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

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/332522980

Comparisons of service quality perceptions between full service carriers and

low cost carriers in airline travel

Article  in  Current Issues in Tourism · April 2019

DOI: 10.1080/13683500.2019.1604638

CITATIONS

40 READS

2,379

1 author:

Juhwan Lim

Kansas State University

2 PUBLICATIONS   40 CITATIONS   

SEE PROFILE

All content following this page was uploaded by Juhwan Lim on 29 July 2019.

The user has requested enhancement of the downloaded file.

Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=rcit20

Current Issues in Tourism

ISSN: 1368-3500 (Print) 1747-7603 (Online) Journal homepage: https://www.tandfonline.com/loi/rcit20

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

To link to this article: https://doi.org/10.1080/13683500.2019.1604638

View supplementary material

Published online: 18 Apr 2019.

Submit your article to this journal

View Crossmark data

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 resource

Collepals.com Plagiarism Free Papers

Are you looking for custom essay writing service or even dissertation writing services? Just request for our write my paper service, and we'll match you with the best essay writer in your subject! With an exceptional team of professional academic experts in a wide range of subjects, we can guarantee you an unrivaled quality of custom-written papers.

Get ZERO PLAGIARISM, HUMAN WRITTEN ESSAYS

Why Hire Collepals.com writers to do your paper?

Quality- We are experienced and have access to ample research materials.

We write plagiarism Free Content

Confidential- We never share or sell your personal information to third parties.

Support-Chat with us today! We are always waiting to answer all your questions.