RELATIVE IMPORTANCE OF SERVICE RECOVERY ATTRIBUTES IN AN
ONLINE SELF-SERVICE TECHNOLOGY CONTEXT
A Conjoint Approach
By
JEROEN HILLEMANS
University of Groningen
Faculty of Economics and Business
Msc Marketing Management & Marketing Research
1
ABSTRACT
Service recovery is considered an important part of building and improving a company’s relationship with their customers. Based on previous literature, this research presents an empirical analysis of the role several of the most prevalent service recovery attributes play in an online self-service technology context. Furthermore, several moderating variables are included in the analysis to determine if segments can be identified. This study uses data collected through 199 individually completed questionnaires that contained two experimental conditions (severity of the service failure and relationship quality) as well as a conjoint task. Using this data, a traditional conjoint analysis (both aggregate and segmented analyses) is performed in order to find out which attributes are the most important. The results show that timeliness is the most important service recovery attribute, followed by attentiveness and credibility. Also, the implications of this research are mostly managerial since the segments that were found can be used by e-tailers to determine an optimal online service recovery. However, this research does provide a basis for other researchers to further study online service recoveries and the importance of recovery attributes.
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1.Introduction
In today’s service-focused society it is becoming increasingly important for service organizations to handle service failures in a way that is satisfactory for their customers. These service recoveries are actions a company takes in response to a service failure (Gronroos, 1988). Service failures and failed recoveries are among the most important reasons for customers to switch service providers (Keaveney, 1995; Smith, Bolton & Wagner, 1999). Furthermore, research shows that 48% of consumers tell ten or more people when they experience bad service as opposed to only 23% when the service was satisfactory (Dixon, Freeman & Toman, 2010). This means quality service recoveries are a crucial element in building relationships with customers and preventing customer defection and can even be seen as a substitute for offensive marketing techniques such as advertising or price (Fornell & Wernerfelt, 1987).
According to Berry and Parasuraman (1991) only 50% of customers that experience a service failure are satisfied with the recovery provided by the company. This indicates that even though research shows that service recovery can be a very profitable endeavor (Hart, Heskett & Sasser, 1990), (e-tail) companies do not always pay enough attention to it yet. Furthermore, the number of customers that are satisfied with the recovery provided by the company is even lower in the case of self-service technologies (Forbes, 2008). A self-service technology (SST) can be described as a technological interface that allows consumer to produce a good or service themselves, without any direct contact with the service provider (Meuter, Ostrom, Roundtree & Bitner, 2000). Examples of SST’s include internet purchasing, ATM machines, ticket machines and telephone banking (Forbes, 2008).
3 Taking these previous arguments into account, it is also surprising there are so few large-scale studies that have focused on service recovery in a SST context. Despite the staggering growth in online spending and online SST’s (Forbes, Kelley & Hoffman, 2005), research on service recovery issues has mostly been limited to traditional service encounters, which are service situations in which the consumer and the service provider interact with each other face-to-face. For an overview of research on traditional service recovery, see Orsingher, Valentini & De Angelis (2010) or Gelbrich & Roschk (2011). Failure and recovery issues regarding SST’s that were already considered in previous research include: Typologies of failure and recovery strategies in a SST context (Forbes et al., 2005; Forbes,2008), investigation of satisfying and dissatisfying incidents in SST’s (Meuter et al., 2000), outcomes associated with recovery strategies in online SST’s (Fan, Wu & Wu, 2010; Sousa & Voss, 2009; Wang, Lin & Phongkusolchit, 2010) and factors influencing the choice for a SST (Gelderman, Gheisen & van Diemen, 2011). However, the relative importance of different attributes related to this type of service recovery has not been covered yet.
In order to gain more insight in the importance of service recovery attributes, the following main research question will be covered in this paper:
‘Which attributes of service recovery are the most important when considering satisfaction with the service recovery in an online self-service technology context?’
This research question has been split up into several sub-questions which will help answer the main question. These are:
1. Which are the most prevalent service recovery attributes according to literature? 2. What are the differences between online and offline service recovery and how
important is each attribute in the online context?
3. Which factors have a moderating effect on the relationship between the recovery attributes and satisfaction with the service recovery?
4. How can practitioners use the importance of service recovery attributes to develop the ‘ideal’ service recovery?
4 important to gain more knowledge about the relative importance of service recovery attributes in this specific context, which will also allow practitioners to provide customers with the ‘ideal’ service recovery. This study will also look at several relevant moderators, to see whether or not these have an impact on the relative importance of the recovery attributes.
Currently, there is a lack of understanding of how customers evaluate and react to recovery efforts by online firms and what is considered a successful recovery in this context (Holloway & Beatty, 2003; McCollough, Berry & Yadav, 2000). Also, Sousa & Voss (2009) argue that it is important to understand what attributes differentiate recovery that leads to high satisfaction from recovery that leads to low satisfaction. As was mentioned previously, only a handful of papers have investigated the impact of different aspects of service recovery on postcomplaint customer behavior (e.g. Smith et al., 1999; Tax, Brown & Chandrashekaran, 1998) and none of these focus on the importance of these attributes in an online context. Being able to quantify the costs and benefits of service recovery actions will have a large impact on customer loyalty and customer retention theory (Davidow, 2003). Therefore, the purpose of this research is to fill this gap in current literature by finding out which of the service recovery attributes are the most important to consumers by using a conjoint approach. This means that, on the one hand, this paper will provide a theoretical basis for service recovery in an online SST context, while on the other hand, it also provides useful practical knowledge for e-tail marketing managers.
