Teikake, 2012 conducted studies on determination of degree of influence the level of customer satisfaction has on airline selection based on airline on-ground and on-board services in Kiribati based on six evaluation criterions. Out of 6 criterions, most of the airlines possessed 5 of them which are named as “Check-in process”, “Boarding Procedure”, “On-board performance”, “Cabin Staff Performance” and “destination services”. Each of the above mentioned variables are utilized by customer to adjudicate the airline service quality differently depending upon the level of importance the AQR variable as per customer perception.

Alamdari, 1999 conducted surveys to investigate correlation between on-board airline service quality and customer satisfaction level in order to determine the fruitfulness of investment made by airline management on on-board services to attract customer. On-board services taken into consideration include “on-board entertainment (OBE)”, “passenger perception about OBEs”, “Willingness to pay extra for OBEs” and “ability to create PoD through OBEs in market”. Post-2000 era, OBEs were in fact considered as PoD in airline industry was IT sector was booming across the globe. But, outcome of studies reflected that most of the air travellers were expecting entry of OBE in late-2000. Hence, introduction of OBEs had diminishing returns in early 1990s. It further reflects that OBEs are rather secondary factors for any airline selection criterion. The primary criterion identified through research were as follows, CORE: “Safety”, “Schedule” and “Reliability”, EXPECTED: “Seat Comfort”, “Baggage”, “”Lounge”, “Cleanliness” and “Food and Drinks” and AUGMENTED: “OBEs”, “Limousine Services”, “Shower Facility”, “Lounge Entertainment”, “Pre-flight meals” and ”Massage.”

Vink et. al. 2012, conducted studies on determination of correlation between in-flight comfort levels (IFCLs) experienced by traveller on repeat purchase behaviour. On-board comfort levels were measured in terms of “leg-room space”, “hygiene”, “crew attention” and “seat and personal space”. The research revealed that indeed tendency to repeat purchase is dependent on in-flight comfort levels. Earlier research conducted by Richards and Jacobson, 1977 also indicates that leg-room space, firmness, width and shape of the seat are main factors contributing towards overall IFCLs. However, given the fact that such study was being conducted almost 30 yrs. ago and with advents of latest technology and aircraft interior design over such long period of time, we can say that such findings may become questionable in nature in today’s era.

Ostrowski et. al., 1993 conducted studies to determine the influence of air traveller’s perceived service quality and customer royalty in US Air Space. Extensive surveys were conducted at the point of on-boarding or de-boarding based on parameters such as “flight schedule”, “price”, “frequent flyer program”, “airline preference while booking”. Deplaning passengers were asked to rate the airline service quality based on 16 service quality factors. Both, data sets from on-boarding and deplaning clearly indicates that customer royalty is indeed function of service quality variables.

Chen and Chang, 2005 conducted service quality gap analysis for particular Taiwanese domestic airline for all routes possible. Study revealed that 32 service quality factors influences the service quality gap which includes 17 variables from on-ground service and rest 15 from on-board services. Author of the research paper claimed that these variables can accurately model the correlation between AQRs and airline ticket purchase behaviour. However, we are of the view that since the sample referred to specific airline in Taiwanese Air Space. Model cannot accurately reflect the scenario which warrants senior management calls.

Bowen and Headley, 2000 conducted extensive research into AQR after making certain modifications in highly cited 9 AQRs studies in United States and were internationally accepted a decade prior (Gilbert and Wong, 2003). The studies indicate that variables such as “On-time performance”, “Safety”, “Misplaced Baggage”, “Denied Boarding” and “Handling of customer complaints” are considered most important variables in post-2000 era.

Wong and Chung, 2007 measured the airline passenger satisfaction based on AQRs in Taiwanese Air Space. But, unlike Chen and Chang, 2005 they have included all possible airline flying in or out through Taiwan to improve the accuracy of the study. The same study was further used by to determine the tendency of repeat purchase and it was found to be accurately predicting the shift in consumer base with fluctuation in AQRs.

Aksoy et. al., 2003 research studies reveals that sometimes AQRs are dependent on air crew’s ability to speak in multiple languages as same has strong influence on customer satisfaction levels.

