The dark side of customer co-creation: exploring the consequences of failed co-created services

ORIGINAL EMPIRICAL RESEARCH

The dark side of customer co-creation: exploringthe consequences of failed co-created services

Sven Heidenreich & Kristina Wittkowski &Matthias Handrich & Tomas Falk

Received: 24 April 2013 /Accepted: 14 April 2014# Academy of Marketing Science 2014

Abstract Whereas current literature emphasizes the positiveconsequences of co-creation, this article sheds light on poten-tial risks of co-created services. Specifically, we examine theimplications of customer co-creation in service failure epi-sodes. The results of four experimental studies show that ina failure case, services high on co-creation generate a greaternegative disconfirmation with the expected service outcomethan services low on co-creation. Moreover, we examine theeffectiveness of different service recovery strategies to restorecustomer satisfaction after failed co-created services. Accord-ing to our results, companies should follow a matching strat-egy by mirroring the level of customer participation in servicerecovery based on the level of co-creation during servicedelivery. In particular, flawed co-creation promotes internalfailure attribution which in turn enhances perceived guilt. Ourresults suggest that in such case customer satisfaction is bestrestored by offering co-created service recovery.

Keywords Customer co-creation . Service failure . Servicerecovery . Technology-based services

Introduction

In services, “innovation always starts with customers’unmet needs” (Ostrom et al. 2010, p. 16). One promi-nent strategy for companies to capture customers’ needsis to actively engage them in the service delivery pro-cess (Payne et al. 2008). As such, an increasing numberof firms (e.g., Nike, Mymuesli) provide Internet-basedself-services, enabling them not only to outsource valuecreation to customers but also to customize offerings(Moraeu and Herd 2010). In general, customer partici-pation in service delivery, referred to as customer co-creation (Prahalad and Ramaswamy 2004; Witell et al.2011), yields benefits for both service providers andcustomers. On the one hand, co-creation enables com-panies to effectively adapt to changing customer needs(Etgar 2008). On the other hand, it provides customerswith a feeling of accomplishment that enhances satis-faction (Meuter et al. 2000).

In particular, co-created services reveal their potentialto strengthen customer relationships when service deliv-ery is successful (Chan et al. 2010; Witell et al. 2011).Yet, high customer involvement in service delivery re-sults in more contact points between customers andservice providers. This increases service complexityand, ultimately, the probability of service failures(Parasuraman 2006). Most important, as customers in-vest considerable time and effort in co-creation, theymight feel an augmented disappointment when the co-created service delivery fails. More precisely, with theircomprehensive engagement in co-created services, cus-tomers are likely to formulate higher-quality expecta-tions of service provision (Childers et al. 2001). Assuch, if higher expectations are only met with poorperformance, disappointment with the co-created servicemay be inflated.

S. Heidenreich (*) :M. HandrichDepartment of Innovation and Entrepreneurship, EBS BusinessSchool, Rheingaustraße 1, 65375 Oestrich-Winkel, Germanye-mail: [email protected]

M. Handriche-mail: [email protected]

K. Wittkowski : T. FalkDepartment of Marketing, EBS Business School, Rheingaustraße 1,65375 Oestrich-Winkel, Germany

K. Wittkowskie-mail: [email protected]

T. Falke-mail: [email protected]

T. FalkAalto University School of Business, Helsinki, Finland

J. of the Acad. Mark. Sci.DOI 10.1007/s11747-014-0387-4

Surprisingly, research examining this “dark side” of cus-tomer co-creation (i.e., the consequences of failed co-createdservices) is lacking. Existing knowledge on the implicationsof service co-creation focuses solely on the effects of success-ful service delivery (e.g., Chan et al. 2010;Meuter et al. 2000).To close this research gap, this study examines possibledownsides of failed co-creation on customer satisfaction.

Previous studies also emphasize the positive effects ofcustomer co-creation in service recovery. More specifically,research has shown that customer participation in the recoveryprocess increases customer satisfaction (e.g., Dong et al.2008). Nevertheless, existing studies seem to disentangle co-creation during recovery from the level of co-creationemployed in the original service delivery (e.g., Dong et al.2008; Roggeveen et al. 2012; Zhu et al. 2013). Our researchadds to this perspective by investigating the effectiveness ofco-creation in service recovery depending on the level of co-creation in the initial service delivery. In particular, customersmay be increasingly motivated to take an active role in over-coming a co-created failure, assuming a perceived responsi-bility for the flawed service. In contrast, service failure low onco-creation should promote company-led recovery, due toexternalization of blame for the failure (Folkes and Kotsos1986). As our second contribution, we empirically validate thedepicted optimal pattern of co-creation levels in service deliv-ery and recovery.

Finally, previous literature has not explored the underlyingpsychological processes of the effects of co-creation in servicedelivery and recovery on customer satisfaction. To gain adeeper understanding of this matter, we empirically assessperceived disconfirmation as a mediating variable betweenthe level of co-creation in service provision and customersatisfaction. In addition, we show that internal failure attribu-tion mediates the link between the level of co-creation inservice delivery and perceived guilt. Additionally, perceivedguilt moderates the relationship between the level of co-creation in service recovery and customer satisfaction. Theempirical validation of these chains of effects constitutes ourthird contribution.

The remainder of this article is structured into four majorparts. The first part develops the conceptual framework of thisresearch. The second part comprises Study 1 (n=243), whichexplores the consequences of failed co-created services oncustomer satisfaction, and Study 2 (n=266), which replicatesthe findings of Study 1 and illustrates the mediating role ofperceived disconfirmation. The third part consists of Study 3(n=338) and Study 4 (n=265), which test the effectiveness ofco-created and non-co-created service recovery strategies,respectively. Here, we introduce the concepts of internal fail-ure attribution and perceived guilt to better understand thelinkages between failed co-creation and customer satisfaction.The fourth part concludes with implications for theoryand practice.

Co-creation, service failure and customer satisfaction:the case of initial service delivery

Co-creation

Literature on customer co-creation and related concepts isdiverse. Overall, there is significant consensus that co-creation principally refers to joint value creation by the com-pany and the customer (Etgar 2008; Prahalad andRamaswamy 2004). In a product context, customers co-create during product development by customizing productfunctionality (Simonson 2005), design (Franke et al. 2009), orboth (e.g., assembly of IKEA furniture) (Dahl and Moraeu2007). In services, co-creation activities can take place acrossthe entire value chain (Yi and Gong 2013)—that is, in both theactual service encounter and the recovery process (Dong et al.2008; Roggeveen et al. 2012). Oftentimes, firms usetechnology-based services (TBSs) that enable customersto shape service design or functionality without directinvolvement of frontline employees (Meuter et al. 2005)to facilitate co-creation.

Independent of potential context-specific peculiarities,three constitutive dimensions of co-creation have emerged:(1) customization, as its key benefit (e.g., Etgar 2008), (2)effort (e.g., Hoyer et al. 2010), and (3) information sharing(e.g., Chan et al. 2010), as its primary costs. Overall, cus-tomers will be satisfied with a co-created service only if theircommitment yields a benefit (i.e., high level of customization)that outweighs the effort invested and the uncomfortable stateto use, store, and distribute personal information (Xie et al.2008). In the case of successful service delivery, co-createdservices can ultimately boost customer satisfaction (Chanet al. 2010). Yet scant research has examined how co-creation affects customer reactions after a service provisionhas failed. While current findings reveal that unexpected co-created outcomes in online innovation communities may trig-ger negative customer reactions (Gebauer et al. 2013), re-search remains silent on the empirical assessment of cus-tomers’ reactions after co-created service failure. Studies 1and 2 address this research gap.

