Evaluating the Airline Experience and Service Quality

In preparation for this research effort, review the following publications and familiarize yourself with the service experience of an airline passenger. Each publication is located within the attachment’s sections.

Nam, S, Ha, C, Lee, H. (2018). Redesigning in-flight service with service blueprint based on text analysis. MDPI.com, Sustainability|an Open Access Journal. 10 (12):4492.

Lim, J. & Lee, H. (2019). Comparisons of service quality perceptions between full service carriers and low cost carriers in airline travel. Current Issues in Tourism, 1-16.

Next, research information pertaining to the services provided and customer reviews of Delta Airlines. There are many online resources to aid in your search effort, including but not limited to:

A-Z Airline Reviews

https://www.tripadvisor.com/Airlines

Develop a comprehensive report of the airline-passenger service encounters (as identified by Nam and Lee, 2018) and propose recommendations for improving service quality

At a minimum, your report should include the following topics:

· Identify the airline’s position (as a legacy or low-cost carrier) and describe how the service mix caters to their target market.

· Evaluate the eight service encounters based on customer reviews and secondary research.

· Explain the importance of innovative in-flight services.

· Describe the significance of service quality and how it can be measured.

At a minimum, your report should include the following:

· Written with at least three sections, a 150-word introduction, body content containing subject headings, and wrap-up or summary.

· Include a title page with your name, course, assignment number, and title.

· Use current APA. In-text citations and include a reference page at the end.

· A minimum of 4 pages, double-spaced (not including the title or reference pages).

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Comparisons of service quality perceptions between full service carriers and

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Comparisons of service quality perceptions
between full service carriers and low cost carriers
in airline travel

Juhwan Lim & Hyun Cheol Lee

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Comparisons of service quality perceptions between full service
carriers and low cost carriers in airline travel
Juhwan Lim a and Hyun Cheol Lee b

aSchool of Business and Technology Management, KAIST, Daejeon, South Korea; bSchool of Business, Korea
Aerospace University, Goyang, South Korea

ABSTRACT
We apply latent Dirichlet allocation topic modeling to a vast number of
passenger-authored online reviews for airline services to compare
service quality between full service carriers (FSCs) and low cost carriers
(LCCs). Representing key features of airline service quality, topics are
extracted from the reviews and matched to the five typical dimensions
used by the SERVQUAL model. Based on the measure of word frequency
statistically distributed to topics, we quantitatively determine the
dimensions of service quality that are deemed as most essential by
travelers. The results show that the most significant dimensions for FSCs
and LCCs are tangibles and reliability, respectively. The least significant
dimensions are assurance and empathy, respectively. By comparing
extracted features in detail, we discover specific differences in traveler
perceptions between FSCs and LCCs. Air carriers should be aware of
these differences, as it would help them better differentiate themselves.
Moreover, inflight meal services and seats, which have typically been
regarded as tangible features, are subdivided into different topics, and
the subdivisions are simultaneously matched to multiple dimensions (eg
tangibles, empathy, and reliability). This suggests that research needs to
reflect the diverse aspects of traveler perceptions for primary service items.

ARTICLE HISTORY
Received 17 July 2018
Accepted 1 April 2019

KEYWORDS
Airline service; airline
travelers; latent
Dirichlet allocation; online
review; service quality
feature; text analysis

Introduction

Competition between low cost carriers (LCCs) and full service carriers (FSCs) has intensified in the
global air travel market (Han & Hwang, 2017; O’Connell & Williams, 2005). For example, LCCs, rela-
tively recently introduced in Asian and emerging air travel markets, are gradually increasing their
market share while concentrating on cost reduction strategies to capture cost-sensitive travelers
(Baum & Kua, 2004; Martinez-Garcia & Royo-Vela, 2010; O’Connell & Williams, 2005). To respond to
the challenges from LCCs, FSCs are strategically focusing on their hub airports, a strategy that runs
counter to the point-to-point strategy used by LCCs. FSCs are also providing higher levels of
service quality and strengthening their alliances to retain their loyal customers and avoid customer
switching behavior (Dennis, 2007, 2010). In such a competitive environment, increased importance
has been placed on acquiring a better understanding of the key differences in perceived service
quality between LCC and FSC customers to differentiate service strategies and achieve business sus-
tainability (Koklic, Kukar-Kinney, & Vegelj, 2017; Lee et al., 2018).

To measure service quality, researchers rely mainly on surveys that are designed using the
existing literature. Using surveys, many studies have explored customer perceptions regarding

CONTACT Hyun Cheol Lee hclee@kau.ac.kr
Supplemental data for this article can be accessed http://dx.doi.org/10.1080/13683500.2019.1604638

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http://orcid.org/0000-0003-4110-7528

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mailto:hclee@kau.ac.kr

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the service quality of airlines and showed significant differences in perceptions between LCCs and
FSCs (Ahn & Lee, 2011; Chiou & Chen, 2010; Curras-Perez & Sanchez-Garcia, 2016; Koklic et al.,
2017; O’Connell & Williams, 2005; Rajaguru, 2016). Despite the utility of being able to draw
upon standardized research designs, circumstances have highlighted typical drawbacks – the
amount of time needed to collect complete datasets (Kothari, 2004), the restricted expandability
of research (Lee & Bradlow, 2011), sample size limitations (Bartlett, Kotrlik, & Higgins, 2001) and so
forth. Online reviews have the exact the opposite disadvantage – the absence of a standardized
research process. Compared to survey data, they can serve as objects of exploratory research and
reveal new aspects of service quality. Nevertheless, because online reviews are regarded as one of
the most critical factors in customer purchase decisions (Archak, Ghose, & Ipeirotis, 2011; Duan,
Gu, & Whinston, 2008; Godes & Mayzlin, 2004), they have lately received considerable attention in
a number of business research areas for numerous reasons, including the following. First, they
have sizable volumes. The inference-derived results can be considered reliable when drawing
the results from trusted review sites with very large datasets. Second, online reviews preserve
the real-time perceptions held by customers (Dellarocas, Zhang, & Awad, 2007; Duan et al.,
2008). They are one of the most immediate measures of service experience. Finally, they show
voluntary, unrefined, and direct experience or feedback from customers (Mudambi & Schuff,
2010).

In this study, we apply latent Dirichlet allocation (LDA) topic modeling, a widely-used text analysis
technique, to a vast number of passenger-written online reviews for airline services to analyze and
compare service quality (Blei & Lafferty, 2007; Blei, Ng, & Jordan, 2003). Representing key features
of airline service quality, the topics discerned by the modeling are (with the help of academic
researchers) matched to the five traditional dimensions – responsibility, assurance, tangibles,
empathy, and responsiveness – employed by the SERVQUAL model to determine experience-
based service quality. This enables us to quantitatively determine the dimensions of service quality
that travelers deem most essential based on the measure of word frequency statistically distributed
across topics. A major incentive to employ the five dimensions is that it is the most widely accepted
measure of service quality, and it is thus easy to compare the current results to previous results. To
minimize the validity issue with the five dimensions, we thoroughly review varied versions of newly-
developed and different dimensions for airline service quality in the following section. This helps us
understand how dimensions should be defined and which dimension type is required to incorporate
airline service-specific characteristics. Accordingly, it is possible to match topics to the dimensions as
appropriately as possible while integrating domain-specific characteristics that varied models have
proposed. We also carry out a sentiment analysis to uncover customer emotions or attitudes regard-
ing the quality of airline services (Liu & Zhang, 2012). In summary, the aim of this study is to answer
the following research questions (RQs).

RQ1. Can service features of airline service quality be extracted from online reviews and be properly represented
in a service quality model?

RQ2. How can the significance of service features be quantified?

RQ3. What are the differences between FSCs and LCCs in terms of SERVQUAL dimensions and features?

RQ4. Compared to previous studies using surveys, what are the new aspects of the customers’ perceptions of
airline service quality?

RQ5. What are the customers’ sentiments about service quality?

By answering these questions, this study offers meaningful insights for air carriers with respect to pro-
viding differentiated services to travelers. Likewise, it provides understandings for researchers
attempting to determine the types of features that should be considered or added when designing
studies on airline service quality.

2 J. LIM AND H. C. LEE

Literature review

Airline service quality models

Many studies on airline service quality have worked with SERVQUAL, which was postulated by Para-
suraman, Zeithaml, and Berry (PZB) (1988), and its variations that reorganize the structure of dimen-
sions to improve model validity by adopting domain-specific characteristics. Fick and Brent Ritchie
(1991) and Gourdin and Kloppenborg (1991) studied service quality in the air transport industry
using PZB’s model. Fick and Brent Ritchie (1991) measured service quality for four kinds of businesses,
including airline service, but they could not measure the relative effects of the SERVQUAL items
(Young, Cunningham, & Lee, 1994). Using PZB’s model of service quality developed in 1985,
Gourdin and Kloppenborg (1991) surveyed customers, airline employees, and officials in the US
Department of Transportation and the Federal Aviation Agency. While they showed statistical signifi-
cance in several variables, their approach was not complete in terms of sample representativeness,
variable origin, and model reference (Young et al., 1994).

Tsaur, Chang, and Yen (2002) implemented the fuzzy set theory to resolve the briefness issue of
the Likert scale for service quality measurement. Using the fuzzy approach, they tried to measure
vague human judgements such as customer satisfaction in a more explicit manner. Their study con-
cluded that the most and least important dimensions were tangibles and empathy, respectively,
among the five dimensions in PZB’s SERVQUAL. Gilbert and Wong (2003) measured airline service
quality by modifying SERVQUAL’s original form. The tangible dimension was subdivided into facilities,
employees, and flight patterns, and the empathy dimension was renamed as customization. Assur-
ance was considered to be most important, whereas customization and facilities were not important.
They also found that service expectations varied in different market segments by showing statistical
differences depending on ethnic groups, nationalities, and travel purposes. Park, Robertson, and Wu
(2005) proposed the application of structural equation modeling to test simultaneous relationships.
However, the applicability of their research results was restricted because their data only represented
international economy class travelers.

Studies on LCCs have only appeared recently, and there are thus fewer of them than studies on
FSCs. Saha and Theingi (2009) found that service quality was still a determinant of customer satisfac-
tion in LCCs, and behavioral intentions such as repurchase intentions and feedback were affected by
service quality and customer satisfaction. They also showed that customer satisfaction and feedback
were positively correlated. Chiou and Chen (2010) adopted the research frame provided by Park,
Robertson, and Wu (2004) to investigate factors that affected the behavioral intentions of travelers
between FSCs and LCCs. Their study showed that service perception had a considerable influence
on the behavioral intentions of FSC travelers whereas service value had a large influence on those
of LCC travelers. These contrasting results suggested that there existed a nontrivial gap between cus-
tomer perspectives for FSC and LCC airline services. In particular, price might have been much more
critical for LCC passengers than for FSC passengers. In Table 1, we summarize selected published
results that have used the conventional five dimensions or variations thereof. We retain the meanings
of the original or modified dimensions in the selected literature when matching topics to the five
dimensions.

Text analysis-based service quality in airline travel

With the growth of mobile web platforms, online reviews have become one of the most popular
methods of customer assessment for overall service quality (Lee & Lin, 2005; Mudambi & Schuff,
2010; Palese & Piccoli, 2016). Reviews are mainly composed of comments reflecting direct percep-
tions of service performance and experience (Guo, Barnes, & Jia, 2017; Humphreys & Wang, 2017;
Miguéis & Nóvoa, 2017). Many studies have analyzed reviews for products and service areas such
as tourism and hotel businesses (Archak et al., 2011; Berezina, Bilgihan, Cobanoglu, & Okumus,

CURRENT ISSUES IN TOURISM 3

2016; Mankad, Han, Goh, & Gavirneni, 2016; O’connor, 2010). Recently, several studies in the air travel
area have used online text data to investigate customer perceptions (Gitto & Mancuso, 2017, 2019;
Lee & Yu, 2018; Martin-Domingo, Martín, & Mandsberg, 2019; Misopoulos, Mitic, Kapoulas, & Karapi-
peris, 2014; Yee Liau & Pei Tan, 2014).

Misopoulos et al. (2014) utilized 67,953 Tweets to identify important customer service factors. They
produced relevance ratings of Tweet messages based on the similarity coefficient, and they analyzed
customer sentiments to investigate opinions regarding airline service. They found that services
related to flight delays, lost baggage, and check-in/boarding problems caused negative sentiments,
while those related to check-in in mobile applications, reasonable prices, and on-board entertain-
ment generated positive sentiments. However, their study was limited in that they only analyzed
20 keywords in the dataset. Similarly, Yee Liau and Pei Tan (2014) analyzed 10,895 Tweets to study
customer opinions about LCCs in Malaysia. They employed a k-means clustering algorithm to
group Tweets and spherical k-means clustering to enhance the efficiency of the analysis. The
results reported that clusters of customer service, booking management and ticket promotions col-
lected more positive emotions. On the contrary, the flight cancelation cluster acquired more negative
sentiments.