Studies conducted by Mason, 2000 and Fourie and Lubbe, 2006 suggests that following are the most important variable influencing the AQRs in order of precedence are “In-flight comfort”, “Seating”, “Flight Schedule” and “Frequency”. From Business travellers view point, flight schedule is considered as most important AQR (Chin, 2002). Evangelho et. al., 2005 research survey show that reliability, flight frequency, multi-options in ticket booking, price, frequent flyer programs, in-flight services and lounge services plays major role in creation of +AQRs.

2.1.3 SERVQUAL IN AIRLINE INDUSTRY:

Parasuraman et. al., 1988 proposed an instrument to measure customer’s perspective of service quality which is known as SERVQUAL. The main objective of the SERVQUAL model is to explain the gap between consumer expectation and actual service rendered by service provider. The gap between customer perception and rendered service is called as service quality. Initial model had 10 service dimension, the refined model had 5 has depicted in Table no. 2 (Parasuraman, Berry and Zeithaml, 1991). This model has been quite successful in recent times in both national and international markets (Parasuraman et al.,1988; Sultan and Simpson, 2000). This model relevant in this research paper as it helps us in designing selection criteria based on which we measure pre-purchase and post-purchase expectation.

Table No. 2: 5 Dimensions of SERVQUAL Model (After Parasuraman et al.,1988)

SERVICE DIMENSIONS	DESCRIPTION
Tangibles	Physical Facilities, Equipment and appearance of the frontline employees
Reliability	Ability to accurately and dependably perform the promised services
Responsiveness	Promptness and willingness to assist the customer
Assurance	It is combination of trust and confidence inducted by frontline employee on customer based on knowledge and courtesy
Empathy	Providing personalized attention to customer

Parasuraman et. al., 1988 used combination of 44 questionnaires in 5 point Likert-scale, out of which 22 questions were designed to capture post-purchase behaviour of the customer in terms of customer royalty, repeat purchase etc. In earlier-1990s, SERVQUAL model was used by companies operating in the field of “Banking”, “Credit Card Services”, “Product maintenance and repair industry” and “Telecom Sector”. Factor analysis was conducted on data sets to determine the dimensions of SERVQUAL Model and R² values were taken into consideration to determine consistency of five dimensions across all four industries.

Teas, 1993 has suggested that SERQUAL model is used by service provider to measure the pre-purchase behaviour of the potential or existing customer in 6 different ways as mentioned below;

a) Forecasted Importance: Customer’s pre-purchase thought process about expected level of service quality based on personalized assumption.

b) Deserved performance: Customer’s pre-purchase thought process about expected level of service quality based on Cost-Benefit Analysis.

c) Equitable performance: Customer’s pre-purchase thought process about expected level of service quality based on opportunity cost.

d) Minimum Tolerance: It is treated as baseline level of service quality based on customer’s perception in terms minimum acceptable quality level vis-à-vis financial investment.

e) Ideal performance: It is treated as optimal level of service quality that can be rendered by the service provider in most of the cases.

f) Service attribute importance: It is subjected to individual customer’s bias in terms of precedence of several of service quality variables in deceasing order of importance.

A research survey conducted by Sultan and Simpson, 2000 reflects that there is strong correlation between customer expectation about airline service quality and country of origin of the passenger for code-share flights. Hence, outcome of his study cannot be relied upon completely due to change over of the airline brands based on alliance network. Gilbert and Wong, 2003 improvised over the preceding author by focusing on non- code-share flights and conducted studies on influence of nationality of the passenger on the pre-purchase expectation based on SERQUAL model. Park et. al., 2006 based on SERVQUAL model formulated 3 factors to depict AQRs as mentioned here-in (a) Reliability and Customer Service (b) Convenience and accessibility and (c) In-flight service based on qualitative studies by interviewing the air crew and travellers. Their studies clearly depicts that indeed repeat purchase behaviour is dependent on AQRs. Pakdil and Aydun, 2007 found that customer’s perception of service quality rendered is also dependent on educational level based on research surveys conducted in Turkish Airlines, Chou et. al. 2011, conducted surveys in multiple Taiwanese Airlines using fuzzy weighted SERVQUAL framework and found that “Safety”, “Customer Complaint Handling” and “Courtesy of crew” to be most important variables of AQRs.

