Variations in airline passenger expectation of service quality across the globe

Tourism Management 75 (2019) 491–508

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Tourism Management journal homepage: www.elsevier.com/locate/tourman

Variations in airline passenger expectation of service quality across the globe

T

Aymeric Punela, Lama Al Hajj Hassana, Alireza Ermagunb,∗ a b

Department of Civil and Environmental Engineering, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA Department of Civil and Environmental Engineering, Mississippi State University, 501 Hardy Road, Mississippi State, MS 39762, USA

ARTICLE INFO

ABSTRACT

Keywords: Airline Culture differences Electronic word of mouth Flight experience Service quality Perception Marketing

This study explores the interdependence between passenger travel experience and service quality in the airline industry for ten geographical regions across the globe. We extract 40,510 passenger reviews and rating information from Skytrax dataset between October 2011 and January 2018. To understand whether and to what extent passenger travel experience varies across geographical regions and their flight classes, we test sentiment score analysis of the reviews and path analysis methods. The results support the hypothesis that the geographical regions shaped by the country of residence of passengers impact travel experience, perception, and evaluation of airline services. North American passengers complain more about their national airline, while East and Southeast Asian passengers are more satisfied with Asian airlines. North American passengers care essentially about the money they pay for their flight and they pay less attention to in-flight services. East Asian passengers care more about in-flight services. Across all geographical regions, seat comfort is the most important factor to evaluate the value for money of the flight. Cabin staff service, however, is the main feature to rate overall flight experience. The results also corroborate that the expectation of passengers is different between the first or business classes and the economy class. Passengers in first or business class are more concerned about seat comfort, food and beverages, and in-flight entertainment. Passengers in economy class are more concerned about the value for money.

1. Introduction The rise of Information and Communications Technology (ICT) and the advent of social media platforms have led to the creation of “electronic word-of-mouth” (eWOM) (Cheung & Lee, 2012). For travelers, social networks have become a gateway to express their opinion and share their experience, which is considered a solid reference for other users (Punel & Ermagun, 2018; Filieri, Alguezaui, & McLeay, 2015; Sparks & Browning, 2011). For the travel and tourism industry, this is a great source of information regarding the behavior of travelers (Litvin, Goldsmith, & Pan, 2008), and is utilized to not only gain publicity, but to hear both negative and positive comments. Considering the opinion and feedback of travelers, hence, helps provide better service in the competitive airline industry (Beneke, Mill, Naidoo, & Wickham, 2015; Lee, Lee, Chuang, & Wu, 2014; Lerrthaitrakul & Panjakajornsak, 2014). Expressing opinions, however, varies among individuals due to cultural differences (Mooradian & Swan, 2006). In airline marketing, an effective practical strategy to maximize satisfaction is taking this variation into account. A “multicultural” strategy helps airlines boost their

∗

businesses as understanding the opinions of individuals due to their geographical and cultural background impacts:

• Customer Satisfaction: For each market, airlines will be able to • •

tailor the needs of their passengers and put more effort into addressing their major concerns. Their offers will be segmented and adapted to each region to better answer customer expectations and to focus on the specific services they value the most. Cost Policy: Airlines will be able to better manage their costs by adapting the features and services offered on planes for a specific market, and consequently optimize their costs. Crew Management: Flight attendants do not have the same experience or expertise. Based on the expectations and preferences of a specific market and its attitude toward in-flight staff, airline companies will be able to coordinate the schedule of their crew members.

Despite the variations in airline passenger expectation of service quality, to the best of the authors' knowledge, there have been few

Corresponding author. E-mail address: [email protected] (A. Ermagun).

https://doi.org/10.1016/j.tourman.2019.06.004 Received 28 October 2018; Received in revised form 8 May 2019; Accepted 5 June 2019 Available online 01 July 2019 0261-5177/ © 2019 Elsevier Ltd. All rights reserved.

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attempts to compare the expectations of passengers across the globe. The contribution of the current research in the existing body of the literature related to the airline industry, therefore, is threefold. First, we assess the expectations and attitudes of passengers across the globe considering their country of residence and their flight classes. Second, we study the direct and indirect effects of service characteristics and sentiment scores extracted from published reviews on the overall flight experience and the value for money. Third, we provide an in-depth discussion on how airline companies might benefit from the results through the lens of policy, planning, and regulations. In particular, we test the following hypotheses:

and Pearson (2015) studied the impact of customer service satisfaction on airline financial performance. They also examined possible strategies for improving performance in terms of profitability or service levels. They developed regression models and tested them using the information of 116 airlines extracted from Skytrax. They used the two ranking schemes to examine whether customer rankings and Skytrax rankings have distinct effects on airline profitability. The results demonstrated that the Skytrax rankings and customer rankings have an insignificant impact on profitability, while customer and Skytrax rankings are highly correlated. Yakut et al. (2015) applied feature and cluster-based models using customer review data for in-flight services. In feature-based models, they grouped customers according to their airline and cabin flown. In cluster-based models, they used k-means clustering. They then developed multivariate regression analysis to model customer groups in each case. Using the Skytrax data, they concluded that when customers are grouped by cabin class flown, value for money is the most significant factor followed by staff service. They also noted that business class customers are more critical of food and beverages than other travelers, but they have lower expectations when it comes to entertainment. Yao et al. (2015) developed a research framework based on text mining to explore airline service features that are valued by customers. They examined the reviews from three main perspectives: (1) the airline industry, (2) the service evaluation, and (3) the airline company levels. Applying a vector space model on Skytrax reviews, they suggested that the proposed methodology succeeded in capturing customer opinion of airline service features. Considering the airline industry as a whole, the findings indicated that the customers' concerns were similar across different airlines. The analysis also revealed that the unique airline features were mainly regarding routes, in-flight services such as food, seat comfort and entertainment, and that the greater the gap is between the star levels of airlines, the bigger the gap becomes in their quality of service.

Hypothesis 1. Flight service characteristics have direct and indirect impacts on the overall flight experience. Hypothesis 2. The expectation of passengers is different among geographical regions shaped by their country of residence. Hypothesis 3. The expectation of passengers is different between the first or business classes and the economy class. To test the hypotheses, we extract 40,510 passenger reviews and ratings information from Skytrax dataset between October 2011 and January 2018. Using the country of residence for passengers, we aggregate the data encompassing 161 countries to ten geographical regions including (1) Africa, (2) East Asia, (3) Eastern Europe, (4) North America, (5) Oceania, (6) South America, (7) South-Central Asia, (8) Southeast Asia, (9) Western Asia, and (10) Western Europe. We further segment the data into two flight classes: (1) first or business class and (2) economy class. For each geographical region and its flight class, we use text mining techniques to determine the most frequent words used by the reviewers and to evaluate the attitude of their text message. To quantitatively assess the direct and indirect effects of in-flight services and airlines features on the overall opinion of the air carrier, we develop a path analysis using passengers' stated scores and the results of the sentiment analysis. The remainder of the paper is structured as follows. First, we review the literature analyzing the Skytrax dataset and articles exploring passengers' attitudes and preferences toward in-flight services. Second, we elaborate on the data used in the analysis and explore its content to get preliminary insights regarding the characteristics of the reviews. Third, we use text mining techniques to evaluate the content of the messages. Fourth, we run a path analysis to quantitatively assess the differences between the markets in terms of passengers' preferences and opinion. Fifth, we discuss the results and their implications for airlines and their business. Sixth, we explain how the findings contribute to planning strategies for airline companies. We, finally, conclude by summarizing the main findings of the study and bringing attention to possible future research avenues.