Co-creation and customer satisfaction

Research on customer satisfaction is traditionally based onexpectation-disconfirmation theory (EDT) (Oliver 1997;Spreng and Page 2003), in which satisfaction results fromthe discrepancy between expectations and perceived perfor-mance. In particular, expectations represent a reference point.Performance outcomes poorer than expected are rated belowthe reference point and lead to dissatisfaction (negative dis-confirmation). Performance outcomes can also meet the ref-erence point (confirmation) or even exceed it (positive dis-confirmation), both triggering satisfaction (Swan and Trawick

J. of the Acad. Mark. Sci.

1981). Applying EDT to co-creation in general, we couldassume that providers of co-created services have a highlikelihood of achieving higher customer satisfaction becauseof improved value generation (i.e., performance) for cus-tomers. Previous research even offers empirical evidence forthe interrelationship between successful co-creation and in-creased customer satisfaction (Chan et al. 2010). Successfulco-created services provide customers with superior economicbenefits accruing from, for example, greater control, increasedgoal achievement, reduced financial and performance risks,and enhanced relational benefits resulting frommore empathyfor customers’ needs on behalf of the service provider (Chanet al. 2010; Claycomb et al. 2001).

However, services are generally susceptible to failuresbecause of their intangibility, dependence on human perfor-mance, and the inseparability of service provision and con-sumption (Patterson et al. 2006). In an attempt to reduce theprobability of service failure, many companies have increasedtheir efforts to implement co-creation strategies (Dong et al.2008; Payne et al. 2008). While higher levels of customerefforts coupled with more comprehensive information sharingmight indeed reduce service failure rates, “achieving 100%service reliability will be cost prohibitive, if not impossible”(Parasuraman 2006, p. 591).

As mentioned previously, in highly co-created services,customers invest significant effort and share information inreturn for a customized service experience (Etgar 2008; Hoyeret al. 2010). In such cases, customers take over activitiesotherwise performed by service employees. As a result of thiscomprehensive involvement, customers are likely to expecttailored results. In other words, in line with tit-for-tat logic,higher customer inputs in terms of co-creation should bematched by a superior service output (Maxham 2001). How-ever, according to EDT logic, in the event of a service failure,these increased expectations are not fulfilled, resulting ingreater negative disconfirmation. A service failure may alsoestablish a “loss frame,” highlighting the ineffective inputsprovided by the customer. Because customers tend to em-phasize negative aspects after service failures, theymight categorize the whole service as low quality(Folkes and Patrick 2003). The discrepancy betweenexpectations and perceptions should therefore be consid-erably higher for customers who experience a servicefailure using highly co-created services than for cus-tomers using services low on co-creation. In line withthis, a highly co-created service failure is likely togenerate greater negative disconfirmation and, thus, low-er satisfaction levels than failed services low on co-creation. Thus, we conjecture the following:

H1: In the case of service failure, customers using serviceshigh on co-creation will be less satisfied than customersusing the same service low on co-creation.

H2: In the case of service failure, disconfirmation of expec-tations mediates the negative effect of level of co-creation on customer satisfaction.

Study 1

Setting and data collection

Study 1 aims to empirically validate H1 by evaluating themutual influence of co-creation in service delivery and servicefailure on customer satisfaction. We use an online real-lifeexperiment to test the proposed effect.

We chose Internet-based services for our research contextbecause customer co-creation is increasingly implementedusing such technologies (Weitjers et al. 2007). Moreover,TBS allow for variation in the degree of co-creation (VanBeuningen et al. 2009). We programmed two Internet-basedservices: one online flight-booking platform named PlaneJourney and one online railway-ticketing service called RailJourney. We employed a self-administered online question-naire and recruited participants through an online panel of aGerman market research institution. We combined the twosubsamples of the Plane Journey and Rail Journey scenariosafter performing Box’s M test (p=0.6), which confirmedequality of the covariance matrices. The overall size amountedto 244 participants (for details, see Table 3 in Appendix 1).

Method

The study was a 2 (level of co-creation in service delivery:high vs. low) × 2 (service outcome: failed vs. successfulbooking) between-subjects experiment. Participants were ran-domly assigned to one of the four scenarios. We manipulatedthe level of co-creation by differing degrees of effort andinformation required from the participants to purchase a planeor train ticket and by different levels of customization. In thehigh co-creation condition, participants were able to plan thedetails of their journey (e.g., time of departure and arrival,airline/train company, seat reservation) and had to providecomprehensive personal information (name, age, address,telephone number, and e-mail). In the final stage of the highco-creation booking process, participants could choose fromseveral add-on services (e.g., on-flight/on-train meals andreadings). In the low co-creation scenario, participants hadto enter their names and e-mail addresses and could decide onthe travel dates and the departure and destination locations.

For the second independent variable, service outcome ofthe booking process, we chose two contrasting scenarios. Inthe service failure condition, an error message appeared afterparticipants had completed the booking process and tried toconfirm the order. Participants were notified that the booking


process had been interrupted and that the request was can-celled. They were then asked to try booking again. In theservice success condition, participants were able to confirmtheir order at the end of the booking process. We assessedcustomer satisfaction as the dependent variable using threeitems adapted from Voss et al. (1998). All items were mea-sured on seven-point Likert scales, anchored by totally dis-agree (1) and totally agree (7) (see Appendix 1).

We conducted a pretest (n=94) and used manipulationchecks to assess the level of co-creation in terms of degreeof customization (“The booking service offered me severaloptions to customize the tickets to my needs”), effort (“Be-cause of the many menus and requests, I had to spend a lot oftime and energy in order to use the booking service properly”),and information sharing (“I had to provide a lot of personalinformation in order to use the booking service properly”).These items were also assessed on seven-point Likert scaleswith the endpoints totally disagree (1) and totally agree (7).Furthermore, we checked for the manipulation of the outcomeof the booking process by asking whether a service failure hadoccurred. Both manipulations proved successful (for details,see Table 4 in Appendix 1).

Main study results

To test the effectiveness of our manipulations, we employedthe same manipulation checks as in the pretest. The manipu-lations worked as intended. The ratings of degree of custom-ization (Mcust_high=5.69, Mcust_low=4.39; F(1, 243)=36.06,p < 0.01) , effor t (Meff_h i gh = 4.00, Meff_ l ow = 2.58;F(1, 243)=38.86, p<0.01), and information sharing

(Minf_high=4.12, Minf_low=2.50; F(1, 243)=51.11, p<0.01)were significantly different from each other across the twolevels of co-creation. Moreover, we checked for the manipu-lation of service outcome using one item that captured wheth-er a service failure had occurred (1 = no failure, 2 = failure).Again, the manipulation was successful. Participants correctlyindicated whether the booking failed (Mfail=1.94) or wassuccessful (Msucc=1.01; F(3, 239)=1657.1, p<0.01).

As our data were not normally distributed (D(243)=2.193,p<0.01), we employed the aligned rank transform (ART)procedure for hypothesis testing. For non-normal distributeddata, the ART procedure is more robust and powerful than thetraditional analysis of variance (ANOVA) (Leys andSchumann 2010). In summary, the results provide empiricalsupport for H1 (see Fig. 1 and Table 1).We found a significantinteraction effect of level of co-creation and service outcomeon customer satisfaction (F(3, 239)=5.137, p<0.05). In thecase of service failure, customers using a highly co-createdservice were less satisfied (Mcc_high=3.16) than customersusing the same TBS low on co-creation (Mcc_low=3.66). Thus,the main effect of co-creation on satisfaction in the case ofservice failure was significant (F(1, 105)=2.929, p<0.10).Moreover, we evaluated the direct effect of level of co-creation on satisfaction in the case of service success. Cus-tomers in the high co-creation group tended to be more satis-fied (Mcc_high=5.30) than customers in the low co-creationgroup (Mcc_low=4.75; F(1, 136)=6.055, p<0.05). We alsofound support for the main effect of service outcome oncustomer satisfaction (F(3, 239)=72.083, p<0.01). Customersatisfaction was significantly lower in the case of a servicefailure (Mfail=3.42) than in the success case (Msucc=5.01).