Gitto and Mancuso (2017) worked with online reviews collected from five major European airports
on the Skytrax website. They analyzed 895 sentences, two third of which were related to non-aviation
services and one third of which were aviation services. They found that slightly more than half (55%)
of the sentiments were positive in the non-aviation services, while one third (33%) of them were posi-
tive in the aviation services. Related to the non-aviation services, the most frequent opinions referred
to food and beverages and shop service. On the contrary, check-in and baggage claim services were
most frequently addressed in the aviation services. Lee and Yu (2018) investigated Google reviews for
the top 100 airports to show that online reviews could be used to measure airport service
quality (ASQ). They found that the sentiment scores of reviews adequately predicted Google star
ratings. They also demonstrated that sentiment scores and Google star ratings had a sizable relation-
ship with ASQ ratings. Furthermore, they revealed that 25 topics extracted from the LDA analysis were
well matched to the ASQ service attributes. They proposed a future study on a relative importance
investigation for attributes of different groups such as FSCs versus LCCs, which could be one of
the results of the present study.

Table 1. Selected studies for airline service quality. We finally select 13 results from 21 papers with five dimensions or varied
dimensions employed among the total of 45 papers reviewed for airline service quality. Due to the lack of space, features of
the dimensions are displayed in Appendix A (in Supplementary Material). The dimensions used by Aksoy et al. (2003) are only
for domestic airline service. The dimensions for international airline service are included in Appendix A.

Type Researcher Dimensions

FSC Ostrowski et al.,
(1993)

No dimension suggested

FSC Young et al., (1994) Baggage handling, Bumping procedures, Operations and safety, Inflight comfort, Connections
FSC Tsaur et al., (2002) Tangibility, Reliability, Responsiveness, Assurance,

Empathy

FSC Chang and Yeh

(2002)
On-board comfort, Airline employees, Reliability of service, Convenience of service, Handling of
abnormal, Conditions

FSC Gilbert and Wong
(2003)

Reliability, Assurance, Facilities, Employees, Flight patterns, Customization,

Responsiveness

FSC Aksoy et al., (2003) Cabin features and personnel, Country of origin and promotion, Food and beverage services, In-
flight activities, Internet services, Punctuality and speed, Free alcoholic beverages, Price

FSC Park et al., (2005) Reliability and customer service, Convenience and accessibility, In-flight service
FSC Pakdil and Aydın

(2007)
Employees, Tangibles, Responsiveness, Reliability and Assurance, Flight Patterns, Availability,
Image, Empathy

LCC Saha and Theingi
(2009)

Tangible features, Schedules, Services provided by ground staff, Services provided by flight
attendants

LCC Kim and Lee (2011) Tangibles, Reliability, Responsiveness, Assurance, Empathy
FSC Liou et al., (2011) Booking service, Ticketing service, Check-in, Baggage handling, Boarding process, Cabin service,

Baggage claim, Responsiveness
LCC Jiang (2013) Ground service, Flight experience, Service reliability, Airfare and schedule
FSC Hussain et al., (2015) Reliability, Responsiveness, Assurance, Tangibility, Security and safety, Communication

4 J. LIM AND H. C. LEE

Gitto and Mancuso (2019) used the Twitter accounts of 118 airports to determine the brand per-
ceptions of airports based on attributes of the airport industry, including environment, disability, and
luxury. Using a cluster analysis and social perception scores, they explained the passengers’ clustered
perceptions of airports. Martin-Domingo, Martín, and Mandsberg (2019) attempted to measure ASQ
using sentiment analysis with a dataset of 4,392 Tweets. They determined 23 service attributes com-
posed of 108 keywords and compared them to 34 attributes of ASQ. The research results revealed
that passengers frequently mentioned attributes regarding waiting and ground transport, but they
mentioned shopping or washrooms (WC) only in 1% of their Tweets. In the sentiment analysis, cus-
tomers had a positive attitude regarding WiFi, WC, food and beverages, and lounge services, while
they showed negative sentiments regarding waiting, parking, arrival, staff, and passport control.

Research model

With respect to analyzing the meaning of the words and content in the documents in the topic
model, the topic model assumes that a topic is a probability distribution of words, and a document
comprises a mixture of topics (Steyvers & Griffiths, 2007). LDA is the most common topic model (Blei
et al., 2003). It generates topics that have been latent in documents based on the Dirichlet distri-
bution. LDA can be easily implemented via software (eg R, Matlab and Python) after model input par-
ameters such as the number of topics (=k) are adequately set. (See details of data preprocessing and
model parameter setting in Appendix B.) As a result of the LDA modeling, a topic is the probability
distribution of words from online reviews that contain customer perceptions on service experience,
and it represents a reorganized form that expresses the feature of service quality in the k-dimensional
space. That is, a topic suggests a feature of a specific dimension of service quality.

The research model is depicted in Figure 1. First, online reviews are collected via web crawling.
Second, the collected reviews are preprocessed to make them suitable as input for the LDAmodeling.
Data is arranged into a document-term matrix format, ie a matrix of a preprocessed corpus. Third,
when extracting topics from the online reviews, the LDA algorithm reduces the uncontrollable
dimensional space into a controllable k-dimensional space (30 topics for FSCs and 20 topics for
LCCs; see explanations in Appendix B). This makes the data sufficiently manageable in the sub-
sequent stage. Fourth, the extracted topics are named and matched to the dimension regarded as
the best fit among the five choices (RQ1). This is achieved with the help of an advisory group.
Namely, we perform another dimension reduction (from 30 and 20 to five each for the FSCs and
LCCs, respectively) based on the group members’ survey and interview. The group is composed of
three professors and six graduate students whose specialties cover diverse majors in aviation man-
agement, including airline marketing, airport operations, airline service, human resources, finance,

Online
Review

Preprocessing LDA modeling

Tangibles

Reliability

Responsiveness

Assurance

Empathy

Expert survey &
interview

Data crawling Data preprocessing

Topic modeling

Topic naming/matching Sentiment analysis Comparison

LCC

FSC

Sentiment
dictionary

Sentiment scoring

Figure 1. Research model.

CURRENT ISSUES IN TOURISM 5

MIS, and aviation policy and strategy. Next, we decide which dimension among the five dimensions is
the most significant by measuring word frequency (RQ2). All of the stages are repeated twice for both
the FSCs and LCCs. Then we compare the differences between the service qualities (RQ3) and explain
what aspects of the research in airline service quality should be newly considered (RQ4). Finally, a
sentiment analysis is applied to topics and dimensions to investigate customer attitudes based on
the widely-used word dictionary (Hu & Liu, 2004) (RQ 5).

Data

We use online reviews from airlinequality.com in which travelers voluntarily and individually write
reviews of their service experiences. To compare, we choose the top 10 of the world’s top 100
ranked airlines for both categories of service carriers (airlinequality.com/review-pages/top-10-air-
lines/). Using a data crawling package in R, we retrieve all of the online reviews of the selected airlines
for both LCC and FSC categories at the time of review gathering. Table 2 summarizes the data
employed in this study.

We statistically examine whether collected online reviews are representative of the population
using the method offered by Aggarwal and Singh (2013). They determined sample representative-
ness by conducting t-tests for critical variables between sample and population groups. If there
was no significant difference in the critical variable between groups, it was concluded that the
sample was representative. We consider two critical variables for the LDA algorithm. One is the
number of words per review, and the other is the number of occurrences of words per review
(Blei et al., 2003; Wei & Croft, 2006). For the first t-test, the average number of words per review is
compared. When we collect the online reviews (11,031) for 20 airlines, there are 52,506 reviews for
84 airlines, including 23 LCC carriers. The test results are summarized in Table 3. For the most frequent
1,000 words, which explain 82.6% of the total occurrences of all words, the average number of occur-
rences per review is compared at the second t-test. Because a lager number of reviews leads to a
larger number of word occurrences, we use statistics through dividing word occurrences by the
number of reviews in each group for the most frequent 1,000 words. Table 4 displays the test out-
comes, which demonstrate that there are no statistically significant differences between the
groups in terms of the critical variables in topic modeling.

Topic modeling

Topic naming

The LDA modeling results in Appendix C reveal the extracted features of the customer online reviews
(ie topics), and are now composed of probabilistic distributions of words. If we can give an appropri-
ate name to every topic while considering the meaning of words distributed to each topic, the

Table 2. Data summary.

FSC LCC

rank airline time periods # of reviews rank (actual) airline time periods # of reviews

1 EK 2013.12∼16.10 1091 1 (23) AK 2010.08∼16.10 406
2 QR 2013.08∼16.10 840 2 (25) VX 2009.10∼16.10 261
3 SQ 2013.06∼16.10 647 3 (30) DY 2009.12∼16.10 669
4 CX 2013.08∼16.10 760 4 (38) U2 2013.04∼16.10 677
5 NH 2009.08∼16.10 341 5 (46) JQ 2012.05∼16.10 506
6 EY 2013.06∼16.10 784 6 (48) D7 2010.01∼16.10 280
7 TK 2013.08∼16.10 895 7 (50) WS 2013.02∼16.10 179
8 BR 2010.10∼16.10 376 8 (51) 6E 2009.12∼16.10 148
9 QF 2013.09∼16.10 848 9 (53) B6 2010.06∼16.10 336
10 LH 2014.01∼16.10 880 10 (54) 3K 2009.08∼16.10 107
Total 7462 3569

6 J. LIM AND H. C. LEE

contents of all of the reviews will be expressed by interpretable topics. This naming process is carried
out in two steps. In the first step, we provide the topic modeling results with a questionnaire form
(Appendix C without topic names (second row), topic significance (third row), and highlighting) to
members of the advisory group, and they fill in the empty name of each topic independently. This
step is designed to collect various interpretations from the modeling results. During this step, we
provide the subjects with two explanations. First, we inform them that a word with a larger prob-
ability in a topic has more explanatory power than a word with a smaller probability. Second, we
ask them to focus on distinguishable words that could represent differences among topics rather
than similar words that simultaneously exist in multiple topics. For example, the names for FSC
Topic 5 (inflight meal (punctuality)) and FSC Topic 11 (inflight meal (menu variety)) are more likely
to be determined by words such as short, quick, and prior for Topic 5 and choic(e), cours(e), and
option for Topic 11 rather than by words such as serv(e), breakfast, and dinner that simultaneously
exist in both topics. The second step is needed only when there exists an obvious disagreement
among the collected names. Opinions from the professors are adjusted until reaching an agreement,
and then the agreed upon name is finalized via consensus from the graduate students. If opposition
still exists as the students finalize their work, the adjusting process with the professors is repeated
until all conditions are satisfied. Figure 2 depicts the process.

Topic matching with five dimensions

After being named, the topics are matched to the five dimensions based on the opinions of the advi-
sory group. The matching process is conducted in a similar manner to the naming process, but only
the second step (in Figure 2) is carried out. During the naming process, both broad interpretations as
well as precise interpretations are necessary from the experts. However, when matching, precision
alone is sufficient. We gather the reliable and valid opinions provided by the professors from the
initial process stage and concentrate on the consensus within the advisory group while striving to
integrate the airline service-specific characteristics that we have outlined in the broad literature
review. The topic matching results with five dimensions are summarized in Table 5. Four of the
topics – customer satisfaction, recommendation intention, and model residual for the FSCs; and
price for the LCCs – are not assigned. They are excluded because they are not regarded as typical
dimensions of service quality. The model residual indicates an uninterpretable topic driven by
noisy data and is used to enhance the coherence of the rest of the topics in the topic modeling
(DiMaggio, Nag, & Blei, 2013).

We use word frequency to quantify the significance of the topic (Yu, Zha, Wang, & Chua, 2011;
Zhang, Narayanan, & Choudhary, 2010). In terms of word frequency, we assume that a topic is men-
tioned more frequently when it is more significant. Thus, the more frequently that words are men-
tioned by travelers, the more significant they are, and the more those words are contained in a

Table 3. t-test for the number of words.

all airlines
(n = 52,506)

selected airlines
(n = 11,031)

M SD M SD df T Sig.

# of words 55.130 32.873 54.750 33.347 15854.604 1.098 0.272

Table 4. t-test for the average number of occurrences of 1,000 words that are the most frequent in each group.

all airlines
(n = 1,000)

selected airlines
(n = 1,000)

M SD M SD df T Sig.

# of occurrences of words/# of reviews 0.045 0.101 0.045 0.099 1997.614 0.037 0.971

CURRENT ISSUES IN TOURISM 7

particular topic, the more significant the topic is. For example, the words seat, comfortable, and pitch
are more probable than the words meal, food, and water in the reviews given by travelers who
express that seat comfort is significant. The significance of the kth topic (k = 1… K ) based on
word frequency is calculated as

Tk =

∑N

n=1

wn/k/

∑K

k=1

∑N

n=1
wn/k (1)

wn/k : the frequency of the nth word in the kth topic (n = 1… N, k = 1… K ).
Note that the probability of the nth word in the kth topic (n = 1… N, k = 1… K ) in Appendix C is

represented by p(wn/k) = wn/k/
∑N

n=1 wn/k .