2.2. Passenger perception and preferences towards inflight services Several studies sought to identify the airline characteristics and inflight services valued by customers. They developed methodologies based on text mining reviews, satisfaction analysis, SERVQUAL framework, and regression analysis (O’Connell & Williams, 2005; Hussain, Al Nasser, & Hussain, 2015; Liu & Lee, 2016; Tsafarakis, Kokotas, & Pantouvakis, 2018), and succeeded in highlighting the relation between the various service dimensions and customer perception and satisfaction. O’Connell and Williams (2005) analyzed the difference in customer attitudes toward full service airlines and low-cost carriers and tried to identify the motivation that drove customers to choose one over the other. They surveyed passengers of regular and low-cost carriers in a European and an Asian airport. The survey outcomes indicated that passengers chose full service carriers based on service reliability, service quality, comfort, and safety. However, passengers flying with lowcost carriers were mainly driven by cost. Hussain et al. (2015) explored the relationship between various airline service features, including quality, carrier image, customer perceived value, customer expectations and satisfaction, and loyalty. They used SERVQUAL framework to extract the determinants of service quality of a Dubai-based airline. They then used service quality as an antecedent to quantify customer satisfaction. Service quality dimensions included reliability, responsiveness, safety, tangibility, communication, and assurance. A survey was then conducted to capture traveler perceptions. Using a structural equation model, they showed service quality, perceived value, and brand image are significantly and positively related to customer satisfaction. Liu and Lee (2016) formulated and tested the relationship between

2. Literature review This section presents a comprehensive review on the existing literature using Skytrax, a website through which travelers share their airline and airport experiences, and similar airline ranking databases. This is followed by a discussion of research on the general passenger perception of airline services. Finally, this section discusses previous work on differences in passenger perception and experience based on their country of residence. 2.1. Analysis of Skytrax reviews Skytrax dataset has been used to analyze several dimensions of traveler satisfaction with airline services (Merkert & Pearson, 2015; Yakut, Turkoglu, & Yakut, 2015; Yao, Yuan, Qian, & Li, 2015). Merkert

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service quality, price perception, word of mouth, and revisited intention using structural equation models and accompanying statistical methods. They tested their proposed method on a sample of 484 lowcost airlines. They defined service quality as an outcome of five dimensions reflecting the physical state of the aircraft, reliability, and relations with customer services representatives, and employee responsiveness and empathy. The results indicated service quality is positively related to service price perception resulting from increased passenger word of mouth. They also found that this positive relationship increases a passenger's revisit intention. Tsafarakis et al. (2018) used multi-criteria satisfaction analysis (MUSA) to quantify passenger satisfaction given several service dimensions. These dimensions were grouped into six main categories examining customer satisfaction by: pricing, website, flight schedule and routes, airport services, during flight, and after landing. They tested the proposed method based on a survey conducted among Aegean Airlines passengers. The results indicated that customers were satisfied with all service criteria except pricing, and the most significant criterion for the surveyed passengers was After Landing services category, which accounted for satisfaction with disembarking efficiency and luggage pick up time. Other criteria such as cabin crew service, seat comfort, food and drinks, and entertainment had equal weights with food and drink satisfaction slightly in the lead.

leisure travelers were mostly concerned by prices and had low expectations across all service dimensions. De Jager et al. (2012) investigated the different levels of importance associated with domestic airline service characteristics among Italian and South African travelers. They conducted a survey among Italian and South African tourists who travelled by domestic carriers during the previous year. Participants filled out a questionnaire with 24 items representing different flight services using a seven-point Likert scale rating. To identify factors impacting travelers' perception of the quality of airline service, an exploratory principal component analysis was conducted. The results indicated that South African and Italian passengers had a similar ranking structure of the various service dimensions. Travelers from both countries attributed the highest importance rating to on-time flight performance followed by in-flight services. Nevertheless, the results showed that Italian passengers gave more importance to cabin and cabin crew service than to food and entertainment. Finally, carrier country of origin had the least importance as a factor affecting service perception. Lim and Tkaczynski (2017) studied the in-flight service preferences of international students. They surveyed international students studying in Australia and identified the service expectations and the differences between them based on country of origin, source of funding, and employment status. The respondents include students from China, Singapore, and other Asian origins. The results indicated that student employment status significantly affected student expectations of ancillary services. They found a relation between various service indicators and student nationality. The study confirmed the variation in perception as a function of country of origin and this was revealed in the difference in ratings of ancillary service, employee service, and other service indicators. However, there was no significant relation between the source of funding and the service quality expectation. The findings demonstrated that 23 out of 27 service quality items received a higher than average score, which means international students had high expectations for the level of service quality when choosing an airline.

2.3. Cultural and national impact on customer behavior Previous research summarized in the preceding subsections unanimously demonstrates that different travelers have different perceptions toward the in-flight services. However, none of the research segmented in the analysis to compare perceptions and attitudes across different geographical regions using the country of residence of passengers. An independent, yet related line of research indicated that national identity has an impact on customer behavior. Christodoulides, Michaelidou, and Argyriou (2012), for instance, aimed at identifying how the influence of the reviews differed with customer nationality by comparing the effects of electronic reviews on purchase intentions of UK and Chinese customers. They showed Chinese customers were vulnerable to recent reviews regardless of their valence, or the way the information was framed, which is contrary to UK customers who were more fixated on negative reviews independent of the order in which the information was revealed. These differences in customer behavior based on the nationality and culture have also been identified in the airline industry (Bruning, 1997; De Jager, Van Zyl, & Toriola, 2012; Gilbert & Wong, 2003; Lim & Tkaczynski, 2017). Bruning (1997) examined the role of patriotism in the choice of airline carrier. He formulated the relationship between social identity, nationalism, and consumer ethnocentrism. The proposed model was tested on a sample of 427 travelers at a Canadian airport. Using an Ordered Probit model, he concluded that nationalism was the second most important factor after price when it comes to selecting air carrier. The results also indicated a difference in the impact of patriotism across the traveler segments that accounted for gender, income, profession, and travel frequency. The segment analysis showed the most loyal customer represented a small percentage of all the travelers whereas most travelers were willing to choose a foreign carrier for lower prices. Gilbert and Wong (2003) studied the service dimensions valued by travelers departing from the Hong Kong airport. The survey respondents consisted of Chinese, North American, Japanese, and Western European travelers. They implied there was no difference in service perception between passengers who did or did not choose the airline. Nevertheless, the findings highlighted that passengers from different ethnic groups and with various travel goals perceive the services differently. Japanese travelers tend to have high expectations, and they along with Chinese travelers, value in-flight entertainment. Finally,

2.4. Summary of findings Reviewing the literature leads us to the following findings:

• Skytrax rankings are reliable and well-established indicators of customer satisfaction with airline services; • Customer service perception may not be impacted by the carrier business model; • Skytrax traveler reviews may not fully capture traveler satisfaction, • •

which justifies the need for an in-depth analysis, rather simply reviewing the comments; There is a significant relation between customer satisfaction and inflight services; Published reviews and the country of residence might impact customer choices.

3. Skytrax data The core data used in this study are extracted from Skytrax website (www.airlinequality.com). Skytrax is a United Kingdom based consulting company specialized in the airline industry. The company releases the ranking of each airline through the World Airline Awards on a yearly basis. Skytrax also provides a platform for airline passengers to share their opinion on their flight experience in three categories: airline, lounges, and seats. The information regarding the airline category is the focus of the current research. Passengers can review their flight by ranking the flight experience and the quality of service offered by the airline on a scale of 1 (lowest score) to 5 (highest score), and by writing a text comment. They can also indicate their type of cabin, the

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purpose of their trip, and both the time and the route of the flight. The Likert scale questions regarding the flight experience and the quality of service include Overall rating, Seat Comfort, Cabin Staff Service, Food and Beverages, In-flight Entertainment, and Value for Money. Finally, passengers indicate their country of residence. We extracted 68,174 reviews published between January 6th, 2002 and January 12th, 2018. We then removed reviews lacking responses to any Likert scale questions. This resulted in 40,510 cleaned reviews covering October 2011 through January 2018. We augmented the data by examining whether the airline is low-cost1 and providing the Skytrax Airline Rewards ranking of a prior year.2 Table 1 depicts the description of the variables used in this study. For the sake of analysis, we aggregated the extracted observations into ten geographical regions according to the residential location of passengers shown in Fig. 1. This figure portrays the number of reviews written by passengers in each geographical region. This aggregation clusters countries based upon their general cultural similarities as we speculate passengers within a same cluster tend to have the same level of expectation. Looking at Fig. 1, it is found that one third of reviewers belong to Western Europe, while only 1.5% of reviews were published by African travelers. Fig. 2 portrays the nationality of airline. As shown, 42%, 25%, and 22% of the reviews belong to Asian, North American, and European airlines, respectively, and the remaining 11% includes Africa, Oceania, and South America airlines.

