive response showed a significantlyhigher purchase intention than those receiving no response or anaccommodative response (bD2

¼ 1:12, p ¼ 0:00). When confront-ing a product failure review, consumers receiving an accommoda-tive response exhibited a significantly higher purchase intentionthan those receiving no response or a defensive response (bD1

¼:94, p<0:00). Second, we regressed the causal attribution on thesame independent variables. The defensive response was effectiveat reducing the causal attribution of a negative review to the brand

for the ordinary negative review (bD2¼ � :47, p ¼ 0:08) but not for

the product failure review (bD2¼ � 0:42, p> :10). The accommo-

dative response was effective at decreasing consumers’ causalattribution of a negative review to the brand for the product failurereview (bD1

¼ � :49, p ¼ 0:08) but not for the ordinary negativereview (bD1

¼ � :04, p>0:50). Last, both the direct and indirecteffects were included in the models. The results indicated that thedirect effects of both the accommodative response and the defen-sive response on purchase intention were reduced (bD2

¼ 1:05, p ¼0:00; bD1

¼ :74, p<0:01). The causal attribution significantlyreduced purchase intention in both conditions (bON ¼ �:15;p ¼ 0:08; bPF ¼ � :38, p ¼ 0:00). The bootstrapping method wasused to generate a 95% confidence interval (CI) around the indirecteffect of attribution. The indirect effect of the accommodativeresponse on purchase intention through causal attribution wassignificant for the product failure reviews (95% CIs: 0.08e0.330) butnot for the ordinary negative reviews (95% CIs: �0.166e0.130),while the indirect effect of the defensive response on purchaseintention was significant for the ordinary negative reviews (95%CIs: 0.041e0.359) but not for the product failure reviews (95%CIs: �0.060e0.229).

5. Discussion and conclusions

The increasing proliferation of social media platforms hasenabled the generation and distribution of consumer-generatedcontent. Instead of being passive bystanders, firms are motivatedto proactively manage customer-to-customer communication suchas online reviews. During the review generation process, firms maystrive to foster exogenously created positive reviews (Godes &Mayzlin, 2009; Kozinets, Wojnicki, Wilner, & De Valck, 2010) andsuppress negative reviews (Hu, Bose, Koh, & Liu, 2012; Xiao &Benbasat, 2011). For posted customer reviews, managementresponse appears to be a prominent intervention in the flow oforiginally consumer-dominated conversations. With such in-terventions, firms seek to stem negative sentiments and improvecustomer relationships (Ma et al., 2015).

Although management response is increasingly being used, thequestion of its real-world effectiveness remains largely open. Moststudies have relied solely on experiments in which consumers areexposed to only one response in addition to a review (e.g., Van Laer& de Ruyter, 2010; Wei et al., 2013). Limited research has providedfield evidence on the efficacy of response but has ignored its se-mantic content (Proserpio & Zervas, 2017; Xie et al., 2014). Relyingon two complementary studies, our study explicitly highlighted the

Table 3Moderated mediation analysis results of Study Two.

IV Model 1 Model 2 Model 3

DV: Purchase intention DV: Attribution towards seller DV: Purchase intention

Nature of review

Ordinary negativereview

Product failurereview

Ordinary negativereview

Product failurereview

Ordinary negativereview

Product failurereview

Accommodative response(D1)

0.23 (0.86) 0.94*** (3.17) �0.04 (�0.15) �0.49* (�1.76) 0.23 (0.85) 0.74*** (2.68)

Defensive response (D2) 1.12*** (4.10) 0.44 (1.48) �0.47* (�1.74) �0.42 (�1.48) 1.05*** (3.84) 0.28 (1.01)Causal attribution toward

sellere e e e �0.15* (�1.77) �0.38*** (�4.60)

Control variablesReliance on the product

reviews0.01 (0.16) 0.01 (0.12) 0.05 (0.61) �0.17 (�1.56) 0.02 (0.25) �0.05 (�0.48)

Experience in purchasingshoes

�0.11* (�1.77) �0.06 (�0.84) 0.06 (0.91) 0.03 (0.50) �0.10 (�1.64) �0.05 (�0.70)

Online purchase experience �0.00 (�0.07) 0.02 (0.52) �0.02 (�0.64) �0.01 (�0.41) �0.00 (�0.16) 0.01 (0.40)Constant 4.60*** (8.07) 2.92*** (3.99) 2.38*** (4.23) 5.36*** (7.70) 4.95*** (8.26) 4.97*** (6.09)

Notes: (1) *: marginal significant at 0.10 level; ** Indicates significant at .05 level; *** Indicates significant at .01 level. (2) The figures in the parentheses are the t-values.

