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sustainability
Article
Redesigning In-Flight Service with Service BlueprintBased on Text Analysis
Seungju Nam 1, Chunghun Ha 2 and Hyun Cheol Lee 1,*1 School of Business, Korea Aerospace University, Hwajeon-dong, Goyang-si 10540, Korea;
narmsung@kau.ac.kr2 Department of Industrial Engineering, Hongik University, 94 Wausan-ro, Seoul 04066, Korea;
chunghun.ha@hongik.ac.kr* Correspondence: hclee@kau.ac.kr; Tel.: +82-2-300-0092
Received: 30 October 2018; Accepted: 26 November 2018; Published: 29 November 2018 �����������������
Abstract: Airline services should be passenger-focused to be sustainable. In this study, we redesign anin-flight service process using a service blueprint while incorporating direct customer perceptions ofservice experiences. To incorporate these, we apply topic modeling to 64,706 passenger-written onlinereviews of airline services. Passenger experiences of in-flight services are the sum of experiencesfrom service encounters in all the subsequent steps and we assume that their direct perceptions oftheir experiences are faithfully contained in the online reviews. Topics extracted from the reviewscan be regarded as service encounters based strongly on passenger experiences. Then, the serviceencounters are reorganized within the framework of a service blueprint. The results show thatthe complexity, a number of service steps, decreases by 38% compared to the benchmark serviceblueprint. However, the divergence, a latitude of service steps, should increase for a couple ofservice encounters. Moreover, we quantitatively analyze the divergence using the probability ofword frequency statistically distributed across topics. The in-flight service using the proposed designcould be sustainable with respect to customer-focused service while considering direct customerexperiences in real-time.
Keywords: latent Dirichlet allocation; online review; passenger-focused; service encounter; serviceblueprint; sustainable in-flight service
1. Introduction
Through the liberalization of air transport service agreements, the airline industry has grownwith the arrival of new entries, which comprise various types of air transport service providers,including low-cost carriers [1]. Industry growth and increased competition have expedited thediversification of customers’ needs by expanding multiple layers of air traffic demands, and servicecustomization, which makes it possible to address each customer’s needs, is a common strategyfor achieving competitive advantages [2–4]. It has been repeatedly emphasized that airlines coulddeliver customized service processes which optimize diversified demands for customers in the airlineindustry [5–8].
The airplane cabin is a space where a service is simultaneously created and consumed [9].Since passengers must remain in the space for most of the flight, while being exposed to the service,the cabin is very important for service experience perception [10]. However, it is not easy to honeservice customization among airlines for the following reasons. Duopoly manufacturers mostlyprovide aircrafts to airlines, and there is no significant difference in terms of technological performanceand characteristics [11]. Customization in relation to intangible factors is also not flexible as airlinesmust follow national and international air transport regulations, the chief aim of which is safety.
Sustainability 2018, 10, 4492; doi:10.3390/su10124492 www.mdpi.com/journal/sustainability

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Customization is only permitted when the safe operations and conditions of an aircraft are guaranteed.Therefore, airlines try to focus on in-flight service for customization as much as possible. Customersatisfaction through customized service could be the minimum requirement for providing a sustainableservice [12].
This study proposes a redesign method for the in-flight service blueprint (SB) based on customerperceptions. In order to identify exact customer perceptions of in-flight service experiences, we useonline reviews for airline services as a dataset [13]. The data is self-generated by passengers andthis is regarded as one of the most direct and immediate forms of customer service experienceresponse [14–16]. As various types of mobile web platforms appear rapidly, studies using onlinereviews that contain real-time perceptions of customer experiences are quite frequent in business andmanagement research [17–24]. The data is described as naturally unrefined and voluntary rather thandesigned or intended, the characteristics of survey data [14]. We make full use of the characteristics ofonline reviews while redesigning service processes from the customer’s perspective.
Specifically, we apply latent Dirichlet allocation (LDA) topic modeling [25,26], a text analysistechnique, to a vast amount of passenger-written online reviews. LDA modeling has been extensivelyused and is one of the Bayesian probabilistic clustering approaches for text data. Based on theco-occurrence probabilities of observed words in documents, the LDA approach can derive latenttopics of documents, which are characterized by a distribution of words. LDA modeling producestopics, which are groups of words with similar characteristics. Through the application of LDA tothe online reviews, we represent topics as interpretable service encounters, critical components ofthe SB based on passenger experiences, and redesign in-flight service in accordance with passengerperceptions by reorganizing service encounters. When redesigning the SB, we employ (re)designprinciples of complexity (a quantitative variable of SB) and divergence (a qualitative variable of SB)proposed by Shostack [27]. The variables are widely used in service (re)design with the SB to produceservices efficiently (e.g., [28–31]), as discussed in detail in Section 2.2.
The primary contributions and findings of this study are as follows. First, we optimize the properdegree of divergence and complexity of the in-flight service process based on the passenger-focusedstandard. As a result, the number of service steps decreases by 38% compared to the benchmarkservice, but the divergence degree should increase for a couple of service steps. Second, we determinethe direction and size of changes in the customization level for service encounters since we analyzethe divergence by investigating the probability of word frequency, a quantitative measure. This alsoexposes the possibility of the quantification of divergence in contrast to previous studies. Lastly,the proposed redesign method could update a service process periodically while communicating withreal-time online reviews. Since a sustainable service requires continuous improvement during a wholeservice lifecycle, this method helps providers achieve that goal by applying immediate feedback [32,33].
This paper is organized as follows. Section 2 reviews the previous literature on SB applicationsin various service fields and related research, as well as introducing the benchmark model. Section 3explains in detail the research model and the dataset used in this study. The LDA model and itsmodeling procedures are briefly discussed. Section 4 presents the modeling results of the LDA topicanalysis and topic naming. Through the redesign principles of complexity and divergence, this sectionprovides the final form of the proposed SB. The related analysis processes and findings are alsodiscussed. Finally, Section 5 summarizes the implications of this study and draws conclusions.
2. Related Review and Knowledge
2.1. Service Blueprint Based Redesign
The improvement of a service starts with an accurate, specific understanding of service processesand components [34]. The SB has become one of the most useful tools for visualizing andconceptualizing the whole service process in service design and innovation [30,35,36]. The SB has beenextensively applied to the analysis of service processes, customer and employee behaviors in a broad