4.2. Text mining and sentiment analysis To quantify the potential differences across the geographical regions and flight classes, we apply text mining techniques on the extracted reviews. The text mining technique helps find the most frequent word in the reviews belonging to each geographical region, and consequently the major concerns of the passengers. Fig. 3 presents the average number of appearances for the top 20 words in each geographical region. As shown in Fig. 3, Seat is the most frequent word in each geographical region. This means seat comfort is a major concern for all passengers regardless of their country of residence. Although Service is the second most frequent word in Africa, East Asia, Oceania, South America, Southeast Asia, and Western Asia, it is replaced by Time in Eastern Europe, Hour in North America, Food in South Central Asia, and Good in Western Europe. Looking at the most frequent words, it is found that North American passengers use Cabin, Crew, Entertainment, Food and Staff less frequently in their reviews, but use Hour and Time more frequently than passengers of other regions. This indicates North American passengers value the reliability of the flight more than other characteristics. Although the text mining technique reveals the most and least frequent words used in the review, it comes short of detecting the attitude and opinion of the reviewer. We, therefore, use the sentiment analysis to extract further information about the attitude of the reviewers. Sentiment analysis falls into three categories of techniques (Collomb, Costea, Joyeux, Hasan, & Brunie, 2014): (1) machine learning, (2) lexicon-based approach, and (3) rule-based approach. We chose the lexicon-based approach for the analysis as our intention is to measure the subjectivity and opinion in text. To rate each review, we use a polarity classification, which permits us to identify the positive and negative words, and we define “sentiment score” as the difference between positive and negative words divided by the sum of positive and negative words. To distinguish positive words from negative words, we use Hu and Liu's opinion lexicon (Hu & Liu, 2004), which is a list of about 6800 English positive and negative opinion words or sentiment words. Comment's sentiment scores range from −1 to 1 and are computed by R package SentimentAnalysis (Feurriegel & Proellochs, 2018) using R version 3.4.1 (R Core Team, 2014). Fig. 4 represents the average sentiment score by rating for each geographical region. For the ease of comparison, we depict the most important differences between each pair of geographical regions in Fig. 5. Looking at Fig. 4, it is observed that the average sentiment score increases as the overall rating increases. This means passengers use more positive words and less negative words when they give a higher score to their flight. However, the increasing trend varies from region to region. As alluded to previously, North American passengers complain about their airline companies more than other geographical regions and report at the lowest rate. However, they are gentle with word usage in the review as their average sentiment score is the highest for low ratings, and is the lowest for high ratings. Fig. 5 a shows North American passengers' sentiment score is significantly lower than South Central Asian travelers' one for positive rating, and the difference is increasing as the rating increases. Fig. 5 b shows opinion in the text of North American reviews is very similar to Western European ones. Fig. 5 c portrays that for low rating, Western Europeans travelers tend to be distinguished from East Asian flyers, as their sentiment score is on average higher. Finally, Fig. 5 d displays similarities between Southeast Asia and Western Asia. In short, Figs. 4 and 5 indicate the positive correlation between the comments of travelers and their final rating. There is also a positive correlation between the sentiment score, which expresses the type of words used and the subjectivity of the message, and the overall rating in each geographical region. On average, for a specific rating, the sentiment score differs between the review of travelers from different countries.

4. Qualitative analysis 4.1. Exploratory Analysis To give the reader a qualitative sense about the variation of ratings across the ten geographical regions, we depict the average ratings given by the passengers of each geographical regions for each criterion in Table 2. Each row of the table gathers the average ratings when considering only the observations from one of the ten geographical regions. We further calculated the proportional t-test for each mean comparison of the scores depicted in Table 2 and reported the results in Appendix I. The results indicate that most of the differences are significant at the 95% confidence interval. The intensity of the color in each cell varies in function of the value of the rating, as the higher the average rating compares to others for a criterion, the darker the color of the associated cell. As shown, the intensity of the color is on average uniform by line but varies from one row to another, which indicates the existence of differences in rating according to passenger's geographical region. While the top third of the table, which represents the ratings when considering all the reviews supports this claim, it also hides several differences depending on whether passengers and airlines have the same nationality. The two remaining thirds of the table show that passengers of each geographical region respond differently when reviewing the service quality of a local and non-local airlines. More precisely, North American passengers are especially critical toward their local airlines as they give the lowest ratings for each category when they review North American airlines. Their judgement is less extreme, but remains below average when they fly with non-local airlines. This indicates either North American passengers have a higher expectation than other passengers or North-American airlines provide poor service on domestic flights. On the contrary, East Asian and Southeast Asian passengers report the highest rating for Asian airlines and more moderate rating for non-Asian airlines. Finally, MiddleEastern passengers complain more about non-Asian companies, which do not provide the same level of service as the Gulf airlines. 1 Web document: https://www.icao.int/sustainability/Documents/LCC-List. pdf (Access: July 31st, 2018). 2 Web document: https://www.worldairlineawards.com/worlds-top-10airlines-2018/(Access: July 31st, 2018).

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Table 1 Description of the variables used in the analysis. Variable

Description

Review Rating Cabin Staff Service Food and Beverages Inflight Entertainment Seat Comfort Value For Money Rating Review Features Airline Ranking Cabin Flown Low-Cost Text Mining and Sentiment Analysis Sentiment Score Word Cabin Word Comfort Word Crew Word Entertain Word Food Word Meal Word Seat Word Service

Average

Std. Dev.

5-point scale rating for “Cabin Staff Service” 5-point scale rating for “Food and Beverages” 5-point scale rating for “Inflight Entertainment” 5-point scale rating for “Seat Comfort” 5-point scale rating for “Value for Money” 10-point scale rating for overall experience

3.45 3.06 2.94 3.20 3.29 5.75

1.50 1.45 1.48 1.39 1.50 3.33

1: If airline is among top 10 airlines /0: Otherwise 1: First Class or Business Class / 0: Otherwise 1: Low-cost airline / 0: Otherwise

0.20 0.24 0.10

– – –

Sentiment score ranging from -1 to 1 Number of times “Cabin” appears in a comment Number of times “Comfort” appears in a comment Number of times “Crew” appears in a comment Number of times “Entertain” appears in a comment Number of times “Food” appears in a comment Number of times “Meal” appears in a comment Number of times “Seat” appears in a comment Number of times “Service” appears in a comment

0.289 0.39 0.36 0.47 0.30 0.62 0.32 1.14 0.72

0.585 0.76 0.63 0.82 0.54 0.79 0.74 1.55 0.97

A A A A A A

Fig. 1. Visualization of the ten geographical regions according to the residential location of passengers.

5. Quantitative analysis

Path analysis is a special case of Structural Equation Modeling, which tests both the direct and indirect effect of variables, but is not able to capture latent effects (Bergan, 2013). As depicted in Fig. 6, there is a direct effect between variable “A” and variable “C” when variable “A” affects variable “C” without the need for a mediation variable “B”. While there is an indirect effect between variable “A” and variable “C” when variable “A” affects variable “C” by having variable “B” in between. We develop a conceptual framework represented in Fig. 7. This framework tests both the direct and indirect impacts of explanatory

To quantitatively measure the response difference in service quality assessment across geographical regions and flight classes, we develop a path analysis for each geographical region and flight class. Wright (1934) explained that path analysis examines the causal relationship between variables. In path analysis, the variables are arranged based on a hypothesis regarding the functional relations between them. The relationship between variables is represented using branched directional arrows. Residuals could also be added to the overall path structure.