Fig. 1 Study 1: interaction effectbetween level of co-creation andservice failure on customersatisfaction with TBS


Study 2


Study 2 aims to (1) replicate the findings from Study 1 and (2)empirically test H2—that is, whether disconfirmation of ex-pectations mediates the negative effect of level of co-creationon customer satisfaction. In this study, similar to previousresearch on co-creation (e.g., Bendapudi and Leone 2003;Grewal et al. 2008), we used a scenario-based experiment.

To generalize findings across different types of co-created services, we chose a different study contextthan in Study 1. On the basis of extensive pretests,we developed a scenario description for a fictionalInternet-based service named DYS—Design YourShoes. At the beginning of the questionnaire, partici-pants read a brief description of the service providerand its offerings. Participants were informed that theservice provided by DYS—Design Your Shoes allowsthem to personalize sport shoes according to their in-dividual preferences. Data collection was conducted bymeans of an online survey that recruited 266 partici-pants from a German consumer panel (for details, seeTable 3 in Appendix 1).

Method

Similar to Study 1, we used a 2 (level of co-creation: high vs.low) × 2 (service outcome: wrong vs. correct shoes) between-subjects experimental design. In the high co-creation condi-tion, participants had to invest considerable time and effort todesign the shoes and to place the order. They also had toprovide a substantial amount of personal information. Partic-ipants were informed about the option to customize the shoesaccording to their preferences by selecting specific material orcolor and by putting their names or initials on the shoes. In thelow co-creation condition, the description indicated thatthe service was effortless to use, required limited per-sonal information, and offered the possibility of custom-izing the color of the shoes.

In the success scenario, participants read that they couldpick up the personalized shoes they ordered (i.e., the correctversion of the shoes) from the store. The participants assignedto the failure scenario were asked to imagine that they re-ceived a wrong version of the designed shoes when they triedto pick up their order at the store (for detailed scenario de-scriptions, see Appendix 2).

We captured customer satisfaction with three items adaptedfrom Voss et al. (1998). We assessed disconfirmation of ex-pectations on the basis of the inferred disconfirmation conceptproposed by Swan and Trawick (1981). In the case of failed(successful) delivery of the shoes, we measured negative(positive) disconfirmation by calculating the difference be-tween participants’ pre-rating (post-rating) and post-rating(pre-rating) of the service outcome, using the items proposedby Voss et al. (1998). Finally, internal failure attribution wasassessed using four items adapted fromDong et al. (2008). Allitems were measured on seven-point Likert scales, anchoredby totally disagree (1) and totally agree (7) (see Appendix 1).To operationalize the level of co-creation, we used dichoto-mous (binary) information to construct a dummy variable (0 =low co-creation, and 1 = high co-creation).

We again conducted a pretest (n=114). We used the samemanipulation checks as in Study 1. The results indicate suc-cessful manipulations for both independent variables. Further-more, we used three items anchored on seven-point Likertscales that were adapted from Dabholkar and Bagozzi (2002)to check whether participants perceived the scenarios as real-istic. The realism check was also successful (see Table 4 inAppendix 1).

Main study results

First, we tested the effectiveness of our manipulations, usingthe same manipulation checks as in the pretest. The manipu-lation checks indicated effectiveness of our manipulations.Participants rated customization (Mcus_high=5.70, Mcus_low=4.98; F(1, 266)=15.22, p<0.01), effort (Meff_high=4.60,Meff_low=4.01; F(1, 266)=7.94, p<0.01), and informationsharing (Minf_high=5.36, Minf_low=4.25; F(1, 266)=30.35,p<0.01) differently across the distinct dimensions of co-creation. Participants also correctly identified the service fail-ure situations (Mfail=1.87) and the service success condition(Msucc=1.14; F(1, 266)=298.96, p<0.01). In addition, theyperceived the scenarios as realistic, given the high averagescore on the realism check items (Mreal=5.14).

We used structural equation modeling to assess the linkamong co-creation, disconfirmation of expectations, and cus-tomer satisfaction. As the data were not normally distributed(D(265)=2.623, p<0.01), we employed the partial leastsquares (PLS) method (Hair et al. 2012). We evaluated twoseparate models (a service failure model and a service successmodel) using SmartPLS 2.0 (Ringle et al. 2005).

Table 1 Study 1: means and standard deviations for customer satisfac-tion with TBS

Means (μ) for customer satisfaction with TBS

Independent variables Service outcome

Service failure Service success

Level of customer co-creation Low 3.66a (1.611) 4.75a,b (1.645)

High 3.16b (1.507) 5.30a,b (1.436)

Standard deviations are in parentheses. Values with the same superscriptare significantly different from each other at p<0.05; all other valuessignificantly differ at p<0.10


To assess the psychometric properties of our constructmeasures, we checked for content, indicator, construct, con-vergent and discriminant validity (Götz et al. 2010; Hair et al.2012). As Table 5 in Appendix 1 shows, all measurementcriteria exceed conventional thresholds (Fornell and Larcker1981). Next, we estimated the structural model with co-creation as the independent variable, expectancy disconfirma-tion as the mediator variable, and customer satisfaction as thedependent variable. We created an index score to captureexpectancy disconfirmation by using the difference inmean values of participants’ pre-ratings and post-ratingsof the service outcome. We calculated path coefficientsand their respective significance levels on the basis ofan effective sample size of 123 observations for thefailure case scenario.

Overall, the estimations fit the data well. The explainedvariance (R2) for the endogenous construct is 0.42. The Q-square value of 0.33 confirms the model’s predictive power(Tenenhaus et al. 2005). A variance inflation factor (VIF) of1.545 indicates that multicollinearity is not an issue at thestructural model level. To test the mediation hypothesis, wefollowed the procedure suggested by Hair et al. (2013). In afirst step, we tested whether the direct effect proposed in H1 issignificant when the mediators are not included. We found asignificant, negative effect of co-creation on customer satis-faction (β=−0.19, p<0.05), which lends additional support toH1. In a second step, we included negative disconfirmation asa mediator variable in the model. The level of co-creation hada significant, positive effect on negative disconfirmation(β=0.21, p<0.01), and negative disconfirmation had a signif-icant, negative effect on customer satisfaction (β=−0.55,p<0.01). Moreover, the direct effect of level of co-creationof satisfaction turned out to be non-significant. These resultsprovide empirical support for H2. To test the significance ofthe mediation, we bootstrapped the sampling distribution ofthe indirect effect (Preacher and Hayes 2008) and found thatthe effect of level of co-creation on customer satisfactionthrough inferred disconfirmation was significant (β=−0.12,p<0.05). The variance accounted for (VAF) of 0.57 indicatespartial mediation (Hair et al. 2013).

Additional analyses

Attribution theory is often used to explain the co-creation–satisfaction link (Bendapudi and Leone 2003; Roggeveenet al. 2012), and thus we estimated an alternative model thatcontrolled for a possible effect of internal failure attribution asan additional mediator between the level of co-creation andcustomer satisfaction. When using services high on co-creation, customers are comprehensively involved in servicedelivery. When such services fail, customers tend to perceivethe failure also as co-created (Zhu et al. 2013). Therefore, co-creation in failed service delivery might prompt internal

failure attribution, which in turn enhances negative feelingsthat result in lower satisfaction with the service outcome.While we found a significant, positive effect of co-creationon internal failure attribution (β=0.20, p<0.01), our resultsdid not reveal a significant effect of internal failure attributionon satisfaction (β=0.07, ns). Similarly, the results ofbootstrapping the sampling distribution of the indirect effectalso refute a mediation effect by internal failure attribution(β=0.01, ns). However, the mediation effect of negative dis-confirmation remained significant (β=−0.11, p<0.01). Thus,EDTappears to be a robust theoretical rationale for explainingthe psychological processes underlying customer judgmentsof co-created service failures.