Result comparisons and discussions

Dimension level comparisons

Carrying out comparisons in the dimension level facilitates an overview of the differences in tra-
veler perceptions of service quality between FSCs and LCCs. Tangibles and reliability are the most
significant dimensions for FSCs and LCCs, respectively. On the contrary, the least significant
dimensions for FSCs and LCCs are assurance and empathy, respectively. As we can see in Table
5, seat-related topics, which have usually been considered as tangible features, are prevalent in
FSCs. In addition, they are principle contributors to the significance of tangibles. For LCCs, at
almost 50% (≈48%), the significance of reliability dominates all other dimensions. This means
that LCC customers are particularly critical of how accurately promised services are performed,
and they are less focused on the performance of additional services (eg inflight entertainment
(IFE), wider seats, and faster staffing). Because of the low price of the service, LCC travelers tend
to have lowered levels of confidence in the service quality, and this lowered confidence affects
the significance of reliability (Bhadra, 2009; Seo, Moon, & Lee, 2015; Wittman, 2014). Likewise, it
can be understood that empathy appears as the least significant dimension for LCCs. Assurance
has a relatively small level of significance for both service carriers. This is similar to the results
reached by Tsaur et al. (2002) whose study determined that assurance was ranked fourth in
terms of importance within the five dimensions for FSCs. Kim and Lee (2011) also showed that
assurance, including reliability and empathy, did not significantly affect customer satisfaction
for LCCs. These comparisons indicate that there exists a considerable difference in passenger

Figure 2. Topic naming and matching process.

8 J. LIM AND H. C. LEE

perceptions between FSCs and LCCs. This conclusion was also reached by O’Connell and Williams
(2005).

Feature level comparisons

We can differentiate the service quality between FSCs and LCCs by examining topics that simul-
taneously do not belong to either type of air carrier since those topics represent the uniqueness of

Table 5. Matching results in order of significance. The percentage value in parenthesis indicates the dimension significance
excluding topics not matched. Highlighted topics are unique topics that simultaneously do not belong to either type of air carrier.

FSC LCC

Topic
Topic

significance
Dimension

Dimension
significance

Topic
Topic

significance
Dimension

Dimension
significance

Seat comfort
(space & location) # 2

3.50%

Tangibles
29.16%

(32.23%)

Change & cancellation
(in reservation service) # 18

5.61%

Reliability
45.31%

(47.67%)

Inflight drink service # 7 3.42%

Carry-on baggage # 20 5.31%Inflight entertainment
# 30

3.40%

Lounge service # 14 3.30%
Inflight meal purchase # 4 5.26%

Seat comfort
(aircraft type) # 18

3.18%

Reservation service # 3 5.10%Seat comfort
(flight distance) # 9

3.12%

Aircraft condition
(interior, age) # 8

3.11% Arrival & departure
punctuality # 13

5.07%

Seat
(overall evaluation)

# 22

3.07%

Flight delay # 1 4.91%
Aircraft (A380) # 24 3.05%

Reservation service
(including change &

cancellation) # 29
3.88%

Reliability
20.68%

(22.85%)

Paid ancillary service # 8 4.77%

Transit and transfer service
# 19

3.65%

Transfer service # 11 4.68%Arrival & departure
punctuality # 6

3.57%

Inflight meal
(punctuality) # 5

3.38% Service consistency over
time # 5

4.60%

Service consistency over
time # 20

3.17%

Reaction to
flight delay # 9

5.61%

Responsive-
ness

20.51%
(21.58%)

Service consistency
in & outbound flight # 26

3.03%

Inflight meal
(menu variety) # 11

3.62%

Empathy
19.84%

(21.93%)

Waiting (check-in) # 16 5.35%

Family seat request
# 12

3.37%

Waiting (boarding) # 10 4.81%
Sleep comfort # 17 3.35%

Service differentiation by
class # 28

3.26%
Customer complaint # 2

4.74%

Frequent flyer program
# 1

3.16%

Aircraft (B787) # 15 5.07%

Tangibles
14.82%

(15.59%)

Inflight meal
(special demand) # 21

3.07%

Reaction to flight delay # 4 4.24%

Responsive-
ness

14.38%
(15.89%)

Seat comfort
(space & location) # 6

4.89%

Waiting
(boarding & check-in) # 13

3.51%

Seat comfort
(flight distance) # 7

4.86%
Flight attendant call # 16 3.32%

Baggage service # 27 3.31% Employee’s quality
(positive perspective) # 17

4.74%

Assurance
9.32%

(9.81%)
Employee’s quality
(language skill) # 10

3.22%

Assurance
6.42%

(7.10%) Employee’s quality
(negative perspective) # 14

4.59%Employee’s quality
(courtesy) #3

3.20%

Model residual # 25 3.29%

Not matched 9.53%

Service differentiation by
class # 19

5.09% Empathy
5.09%

(5.36%)

Customer satisfaction # 15 3.20%

Price # 12 4.95% Not matched 4.95%Recommendation intention
# 23

3.04%

Total 100% 100% (100%) Total 100% 100% (100%)

CURRENT ISSUES IN TOURISM 9

each category. When we exclude FSC topics identical or similar to LCC topics, there exist 15 unique
topics out of the 27 total topics. The unique topics are highlighted in Table 5. Of note, frequent
flyer program (Topic 1), lounge service (Topic 14), and inflight entertainment (Topic 30) are topics
exclusive to FSCs. These distinctive services can increase the probability that passengers will choose
a particular FSC (Baker, 2013). Topics of employee quality (language skill) (Topic 10), sleep comfort
(Topic 17), and inflight entertainment (Topic 30) are also more relevant for FSCs because FSCs are
more likely to frequently cover long distances and fly international routes (Fourie & Lubbe, 2006;
Gillen &Morrison, 2003; Kappes &Merkert, 2013). The fact that language skill appears for FSCs suggests
that FSCs place greater focus on international routes than LCCs (Kappes & Merkert, 2013). Sleep
comfort also represents a major feature of FSCs that usually operate long-distance flights (Francis,
Dennis, Ison, & Humphreys, 2007). Longer flights lead to longer sleeping times. Therefore, as flight
time increases, the need for more attentive service to ensure sleep comfort also increases.

Interestingly, unique FSC topics are distributed in all of the five dimensions of service quality. On
the contrary, four of the five unique topics, out of 19 LCC topics, are placed only in the reliability
dimension. This indicates that FSCs should not ignore any dimension of service quality, though tan-
gibles is the most critical dimension. On the other hand, LCCs need to focus on service items related
to the dimension of reliability for better differentiation. This is because FSC travelers tend to recog-
nize quality in various aspects of airline service (Gillen & Morrison, 2003; Hunter, 2006; Zhang, Lin, &
Newman, 2016). They consider the entire range of services from basic to sophisticated, even includ-
ing the physical appearance of the aircraft (Topic 8). When the most basic and sophisticated services
are assumed to be related to reliability and empathy, the unique topics of service consistency (Topic
26) and family seat request (Topic 12) are categorized into each dimension. The rest of the unique FSC
topics are related to seats and inflight meal services, which almost match with the tangibles and
empathy dimensions. These dimensions are closely related to the lower price sensitivity of FSC cus-
tomers when compared to LCC customers (O’Connell & Williams, 2005; Wittman, 2014). FSC travelers
tolerate higher expenses to obtain greater benefits via various services (Seo et al., 2015).

Of the 19 LCC topics, five of them are unique. Among them, inflight meal purchase (Topic 4), paid
ancillary service (Topic 8), and carry-on baggage (Topic 20) are closely related to specific LCC prop-
erties. The inflight meal purchase topic aptly reflects the fact that most LCCs charge fees for inflight
meals (O’Connell & Warnock-Smith, 2013). Words such as intern(et) and prebook in this topic reveal a
recent trend in the LCC business environment (Baker, 2013; Bigné, Hernández, Ruiz, & Andreu, 2010;
Hunter, 2006). The paid ancillary service topic has been stressed as a major business strategy for LCCs
and is an important contributing factor for LCC profits (Doganis, 2006; O’Connell & Warnock-Smith,
2013). This service incorporates seat selection, seat upgrades, IFE, WiFi service, and inflight food
and beverages, and related words such as snacks, wifi, legroom, and drinks appear in the topic.
LCCs need to concentrate on services related to these words to achieve better differentiation. The
carry-on baggage topic also reflects the business nature of LCCs. Most LCCs try to implement strict
policies regarding checked baggage, including the implementation of fees, to reduce related costs
(O’Connell & Warnock-Smith, 2013). To save on travel costs, LCC travelers tend to prefer using
carry-on baggage (Aldamari & Fagan, 2005).

Further divided features

Also of interest, the inflight meal service and seat topics are subdivided. For FSCs, meal services are
divided into four subtopics – inflight meal (punctuality) (Topic 5), inflight drink service (Topic 7),
inflight meal (menu variety) (Topic 11), and inflight meal (special demand) (Topic 21). Also for
FSCs, seats are divided into five subtopics – seat comfort (space and location) (Topic 2), seat
comfort (flight distance) (Topic 9), family seat request (Topic 12), seat comfort (aircraft type) (Topic
18), and seats (overall evaluation) (Topic 22). For LCCs, seats are divided into two subtopics – seat
comfort (space and location) (Topic 6) and seat comfort (flight distance) (Topic 7). However, for
LCCs, there is only a single subtopic for meal services – inflight meal purchase (Topic 4). The

10 J. LIM AND H. C. LEE

subdivision is specifically evident for FSCs. Moreover, the subdivided topics do not match single
dimensions, but are instead separated into multiple dimensions. In most of the previous studies, fea-
tures related to inflight meal services and seats have belonged to tangibles. Although they mainly
belong to tangibles in this study (five of nine for FSCs and two of three for LCCs), some of the
topics match with empathy and reliability. This indicates that diverse aspects of customer perceptions
exist with respect to quality, especially for primary service items. As such, they cannot be simply
measured as belonging to tangible dimensions as they have been in previous studies. If studies
are carried out using surveys, the surveys must be designed in a more sophisticated and nuanced
manner to accurately reflect the diverse aspects of traveler perceptions. This is particularly true for
meal services and seats. Indeed, food service is a complex mixture of multiple components, including
ingredient freshness, menus, drinks, moods, employee courtesy, and so forth. The service is also
affected by cultural and social factors (Aksoy, Atilgan, & Akinci, 2003).

Inflight meal services have played an important role in airline service marketing. Good meal ser-
vices create a positive effect on word of mouth among customers, which serves as important infor-
mation in airline selection (Heide, Grønhaug, & Engset, 1999). From this, it is reasonable to think that
the importance of the meal service leads to the division into four distinctive subtopics in this study.
Through words such as short, quick, takeoff, welcome, and prior, we see that customers recognize the
punctuality of meal services. From the word distribution (chicken, fruit, salad, bread, snack, beef, and so
forth) for the topic of inflight meal (menu variety), customers recognize which type of menu is served
and how that menu varies throughout the service. Aksoy et al. (2003) showed that the punctuality
and menu variety of inflight meals were important service measures in both foreign and domestic
airline services. Laws (2005) also found that there was a more diverse demand for meals as the
flight distance increased.

The inflight meal (special demand) topic differs from the inflight meal (menu variety) topic in
terms of the degree of service customization. Offering a variety of items on a menu is indicative of
good service, but providing customized meals for individual passengers suggests a higher level of
quality. When we look at the word distribution for inflight meal (special demand), words such as
special, avail(able), request, and vegetarian appear. This indicates that FSCs should attend to customer
requests. Examples include meals for passengers that have specific dietary restrictions, including veg-
etarians, infants, and religious adherents. Dana (1999) also asserted that airlines should provide
special meals that reflect the dietary or religious needs of travelers. The inflight drink service topic
seems to be recognized as a separate service by customers rather than as a part of the overall
meal service. In fact, words associated with drinks are not present in the other three meal-related
topics. This suggests that the importance of drink service cannot be overlooked by FSCs. For the
meal-related topics in LCCs, inflight meal purchase is the only topic.

Together with the meal service, seats are another primary service item when evaluating airline
service quality. Seat comfort (space and location) (Topic 2 for FSCs and Topic 6 for LCCs) is a
service feature dealt with in almost every study on airline service quality (Aksoy et al., 2003; Chang
& Yeh, 2002; Gilbert & Wong, 2003; Hussain, Al Nasser, & Hussain, 2015; Jiang, 2013; Ostrowski,
O’Brien, & Gordon, 1993; Pakdil & Aydın, 2007; Park et al., 2005; Saha & Theingi, 2009; Tsaur et al.,
2002; Young et al., 1994), and it typically has a trade-off relationship with price (Balcombe, Fraser,
& Harris, 2009). Based on the measure of seat comfort with seat width and pitch, low values in
terms of seat comfort are associated with increased seating capacity. This increased capacity
results in lower unit operating costs, but it also reduces the level of service quality onboard (Lee &
Luengo-Prado, 2004). On the other hand, LCC strategies generally rely on this idea. Because the
same negative word (uncomfort(able)) is found in both the carriers, we see that customers are sensi-
tive to this feature of service quality.