This paper will continue by discussing the theoretical framework underlying the research subject in chapter two. Also, it will cover previous research on relevant service recovery attributes and clarify the boundaries of the study. Furthermore, research on service recovery and self-service technologies will be reviewed and the differences between online and offline SST’s with respect to service recovery will be discussed. In chapter three the methodology of this research will be explained and after that the results of the statistical analyses will be presented in chapter four. Finally, chapter five will use these results to formulate a conclusion in the form of theoretical- and practical implications, the limitations of the study and suggestions for further research will be given.
2. Theory
5 discussed and the relevant concepts will be defined for the purpose of this research. Finally, the service recovery attributes found in literature will be discussed as well as the moderating variables (see figure 1 below for the conceptual framework).
2.1 Service failure and recovery
Organized service recovery procedures and programs are crucial tools for organizations that can be used to maintain customer satisfaction and loyalty (Fornell & Wernerfelt, 1987; Holloway & Beatty, 2003; Wang et al., 2010). Bitner, Booms & Tetreault (1990) found that proper service recovery can transform service failure into a satisfactory experience. A service failure can be described as a real or perceived breakdown of the service in the form of a process- or outcome failure (Duffy, Miller & Bexley, 2006) or in a more general sense as any service mishap or problem that occurs during a customer’s experience with a company (Maxham, 2001). Furthermore, Holloway & Beatty (2003) argue that a service failure occurs when the actual (perceived) service experience falls below the expected level. Such service failures are costly to firms and can result in declining customer loyalty and negative word of mouth (Bitner, Brown & Meuter, 2000). Moreover, on the other side of the medallion satisfaction with the service recovery has a direct impact on trust and commitment (Tax et al., 1998) which means effective service recovery procedures should be in place. Gronroos (1988) defined service recovery as actions the focal company takes in response to a perceived service failure in order to address a customer’s complaint, whereas Miller, Craighead & Karwan (2000) described it as those actions that were designed to resolve problems, change negative attitudes of dissatisfied consumers in order to retain these consumers. As can be seen, the second definition adds some depth to the classic definition by Gronroos (1988). The whole service failure/recovery encounter was described by Smith et al. (1999: 357) as: ‘an exchange in which the customer experiences a loss due to the service failure and the organization attempts to provide a gain, in the form of a recovery effort, to make up for the customer’s loss’. This provides a clear explanation of the general dynamic of a service recovery encounter and is applicable even in the context of this paper.
6 the impersonal nature of online SST’s there are several differences between offline and online service recovery. These differences will be discussed in the next section.
2.2 Differences between online and offline service recovery
Recent developments in the field of services has lead to a stronger emphasis on online services and by extension service recovery (Sousa & Voss, 2009; Wang et al., 2010). Previous literature found that the Internet does not fundamentally change traditional marketing principles (Barwise, Elberse & Hammond, 2002) which means that insights gathered from offline service recovery research are also relevant in an online context (Holloway & Beatty, 2003). However, there are still several important differences that are important for this paper and will therefore be discussed in this section.
The first difference is the fact that the interpersonal interaction that is so crucial to traditional service recovery has been replaced by technology in the online world (Holloway & Beatty, 2003; Holloway, Wang & Parish, 2005). Previous research shows that the perception of an interpersonal service encounter is positively influenced by the development of a service relationship (Gutek, Bhappi, Liao-Troth & Cherry, 1999). Since these kinds of relationships are very difficult to achieve online, these advantages are not present for e-tail organizations. Furthermore, the presence of social contact in general can help mitigate the damage done by a service failure in a traditional setting, but not in an online setting (Forbes et al.,2005) which indicates it is harder to effectively recover from service failure online.
Second, service failures in an online environment are relatively objective and obvious (Meuter et al., 2000) when it concerns a technology failure, since you can immediately see it on your computer screen when something goes wrong which leads to a greater transparency concerning these kinds of failures online (Forbes et al., 2005). Other types of failures might take longer to identify (e.g. something is ordered online, but the wrong product is sent). However, the transparency associated with online technology failures means customers have an easier time objectively identifying this type of service failure and because the consumer has the ability to exit the relationship with a click of the mouse (Holloway & Beatty, 2003), minimizing and effectively recovering from service failures is crucial to online retailers (Wang et al.,2010).
7 argue that consumers perceive online service failures as more severe than their offline counterparts. This can also be related to the absence of interpersonal contact that was mentioned before, since the presence of an employee cannot relieve the severity of the service failure in this setting (Forbes et al., 2005). Another reason for the perceived severity of these failures is that online failures tend to be either process or technology failures (Holloway & Beatty, 2003; Meuter et al.,2000) which are both considered to be severe failures from the customer’s point of view. A process failure causes large problems for consumers, because they are under the impression that the transaction was completed, while in reality, a process failure occurred that may not be discovered until later (Meuter et al.,2000). Technology failures are similarly critical because they prevent customers from using the technology (Meuter et al.,2000). Keeney (1999) states that these kind of failures are especially bad because consumers expect convenience and control in an online environment.
Fourth, an important difference between online and offline service recovery is the propensity to complain. Although the percentage of customers that are willing to complain in offline settings is fairly low, Holloway & Beatty (2003) found that a higher percentage of online customers complain when they are dissatisfied. Traditionally, some customers do not feel strong enough to voice their complaints in offline settings (Andreason & Manning, 1990). However, following the findings of Holloway & Beatty (2003) it might be that complaining might be less embarrassing online. The relevant difference here is that it appears customers are more prone to complain online, which means e-tailers can take advantage of this.
8 al., 2005). The next section will deal with the different service recovery attributes that are relevant for online service recovery.