C. Li et al. / Computers in Human Behavior 84 (2018) 272e284280

necessity of semantically tailoring the responses according to thecontent of each review. We distinguished between a defensiveresponse and an accommodative response and postulated that bothcan be effective when implemented appropriately. Specifically, thefield investigation and controlled experiment both showed that toachieve the expected positive effect of the response, the differentnatures of negative reviews should be accounted for. An accom-modative response worked for product failure reviews but not forordinary negative reviews, while a defensive response increasedsales performance for ordinary negative reviews but not for prod-uct failure reviews. Study Two also demonstrated a moderatedmediation effect of consumers' causal attribution of negative re-views. That is, the effectiveness of an appropriate response (i.e., adefensive response to an ordinary negative review and an accom-modative response to a product failure review) was due to areduced causal attribution of the negative review to the firm andconsequently enhanced consumers’ purchase intentions.

5.1. Managerial implications

Increasing numbers of companies are using social mediacommunication as their primary digital tool to reach customers.The potency of social media lies in their ability to amplify the effectof online reviews. Yet few executives know how to harness thepower of social media. Firms diligently establish Twitter feeds andbranded Facebook pages, but few have a deep understanding ofhow to take advantage of social media to effectively interact withconsumers to leverage their product and brand recognition, drivesales and profitability and engender loyalty.

As an increasingly popular form of firm intervention on socialmedia, management response to negative review is challengingdue to the transparency, permanence and observability of theposted responses. The mere action of response itself is not neces-sarily effective at mitigating the detrimental impact of negativereviews. The implications of our findings enable the effective tar-geting and crafting of firm intervention. Firms should draft theirresponses according to the nature of the reviews. An accommo-dative response is not always beneficial for all negative reviews, butit works for product failure reviews. In contrast, a defensiveresponse can effectively counteract the impact of ordinary negativereviews but not of product failure reviews. By tailoring responses

according to the nature of the negative reviews, firms show thatthey listen to their customers and care about their experiencesbeyond making profits. Study One also reveals that neither irrele-vant responses nor responses to positive reviews help to enhancesales revenue. While positive reviews usually dominate online re-view systems, this result calls for firms to consider their resourceswhen responding to positive reviews. The inefficacy of an irrelevantresponse reveals that management response should aim to solveproblems and address customer complaints.

5.1.1. Limitations and future directionsThis study is subject to several limitations, some of which may

deserve future research. First, different response strategies can becategorised along a continuum from accommodative to defensiveresponses. However, in both the controlled experiment and thefield investigation, all of the responses were dummy coded intoeither accommodative or defensive responses. A more flexiblemeasure gauging the different extents of response accommoda-tiveness or defensiveness would enrich our findings.

We also categorised negative reviews into ordinary negativereviews and product failure reviews. Such a distinction is importantin that different reviews evoke different trade-offs whenresponding. Other categorisations of negative reviews may also beintriguing. For instance, due to the positivity bias of competence-relevant information and the negativity bias of integrity-relevantinformation (Madon, Jussim, & Eccles, 1997; Martijn, Spears, Vander Pligt, & Jakobs, 1992), a defensive response (accommodativeresponse) may be more effective at mitigating the undesirable ef-fect of competence (integrity) on a negative review.

Finally, our empirical study took the perspective of observingconsumers and did not consider the dynamic relationship betweencustomer reviews and management response. Future studies couldexplore the situations that may affect firms' choice of specificmanagement responses. Fluctuations of the valence, density andvariance of review ratings may influence management response.Reviewer characteristics may shape firms' decisions about whetherand how to respond. Competitors’ response may affect the reviewsand performance of others. Investigation of these issues willenhance our understanding of the effect of management responseon online platforms.