Looking at the words associated with the seat comfort (flight distance) topic (Topic 9 for FSCs and
Topic 7 for LCCs), we can infer that customer perceptions for seat comfort are dependent on flight
distance. Recently, many LCCs have tried to expand their business territories to include long haul
markets (Francis et al., 2007). Therefore, LCCs need to pay special attention to seat comfort for

CURRENT ISSUES IN TOURISM 11

long-distance flights. For example, LCCs might consider the introduction of an upper class service
such as premium economy (see Topic 19) to compensate for this weakness (Morrell, 2008).

Sentiment analysis

The sentiment analysis is a useful tool to elicit customer perceptions of service and new perspectives
to improve service features based on customer opinions (Misopoulos et al., 2014; Wei, Chen, Yang, &
Yang, 2010). An opinion can be a positive, negative, or neutral emotion or attitude from customers
towards a service, product, or topic (Liu & Zhang, 2012). We employ a well-established word diction-
ary made by Hu and Liu (2004) to analyze sentiments. By applying the same stemming method in
Appendix B to exactly match our data to the dictionary, we produce the optimal form of the diction-
ary to the data. To read sentiments in terms of topic level, the sentiment score of the kth topic (k = 1
… K ) is calculated as (Guzman & Maalej, 2014).

TSk =
∑N

n=1

wn/k · wsn/
∑N

n=1

wn/k

where wsn has a value of + 1 if the nth word in the kth topic matches with the positive word in the
dictionary, a value of −1 if the nth word in the kth topic matches with the negative word in the dic-
tionary, and a value of 0 otherwise. wn/k has the same definition as in Equation (1).

Similarly to Hu and Liu (2004), we obtain a sentiment score of the specific dimension by adding up
the sentiment scores of topics (TSk) that belong to the specific dimension. Then we compare the sen-
timent scores of the FSCs and LCCs. We briefly introduce the comparison results of sentiments within
the dimension level. In terms of the total sum of sentiment scores, LCC travelers have slightly lower

Figure 3. Sentiment score distributions without neutral scores. Top panel is for FSCs and bottom panel is for LCCs. Distributions in
the topic level are available upon request from the authors.

12 J. LIM AND H. C. LEE

negative perceptions of the airline service than FSC travelers. When we observe the sentiment distri-
butions in Figure 3, negative words appear more in the three dimensions of the FSCs, although there
is not a large difference in empathy. In a similar vein, LCCs have two negative dimensions. Reliability
and responsiveness are negatively perceived for both FSCs and LCCs. While reliability is the most sig-
nificant dimension of LCCs, the dimension includes features that are likely to cause negative percep-
tions such as flight delay (Topic 1), punctuality (Topic 13), and change & cancelation (Topic 18). In
FSCs, reliability is the second most significant dimension, and it covers negatively perceived features
such as punctuality (Topics 5 and 6) and reservation including cancelation (Topic 29). This result is
consistent with previous studies (Misopoulos et al., 2014; Yee Liau & Pei Tan, 2014). Such studies
showed that Twitter messages regarding flight delay and cancelation received more negative senti-
ments. Although responsiveness has a smaller significance than reliability, its features are mainly
related to waiting situations such as waiting for check-in & boarding, flight delays and employee reac-
tions. Unless the waiting time is almost zero, it is likely to elicit negative sentiments from travelers. In
contrast, tangibles and assurance are positively perceived for both FSCs and LCCs. Features regarding
seats and aircraft conditions, which principally comprise the tangibles dimension, contribute to
obtain positive sentiments for both types of air carriers. Assurance is composed of features of
general employee quality, and positive perceptions are dominant for both types of carriers. FSCs
should sharpen tangibles features to showmore positive sentiments since the dimension is positively
perceived and the most significant.

Conclusions

Competition between FSCs and LCCs has intensified. To determine differences in service quality, we
investigated customer perceptions of quality based on the LDA topic model. As a result of the LDA
modeling, we extracted 27 and 19 features of perceived service quality for FSCs and LCCs, respect-
ively, from passenger-authored online reviews. Also we further reduced the dimensions of service
quality by matching them to five dimensions for an overview. Through this, we resolved the RQ1.
From the quantified comparisons, we learned that there was a reasonable difference in traveler per-
ceptions between the FSCs and LCCs. In terms of dimension comparisons, the most significant dimen-
sions for the FSCs and LCCs were tangibles and reliability, respectively. On the other hand, the least
significant dimensions were assurance and empathy, respectively. To differentiate more specifically
between services, we compared the results in terms of unique features. There were a number of
unique features (15 of 27 for the FSCs and five of 19 for the LCCs) for service quality that simul-
taneously did not belong to either type of air carrier. We showed how each type of air carrier
should focus on or improve specific features of their service to sharpen service differentiation
through discussions of unique features and reviews from the literature. The conclusions were able
to provide answers for RQs 2 and 3.

With respect to academic research perspectives, this also suggested that the service features in
survey forms used for FSCs and LCCs, which have resembled one another thus far, needed to be
modified to incorporate specific features that might be revealed in the unique topics of this study.
Moreover, a couple of primary service items (inflight meal services and seats), which have typically
been regarded as part of a specific dimension (tangibles), were subdivided and matched to multiple
dimensions simultaneously. This finding also needs to be considered for future research designs and
covers RQ4.

Using the brief analysis of sentiments to provide an answer for RQ5, we found that LCC customers
generally held less negative emotions than FSC customers, although the difference was not signifi-
cant. Because the reliability was negatively perceived and the most significant, it was concluded
that LCCs should hone their features to avoid negative sentiments. On the contrary, FSCs need to
sharpen tangibles features with respect to providing more positive sentiments since the dimension
is positively perceived and the most significant.

CURRENT ISSUES IN TOURISM 13

This study is limited in that the LDA modeling itself cannot provide the causes of differentiation
between FSCs and LCCs. For example, the results do not explain why such topics occur and how they
are specifically related. To do this, additional investigations, including co-occurrence and trend ana-
lyses, might be employed. Through such additional analyses, we would be able to expand the knowl-
edge regarding service quality differentiation in the air transport industry.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Juhwan Lim http://orcid.org/0000-0003-4110-7528
Hyun Cheol Lee http://orcid.org/0000-0003-4698-065X

References

Aggarwal, R., & Singh, H. (2013). Differential influence of blogs across different stages of decision making: The case of
venture capitalists. MIS Quarterly, 37(4), 1093–1112.

Ahn, T. H., & Lee, T. J. (2011). Service quality in the airline industry: Comparison between traditional and low-cost airlines.
Tourism Analysis, 16(5), 535–542.

Aksoy, S., Atilgan, E., & Akinci, S. (2003). Airline services marketing by domestic and foreign firms: Differences from the
customers’ viewpoint. Journal of Air Transport Management, 9(6), 343–351.

Aldamari, F., & Fagan, S. (2005). Impact of the adherence to the original low-cost model on the profitability of the low-cost
airline. Transport Reviews, 25, 377–392.

Archak, N., Ghose, A., & Ipeirotis, P. G. (2011). Deriving the pricing power of product features by mining consumer reviews.
Management Science, 57(8), 1485–1509.

Baker, D. M. A. (2013). Service quality and customer satisfaction in the airline industry: A comparison between legacy air-
lines and low-cost airlines. American Journal of Tourism Research, 2(1), 67–77.

Balcombe, K., Fraser, I., & Harris, L. (2009). Consumer willingness to pay for in-flight service and comfort levels: A choice
experiment. Journal of Air Transport Management, 15(5), 221–226.

Bartlett, J. E., Kotrlik, J. W., & Higgins, C. C. (2001). Organizational research: Determining appropriate sample size in survey
research. Information Technology, Learning, and Performance Journal, 19(1), 43–50.

Baum, T. G., & Kua, J. (2004). Perspectives on the development of low cost airlines in South East Asia: Evidence from the
regional press. Current Issues in Tourism, 7(3), 262–276.

Berezina, K., Bilgihan, A., Cobanoglu, C., & Okumus, F. (2016). Understanding satisfied and dissatisfied hotel customers:
Text mining of online hotel reviews. Journal of Hospitality Marketing & Management, 25(1), 1–24.

Bhadra, D. (2009). Race to the bottom or swimming upstream: Performance analysis of US airlines. Journal of Air Transport
Management, 15(5), 227–235.

Bigné, E., Hernández, B., Ruiz, C., & Andreu, L. (2010). How motivation, opportunity and ability can drive online airline
ticket purchases. Journal of Air Transport Management, 16(6), 346–349.

Blei, D. M., & Lafferty, J. D. (2007). A correlated topic model of science. Annals of Applied Statistics, 1(1), 17–35.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Chang, Y. H., & Yeh, C. H. (2002). A survey analysis of service quality for domestic airlines. European Journal of Operational

Research, 139(1), 166–177.
Chiou, Y. C., & Chen, Y. H. (2010). Factors influencing the intentions of passengers regarding full service and low cost car-

riers: A note. Journal of Air Transport Management, 16(4), 226–228.
Curras-Perez, R., & Sanchez-Garcia, I. (2016). Antecedents and consequences of consumer commitment in traditional and

low-cost airlines. Journal of Travel & Tourism Marketing, 33(6), 899–911.
Dana, L. P. (1999). Korean Air Lines. British Food Journal, 101(5/6), 365–383.
Dellarocas, C., Zhang, X. M., & Awad, N. F. (2007). Exploring the value of online product reviews in forecasting sales: The

case of motion pictures. Journal of Interactive Marketing, 21(4), 23–45.
Dennis, N. (2007). End of the free lunch? The responses of traditional European airlines to the low-cost carrier threat.

Journal of Air Transport Management, 13(5), 311–321.
Dennis, N. (2010, October). Old conflicts, new rivals? Airline competition in the European market. Proceedings European

transport Conference 2010.
DiMaggio, P., Nag, M., & Blei, D. (2013). Exploiting affinities between topic modeling and the sociological perspective on

culture: Application to newspaper coverage of US government arts funding. Poetics, 41(6), 570–606.

14 J. LIM AND H. C. LEE

http://orcid.org/0000-0003-4110-7528

http://orcid.org/0000-0003-4698-065X

Doganis, R. (2006). The airline business (2nd ed.). London: Routledge.
Duan, W., Gu, B., & Whinston, A. B. (2008). Do online reviews matter?—An empirical investigation of panel data. Decision

Support Systems, 45(4), 1007–1016.
Fick, G. R., & Brent Ritchie, J. R. (1991). Measuring service quality in the travel and tourism industry. Journal of Travel

Research, 30(2), 2–9.
Fourie, C., & Lubbe, B. (2006). Determinants of selection of full-service airlines and low-cost carriers—A note on business

travellers in South Africa. Journal of Air Transport Management, 12(2), 98–102.
Francis, G., Dennis, N., Ison, S., & Humphreys, I. (2007). The transferability of the low-cost model to long-haul airline oper-

ations. Tourism Management, 28(2), 391–398.
Gilbert, D., & Wong, R. K. (2003). Passenger expectations and airline services: A Hong Kong based study. Tourism

Management, 24(5), 519–532.
Gillen, D., & Morrison, W. (2003). Bundling, integration and the delivered price of air travel- are low cost carriers full service

competitors. Journal of Air Transport Management, 9(1), 15–23.
Gitto, S., & Mancuso, P. (2017). Improving airport services using sentiment analysis of the websites. Tourism Management

Perspectives, 22, 132–136.
Gitto, S., & Mancuso, P. (2019). Brand perceptions of airports using social networks. Journal of Air Transport Management,

75, 153–163.
Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication.Marketing Science, 23

(4), 545–560.
Gourdin, K. N., & Kloppenborg, T. J. (1991). Identifying service gaps in commercial air travel: The first step toward quality

improvement. Transportation Journal, 31(1), 22–30.
Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using

latent dirichlet allocation. Tourism Management, 59, 467–483.
Guzman, E., & Maalej, W. (2014, August). How do users like this feature? A fine grained sentiment analysis of app reviews.