2.3 Service recovery attributes
Although, to the author’s knowledge, there is hardly any research that discusses service recovery attributes in an online context, there are several frameworks originating from traditional service recovery research that can be used as a basis for the theoretical framework of this study. Boshoff (1999) developed and validated a measuring tool for six aspects of service recovery, which are communication, empowerment, feedback, atonement, explanation and tangibles. However, the tool was never tested and aspects such as tangibles (how is the employee processing your complaint dressed?) and empowerment (is the complaint passed around from employee to employee?) are not relevant in an online context. Wirtz & Mattila (2004) investigated the role of three service recovery dimensions (speed of recovery, apology and compensation) and their relation with satisfaction and post-recovery behaviors. Smith et al. (1999) also investigated satisfaction with service failure/recovery encounters and utilized several relevant recovery attributes, although the framework is still missing some important attributes. Finally, Wang et al. (2010) is a notable exception to this lack of research in an online context. However, they only focus on two service recovery attributes (i.e. speed and magnitude of the recovery effort), which makes the framework too narrow to serve as a basis for this paper. According to Davidow (2000) there are six different service recovery attributes. These are redress, timeliness, credibility, apology, attentiveness and facilitation. This empirically tested model successfully differentiated between these attributes and satisfaction and is also the first paper that discussed more than three attributes at once, which makes it very suitable to use as the basis for a framework. Because there is a lack of consensus in this research field regarding the service recovery attributes, several of these are labeled differently across studies. However, the dimensions discussed by Davidow (2000) and reviewed by Davidow (2003) cover all attributes as discussed by Boshoff (1999), Smith et al. (1999), Wang et al. (2010) & Wirtz & Matilla (2004) and will therefore be used as the basis for this paper’s framework (figure 1).
9 that often postcomplaint behavior such as WOM and repurchase intentions are used as outcomes of service recovery studies (Davidow, 2003; Orsingher et al., 2010). However, research shows that recovery satisfaction fully mediates the relationship between recovery attributes and behavioral intentions (Wirtz & Matilla, 2004). Before we proceed to discuss the service recovery attributes in detail in the next section, two important remarks have to be made about the model.
First, it is important to note that facilitation, which can be described as: ’the policies, procedures and structures a company has in place to support customers engaging in
complaints and communications (Davidow, 2003)’, will be excluded from this research since it is not an attribute of a service recovery, but relates more to the policy of the focal company. Since the goal of this paper is to find out what the ideal composition of an online service recovery is, policies and regulations cannot be taken into account even though previous research does indicate it has a positive effect on satisfaction (Boshoff & Leong, 1998).
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Figure 1. Conceptual framework
Redress
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and previous research shows that redress is expected by customers when a failure occurs (Johnston & Fern, 1999). However, a study in the restaurant industry found that overspending on redress is not necessarily effective (Mack, Mueller, Crotts & Broderick, 2000). It is important to note that all these studies were focused on traditional services and their results might not translate completely to the online channel.
Timeliness
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when an outcome failure occurs, which is basically the opposite of the previous argument. This indicates literature is conflicted on the role of timeliness and this empirical study should provide some indication of the importance of timeliness in online settings.
Credibility
Credibility is: ‘an organization’s willingness to present an explanation or account for the problem’ (Davidow, 2003: 232). Besides the more obvious service recovery attributes, like redress and timeliness, the consumer also wants to know how the company will prevent future failures from happening and how the failure happened in the first place. These elements are all covered by credibility, which potentially increases satisfaction with the service recovery (Davidow, 2003). Therefore, it appears the way a service recovery is presented and interpreted by the consumer can be more important than the recovery itself. Conlon & Murray (1996) found that companies that explain and take responsibility for service failures achieve a higher satisfaction with the service recovery than companies that do not. Previous research shows that making an excuse or providing an explanation in a written response made consumers belief that the company could not have avoided or had control over the problem and thus increasing satisfaction (Baer & Hill, 1994). Johnston & Fern (1999) argue that consumers demand information regarding the service failure, as well as an explanation and assurance the problem will not occur again. Boshoff & Leong (1998) looked at different types of explanations and excuses regarding service recovery and concluded that the company taking the blame is the best, instead of blaming other parties. Furthermore, previous literature suggests there is some kind of interaction with redress (Sparks & Callan, 1995) since they found that redress without an explanation could be considered as an admission of guilt and would therefore reduce satisfaction.
Apology
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considered apology a separate attribute instead of being part of the redress dimension will be considered here.
Smith et al. (1999) found that apology had an indirect positive effect on satisfaction with the service recovery (through interactional justice). However, results on the apology dimension are mixed, especially when looking at the effect of apology on other outcomes, such as repurchase intentions and customer satisfaction. Therefore, the apology dimension might be the most useful when used in conjunction with the other attributes. An example of this is the reported interaction effect between redress and apology. For example, Boshoff (1997) found that an apology without redress was not significant, while Goodwin & Ross (1992) argued that an apology affected satisfaction more when redress was involved.
Attentiveness
Attentiveness refers to ‘The interpersonal communication and interaction between the organizational representative and the customer’ (Davidow, 2003:232). The attribute consists of four elements: effort, respect, empathy and a willingness to listen to the consumer (Davidow, 2003). Due to the intangible nature of the Internet it is a lot harder to interact with customers which makes attentiveness harder to achieve in the online channel. However, research shows that in the traditional service setting attentiveness is the most important attribute affecting satisfaction (Davidow, 2000; Estelami, 2000) which might mean consumers also feel it is very important when shopping online. Hocutt et al. (1997) found that empathy (which is a part of attentiveness) had a significant influence on satisfaction with the service recovery. McCollough (2000) complemented those findings by showing that effort and courtesy both significantly influence complaint handling satisfaction.