C. Li et al. / Computers in Human Behavior 84 (2018) 272e284 281

Appendix

Table A1Literature Review on Management Response

Author (Year) Focal Variables Effects D

Lee and Cranage(2014)

Different responsestrategies to NWOM(Moderator: NWOM

consensus (# of Neg# of Pos))

Prospective consumers' attituderecovery after receiving managerialresponses

2d

Wei et al. (2013) Generic vs specificresponse (Moderator:valence of review)

Prospective consumers' trust andperceived quality of response

Asp

Lee and Song(2010)

Different responsestrategies to NWOM(Moderator:consensus andvividness of NWOM)

Prospective consumers' attribution ofNWOM to company and companyevaluation

ScS

Min et al. (2015) Different responsestrategies to NWOM

Prospective consumers' satisfaction ofthe response

A(p(qe

van Noort andWillemsen(2012)

Different responsestrategy to NWOM(Moderator: Differentplatforms)

Prospective consumers' brandevaluation

A(cb

Van Laer and deRuyter (2010)

The content andsource of managementresponse

Prospective consumers' integrityrestoration after integrity violating

Snnce

Sparks et al. (2016) Presence or absence ofresponse to NWOM;source, voice, speed,and action frame ofresponse

Prospective customers' inferences of abusiness's trustworthiness and itscaring about customers

A(pvspbre

Rose and Blodgett(2016)

Presence of responseto NWOM andresponse content

(Moderator: # of Neg# of Pos

and controllability)

Prospective consumers' perception ofcompany reputation

Svre(c(a

Crijns et al. (2017) Tone of voiceorganizationalresponse (moderator:valence of customercomment)

Prospective consumers' perception ofcompany reputation

A(pc

Mauri and Minazzi(2013)

Presence of responseto NWOM (Moderator:# of Neg# of Pos)

Prospective consumers' purchaseintention and expectations

Av

Xie et al. (2014) The cumulativefrequency ofresponses (Moderator:valence, volume, andvariance)

Hotel revenue per available room AqT

esign Main Conclusion

(1/3 vs 3/1)� 3 (no, accommodative, vsefensive) between-subject experiment

A defensive response is themost effectivefor low NWOM consensus, whereas anaccommodative response or no responseis more effective for high consensusNWOM.

2 (positive vs negative)� 2 (generic vsecific) between-subject design

For negative reviews, specific response ismore effective. But for positive reviews,no difference.

tudy 1: 2(low vs highonsensus)� 2(low vs high vividness);tudy 2: defensive, accommodative vs no

Defensive response (but accommodativeresponse did not) lead to moreattribution to company than no responsesituation; accommodative response leadto better company evaluation thandefensive or no response.

2 (empathetic vs nonempathetic)� 2araphrased vs nonparaphrased)� 2uick vs slow) between-subjectxperiment

Response with empathy or paraphrasing,or in a quick manner were more favored.

3 (proactive, reactive vs no)� 2ustomer-generated vs company blog)etween-subject experiment

Response is effective to counter NWOMeffect. Proactive response is moreeffective on brand-generated platform,where reactive response is similarlyeffective both platforms. The effect ismediated by consumers' perceivedconversational human voice of response.

tudy 1 & 2: 2 (analytical, narrative, vso)� (apology vs denial); Study 3:arrative apology with low (issued byompany spokesman) vs high (issued bymployee) empathy

Denial in analytical and apology innarrative outperform other combinations(Study 1). The effectiveness is mediatedby transportation (Study 2). Response byemployees are more effective than thatissued by the company's spokesperson(Study 3).

2 (high vs low position)� 2rofessional vs conversational humanoice)� 3 (fast, moderate, vs. loweed)� 2 (past vs future action frame)etween-subject experiment (nosponse: control)

The presence (vs. absence) of a response,using a human (vs professional) voice, ora timely (vs moderate or slow) responseyield more favorable customer inferenceof trustworthiness and caring.

tudy 1: 2 (1/5 vs 2/5)� 2 (controllables uncontrollable)� 2 (response vs nosponse); Study 2: 2 (1/5 vs 2/3)� 2ontrollable vs uncontrollable)� 2ssurance of future vs corrective action)

Study 1: Company reputation was morefavorable when hotel responded. Theeffect of a response was not significantdifferent between 1/5 and 2/5. Responsewas more effective in enhancingreputation when the problem wascontrollable.Study 2: the two response types areequally effective.