Proceedings of 2014 IEEE 22nd International Requirements Engineering Conference.
Han, H., & Hwang, J. (2017). In-flight physical surroundings: Quality, satisfaction, and traveller loyalty in the emerging low-

cost flight market. Current Issues in Tourism, 20(13), 1336–1354.
Heide, M., Grønhaug, K., & Engset, M. G. (1999). Industry specific measurement of consumer satisfaction: Experiences from

the business travelling industry. International Journal of Hospitality Management, 18(2), 201–213.
Hu, M., & Liu, B. (2004, August). Mining and summarizing customer reviews. Proceedings of the 10th ACM SIGKDD inter-

national conference on Knowledge Discovery and Data Mining.
Humphreys, A., & Wang, R. J. H. (2017). Automated text analysis for consumer research. Journal of Consumer Research, 44

(6), 1274–1306.
Hunter, L. (2006). Low cost airlines- business model and employment relations. European Management Journal, 24(5),

315–321.
Hussain, R., Al Nasser, A., & Hussain, Y. K. (2015). Service quality and customer satisfaction of a UAE-based airline: An

empirical investigation. Journal of Air Transport Management, 42, 167–175.
Jiang, H. (2013). Service quality of low-cost long-haul airlines–The case of Jetstar Airways and AirAsia X. Journal of Air

Transport Management, 26, 20–24.
Kappes, J. W., & Merkert, R. (2013). Barriers to entry into European aviation markets revisited: A review and analysis of

managerial perceptions. Transportation Research Part E: Logistics and Transportation Review, 57, 58–69.
Kim, Y. K., & Lee, H. R. (2011). Customer satisfaction using low cost carriers. Tourism Management, 32(2), 235–243.
Koklic, M. K., Kukar-Kinney, M., & Vegelj, S. (2017). An investigation of customer satisfaction with low-cost and full-service

airline companies. Journal of Business Research, 80, 188–196.
Kothari, C. R. (2004). Research methodology: Methods and techniques (2nd ed.). New Delhi: New Age International.
Laws, E. (2005). Managing passenger satisfaction: Some quality issues in airline meal service. Journal of Quality Assurance

in Hospitality & Tourism, 6(1–2), 89–113.
Lee, C. K. M., Ng, K. K. H., Chan, H. K., Choy, K. L., Tai, W. C., & Choi, L. S. (2018). A multi-group analysis of social media

engagement and loyalty constructs between full-service and low-cost carriers in Hong Kong. Journal of Air
Transport Management, 73, 46–57.

Lee, D., & Luengo-Prado, M. J. (2004). Are passengers willing to pay more for additional legroom? Journal of Air Transport
Management, 10(6), 377–383.

Lee, G. G., & Lin, H. F. (2005). Customer perceptions of e-service quality in online shopping. International Journal of Retail &
Distribution Management, 33(2), 161–176.

Lee, K., & Yu, C. (2018). Assessment of airport service quality: A complementary approach to measure perceived service
quality based on Google reviews. Journal of Air Transport Management, 71, 28–44.

Lee, T. Y., & Bradlow, E. T. (2011). Automated marketing research using online customer reviews. Journal of Marketing
Research, 48(5), 881–894.

Liou, J. J., Tsai, C. Y., Lin, R. H., & Tzeng, G. H. (2011). A modified VIKOR multiple-criteria decision method for improving
domestic airlines service quality. Journal of Air Transport Management, 17(2), 57–61.

CURRENT ISSUES IN TOURISM 15

Liu, B., & Zhang, L. (2012). A survey of opinionmining and sentiment analysis. In C. C. Aggarwal & C. Zhai (Eds.),Mining text
data (pp. 415–463). New York, NY: Springer.

Mankad, S., Han, H. S., Goh, J., & Gavirneni, S. (2016). Understanding online hotel reviews through automated text analysis.
Service Science, 8(2), 124–138.

Martin-Domingo, L., Martín, J. C., & Mandsberg, G. (2019). Social media as a resource for sentiment analysis of Airport
Service Quality (ASQ). Journal of Air Transport Management. doi:10.1016/j.jairtraman.2019.01.004

Martinez-Garcia, E., & Royo-Vela, M. (2010). Segmentation of low-cost flights users at secondary airports. Journal of Air
Transport Management, 16(4), 234–237.

Meyer, D., Hornik, K., & Feinerer, I. (2008). Text mining infrastructure in R. Journal of Statistical Software, 25(5), 1–54.
Miguéis, V. L., & Nóvoa, H. (2017). Exploring online travel reviews using data analytics: An exploratory study. Service

Science, 9(4), 315–323.
Misopoulos, F., Mitic, M., Kapoulas, A., & Karapiperis, C. (2014). Uncovering customer service experiences with Twitter: The

case of airline industry. Management Decision, 52(4), 705–723.
Morrell, P. (2008). Can long-haul low-cost airlines be successful? Research in Transportation Economics, 24(1), 61–67.
Mudambi, S. M., & Schuff, D. (2010). What makes a helpful review? A study of customer reviews on Amazon. Com. MIS

Quarterly, 34(1), 185–200.
O’Connell, J. F., & Warnock-Smith, D. (2013). An investigation into traveler preferences and acceptance levels of airline

ancillary revenues. Journal of Air Transport Management, 33, 12–21.
O’Connell, J. F., & Williams, G. (2005). Passengers’ perceptions of low cost airlines and full service carriers: A case study

involving Ryanair, Aer Lingus, Air Asia and Malaysia Airlines. Journal of Air Transport Management, 11(4), 259–272.
O’connor, P. (2010). Managing a hotel’s image on tripadvisor. Journal of Hospitality Marketing & Management, 19(7), 754–

772.
Ostrowski, P. L., O’Brien, T. V., & Gordon, G. L. (1993). Service quality and customer loyalty in the commercial airline indus-

try. Journal of Travel Research, 32(2), 16–24.
Pakdil, F., & Aydın, Ö. (2007). Expectations and perceptions in airline services: An analysis using weighted SERVQUAL

scores. Journal of Air Transport Management, 13(4), 229–237.
Palese, B., & Piccoli, G. (2016). Online reviews as a measure of service quality. Proceedings of Pre-ICIS SIGDSA/IFIP WG.
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring Consumer percep-

tions of service quality. Journal of Retailing, 64(1), 12–40.
Park, J. W., Robertson, R., & Wu, C. L. (2004). The effect of airline service quality on passengers’ behavioural intentions: A

Korean case study. Journal of Air Transport Management, 10(6), 435–439.
Park, J. W., Robertson, R., & Wu, C. L. (2005). Investigating the effects of airline service quality on airline image and pas-

sengers’ future behavioural intentions: Findings from Australian international air passengers. Journal of Tourism
Studies, 16(1), 2–11.

Rajaguru, R. (2016). Role of value for money and service quality on behavioural intention: A study of full service and low
cost airlines. Journal of Air Transport Management, 53, 114–122.

Saha, G. C., & Theingi. (2009). Service quality, satisfaction, and behavioural intentions: A study of low-cost airline carriers in
Thailand. Managing Service Quality: An International Journal, 19(3), 350–372.

Seo, K., Moon, J., & Lee, S. (2015). Synergy of corporate social responsibility and service quality for airlines: The moderating
role of carrier type. Journal of Air Transport Management, 47, 126–134.

Steyvers, M., & Griffiths, T. (2007). Probabilistic topic models. In T. K. McNamara, D. S. Dennis, & W. Kintsch (Eds.), Handbook
of latent semantic analysis: A road to meaning (pp. 424–440). Mahwah, NJ: Lawrence Erlbaum Associates.

Tsaur, S. H., Chang, T. Y., & Yen, C. H. (2002). The evaluation of airline service quality by fuzzy MCDM. TourismManagement,
23(2), 107–115.

Wei, C. P., Chen, Y. M., Yang, C. S., & Yang, C. C. (2010). Understanding what concerns consumers: A semantic approach to
product feature extraction from consumer reviews. Information Systems and E-Business Management, 8(2), 149–167.

Wei, X., & Croft, W. B. (2006, August). LDA-based document models for ad-hoc retrieval. Proceedings of the 29th annual
international ACM SIGIR conference on Research and Development in Information Retrieval (pp. 178–185). ACM.

Wittman, M. D. (2014). Are low-cost carrier passengers less likely to complain about service quality? Journal of Air
Transport Management, 35, 64–71.

Yee Liau, B., & Pei Tan, P. (2014). Gaining customer knowledge in low cost airlines through text mining. Industrial
Management & Data Systems, 114(9), 1344–1359.

Young, C., Cunningham, L., & Lee, M. (1994). Assessing service quality as an effective management tool: The case of the
airline industry. Journal of Marketing Theory and Practice, 2(2), 76–97.

Yu, J., Zha, Z. J., Wang, M., & Chua, T. S. (2011, June). Aspect ranking: Identifying important product aspects from online con-
sumer reviews. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics.

Zhang, C., Lin, Y. H., & Newman, D. G. (2016). Investigating the effectiveness of repositioning strategies: The customers’
perspective. Journal of Travel & Tourism Marketing, 33(9), 1235–1250.

Zhang, K., Narayanan, R., & Choudhary, A. (2010). Voice of the customers: Mining online customer reviews for product feature-
based ranking. Proceedings of the 3rd workshop on Online Social Networks.

16 J. LIM AND H. C. LEE

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https://doi.org/10.1016/j.jairtraman.2019.01.004

https://www.researchgate.net/publication/332522980

Abstract

Introduction

Literature review

Airline service quality models

Text analysis-based service quality in airline travel

Research model

Data

Topic modeling

Topic naming

Topic matching with five dimensions

Result comparisons and discussions

Dimension level comparisons

Feature level comparisons

Further divided features

Sentiment analysis

Conclusions

Disclosure statement

ORCID

References

sustainability

Article

Redesigning In-Flight Service with Service Blueprint
Based 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.kr
2 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 an
in-flight service process using a service blueprint while incorporating direct customer perceptions of
service experiences. To incorporate these, we apply topic modeling to 64,706 passenger-written online
reviews of airline services. Passenger experiences of in-flight services are the sum of experiences
from service encounters in all the subsequent steps and we assume that their direct perceptions of
their experiences are faithfully contained in the online reviews. Topics extracted from the reviews
can be regarded as service encounters based strongly on passenger experiences. Then, the service
encounters are reorganized within the framework of a service blueprint. The results show that
the complexity, a number of service steps, decreases by 38% compared to the benchmark service
blueprint. However, the divergence, a latitude of service steps, should increase for a couple of
service encounters. Moreover, we quantitatively analyze the divergence using the probability of
word frequency statistically distributed across topics. The in-flight service using the proposed design
could be sustainable with respect to customer-focused service while considering direct customer
experiences in real-time.

Keywords: latent Dirichlet allocation; online review; passenger-focused; service encounter; service
blueprint; sustainable in-flight service

1. Introduction

Through the liberalization of air transport service agreements, the airline industry has grown
with 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 the
diversification of customers’ needs by expanding multiple layers of air traffic demands, and service
customization, which makes it possible to address each customer’s needs, is a common strategy
for achieving competitive advantages [2–4]. It has been repeatedly emphasized that airlines could
deliver customized service processes which optimize diversified demands for customers in the airline
industry [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 hone
service customization among airlines for the following reasons. Duopoly manufacturers mostly
provide aircrafts to airlines, and there is no significant difference in terms of technological performance
and characteristics [11]. Customization in relation to intangible factors is also not flexible as airlines
must 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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Sustainability 2018, 10, 4492 2 of 2

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. Customer
satisfaction through customized service could be the minimum requirement for providing a sustainable
service [12].

This study proposes a redesign method for the in-flight service blueprint (SB) based on customer
perceptions. In order to identify exact customer perceptions of in-flight service experiences, we use
online reviews for airline services as a dataset [13]. The data is self-generated by passengers and
this is regarded as one of the most direct and immediate forms of customer service experience
response [14–16]. As various types of mobile web platforms appear rapidly, studies using online
reviews that contain real-time perceptions of customer experiences are quite frequent in business and
management research [17–24]. The data is described as naturally unrefined and voluntary rather than
designed or intended, the characteristics of survey data [14]. We make full use of the characteristics of
online reviews while redesigning service processes from the customer’s perspective.

Specifically, we apply latent Dirichlet allocation (LDA) topic modeling [25,26], a text analysis
technique, to a vast amount of passenger-written online reviews. LDA modeling has been extensively
used and is one of the Bayesian probabilistic clustering approaches for text data. Based on the
co-occurrence probabilities of observed words in documents, the LDA approach can derive latent
topics of documents, which are characterized by a distribution of words. LDA modeling produces
topics, which are groups of words with similar characteristics. Through the application of LDA to
the online reviews, we represent topics as interpretable service encounters, critical components of
the SB based on passenger experiences, and redesign in-flight service in accordance with passenger
perceptions by reorganizing service encounters. When redesigning the SB, we employ (re)design
principles 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 produce
services 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 proper
degree of divergence and complexity of the in-flight service process based on the passenger-focused
standard. As a result, the number of service steps decreases by 38% compared to the benchmark
service, but the divergence degree should increase for a couple of service steps. Second, we determine
the direction and size of changes in the customization level for service encounters since we analyze
the divergence by investigating the probability of word frequency, a quantitative measure. This also
exposes 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 with
real-time online reviews. Since a sustainable service requires continuous improvement during a whole
service 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 applications
in various service fields and related research, as well as introducing the benchmark model. Section 3
explains in detail the research model and the dataset used in this study. The LDA model and its
modeling procedures are briefly discussed. Section 4 presents the modeling results of the LDA topic
analysis and topic naming. Through the redesign principles of complexity and divergence, this section
provides the final form of the proposed SB. The related analysis processes and findings are also
discussed. 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 processes
and components [34]. The SB has become one of the most useful tools for visualizing and
conceptualizing the whole service process in service design and innovation [30,35,36]. The SB has been
extensively applied to the analysis of service processes, customer and employee behaviors in a broad

Sustainability 2018, 10, 4492 3 of 21

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 service
process performs efficiently while meeting the exact needs of customers at the actual field where the
service is provided [41]. There are two main approaches for modifying the SB. One class of method
is 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 inventive
problem solving (TIPS) principles. They identified problems and found solutions for the service based
on 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 critical
points 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 from
online reviews using text mining and explained possible applications including an SB improvement
to 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 new
components of channel, and smart devices and technology. There are also a few published research
results 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 applying
content analysis to the website information. Hartman et al. [47] proposed a public-sector service design
through the application of content analysis to blogs and YouTube.