2.4 Moderating variables
14 service quality will change (Cadotte, Woodruff & Jenkins, 1987). Parasuraman, Zeithaml & Berry (1985) found that when consumers do not have a lot of experience, their expectations are usually focused on what they feel a service provider should do for them, instead of having realistic expectations based on experience. Holloway et al. (2005) studied this phenomenon in the context of online purchasing experience. They found that more experienced online users have more realistic expectations of the potential for service failures and were less dissatisfied when a service failure occurred, while the same service failure had a more severe impact for the less experienced purchasers. Therefore, previous online purchasing experience acts as a buffer for service failures (Holloway et al., 2005), which indicates that the strength of the relationship between recovery attributes and satisfaction with the service recovery might vary when a user is more or less experienced.
Second, technology readiness is added to the model. Technology readiness can be described as a person’s propensity to embrace and use new technologies in their daily lives (Parasuraman, 2000). Considering the large role of technology in the online SST setting and the service failures and recoveries that might take place there, it seems reasonable to assume a person with a high score for technology readiness might evaluate certain service recovery attributes differently than a person with a low score. For example, it might be that someone with low technology readiness values attentiveness and apology higher due to their aversion of technology. This suggests a moderating influence that has to be taken into account in this study.
15 behind this is that they are already frustrated easily when searching for products online, but if a service failure occurs, they will need additional reassurance when compared to younger people. These differences might moderate the relationship between the recovery attributes and satisfaction with the service recovery.
Fourth, the severity of the failure will also be discussed. Previous research suggests that service providers should customize their recoveries based on the type of failure that has occurred (Craighead, Karwan & Miller, 2004). Smith et al. (1999) argued that customer satisfaction with recoveries will differ with the severity of the failure. Furthermore, the severity of the failure will influence the customers’ evaluation of the service recovery since the failure context serves as a starting point from which customers view a service recovery (Smith et al., 1999). They also found that compensation had a smaller indirect effect on satisfaction with the recovery when severity of the failure was low, while apology and credibility did not differ when severity of the failure changed.
Finally, relationship quality will be taken into account. There are two steams concerning relationship quality and its effect regarding service recovery. On the one hand, several researchers have found that a high relationship quality acts as some sort of buffer when a failure occurs, which means customers are more likely to forgive the company for a service failure (Singh & Sirdeshmukh, 2000; Tax et al., 1998). On the other hand, it was also found that a high relationship quality may enhance a customer’s reaction following a service failure because they feel betrayed (Holloway, Wang & Beatty, 2009; Kelley & Davis, 1994). Furthermore, high relationship quality customers will respond less strongly to an organization’s recovery efforts (Holloway et al., 2009). These findings suggest that the strength of the relationship between the attributes and satisfaction with the recovery might vary due to relationship quality, indicating a moderating influence.
3.Methodology
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3.1 Traditional conjoint analysis
This research employs a traditional conjoint analysis (TCA) in order to determine which of the service recovery attributes are the most important in an online SST setting. TCA is appropriate in this context for several reasons. First, TCA matches the goals of this study because it assesses how consumers balance benefits that are associated with product or service attributes (e.g. Wittink & Cattin, 1989; Wittink, Vriens & Burhenne, 1994). Second, the downsides associated with the technique are not relevant given the research design used in this study since people do not ‘buy’ an online service recovery. Therefore, it is fine that TCA only covers the preference formation stage of the decision process and is unable to distinguish between buyers and non-buyers (Backhaus, Hillig & Wilken, 2007). Finally, the dependent variable in this study is ‘satisfaction with the service recovery’ and TCA allows us to use a metric rating scale (Hair, Black, Babin & Anderson, 2006). Furthermore, the limited number of attributes used in this study means TCA is appropriate because it allows for a maximum of nine attributes (Hair et al., 2006).
3.2 Data collection procedure
Empirical data for this study was gathered by using a sample of Dutch consumers and specifically consumers that have bought products online. In order to attain a sufficiently heterogeneous sample the snowball sampling procedure was used through social media (Facebook and LinkedIn). Data was collected through individually completed questionnaires filled out online.
3.3 Measurement scales
17 period while high severity means the web shop delivered the wrong product to the consumer. The scenarios for relationship quality are based on a measurement scale by Holloway et al. (2009). Low relationship quality indicates the respondent is unfamiliar with the web shop and does not know yet if it can be trusted or is honest while high relationship quality means the consumer feels like they are a loyal customer and the organization can be trusted.
3.4 Experimental design
A 2x2 between subjects factorial design was used to manipulate the effects of severity of the failure (low versus high) and relationship quality (low versus high). Each subject was randomly exposed to one of these four failure scenarios and within these the respondents had to complete a conjoint task of fifteen profiles within which they had to rate their satisfaction with the depicted service recovery on a ten point scale. Regardless of the scenario respondents were exposed to, each of them completed the same conjoint task. The scenarios can be found in appendix 2.
Scenarios were chosen for several reasons. First, it eliminates problems with observations or manipulation of actual service failure/recovery, which includes expenses, ethical considerations and unwillingness to intentionally expose customers to service failures (Smith et al., 1999). Second, they also argue that the use of scenarios minimizes memory bias as well as rationalization tendencies which is common in self-reports of service failure (Smith et al., 1999). Third, it reduces problems with individual differences in response styles and personal circumstances since respondents all face the same situation more or less (Wirtz & Bateson, 1999).
18 provided by Davidow (2003) discussed previously. Redress meant the complaint would be resolved for all customers but the ‘high’ level of the attribute would add some extra benefit as compensation. Pre-testing showed a discount of 50% was appropriate. Timeliness was also determined by pre-testing which showed that recovery within a week was considered desirable for online services while a month was outside the norm, but not unbelievable.