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range of tourism and hospitality fields, including shoe washing services [36,37], hotel services [38],banking services [27,39], and restaurant services [40].
It is necessary to modify an SB to incorporate field-specific characteristics so that the serviceprocess performs efficiently while meeting the exact needs of customers at the actual field where theservice is provided [41]. There are two main approaches for modifying the SB. One class of methodis an attempt to improve existing processes by applying advanced models and concepts to the SB.Lee, Wang, and Trappey [42] redesigned parking service processes using the theory of inventiveproblem solving (TIPS) principles. They identified problems and found solutions for the service basedon the TIPS. Ru Chen, and Cheng [43] improved the blueprint with respect to total quality management(TQM) using ISO 9001. Botschen, Bstieler, and Woodside [44] redesigned the SB to determine criticalpoints such as service encounters and points where service fails from the service provider’s perspective.A few redesign methods used text analyses. Ordenes et al. [13] analyzed customer perspectives fromonline reviews using text mining and explained possible applications including an SB improvementto combine customer perceptions. Ryu, Lim, and Kim [45] identified the definition, characteristics,and keywords of online-to-offline service by using a text analysis and modified the SB by adding newcomponents of channel, and smart devices and technology. There are also a few published researchresults on service design issues using content analyses, which can apply to visual as well as textual data.Cristobal-fransi et al. [46] analyzed the service design of ski-resorts for climate change by applyingcontent analysis to the website information. Hartman et al. [47] proposed a public-sector service designthrough the application of content analysis to blogs and YouTube.
The other class of methods varies the SB components. This type of modification can be commonlyobserved when an industry or an innovative new technology, which has never been introduced beforein service blueprinting, is applied. Patrício, Fisk, and Cunha [48] suggested a service experienceblueprint (SEB) adding a component called an interface to correspond to information technologiesintroduced in internet banking financial services. In addition, Patrício et al. [49] extended the scopeof SB to retail industries combined with financial services. In order to represent the service deliveryprocess clearly, Lim and Kim [50] modified the SEB by adding an information delivery system in theinformation-intensive service industry. Pöppel, Finsterwalder, and Laycock [51] reflected changes inthe service process resulting from the introduction of digitization in the film industry by modifyingthe SB component. Barbieri et al. [52] considered a sociogram as a human factor dimension to visualizethe reception service process of luxury hotels. The proposed SB of this study is rather close to the latterclass of redesign approaches as it reorganizes the service encounters based on customer perceptions ofthe service while employing LDA text analysis in service blueprinting.
2.2. Reorganizing Service Encounters in Service Blueprint
One of the key components in the SB is the service encounter. Throughout the paper, we assumethere is a one-to-one relationship between the service encounter and the service step as describedin Reference [34]. The SB consists of customer actions, actions in front-stage encounters, actionsin back-stage encounters, support processes, visible lines that distinguish between the front andback-stage, and physical evidence that a customer can see or experience [35,36,53]. The serviceencounter, the core of service delivery, is the moment when and/or the place where direct interactionsbetween a customer and a service provider with proper physical components occur [30,54]. The serviceperformance during service encounters affects service quality [55,56] and service quality has a positiveinfluence on customer loyalty and satisfaction [57–59]. Customer perception of service experiences isthe sum of experience perceptions from every service encounter in the subsequent process steps [55].Therefore, it is very crucial to give an accurate configuration of service encounters when redesigningthe service process and providing customized services. Scandinavian Airlines, for example, achievedpositive corporate performances by adequately altering service encounters [53,60].
Since a customer prefers a flexible and personalized service, changes in the service encountersare unavoidable to accommodate customer needs [61,62]. Shostack [27] noted the redesign principles

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of SB as depicting various changes in the actual service delivery examples. These are complexity,the number and intricacy of the service delivery steps expressed in the blueprint, and divergence,the level of uniqueness permitted in a service step. Hence, a divergent service can be greatly affectedby the service provider’s capabilities which includes proficiency, specific response behaviors tosituations, response skills for predictable and unpredictable changes, self-control, adaptability tosituations, and so on. In particular, the cabin crew’s capabilities should be emphasized in the airlineservice because the industry truly relies on services related to human resources against other serviceindustries [63]. Thus, the complexity shows a quantitative variation in the SB whereas the divergenceis closely related to the degree of employee competence and represents a qualitative variation in the SB.For example, decreasing divergence results in a standardized service, whereas increasing divergencemeans a customized service [27].
By adjusting the complexity, Paquet et al. [64] redesigned the SB to be an effective distributingprocess for meal services in a medical hospital. Kim and Kim [65] proposed an efficient servicedelivery by rationalizing the design of the customer service process. The simplification of service stepsled to a decrease in complexity. Geum and Park [66] suggested a redesign method for the medicalservice process in terms of complexity by integrating the product-service system. Hossain, Enam andFarhana [67] investigated the limitations of the existing restaurant service process using interviews,and presented a new SB with greater complexity that split the behavior of customers and employees.Although relevant results with respect to research conducted on the complexity are relatively plentiful,there are few study results for modifying the divergence, especially working with quantitative methods,for the SB redesign. In terms of the improved design of in-flight service, we balance the complexity ofin-flight service steps and the proper divergence of customization by investigating the probability ofword frequency statistically distributed across topics and related service encounters [27].
2.3. Prior Works and Benchmark of the In-Flight Service Process
Research into air transport services has mainly focused on service quality and the investigation offactors that have major effects on and correlations with quality (See e.g., References [68–72]). There arenot many published research results regarding the design and upgrade of the airline service process.Bamford and Xystouri [73] analyzed airline service points where the service fails and Kim, Bong, andCho [74] modified the airline service process for specialized infant services. Lee, Kim, and Lee [75] andGo and Kim [76] applied the negative customer-to-customer interaction (NCCI) and Kano models tothe SB for redesigning purposes, respectively, in order to estimate fail points and bottleneck processesin the airline service.
We choose the in-flight SB of a Korean airline as a benchmark and propose a modified versionof the benchmark using the redesign principles described previously. The benchmark blueprintconsists of 13 service steps with the equivalent number of service encounters when a passengerboards an aircraft [74–76]. Some service steps are only applicable to long-haul and internationalroutes. This benchmark is the only publicized in-flight service in the form of an SB, to the best ofour knowledge, and the service received an excellence award for ten years in a row until 2017 in thearea of in-flight services [77]. Appendix A summarizes the descriptions of every service step andcorresponding physical evidence.
3. Methodology
3.1. Research Model
To obtain a sustainable service as close to customer needs as possible, we used 64,706 passenger-written online reviews, which are naturally unrefined and voluntary. Online reviews contain morestraightforward customer tastes and perceptions than standard survey data [78,79]. Customerperceptions are derived from customer experiences and customer experiences are defined as thesum of experiences at every service encounter [55]. We assumed that direct perceptions of customer

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experiences at every service encounter were contained in the online reviews [13,80]. The customerperceptions preserved were analyzed by employing LDA topic modeling [25,26]. As a result ofLDA modeling, a topic, one of the k-dimensional space, becomes a probability distribution of wordfrequency from online reviews containing customer perceptions of the in-flight service. Here, k denotesthe number of topics and is determined by the perplexity function, one of the measures for goodnessof fit of statistical models. In this modeling, k is chosen to be 18 since the derivative of the perplexityfunction does not change significantly from the value. Therefore, the topic was weighed by the size ofprobability based on word frequencies. This suggests that the more frequently mentioned words bycustomers, the more important they are, and the more those words are included in the specific topic,the more important the topic is.
The extracted topics were named interpretable service encounters by conducting a two-stepsurvey of researchers in the aviation management field. The group of researchers was composed of3 professors and 12 graduate students of various majors in the aviation management field. Their specificmajors included airline marketing, airport operations, airline service, revenue management, humanresource management, finance, MIS and aviation policy and strategy. The professor group, includingthe authors, selected proper service encounters as compared to the benchmark and provided temporarytopic names in the first step. New service encounters can be created if there are no suitable ones in thebenchmark. On the contrary, existing service encounters can be deleted from the benchmark if they donot properly correspond to current topics. In the second step, every participant of the graduate studentgroup independently provided final topic names as matching service encounters. The authors wereexcluded from this step. Then, we reorganized the service encounters using the redesign principles.
In the research frame, there were two main assumptions for the redesign of in-flight services.First, we assumed that all the actual service steps delivered should be defined in the in-flight SBwithout any omissions. This assumption gave us a legitimate opportunity to exclude passengerperceptions of service encounters undefined in the SB. In fact, the standard operating procedure (SOP)in the employee manual of cabin crews specifies all the service steps in the SB. Since crews shouldfollow the SOP as per their training, the first assumption can be justified. Second, we assumed thatthere was no significant variation in the level of service quality among the top 10 ranked airlines thatwe chose [81]. Further details can be found in Section 3.2. Thus, the assumption enabled us to treat thewhole dataset of 10 airlines as a similar level of data without having to distinguish between the chosenairlines. Since the survey evaluated around 330 airlines in the world at the same time, 3%, the top 10airlines’ portion, suggests very exclusive and high-quality airline services. It is reasonable to treat thedifference among them as insignificant. Figure 1 briefly depicts the whole modeling process of thestudy and Figure 2 dissects only the naming process in the dashed box of Figure 1.
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frequency from online reviews containing customer perceptions of the in-flight service. Here, k
denotes the number of topics and is determined by the perplexity function, one of the measures for
goodness of fit of statistical models. In this modeling, k is chosen to be 18 since the derivative of the
perplexity function does not change significantly from the value. Therefore, the topic was weighed
by the size of probability based on word frequencies. This suggests that the more frequently
mentioned words by customers, the more important they are, and the more those words are included
in the specific topic, the more important the topic is.
The extracted topics were named interpretable service encounters by conducting a two-step
survey of researchers in the aviation management field. The group of researchers was composed of 3
professors and 12 graduate students of various majors in the aviation management field. Their
specific majors included airline marketing, airport operations, airline service, revenue management,
human resource management, finance, MIS and aviation policy and strategy. The professor group,
including the authors, selected proper service encounters as compared to the benchmark and
provided temporary topic names in the first step. New service encounters can be created if there are
no suitable ones in the benchmark. On the contrary, existing service encounters can be deleted from
the benchmark if they do not properly correspond to current topics. In the second step, every
participant of the graduate student group independently provided final topic names as matching
service encounters. The authors were excluded from this step. Then, we reorganized the service
encounters using the redesign principles.
In the research frame, there were two main assumptions for the redesign of in-flight services.
First, we assumed that all the actual service steps delivered should be defined in the in-flight SB
without any omissions. This assumption gave us a legitimate opportunity to exclude passenger
perceptions of service encounters undefined in the SB. In fact, the standard operating procedure
(SOP) in the employee manual of cabin crews specifies all the service steps in the SB. Since crews
should follow the SOP as per their training, the first assumption can be justified. Second, we assumed
that there was no significant variation in the level of service quality among the top 10 ranked airlines
that we chose [81]. Further details can be found in Section 3.2. Thus, the assumption enabled us to
treat the whole dataset of 10 airlines as a similar level of data without having to distinguish between
the chosen airlines. Since the survey evaluated around 330 airlines in the world at the same time, 3%,
the top 10 airlines’ portion, suggests very exclusive and high-quality airline services. It is reasonable
to treat the difference among them as insignificant. Figure 1 briefly depicts the whole modeling
process of the study and Figure 2 dissects only the naming process in the dashed box of Figure 1.

