This paper examines consumer service switching from the perspective of case-based decision theory (CBDT), developed by Gilboa and Schmeidler. In contrast with the consumption of physical goods, it is difficult for consumers to evaluate their utility from service provision in advance because of intangibility. CBDT is a decision criterion that re ects consumers' past experiences, and enables us to examine their reasoning for service switching. Our paper empirically examines consumer choice behavior based on past experiences, using data from Japanese hairdressing salons, which consist of salon introductions and individual reviews of those salons. We focus on bad service experiences because CBDT suggests that after experiencing bad service, consumers will choose services that are less similar for their next salon appointment. Our paper examines whether CBDT accurately predicts the switching process for the service consumption. The results indicate that prior experiences have no signi cant effect on service choices.
The paper examines a behavior hypothesis whereby people switch their service
choice after a bad experience, which is derived from case-based decision
theory (CBDT). CBDT is developed by Gilboa and Schmeidler [
decision theory literature on and it employs case-based reasoning, as shown by
Schank [
Our research is related to the emerging literature of service science, which considers the characteristics and improvement of the service industry. In contrast to physical goods, consumers cannot correctly estimate their utility before consuming a service. The qualities of services depend on both the ability of service providers and the chemistry between purveyors and consumers. In this sense, several different uncertainties affect service provision.
Therefore, researchers considering services focus on consumers' experiences and investigate how/what service should be provided given their various experiences. In this paper, we make use of CBDT to represent the consumers' decision criteria regarding their experiences, and examine how consistent their behavior is with the theory. In particular, our focus is the `bad' service experiences because consumers may switch their choices after bad experiences. CBDT potentially has a role in explaining the process of consumers' choices.
Indeed, failure in service provision can often occur because of the different
qualities involved and a 'lack of chemistry' between service providers and
consumers. Once a consumer becomes disgruntled or dissatis ed with a service, he
or she will not visit the service provider again. Naturally, service providers try
to prevent such situations and improve on poor service provision. One task of
service science researchers is to examine how services can be improved after such
failures [
In this paper, we investigate how the bad services experiences in uence
service switching using a unique data set on Japanese hairdressing salon services
available online (Recruit [
The remainder of this paper is organized as follows. The next section explains the composition and characteristics of the Japanese hairdressing salon data that we use in our analysis. Section 3 provides our empirical results from analyzing the decision-making of consumers after they have had bad service experiences. The nal section summarizes the research and provides some comments regarding future research. 2
Hot Pepper Beauty: Japanese Beauty Salon Data
We use data from the largest booking website for Japanese hairdressing salons,
Hot Pepper Beauty, provided by Recruit Technologies Co., Ltd [
As stated in the Introduction, the services offered by hairdressing salons are a typical service good in that the quality and satisfaction level of customers cannot be evaluated in advance. As an example, in the case of hairdressers, each customer will have a speci c preference regarding hair styles and he or she may fail to communicate these preferences effectively to the hairdresser. Even when a customer explains his or preferences in detail, different hairdressers may understand the instructions in different ways. Moreover, the satisfaction level of customers may be decided not only by the actual hair styles achieved, but also by the atmosphere of the salon and the customers' communication with the hairdressers. Therefore, the Hot Pepper Beauty data set can assist us to understand the process of consumers' salon choices/switches because we can observe their repeated uses of the service every few months. 2.1
The data set is provided through a Japanese research organization, the National Institute of Informatics, and it is public data that are recorded from January 11, 2012 to January 9th, 2014. We use information provided by the salon on the hairdressers and information contained in reviews in the data. We excluded some incomplete data on hairdressers. Table 1 reports the descriptive statistics of the data that we actually used in our study. Each component of the table is explained as follows: Review Information: Each date-stamped review consists of the review text and an evaluation of the hairdresser who provided the service to the customer who has wrote the review. Each of the hairdressers is assigned an ID number. Then, we use this to refer to the hairdressers that each customer reviews. The review information contains an evaluation of the service, indicated by a number from 1 to 5, where a higher number indicates a better service. As each reviewer has an ID that is provided in the data set information with each review, we can develop a time series of review information by each reviewer.
Hairdresser Information: Hairdresser information in the data set includes the hairdressers who belong to a hairdressing salon. The hairdressers also provide three types of information for consumers: their selling points, strengths, and a self introduction. All three types of information provided by the hairdressers are collected together in one long document, which we then use to estimate topics, as explained below.