Also, each attribute has a linear preference function since they each only have two levels of which the higher level should reasonably have a higher utility. Because of this, the loss of accuracy is minimal while maintaining statistical efficiency using a linear preference function (Hair et al., 2006). Table 1 describes the levels of each attribute in more detail.
TABLE 1.
Recovery attributes and their levels Attribute: Levels:
Redress High: The organization provides you with compensation that covers the expenses
caused by the service failure and a discount coupon for 50% off your next purchase
Low: The organization provides you with compensation that covers the expenses
caused by the service failure
Timeliness High: Your complaint is resolved within the week Low: The resolution of your complaint takes a month
Credibility High: The organization takes responsibility for the failure and provides an explanation Low: The organization does not explicitly take responsibility for the failure and does
not provide an explanation
Apology Yes: The company apologizes for the failure No: The company does not apologize for the failure
Attentiveness High: The employee handling your complaint shows respect, empathy and a
willingness to listen and makes a real effort to make sure you are satisfied
Low: The employee handling your complaint does the minimum necessary to resolve
your complaint, but does not show empathy and a willingness to listen.
The presentation method that was chosen for this study is the full profile method, which allowed us to reduce the number of comparisons by using a fractional factorial design. This method allows for a more realistic portrayal of the recovery procedure by showing a level for each recovery attribute and a more explicit view of the trade-offs among all attributes (Hair et al., 2006). Furthermore, since six or fewer factors are involved, the full profile method is recommended (Hair et al., 2006).
19 which no direct effect or factor interaction is aliased with other direct effects or two-factor interactions (Montgomery, 1997). What this means is that half of the possible profiles are eliminated while maintaining orthogonality ( because we have five attributes with two levels each). Since we have a limited amount of attributes with only two levels each the highest possible resolution can be used in this research design which allows for less restrictive assumptions regarding interactions that are negligible (Montgomery, 1997). In the end this led to sixteen profiles being included in the research design. Finally, one profile was excluded1since all attributes had the lowest level which could lead to response bias (Hair et al., 2006), therefore the final research design contained fifteen recovery profiles.
3.5 Data analysis
As was argued before, TCA was used to analyze the importance of the service recovery attributes. Utilities of each attribute were found using a multiple regression approach using satisfaction with the service recovery as the dependent variable. Furthermore, latent class analysis was performed to see if heterogeneity was present in the estimated parameters. Segmenting the respondents based on the moderating variables proposed in our model means that companies can modify their behavior to the type of customer they are dealing with (Green & Krieger, 1991) if heterogeneity is indeed present. Finally, predictive validity was ensured by determining three hold-out sets at random. This means that twelve sets were used for the estimation of the model. The hold-out sets were used to check how accurate the estimated model predicts these hold-out sets (Hair et al., 2006).
4 Results
This chapter will deal with the results from the aggregate conjoint analysis (using regression) as well as the results from the latent class analysis. This was done in order to determine if there is any heterogeneity in the estimated parameters based on the moderating variables. However, this section will start with a description of the sample that was used, followed by a discussion on scale purification for technology readiness using multi-item scales. Finally, the
1 Please note that the profile with the highest level for each attribute was already excluded due to the nature
20 results of the aggregate and segmented conjoint analyses as well as the validation of the model will be discussed.
4.1 Sample
The online survey resulted in a total of 238 respondents of which 39 respondents had to be omitted due to a very high number of missing values (<10% of the questionnaire completed), which resulted in a final sample of 199 respondents. In order to obtain a reliable result for a conjoint analysis with a moderate amount of attributes (<9) a sample size of 200 is usually recommended (Hair et al., 2006). As can be seen in table 2, the sample consists of a range of age groups (although the age group of 20 through 30 dominates the sample with a share of 49.3%). Furthermore, 50.8% of the online shoppers were male while 49.2% were female. Moreover, the sample seems to be relatively active in the online shopping environment. The majority of the respondents report to purchase products online either weekly (9.5%), monthly (40.7%) or quarterly (33.7%) which shows that the sample is relatively active in the online shopping environment.
TABLE 2.
Characteristics of the sample (N=199)
Number of respondents Percentage
Gender Male 101 50.8% Female 98 49.2% Age <20 years 58 29.1% 20 – 30 years 98 49.3% 31 – 40 years 10 5.0% 41-50 years 10 5.0% >50 years 23 11.6%
Frequency Online Purchase
Weekly 19 9.5%
Monthly 81 40.7%
Quarterly 67 33.7%
Yearly 32 16.1%
Never 0 0.0%
Quantity Online Purchase
1-3 products 69 34.7%
4-7 products 59 29.6%
7-10 products 26 13.1%
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Average Spending per Purchase
Less than €20 33 16.6%
20-€60 119 59.8%
€61-€100 31 15.6%
More than €100 16 8.0%
Average Spending per 6 Months Less than €50 29 14.6% €50-€225 92 46.2% €226-€400 36 18.1% €401-€575 24 12.1% More than €575 18 9.0%
4.2 Measurement purification
Before the results of the conjoint analysis can be discussed, it is important to purify the measurement scales that consist of multiple items (Hair et al., 2006). In the case of this study, this applies to the moderating variable technology readiness2. Therefore, reliability and validity of the measurement instrument that was used for the construct technology readiness will be assessed by using convergent validity, discriminant validity and face validity (Hair et al.,2006). As was mentioned before, the items used for technology readiness were previously validated and originate from the ‘technology readiness index’ developed by Parasaruman (2000). Furthermore, all questions in the scale used a 5-point Likert scale (1 = Completely disagree; 5 = Completely agree). The different types of validity will be discussed in this section in order to enable summated scales for technology readiness in which a number of items are transformed into a composite measure.