Figure 1. The proposed research model.
More specifically, in the first step service encounters were selected while matching 18 topic
modeling results to 13 service encounters in the benchmark model. We screened out one by one from
the pool of topics and service encounters. If more than half of the participants regarded the specific
pair of service encounter and topic as the right one, the pair was determined to be necessary for the
redesign. For the non-matched topics and service encounters, an additional discussion within the
Figure 1. The proposed research model.

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More specifically, in the first step service encounters were selected while matching 18 topicmodeling results to 13 service encounters in the benchmark model. We screened out one by one fromthe pool of topics and service encounters. If more than half of the participants regarded the specificpair of service encounter and topic as the right one, the pair was determined to be necessary for theredesign. For the non-matched topics and service encounters, an additional discussion within theprofessor group determined whether new service encounters should be created or whether existingservice encounters should be removed. The results of the first step show seven service encounters thatwere highly recognized by passengers.
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professor group determined whether new service encounters should be created or whether existing
service encounters should be removed. The results of the first step show seven service encounters
that were highly recognized by passengers.

Figure 2. Naming process. The left and right panels depict the first and second steps in naming topics,
respectively. The first step is the selection process of service encounters while comparing the
benchmark and LDA results with the help of the professor group, including the authors. In the left
panel, the first and second columns represent the selection process during the comparison and the
third column shows the selected results after the comparison. The dashed boxes denote removed or
created encounters. The second step is the final naming process of topics as matching topics to the
selected service encounters in the first step with the help of a graduate student group, excluding the
authors.
We performed a survey that matched 18 topics using the selected seven service encounters in
the second step. The purpose of this survey was to confirm which service encounter among seven
choices was the best fit for the specific topic. This survey used Appendix B, which summarized the
LDA results composed of probabilistic distributions of words, although the appendix now contains
the names. Specifically, we provided a questionnaire form with blanks in the second row of the table.
Then 12 participants filled in the empty name of each topic from seven choices with the following
naming directions. The first direction was that a word with a larger probability in a topic had a greater
explanatory power than a word with a smaller probability. The second direction was to focus on
dissimilar words that could represent differences among topics rather than similar words that existed
in multiple topics at the same time. All participants were requested to mark words that were strongly
associated with the specific service encounter during the survey, and these words were highlighted
in the appendix. The final naming result was determined for the specific topic if more than half of the
participants had given the identical answer.
In addition, all the participants were asked to highlight words for the divergence analysis. In
order to analyze the capability of cabin attendants in terms of word frequency probability, all the
participants were requested to mark two types of words strongly related to the capability in the
questionnaire. One type of words expressed specific actions of cabin crews and the other type of
words were evaluation expressions for the competence of cabin crews (see details in Section 4.3).
3.2. Data
We collected 64,706 online reviews from TripAdvisor for airline services from 1 February 2016
to 31 January 2017. To include a high level of quality in airline services in this analysis, we chose the
Figure 2. Naming process. The left and right panels depict the first and second steps in namingtopics, respectively. The first step is the selection process of service encounters while comparing thebenchmark and LDA results with the help of the professor group, including the authors. In the leftpanel, the first and second columns represent the selection process during the comparison and the thirdcolumn shows the selected results after the comparison. The dashed boxes denote removed or createdencounters. The second step is the final naming process of topics as matching topics to the selectedservice encounters in the first step with the help of a graduate student group, excluding the authors.
We performed a survey that matched 18 topics using the selected seven service encounters inthe second step. The purpose of this survey was to confirm which service encounter among sevenchoices was the best fit for the specific topic. This survey used Appendix B, which summarized theLDA results composed of probabilistic distributions of words, although the appendix now containsthe names. Specifically, we provided a questionnaire form with blanks in the second row of the table.Then 12 participants filled in the empty name of each topic from seven choices with the followingnaming directions. The first direction was that a word with a larger probability in a topic had a greaterexplanatory power than a word with a smaller probability. The second direction was to focus ondissimilar words that could represent differences among topics rather than similar words that existedin multiple topics at the same time. All participants were requested to mark words that were stronglyassociated with the specific service encounter during the survey, and these words were highlighted inthe appendix. The final naming result was determined for the specific topic if more than half of theparticipants had given the identical answer.
In addition, all the participants were asked to highlight words for the divergence analysis. In orderto analyze the capability of cabin attendants in terms of word frequency probability, all the participantswere requested to mark two types of words strongly related to the capability in the questionnaire.One type of words expressed specific actions of cabin crews and the other type of words were evaluationexpressions for the competence of cabin crews (see details in Section 4.3).

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3.2. Data
We collected 64,706 online reviews from TripAdvisor for airline services from 1 February 2016 to31 January 2017. To include a high level of quality in airline services in this analysis, we chose the top10 ranked airlines assessed by Skytrax [81]. Table 1 presents the airlines selected in alphabetical orderand the summary of the online reviews.
Table 1. Online review data.
BR CA EK EY GA HU LH NH SQ QR
Review #s 1506 5377 16,200 10,789 6296 350 7080 1358 7960 7790Rank 6 5 4 8 10 9 7 3 2 1
4. Results
4.1. Service Encounters Using LDA Modeling
As shown in Appendix B, 18 topics, the results of LDA modeling, were finally named as sevenservice encounters using the two-step survey. These were: reservation, pre-boarding service, boarding& ground service, take-off safety check, meal & beverage service, passenger relaxation, and deplaning &post-deplaning. The survey used the top 15 words based on the probability size in the naming of topics,and the words explained topics by the amount of 55.6% on average (refer to the last rows of tablesin Appendix B). Table 2 arranges the LDA service encounters of in-flight service in the sequence ofoccurrence, together with the definition, matched topics, and the importance of service encounters.Among them, two new service encounters—reservation and pre-boarding service—were added and fourof the existing service encounters were removed from the benchmark model. The rest of the serviceencounters were identical or renamed by integrating related service encounters from the benchmark.
Table 2. LDA naming results. The importance is the sum of the importance of related topics. The formof service encounters in the parenthesis denotes the shortened form of the service encounters.
Service Encounter Definition Topics Importance
Reservation Related to reservations T3, T12, T16 17%
Pre-boarding service(pre-boarding)
Related actions from airportcheck-in to boarding gate arrival T5, T7, T9 17%
Boarding & ground service (boarding) Related actions from boarding totaking a seat T2, T17 11%
Take-off safety check(take-off)
Actions related to take-off andsafety check T14, T15 11%
Meal & beverage service(meal service) Actions related to meal service T6 6%
Passenger relaxation Actions related to personal restingand entertaining within a flight T1, T8, T10, T11, T13, T18 33%
Deplaning & post-deplaning(deplaning)
Actions related to landingand deplaning T4 5%
For new service encounters generated by LDA modeling, reservation corresponds to T3, T12,and T16, and takes 17% of the importance. In particular, a word such as ‘social media’ (originallysocialmedia in the modeling result) represents a recent change in customer trends as a new typeof word-of-mouth [82]. Pre-boarding also takes 17% of the importance. The service process beforeboarding is related to sets of words such as carried baggage (e.g., bag, luggage), services provided byan airport (e.g., service, serve, efficient, eat, bar), flight information guides (e.g., travel, screen, delay,passenger), and physical evidence for boarding (e.g., ticket, passport).
For renamed service encounters from the typical ones, boarding includes T2 and T17 and takes 11%of the importance in the LDA results. This service encounter contains words related to the boardingprocess (e.g., check, available) and seating (e.g., economy, cabin, seat, short, forward, order). Take-off