The other class of methods varies the SB components. This type of modification can be commonly
observed when an industry or an innovative new technology, which has never been introduced before
in service blueprinting, is applied. Patrício, Fisk, and Cunha [48] suggested a service experience
blueprint (SEB) adding a component called an interface to correspond to information technologies
introduced in internet banking financial services. In addition, Patrício et al. [49] extended the scope
of SB to retail industries combined with financial services. In order to represent the service delivery
process clearly, Lim and Kim [50] modified the SEB by adding an information delivery system in the
information-intensive service industry. Pöppel, Finsterwalder, and Laycock [51] reflected changes in
the service process resulting from the introduction of digitization in the film industry by modifying
the SB component. Barbieri et al. [52] considered a sociogram as a human factor dimension to visualize
the reception service process of luxury hotels. The proposed SB of this study is rather close to the latter
class of redesign approaches as it reorganizes the service encounters based on customer perceptions of
the 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 assume
there is a one-to-one relationship between the service encounter and the service step as described
in Reference [34]. The SB consists of customer actions, actions in front-stage encounters, actions
in back-stage encounters, support processes, visible lines that distinguish between the front and
back-stage, and physical evidence that a customer can see or experience [35,36,53]. The service
encounter, the core of service delivery, is the moment when and/or the place where direct interactions
between a customer and a service provider with proper physical components occur [30,54]. The service
performance during service encounters affects service quality [55,56] and service quality has a positive
influence on customer loyalty and satisfaction [57–59]. Customer perception of service experiences is
the 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 redesigning
the service process and providing customized services. Scandinavian Airlines, for example, achieved
positive corporate performances by adequately altering service encounters [53,60].

Since a customer prefers a flexible and personalized service, changes in the service encounters
are unavoidable to accommodate customer needs [61,62]. Shostack [27] noted the redesign principles

Sustainability 2018, 10, 4492 4 of 21

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 affected
by the service provider’s capabilities which includes proficiency, specific response behaviors to
situations, response skills for predictable and unpredictable changes, self-control, adaptability to
situations, and so on. In particular, the cabin crew’s capabilities should be emphasized in the airline
service because the industry truly relies on services related to human resources against other service
industries [63]. Thus, the complexity shows a quantitative variation in the SB whereas the divergence
is 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 divergence
means a customized service [27].

By adjusting the complexity, Paquet et al. [64] redesigned the SB to be an effective distributing
process for meal services in a medical hospital. Kim and Kim [65] proposed an efficient service
delivery by rationalizing the design of the customer service process. The simplification of service steps
led to a decrease in complexity. Geum and Park [66] suggested a redesign method for the medical
service process in terms of complexity by integrating the product-service system. Hossain, Enam and
Farhana [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 of
in-flight service steps and the proper divergence of customization by investigating the probability of
word 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 of
factors that have major effects on and correlations with quality (See e.g.,

References

[68–72]). There are
not 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, and
Cho [74] modified the airline service process for specialized infant services. Lee, Kim, and Lee [75] and
Go and Kim [76] applied the negative customer-to-customer interaction (NCCI) and Kano models to
the SB for redesigning purposes, respectively, in order to estimate fail points and bottleneck processes
in the airline service.

We choose the in-flight SB of a Korean airline as a benchmark and propose a modified version
of the benchmark using the redesign principles described previously. The benchmark blueprint
consists of 13 service steps with the equivalent number of service encounters when a passenger
boards an aircraft [74–76]. Some service steps are only applicable to long-haul and international
routes. This benchmark is the only publicized in-flight service in the form of an SB, to the best of
our knowledge, and the service received an excellence award for ten years in a row until 2017 in the
area of in-flight services [77]. Appendix A summarizes the descriptions of every service step and
corresponding 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 more
straightforward customer tastes and perceptions than standard survey data [78,79]. Customer
perceptions are derived from customer experiences and customer experiences are defined as the
sum of experiences at every service encounter [55]. We assumed that direct perceptions of customer

Sustainability 2018, 10, 4492 5 of 21

experiences at every service encounter were contained in the online reviews [13,80]. The customer
perceptions preserved were analyzed by employing LDA topic modeling [25,26]. As a result of
LDA modeling, a topic, one of the k-dimensional space, becomes a probability distribution of word
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.

Sustainability 2018, 10, x FOR PEER REVIEW 5 of 23

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.

Sustainability 2018, 10, 4492 6 of 21

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
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.

Sustainability 2018, 10, x FOR PEER REVIEW 6 of 23

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 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).

Sustainability 2018, 10, 4492 7 of 21

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 top
10 ranked airlines assessed by Skytrax [81]. Table 1 presents the airlines selected in alphabetical order
and 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 7790
Rank 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 seven
service 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 tables
in Appendix B). Table 2 arranges the LDA service encounters of in-flight service in the sequence of
occurrence, 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 four
of the existing service encounters were removed from the benchmark model. The rest of the service
encounters 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 form
of 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 airport
check-in to boarding gate arrival T5, T7, T9 17%

Boarding & ground service (boarding) Related actions from boarding to
taking a seat T2, T17 11%

Take-off safety check
(take-off)

Actions related to take-off and
safety check T14, T15 11%

Meal & beverage service
(meal service) Actions related to meal service T6 6%

Passenger relaxation Actions related to personal resting
and entertaining within a flight T1, T8, T10, T11, T13, T18 33%

Deplaning & post-deplaning
(deplaning)

Actions related to landing
and 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’ (originally
socialmedia in the modeling result) represents a recent change in customer trends as a new type
of word-of-mouth [82]. Pre-boarding also takes 17% of the importance. The service process before
boarding is related to sets of words such as carried baggage (e.g., bag, luggage), services provided by
an 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 boarding
process (e.g., check, available) and seating (e.g., economy, cabin, seat, short, forward, order). Take-off

Sustainability 2018, 10, 4492 8 of 21

(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% of
the 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 about
the service (e.g., busy, available, service). Passenger relaxation is matched to the largest number of
topics (T1, T8, T10, T11, T13, and T18) and has the highest importance. The service encounter
includes 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 the
importance. This service encounter is supported by words (e.g., destination, transfer, hotel) and
passenger 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 service
encounters from the LDA topic modeling while reflecting passenger perceptions contained in online
reviews. Reservation is excluded from the proposed SB since the actual in-flight service does not cover
the service encounter. However, it is reasonable to assume that online reviews involve a great deal of
expressions for the reservation because online booking systems are commonly utilized today. Although
the reservation is not dealt with as an in-flight service encounter, service providers should be aware of
its importance (17%)—not a small amount, in our analysis. This suggests that the reservation is one of
the service processes that is highly recognized by passengers. We included this service encounter in
the divergence analysis for this reason in Section 4.3.

Pre-boarding service asks for changes in the conventional process of in-flight services, since the
service encounter is not the existing service encounter in the benchmark. The service encounter
shows 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 frequent
than cabin experience topics that can be characterized in the existing service encounter. This means
that the importance of services provided before boarding should not be overlooked. If passengers
seriously 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 by
incorporating 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 cooperation
and coordination between related organizations are inevitable. Specified action plans for this service
step 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 service
encounters (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 think
that passengers would note little difference among the existing service encounters. These four service
steps tend to be performed at the same time between boarding and take-off and passengers recognize
the service encounters as almost the same one. This results in the integrated service encounter of
boarding & ground service. The integration of the service encounters causes a decrease in complexity by
reducing the service encounter numbers.

Take-off safety check and meal & beverage service remain the identical forms of the benchmark. Take-off
is 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 as
a separate service encounter. Although meal service, in terms of the characteristics of the service,

Sustainability 2018, 10, 4492 9 of 21

can be seen as an extended one from passenger relaxation explained in the next paragraph, the service
encounter 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 reveal
a significant difference in customer perceptions and have been combined as a renamed service
encounter: 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 the
connected topics to support the service encounter. With the prevalent help of IFEs, watching a movie
as part of personal relaxing has become a normal form of in-flight leisure. In particular, the order of
movie watching is not critical to service providers in the whole service sequence because passengers
experience the service with wide applications of IFEs regardless of the service sequence whenever the
service is ready. That is, the service encounter of movie watching is inclusively recognized within the
service encounter of passenger relaxation in a broader sense.

Deplaning means the termination of in-flight service and also leaves an independent and strong
impression on customers likewise in take-off. As the same service encounter as the benchmark, there is
no change in the complexity.

In terms of customer perceptions, four typical service encounters—in-flight sales, preparing
immigration documents, preparing landing, and landing—are removed from the benchmark. In-flight
sales is the service encounter of passengers’ convenience for shopping. Since in-flight sales is used as
an additional income source for airlines, airlines treat this service as an important one [83]. In order
to provide a diversified and customized shopping service, air carriers deploy a passenger-friendly
marketing strategy based on products that consider the characteristics of passengers for individual
routes and shopping counters that can achieve strong perceptions of the service. However, the current
LDA modeling results do not disclose such efforts by airlines and neither do the results of the
survey. This might be because the service encounter is not mandatory for every route and only
applicable to part of long-haul or international routes. In preparing for landing, cabin crews provide
destination information via announcements and take back used or reusable goods for the in-flight
service. Passengers are usually static in the service encounter, being informed and returning goods
according to the instructions. The degree of interaction between passengers and crews is lower than
that in any other service encounter and the lower level of interaction has a restricted impact on
customer perceptions of the experience [84].

Both preparing immigration documents and landing are the essential service encounters in air
transport services although they did not emerge in the LDA topic modeling results. Customer
perceptions of the service encounters are not strong enough to be revealed in the modeling since
the presence of service encounters is naturally accepted in the in-flight service process. The service
encounters remain in the same form in the proposed SB. Table 3 presents the results of the reorganized
in-flight SBs in terms of complexity.

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%.

Sustainability 2018, 10, 4492 10 of 21

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 seats
Baggage service
Ground 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 relaxation
Personal 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 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.

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.

Sustainability 2018, 10, 4492 11 of 21

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 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.

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, 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 indicates
that the level of service expectation could also be diversified. This can generate gaps between the
service expectation formed by prior experiences and the performance actually perceived [86]. The
gap causes passenger dissatisfaction with the service. Therefore, it is essential for airlines to meet
different passenger needs by interpreting them as accurately as possible and perform the service
based on their understandings. Although service providers fail to properly respond to the diverse
level of customer expectations, 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 services
related to the competence level of cabin attendants [63]. Therefore, airlines should be equipped with
cabin attendants’ capabilities of service delivery processes to promptly respond to diversified needs.

reservation,
0.84

pre boarding,
0.60

boarding, 1.07

take-off,
0.91

passenger relaxation,
1.37

meal service,
1.60

0.4

0.6

0.8

1.2

1.4

1.6

1.8

0 1 2 3 4 5 6 7

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, 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 indicates
that the level of service expectation could also be diversified. This can generate gaps between the
service expectation formed by prior experiences and the performance actually perceived [86]. The gap
causes passenger dissatisfaction with the service. Therefore, it is essential for airlines to meet different
passenger needs by interpreting them as accurately as possible and perform the service based on their
understandings. Although service providers fail to properly respond to the diverse level of customer
expectations, it is still possible to improve customer loyalty when the service recovery succeeds [87–89].

Sustainability 2018, 10, 4492 12 of 21

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.

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 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.

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

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 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.

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 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
the level of divergence is determined by the size of the angle in the sector. A solid line denotes the
perceived level and a dotted line denotes the proposed (ideal) level of divergence. Pre-boarding and
boarding need to increase the level and take-off; meal service and relaxation can maintain the current
level of divergence.

Sustainability 2018, 10, 4492 14 of 21

5. Summary and Conclusions

The main conclusions of the proposed in-flight SB are shared with respect to service
design perspectives. First, we redesigned a customer-focused in-flight SB while understanding
customer perceptions of the service through the application of topic modeling based on 64,706
passenger-authored online reviews for airline services. To do so, we derived the service encounters of
in-flight service processes while extracting passenger perceptions of the service encounter experiences
using LDA text analysis. We finally depicted the redesigned service using the SB frame with the
redesign principles of complexity and divergence. To make sustainable in-flight service, we balanced
the complexity and the proper divergence degree of in-flight service by investigating the probability
of word frequency statistically distributed to topics and related service encounters. Second, in terms
of 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 because
it covers 17% of the total importance. This suggests that airlines need to expand the actual scope
of services in a more proactive way to provide better in-flight services. Feasible action plans were
discussed with specific examples in the previous section. Airlines may sustain the service capability
for 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 help
them better differentiate themselves. Lastly, airlines need to provide more customized services than the
currently perceived level at a couple of service encounters (pre-boarding and boarding). This conclusion
was reached by Shostack [27]; a service should be designed by considering the unique features of
service encounters as carefully as possible. In particular, the results of the divergence analysis are
established using a quantitative method with the probability of word occurrence.