2 (personalized vs corporate tone)� 2ositive vs negative consumeromment)

When consumer comments are positive,a personalized organizational responsedamages organizational reputation dueto increased consumer skepticism; Whenconsumer reactions are negative,however, personalizing theorganizational response is beneficial fororganizational reputation due to reducedconsumer skepticism.

nested design: (7/3 vs 3/7): (responses no response)

The presence of response to NWOMhurtsobserving consumers' purchaseintention.

panel data for 843 hotels over 10uarters (at the hotel level) fromripAdvisor

The cumulative frequency of responsehas a significant negative effect on hotelperformance. It strengthens the positiveeffect of location but weakens thepositive effect of cleanness, suggestinghotels should selectively respond tospecific consumer reviews.

(continued on next page)

Table A1 (continued )

Author (Year) Focal Variables Effects Design Main Conclusion

Li et al. (2017) Weekly frequency,speed, and length ofresponse (Moderator:budget vs premiumhotel)

Prospective customer's engagement asindicated by review volume, reviewvalence, voting for helpful reviews andpopularity ranking

A panel data from TripAdvisor (108,410reviews for 212 hotels)

The frequency and speed of responsesignificantly enhance travelers'engagement as indicated by morereviews, higher average valence, morevotes for helpfulness, and higherpopularity ranking. Furthermore, thefrequent and speedy response is moreeffective for budget (vs. premium) hotels.

Proserpio andZervas (2017)

The initial response ofeach hotel

The valence and volume of new reviews A cross-section data from TripAdvisor(314,776 reviews)

Responding hotels receive a 0.12-starincrease in ratings and a 12% increase inreview volume.

Wang andChaudhry (In-Press)

The initial response ofeach hotel(Moderator: tailoringof response)

The valence of new reviews The hotel data from multiple websites Manager responses to negative reviewspositively, whereas manager responsesto positive reviews negatively, impactthe valence of new reviews. Responsetailoring amplifies such effect.

Chevalier et al.(forthcoming)

The initial response ofeach hotel

The valence and length of new reviews The hotel data from multiple websites The initial response has a negative effecton subsequent review generation asindicated more and longer negativereviews.

Gu and Ye (2014) Presence or absence ofresponse (Moderator:very low rating (i.e., 1and 2))

The subsequent rating of complainerswhose first review was responded orthat of complainers who observedresponses to others but did not receiveany themselves.

A panel data from Ctrip (316,568customer reviews for 5831 hotels)

Response exerts no effect in enhancingthe subsequent rating of complainers.But it helps recover satisfaction of verydissatisfactory customers who receivethe response after first ratings butdeteriorates satisfaction of verydissatisfactory customers who observeresponse to others but do notthemselves.

Ma et al. (2015) Presence or absence ofresponse

The valence of complainers' subsequentTwitts

Twitts data from 714 customers Although response improves customerrelationships, it encourages thecomplainer to complain more

Note: Table A1 is adapted from Li, Cui, and Peng (2017) with necessary updates in the literature.

Table A2Definitions and examples for the key variables

Terms Definition Example

Ordinarynegativereview

The review describing consumption experiences related to dislike,mismatched preferences, unrealistic expectations or occasionallyunreasonableness on the part of the customer.

The carpet in the rooms seemed to have many stains despite the stain-hiding pattern. I made sure not to walk barefoot on it or put a bag downon it. Looking at it, I also wanted to look for bedbugs. The hotel felt old.The corridors smelled of age and plumbing. The best I can say is that thesheets were clean. Thin and worn from many washings, but clean. The staffwas nice too.

Product failurereview

The reviews involving product failure experiences when the product doesnot provide the core benefits promised or perform its fundamentalfunctions or when the delivery of the core service is flawed or deficient.

The first time I was really drunk, so I didn't notice how dumpy that placewas. This time I was buzzed, but sober enough: how loud the place is, youcan hear EVERYTHING- how badly the room smelled- the numerousHOLES in the bedding- how disgusting the elevator is. For $100 per night,it is NOT worth staying there!

Defensiveresponse

The firm either denies that the mentioned problems are attached to theproduct or itself (justifications) or negates the accused responsibility(excuses).