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(T4, T15) takes 11% of the importance and shows words related to the take-off process (e.g., attendant,takeoff, departure, request, gate). Meal service corresponds to a single topic, T6, and covers 6% ofthe importance. The service encounter is represented by word sets, such as in-flight meal (e.g., meal,wine, snack), service quality evaluation (e.g., love, nice, awesome), and general impressions aboutthe service (e.g., busy, available, service). Passenger relaxation is matched to the largest number oftopics (T1, T8, T10, T11, T13, and T18) and has the highest importance. The service encounterincludes several word sets such as seat experience (e.g., premium economy, sit, comfort, inconvenient),in-flight entertainment (IFE) (e.g., entertainment, book, online, movie, film), food (beverage, food),service providers (e.g., provide, hostess, staff, crew), and customer perceptions regarding the service(e.g., feel, happy, amaze, enjoy, pleasant, nice). Deplaning corresponds to T4 and covers 5% of theimportance. This service encounter is supported by words (e.g., destination, transfer, hotel) andpassenger perceptions of landing and deplaning (e.g., smile, welcome, miss).
4.2. Service Blueprinting in Terms of Complexity
As shown in the previous section, reservation and pre-boarding service are the newly derived serviceencounters from the LDA topic modeling while reflecting passenger perceptions contained in onlinereviews. Reservation is excluded from the proposed SB since the actual in-flight service does not coverthe service encounter. However, it is reasonable to assume that online reviews involve a great deal ofexpressions for the reservation because online booking systems are commonly utilized today. Althoughthe reservation is not dealt with as an in-flight service encounter, service providers should be aware ofits importance (17%)—not a small amount, in our analysis. This suggests that the reservation is one ofthe service processes that is highly recognized by passengers. We included this service encounter inthe divergence analysis for this reason in Section 4.3.
Pre-boarding service asks for changes in the conventional process of in-flight services, since theservice encounter is not the existing service encounter in the benchmark. The service encountershows a few similarities with the existing service encounter of boarding a plane in the benchmark.However, it appears that topics of passenger experiences before the boarding stage (e.g., airport service,baggage handling, flight information, physical evidence of boarding) are relatively more frequentthan cabin experience topics that can be characterized in the existing service encounter. This meansthat the importance of services provided before boarding should not be overlooked. If passengersseriously recognize the airline service from ticket issues, shopping, flight information acquisition,wandering and rests while waiting to board, air carriers need to be proactive in serving passengers byincorporating a wider range of new service encounters that have not been covered yet. As expected,some of the services mentioned cannot be easily reached by air carriers themselves and cooperationand coordination between related organizations are inevitable. Specified action plans for this servicestep are discussed in Section 4.4. The introduction of a new service encounter increases the complexity.
Boarding & ground service is a renamed service encounter as integrated in four existing serviceencounters (boarding a plane, finding seats, baggage service, ground service) in the benchmark.Since words associated with the 4 existing service encounters, such as boarding (e.g., check, cabin,available), finding a seat (e.g., economy, seat, short, forward), carried baggage service (e.g., put, high)and cabin service (e.g., drink, order) coexist in the related topics simultaneously, it is plausible to thinkthat passengers would note little difference among the existing service encounters. These four servicesteps tend to be performed at the same time between boarding and take-off and passengers recognizethe service encounters as almost the same one. This results in the integrated service encounter ofboarding & ground service. The integration of the service encounters causes a decrease in complexity byreducing the service encounter numbers.
Take-off safety check and meal & beverage service remain the identical forms of the benchmark. Take-offis the service encounter that gives a start signal for actual flight after a few service steps have finished.Therefore, passengers independently recognize this service encounter from others and regard it asa separate service encounter. Although meal service, in terms of the characteristics of the service,

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can be seen as an extended one from passenger relaxation explained in the next paragraph, the serviceencounter is determined to be different since discernible meal-related words (e.g., meal, wine, snack)have appeared in the topic.
Two conventional service encounters, movie watching and personal relaxing, do not reveala significant difference in customer perceptions and have been combined as a renamed serviceencounter: passenger relaxation. There are a great deal of word sets that are IFE-associated(e.g., entertainment, book, online, movie, film) and leisure-associated (e.g., book, sleep, sit) in theconnected topics to support the service encounter. With the prevalent help of IFEs, watching a movieas part of personal relaxing has become a normal form of in-flight leisure. In particular, the order ofmovie watching is not critical to service providers in the whole service sequence because passengersexperience the service with wide applications of IFEs regardless of the service sequence whenever theservice is ready. That is, the service encounter of movie watching is inclusively recognized within theservice encounter of passenger relaxation in a broader sense.
Deplaning means the termination of in-flight service and also leaves an independent and strongimpression on customers likewise in take-off. As the same service encounter as the benchmark, there isno change in the complexity.
In terms of customer perceptions, four typical service encounters—in-flight sales, preparingimmigration documents, preparing landing, and landing—are removed from the benchmark. In-flightsales is the service encounter of passengers’ convenience for shopping. Since in-flight sales is used asan additional income source for airlines, airlines treat this service as an important one [83]. In orderto provide a diversified and customized shopping service, air carriers deploy a passenger-friendlymarketing strategy based on products that consider the characteristics of passengers for individualroutes and shopping counters that can achieve strong perceptions of the service. However, the currentLDA modeling results do not disclose such efforts by airlines and neither do the results of thesurvey. This might be because the service encounter is not mandatory for every route and onlyapplicable to part of long-haul or international routes. In preparing for landing, cabin crews providedestination information via announcements and take back used or reusable goods for the in-flightservice. Passengers are usually static in the service encounter, being informed and returning goodsaccording to the instructions. The degree of interaction between passengers and crews is lower thanthat in any other service encounter and the lower level of interaction has a restricted impact oncustomer perceptions of the experience [84].
Both preparing immigration documents and landing are the essential service encounters in airtransport services although they did not emerge in the LDA topic modeling results. Customerperceptions of the service encounters are not strong enough to be revealed in the modeling sincethe presence of service encounters is naturally accepted in the in-flight service process. The serviceencounters remain in the same form in the proposed SB. Table 3 presents the results of the reorganizedin-flight SBs in terms of complexity.
In summary, among the newly derived service encounters, reservation is excluded and pre-boardingservice is added in the redesigned SB as increasing the complexity. However, the overall number ofservice encounters decreases when aggregating the four consecutive service encounters from boardinga plane to ground service in the benchmark as boarding & ground service, and combining the serviceencounters from movie watching to personal relaxing as passenger relaxation. Although passengerperceptions of the traditional service encounters of in-flight sales, preparing immigration documents,preparing for landing and landing are not strong enough to be regarded as important, we includedtwo fundamental service encounters (preparing immigration documents, landing) in the redesignedSB. Finally, the SB is composed of the eight service encounters and is less complex than the benchmarkSB by 38%.