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Fig. 2. Proportions of reviews according to the nationality of airline. Table 2 Average rating score by Author's geographical region. Group

Rating

All Reviews Africa 6.01 East Asia 7.21 Eastern Europe 6.56 North America 4.69 Oceania 6.34 South America 5.81 South-central Asia 5.99 Southeast Asia 6.78 Western Asia 5.98 Western Europe 6.00 Considering Reviews for the Same Continent Africa 5.63 East Asia 7.41 Eastern Europe 6.51 North America 3.73 Oceania 6.28 South America 5.46 South-central Asia 6.05 Southeast Asia 6.92 Western Asia 6.19 Western Europe 5.78 Considering Reviews for the Other Continents Africa 6.36 East Asia 6.16 Eastern Europe 6.64 North America 5.72 Oceania 6.37 South America 6.07 South-central Asia 5.64 Southeast Asia 5.94 Western Asia 5.35 Western Europe 6.19

Cabin Staff Service

Beverages

Inflight Entertainment

Seat Comfort

Value for Money

3.54 4.03 3.71 3.10 3.65 3.46 3.45 3.83 3.53 3.52

3.29 3.56 3.45 2.67 3.27 2.96 3.19 3.43 3.24 3.14

3.05 3.46 3.12 2.61 3.20 2.85 2.94 3.22 3.00 2.98

3.32 3.79 3.50 2.76 3.45 3.11 3.34 3.64 3.31 3.30

3.41 3.91 3.63 2.80 3.58 3.20 3.45 3.77 3.38 3.42

3.37 4.13 3.73 2.70 3.65 3.21 3.47 3.89 3.61 3.48

3.13 3.65 3.29 2.20 3.18 2.75 3.23 3.48 3.38 2.99

2.69 3.52 2.94 2.13 3.16 2.73 2.96 3.28 3.14 2.69

3.26 3.89 3.44 2.36 3.38 2.98 3.38 3.72 3.42 3.14

3.22 4.00 3.60 2.34 3.41 3.02 3.47 3.82 3.48 3.25

3.69 3.48 3.69 3.53 3.65 3.64 3.36 3.46 3.27 3.55

3.44 3.13 3.69 3.17 3.31 3.11 2.98 3.17 2.80 3.26

3.36 3.12 3.40 3.13 3.22 2.93 2.86 2.89 2.58 3.22

3.37 3.28 3.59 3.18 3.47 3.21 3.10 3.21 2.95 3.43

3.58 3.47 3.68 3.30 3.66 3.34 3.32 3.48 3.08 3.55

variables on general ratings and the value for money. The explanatory variables used in this study fall into three categories: (1) airline information, (2) sentiment score obtained from sentiment analysis, and (3) Likert score ratings of service characteristics. The variable Value for Money is used as the mediator as we consider that passenger's perception of inflight service depends on how much they pay for their flight. We apply the framework to each of the 10 geographical regions representing where the authors live, and further segment the analysis by flight classes, leading to a total of 20 models. For the First or Business Class models, we do not consider the variable Low-Cost. Results of these models for Economy Class and First or Business Class are summarized in

Table 3 through Table 6. 6. Results and discussion This section provides an in-depth discussion on the direct and indirect relationship between in-flight characteristics, sentiment score, Likert score ratings of service characteristics, overall rating, and the value for money. As expectations might vary between cabin class flown, we first highlight the main differences of the results between the Economy Class models and First or Business Class models. We then discuss to what extent in-flight services have an impact on the value for

496

seat

Word

board

i. Western Asia

497

hour

0.00

j. Western Europe

Fig. 3. Average number of appearance of top 20 words in each region. staff

meal

hour

class

cabin

entertain

passeng

comfort

one

entertain

get

fli nice

entertain

even

cabin

check

staff

airport

back

first

will class

crew

good

delay

one

food

air

airport

Word

meal

fli plane

plane

1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00

board

d. North America

arriv

one

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busi

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Word

drink

time

time servic

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1.20 singapor

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h. South-East Asia busi

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g. South-Central Asia

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e. Oceania class

0.00

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crew

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me

0.60

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0.80

food

1.20

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c. Eastern Europe

food

a. Africa

good

0.00 Frequency by Comment

0.20

servic

seat

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busi

passeng

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one

plane

airport

via

johannesburg

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0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 seat

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1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 seat

airport

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economi

one

fli

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hour

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busi

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time

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Frequency by Comment

1.20

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air experi

food good

seat time

Frequency by Comment

1.00

good

seat

plane

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dubai

passeng

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one

class

serv

Word

entertain

Word travel

meal

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hour

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servic

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good

seat servic

Frequency by Comment

Word

airport

seat food

Frequency by Comment

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one

Frequency by Comment

1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00

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0.60

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b. East Asia 1.20

1.00

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f. South America

1.20

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Fig. 4. Average sentiment score by rating for each area.

Fig. 5. Average sentiment score by rating for specific areas.

money perceived by passengers and highlight the differences among geographical regions. We finally discuss how insights from text mining and sentiment analysis describe the review of passengers.

6.1. Impact of cabin class flown on Passenger's expectations To assess how the type of cabin influences passenger's attitudes and expectations of their flight experience, we run the path analysis model on both First or Business Class and Economy Class in all geographical 498

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6.2. Value for money As far as in-flight services are concerned, the results demonstrate that Seat Comfort, Cabin Staff Service, Food and Beverages, and Sentiment Score are significantly and positively correlated with Value for Money in all regions and for both flight classes. Inflight Entertainment is also significantly and positively correlated with Value for Money in all regions except South America and South-Central Asia in both classes as well as in Eastern Europe, Oceania, and Western Asia in First or Business class. This supports our hypothesis regarding the disparity in satisfaction between the different classes since Value for Money can be considered a measure of satisfaction. Looking at the magnitude of the variables, in most regions the variable with the highest value is Sentiment Score. Considering the Economy class, in all regions except Africa, South America, and Western Asia, the order of the inflight service variables is the same with Seat Comfort first, Cabin Staff Service second, Food and Beverages third, and Inflight Entertainment fourth. For African and South American travelers, the major element of the flight experience is Cabin Staff Service, while Western Asia passengers equally value Seat Comfort and Cabin Staff Service. Considering the First or Business class, in all regions except Africa, Eastern Europe, South America, South Central Asia and Western Asia, the order of the inflight service variables is the same with Seat Comfort first, Cabin Staff Service second, Food and Beverages third, and Inflight Entertainment fourth. For African travelers, the major element of the flight experience is Food and Beverages, while Eastern Europe, South America, South Central Asia and Western Asia value Food and Beverages more than Cabin Staff Service. To give the reader a sense of how and to what extent the Likert score ratings of service characteristics impact the value of money across geographical regions, we normalize the coefficients of each explanatory variable based upon the related coefficients for the North America region. The results are depicted in Figs. 9 and 10. As shown, Seat Comfort has the least magnitude for African passengers in both classes whereas

Fig. 6. Direct and indirect effects between variables.

regions. Tables 3 and 4 depict the results. It is inferred that passengers in First or Business Class tend to have a higher concern about seat comfort, food and beverages, and in-flight entertainment as values for these variables are often greater. On the opposite, passengers in Economy class are more concerned regarding the impact value for money on their overall flight experience. We made a quantitative comparison between the estimated parameters of First or Business Class and the estimated parameters of Economy Class to make the differences tangible. The ratio of First or Business Class to Economy Class estimated parameters is calculated to observe the magnitude of difference between the two models in all geographical regions. To calculate the ratio, either First and Business Class or Economy Class estimated parameters with a larger absolute value is divided by the other, and the cumulative distribution function of this ratio is plotted in Fig. 8. Only the ratios with significant values for both classes are reported. According to this graph, in all cases the estimated parameters have the same sign but different magnitude. As for each case, we only calculate the ratio by considering the largest absolute value as the numerator, and all the ratios are greater than 1. Fig. 8 indicates that in half of the cases, the difference in magnitude is greater than 20%, and can even be as large as twice between the variable for the Economy Class model and First or Business Class model. This highlights a notable difference between the attitude and expectations of passengers flying in First or Business Class and passengers flying in Economy Class as they do not have the same expectations regarding in-flight services.