We appreciate the positive feedback. However, your perception of theFront Office staff does surprise me, as they really are the friendliestgroup of individuals; and I feel blessed to have them working for me. Iwould like to hear from you and find out who it was that interacted withyou; and why you felt he or she might have been rude. You may sendme anemail, bgallegos@wyndham.com or call the hotel directly and ask for me.

Accommodativeresponse

Full apologies when firms confess and takes full or substantial responsibilityfor negative events, acknowlege the existence of the mentioned problems,express remorse, and attempt to remedy the damage.

Thank you for sharing your comments. We extend our sincere apologiesfor your guest room thermostat malfunction and low water pressure;this is certainly not the type of service wewant our guests to experience.We are currently engaged in an enhancement project, of which ourentire hotel is being enhanced to a more modern and beautiful decor.We hope you will reconsider visiting us again, as we want to make everywrong right; further we want you to experience our new rooms and allowus another opportunity to demonstrate our commitment to you as a valuedguest.

C. Li et al. / Computers in Human Behavior 84 (2018) 272e284282

References

Adaval, R., & Monroe, K. B. (2002). Automatic construction and use of contextualinformation for product and price evaluations. Journal of Consumer Research,28(No. 4), 572e588.

Anderson, E. W. (1998). Customer satisfaction and word of mouth. Journal of Service

Research, 1(1), 1e14.Basuroy, S., Chatterjee, S., & Ravid, S. A. (2003). How critical are critical reviews? The

box office effects of film critics, star power, and budgets. Journal of Marketing,67(4), 103e117.

Benoit, W. L. (1995). Accounts, excuses, and apologies: A theory of image restorationstrategies. NY: State University of New York Press Albany.

Boulding, W., & Kirmani, A. (1993). A consumer-side experimental examination of

C. Li et al. / Computers in Human Behavior 84 (2018) 272e284 283

signaling theory: Do consumers perceive warranties as signals of quality?Journal of Consumer Research, 111e123. Vol.

Boulding, W., & Staelin, R. (1995). Identifying generalizable effects of strategic ac-tions on firm performance: The case of demand-side returns to R&D spending.Marketing Science, 14(3), 222e236.

Chen, Y., Liu, Y., & Zhang, J. (2012). When do third-party product reviews affect firmvalue and what can firms do? The case of media critics and professional moviereviews. Journal of Marketing, 76(2), 116e134.

Chen, Z., & Lurie, N. H. (2013). Temporal contiguity and negativity bias in the impactof online word of mouth. Journal of Marketing Research, 50(4), 463e476.

Chen, Y., & Yao, S. (2015). Sequential search with refinement: Model and applicationwith click-stream data. Available at: SSRN 2130646, Vol.

Chevalier, J. A., Dover, Y., Mayzlin, D.., (forthcoming), "Channels of Impact: Userreviews when quality is dynamic and managers respond", Marketing Science,Vol..

Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on Sales: Onlinebook reviews. Journal of Marketing Research, 43(3), 345e354.

Conlon, D. E., & Murray, N. M. (1996). Customer perceptions of corporate responsesto product complaints: The role of explanations. Academy of ManagementJournal, 39(4), 1040e1056.

Coombs, W. T. (1999). Information and compassion in crisis responses: A test oftheir effects. Journal of Public Relations Research, 11(2), 125e142.

Crijns, H., Cauberghe, V., Hudders, L., & Claeys, A.-S. (2017). How to deal with onlineconsumer comments during a crisis? The impact of personalized organizationalresponses on organizational reputation. Computers in Human Behavior.

Cui, G., Lui, H. K., & Guo, X. N. (2012). The effect of online consumer reviews on newproduct sales",. International Journal of Electronic Commerce, 17(1), 39e57.

Davidow, M. (2003). Organizational responses to customer complaints: What worksand what doesn't. Journal of Service Research, 5(3), 225e250.

Dawar, N., & Pillutla, M. M. (2000). Impact of product-harm crises on brand equity:The moderating role of consumer expectations. Journal of Marketing Research,37(2), 215e226.

Driscoll, J. C., & Kraay, A. C. (1998). Consistent covariance matrix estimation withspatially dependent panel data. The Review of Economics and Statistics, 80(4),549e560.