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Table 3. Reorganized in-flight service encounters.
Benchmark Service Encounters Topic Modeling Results Reorganized Service Encounters
– Reservation –
– Pre-boarding service Pre-boarding service
Boarding a plane
Boarding & ground service Boarding & ground serviceFinding seatsBaggage serviceGround service
Take-off safety check Take-off safety check Take-off safety check
In-flight food service Meal & beverage service Meal & beverage service
In-flight sales – –
Preparing immigration documents – Preparing immigration documents
Movie watching Passenger relaxation Passenger relaxationPersonal relaxing
Preparing landing – –
Landing Landing
Deplaning Deplaning Deplaning & post-deplaning
4.3. Service Blueprinting in Terms of Divergence
The divergence represents the level of uniqueness and customization of the service and is closelyrelated to the capabilities of service providers. In terms of the text analysis, the divergence can berevealed by word frequencies related to the capabilities of service providers. These can be expressionsfor specific actions relating to service delivery and customer evaluations of service competence.The current LDA results show that word sets associated with specific behaviors for service delivery(e.g., entertainment, check, service, crew, arrive, select, staff, offer, connect, steward) and word setsassociated with customer assessments of service competence (e.g., good, great, comfort, busy, plenty,disappoint, quality, nice, friendly, happy) appear together within the relevant topics. Since thecorrelation among words is analyzed by using their frequency of simultaneous appearances in aset of documents in the LDA, the words that appear in the same topic are closely related to eachother [25,26,85]. As explained previously, the word sets are collected from the survey of participantsand the probabilities of two types of word sets can be utilized for quantitative evidence with respect tothe divergence analysis in this redesign.
We defined the former word sets that belong to specific actions for service delivery as category 1,and the latter word sets belonging to customer evaluations of service competence as category 2.Figure 3 displays the word probabilities of categories in every topic and Appendix C summarizesthe corresponding words for each topic. Three topics (T1, T4, and T5) are excluded from the analysisbecause they have only one of the two categories. Therefore, deplaning, which is only matched to T4,cannot be discussed here. As shown in Figure 3, the sum of probabilities of two categories varies from11% to 47% and the proportion of words included in two categories is around 24%, which is sufficientto express the divergence, in total word frequency counts.
For the quantitative analysis, we divided the sum of probabilities of category 1 by that of category2 for each service encounter after reuniting the topics that belong to the specific service encounter asdisplayed in Table 2. For example, boarding consisted of T2 and T17, and the ratio was 1.07 (=21.4/20.0)when we divided the sum of probabilities of category 1 (5.09 + 16.31 = 21.4) by that of category2 (6.69 + 13.31 = 20.0). The ratio measures the word frequency of specific service actions per the wordfrequency of customer evaluations of the service capability. If the ratio was close to 1, we approximatedthat service actions were equally performed for service assessments in the service encounter. If theratio was greater than 1, more service actions were provided for a service capability evaluation.This indicates that the crew actions for the service were relatively diverse and frequent to obtain oneassessment. The service enables passengers to recognize a relatively high level of customization in theservice encounter. If the ratio was smaller than 1, we deemed that the exact opposite was true.

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Sustainability 2018, 10, x FOR PEER REVIEW 10 of 23
Deplaning Deplaning Deplaning & post-deplaning
In summary, among the newly derived service encounters, reservation is excluded and pre-
boarding service is added in the redesigned SB as increasing the complexity. However, the overall
number of service encounters decreases when aggregating the four consecutive service encounters
from boarding a plane to ground service in the benchmark as boarding & ground service, and
combining the service encounters from movie watching to personal relaxing as passenger relaxation.
Although passenger perceptions of the traditional service encounters of in-flight sales, preparing
immigration documents, preparing for landing and landing are not strong enough to be regarded as
important, we included two fundamental service encounters (preparing immigration documents,
landing) in the redesigned SB. Finally, the SB is composed of the eight service encounters and is less
complex than the benchmark SB by 38%.
4.3. Service Blueprinting in Terms of Divergence
The divergence represents the level of uniqueness and customization of the service and is closely
related to the capabilities of service providers. In terms of the text analysis, the divergence can be
revealed by word frequencies related to the capabilities of service providers. These can be expressions
for specific actions relating to service delivery and customer evaluations of service competence. The
current LDA results show that word sets associated with specific behaviors for service delivery (e.g.,
entertainment, check, service, crew, arrive, select, staff, offer, connect, steward) and word sets
associated with customer assessments of service competence (e.g., good, great, comfort, busy, plenty,
disappoint, quality, nice, friendly, happy) appear together within the relevant topics. Since the
correlation among words is analyzed by using their frequency of simultaneous appearances in a set
of documents in the LDA, the words that appear in the same topic are closely related to each other
[25,26,85]. As explained previously, the word sets are collected from the survey of participants and
the probabilities of two types of word sets can be utilized for quantitative evidence with respect to
the divergence analysis in this redesign.
We defined the former word sets that belong to specific actions for service delivery as category
1, and the latter word sets belonging to customer evaluations of service competence as category 2.
Figure 3 displays the word probabilities of categories in every topic and Appendix C summarizes the
corresponding words for each topic. Three topics (T1, T4, and T5) are excluded from the analysis
because they have only one of the two categories. Therefore, deplaning, which is only matched to T4,
cannot be discussed here. As shown in Figure 3, the sum of probabilities of two categories varies from
11% to 47% and the proportion of words included in two categories is around 24%, which is sufficient
to express the divergence, in total word frequency counts.

Figure 3. Word probability distribution of divergence.
5.09%
14.15%
9.06%
17.67%
8.42%
26.83%
13.58%11.30%
18.94%
6.59%10.16%
4.25%6.82%
10.08%
16.31%
40.17%
12.40%
2.37%
6.69%
22.04%
21.22%
11.06%
25.85%
11.15%
10.72%14.32%
25.11%
8.36%
21.07%
6.84%5.32%
6.21%
13.31%
6.95%
11.85%
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
50.00%
Category1 Category2
Figure 3. Word probability distribution of divergence.
As shown in Figure 4, the service encounters are grouped according to the ratio size. The firstgroup, passenger relaxation and meal service, has ratio values of 1.37 and 1.60, respectively. We concludedthat this was the group in which service encounters present a high level of divergence perceived bypassengers. The second group, boarding, has a value of almost 1 and was concluded as the mid-levelservice encounter in terms of divergence. Likewise, the last group, reservation, pre-boarding and take-off,can be concluded as low-level service encounters in terms of divergence as the ratio is less than 1.
Sustainability 2018, 10, x FOR PEER REVIEW 11 of 23
For the quantitative analysis, we divided the sum of probabilities of category 1 by that of category 2 for each service encounter after reuniting the topics that belong to the specific service encounter as displayed in Table 2. For example, boarding consisted of T2 and T17, and the ratio was 1.07 (=21.4/20.0) when we divided the sum of probabilities of category 1 (5.09 + 16.31 = 21.4) by that of category 2 (6.69 + 13.31 = 20.0). The ratio measures the word frequency of specific service actions per the word frequency of customer evaluations of the service capability. If the ratio was close to 1, we approximated that service actions were equally performed for service assessments in the service encounter. If the ratio was greater than 1, more service actions were provided for a service capability evaluation. This indicates that the crew actions for the service were relatively diverse and frequent to obtain one assessment. The service enables passengers to recognize a relatively high level of customization in the service encounter. If the ratio was smaller than 1, we deemed that the exact opposite was true.
As shown in Figure 4, the service encounters are grouped according to the ratio size. The first group, passenger relaxation and meal service, has ratio values of 1.37 and 1.60, respectively. We concluded that this was the group in which service encounters present a high level of divergence perceived by passengers. The second group, boarding, has a value of almost 1 and was concluded as the mid-level service encounter in terms of divergence. Likewise, the last group, reservation, pre-boarding and take-off, can be concluded as low-level service encounters in terms of divergence as the ratio is less than 1.
Figure 4. Groups by divergence ratio. On the basis value of 1, the service encounters are divided into three groups: high (>1, meal service and passenger relaxation), medium (≈1, boarding), low (1, meal service and passenger relaxation), medium (≈1, boarding), low (<1,reservation, pre-boarding and take-off). Reservation, dashed point, is not included in the actual SB.
The existence of various forms of service evaluations on the same service performance indicatesthat the level of service expectation could also be diversified. This can generate gaps between theservice expectation formed by prior experiences and the performance actually perceived [86]. The gapcauses passenger dissatisfaction with the service. Therefore, it is essential for airlines to meet differentpassenger needs by interpreting them as accurately as possible and perform the service based on theirunderstandings. Although service providers fail to properly respond to the diverse level of customerexpectations, it is still possible to improve customer loyalty when the service recovery succeeds [87–89].
When compared to other service businesses, the airline service is quite dependent on servicesrelated to the competence level of cabin attendants [63]. Therefore, airlines should be equipped withcabin attendants’ capabilities of service delivery processes to promptly respond to diversified needs.As investigated using the divergence analysis, the service encounters of meal service and passenger