Convergent validity means that separate items within a construct should converge or, in other words, share a high percentage of variance (Hair et al.,2006). The measure most often used for this type of validity is Cronbach’s Alpha (Peterson, 1994), which determines the internal reliability of separate items within a construct. Although there is some debate on this topic in academic literature, a Cronbach’s Alpha of .7 is usually regarded as the minimum for a reliable scale (Peterson, 1994).
Before the Cronbach’s Alpha was calculated, some of the items were recoded in such a way that all questions were measured in the same direction. After determining the Cronbach’s Alpha for all items in the measurement scale of technology readiness, it appears the internal reliability of the entire scale is somewhat low (α = 0.653). This is especially relevant since it
2 The moderating variable ‘cumulative online shopping experience’ is also a multi-item scale, but since the
22 concerns a previously validated scale, which usually implies a higher value for Cronbach’s Alpha is required (Peterson, 1994).
Since the internal reliability of the entire scale is insufficient, a factor analysis with varimax rotation was performed in order to determine the underlying constructs. Varimax rotation was used since it is an orthogonal rotation method that maximizes the sum of variances of required loadings for the factor matrix and seems to give a clearer separation of factors when compared to other orthogonal rotation methods (Hair et al.,2006; Malhotra, 2010).
After examining the initial results of the factor analysis, it appeared some variables had communalities that were insufficient. These items were omitted from the analysis in a stepwise manner after which the analysis was performed again. The item ‘we should be careful with replacing important human tasks with technology, since technology can break down or cause mistakes’ was omitted from the analysis (communality = 0.375) after which all communalities were above .5.
The results show that Bartlett’s test of sphericity was significant (2
/df=4653.526/55, p = .000) which indicates there are at least some correlations between the variables included in the analysis (Hair et al.,2006). The Kaiser-Meyer-Olkin measure of sampling adequacy (MSA) is .666 which indicates the items are at least partly explained by the other variables. Furthermore, none of the individual MSA’s fall below .5 which means the sampling adequacy is sufficient (Hair et al.,2006). Table 3 shows the eigenvalues and extracted variance per factor.
TABLE 3.
Eigenvalues and extracted variance Factor Eigenvalue Cumulative variance extracted
1 2.511 22.8% 2 1.699 38.3% 3 1.446 51.4% 4 1.141 61.8% 5 .901 70.0% 6 .739 76.7%
23 factors that were found by Parasuraman (2000) which provides additional theoretical proof to choose a four factor solution. The initial solution showed some cross loadings which lead us to interpret the Varimax rotated solution instead. Table 4 shows the final factor solution as well as the Cronbach’s Alpha of the four factors and the value when an item would be deleted.
TABLE 4.
Factor Solution and Reliability
Items Factor Loadings Alpha If Item
Deleted
Cronbach Alpha For Scale
Factor Analysis Technology Readiness
Innovativeness (Factor 1): .790
‘Other people come to you for advice about new technology’ .880 .612 ‘In general, you are among the first in your circle of friends to
acquire new technology when it appears’
.840 .727 ‘You can usually figure out new high-tech products and services
without any help from others’
.761 .789
Optimism (Factor 2): .593
‘Technology gives you more freedom of mobility’ .774 .427 ‘Technology gives people more control over their daily lives’ .766 .455 ‘Products and services that use the newest technology are much
more convenient to use’
.585 .583
Security (Factor 3): .531
‘You do not consider it safe to give out credit card information over the internet’
.719 .424 ‘Any business transaction that you do electronically should be
confirmed later with something in writing’
.703 .486 ‘If you provide information to a machine or over the internet, you
can never be sure it really gets to the right place’
.696 .384
Comfort (Factor 4): .335
‘It is embarrassing when you have trouble with a high tech gadget while people are watching’
.773 -
‘Technology always seems to fail at the worst possible time’ .699 -
Even though the measurement scale was previously validated by Parasuraman (2000) the Alpha values found for this sample are insufficient, except for the factor innovativeness (α = .79), which lead us to apply weighted factor scores instead of summated scales. This enhances internal reliability by using factor loadings as a weight factor in the calculation of the summated variables. Thus making sure the items with the highest correlation to the factor are also assigned the highest weight.
24 Please note that the original constructs were called insecurity and discomfort, but since we recoded these variables to make sure all items are measured in the same direction, they were renamed.
4.3 Aggregate conjoint analysis
This section will discuss the aggregate conjoint analysis which will give an initial indication of the importance of each attribute for the entire sample. The multiple regression with ‘satisfaction with the service recovery’ as the dependent variable and the five recovery attributes that were discussed previously as independent variables showed an aggregate R² of .2106 which indicates 21% of the variance in the dependent variable is explained by the chosen attributes. Table 5 shows the results of the aggregate analysis, including the relative importance of each attribute and their significance. All five attributes significantly explain the consumer’s satisfaction with the service recovery (p<0.001). The results show that timeliness is the most important attribute (33.4%) followed by attentiveness (21.9%), while redress is the least important attribute (10.1%) in the case of an online service recovery. The model was re-run three times in which the maximum number of iterations was increased from the standard number of 250 up to 500 which was done to ensure the model was stable. The results showed minimal variation which indicates a stable model. Next, a segmented conjoint analysis will be performed in order to see if any differences in the parameters per segment can be detected.
TABLE 5.
Output aggregate conjoint analysis
Attribute Parameter Wald statistic Significance Range Relative
importance Attentiveness .1643 149.4393 .000 .3286 21.9% Credibility .1413 106.4863 .000 .2826 18.9% Apology .1183 68.6094 .000 .2366 15.8% Timeliness .2505 279.2443 .000 .5010 33.4% Redress .0755 35.0022 .000 .1510 10.1%
4.4 Segmented conjoint analysis
25 2006; Malhotra, 2010). The questions regarding cumulative online shopping experience were recoded into dummy variables, which resulted in thirteen dummy variables (three variables per item and four for total expenditures, since there were four categories except for total expenditures which consisted of five categories). This was done because only continuous or nominal variables could be used in the analysis.