Fig. 7. Path Analysis Structure. Note: Green color represents airline's information, red color represents findings from text mining and sentiment analysis, and blue color represents passengers' score rating. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

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Word Comfort Word Seat Airline Ranking Low-Cost Word Crew Word Service Airline Ranking Low-Cost Word Food Word Meal Airline Ranking Low-Cost Word Entertainment Airline Ranking Low-Cost Airline Ranking Low-Cost Seat Comfort Cabin Staff Service Food and Beverages Entertainment Sentiment Score Value for Money Seat Comfort Cabin Staff Service Food and Beverages Entertainment Sentiment Score

Seat Comfort

500

0.42 (0.00) −0.16 (0.00) 0.23 (0.078) −0.66 (0.10) −0.05 (0.51) −0.007 (0.93) 0.40 (0.01) −0.91 (0.05) −0.08 (0.36) −0.14 (0.08) 0.56 (0.00) −1.46 (0.00) −0.07 (0.51) 1.001 (0.00) −0.67 (0.12) 0.11 (0.09) −0.36 (0.07) 0.47 (0.00) 0.56 (0.00) 0.22 (0.00) 0.012 (0.82) 0.84 (0.00) 0.84 (0.00) 0.20 (0.00) 0.28 (0.00) 0.20 (0.00) 0.12 (0.00) 0.36 (0.00)

Africa 0.21 (0.00) −0.11 (0.00) 0.24 (0.00) −0.49 (0.03) 0.12 (0.02) −0.02 (0.66) 0.14 (0.10) −0.82 (0.00) −0.16 (0.00) 0.02 (0.68) 0.42 (0.00) −0.76 (0.01) 0.04 (0.55) 0.48 (0.00) −1.09 (0.00) 0.01 (0.734) −0.15 (0.19) 0.28 (0.00) 0.65 (0.00) 0.28 (0.00) 0.18 (0.00) 0.64 (0.00) 0.77 (0.00) 0.28 (0.00) 0.24 (0.00) 0.16 (0.00) 0.11 (0.00) 0.28 (0.00)

East Asia 0.24 (0.01) −0.24 (0.00) −0.04 (0.76) −0.37 (0.03) −0.02 (0.74) −0.02 (0.72) −0.007 (0.96) −0.24 (0.21) −0.06 (0.46) 0.08 (0.256) 0.36 (0.01) −0.48 (0.01) 0.21 (0.03) 0.54 (0.00) −0.80 (0.00) 0.012 (0.82) −0.18 (0.02) 0.45 (0.00) 0.54 (0.00) 0.24 (0.00) 0.13 (0.00) 0.87 (0.00) 0.82 (0.00) 0.29 (0.00) 0.27 (0.00) 0.16 (0.00) 0.11 (0.00) 0.29 (0.00)

Eastern Europe 0.21 (0.00) −0.11 (0.00) 0.67 (0.00) −0.54 (0.00) 0.20 (0.00) −0.03 (0.05) 0.59 (0.00) −0.26 (0.00) 0.13 (0.00) 0.11 (0.00) 0.81 (0.00) −0.44 (0.00) 0.23 (0.00) 1.09 (0.00) −0.63 (0.00) 0.22 (0.00) −0.05 (0.00) 0.28 (0.00) 0.37 (0.00) 0.17 (0.00) 0.14 (0.00) 0.52 (0.00) 1.17 (0.00) 0.36 (0.00) 0.27 (0.00) 0.18 (0.00) 0.12 (0.00) 0.35 (0.00)

North America 0.14 (0.00) −0.16 (0.00) 0.07 (0.1) −0.27 (0.00) 0.06 (0.05) −0.08 (0.00) 0.11 (0.04) −0.25 (0.00) −0.14 (0.00) −0.17 (0.00) 0.28 (0.00) −0.43 (0.00) −0.08 (0.01) 0.59 (0.00) −0.76 (0.00) −0.005 (0.79) −0.12 (0.00) 0.38 (0.00) 0.56 (0.00) 0.25 (0.00) 0.17 (0.00) 0.73 (0.00) 0.80 (0.00) 0.31 (0.00) 0.27 (0.00) 0.21 (0.00) 0.07 (0.00) 0.29 (0.00)

Oceania 0.11 (0.40) −0.10 (0.06) 0.48 (0.04) −0.06 (0.78) 0.26 (0.04) 0.11 (0.12) 0.77 (0.00) −0.07 (0.75) 0.20 (0.05) 0.32 (0.02) 0.57 (0.03) −0.19 (0.44) 0.18 (0.16) 0.39 (0.12) −0.86 (0.00) 0.273 (0.01) −0.02 (0.88) 0.31 (0.00) 0.49 (0.00) 0.14 (0.05) 0.19 (0.00) 0.73 (0.00) 0.99 (0.00) 0.29 (0.00) 0.33 (0.00) 0.16 (0.00) 0.01 (0.80) 0.34 (0.00)

South America 0.40 (0.00) −0.15 (0.00) −0.01 (0.92) −0.73 (0.00) 0.03 (0.64) 0.03 (0.66) 0.15 (0.25) −0.59 (0.01) −0.08 (0.17) −0.12 (0.05) 0.21 (0.10) −1.05 (0.00) −0.15 (0.11) 0.75 (0.00) −1.32 (0.00) −0.01 (0.82) −0.18 (0.05) 0.27 (0.00) 0.56 (0.00) 0.17 (0.00) 0.14 (0.00) 0.64 (0.00) 0.95 (0.00) 0.34 (0.00) 0.21 (0.00) 0.22 (0.00) 0.02 (0.35) 0.41 (0.00)

South Central Asia

Notes: 1Number in parentheses represents the p-value of the coefficient. 2Insignificant coefficients at 90% confidence interval are marked in italic.

Value for Money

Overall Rating

Sentiment Score

Entertainment

Food and Beverages

Cabin Staff Service

Explanatory Variable

Dependent Variable

Table 3 Path analysis results for economy class reviews.

0.16 (0.00) −0.14 (0.00) 0.31 (0.00) −0.63 (0.00) 0.02 (0.61) −0.10 (0.00) 0.33 (0.00) −0.45 (0.00) −0.005 (0.88) −0.05 (0.13) 0.43 (0.00) −0.55 (0.00) −0.05 (0.22) 0.71 (0.00) −1.23 (0.00) 0.08 (0.00) −0.21 (0.00) 0.34 (0.00) 0.60 (0.00) 0.24 (0.00) 0.13 (0.00) 0.71 (0.00) 0.78 (0.00) 0.31 (0.00) 0.24 (0.00) 0.17 (0.00) 0.07 (0.00) 0.36 (0.00)

Southeast Asia

0.39 (0.00) −0.15 (0.00) −0.06 (0.57) −0.91 (0.00) −0.01 (0.90) −0.01 (0.82) −0.05 (0.62) −0.84 (0.00) −0.03 (0.62) −0.17 (0.01) 0.10 (0.34) −1.02 (0.00) −0.08 (0.29) 0.67 (0.00) −1.01 (0.00) −0.03 (0.38) −0.33 (0.00) 0.37 (0.00) 0.42 (0.00) 0.23 (0.00) 0.17 (0.00) 0.83 (0.00) 0.90 (0.00) 0.26 (0.00) 0.26 (0.00) 0.20 (0.00) 0.08 (0.00) 0.35 (0.00)

