Evaluating the Airline Experience and Service Quality

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Comparisons of service quality perceptions between full service carriers and

low cost carriers in airline travel

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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

To cite this article: Juhwan Lim & Hyun Cheol Lee (2019): Comparisons of service quality perceptions between full service carriers and low cost carriers in airline travel, Current Issues in Tourism, DOI: 10.1080/13683500.2019.1604638

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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

© 2019 Informa UK Limited, trading as Taylor & Francis Group

CONTACT Hyun Cheol Lee [email protected] Supplemental data for this article can be accessed http://dx.doi.org/10.1080/13683500.2019.1604638

CURRENT ISSUES IN TOURISM https://doi.org/10.1080/13683500.2019.1604638

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

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