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relaxation should keep up the current high level of divergence. The service encounter of take-off canalso maintain the current level of divergence when it has been regarded as an almost standardizedservice process. However, the service encounters of pre-boarding and boarding should strengthen thecapabilities of cabin attendants and make efforts to develop further customized services with respectto the characteristics of the service encounters. The meaningful level of divergence should be increasedin the service encounters.
4.4. Redesigned In-Flight Service Blueprint
For every service encounter, the correlation between complexity and divergence is drawn in thecomplexity-divergence matrix in Figure 5. The level of divergence is determined by the base value of 1in the ratio analysis and that of complexity is defined by the number of integrated service encountersfrom the benchmark. We noted that the complexity in this matrix should be interpreted with caution.If a service encounter is integrated with old service encounters, the complexity of the service encounteritself increases but the complexity decreases with respect to the SB level with a reduction in the servicesteps. With respect to service encounter level, the integration increases the intricacy of the serviceencounter as the single service encounter gathers service delivery procedures and elements of serviceencounters integrated [27]. The matrix covers only five service encounters that have been investigatedby both dimensions (preparing immigration documents and landing not covered by the complexity anddeplaning not covered by the divergence). For example, boarding is located in the middle part of thedivergence axis and on the right side of the complexity axis since the service encounter has a valueclose to 1 and is integrated with four existing service encounters from the benchmark. Passengerrelaxation is placed in the upper side of the divergence axis and in the middle part of the complexityaxis because the ratio is 1.37 and two old service encounters are merged at the service encounter.In a similar vein, the positions of take-off and meal service are determined in the matrix. In particular,the newly derived pre-boarding is located in the high complexity region based on its typical features.
Sustainability 2018, 10, x FOR PEER REVIEW 12 of 23
As investigated using the divergence analysis, the service encounters of meal service and passenger
relaxation should keep up the current high level of divergence. The service encounter of take-off can
also maintain the current level of divergence when it has been regarded as an almost standardized
service process. However, the service encounters of pre-boarding and boarding should strengthen the
capabilities of cabin attendants and make efforts to develop further customized services with respect
to the characteristics of the service encounters. The meaningful level of divergence should be
increased in the service encounters.
4.4. Redesigned In-Flight Service Blueprint
For every service encounter, the correlation between complexity and divergence is drawn in the
complexity-divergence matrix in Figure 5. The level of divergence is determined by the base value of
1 in the ratio analysis and that of complexity is defined by the number of integrated service
encounters from the benchmark. We noted that the complexity in this matrix should be interpreted
with caution. If a service encounter is integrated with old service encounters, the complexity of the
service encounter itself increases but the complexity decreases with respect to the SB level with a
reduction in the service steps. With respect to service encounter level, the integration increases the
intricacy of the service encounter as the single service encounter gathers service delivery procedures
and elements of service encounters integrated [27]. The matrix covers only five service encounters
that have been investigated by both dimensions (preparing immigration documents and landing not
covered by the complexity and deplaning not covered by the divergence). For example, boarding is
located in the middle part of the divergence axis and on the right side of the complexity axis since the
service encounter has a value close to 1 and is integrated with four existing service encounters from
the benchmark. Passenger relaxation is placed in the upper side of the divergence axis and in the
middle part of the complexity axis because the ratio is 1.37 and two old service encounters are merged
at the service encounter. In a similar vein, the positions of take-off and meal service are determined in
the matrix. In particular, the newly derived pre-boarding is located in the high complexity region based
on its typical features.

Figure 5. Complexity-divergence matrix of service encounters. A solid oval means the current
perceived status of a service encounter in the complexity and divergence matrix. A dotted oval
denotes the proposed (ideal) status of a service encounter in the matrix and demands changes in the
current level. Pre-boarding and boarding should increase the level of divergence and take-off; meal
service and passenger relaxation may maintain the current position.
Figure 5. Complexity-divergence matrix of service encounters. A solid oval means the currentperceived status of a service encounter in the complexity and divergence matrix. A dotted oval denotesthe proposed (ideal) status of a service encounter in the matrix and demands changes in the currentlevel. Pre-boarding and boarding should increase the level of divergence and take-off; meal serviceand passenger relaxation may maintain the current position.
Pre-boarding is perceived to be complicated but not very customized by passengers. However,the amount of time and experiences consumed in this service encounter are not trivial with respect tothe characteristics of the air transport service. As suggested in Figure 5, air carriers need to make their

Sustainability 2018, 10, 4492 13 of 21
passengers aware of more customized services by increasing the degree of divergence. For example,they can strengthen the service capabilities of special care for passengers, such as pregnant women,elderly people, infants, and wounded veterans until flight departure. They can also sharpen loungeservice differentiation before boarding for unique service experiences. Especially close cooperationbetween airlines and relevant authorities, such as an airport and customs service, is essential. Examplesof service collaboration include shopping at duty free shops, notices and updates of flight information,services in amusement facilities such as restaurants, play zones and shopping malls, and so forth.Because the related topics cover 17% of the importance, there is a sufficient reason to improve theservice capabilities of providers for this new service encounter.
Boarding is recognized as the service encounter with high complexity and medium divergence.Positive and strong passenger perceptions of this service encounter are important because theservice encounter is the moment of truth when customers actually encounter the in-flight service.Thus, customized service is vital in the service encounter, intensifying the level of divergence. Take-offis the service encounter with low complexity and low divergence as perceived as a standardizedservice that involves simple safety checks. Meal service is perceived to be not very complicated buthighly customized by customers. To deal with each customer’s needs, including menu variety andspecial demands, diversified scenarios of meal service delivery can be used as a viable strategy toachieve competitive advantages in the airline industry. Passenger relaxation is recognized as the serviceencounter with medium complexity and high divergence by passengers and needs to be highlydivergent for maintaining the current level of customization. This is primarily because customerstend to experience the service encounter from the closest distance and spend most of the time at theservice encounter; 33% topic importance supports this reasoning. To effectively respond to diversifiedcustomer needs and gain a competitive advantage, airlines should provide sophisticated, highlycustomized, and more service encounter-specific characterized services [27]. Figure 6 shows the finalform of the redesigned in-flight SB based on customer perceptions of the service.
Sustainability 2018, 10, x FOR PEER REVIEW 13 of 23
Pre-boarding is perceived to be complicated but not very customized by passengers. However,
the amount of time and experiences consumed in this service encounter are not trivial with respect
to the characteristics of the air transport service. As suggested in Figure 5, air carriers need to make
their passengers aware of more customized services by increasing the degree of divergence. For
example, they can strengthen the service capabilities of special care for passengers, such as pregnant
women, elderly people, infants, and wounded veterans until flight departure. They can also sharpen
lounge service differentiation before boarding for unique service experiences. Especially close
cooperation between airlines and relevant authorities, such as an airport and customs service, is
essential. Examples of service collaboration include shopping at duty free shops, notices and updates
of flight information, services in amusement facilities such as restaurants, play zones and shopping
malls, and so forth. Because the related topics cover 17% of the importance, there is a sufficient reason
to improve the service capabilities of providers for this new service encounter.
Boarding is recognized as the service encounter with high complexity and medium divergence.
Positive and strong passenger perceptions of this service encounter are important because the service
encounter is the moment of truth when customers actually encounter the in-flight service. Thus,
customized service is vital in the service encounter, intensifying the level of divergence. Take-off is the
service encounter with low complexity and low divergence as perceived as a standardized service
that involves simple safety checks. Meal service is perceived to be not very complicated but highly
customized by customers. To deal with each customer’s needs, including menu variety and special
demands, diversified scenarios of meal service delivery can be used as a viable strategy to achieve
competitive advantages in the airline industry. Passenger relaxation is recognized as the service
encounter with medium complexity and high divergence by passengers and needs to be highly
divergent for maintaining the current level of customization. This is primarily because customers
tend to experience the service encounter from the closest distance and spend most of the time at the
service encounter; 33% topic importance supports this reasoning. To effectively respond to
diversified customer needs and gain a competitive advantage, airlines should provide sophisticated,
highly customized, and more service encounter-specific characterized services [27]. Figure 6 shows
the final form of the redesigned in-flight SB based on customer perceptions of the service.