Western Asia

0.18 (0.00) −0.15 (0.00) 0.33 (0.00) −0.22 (0.00) 0.003 (0.84) −0.09 (0.00) 0.29 (0.00) −0.16 (0.00) −0.09 (0.00) −0.07 (0.00) 0.51 (0.00) −0.47 (0.00) 0.02 (0.36) 0.90 (0.00) −0.42 (0.00) 0.08 (0.00) −0.10 (0.00) 0.37 (0.00) 0.53 (0.00) 0.23 (0.00) 0.15 (0.00) 0.66 (0.00) 0.9 (0.00) 0.32 (0.00) 0.25 (0.00) 0.21 (0.00) 0.09 (0.00) 0.35 (0.00)

Western Europe

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Word Comfort Word Seat Airline Ranking Word Crew Word Service Airline Ranking Word Food Word Meal Airline Ranking Word Entertainment Airline Ranking Airline Ranking Seat Comfort Cabin Staff Service Food and Beverages Entertainment Sentiment Score Value for Money Seat Comfort Cabin Staff Service Food and Beverages Entertainment Sentiment Score

0.09 (0.60) −0.20 (0.03) 0.53 (0.04) −0.03 (0.83) 0.05 (0.70) 0.67 (0.02) 0.13 (0.38) −0.10 (0.61) 0.73 (0.00) −0.49 (0.02) 1.04 (0.00) 0.06 (0.59) 0.58 (0.00) 0.55 (0.00) 0.25 (0.05) 0.22 (0.03) 0.53 (0.03) 0.58 (0.00) 0.23 (0.00) 0.23 (0.00) 0.33 (0.00) 0.12 (0.07) 0.24 (0.09)

0.19 (0.01) −0.21 (0.00) 0.16 (0.16) 0.07 (0.16) −0.03 (0.53) −0.05 (0.68) −0.19 (0.00) −0.21 (0.00) 0.17 (0.17) −0.30 (0.01) 0.41 (0.00) −0.01 (0.77) 0.40 (0.00) 0.49 (0.00) 0.27 (0.00) 0.18 (0.00) 0.62 (0.00) 0.74 (0.00) 0.26 (0.00) 0.24 (0.00) 0.17 (0.00) 0.12 (0.00) 0.28 (0.00)

East Asia 0.37 (0.05) −0.23 (0.02) 0.44 (0.09) −0.09 (0.50) −0.07 (0.46) 0.10 (0.70) −0.15 (0.28) −0.11 (0.53) 0.47 (0.08) −0.19 (0.46) 1.37 (0.00) 0.09 (0.42) 0.12 (0.45) 0.47 (0.03) 0.45 (0.01) 0.18 (0.09) 0.49 (0.19) 1.04 (0.00) 0.29 (0.00) 0.23 (0.01) 0.26 (0.00) 0.07 (0.29) 0.46 (0.01)

Eastern Europe 0.30 (0.00) −0.17 (0.00) 0.49 (0.00) 0.07 (0.07) −0.07 (0.01) 0.41 (0.00) 0.01 (0.62) −0.19 (0.000) 0.63 (0.000) −0.21 (0.00) 0.65 (0.00) 0.11 (0.00) 0.36 (0.00) 0.45 (0.00) 0.23 (0.00) 0.17 (0.00) 0.71 (0.00) 0.98 (0.00) 0.32 (0.00) 0.29 (0.00) 0.24 (0.00) 0.08 (0.00) 0.33 (0.00)

North America 0.19 (0.00) −0.21 (0.00) 0.03 (0.61) −0.04 (0.25) −0.08 (0.01) −0.14 (0.03) −0.06 (0.08) −0.25 (0.00) 0.01 (0.85) −0.27 (0.00) 0.42 (0.00) −0.05 (0.06) 0.36 (0.00) 0.46 (0.00) 0.39 (0.00) 0.18 (0.00) 0.54 (0.00) 0.76 (0.00) 0.39 (0.00) 0.30 (0.00) 0.26 (0.00) −0.02 (0.34) 0.26 (0.00)

Oceania 0.13 (0.27) −0.13 (0.05) 0.59 (0.00) 0.22 (0.12) −0.19 (0.06) 0.73 (0.00) −0.02 (0.78) −0.15 (0.39) 0.71 (0.00) −0.37 (0.03) 0.92 (0.00) 0.12 (0.15) 0.35 (0.00) 0.58 (0.00) 0.21 (0.03) 0.02 (0.77) 0.74 (0.00) 0.86 (0.00) 0.35 (0.00) 0.24 (0.00) 0.27 (0.000) 0.07 (0.16) 0.31 (0.01)

South America 0.38 (0.02) −0.25 (0.00) 0.07 (0.74) −0.03 (0.78) −0.15 (0.20) −0.04 (0.87) −0.03 (0.82) −0.45 (0.00) 0.0002 (1.00) 0.34 (0.10) 0.34 (0.10) −0.04 (0.68) 0.46 (0.00) 0.59 (0.00) 0.34 (0.01) 0.17 (0.05) 0.88 (0.00) 0.63 (0.00) 0.35 (0.00) 0.16 (0.04) 0.31 (0.00) 0.04 (0.50) 0.25 (0.05)

South Central Asia

Notes: 1Number in parentheses represents the p-value of thecoefficient. 2Insignificant coefficients at 90% confidence interval are marked in italic.

Value for Money

Sentiment Score Overall Rating

Entertainment

Food and Beverages

Cabin Staff Service

Seat Comfort

Africa

Table 4 Path analysis results for first and business class reviews.

0.09 (0.16) −0.13 (0.00) 0.21 (0.02) −0.03 (0.51) −0.03 (0.36) 0.21 (0.02) −0.10 (0.05) −0.02 (0.69) 0.36 (0.00) −0.24 (0.01) 0.66 (0.00) −0.01 (0.66) 0.38 (0.00) 0.49 (0.00) 0.33 (0.00) 0.21 (0.00) 0.68 (0.00) 0.77 (0.00) 0.34 (0.00) 0.24 (0.00) 0.21 (0.00) 0.05 (0.06) 0.25 (0.00)

Southeast Asia

0.32 (0.00) −0.23 (0.00) 0.05 (0.72) −0.17 (0.02) 0.04 (0.52) −0.13 (0.32) −0.16 (0.07) 0.18 (0.21) 0.14 (0.30) −0.19 (0.10) 0.78 (0.00) −0.07 (0.19) 0.37 (0.00) 0.38 (0.00) 0.12 (0.11) 0.22 (0.00) 0.51 (0.00) 1.03 (0.00) 0.34 (0.00) 0.17 (0.00) 0.27 (0.00) 0.03 (0.43) 0.43 (0.00)

Western Asia

0.27 (0.00) −0.17 (0.00) 0.27 (0.00) 0.06 (0.02) −0.11 (0.00) 0.17 (0.00) −0.08 (0.00) −0.21 (0.00) 0.36 (0.00) −0.14 (0.00) 0.66 (0.00) 0.009 (0.64) 0.31 (0.00) 0.50 (0.00) 0.31 (0.00) 0.15 (0.00) 0.63 (0.00) 0.90 (0.00) 0.34 (0.00) 0.26 (0.00) 0.23 (0.00) 0.08 (0.00) 0.34 (0.00)

Western Europe

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0.173 0.238 0.172 0.102

Seat Comfort Cabin Staff Service Food and Beverages Entertainment

(0.00) (0.00) (0.00) (0.00)

0.216 0.188 0.126 0.086

(0.00) (0.00) (0.00) (0.00)

East Asia 0.242 0.223 0.136 0.093

(0.00) (0.00) (0.00) (0.00)

Eastern Europe 0.425 0.315 0.206 0.139

(0.00) (0.00) (0.00) (0.00)