The divergence analysis could be improved by considering the polarity of online reviews (positive
or negative) in further studies, since we only use word frequency to quantify the significance of the
topic. If a sentiment analysis were employed to capture the polarity of the degree of evaluation of
words related to the service evaluation (category 2), the results could add more accurate and wider
interpretations regarding service design. For the same aim of better interpretations, we need to utilize
multiple trusted sources of online reviews simultaneously. Moreover, the characteristics of online
reviews can sometimes cause problems. Since one of the main characteristics of online reviews is
voluntariness, there is a chance of excluding the data of customers who are reluctant to, or for other
reasons do not, express their opinions and thoughts.

We finalize this study by explaining the usability of the proposed design method. Under the
circumstances wherein companies must promptly respond to customer needs and business
environments change, the proposed design method could offer the ability to capture customer needs on
the fly and incorporate them into service improvement. Furthermore, the application of the proposed
design approach could be expanded to other industries with the proper acquisition of relevant datasets
although we focus on the airline service in this study. Finally, the proposed design could play a crucial
role in the further improvement of a service process as a new standard. It is possible to evaluate the
status of service delivery efficiency based on the new standard design. The appropriate evaluation can
be 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. and
H.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 the
graduate students for their participation in the surveys and interviews, as well as for their helpful comments
and 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 with
passengers takes place for the first time

Crew uniform, boarding area facilities,
aircraft outlook

Finding seats Checking in boarding passes managing
congested aisles Boarding pass, seat, interior

Baggage service Managing luggage storages Overhead compartment (bin), coat
room

Ground service Providing background music, reading
materials and beverages Screen, book, audio

Taking off Checking up take-off demonstrating
safety simulation

Individual reading light, seat belt,
in-flight light

In-flight food service Providing meal and beverage service
Menu, meal, beverage, waiting, service
evidence, clearance, attendants’
appearance

Long-haul &

international routes

In-flight sales Providing convenience of shopping for
passengers In-flight sales counter, goods Long-haul &

international routes

Prepare immigration
documents

Support with filling out passenger
immigration 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 service
calls

In-flight environment, thermostat
setting, 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. The
definition, physical evidence of every 13 service encounters are explained, and some of the service encounters are
only applicable for long-haul and/or international routes.

Sustainability 2018, 10, 4492 16 of 21

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 a
two-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 the
importance 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 basis
for 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 superb
awesome 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 send
hostess mention 11.30% good worth inconvenient 14.32% 25.6%

T11 make staff entertainment 18.94% good happy comfort impress
pleasant 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 contains
word sets associated with specific actions for service delivery and category 2 contains word sets associated with
customer assessments on service competence.

References

1. InterVISTAS-ga2 Consulting. The Economic Impact of Air Service Liberization. 2006. Available online: http://
www.iata.org/whatwedo/Documents/economics/liberalization_air_transport_study_30may06 (accessed
on 30 October 2018).

2. Cognizant. How Airlines Can Deliver a Personalized Customer Experience during Operational Disruptions. 2015.
Available online: https://www.cognizant.com/whitepapers/How-Airlines-Can-Deliver-a-Personalized-Customer-
Experience-During-Operational-Disruptions-codex1603 (accessed on 30 October 2018).

3. Bhaskara, V. Airlines Are Giving Customers Exactly What They Want. Forbes. 14 January 2015. Available
online: https://www.forbes.com/sites/airchive/2015/01/14/actually-airlines-are-giving-customers-exactly-
what-they-want/#3364aaef29bb (accessed on 30 October 2018).

4. Treacy, M.; Wiersema, F. Customer Intimacy and Other Value Disciplines Customer Intimacy and Other
Value Disciplines. Harv. Bus. Rev. 1993, 71, 84–93. [CrossRef]

5. Punel, A.; Ermagun, A. Using Twitter network to detect market segments in the airline industry. J. Air
Transp. Manag. 2018, 73, 67–76. [CrossRef]

6. Li, W.; Yu, S.; Pei, H.; Zhao, C.; Tian, B. A hybrid approach based on fuzzy AHP and 2-tuple fuzzy linguistic
method for evaluation in-flight service quality. J. Air Transp. Manag. 2017, 60, 49–64. [CrossRef]

7. Liou, J.J.H.; Yen, L.; Tzeng, G. Using decision rules to achieve mass customization of airline services. Eur. J.
Oper. Res. 2010, 205, 680–686. [CrossRef]

8. Gilbert, D.; Wong, R.K.C. Passenger expectations and airline services: A Hong Kong based study. Tour. Manag.
2003, 24, 519–532. [CrossRef]

9. Doganis, R. Flying Off Course: Airline Economics and Marketing, 4th ed.; Routledge: London, UK, 2010;
ISBN 9780203863992.

10. Nameghi, E.N.M.; Azmi, A.; Arif, M. The measurement scale for airline hospitality: Cabin crew’s performance
perspective. J. Air Transp. Manag. 2013, 30, 1–9. [CrossRef]

11. Pearlstein, S. Boeing and Airbus, the New ‘Super Duopoly’. The Washington Post. 2018. Available
online: https://www.washingtonpost.com/news/wonk/wp/2018/04/25/boeing-and-airbus-the-new-super-
duopoly/?noredirect=on&utm_term=.14b4df53ee16 (accessed on 30 October 2018).

http://www.iata.org/whatwedo/Documents/economics/liberalization_air_transport_study_30may06

https://www.cognizant.com/whitepapers/How-Airlines-Can-Deliver-a-Personalized-Customer-Experience-During-Operational-Disruptions-codex1603

https://www.forbes.com/sites/airchive/2015/01/14/actually-airlines-are-giving-customers-exactly-what-they-want/#3364aaef29bb

http://dx.doi.org/10.1225/93107

http://dx.doi.org/10.1016/j.jairtraman.2018.08.004

http://dx.doi.org/10.1016/j.jairtraman.2017.01.006

http://dx.doi.org/10.1016/j.ejor.2009.11.019

http://dx.doi.org/10.1016/S0261-5177(03)00002-5

http://dx.doi.org/10.1016/j.jairtraman.2013.03.001

https://www.washingtonpost.com/news/wonk/wp/2018/04/25/boeing-and-airbus-the-new-super-duopoly/?noredirect=on&utm_term=.14b4df53ee16

Sustainability 2018, 10, 4492 19 of 21

12. Jones, P.; Clarke-Hill, C.; Comfort, D.; Hillier, D. Marketing and sustainability. Mark. Intell. Plan. 2008, 26,
123–130. [CrossRef]

13. Ordenes, F.V.; Theodoulidis, B.; Burton, J.; Gruber, T.; Zaki, M. Analyzing Customer Experience Feedback
Using Text Mining. J. Serv. Res. 2014, 17, 278–295. [CrossRef]

14. Mudambi, S.M.; Schuff, D. Research Note: What Makes a Helpful Online Review? A Study of Customer
Reviews on Amazon.com. MIS Q. 2010, 34, 185–200. [CrossRef]

15. Duan, W.; Gu, B.; Whinston, A.B. Do online reviews matter?—An empirical investigation of panel data.
Decis. Support Syst. 2008, 45, 1007–1016. [CrossRef]

16. Dellarocas, C.; Zhang, X.; Awad, N.F. Exploring the value of online product reviews in forecasting sales:
The case of motion pictures. J. Interact. Mark. 2007, 21, 23–46. [CrossRef]

17. Berezina, K.; Bilgihan, A.; Cobanoglu, C.; Okumus, F. Understanding Satisfied and Dissatisfied Hotel
Customers: Text Mining of Online Hotel Reviews. J. Hosp. Mark. Manag. 2016, 25, 1–24. [CrossRef]

18. Mankad, S.; Han, H.; Goh, J.; Gavirneni, S. Understanding Online Hotel Reviews through Automated Text
Analysis. Serv. Sci. 2016, 8, 124–138. [CrossRef]

19. Archak, N.; Ghose, A.; Ipeirotis, P.G. Deriving the Pricing Power of Product Features by Mining Consumer
Reviews. Manag. Sci. 2011, 57, 1485–1509. [CrossRef]

20. Lee, M.J.; Singh, N.; Chan, E.S.W. Service failures and recovery actions in the hotel industry: A text-mining
approach. J. Vacat. Mark. 2011, 17, 197–207. [CrossRef]

21. O’Connor, P. Managing a hotel’s image on Tripadvisor. J. Hosp. Mark. Manag. 2010, 19, 754–772. [CrossRef]
22. Gretzel, U.; Yoo, K.H. Use and impact of online travel reviews. In Information and Communication Technologies

in Tourism 2008; Springer: Berlin, Germany, 2008. [CrossRef]
23. Pekar, V.; Ou, S. Discovery of subjective evaluations of product features in hotel reviews. J. Vacat. Mark. 2008,

14, 145–155. [CrossRef]
24. Hu, M.; Liu, B. Mining opinion features in customer reviews. Am. Assoc. Artif. Intell. 2004, 4, 755–760.

[CrossRef]
25. Blei, D.M.; Lafferty, J.D. A correlated topic model of science. Ann. Appl. Stat. 2007, 1, 17–35. [CrossRef]
26. Blei, D.M.; Ng, A.Y.; Jordan, M.I. Latent Dirichlet Allocation. J. Mach. Learn. Res. 2003, 3, 993–1022. [CrossRef]
27. Shostack, G.L. Service Positioning through Structural Change. J. Mark. 1987, 51, 34–43. [CrossRef]
28. Kuang, P.H.; Chou, W.H. Research on Service Blueprint of Food Banks. Des. J. 2017, 20, S3425–S3435.

[CrossRef]
29. Song, W.; Wu, Z.; Li, X.; Xu, Z. Modularizing product extension services: An approach based on modified

service blueprint and fuzzy graph. Comput. Ind. Eng. 2015, 85, 186–195. [CrossRef]
30. Bitner, M.J.; Ostrom, A.L.; Morgan, F.N. Service Blueprinting: A Practical Technique for Service Innovation.

Calif. Manag. Rev. 2008, 50, 66–94. [CrossRef]
31. Tseng, M.M.; Qinhai, M.; Su, C.J. Mapping customers’ service experience for operations improvement.

Bus. Process Manag. J. 1999, 5, 50–64. [CrossRef]
32. Peattie, K.; Belz, F.-M. Sustainability marketing—An innovative conception of marketing. Mark. Rev.

St. Gallen 2010, 27, 8–15. [CrossRef]
33. Polese, F.; Carrubbo, L.; Caputo, F.; Sarno, D. Managing Healthcare Service Ecosystems: Abstracting

a Sustainability-Based View from Hospitalization at Home (HaH) Practices. Sustainability 2018, 10, 3951.
[CrossRef]

34. Zeithaml, V.A.; Bitner, M.J.; Gremler, D.D. Services Marketing: Integrating Customer Focus across the Firm, 6th
ed.; McGraw-Hill: New York, NY, USA, 2013; ISBN 9780078112058.

35. Fließ, S.; Kleinaltenkamp, M. Blueprinting the service company—Managing service processes efficiently.
J. Bus. Res. 2004, 57, 392–404. [CrossRef]

36. Shostack, G.L. Designing services that deliver. Harv. Bus. Rev. 1984, 62, 133–139. [CrossRef]
37. Berkley, B.J. Analyzing service blueprints using phase distributions. Eur. J. Oper. Res. 1996, 88, 152–164.