In this case 21 covariates were added to the model (age, gender, thirteen dummies for online shopping experience, four variables for technology readiness and a variable for relationship quality and severity of the failure) of which five were numeric and sixteen were nominal.
First, models ranging from one class to five classes were run in order to determine the most appropriate number of classes when considering the available data. The model fit likelihood ratio chi-squared statistic (L2) is significant for all five class models at p < .001, which indicates that the estimated frequencies are similar to the observed frequencies (Hair et al., 2006). In order to determine what model has the best fit, several information criteria of different strictness will be used (AIC, BIC, AIC3) and the results can be found in table 6.
The model with the lowest information criteria should be chosen since lower criteria indicate a better fit (Hair et al., 2006). However, even though all three criteria weight fit and parsimony of the model by adjusting the Log Likelihood (LL) to adjust for the amount of parameters in the model (Hair et al., 2006) the BIC is the most restrictive in this regard, followed by the AIC and the AIC3. Since the BIC is often used to determine the correct number of classes (Hair et al., 2006), it will be used here to make a final choice which leads us to choose a four class model (BIC: 8549.48). Another reason for this choice is the distribution of importance regarding the recovery attributes. The four class solution provides
TABLE 6 Model selection
Model LL BIC(LL) AIC(LL) AIC3(LL df P-value R²
1 class -4568.5 9211.0 9164.9 9178.9 185 .000 0.2106 2 class -4230.0 8724.7 8560.0 8610.0 149 .000 0.4594 3 class -4064.1 8583.4 8300.1 8386.1 113 .000 0.5096 4 class -3951.8 8549.5 8147.7 8269.7 77 .000 0.5260 5 class -3872.0 8580.4 8060.0 8218.0 41 .000 0.5613
26 the clearest differentiation regarding relative importance between classes when compared to other solutions (see table 7). Furthermore, the size of the classes is also distributed the most evenly in this solution (a solution including more classes also has a higher number of classes that are significantly smaller than the others).
TABLE 7
Relative importance recovery attributes per segment Relative importance
Attribute Class 1 Class 2 Class 3 Class 4
Attentiveness 23.36% 18.75% 24.26% 17.93% Credibility 18.69% 17.14% 18.48% 21.85% Apology 13.85% 19.75% 19.37% 15.05% Timeliness 35.32% 33.36% 27.12% 26.43% Redress 8.78% 11.00% 10.78% 18.75%
Note: the highest relative importance per attribute is underlined
The differences in relative importance of the recovery attributes will be discussed in detail below within the description of the segments. Also, the final segmented analysis shows a relatively equal distribution of size among the different classes, except for class four which is the smallest (respectively 34%, 33.5%, 26.3% and 6.1%). The results show that nine of the 21 covariates significantly differentiated between the segments. These significant covariates will be used to describe the four segments next and can be found in table 8. The table in appendix 1 depicts all parameters regarding the covariates.
TABLE 8 Significant covariates Covariates p-value Age 0.033 Relationship Quality 0.017 Severity 0.028 Optimism 0.01 Security 0.03 Comfort 0.019
27
Class 1- Technologically insecure youngsters
When looking at the relative difference between the segments, table 7 shows that segment 1 has the strongest focus on timeliness and attentiveness (35.3% and 23.4%), while apology and redress are less important (13.9% and 8.8%). This segment is likely to contain younger people (β = -0.02) when compared to the other segments. The parameters of the covariates relationship quality and severity of the failure for this segment were found to be close to zero (β = 0,05 and β = 0.09 respectively) indicating these do not contribute a lot to the prediction of class membership. Also, this segment is the least likely to be optimistic about technology (β = -0.62) which means this segment feels technology does not add freedom or mobility to their daily lives and they prefer products and services with traditional technology. Furthermore, this segment is the most likely to be secure regarding technology (β = 0.46), which indicates they do feel safe using technology. However, their score for comfort around technology is average when compared to the other segments (β = 0.08). This segment is the second most likely to purchase ten or more products online each half year (β = 1.20) and the most likely to purchase products online quarterly (β = 2.49). In summary, this is a younger segment that is relatively pessimistic about technology, but does feel secure around it. Also, they purchase an above average quantity of products online. When it comes to service recovery in an online context, they strongly focus on timeliness and attentiveness.
Class 2- Young average Joe’s
28
Class 3- Loyal and comfortable seniors
Attentiveness is the most important attribute in this segment (24.3%), when compared to the other segments, while apology also scores relatively high (19.4%). People with a higher age are most likely to belong to this segment (β= 0.04). Furthermore, this segment has the highest likelihood to have a high relationship quality with the organization (β= 0.61) and when a service failure with a high severity occurs, a customer is least likely to belong in this segment when compared to the other segments (β= -0.58). Customers here are relatively secure (β= 0.42) and the most likely to be comfortable with technology (β= 0.55). Also, customers that buy ten or more products each half-year are most likely to belong in this segment (β= 2.14) and buy products online the most frequently (β= 2.55). This means people that are older, with a relatively high security and comfort when it comes to technology and a high relationship quality are the most likely to belong to this segment. When trying to perform service recovery for this segment, the focus should be on attentiveness and apology; while timeliness is also still one of the most important attributes.