Drukker, D. M. (2003). Testing for serial correlation in linear panel-data models.STATA Journal, 3(2), 168e177.

Ferrin, D. L., Kim, P. H., Cooper, C. D., & Dirks, K. T. (2007). Silence speaks volumes:The effectiveness of reticence in comparison to apology and denial forresponding to integrity-and competence-based trust violations. Journal ofApplied Psychology, 92(4), 893e908.

Fiske, S. T., & Taylor, S. E. (1991). Social cognition. New York: McGraw-Hill.Folkes, V. S. (1988). Recent attribution research in consumer behavior: A review and

new directions. Journal of Consumer Research, 14(4), 548e565.Folkes, V. S., & Patrick, V. M. (2003). The positivity effect in perceptions of services:

Seen one, seen them all? Journal of Consumer Research, 30(1), 125e137.Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between

reviews and Sales: The role of reviewer identity disclosure in electronic mar-kets. Information Systems Research, 19(3), 291e313.

Gawronski, B. (2004). Theory-based bias correction in dispositional inference: Thefundamental attribution error is dead, long live the correspondence bias. Eu-ropean Review of Social Psychology, 15(1), 183e217.

Gilbert, D. T., & Malone, P. S. (1995). The correspondence bias. Psychological Bulletin,117(1), 21e38.

Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4), 545e560.

Godes, D., & Mayzlin, D. (2009). Firm-created word-of-mouth Communication:Evidence from a field test. Marketing Science, 28(4), 721e739.

Godes, D., & Silva, J. C. (2012). Sequential and temporal dynamics of online opinion.Marketing Science, 31(3), 448e473.

Gürhan-Canli, Z. (2003). The effect of expected variability of product quality andattribute uniqueness on family brand evaluations. Journal of Consumer Research,30(1), 105e114.

Gu, B., & Ye, Q. (2014). First step in social media: Measuring the influence of onlinemanagement responses on customer satisfaction. Production and OperationsManagement, 23(4), 570e582.

Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional processanalysis: A regression-based approach. Guilford Press.

Hayes, A. F., & Preacher, K. J. (2013). Statistical mediation analysis with a multi-categorical independent variable. British Journal of Mathematical and StatisticalPsychology, 67(3), 451e470.

He, S. X., & Bond, S. D. (2015). Why is the crowd Divided? Attribution for dispersionin online word of mouth. Journal of Consumer Research, 41(6), 1509e1527.

Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronicword-of-mouth via consumer-opinion platforms: What motivates consumers toarticulate themselves on the internet? Journal of Interactive Marketing, 18(1),38e52.

Hoechle, D. (2007). Robust standard errors for panel regressions with cross-sectional dependence. STATA Journal, 7(3), 281.

Hornik, J., Satchi, R. S., Cesareo, L., & Pastore, A. (2015). Information disseminationvia electronic word-of-mouth: Good news travels fast, bad news travels faster!Computers in Human Behavior, 45, 273e280.

Hu, N., Bose, I., Koh, N. S., & Liu, L. (2012). Manipulation of online Reviews: Ananalysis of ratings, readability, and sentiments. Decision Support Systems, 52(3),674e684.

Istanbulluoglu, D. (2017). Complaint handling on social media: The impact ofmultiple response times on consumer satisfaction. Computers in HumanBehavior, 74, 72e82.

Jones, E. E., & Davis, K. E. (1965). From acts to Dispositions: The attribution processin person perception. Advances in Experimental Social Psychology, 2, 219e266.

Kardes, F. R., & Allen, C. T. (1991). Perceived variability and inferences about brandextensions. Advances in Consumer Research, 18(1), 392e398.

Kim, P. H., Ferrin, D. L., Cooper, C. D., & Dirks, K. T. (2004). Removing the shadow ofsuspicion: The effects of apology versus denial for repairing competence-versusintegrity-based trust violations. Journal of Applied Psychology, 89(1), 104e118.

Kirmani, A., & Rao, A. R. (2000). No pain, No gain: A critical review of the literatureon signaling unobservable product quality. Journal of Marketing, 64(2), 66e79.

Koulayev, S. (2010). Estimating demand in online search markets, with applicationto hotel bookings. Federal Reserve Bank of Boston Working Paper, No., 09e16.