Next, we examine consumers' switching choices from the perspective of CBDT by constructing a similarity measure from the data. 2.2
The CBDT developed by Gilboa and Schmeidler [
The situation is formulated as a triple (P; A; R) where P is a set of decision problems, A is a set of acts, and R is a set of possible outcomes. Then, a case (p; a; r) is an element of P A R C. Because C includes all the cases, these cases do not necessarily happen. The decision-maker has only partial experiences of the cases, which are remembered as memory M C.
He or she also has a similarity function s from P P to the closed interval between 0 and 1; s : P P ! [0; 1]. This function represents the degree of similarity between the two cases. We assume that s(p; p) = 1 for any identical problem p, and that 0 s(p; p′) < 1 for any two different cases, p and p′.
As the decision-maker enjoys utilities from past outcomes, we represent them by a utility function u : R ! R. Then, he or she chooses an action a 2 A to maximize the following objective function:
U (a)
∑ (q;a;r)2M
s(p; q)u(r):
The above equation represents the fact that the decision-maker would like to choose an identical action to that which yielded higher utilities on average in similar cases in the past. Note that the function is given as a weighted average of the utilities from his or her choice, which takes similarity into account.
From this perspective, we hypothesize in our data analysis that a customer switches from a hairdresser to a dissimilar hairdresser once he or she has had a bad experience. In the following, we examine how much her choice differs between the past hairdresser and the next hairdresser chosen over two successive periods. 2.3
To analyze choice switching, we introduce some notations. Let the set of individuals be I and the set of review information be R. The review information consists of the set of hairdressers denoted by S and the evaluations E = f1; 2; 3; 4; 5g, that is R = S E. Note that each customer can write an online review anytime and that each is given a time stamp in the review information.
To examine customer switching between hairdressers, we compare the reviews that each of the individuals posted in two successive periods. A set of review information for individual i 2 I is denoted by Ri. We write the t-th review of individual i as rit 2 Ri. We call a pair of individual i's reviews (rit; rit+1) successive-period information. We denote the set of all individual i's successiveperiods information by Pi = f(rit; rit+1)j rit; rit+1 2 E Sg. When we restrict the evaluation of the rst period in the successive periods to E′ E, we write Pi(E′), that is Pi(E′) = f(rit; rit+1)j rit 2 E′ S; rit+1 2 E Sg. For example, in focusing on the reviews where the evaluations consist of 1 and 2, we write Pi(f1; 2g). We denote all the successive information restricted on E by P(E) = [i2I Pi(E).
First, we examine how each customer switches his or her choice, taking into account the evaluations. We denote the set of successive-period information for customers who switch hairdressers by Ph(E). Table 2 reports the numbers for each element of the data. We regard a good evaluation as one where the ranking is higher than 3 and a bad evaluation as one where it is lower than 3. An evaluation of 3 is regarded as neutral. As shown in the table, although less than half of all the consumers chose the different hairdressers in successive periods, around 90 percent of those consumers who provided a bad evaluation did then switch hairdressers in the next period.
Relationships between Evaluations and Switching As our hypothesis is that the customers choose a different type of hairdresser after having a bad experience with the hairdresser previously chosen, we focus on the data for which a different hairdresser is chosen in each of the two periods analyzed. Following the CBDT, we estimate a similarity function s. If our hypothesis is valid, the estimated similarity for the information involving bad evaluations might be lower than that for the information involving better evaluations.
To estimate similarity, we use topic analysis for the documents with the hairdresser information and characterize them based on topics. We evaluate the difference/similarity of the hairdressers that the customers choose based on the estimated similarity function. The construction of similarity is illustrated in Figure 1.
Service Providers Service Providers
① Divide them into words Document ~~~~~~~~ ~~~~~~~~ ~~~~~~~~ Document ~~~~~~~~ ~~~~~~~~ ~~~~~~~~ hair, treatment, reservation,… relaxing,haircut, price, … LDA
Document
~~~~~~~~
~~~~~~~~
~~~~~~~~
Document
~~~~~~~~
~~~~~~~~
~~~~~~~~
② Calculate
distribution of topicsin
documents
(topic1:40%,topic2: 20%,…)
(topic1: 10%, topic2: 50%, …)
② Assign wordsto
distribution of wordsin
topic k
Topic 1: hair, haircut,
hairdressers,…
Topic 2: beautysalon, haircut,
hairdressers,… …
③ Define distribution of topics in a
document as the attribute of a service
provider.