Figure 6. Redesigned service blueprint. The top panel shows the part of the SB form in Shostack [27],
Bitner et al. [30], and Go and Kim [76], and the bottom panel zooms in on the row of front-stage actions
where the proposed service encounters exist. The redesigned SB consists of eight service encounters
in terms of complexity. The divergence of a service encounter is represented by a circular sector and
Figure 6. Redesigned service blueprint. The top panel shows the part of the SB form in Shostack [27],Bitner et al. [30], and Go and Kim [76], and the bottom panel zooms in on the row of front-stage actionswhere the proposed service encounters exist. The redesigned SB consists of eight service encountersin terms of complexity. The divergence of a service encounter is represented by a circular sector andthe level of divergence is determined by the size of the angle in the sector. A solid line denotes theperceived level and a dotted line denotes the proposed (ideal) level of divergence. Pre-boarding andboarding need to increase the level and take-off; meal service and relaxation can maintain the currentlevel of divergence.

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5. Summary and Conclusions
The main conclusions of the proposed in-flight SB are shared with respect to servicedesign perspectives. First, we redesigned a customer-focused in-flight SB while understandingcustomer perceptions of the service through the application of topic modeling based on 64,706passenger-authored online reviews for airline services. To do so, we derived the service encounters ofin-flight service processes while extracting passenger perceptions of the service encounter experiencesusing LDA text analysis. We finally depicted the redesigned service using the SB frame with theredesign principles of complexity and divergence. To make sustainable in-flight service, we balancedthe complexity and the proper divergence degree of in-flight service by investigating the probabilityof word frequency statistically distributed to topics and related service encounters. Second, in termsof complexity, in-flight-service is reorganized by eight service encounters via integration (boarding,passenger relaxation), new appearance (pre-boarding), and removal (in-flight sales, preparing landing).This leads to a 38% reduction in the number of service encounters compared to the benchmark SB.The newly emerged service encounter, pre-boarding, is not negligible for the entire service becauseit covers 17% of the total importance. This suggests that airlines need to expand the actual scopeof services in a more proactive way to provide better in-flight services. Feasible action plans werediscussed with specific examples in the previous section. Airlines may sustain the service capabilityfor people who need special care and sharpen service differentiation for customers who are waiting,i.e., lounge services, before boarding. They should be aware of the importance of this, as it would helpthem better differentiate themselves. Lastly, airlines need to provide more customized services than thecurrently perceived level at a couple of service encounters (pre-boarding and boarding). This conclusionwas reached by Shostack [27]; a service should be designed by considering the unique features ofservice encounters as carefully as possible. In particular, the results of the divergence analysis areestablished using a quantitative method with the probability of word occurrence.
The divergence analysis could be improved by considering the polarity of online reviews (positiveor negative) in further studies, since we only use word frequency to quantify the significance of thetopic. If a sentiment analysis were employed to capture the polarity of the degree of evaluation ofwords related to the service evaluation (category 2), the results could add more accurate and widerinterpretations regarding service design. For the same aim of better interpretations, we need to utilizemultiple trusted sources of online reviews simultaneously. Moreover, the characteristics of onlinereviews can sometimes cause problems. Since one of the main characteristics of online reviews isvoluntariness, there is a chance of excluding the data of customers who are reluctant to, or for otherreasons do not, express their opinions and thoughts.
We finalize this study by explaining the usability of the proposed design method. Under thecircumstances wherein companies must promptly respond to customer needs and businessenvironments change, the proposed design method could offer the ability to capture customer needs onthe fly and incorporate them into service improvement. Furthermore, the application of the proposeddesign approach could be expanded to other industries with the proper acquisition of relevant datasetsalthough we focus on the airline service in this study. Finally, the proposed design could play a crucialrole in the further improvement of a service process as a new standard. It is possible to evaluate thestatus of service delivery efficiency based on the new standard design. The appropriate evaluation canbe another trigger for continuous improvements in a sustainable service.
Author Contributions: Conceptualization, S.N. and H.C.L.; methodology, S.N., C.H. and H.C.L.; software, S.N.and C.H.; validation, S.N., C.H. and H.C.L.; formal analysis, S.N. and H.C.L.; investigation, S.N., C.H. andH.C.L.; resources, S.N. and H.C.L.; data curation, S.N.; writing—original draft preparation, S.N. and H.C.L.;writing—review and editing, S.N., C.H. and H.C.L.; visualization, S.N. and H.C.L.; project administration, H.C.L.;supervision, H.C.L.
Funding: This research received no external funding.

Sustainability 2018, 10, 4492 15 of 21
Acknowledgments: The authors would like to thank the professors (Woon-Kyung Song, Chul-woo Kim) and thegraduate students for their participation in the surveys and interviews, as well as for their helpful commentsand suggestions.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. Benchmark Model of In-Flight Service Encounters.
Service Encounter Definition Physical Evidence Remarks
Boarding a plane The moment when a meeting withpassengers takes place for the first time
Crew uniform, boarding area facilities,aircraft outlook
Finding seats Checking in boarding passes managingcongested aisles Boarding pass, seat, interior
Baggage service Managing luggage storages Overhead compartment (bin), coatroom
Ground service Providing background music, readingmaterials and beverages Screen, book, audio
Taking off Checking up take-off demonstratingsafety simulation
Individual reading light, seat belt,in-flight light
In-flight food service Providing meal and beverage serviceMenu, meal, beverage, waiting, serviceevidence, clearance, attendants’appearance
Long-haul &international routes
In-flight sales Providing convenience of shopping forpassengers In-flight sales counter, goods Long-haul &
international routes
Prepare immigrationdocuments
Support with filling out passengerimmigration documents Immigration documents International routes
Movie watching Providing movies and music Passenger service unit (PSU), movie,screen
Long-haul &international routes
Personal relaxing Touring the cabin, responding to servicecalls
In-flight environment, thermostatsetting, toilet, blanket, cushion
Prepare landing Providing destination information,collecting service items Earphone, pillow Long-haul &
international routes
Landing Checking safety of landing Individual reading light, in-flight light,seat
Deplaning Giving a farewell and taking back goods Attendant appearance, cabin interior
The table presents in-flight service encounters of the benchmark blueprint in order of time of occurrence. Thedefinition, physical evidence of every 13 service encounters are explained, and some of the service encounters areonly applicable for long-haul and/or international routes.