Moreover, we evaluated the role of positive disconfirma-tion as a mediator between the level of co-creation and cus-tomer satisfaction in the case of successful service provision toconfirm the applicability of EDT as theoretical framework forboth service failure and success episodes. Overall, the estima-tion of the structural model yielded satisfactory results(R2=0.28; Q2=0.19; VIF=1.236). When the mediator is ex-cluded from the model, co-creation significantly drives cus-tomer satisfaction (β=0.17, p<0.05). When we include posi-tive disconfirmation as a mediator, co-creation positively af-fects positive disconfirmation (β=0.16, p<0.05), while thelatter significantly influences customer satisfaction (β=0.43,p<0.01). We bootstrapped the sampling distribution of theindirect effect and found that mediation through positivedisconfirmation was also significant in the case of servicesuccess (β=0.07, p<0.05). A VAF of 0.42 indicates partialmediation. Figure 2 illustrates all results.

Discussion of Studies 1 and 2

Studies 1 and 2 strive to extend existing research by examin-ing the dark side of customer co-creation (i.e., customerreactions after a co-created service fails). In line with previousresearch (Chan et al. 2010), we show that in the case of servicesuccess, customers who are highly involved in the co-creationof the outcome are more satisfied than customers who barelyparticipated in the value creation. However, in the case ofservice failure, co-creation triggers a greater imbalancebetween customers’ expectations of service delivery andthe actual outcome (i.e., performance). As a result,negative disconfirmation is enhanced, leading to a sig-nificant decline in satisfaction. Thus, co-creation is ben-eficial in the case of service success but detrimentalwhen the service fails. To overcome such negative ef-fects, also embedding co-creation in recovery measuresmay represent a promising approach (Roggeveen et al.2012; Zhu et al. 2013). However, empirical evidence onthe effectiveness of service recovery depending on thelevel of co-creation in the preceding service delivery islacking. In the following, we aim to address this issue.


Co-creation, service failure and customer satisfaction:the case of service recovery

Service recovery

Service recovery encompasses “the actions of a service pro-vider to mitigate and/or repair the damage to a customer thatresults from the provider’s failure to deliver a service as it isdesigned” (Johnston and Hewa 1997, p. 467). Empirical evi-dence suggests that successful service recovery positivelyinfluences customer satisfaction (e.g., Maxham andNetemeyer 2002b), customer loyalty (e.g., Maxham 2001),and customer repurchase intentions (e.g., Grewal et al. 2008).Smith et al. (1999) even find evidence of a service recoveryparadox and show that the level of satisfaction of customerswho received superior service recovery exceeds that of cus-tomers who did not encounter a problem with the initialservice. With the rise of customer co-creation, recentstudies (Roggeveen et al. 2012; Zhu et al. 2013) havealso begun exploring the effects of customer participa-tion in service recoveries.

Co-creation during service recovery

In general, co-creation during service recovery refers to “thedegree to which the customer is involved in taking actions torespond to a service failure” (Dong et al. 2008, p. 126).Accordingly, in co-created service recoveries (CSRs), cus-tomers help shape the content of the recovery (Roggeveenet al. 2012) and actively participate in finding an appropriatesolution. In contrast, a non-co-created service recovery(NCSR) is solely initiated and executed by the company.Recently, research has demonstrated that co-created recoveryefforts enhance customer satisfaction (Dong et al. 2008).However, this positive effect of customer participation inservice recovery on post-recovery evaluations seems to varywith the nature of the service failure (e.g., service delay)

(Roggeveen et al. 2012). In view of this, we propose the levelof co-creation as another feature of service failure and contendthat the level of co-creation during service delivery yieldssignificant implications for the optimal degree of customerparticipation during recovery.

Co-creation during service provision and service recovery

According to attribution theory (e.g., Folkes 1984; Weiner1986), consumers generally tend to externalize blame in ser-vice failures. As mentioned previously, because customers areactively involved in the value creation process in the case ofhighly co-created services, they often perceive a service fail-ure in such situations also as co-created (Zhu et al. 2013).Thus, customers seem to attribute the responsibility for thepoor outcome to themselves. Self-judgment of responsibilityoften evokes guilt, which in turn prompts actions to amend forthe act that created the problem (Hareli and Hess 2008; Smithet al. 2002). Thus, customers may feel guilty for the flawedservice outcome and feel obliged to solve the problem theycaused. Active engagement in recovery measures might rep-resent an opportunity for customers to alleviate their perceivedguilt. Empirical support for this notion derives from previousresearch indicating that customers only expect a refund whenservice failure is firm related rather than customer related(Folkes 1984). In an effort to transfer these ideas to a co-creation context, we infer that CSR is preferred by customerswho experienced the failure of highly co-created services.

In contrast, in the case of failed services low on co-creation,customers may externalize blame for the failure, given theirlow involvement in the service process. If customers hold thecompany accountable for service failure, they feel little guiltand expect the service provider to resolve the problem auton-omously by providing a company-initiated service recovery(Grégoire et al. 2009). Thus, in failed services low on co-creation, customer participation in service recovery isregarded as an unfair chore and can harm post-recovery

Fig. 2 Study 2: results of thestructural model


evaluations (Mills et al. 1983; Roggeveen et al. 2012). Insummary, we propose the following:

H3: In the case of a failed service high on co-creation,customers will be more satisfied after a CSR than aftera NCSR.

H4: In the case of a failed service low on co-creation, cus-tomers will be more satisfied after an NCSR than after aCSR.

H5: In the case of a failed service high on co-creation,internal failure attribution will be more intense thanfor a failed service low on co-creation.

H6: Internal failure attribution increases perceived guilt.H7: In the case of a failed service high on co-creation, the

positive effect of co-creation in service recovery oncustomer satisfaction strengthens as perceived guiltincreases.

Study 3


Study 3 aims to empirically validate the effects of co-creationin service recovery dependent on the level of co-creation inservice delivery, as proposed in H3 and H4. Similar to Study1, we used a real-life experiment.

In this study, participants were again users of the twoInternet-based services: Plane Journey and Rail Journey. Asin Study 1, participants had to book a plane ticket (throughPlane Journey) or a train ticket (through Rail Journey). Datacollection was conducted by means of an online survey usinga representative German consumer panel. We were able tomerge the Plane Journey and Rail Journey subsamples be-cause the Box’s M test was not significant (p=0.880), whichresulted in a final sample of 338 participants (for details, seeTable 3 in Appendix 1).

Method

In this study, we employed a 2 (level of co-creation in servicedelivery: high vs. low) × 3 (type of service recovery: noservice recovery [NSR] vs. NCSR vs. CSR) between-subjects design. We manipulated the independent variablelevel of co-creation in service delivery similar to the manipu-lation used in Study 1 and the independent variable type ofservice recovery in line with Dong et al. (2008). In all threescenarios, participants received an error message after havingcompleted the booking process. In the NSR condition, partic-ipants were notified that their booking had failed andwere askedto try booking again. In the NCSR condition, participants re-ceived an error message stating that the booking had failed, but

since all provided information was restored, an e-mail would besent shortly, including the booking details and a coupon for theon-board shop of the plane/train as compensation. In the CSRcondition, a user interface appeared, and the participant wasasked to enter a detailed description of the failure and to suggestan appropriate solution. Thereafter, participants received theinformation that their booking was restored, due to their detailedfailure description, and that they would soon be sent both thebooking confirmation and compensation (i.e., coupon for theon-board shop of the plane/train). Similar to Studies 1 and 2, weused three items adapted from Voss et al. (1998) to capture thedependent variable customer satisfaction.