2.1.4 BRANDING AND LOYALTY IN AIRLINE SERVICE EVALUATION

Brand is defined as “Name”, “Term”, “Sign”, “Symbol” or “Design” or Combination of them” which helps in identification of products or services of a particular organization and creates PoD from me-too firms (Kotler, 1994). Keller, 1993 defined the term “Brand equity” as the commercial value in form of incremental future cash flows that can be obtained from consumer perception of the brand name of a particular product or service, rather than from the product or service itself.

Research conducted by multiple researchers Cobb Walgen and Mohr, 1998; Laroche and Brisoux, 1989; Laroche et.al. 1995 clearly depicts that there is strong correlation between “Brand Equity” and “Brand Perception”, “Decision making” and “Purchase Intentions”. Grace and O’Cass, 2005 and Jamal and Goode, 2001 concluded that sometimes branding has stronger influence on pre-purchase service selection. Hence, it is extremely important for us to conduct brief literature review before proceeding ahead with our survey.

Wong and Musa, 2011 conducted research surveys in terms of airline pre-purchase brand satisfaction in Malaysia based on 9 dimensions of service quality i.e. “Brand”, “Price”, “Core Service” ,”Feeling” ,”Reputation”, “Employee”, “WOM”, “Service Scape”, “Publicity and Advertising”. It accurately predicted the pre-purchase criterion used by passenger for different domestic Malaysian airlines.

Dolnicar et. al., 2011 found that business travellers show stronger sense of brand loyalty then leisure travellers. Hence, frequent flyer programs are of greater relevance for business travellers as it reduces the impact of switching cost and helps the airline in retaining the customer base (Carlsson and Lofgren, 2006). Sarabia and Ostrovskaya, 2014 illustrated the “branding” alone cannot be used as pre-purchase selection criteria, rather it act as faster way to make purchase decision. In many cases, it has found that repeat purchase behaviour is highly influenced by frontline employee’s behaviour (Akamavi et. al., 2015). Though the concept of service recovery can be used to restore the customer’s faith on service provider (Maxhum and Netemeyer, 2002). Service recovery has strong influence over the customer future purchase behaviour. Hence, it must be designed in such a manner to meet perceived justice (Smith and Bolton, 1998). Intention to re-purchase and sharing positive WOM to prospective customer is found to dependent on customer satisfaction level in past experiences (Nadiri et. al., 2008). Dowling and Uncles, 1997 found that frequent flyer programmes have very little impact on pre-purchase or repeat-purchase behaviour of the customer as competitive market forces tends to overshadow the benefits it provides. Furthermore, Nadiri et. al., 2008 contended that service tangibles have more impact on customer purchase behaviour then loyalty programmes such as Frequent Flyer one.

For sake of brevity for readers, authors have consolidated the observations drawn from different literature reviews in tabular form as depicted in Table No. 1 above. Most of researchers conducted surveys to evaluate the post-purchase customer behaviour. Hence, their research outcome has been extensive used in further studies to model the scenario for next-generation air traveller in Indian Aviation Sector only.

3. METHODOLOGY:

A quantitative approach is adopted by administering a questionnaire among the respondents for primary data to evaluate the quality of service provided by the domestic airliner’s in which they have travelled, the perceived service quality of each variable was measured through questions designed on a 5 point Likert scale ranging from Important (1) to Not at all important (5). The questionnaire was administered to 153 customers/passengers. “Factor Analysis” and “Discriminant Analysis” was performed on the data.

a) Factor Analysis: A survey was conducted to determine the factors which influence the Airline Service Quality. The survey was conducted with a sample of 153 air travellers from different age and income groups. The respondents were a mix of male and female. The question was measured using a 5 point Likert scale (1- Important; 5-Not at all Important).

b) Discriminant Analysis: In additional to above, a study was conducted to identify the variables which impacts the air traveller’s airline selection decision based on Airline Service Quality. A questionnaire was designed for the purpose. The online survey was conducted on a sample of 153 air travellers in the age group of 21 to 40. We have divided the sample into two groups-one that has high airline selection impact factor (High-1) and the second which has low airline selection impact factor (Low: 2 to 5).