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Appendix B
Table A2. LDA Topic Naming Results.
T1 T2 T3 T4 T5 T6
Passenger relaxation Boarding Reservation Deplaning Pre-boarding Meal Service
5.53% 5.54% 5.53% 5.46% 5.56% 5.55%
food 8.94% economy 8.69% comfort 9.20% excel 14.81% travel 11.38% service 17.67%change 7.09% cabin 6.75% staff 7.09% leg 5.74% food 10.28% meal 7.14%hope 5.22% seat 5.71% little 5.85% try 4.17% delay 7.73% busy 6.16%
upgrade 4.69% disappoint 5.48% choose 4.89% class 3.96% serve 7.08% love 2.11%board 4.37% high 2.31% steward 4.88% bad 3.30% quit 5.97% return 1.32%easy 2.99% forward 2.18% special 4.50% friendly 3.11% haul 5.04% legroom 1.19%
appreciate 2.37% issue 2.01% left 4.42% smile 2.66% find 4.92% wine 1.18%bed 2.35% several 1.89% free 4.40% welcome 2.62% long 3.17% snack 1.00%
premiumeconomy 2.32% order 1.74% poor 3.65% destination 2.07% toilet 2.34% nice 0.93%sleep 1.52% level 1.62% trip 2.72% improve 1.94% passport 2.31% awesome 0.93%direct 1.48% menu 1.25% superb 2.70% start 1.94% bar 2.30% number 0.83%future 1.30% glad 1.21% show 2.18% transfer 1.68% route 2.21% schedule 0.63%
leg 1.13% put 1.17% awesome 1.99% miss 1.19% staff 1.98% case 0.61%bag 0.58% gate 1.10% start 1.62% regret 1.16% screen 1.50% prefect 0.52%
apology 0.55% leave 0.92% front 1.57% hotel 1.16% carrier 1.20% available 0.41%
46.95% 44.07% 61.71% 51.56% 69.47% 42.69%
T7 T8 T9 T10 T11 T12
Pre-boarding Passenger relaxation Pre-boarding Passenger relaxation Passenger relaxation Reservation
5.64% 5.52% 5.53% 5.61% 5.59% 5.54%
great 13.10% entertainment 21.84% service 12.27% 13.61% 10.13% make 9.46% seat 13.61%service 8.42% book 7.71% experience 8.32% 8.92% 8.66% good 6.33% clean 8.92%
nice 5.05% enjoy 4.67% passenger 7.44% 6.59% 6.85% staff 6.16% offer 6.59%kind 4.70% give 3.79% price 6.04% 4.71% 4.49% feel 5.54% expect 4.71%
ticket 4.67% pay 3.25% efficient 4.99% 4.30% 3.32% happy 5.01% share 4.30%ground 4.63% pleasant 2.36% bag 4.99% 4.14% 3.23% comfort 4.60% option 4.14%average 2.32% problem 2.36% sorry 3.19% 4.14% 2.77% reason 4.43% luggage 4.14%fantastic 2.19% perfect 2.15% frequent 3.10% 3.68% 2.23% top 4.23% polite 3.68%return 1.57% quick 2.03% ticket 3.10% 3.21% 2.22% home 4.07% travel 3.21%
eat 1.21% nice 1.97% fault 2.54% 3.14% 2.08% impress 3.99% socialmedia 3.14%system 1.19% end 1.57% screen 2.35% 3.06% 1.96% entertainment 3.32% route 3.06%
big 1.07% meal 1.47% recliner 2.35% 2.66% 1.85% pleasant 2.75% detail 2.66%onboard 0.93% facility 1.21% luggage 2.08% 2.63% 1.79% amaze 2.43% book 2.63%
water 0.82% manage 1.20% write 1.44% 2.34% 1.64% breakfast 2.20% decent 2.34%late 0.81% happen 1.20% serve 1.31% 2.34% 1.45% part 2.05% pleasure 2.34%
52.76% 58.84% 65.58% 54.74% 66.62% 69.52% 69.52%

Sustainability 2018, 10, 4492 17 of 21
Table A2. Cont.
T13 T14 T15 T16 T17 T18
Passenger relaxation Take-off Take-off Reservation Boarding Passenger relaxation
5.54% 5.57% 5.55% 5.59% 5.54% 5.54%
good 17.21% food 12.39% airplane 10.61% board 9.45% check 14.046 crew 11.65%experience 5.24% friendly 5.05% service 5.49% room 6.86% airport 11.275 arrive 11.22%
attend 4.19% choice 3.52% drink 4.84% connect 5.04% lounge 8.31% select 10.77%feedback 4.07% review 2.51% work 4.00% full 4.71% quality 5.09% space 8.72%
amaze 3.86% attendant 2.33% compare 2.47% found 3.64% drink 4.78% plenty 5.74%different 3.05% journey 2.33% free 2.34% professional 3.08% available 4.68% wait 4.18%
sleep 2.34% takeoff 2.27% spacious 2.30% care 2.73% include 2.70% start 2.35%row 2.30% departure 1.84% point 2.13% stopover 2.35% legroom 2.53% big 2.26%
online 2.18% baggage 1.33% smooth 1.92% smooth 1.82% economy 2.32% premiumeconomy2.21%treat 1.90% television 1.21% prefect 1.48% surprise 1.31% short 2.32% small 1.98%huge 1.26% request 1.17% contact 1.33% staff 1.23% investigate 2.26% room 1.95%cause 1.24% airplane 1.09% takeoff 1.27% line 1.21% please 2.25% front 1.55%
terminal 1.23% attentive 0.90% pretty 1.21% flat 1.20% fine 1.28% film 1.37%send 1.21% good 0.89% gate 1.04% call 1.08% recent 1.22% large 1.23%
media 1.21% attend 0.75% courteous 0.71% value 0.97% message 1.14% pleasant 1.21%
52.56% 39.64% 43.21% 46.74% 66.26% 68.45%
The table in this appendix represents topics derived using LDA modeling. It contains the topic number (first row), the name (second row)−the result of the naming process using atwo-step survey, and the importance (third row) of 18 LDA topics. As the top 15 words are arranged according to the probability size, the values in the third and last rows denote theimportance of the topic and amount of explanation (sum of probabilities) of the 15 words for the topic, respectively. The words in bold are strongly related to each topic and are the basisfor naming the topic.

Sustainability 2018, 10, 4492 18 of 21
Appendix C
Table A3. Words List for Divergence Analysis.
Related Words%
Category 1 Category 2
T1 – – appreciate 2.37% 2.4%T2 forward order put 5.09% disappoint glad 6.69% 11.8%
T3 staff steward show 14.15% comfort special poor superbawesome 22.04% 36.2%
T4 – – excel bad friendly 21.22% 21.2%T5 serve staff 9.06% – – 9.1%T6 service 17.67% busy love nice awesome perfect 11.06% 22.2%T7 service 8.42% great nice kind fantastic late 25.85% 34.3%T8 entertainment give manage 26.83% enjoy pleasant perfect nice 11.15% 38.0%T9 service serve 13.58% efficient sorry fault 10.72% 24.3%
T10 provide recommend sendhostess mention 11.30% good worth inconvenient 14.32% 25.6%
T11 make staff entertainment 18.94% good happy comfort impresspleasant amaze 25.11% 44.1%
T12 offer 6.59% polite decent pleasure 8.36% 15.0%T13 attend feedback treat send 10.16% good amaze 21.07% 31.2%T14 attendant attend 4.25% friendly attentive good 6.84% 11.1%T15 service contact 6.82% smooth perfect pretty courteous 5.32% 12.1%T16 connect care staff call 10.08% professional smooth surprise 6.21% 16.3%T17 check investigate 16.31% quality available please fine 13.31% 29.6%T18 crew arrive select wait start 40.17% plenty pleasant 6.95% 47.1%
This table summarizes the words related to the capability of cabin crews in the online reviews. Category 1 containsword sets associated with specific actions for service delivery and category 2 contains word sets associated withcustomer assessments on service competence.
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Introduction
Related Review and Knowledge

Service Blueprint Based Redesign
Reorganizing Service Encounters in Service Blueprint
Prior Works and Benchmark of the In-Flight Service Process

Methodology

Results

Service Encounters Using LDA Modeling
Service Blueprinting in Terms of Complexity
Service Blueprinting in Terms of Divergence
Redesigned In-Flight Service Blueprint

Summary and Conclusions

References

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