We again conducted a pretest to assess the effectiveness ofour manipulations (n=94). To check for the recovery manip-ulations, we asked the participants whether the TBS (1) had nofailure, (2) had a failure that was not recovered, (3) had afailure that was recovered solely by the company, or (4) had afailure that was recovered with the help of the customer. Asthe vast majority of the participants (1=97.62%, 2=95%,3=90%, and 4=100%) recognized the correct service recov-ery, we consider our manipulation successful. To checkwhether participants who co-created the service were morelikely to attribute service failure internally, we asked all par-ticipants to rate the responsibility for the service failure on aseven-point scale (1 = participant himself/herself, 7 = serviceprovider). According to our results, customers who co-createdthe service were more likely to attribute the failure internally(Mcc_high=2.80) than customers who were not actively in-volved in service delivery (Mcc_low=5.15; F(1, 92)=68.362,p<0.01). To assess the recovery participation manipulation,participants had to indicate their involvement in the servicerecovery process (1 = not actively involved, 7 = activelyinvolved). Participants claimed to be significantly more ac-tively involved in the CSR (Mcsr=5.50) than the NCSR(Mncsr=2.40) condition, providing support for the validity ofour manipulation (F(1, 38)=96.10, p<0.01).

Main study results

Our manipulation checks indicated an effective experimentaldesign. In order to assess the degree of co-creation manipula-tion, we used the same measures as in the preceding studies.Across the two levels of co-creation, significant differencesexisted between the degree of customization (Mcus_high=4.38,Mcus_low = 2.33; F(1, 336) = 129.61, p<0.01), effort(Meff_high=5.70, Meff_low=3.93; F(1, 336)=83.12, p<0.01),and information sharing (Minf_high=4.52, Minf_low=2.38;F(1, 336)=141.18, p<0.01). Moreover, we checked for therecovery participation manipulation using the same measuresas in the pretest. This manipulation check was also successful(Mncsr=2.30, Mcsr=5.36; F(1, 336)=539.15, p<0.01).

To test our hypotheses, we again employed the ARTprocedure, as the data were not normally distributed


(D(338)=2.599, p<0.01) (Sawilowsky 1990). The resultsof the adjusted ART ANOVA show a significant interactioneffect of level of co-creation and type of service recovery oncustomer satisfaction (F(5, 332)=7.580, p<0.01). In addition,participants who experienced a service failure after being high-ly involved in the service delivery (high co-creation) weremoresatisfied with CSR (Mcsr=4.75) than with NCSR (Mncsr=4.00;F(1, 115)=5.239, p<0.05), lending empirical support to H3. Inthe case of low co-creation in the service delivery phase, post-recovery satisfaction was greater after an NCSR (Mncsr=5.08)than after a CSR (Mncsr=3.84 ; F(1, 113)=14.996, p<0.01),confirming H4. A detailed description of these results appearsin Table 2 and Fig. 3.

Study 4


In addition to replicating the results found in Study 3, this studyinvestigates the psychological processes underlying the effects

of co-creation in service recoveries dependent on the level ofco-creation employed in service delivery. By using a scenario-based experiment, we intend to empirically evaluate H5 to H7.

We used the same scenario-based experimental design as inStudy 2 (i.e., the fictional Internet-based service DYS—De-sign Your Shoes) as the setting for Study 4. Similar to Study 2,participants received a brief scenario description of the serviceprovider, its offerings, and their role as a customer in the study.Our final sample consisted of 265 participants recruited from aGerman consumer panel (for details, see Table 3 inAppendix 1).

Method

We used a 2 (level of co-creation in service delivery: high vs.low) × 3 (type of service recovery: NSR vs. NCSR vs. CSR)between-subjects design. In this study, we used the samemanipulations for level of co-creation in service delivery asin Study 2. Similar to the manipulation used in Study 3 and inline with Dong et al. (2008), we differentiated three recoveryscenarios in which the service provider offers NSR, an NCSR,

Table 2 Study 3: means and standard deviations for customer satisfaction with TBS


Independent variables Type of service recovery

NCSR CSR NSR

Level of customer co-creation Low 5.08a,b,c (1.471) 3.84c (1.962) 3.64b (1.610)

High 4.00a (1.889) 4.75a,b,c (1.632) 3.11a,b,c (1.528)

Standard deviations are in parentheses. Values with the same superscript are significantly different from each other at p<0.05; all other values do notsignificantly differ

Fig. 3 Study 3: interaction effectbetween level of co-creation andservice recovery on customersatisfaction with TBS


or a CSR. In the NSR scenario, participants were informedthat the service failed and a wrong version of the shoe wasdelivered; they were not offered compensation. Participants inthe NCSR condition read that after being informed of theservice failure (i.e., that the wrong version of the shoe wasdelivered), a service employee autonomously initiated therecovery process and proactively provided compensation (of-fering a 10% discount and letting customers choose from threealternative pairs of shoes). In the CSR condition, recovery wasdescribed as rather laborious, including intense customer par-ticipation. Participants were told that they were activelyparticipating in the recovery process, searching for al-ternative products, and discussing the fit of possiblealternatives with the employee. Participants in the CSRcondition received the alternative product they selectedand a 10% discount as compensation (for detailed sce-nario descriptions, see Appendix 2).

Participants were asked to report their level of satisfactionwith the described TBS on a scale proposed by Voss et al.(1998). We measured internal failure attribution using fouritems adapted from Dong et al. (2008). We assessed perceivedguilt following Lau-Gesk and Meyers-Levy (2009). All itemswere measured on seven-point Likert scales, anchored bytotally disagree (1) and totally agree (7) (see Table 5 inAppendix 1). For the level of co-creation in service delivery,we again used dichotomous (binary) information to construct adummy variable (0 = low co-creation, and 1 = high co-creation).Similarly, for the level of co-creation in service recovery, wealso used dichotomous (binary) information to construct a dum-my variable (0 = NSR, and 1 = NCSR or CSR).

In the pretest (n=114), we used the same manipulationchecks as in Study 3. As most of the participants(1=89.66%, 2=90.65%, 3=88%, and 4=95.83%) recognizedthe right service recovery, we consider our manipulation suc-cessful. Furthermore, a significant effect of level of customerco-creation on failure attribution (F(1, 47)=20.33, p<0.01)was confirmed; participants who co-created the service deliv-ery were more likely to attribute the failure internally(Mcc_high=4.73), while participants who did not co-create theservice were more likely to externally attribute the servicefailure (Mcc_low=6.24). The manipulation of service recoverywas also successful; participants felt more actively involved inthe CSR than the NSR condition (MCSR=4.65, MNCSR=2.78;F(1, 36)=10.71, p<0.01).

Main study results

First, we tested the effectiveness of our manipulations,employing the same measures as in Study 3. The manipula-tions worked as intended. Participants rated the degree ofcus tomiza t ion (Mcu s_h i g h = 5.59, Mcu s_ l ow = 4.86;F(1, 265) = 16.20, p<0.01), effort (Meff_high = 4.48,Meff_low=3.74; F(1, 265)=15.73, p<0.01), and information

sharing (Minf_high=5.33, Minf_low=3.92; F(1, 265)=53.97,p<0.01) differently across the two levels of co-creation.Moreover, the manipulation check of degree of participationin service recovery was successful (MNCSR= 2.30,MCSR=5.36; F(1, 265)=539.15, p<0.01).