4. RESULTS AND DISCUSSIONS:

4.1 FACTOR ANALYSIS:

KMO measure of sampling adequacy and Bartlett’s test of sphericity for judging the appropriateness of a factor model. KMO statistic compares the magnitude of the observed correlation coefficient with the magnitude of the observed correlation coefficient with the magnitude of the partial correlation coefficient. A high value of this statistic (from 0.5 to 1) indicates the appropriateness of the factor analysis. Kaiser has presented the range as follows: statistic 1-0.5 acceptable and < 0.5 unacceptable. Computed KMO statistic in this case was 0.864, which indicates the value in acceptable zone of the factor analysis.

The communalities describe the amount of the variance a variable shares with all other variables taken into the study. The extracted communalities as shown in third column of the Table No. 4 is the estimate of the variance in each variable, which can be attributed to the factors in the factor solution. Relatively small value of the communality suggests that the concerned variable is a misfit for the factor solution and can be dropped out from the factor analysis.

Table No. 3: Criterion used for analysing communalities data

Basic Requirement:
		Questionnaire Quality
Extraction Communalities Criteria	Max >= 0.8	Good Questionnaire
Extraction Communalities Criteria	Min < = 0.5	Bad Questionnaire
S.No.	Criteria	Solution
1	Initial Communalities = Extraction Communalities	Variable should be continued factor analysis
2	Initial Communalities <<<< Extraction Communalities	Variable should be discarded from factor analysis

Hence, based on above mentioned criteria following variables will be discarded or continued further for factor analysis

Table No. 4: Fit / Unfit Status of variables for factor analysis

Communalities
	Initial	Extraction	Decision
Flight_ Timings	1.000	.832	Fit for factor analysis
No._of_ Stop_overs	1.000	.796	Fit for factor analysis
Air_Fare	1.000	.733	Fit for factor analysis
Airline's_ Frequent_ Flyer_Program	1.000	.653	Fit for factor analysis
In-flight_ services	1.000	.594	Fit for factor analysis
Efficacy_of_ Ground_services	1.000	.511	Fit for factor analysis
Personal_ Preference	1.000	.550	Fit for factor analysis
Third_Party_ Influence	1.000	.846	Fit for factor analysis
Aircraft's_ Amenities	1.000	.791	Fit for factor analysis

Since, we have adopted “principal component method” as the method of analysis in a factor model. The Criterion for extraction of factors: In Rotation Sums of Squared Loadings are as follows;

i. Total Eigen Values must be greater than 1 and arranged in descending order.

ii. Cumulative % Variance must be greater than 60% atleast.

In this case, cumulative % variance is 70.052% and at a same time total eigen values were greater than 1 and arranged in descending order. Hence, it is possible to extract factors from the components.

Rotated component matrix is often referred as the “pattern matrix for oblique rotation”. The columns in this Table No. 5 represent the factor loading for each variable, for the concerned factor after rotation. The figure clearly shows that interpretability importance of the rotation. The widely applied method of rotation is the “Varimax Procedure”. Although a number of rotation methods have been developed, varimax has been generally regarded as the best orthogonal rotation and is overwhelmingly the most widely used orthogonal rotation in psychological research (Fabriger et. al., 1999).

Table No. 5: Rotation Component matrix

Cut-off score of loading: a) Max = 0.8 and Min = 0.6 b) Cross-loading check done
Variables		Factors
Variables		F1	F2	F3
Flight_Timings	X1	.874	.198	-.172
No._of_Stop_overs	X2	.849	-.149	.229
Air_Fare	X3	.623	-.133	.573
Airline's_Frequent_Flyer_Program	X4	.067	.796	.121
In-flight_services	X5	-.050	.769	.032
Efficacy_of_Ground_services	X6	.528	.303	.375
Personal_Preference	X7	.109	.712	.173
Third_Party_Influence	X8	-.289	.479	.730
Aircraft's_Amenities	X9	.305	.151	.821

Therefore, following variables are assigned to factors as mentioned below;

Factors	Variables
F1	X1	X2
F2	X4	X5	X7
F3	X8	X9

4.2 DISCRIMINANT ANALYSIS:

Group Statistics: It shows the means and standard deviations for both groups. From Table No. 6, few preliminary observations about the groups can be made and it clearly shows that two groups of flyers are widely separated with respect to three variables “Airline’s Frequent Flyer Program”, “Personal Preference” and “Third Party Influence”.