North America 0.25 (0.00) 0.216 (0.00) 0.172 (0.00) 0.059 (0.00)

Oceania 0.291 0.323 0.164 0.010

(0.00) (0.00) (0.00) (0.81)

South America 0.330 0.205 0.209 0.025

(0.00) (0.00) (0.00) (0.36)

South Central Asia

502

0.134 0.133 0.196 0.072

Seat Comfort Cabin Staff Service Food and Beverages Entertainment

(0.05) (0.01) (0.00) (0.10)

0.194 (0.00) 0.179 (0.00) 0.13 (0.00) 0.088 (0.01)

East Asia 0.305 0.240 0.275 0.073

(0.01) (0.04) (0.05) (0.30)

Eastern Europe 0.314 0.280 0.235 0.075

(0.00) (0.00) (0.00) (0.00)

North America 0.297 (0.00) 0.226 (0.00) 0.196 (0.00) −0.014 (0.38)

Oceania

0.302 0.204 0.231 0.065

(0.00) (0.00) (0.00) (0.21)

South America

0.223 0.104 0.194 0.028

(0.00) (0.10) (0.00) (0.58)

South Central Asia

Notes: 1Number in parentheses represents the p-value of the coefficient. 2Insignificant coefficients at 90% confidence interval are marked in italic.

Africa

Independent Variable

Indirect Effect through Value for Money

Table 6 Mediation analysis for first & business class reviews.

Notes: 1Number in parentheses represents the p-value of the coefficient. 2Insignificant coefficients at 90% confidence interval are marked in italic.

Africa

Independent Variable

Indirect Effect through Value for Money

Table 5 Mediation analysis for economy class reviews.

(0.00) (0.00) (0.00) (0.00)

0.262 0.187 0.161 0.042

(0.00) (0.00) (0.00) (0.1)

Southeast Asia

0.242 0.185 0.136 0.060

Southeast Asia (0.00) (0.00) (0.00) (0.00)

0.354 0.181 0.283 0.037

(0.00) (0.01) (0.00) (0.48)

Western Asia

0.235 0.238 0.177 0.081

Western Asia

0.311 0.238 0.211 0.075

(0.00) (0.00) (0.00) (0.00)

Western Europe

0.29 (0.00) 0.227 (0.00) 0.191 (0.00) 0.082 (0.00)

Western Europe

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Fig. 8. Density (purple) and cumulative probability (black) of the coefficient ratio of first and business class to economy class. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 9. Comparison of the normalized values of the economy class explanatory variables for Value for money (base: North America).

Fig. 10. Comparison of the normalized values of the first/business class explanatory variables for Value for money (base: North America).

it has the most impact for North American passengers flying Economy and Oceania passengers in the First or Business class. The Cabin Staff Service has the least magnitude for South Central Asian passengers in both classes while it has the most impact for South American passengers in Economy and Oceania passengers in the First or Business class.

Interestingly, Food and Beverages rating has the least magnitude for East Asian passengers regardless of the class flown and the most magnitude for South Central Asian passengers in Economy and African passengers in First or Business class. Looking at the impact of Inflight Entertainment on the Value for Money across the geographical regions, it is found that

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Fig. 11. Comparison of the normalized values of the economy class explanatory variables for overall rating (base: North America).

Fig. 12. Comparison of the normalized values of the first or business class explanatory variables for overall rating (base: North America).

the least impact belongs to South American passengers in Economy and Oceania passengers in First or Business class. Inflight Entertainment has the most impact on Value for Money in African passengers in both classes. These differences across the 10 geographical areas corroborate Hypothesis 2.

coefficients of each explanatory variable based upon the related coefficients for the North America region. The results are depicted in Figs. 11 and 12. Analyzing Value for Money among travelers in Economy class, it has by far the highest magnitude for North-American travelers and the least impact belongs to East and Southeast Asian individuals. In First or Business class, Value for Money has the highest magnitude for Eastern European and Western Asian travelers and the least impact belongs to African travelers. There are some interesting disparities regarding the magnitude of Seat Comfort. In Economy class, the highest is for African passengers, followed by Eastern European passengers and Oceania. The lowest, however, is for economy passengers of South-Central Asian. In First or Business class, the highest magnitude is also for African passengers whereas the lowest is for Eastern European passengers. The lowest magnitude of Cabin Staff Service variable is for North American travelers in Economy and Western Asia in First or Business class. Except for Western Asia, the magnitude for all other regions is at least 40% higher and the highest magnitude being for East Asia, which is 70% greater than North America in Economy class. Looking at the impact of Inflight Entertainment on the Value for Money across the geographical regions, the least impact belongs to African Economy travelers and South American First or Business class travelers, while the greatest impact belongs to South American Economy travelers and African First or Business class travelers. Finally, Table 5 and Table 6 summarize the indirect effect of each of the inflight services through Value for Money, the mediator, on the

6.3. Overall rating As far as in-flight services are concerned, the results demonstrate that Seat Comfort, Cabin Staff Service, Food and Beverages, Inflight Entertainment, Sentiment Score, and Value for Money are all significantly and positively correlated with Rating in all regions for passengers flying Economy except Inflight Entertainment for African passengers. Considering First or Business class passengers, Seat Comfort and Inflight Entertainment are not significant for Eastern European passengers. Similarly, Inflight Entertainment is not significant for South American passengers and Food and Beverages is not significant for Western Asian passengers. Excluding these exceptions, the remaining coefficients for the First or Business class are significantly and positively correlated with Overall Rating. Looking at the magnitude of the variables in both classes, Value for Money has a higher value to explain the rating in most regions, followed by the Sentiment Score. Seat Comfort is not the highest in-flight characteristic variable anymore, but is replaced by Cabin Staff Service. While passengers essentially link the comfort of their seat to the value they pay for their flight, they summarize their flight experience mainly through the service offered by the staff on board. Same as for Value for Money interpretation, we normalize the

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Overall Rating. For all variables in both classes and across all regions, the indirect effects are positive and statistically significant except for the effect of Inflight Entertainment among South American and South Central Asian economy travelers and Oceania, South American, Eastern European, South Central Asian and Western Asian First or Business class travelers. These significant results allow us to confirm hypothesis H1.

service of the planes operating in this market to increase customer satisfaction. In general, in Asia, airline companies should pay more attention to the quality of service offered by the flight attendants. As far as cost policy is concerned, the results suggest that companies should not significantly invest in in-flight entertainment services for their whole fleet as the In-flight Entertainment variable has the lowest magnitude in the path analysis to explain Value for Money and Overall Rating for each geographical region. In-flight entertainment generally relates to either personal screens at each seat, shared screens, or streaming platform on passenger's own personal device. With the development and democratization of the new technologies, passengers nowadays are more likely to travel with a high-tech smartphone or tablet, often more powerful than the back-seat screens in the planes. Consequently, they prefer to use their own device rather than the onboard technology. Instead of investing in hardware and materials, airline companies should foster the use of passenger's personal devices by proposing a large content of movies, TV shows, music, and games available to download, as well as providing USB ports to charge the devices. East Asia, Oceania, and Western Asia may remain the markets with the highest interest for on-board entertainment devices. Regarding the food and beverages offered on board, this aspect of the inflight services has a less important impact on the value for money for passengers from East Asia, Eastern Europe, North America, and Southeast Asia. However, for these regions except North America, as well as Oceania, passengers do consider that the Food and Beverages service influence their overall perception of the flight. Such finding encourages air carriers to maintain a high level of service and quality regarding the selection of drinks and meals either complimentary or for purchase for passengers from these regions of the world. As far as crew planning management is concerned, the results of the analysis may encourage airlines to select and assign their flight attendants depending on the market. Thus, North American air carriers should not assign their experienced flight attendants on the domestic market as American passengers' satisfaction is not mainly driven by this aspect of flight. Instead, they, as well as other airlines, should assign their best crew to the Asian market as passengers have higher expectations in terms of flight attendants' service.