[CrossRef]
38. Scheuing, E.E.; Christopher, W.F. The Service Quality Handbook; Amacom: New York, NY, USA, 1993;

ISBN 9780814401194.
39. Michel, S. Analyzing service failures and recoveries: A process approach. Int. J. Serv. Ind. Manag. 2001, 12,

20–33. [CrossRef]

http://dx.doi.org/10.1108/02634500810860584

http://dx.doi.org/10.1177/1094670514524625

http://dx.doi.org/10.2307/20721420

http://dx.doi.org/10.1016/j.dss.2008.04.001

http://dx.doi.org/10.1002/dir.20087

http://dx.doi.org/10.1080/19368623.2015.983631

http://dx.doi.org/10.1287/serv.2016.0126

http://dx.doi.org/10.1287/mnsc.1110.1370

http://dx.doi.org/10.1177/1356766711409182

http://dx.doi.org/10.1080/19368623.2010.508007

http://dx.doi.org/10.1007/978-3-211-77280-5_4

http://dx.doi.org/10.1177/1356766707087522

http://dx.doi.org/10.1002/j.1532-2149.2013.00312.x

http://dx.doi.org/10.1214/07-AOAS114

http://dx.doi.org/10.1162/jmlr.2003.3.4-5.993

http://dx.doi.org/10.2307/1251142

http://dx.doi.org/10.1080/14606925.2017.1352846

http://dx.doi.org/10.1016/j.cie.2015.03.013

http://dx.doi.org/10.2307/41166446

http://dx.doi.org/10.1108/14637159910249126

http://dx.doi.org/10.1007/s11621-010-0085-7

http://dx.doi.org/10.3390/su10113951

http://dx.doi.org/10.1016/S0148-2963(02)00273-4

http://dx.doi.org/10.1225/84115

http://dx.doi.org/10.1016/0377-2217(94)00157-X

http://dx.doi.org/10.1108/09564230110382754

Sustainability 2018, 10, 4492 20 of 21

40. Hummel, E.; Murphy, K.S. Using service blueprinting to analyze restaurant service efficiency. Cornell Hosp. Q.
2011, 52, 265–272. [CrossRef]

41. Buhalis, D.; Law, R. Progress in information technology and tourism management: 20 years on and 10 years
after the Internet-The state of eTourism research. Tour. Manag. 2008, 29, 609–623. [CrossRef]

42. Lee, C.H.; Wang, Y.H.; Trappey, A.J.C. Service design for intelligent parking based on theory of inventive
problem solving and service blueprint. Adv. Eng. Inform. 2015, 29, 295–306. [CrossRef]

43. Ru Chen, H.; Cheng, B. Applying the ISO 9001 process approach and service blueprint to hospital
management systems. TQM J. 2012, 24, 418–432. [CrossRef]

44. Botschen, G.; Bstieler, L.; Woodside, A.G. Sequence-oriented Problem Identification within Service
Encounters. J. Euromark. 1996, 5, 19–52. [CrossRef]

45. Ryu, D.-H.; Lim, C.-H.; Kim, K.-J. Development of online-to-offline service blueprint. In Proceedings of the
2016 Informs Annual Meeting, Nashville, TN, USA, 13–16 November 2016.

46. Cristobal-Fransi, E.; Daries, N.; Serra-Cantallops, A.; Ramón-Cardona, J.; Zorzano, M. Ski Tourism and Web
Marketing Strategies: The Case of Ski Resorts in France and Spain. Sustainability 2018, 10, 2920. [CrossRef]

47. Hartman, A.; Jain, A.N.; Ramanathan, J.; Ramfos, A. Participatory Design of Public Sector Services.
In Proceedings of the 2010 Electronic Government and the Information Systems Perspective (EGOVIS
2010), Bilbao, Spain, 31 August–2 September 2010. [CrossRef]

48. Patrício, L.; Fisk, R.P.; Falcão e Cunha, J.; Cunha, J. Designing multi-interface service experiences: The service
experience blueprint. J. Serv. Res. 2008, 10, 318–334. [CrossRef]

49. Patrício, L.; Fisk, R.P.; Falcão e Cunha, J.; Constantine, L. Multilevel service design: From customer value
constellation to service experience blueprinting. J. Serv. Res. 2011, 14, 180–200. [CrossRef]

50. Lim, C.-H.; Kim, K.-J. Information Service Blueprint: A Service Blueprinting Framework for Information-
Intensive Services. Serv. Sci. 2014, 6, 296–312. [CrossRef]

51. Pöppel, J.; Finsterwalder, J.; Laycock, R.A. Developing a film-based service experience blueprinting technique.
J. Bus. Res. 2018, 85, 459–466. [CrossRef]

52. Barbieri, S.; Fragniere, E.; de Grandbois, Y.; Moreira, M.P. Measuring Human Risks in Service: A New Model.
J. Serv. Sci. Manag. 2017, 10, 518–536. [CrossRef]

53. Fitzsimmons, J.A.; Fitzsimmons, M.J.; Bordoloi, S. Service Management: Operations, Strategy, and Information
Technology, 8th ed.; McGraw-Hill: New York, NY, USA, 2014; ISBN 9780077841201.

54. Surprenant, C.F.; Solomon, M.R. Predictability and Personalization in the Service Encounter. J. Mark. 1987,
86–96. [CrossRef]

55. Voorhees, C.M.; Fombelle, P.W.; Gregoire, Y.; Bone, S.; Gustafsson, A.; Sousa, R.; Walkowiak, T. Service
encounters, experiences and the customer journey: Defining the field and a call to expand our lens. J. Bus. Res.
2017, 79, 269–280. [CrossRef]

56. Parasuraman, A.; Zeithaml, V.; Berry, L. Reassessment of expectations as a comparison standard in measuring
service quality: Implications for further research. J. Mark. 1994, 111–124. [CrossRef]

57. Lynn, M.L.; Lytle, R.S.; Bobek, S. Service Orientation in Transitional Markets: Does it matter? Eur. J. Mark.
2000, 34, 279–298. [CrossRef]

58. Gremler, D.D.; Brown, S.W. The loyalty ripple effect. Int. J. Serv. Ind. Manag. 1999, 10, 271–293. [CrossRef]
59. Solomon, M.R.; Surprenant, C.; Czepiel, J.A.; Gutman, E.G. Role Theory Perspective on Dyadic Relations:

The service encounter. J. Mark. 1985, 49, 99–111. [CrossRef]
60. Carlzon, J. Moments of Truth; Harper & Row: New York, NY, USA, 1987; ISBN 9780060915803.
61. Mittal, B.; Lassar, W.M. The role of personalization in service encounters. J. Retail. 1996, 72, 95–109. [CrossRef]
62. Bitner, M.J.; Booms, B.H.; Tetreault, M.S. The Service Encounter: Diagnosing Favorable and Unfavorable

Incidents. J. Mark. 1990, 71–84. [CrossRef]
63. Wirtz, J.; Heracleous, L. Managing human resources for service excellence and cost effectiveness at Singapore

Airlines. Manag. Serv. Qual. 2008, 18, 4–19. [CrossRef]
64. Paquet, C.; St-Arnaud-McKenzie, D.; Ferland, G.; Dubé, L. A blueprint-based case study analysis of nutrition

services provided in a midterm care facility for the elderly. J. Am. Diet. Assoc. 2003, 103, 363–368. [CrossRef]
[PubMed]

65. Kim, H.-W.; Kim, Y.-G. Rationalizing the customer service process. Bus. Process Manag. J. 2001, 7, 139–156.
[CrossRef]

http://dx.doi.org/10.1177/1938965511410687

http://dx.doi.org/10.1016/j.tourman.2008.01.005

http://dx.doi.org/10.1016/j.aei.2014.10.002

http://dx.doi.org/10.1108/17542731211261575

http://dx.doi.org/10.1300/J037v05n02_03

http://dx.doi.org/10.3390/su10082920

http://dx.doi.org/10.1007/978-3-642-15172-9

http://dx.doi.org/10.1177/1094670508314264

http://dx.doi.org/10.1177/1094670511401901

http://dx.doi.org/10.1287/serv.2014.0086

http://dx.doi.org/10.1016/j.jbusres.2017.10.024

http://dx.doi.org/10.4236/jssm.2017.106040

http://dx.doi.org/10.2307/1251131

http://dx.doi.org/10.1016/j.jbusres.2017.04.014

http://dx.doi.org/10.1177/002224299405800109

http://dx.doi.org/10.1108/03090560010311858

http://dx.doi.org/10.1108/09564239910276872

http://dx.doi.org/10.1177/002224298504900110

http://dx.doi.org/10.1016/S0022-4359(96)90007-X

http://dx.doi.org/10.1177/002224299005400105

http://dx.doi.org/10.1108/09604520810842812

http://dx.doi.org/10.1053/jada.2003.50047

http://www.ncbi.nlm.nih.gov/pubmed/12616261

http://dx.doi.org/10.1108/14637150110389713

Sustainability 2018, 10, 4492 21 of 21

66. Geum, Y.; Park, Y. Designing the sustainable product-service integration: A product-service blueprint
approach. J. Clean. Prod. 2011, 19, 1601–1614. [CrossRef]

67. Hossain, M.Z.; Enam, F.; Farhana, S. Service Blueprint a Tool for Enhancing Service Quality in Restaurant
Business. Am. J. Ind. Bus. Manag. 2017, 7, 919–926. [CrossRef]

68. Gupta, H. Evaluating service quality of airline industry using hybrid best worst method and VIKOR. J. Air
Transp. Manag. 2018, 68, 35–47. [CrossRef]

69. Tsafarakis, S.; Kokotas, T.; Pantouvakis, A. A multiple criteria approach for airline passenger satisfaction
measurement and service quality improvement. J. Air Transp. Manag. 2018, 68, 61–75. [CrossRef]

70. Gursoy, D.; Chen, M.H.; Kim, H.J. The US airlines relative positioning based on attributes of service quality.
Tour. Manag. 2005, 26, 57–67. [CrossRef]

71. Park, J.W.; Robertson, R.; Wu, C.L. The effect of airline service quality on passengers’ behavioural intentions:
A Korean case study. J. Air Transp. Manag. 2004, 10, 435–439. [CrossRef]

72. Tsaura, S.H.; Chang, T.Y.; Yen, C.H. The evaluation of airline service quality by fuzzy MCDM. Tour. Manag.
2002, 23, 107–115. [CrossRef]

73. Bamford, D.; Xystouri, T. A case study of service failure and recovery within an international airline.
Manag. Serv. Qual. 2005, 15, 306–322. [CrossRef]

74. Kim, I.; Bong, Y.; Cho, M. Service Blueprint of the Young Kids Customers’ Specialized In-flight Service
Utilizing Kano Model Analysis. J. Tour. Sci. 2015, 39, 71–90.

75. Lee, J.-M.; Kim, Y.-S.; Lee, D.-W. Analyzing the Service Blueprint for Aircraft Cabin Service. J. Korean Soc.
Qual. Manag. 2010, 38, 593–600.

76. Go, M.; Kim, I. In-flight NCCI management by combining the Kano model with the service blueprint:
A comparison of frequent and infrequent flyers. Tour. Manag. 2018, 69, 471–486. [CrossRef]

77. Skytrax. Available online: https://skytraxratings.com/ (accessed on 26 October 2018).
78. Witell, L.; Kristensson, P.; Gustafsson, A.; Löfgren, M. Idea generation: Customer co-creation versis traditional

market research techniques. J. Serv. Manag. 2011, 22, 140–159. [CrossRef]
79. Wirtz, J.; Kuan Tambyah, S.; Mattila, A.S. Organizational learning from customer feedback received by

service employees. J. Serv. Manag. 2010, 21, 363–387. [CrossRef]
80. Gao, B.; Li, X.; Liu, S.; Fang, D. How power distance affects online hotel ratings: The positive moderating

roles of hotel chain and reviewers’ travel experience. Tour. Manag. 2018, 65, 176–186. [CrossRef]
81. Skytrax. Available online: https://www.worldairlineawards.com/the-worlds-top-100-airlines-2017/

(accessed on 26 October 2018).
82. Xiang, Z.; Schwartz, Z.; Gerdes, J.H.; Uysal, M. What can big data and text analytics tell us about hotel guest

experience and satisfaction? Int. J. Hosp. Manag. 2015, 44, 120–130. [CrossRef]
83. Inmarsat Aviation. The Future of Inflight Retail. 2016. Available online: https://www.inmarsataviation.

com/en/benefits/revenue-opportunities/the-future-of-inflight-retail.html (accessed on 30 October 2018).
84. McMillan, S.; Hwang, J. Measures of Perceived Interactivity: An Exploration of the Role of Direction of

Communication, User Control, and Time in Shaping Perceptions of Interactivity. J. Advert. 2002, 31, 29–42.
[CrossRef]

85. Zhong, N.; Li, Y.; Wu, S.T. Effective pattern discovery for text mining. IEEE Trans. Knowl. Data Eng. 2012, 24,
30–44. [CrossRef]

86. Hoffman, K.D.; Bateson, J.E.G. Services Marketing, 4th ed.; South-Western Cengage Learning: Mason, OH,
USA, 2010; ISBN 9781439039397.

87. Ahrholdt, D.C.; Gudergan, S.P.; Ringle, C.M. Enhancing Service Loyalty: The Roles of Delight, Satisfaction,
and Service Quality. J. Travel Res. 2017, 56, 436–450. [CrossRef]

88. Umashankar, N.; Ward, M.K.; Dahl, D.W. The Benefit of Becoming Friends: Complaining After Service
Failures Leads Customers with Strong Ties to increase loyalty. J. Mark. 2017, 81, 79–98. [CrossRef]

89. Keaveney, S.M. Customer Switching Behavior in Service Industries: An exploratory study. J. Mark. 1995, 59,
71–82. [CrossRef]

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).

http://dx.doi.org/10.1016/j.jclepro.2011.05.017

http://dx.doi.org/10.4236/ajibm.2017.77065

http://dx.doi.org/10.1016/j.jairtraman.2017.06.001

http://dx.doi.org/10.1016/j.jairtraman.2017.09.010

http://dx.doi.org/10.1016/j.tourman.2003.08.019

http://dx.doi.org/10.1016/j.jairtraman.2004.06.001

http://dx.doi.org/10.1016/S0261-5177(01)00050-4

http://dx.doi.org/10.1108/09604520510597845

http://dx.doi.org/10.1016/j.tourman.2018.06.034

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http://dx.doi.org/10.1016/j.ijhm.2014.10.013

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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

Research Model

Data

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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