Table No. 6: Group Statistics

In terms of mean only
Airline_Selection_ Impact_Factor	High Airline Selection Impact Factor	Low Airline Selection Impact Factor	\|Difference\|
Flight_Timings	1.27	2.14	0.87
No._of_Stop_overs	1.09	1.79	0.7
Air_Fare	1.09	1.81	0.72
Airline's_Frequent_Flyer_Program	1.00	3.36	2.36
In-flight_services	1.73	2.57	0.84
Efficacy_of_Ground_services	1.45	2.26	0.81
Personal_Preference	1.55	2.71	1.16
Third_Party_Influence	1.45	2.55	1.1
Aircraft's_Amenities	1.36	2.02	0.66

Tests of Equality of Group Means:

It determines the variable that should be included in the model and describes that when predicators (independent variables i.e. Sig. < 0.05) are considered individually, only “Airline’s Frequent Flyer Program”, “Personal Preference” and “Third Party Influence” significantly differ between the two groups. The last column of “Test of Equity of Group Means” was shown below (Sig. < 0.05).

This clearly shows that “p-value” corresponding to “F-value” and confirms that only “Airline’s Frequent Flyer Program”, “Personal Preference” and “Third Party Influence” differ significantly between the two group of flyers.

Table No. 7: Tests of Equality of Group Means

Tests of Equality of Group Means
	Wilks' Lambda	F	df1	df2	Sig.
Flight_Timings	.925	4.139	1	51	.047
No._of_Stop_overs	.928	3.976	1	51	.052
Air_Fare	.922	4.300	1	51	.043
Airline's_Frequent_Flyer_Program	.474	56.597	1	51	.000
In-flight_services	.898	5.816	1	51	.020
Efficacy_of_Ground_services	.899	5.699	1	51	.021
Personal_Preference	.842	9.595	1	51	.003
Third_Party_Influence	.846	9.297	1	51	.004
Aircraft's_Amenities	.908	5.164	1	51	.027

Eigen values:

It indicates the amount of variance explained. The function always accounts for 100% of the variance. A large eigenvalue is an indication of a strong function. Furthermore, to add that canonical correlation measures degree of association between the discriminant scores and the groups (i.e. levels of dependent variables). A high value of the canonical correlation indicates that a function discriminates well between the groups. Canonical correlation associated with function is 0.785, Square of this value is given by (0.785)2 = 0.616225. This indicates that 61.6225 % of variance in the dependent variable can be attributed to this model.

Wilk’s lamba:

It is a statistic that assesses whether the discriminant analysis is statistically significant. To test the significance of each independent variable, the corresponding F-value is used. To test the significance of the discriminant function, chi-sqaure transformation of wilk’s lambda is used. A high value of chi-square indicates that the functions significantly differ from each other.

In this case, chi-square value is found to be 44.566 with corresponding p-value as 0. This value is significant at 99% confidence level. Hence, it can be concluded that population means of all the discriminant functions in all the groups are not equal (acceptance of alternative hypothesis). It indicates that the discriminant function is statistically significant and further interpretation of the function can be proceeded.

Canonical Discriminant Function Coefficients:

It gives an unstandardized coefficient and a constant value for discriminant equation. After substituting the unstandardized coefficient values with corresponding predicator and constant values, the discriminant equations can be written as mentioned below;

D =

- 4.349 - 0.130 (Flight_Timings) – 0.351 (No_of_Stop_Overs) + 0.201 (Air_Fare) + 1.081 (Airline's_Frequent_Flyer_Program) - 0.014 (In-flight_services) + 0.238 (Efficacy_of_Ground_services) + 0.096 (Personal_Preference) - 0.042 (Third_Party_Influence) – 0.007 (Aircraft's_Amenities)

Functions at Group Centroids: These are unstandardized canonical discriminant functions evaluated at group means and are obtained by placing the variable means for each group in the discriminant equations rather than placing the individual variable values. For, the first group, the group centroid is a negative value (-2.430) and the second group, it is an unequal positive value (0.637). From, the discriminant equation mentioned in preceding paragraphs, it can be noted that 5 and 4 coefficients are associated with the predicators that have positive and negative sign respectively. Therefore, it can be held that “Airline’s Frequent Flyer Program”, “Personal Preference”, “Third Party Influence”, Efficacy_of_Ground_services”, “Air_Fare” and “No_of_Stop_Overs” are likely to have high impact on airline selection based on service quality factors.