Because of the non-normal distribution of the data(D(217)=2.794, p<0.01), we again used PLS structural equa-tion modeling for hypotheses testing. In a first step, we eval-uated the construct measurements. As Table 5 in Appendix 1shows, all fit criteria exceeded common thresholds (Bagozziand Yi 1988). To evaluate H3–H7 at a structural model level,we assessed the path coefficients and their significance levelsin two separate analyses.

To evaluate H3 and H4, and thus to replicate our findingsfrom Study 3, we first run a PLS-SEM multigroup analysis(Hair et al. 2013), and compared the direct effects of CSR andNCSR on customer satisfaction in the case of low and highlevels of co-creation. In the model for highly co-created ser-vices, the direct effect of CSR on customer satisfaction(β=0.59, p<0.01; R2=0.39, Q2=0.36) was stronger than thedirect effect of NCSR (β=0.42, p<0.01; R2=0.22, Q2=0.19)on satisfaction. Applying the modified version of the two-independent-samples t-test suggested by Keil et al. (2000), wefound that this difference between coefficients was also sig-nificant (p<0.10). Thus, we find empirical support for H3. Inthe model for low co-creation in service delivery, the directeffect of NCSR on customer satisfaction (β=0.48, p<0.01;R2=0.27, Q2=0.26) was larger than the corresponding effectof CSR (β=0.25, p<0.01; R2=0.15,Q2=0.14). The differencebetween the path coefficients was again significant (p<0.05).Thus, H4 also receives support.

Second, we ran a separate model to validate the underlyingpsychological processes proposed in H5–H7 (see Fig. 4). Wefound that the level of co-creation in the initial service deliveryincreased internal failure attribution (β=0.17, p<0.01), pro-viding support for H5. In turn, internal failure attribution had asignificant, positive effect on perceived guilt (β=0.29,p<0.01), confirming H6. Finally, to evaluate the moderatingeffect of perceived guilt on the link between level of co-creation during service recovery and customer satisfaction,we used the product term approach (Hair et al. 2013). Follow-ing this approach, we multiplied each item representing theindependent construct (co-creation in service recovery, X) byeach item representing the moderating construct (perceivedguilt, Z) to create an interaction term (X, Z). All indicatorvalues were standardized prior to building product terms inorder to minimize multicollinearity. The hypothesis on themoderating effect is supported if the path coefficient of theinteraction term is significant—regardless of the direct rela-tionships of the exogenous and the moderator variable (Baronand Kenny 1986; Henseler and Fassott 2010). In support ofH7, our results confirm a significant and positive effect of theinteraction term on satisfaction (β=0.21, p<0.01). The


positive interaction term can be interpreted as follows (Hairet al. 2013). A medium level of perceived guilt serves as areference point. For this level of perceived guilt, the relation-ship between level of co-creation in service recovery andsatisfaction has a value of 0 as the direct effect was notsignificant (β=−0.08, ns). If perceived guilt would increaseby one standard deviation point, the strength of relationshipbetween level of co-creation in service recovery and satisfac-tion will increase by the size of the interaction term(i.e., 0+0.21=0.21). Hence, in case of high levels of perceivedguilt, co-creation in service recovery has a positive effect onsatisfaction. The opposite holds for lower levels of perceivedguilt (i.e., decrease by one standard deviation point). Here, co-creation in service recovery would have a negative effect (0–0.21=−0.21) on satisfaction.

Discussion of Studies 3 and 4

Studies 3 and 4 evaluate the type of service recovery thatrepresents an effective means to rectify failure of services thatdiffer in their level of co-creation. Our findings indicate thatcustomers who experience a mistake in the service delivery ofa highly co-created service tend to blame themselves for theflawed outcome and thus feel a sense of guilt. In such cases,CSR proves to be the most effective recovery strategyto restore customer satisfaction. More specifically, per-ceived guilt strengthens the link between the level ofco-creation in recovery and satisfaction with recovery.In other words, actively involving customers in over-coming a jointly induced service failure might helpthese customers reduce the negative cognitive disso-nance resulting from perceived guilt for the flawedservice provision.

General discussion

Theoretical implications

Academic research continues to devote considerable attentionto customer co-creation. However, an important but largelyoverlooked research area is the dark side of customer co-creation. The current research strives to contribute to theoryand practice by shedding some light on this issue. Our re-search goals were threefold: (1) to determine the ramificationsof co-creation on customer satisfaction in the case of servicefailure, (2) to investigate the effectiveness of a CSR after afailed co-created service delivery, and (3) to empirically val-idate psychological processes intervening in the relationshipbetween the level of co-creation in service delivery (recovery)and customer satisfaction. The findings of our empirical stud-ies contribute to the current understanding of customer co-creation in several ways.

First, our study is the first to empirically compare theeffects of low and high levels of co-created services on cus-tomer satisfaction after service failure. We extend previousresearch by demonstrating that in the case of service failure, ahigh level of co-creation in the initial service leads to lowercustomer satisfaction caused by negative disconfirmation.Thus, although co-creation by means of TBSs allows serviceproviders to improve service effectiveness in a success case(Weitjers et al. 2007), this strategy represents a double-edgedsword in a failure case. More specifically, providers of TBSstypically struggle with achieving a critical mass of TBS users(Curran et al. 2003). Failed co-creation is therefore harmful intwo respects: when the service performance falls short ofcustomers’ expectations (negative disconfirmation) and whencustomers are dissatisfied with the co-created service. In light

Fig. 4 Study 4: results of thestructural model


of this, functional attributes of TBSs, such as perceived ease ofuse or reliability, gain increased importance for preventing co-creation’s potential dark side (e.g., Dabholkar and Bagozzi2002; Meuter et al. 2005). While functional indicators shouldenhance TBS usability and reduce service failures, hedonicaspects such as fun and excitement should help mitigateoverly negative customer judgments, even if the co-createdservice delivery fails (Childers et al. 2001). According to flowtheory (Csikszentmihalyi 1988), enjoying aspects of the ser-vice encounter helps emphasize the actual activity rather thanthe service outcome. Thus, the one-dimensional focus ofcustomers on the service result is extended by a moreprocess-oriented measure. This holds true particularly forInternet-based service encounters, which yield strong affec-tive cues, such as interactivity and hypermediality, and evokestrong emotional responses (Hoffman and Novak 1996).

Second, we extend previous research by showing that co-creation of service recovery measures does not always en-hance post-recovery satisfaction. In line with Dong et al.(2008), we empirically validate that customer participationin the recovery process results in greater satisfaction, whencustomers co-created the service failure. Our findings alsosuggest that NCSR leads to greater post-recovery satisfactionthan CSR when customers are not involved in the initialservice delivery. In view of this, service providers should holdthe level of co-creation constant across the entire value crea-tion process. These findings extend both research on customerrevenge after failures (e.g., Grégoire et al. 2009) and theservice recovery literature (e.g., De Matos et al. 2007). Withrespect to the former, selecting the appropriate recovery strat-egy offers the chance to forestall retaliatory customer responseto a “double deviation” (i.e., failed service delivery and failedrecovery) (Maxham and Netemeyer 2002a). Regarding thelatter, choosing the optimal level of co-creation in recoverycould help achieve a service recovery paradox (De Matos et al.2007). Our findings suggest that such a service recovery para-dox might only occur when a failed service that the customerbarely helped co-create (i.e., low level of co-creation) is coun-tered with an NCSR. Although the mean difference in satisfac-tion in the case of successful service delivery (Msucc=4.85) andin the case of NCSR (MNCSR=5.08) after a low co-createdservice failure was not significant (F(1, 135)=0.687, ns), it stillprovides directional support for our proposition.