Kozinets, R., Wojnicki, A. C., Wilner, S. J., & De Valck, K. (2010). Networked narra-tives: Understanding word-of-mouth marketing in online communities. Journalof Marketing, 74(2), 71e89.

Laczniak, R. N., DeCarlo, T. E., & Ramaswami, S. N. (2001). Consumers' responses tonegative word-of-mouth communication: An attribution theory perspective.Journal of Consumer Psychology, 11(1), 57e73.

Lee, C. H., & Cranage, D. A. (2014). Toward understanding consumer processing ofnegative online word-of-mouth communication: The roles of opinionconsensus and organizational response strategies. Journal of Hospitality &Tourism Research, 38(3), 330e360.

Lee, Y. L., & Song, S. (2010). An empirical investigation of electronic word-of-mouth:Informational motive and corporate response strategy. Computers in HumanBehavior, 26(5), 1073e1080.

Li, C., Cui, G., & Peng, L. (2017). The signaling effect of management response inengaging customers: A study of the hotel industry. Tourism Management,62(Oct), 42e53.

Ludwig, S., Ruyter, K. . d., Friedman, M., Bruggen, E. C., Wetzels, M., & Pfann, G.(2013). More than Words: The influence of affective content and linguistic stylematches in online reviews on conversion rates. Journal of Marketing, 77(1),87e103.

Madon, S., Jussim, L., & Eccles, J. (1997). In search of the powerful self-fulfillingprophecy. Journal of Personality and Social Psychology, 72(4), 791e809.

Marcus, A. A., & Goodman, R. S. (1991). Victims and Shareholders: The dilemmas ofpresenting corporate policy during a crisis. Academy of Management Journal,34(2), 281e305.

Martijn, C., Spears, R., Van der Pligt, J., & Jakobs, E. (1992). Negativity and positivityeffects in person perception and inference: Ability versus morality. EuropeanJournal of Social Psychology, 22(5), 453e463.

Ma, L., Sun, B., & Kekre, S. (2015). The squeaky wheel gets the greasedan empiricalanalysis of customer voice and firm intervention on twitter. Marketing Science,34(5), 627e645.

Mauri, A. G., & Minazzi, R. (2013). Web reviews influence on expectations andpurchasing intentions of hotel potential customers. International Journal ofHospitality Management, 34, 99e107.

Maxham, J. G., & Netemeyer, R. G. (2002). A longitudinal study of complainingcustomers' evaluations of multiple service failures and recovery efforts. Journalof Marketing, 66(4), 57e71.

Min, H., Lim, Y., & Magnini, V. P. (2015). Factors affecting customer satisfaction inresponses to negative online hotel reviews the impact of empathy, para-phrasing, and speed. Cornell Hospitality Quarterly, 56(2), 223e231.

Moe, W. W., & Schweidel, D. A. (2012). Online product opinions: Incidence, evalu-ation, and evolution. Marketing Science, 31(3), 372e386.

Moe, W. W., & Trusov, M. (2011). The value of social dynamics in online productratings forums. Journal of Marketing Research, 48(3), 444e456.

Mussweiler, T. (2003). Comparison processes in social judgment: Mechanisms andconsequences. Psychological Review, 110(3), 472.

van Noort, G., & Willemsen, L. M. (2012). Online damage control: The effects ofproactive versus reactive webcare interventions in consumer-generated andbrand-generated platforms. Journal of Interactive Marketing, 26(3), 131e140.

Ohbuchi, K.-i., Kameda, M., & Agarie, N. (1989). Apology as aggression control: Itsrole in mediating appraisal of and response to harm. Journal of Personality andSocial Psychology, 56(2), 219e227.

Pennebaker, J. W., Francis, M. E., & Booth, R. J. (2001). Linguistic inquiry and wordcount: LIWC 2001. Mahway: Lawrence Erlbaum Associates.

Proserpio, D., & Zervas, G. (2017). Online reputation management: Estimating theimpact of management responses on consumer reviews. Marketing Science.

Reinstein, D. A., & Snyder, C. M. (2005). The influence of expert reviews on con-sumer demand for experience goods: A case study of movie critics. The Journalof Industrial Economics, 53(1), 27e51.