6.4. Insights from text mining and sentiment analysis As far as findings from the text mining and sentiment analysis are concerned, we extracted several key words and added them as covariates to the relevant models. Looking at Table 3, it is found that the word “Comfort” has a direct, significant, and positive effect on Seat Comfort rating in all regions except South America. This suggests that when “Comfort” is used in reviews, it is mostly to praise and compliment the seat comfort. However, when the word “Seat” is used in the published reviews, it has a direct, negative, and significant effect on Seat Comfort in all regions. This means the passengers typically complain about seat in their reviews and it has a negative connotation. We also tested the impact of the frequency of the words “Service” and “Crew” on Cabin Staff Service. The relation between the frequency of “Service” and Cabin Staff Service is mostly insignificant across regions except in North America, Oceania, Southeast Asia, and Western Europe for which the coefficient has a negative effect. This means “Service” is generally part of the flyer complaint. The frequency of word “Crew” is insignificant in all regions except in East Asia, North America, and South America for which the coefficient is positively correlated with Cabin Staff Service. This correlation indicates “Crew” is typically used as part of compliments. The effect of frequency of the word “Food” in reviews on Food and Beverages rating varies across regions. It has a significant and positive coefficient in North and South America, a significant and a negative coefficient in East Asia, Oceania, and Western Europe, and is insignificant in other geographical regions. Therefore, the context in which the word “Food” is repeated in reviews cannot be generalized across geographical regions. Similarly, the frequency of the word “Meal” in reviews has a significant and positive coefficient in North and South America, a significant and negative coefficient in Oceania, South Central Asia, Western Asia, and Western Europe, and is insignificant in other geographical regions.

8. Summary and conclusion Development of information and communication technologies has provided passengers more opportunities to share their flight experience on social networks and on-line platforms. Through electronic word-ofmouth, they can exchange their feedback and opinion on the quality of the service and their flight experience. Not only is this information fruitful for future passengers, but it is considered an invaluable resource in the airline industry to assess the airline performance from the passenger's point of view. The current research, to the best of the authors' knowledge, was the first attempt to analyze how and to what extent the determinants of value for money and overall rating of a flight experience varies among geographical regions and flight classes. Previous research highlighted that the attitude and behavior of passengers toward their flight experience is not homogenous and needs to be analyzed over geographical regions due to the cultural differences. We extracted 40,510 flight reviews from the Skytrax database published between October 2011 and January 2018, and aggregated the observation into ten geographical regions using the country of residence for passengers. We tested three hypotheses framed in the introduction section. To corroborate Hypothesis 1, we derived the indirect effects of each of the inflight services on the Overall Rating by taking the Value for Money score as the mediator. The results indicate that across all geographical regions and for all services except Inflight Entertainment, the indirect effect is positive and statistically significant. We used the exploratory analysis and the path analysis to corroborate Hypothesis 2 and highlighted how passenger expectations vary among their geographical regions. To test

7. Planning strategies The findings of the text and path analyses have implications for airlines to augment their performance as well as the passenger satisfaction. As far as customer satisfaction is concerned, the results echo the need to adapt the level of in-flight services to maximize performance in each market. In particular, air carriers need to raise the bar when they serve the Middle-East market as travelers from this region have a negative attitude toward non-local airlines. The reason is Gulf air-carriers typically offer a higher-quality service for a low price, which increases the expectation of regional passengers. We also noticed that Western Asian travelers' value more in-flight entertainment than other travelers. As a result, to compete with Gulf air-carriers in the Middle-East market, airlines might need to offer a better quality of in-flight entertainment, including free movies, TV shows, music, and games. Taking North American travelers into account when flying on their national airlines, we found they prioritize the value for money over any other in-flight services more than other travelers. American air carriers, hence, might not necessarily need to change the level of service in the domestic market. Rather, they should boost the reliability of service. On the contrary, East Asian travelers value the in-flight services than other travelers. This agrees with Gilbert and Wong's findings (Gilbert & Wong, 2003). Airlines serving East Asian travelers might need to improve the quality of food and beverages, entertainment, and the overall 505

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Hypothesis 3, we separately developed the path analysis on First or Business and Economy classes and highlighted the variation in rating and expectations among different class flyers. To summarize:

characteristics that are not typically surveyed. Some caveats related to the current research need to be pointed out:

• We aggregated the data into 10 geographical regions rather than

• North American passengers complain more about their national • • • • • • • •

airline, while East and Southeast Asian passengers are more satisfied with Asian airlines. Middle-Eastern passengers are critical toward non-local airline companies. North American passengers care essentially about the money they pay for their flight, and they care less about the in-flight services. East Asian passengers care more about inflight services in their overall opinion of their flights. Asian passengers pay greater attention to the service quality offered by flight attendants. Travelers from Oceania tend to have high expectations in terms of service, especially regarding food and beverages, and their overall satisfaction is not essentially driven by the value for money. South American travelers have lower expectations in terms of inflight amenities, but pay more attention to the price they pay for the service. Across all geographical regions, seat comfort is the most important factor to evaluate the value for money of the flight and constitutes the major concern in passengers' reviews, while cabin staff service is the main feature to summarize overall flight experience. As far as cabin class flown is concerned, travelers in first or business class are more concerned about the importance of seat comfort, food and beverages, and in-flight entertainment on their overall flight experience, while passengers in economy class are more concerned about value for money

•

• •

developing models for at the country level due to the lack of data for each country. The data used in this study hindered capturing the set of norms, behaviors, beliefs, and values shared by the population of a nation in the analysis. We, however, believe national culture has a significant impact on the passengers' expectations. Although we have attempted to partially capture specific characteristics such as language, religion, ethnic and racial identity, and cultural history and traditions by dividing the data into ten segments representing ten geographical regions of the globe, we believe future research is needed to embed national culture in analysis conditional to the existence of rich data. The published reviews used in the current research skewed toward a negative response as system users are more likely to write a review to express a complaint rather than compliment. They are consequently not completely representative of the overall passenger opinion. The given ratings of the in-flight service do not completely explain the overall rating of the flight. General opinion on the flight experience is also driven by other factors such as ground service, cleanliness of the cabin, reliability of the planes, and delays.

Author contribution The authors confirm contribution to the paper as follows. Study conception and design: AE, LA, and AP; data collection: AP; literature review: LA; exploratory analysis: AP; sentiment analysis: AP; path analysis: LA; interpretation of the results: AE, LA, and AP; draft manuscript preparation: AE, LA, and AP; edits and conceptualization: AE. All authors reviewed the results and approved the final version of the manuscript.

Regarding these distinct needs and expectations, airlines should adapt their offers and services to specific regions to increase passenger's satisfaction and optimize costs and crew management. Finally, the study revealed the importance of the sentiment score in the analysis of the reviews as it helps capture service and flight experience

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Results of t-test comparing the mean of each ratings between 10 airlines.

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Aymeric Punel is a data scientist at the Boston Consulting Group. He received a Master of Science in Civil Engineering from Ecole Spéciale des Travaux Publics, Paris (France), as well as a Master of Science and a Ph.D. in Transportation Engineering from Northwestern University, Evanston (USA). His research focuses on applying data analytics techniques on air management, passenger's behavior, and the role of social media in the airline industry.

Lama Al Hajj Hassan is a Ph.D. student in Transportation Engineering at Northwestern University. She is currently a research assistant at Northwestern University Transportation Center. Her research interests revolve around developing operations research and data analytics based solutions for airline passenger and freight operations.

Alireza Ermagun is an Assistant Professor of Transport in the Department of Civil and Environmental Engineering at Mississippi State University. A common theme running through his research activities is to identify novel solutions, develop advanced methods, and introduce innovative theories to understand travel behavior and human aspects of sustainable and smart mobility more realistically. He has published more than 40 refereed articles in peer-reviewed journals, and made over 80 technical presentations. He is on the editorial board of Transport Findings.

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