Class 4- Low budget optimists
Even though segment 4 is the smallest segment by far, it does have the highest relative importance on two of the five attributes, namely credibility (21.9%) and redress (18.8%). This indicates there is a small segment that really values the company taking responsibility for the failure and providing satisfactory compensation. This segment has a low propensity to have a high relationship quality with the company (β= -0.95) and if a problem with high severity occurs, the victim is most likely to belong to this segment. Also, this is the group of people that is very optimistic about technology (β= 0.99), while being the most insecure around technology (β= -0.74). The group is the least likely to buy a large quantity of products each half year (β= -5.54) or buy products frequently (β= -4.71). In summary, this is the least attractive segment due to the low relationship quality, low spending and frequency of online purchases. However, this group feels credibility and redress are especially important when it comes to service recovery.
4.5 Validation
29 give according to our model is compared to the actual value the respondent provided. This study will use the mean absolute percentage error (MAPE) to show the predictive validity of our model, because it is widely accepted as a predictive validity measure and it is easily related to since it is depicted as a percentage (Leeflang, Wittink, Wedel & Naert, 2000; Swanson, Tayman & Bryan, 2007). The MAPE for our final model is 27.4%, which is fairly high. A possible explanation for this might be that a small portion of the observations had a very large difference between the observed and predicted values (27 out of 597 observations), while the rest only had a small error percentage. It is possible these outliers strongly influences the MAPE score, since a few extreme values can mean the MAPE understates predictive validity (Hoaglin, Mosteller and Tukey, 1983). In order to circumvent these problems with the MAPE, another measure was calculated for the model, namely the symmetric mean absolute prediction error (SMAPE), which adjusts the formula to be less sensitive to outliers (Armstrong, 1985). The SMAPE showed a prediction error of 22.9%.
5. Discussion
The goal of this study was to find out which of the five service recovery attributes (redress, timeliness, credibility, apology and attentiveness) were the most important to online shoppers when a service failure occurred online. Furthermore, we set out to distinguish different segments within the population of online shoppers in order to provide companies with some practical guidelines that will help them solve service failures in a way that is most appropriate and satisfactory for their customers. While this study builds on previous literature on service recovery in an offline context (e.g. Davidow, 2003; Smith et al., 1999), it also explores new territory by discussing the specific importance of recovery attributes in the online service recovery world. This section will discuss the most important implications of our research (both theoretical and practical) as well as limitations of the study and directions for further research.
5.1 Theoretical implications
30 important in an online context due to the low switching costs for online customers. Furthermore, the fact that the failures portrayed in our scenarios were process failures (the most common type of failure online) contributed to a higher importance for timeliness, since it is only important in the case of nonmonetary service failures (Gilly & Gelb, 1982). Consequently, future research on online service recovery should at least include timeliness as one of the attributes.
The second most important attribute was attentiveness (21.9%), which was surprising since it was expected that attentiveness would be less important online due to the lack of interpersonal interaction through the online channel (Holloway & Beatty, 2003; Holloway et al., 2005). However, attentiveness is the most important driver of satisfaction with the service recovery in an offline context (Davidow, 2000; Estelami, 2000), which apparently translates over to the online world. A possible explanation for this is that just because in an online context an employee cannot help mitigate damage done by a service failure (Forbes et al.,2005), does not mean customers do not want an attentive employee handling their problem. Online customers apparently value a friendly, helpful and attentive employee just as much as their offline counterparts. Therefore, because it is harder to achieve attentiveness in online retailing, online service companies should focus on fostering attentive employees even more so than traditional service companies.
Next, an attribute of average importance is the credibility of the service provider (18.9%). This indicates that customers do appreciate the company taking responsibility for the failure (since the attribute did contribute significantly to the satisfaction with the service recovery) but its relative importance is low. Similar conclusions can be drawn regarding the attribute apology (15.8%).
31
5.2 Managerial implications
The conjoint methodology combined with a latent class analysis that was used in this study provides e-tail marketing managers with a quantifiable way of determining the ‘ideal’ service recovery for every customer. The company can realize this by finding out several personal characteristics of the complaining customer, based on which the segment to which they are likely to belong can be determined. Once this has been determined, the result section of this paper can be used as a framework to help determine which attributes are most important to that particular customer. After a while, this will result in a database of customers’ individual characteristics, which can be used (perhaps through an automated system) to help service recovery employees determine what type of recovery is most suitable for their customers. When this strategy is combined with recovery analysis to evaluate the effectiveness of the service recovery in the eyes of the customer, it should help to eliminate future failures. This is a very important concern for e-tailers since previous research shows that minimizing service failures is even more important for them when compared to traditional brick and mortar retailers, because customers have a higher propensity to switch providers in an online environment (Forbes, 2005).
32 In order to help e-tailers achieve this, the important characteristics of the profitable segments are discussed next. First, ‘technologically insecure youngsters’ is a segment that purchases an above average quantity of goods online indicating it is worthwhile to tailor service recovery to this segment. The service recovery attributes they value the most are timeliness and attentiveness. Therefore, if a customer is likely to belong to this segment, the e-tailer should make sure the recovery is very quick and the communication between customer and employee runs smoothly.
Second, the ‘young average Joe’s’ are fairly, as the name suggests, average in terms of their online buying behavior and relative importance of attributes. Therefore, this segment is less attractive to tailor service recoveries to than the ‘technologically insecure youngsters’. This segment values an apology and timeliness very highly, which means the customized recovery should be quick and at least contain a sincere apology.
Third, the most attractive segment when it comes to buying behavior since they have both the highest frequency and quantity of online purchases are the ‘loyal and comfortable
seniors’. These are relatively older people that value attentiveness, an apology and of course
timeliness. If possible, e-tailers should attempt to deal with service recovery in this segment first.
5.3 Limitations and directions for further research
33 importance figure for the attribute redress. Future research could attempt to vary in the levels of redress to make sure this is not the case.
34
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