Robbennolt, J. K. (2000). Outcome severity and judgments of “responsibility”: Ameta-analytic Review1. Journal of Applied Social Psychology, 30(12), 2575e2609.

Rose, M., & Blodgett, J. G. (2016). Should hotels respond to negative online reviews?Cornell Hospitality Quarterly, 1, 1e15.

Sahni, N. S. (2016). Advertising spillovers: Evidence from online field-experimentsand implications for returns on advertising. Journal of Marketing Research.

Schlenker, B. R. (1980). Impression Management: The self-concept, social identity, andinterpersonal relations. , CA: Brooks/Cole Publishing Company Monterey.

Schlenker, B. R., & Darby, B. W. (1981). The use of apologies in social predicaments.Social Psychology Quarterly, 271e278.

Smith, A. K., & Bolton, R. N. (1998). An experimental investigation of customer re-actions to service failure and recovery encounters paradox or peril? Journal of

C. Li et al. / Computers in Human Behavior 84 (2018) 272e284284

Service Research, 1(1), 65e81.Smith, A. K., Bolton, R. N., & Wagner, J. (1999). A model of customer satisfaction with

service encounters involving failure and recovery. Journal of Marketing Research,36(3), 356e372.

Snyder, M., & Stukas, A. A. (1999). Interpersonal processes: The interplay ofcognitive, motivational, and behavioral activities in social interaction. AnnualReview of Psychology, 50(1), 273e303.

Sparks, B. A., So, K. K. F., & Bradley, G. L. (2016). Responding to negative onlinereviews: The effects of hotel responses on customer inferences of trust andconcern. Tourism Management, 53, 74e85.

Sridhar, S., & Srinivasan, R. (2012). Social influence effects in online product ratings.Journal of Marketing, 76(5), 70e88.

Tang, T., Fang, E., & Wang, F. (2014). Is neutral really neutral? The effects of neutraluser-generated content on product sales. Journal of Marketing, 78(4), 41e58.

Tirunillai, S., & Tellis, G. J. (2012). Does chatter really matter? Dynamics of user-generated content and stock performance. Marketing Science, 31(2), 198e215.

Tormala, Z. L., & Petty, R. E. (2007). Contextual contrast and perceived knowledge:Exploring the implications for persuasion. Journal of Experimental Social Psy-chology, 43(1), 17e30.

Van Laer, T., & de Ruyter, K. (2010). In stories we trust: How narrative apologiesprovide cover for competitive vulnerability after integrity-violating blog posts.International Journal of Research in Marketing, 27(2), 164e174.

Wang, Y., Chaudhry, A., "When and how managers' responses to online reviewsaffect subsequent reviews", Journal of Marketing Research, (In-Press).

Wei, W., Miao, L., & Huang, Z. J. (2013). Customer engagement behaviors and hotelresponses. International Journal of Hospitality Management, 33, 316e330.

Wojciszke, B., Brycz, H., & Borkenau, P. (1993). Effects of information content andevaluative extremity on positivity and negativity biases. Journal of Personalityand Social Psychology, 64(3), 327e335.

Wooldridge, M. (2009). An introduction to multiagent systems. John Wiley & Sons.Xiao, B., & Benbasat, I. (2011). Product-related deception in e-commer: A theoretical

perspective. MIS Quarterly, 35(1), 169e195.Xie, K. L., Zhang, Z., & Zhang, Z. (2014). The business value of online consumer

reviews and management response to hotel performance. International Journalof Hospitality Management, 43, 1e12.

Yavas, U., Karatepe, O. M., Babakus, E., & Avci, T. (2004). Customer complaints andorganizational responses: A study of hotel guests in northern Cyprus. Journal ofHospitality & Leisure Marketing, 11(2e3), 31e46.

Ybarra, O. (2002). Naive causal understanding of valenced behaviors and its im-plications for social information processing. Psychological Bulletin, 128(3),421e441.

Zeelenberg, M., & Pieters, R. (1999). Comparing service delivery to what might havebeen behavioral responses to regret and disappointment. Journal of ServiceResearch, 2(1), 86e97.

Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on Sales: Themoderating role of product and consumer characteristics. Journal of Marketing,74(2), 133e148.