Third, our findings provide insights into the underlyingpsychological processes shaping customer judgments afterco-created service delivery and recovery. With regard to co-creation in service delivery, our results indicate that negativedisconfirmation is enhanced when highly co-created servicesfail. As a result, customer satisfaction ratings decline. Activecustomer involvement in the service delivery process triggersincreased expectations compared with service provision lowon co-creation. This finding extends literature on EDT to thefield of co-creation (Oliver 1997; Spreng and Page 2003).

With regard to co-creation in service recovery, our resultsconfirm that customer co-creation enhances internal failureattribution and, thus, perceived guilt after initial service pro-vision has failed. This finding adds to research in the field ofconsumer psychology stating that particularly when cus-tomers feel control over a situation, guilt feelings are to beexpected after co-created failures (Hareli and Hess 2008;Weiner 1986). We propose that co-creation increases suchfeelings of control during a service encounter because ofcustomers’ active role during service production. Service pro-viders should be aware of this mechanism after co-createdservice failures and offer broad customer participation (i.e.,control) during the service recovery episode. In doing so,customers might alleviate perceptions of guilt for the initialfailure and restore their emotional equilibrium. This mecha-nism may serve as a theoretical rationale for the already-described matching logic regarding the level of co-creationin service provision and recovery.

Managerial implications

First, managers must be aware of the potential negativeconsequences of offering highly co-created services. Ourfindings show that in the case of highly co-createdservice failure, perceived negative disconfirmationarises, which leads to greater dissatisfaction. Further-more, our results indicate that the volatility of customersatisfaction is much stronger for high levels than forlow levels of co-created services (see Fig. 1). Thus,successfully co-created services can boost customer sat-isfaction because they yield benefits for service pro-viders and customers alike. They allow firms to unlocknew sources of competitive advantage, and they providecustomers with offerings tailored to their needs. How-ever, our findings show that there is also a dark side tohighly co-created services, as customer satisfaction isextremely low in the event of a failure. Given thatcustomer satisfaction leads to customer retention andpositively affects company profits (Anderson andMittal 2000), managers should carefully weigh the risksand benefits of providing co-created offerings and eval-uate which co-creation level best fits their overall busi-ness model. That is, service firms should evaluate theirknowledge, skills, and resources to determine their co-creation potential. Successful co-creation requires orga-nizations to build long-term and interactive relationshipswith their customers, to adapt communication activitiesand value propositions according to these relationships,to encourage customer participation in each stage of thevalue creation process, to focus on operant resources asthe unit of exchange, to support customer learning, andto foster organizational learning (Payne et al. 2008).Thus, companies that fulfill these requirements may be


able to keep service failure rates at a minimum and providesuccessfully co-created services. Service providers that meetthese requirements, but have only limited experience in co-creation, could consider offering services low on co-creationuntil they have gained the necessary expertise to provide suc-cessful highly co-created services.

Second, to harness the benefits of highly co-created ser-vices while minimizing the associated risks, firms shouldinitiate measures to minimize the possibility of co-createdservice failure caused by human mistakes. For example, firmscould optimize the usability aspects of the service and providedetailed and customer-friendly instructions and aids. In thecase of highly co-created TBS, comprehensive supportfunctions, well-arranged content, low downtimes, andintuitive navigation are of high importance (Baueret al. 2006). Furthermore, each stage of the co-creationprocess should be constantly reviewed, and features thatinduce problems should be eliminated. Setting up onlineconsumer communities might help in this respect. Onthe one hand, such platforms allow users to share theirexperiences from failed co-created service encountersand help others avoid making the same mistakes. Onthe other hand, consumer communities allow firms tobetter understand consumers’ value creation processesand to detect and deter user errors. Finally, seminarscould be offered to train customers in using the co-created service and to introduce first-time users to thevalue creation process.

Third, service failures are inevitable in most, if notall, service contexts, and thus service providers shouldstrive to enhance customer experience to increase satis-faction with the overall service. While the service out-come generally yields a strong impact on satisfactionratings, ensuring an exciting co-creation experiencecould mitigate negative consequences after co-createdservice failure. Affective cues might emphasize the ac-tual co-creation experience and, as an additional judg-ment criterion to the mere service outcome, preventpurely result-oriented customer judgments. In otherwords, designing a joyful co-creation experience mightbe an effective approach to put customer evaluations ona broader foundation.

Fourth, firms need to have an effective recovery system inplace to offset the negative effects of service failures. Overall,our findings indicate that service recovery strategies should bealigned with the level of co-creation in service delivery. In thecase of services high on co-creation, companies should offercustomers the possibility of alleviating perceived guilt withinservice recovery and thus use CSR-related measures. TBSproviders especially need to retain customers after a failureand prevent them from switching from the interface after anerror occurs. These firms should not only use automated errormessages but also directly encourage co-creation by

motivating customer to provide suggestions for possible solu-tions. In the case of services low on co-creation, companiesshould merely execute the recovery in terms of NCSR-relatedmeasures. Here, users should be ensured that the service firmtakes responsibility for the recovery. TBS providers shouldmake service employees available to assist and guide con-sumers through the recovery process. Moreover, to immedi-ately provide appropriate compensations, TBS providersshould assess customer preferences from previous interactionsor user profiles in their databases.

Limitations and directions for further research

As with any research effort, this study has certain limitations.First, we chose TBSs for ticket booking and customizingshoes as research contexts. Although these services are prom-inent examples of TBSs, we cannot rule out the possibility thatcustomer reactions will be different for co-created services inother categories. Thus, we propose that future studies try toreplicate our findings in a different service context, such asonline games or social networks. To further enhance thegeneralizability of our findings beyond the realm of TBSs,research could also validate our study for non-technology-based, co-created service settings.

Second, we used cross-sectional data to investigate theeffects of co-created service delivery on customer satisfaction.To confirm our findings, we encourage researchers to assessthe effects of the dark side of co-creation over time. Recurringfailures in highly co-created services might have tremendousnegative effects on customer loyalty. Further research mightreveal such effects on long-term relationships between com-panies and customers using longitudinal data.

Third, in addition to examining the effects of level of co-creation on satisfaction with the service process or outcome,research might explore the ramifications of failed co-creationfor customer satisfaction with the service provider. Morespecifically, our findings suggest that co-creation in failedservice delivery prompts internal failure attribution, which inturn compels the customer to share some blame for the flawedoutcome. As a result, and in contrast with declining satisfac-tion with the service, overall satisfaction with the serviceprovider might not be harmed.

Finally, we manipulated the type of service recoveryby implementing an NCSR that is merely executed bythe company and a CSR that is characterized by highcustomer involvement. Nonetheless, other than the levelof co-creation in the recovery process, both CSR andNCSR can differ in companies’ reactions to servicefailure. Thus, further research could add to our findingsby investigating the negotiation and compensation pro-cesses between customers and service employees andexamining how these mechanisms affect the effective-ness of CSRs and NCSRs.