In our data set, there are no numerical attribute data. Thus, we use documents
for the hairdressers, and derive their attributes using topic analysis. We use latent
Dirichlet allocation (LDA), which is a probabilistic model of topic analysis [
We used mecab-ipadic-NEologd as a dictionary to extract Japanese texts
with a space between words [
Recall that there is a set of hairdressers S. Each hairdresser a 2 S has a document da. Each da has a nite set of words Wda = fw1; w2; : : : wng. In the LDA, using Wda , the topic distribution da = ( da1; :::; daK ) of da is an estimated probability distribution over K topics. Each topic has particular words that characterize it. The word distribution indicates which words belong to each topic.
In this research, we de ne similarity based on the topic distribution da of
hairdresser a's document da. In the prior empirical research based on CBDT,
there are two de nitions of similarity. Gayer et al. [
1 vu K 1 + tu∑( dak k=1 d′ak)2 : (1)
We used the Gensim package in Python to estimate posterior topic
distributions [
First, we extracted words in topics regarding hairdressers from the data. Table 3 provides an example of the results from the retrieval. It shows the top ve relevant words in each topic regarding hairdressers. We translated each Japanese word to English5.
We observed that some topics are difficult to understand and distinguish from
other topics. This is because we use the same genre of documents on hairdressers,
3 In our topic analysis, we did not consider the relationship between two successive
words (e.g., \very good"). To avoid mistaking the effect of these relationships, we
only checked nouns and adjectives.
4 We checked the relationship between the number of topics and the coherence
value [
Next, we estimate average similarity values for successive-period information that relates to hairdressers who experienced customer switching. We estimate similarity values for those with Ph(f1; 2g) and Ph(f4; 5g), and examine how experiences with bad evaluations affected service switching behavior using a Mann-Whitney U test.
Table 4 describes the results of 10 repetitive estimations. It shows that the estimated word distributions of estimations 4, 6, and 10 have signi cant differences at < :01, but most of the cases have no signi cance. That is, our comparisons did not show clear evidence of service switching by consumers to a low similarity service following a bad experience. We discuss these results further in the next section. 3.3
Our estimations show little clear evidence of service switching from the perspective of CBDT. That is, after a bad experience, consumers do not choose a less similar service next time. In other words, bad service experiences do not affect the service choice as far as similarity of services is concerned. Based on the analysis, we provide the following concluding remarks. We focus on the following four aspects of our analysis: the data problem, the similarity function problem, selection bias, and the fact that we excluded those customers who chose the same service in two successive periods from our data set.
First, regarding the data problem, we used review data for our consumer behavior analysis that did not comprise data on the whole usage of the service or satisfaction data which happened in the real world. In CBDT, the summation of the past experiences of an individual is considered. If there is a very large effect arising from these prior experiences, which we did not consider in our analysis, then the posterior experience that we could observe from the review data will have little effect on decision-making. In future research, we could add three or more successive sets of review data to examine whether the effect of the evaluations on service switching behavior changes.
Regarding the problem of estimating similarity, it may not be appropriate to use only topic distributions to form similarity. The estimated topics may differ from the impression that consumers obtain when they read those messages. It is possible that consumers not only see text messages, but also images, prices, menus, and other features. For future research, we may use other variables in combination with topic distribution to form our estimates of similarity.
In addition, there may be a problem of selection bias in that those customers who write bad reviews may differ from those who only write good reviews. For example, customers who write bad reviews may actually be satis ed with a service but have a policy of writing bad reviews to prompt service providers to improve their service further. If this is the case, CBDT decision criteria will suggest a similar service because the customers are satis ed.
Finally, a problem may arise because we excluded those who chose the same service for two consecutive periods and used only data for switching consumers. However, CBDT does not exclude the possibility that consumers will choose the same service. Thus, to conduct a proper analysis, we need to obtain data for choice situations where consumers cannot choose the same service providers in consecutive periods.
Conclusion In this research, we focused on service switching behavior after consumers experienced bad services, and analyzed the similarities of the hairdresser services chosen by consumers. We conducted a topic analysis on marketing messages written by the hairdressers to determine similarities. Our results indicated that there were few signi cant relationships between the evaluations and similarities.
In future research, we will examine service switching behavior using estimations of similarities on a more complete data set and analyze the construction of similarity using additional variables. 15. von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior.
Princeton University Press (1944).