Appendix 1

Table 3 Descriptive analyses of samples

Gender (Age) Education Income

Male Female Graduate degree High-school diploma Secondary school certificate <35,000 €

Study 1 47.9% (47.3 years) 52.1% (40.7 years) 37.3% 22.5% 12.4% 51.7%




Table 4 Study 1 and Study 2 pretest results of experimental manipulations

Scenario manipulations: Study 1

Customization Effort Information sharing

Mean SD f-value Mean SD f-value Mean SD f-value

High level of co-created TBS 5.85 1.13 79.73* 5.95 1.26 131.13* 5.07 1.52 88.46*Low level of co-created TBS 3.38 1.47 2.75 1.39 2.51 1.12

Service failureService failure 1.96 0.19 1027.7*Service success 1.00 0.00

Scenario manipulations: Study 2

Customization Effort Information sharing

Mean SD f-value Mean SD f-value Mean SD f-value

High level of co-created TBS 6.57 1.59 70.16* 5.45 1.78 28.23* 6.21 1.28 47.01*Low level of co-created TBS 4.66 0.59 3.71 1.67 4.23 1.75

Service failureService failure 1.96 0.19 663.36*Service success 1.00 0.00

*Significant at p<0.01

Table 5 Measurement model results

Loading (λi) Sig. (t-value)

Pre-rating CR=0.971 AVE=0.894

If I were to use this TBS, I would receive an excellent overall service. 0.942 73.490

If I were to use this TBS, I would receive a high quality service. 0.947 72.468

If I were to use this TBS, I would receive a highly reliable service. 0.937 65.159

If I were to use this TBS, I would receive a very good service. 0.957 99.425

Post-rating CR=0.973 AVE=0.902

The service provided by the TBS was poor/excellent. 0.965 138.54

The service provided by the TBS was very low quality/very high quality. 0.933 71.28

The service provided by the TBS was unreliable/reliable. 0.940 62.19

The service provided by the TBS was very bad/very good. 0.960 85.46

Satisfaction (failure case of study 2) CR=0.984 AVE=0.955

Unsatisfied/satisfied with the use of the TBS. 0.984 230.52

Unhappy/happy with the use of the TBS. 0.967 103.56

Unpleased/pleased with the use of the TBS. 0.981 203.19


Appendix 2

Introduction of scenario descriptions

In the newspaper, you read that a new service is being offeredat your favorite sports outfitter: “DYS—Design-Your-Shoes”.This service allows you to design a sports shoe accord-ing to your individual preferences and needs directly inthe store using special computer software. After placingthe order, the shoes are customized specifically for you

and can be collected on site after a maximum period of14 days. In order to facilitate the ordering process asmuch as possible, an employee assists you with thehandling of the computer program as well as with thechoice of shoes and the final order.

Please picture the following situation You visit the sportsoutfitter in order to give the new offer “DYS—Design-Your-Shoes” a try and to order a pair of sports shoes. After arrivingat the store, you ask an employee about the new service

Table 5 (continued)

Loading (λi) Sig. (t-value)

Internal Failure Attribution CR=0.971 AVE=0.892

In my view, the service provider is fully responsible for the service failure. (reversed) 0.910 43.75

The problem that led to the service failure was clearly caused by the service provider. (reversed) 0.960 155.18

The service failure that I encountered was entirely service provider’s fault. (reversed) 0.967 164.43

The service provider is solely responsible for the service failure. (reversed) 0.940 75.13

Perceived Guilt CR=0.941 AVE=0.798

With regard to the incidence of the service failure, I feel guilty. 0.926 59.90

With regard to the incidence of the service failure, I feel repentant. 0.864 26.58

With regard to the incidence of the service failure, I feel blameworthy. 0.873 28.77

With regard to the incidence of the service failure, I feel responsible. 0.909 40.00

Satisfaction (recovery case of study 4) CR=0.976 AVE=0.931

Unsatisfied/satisfied with the use of the TBS. 0.968 181.40

Unhappy/happy with the use of the TBS. 0.955 111.46

Unpleased/pleased with the use of the TBS. 0.971 193.40

TBS is used as placeholder for the respective service, i.e. “Train Journey”, “Plane Journey” or “DYS—Design-Your-Shoes”

Table 6 Means for customer satisfaction and PLS estimates for Studies 2 and 4


Service outcome

Independent variable Service success NCSR CSR NSR

Customer co-creation Low 5.27 (1.147)a,b,c 4.83 (1.533)a,b,c 3.81 (1.309)b 3.05 (1.732)a,b,c

High 5.69 (1.110)a,b,c 3.85 (1.778)c 4.33 (1.331)a,b,c 2.41 (1.398)a,b,c

PLS estimates

Effect β t-value

Study 2 Co-creation in service delivery → satisfaction with TBS −0.088 1.421

Co-creation in service delivery → negative disconfirmation 0.207 2.587

Negative disconfirmation→ satisfaction with TBS −0.554 9.092

Co-creation in service delivery → internal failure attribution 0.200 2.384

Internal failure attribution→ satisfaction with TBS 0.068 1.120

Study 4 Co-creation in service delivery → internal failure attribution 0.166 2.786

Internal failure attribution→ perceived guilt 0.296 4.801

Co-creation in service recovery × perceived guilt→ satisfaction with TBS 0.212 3.664

Values with the same superscript are significantly different from each other at p<0.10; all other values do not significantly differ


“DYS—Design-Your-Shoes”. The employee leads you to acomputer and offers to assist you with navigating through themenu. Together you start the program.

Level of co-creation manipulation

Low co-creation The employee explains that you have tocreate a profile in order to use “DYS—Design Your Shoes”.This requires your name, shoe size, and the type of sports youwish to wear the shoes for. You give this information to theemployee, who enters it into the provided template. Afterproviding this information, three types of shoe models thatare most suitable for the specified sport automatically appearon the screen. You choose one of the models and, in the nextstep, can define the color of the shoes. You confirm the colorand the employee finalizes the order.

High co-creation The employee explains that you have tocreate a profile in order to use “DYS—Design YourShoes”. This requires your contact details, includingyour name, address, email, and telephone number, aswell as your body height, weight, and shoe size. Addi-tionally, you are asked for which sport you will wearthe shoes as well as how often (hours per week) and atwhich level (Beginner/Intermediate/Expert) you practicethe sport. Together with the employee you enter theinformation into the provided template. In a next step,the employee measures your feet and analyses yourrunning style on a treadmill. This data is also enteredinto the program to complete the profile.

On the basis of the supplied data, three different models arelisted that are tailored to your particular needs and providedinformation. You choose one of the models. In the next step,you have the opportunity to define the shoe’s color, the colorand texture of the sole, the material to be used, the color of theshoelaces as well as the color of the lining. In addition, youcan have an individual phrase printed on the shoe, for exam-ple, your name at the top of the shoe tongue. You make yourdecisions and together with the employee confirm your choiceand finalize your order.

Service failure manipulation

No service failure After seven working days you visit thesports outfitter in order to collect your shoes. After openingthe shoe carton you notice that the sports shoes exactly meetyour expectations. You pay and leave the store.

Service failure After seven working days you visit the sportsoutfitter in order to collect your shoes. After opening the shoecarton you notice that the sports shoes do not match the modelyou asked for. You approach the employee that assisted youwith your order. He cannot say if the mistake lies in the

production process or if you accidentally entered false infor-mation during the process of ordering.

Service recovery manipulation

NSR The employee apologizes several times, but informs youthat the shoes are not returnable. You pay and leave the storewith the wrong shoe model.

NCSR To spare you from buying the wrong shoe model theemployee offers you an exchange. You accept this offer. Theemployee calls up your “DYS—Design Your Shoe” profileand searches for alternative sports shoes in the warehouse onthe basis of the information your profile provides. After a fewminutes he presents three different sports shoe models for youto choose from. They are all in the design you wished for andtailored to your particular needs. You choose one of the pairs.The employee gives you a 10% discount on the original priceas a compensation for the inconvenience you experienced.You pay and leave the store with your new shoes.

CSR To spare you from buying the wrong shoe modelthe employee offers you an exchange. You accept thisoffer and suggest that you could jointly search for analternative shoe with your preferred design using theprovided profile data. Together you go to the computer,upload your “DYS—Design Your Shoe” profile, andmatch it against all available shoes in the warehouse.You then jointly discuss the respective accuracy of fit ofthe available models. Together you find three sportsshoe models that are all in the design you wished forand tailored to your particular needs. You choose one ofthese pairs. The employee gives you a 10% discount onthe original price as a compensation for the inconve-nience you experienced. You pay and leave the storewith your new shoes.

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