Of the 9 predicators, only three have got significant p-values i.e. “Airline’s Frequent Flyer Program”, “Personal Preference” and “Third Party Influence” (Refer: Tests of Equality of Group Means). Hence, it will be useful to develop a profile with these three statistically significant predicators

5. RECOMMENDATIONS:

a) BASED ON FACTOR ANALYSIS:

The airline’s senior management can use the table cited below to regulate various AQR variables which directly influences the factors of service quality performance of the airline from customer’s point of view in Maharashtra state to retain the customer base and become market leader in sector.

Factors	Variables
Flight Planning and Scheduling (F1)	Flight_ Timings (X1)	No._of_Stop_ Overs (X2)
Brand Loyalty Programs and Services (F2)	Airline's_Frequent_ Flyer_ Program (X4)	In-flight_ services (X5)	Personal_ Preference (X7)
Word-of-Mouth Marketing about Airline Services (F3)	Third_Party _Influence (X8)	Aircraft's_ Amenities (X9)

b) BASED ON DISCRIMINANT ANALYSIS:

Mathematically, the impact of AQRs on air traveller’s airline selection decision can be determined by using below mentioned equation

D =

Here, D = 1 (High Impact); D = 2-5 (Low Impact)

These will help the airline to gauge the impact of factors that influences the consumer decision making behaviour vis-à-vis Airline Quality Ratings (AQRs) factors while booking tickets. So, as to enable the airline’s senior management to make customer driven service design and standard and close the service quality gap that exist between the customer expectation and company’s perception about customer expectation. Thereby, achieving high customer satisfaction, retention rate, positive W-o-M Marketing, brand loyalty etc.

6. REFERENCES:

1. Akamavi, R. K., Mohamed, E., Pellmann, K. and Xu, Y. (2015), Key determinants of passenger loyalty in the low‐cost airline business, Tourism Management, 46, pp 528‐545.

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

3. Alamdari, F. (1999), Airline in‐flight entertainment: the passengers’ perspective, Journal of Air Transport Management, 5(4), pp 203‐209.

4. Archana, R., and Subha, M. (2012), A study on service quality and passenger satisfaction on Indian airlines, International Journal of Multidisciplinary Research, 2(2), pp 50‐63.

5. Badri, M. A., Abdulla, M. and Al-Madani, A. (2005).Service quality assessment and application of SERVQUAL, 22 (8), pp. 819-848.

6. Bitner, M.J. and Hubbert, A.R. (1994), Encounter satisfaction versus overall satisfaction versus quality. In R.T. Rust and R.L. Oliver (Eds.), Service quality: New directions in theory and practice, Thousand Oaks: Sage Publications, pp 72-94.

7. Bowen, B. and Headley, D (2000), Air travel consumer report: The airline quality rating in Washington DC, US Department of Transportation.

8. Carlsson, F. and Löfgren, Å. (2006), Airline choice, switching costs and frequent flyer programmes, Applied Economics, 38(13), pp 1469‐1475.

9. Chang, Y. H., and Yeh, C.H. (2002), Survey analysis of service quality for domestic airlines, European Journal of Operational Research, 139(1), pp 166-177.

10. Chen, F. Y., and Chang, Y. H. (2005), Examining airline service quality from a process perspective, Journal of Air Transport Management, 11(2), pp 79-87.

11. Chou, C. C., Liu, L. J., Huang, S. F., Yih, J. M. and Han, T. C. (2011), An evaluation of airline service quality using the fuzzy weighted SERVQUAL method, Applied Soft Computing, 11(2), pp 2117-2128.

12. Chowdhary, N. andPrakash, M. (2007), Prioritising service quality dimensions. Management Service Quality, 17 (5), pp 493-509.

13. Cobb Walgren, C. J. and Mohr, L. A. (1998), Symbols in service advertisements, Journal of Services Marketing, 12(2), pp 129-‐151.

14. Cunningham, L. F., Gerlach, J. H., Harper, M. D. and Young, C. E. (2005), Perceived risk and the consumer buying process: Internet airline reservations, International Journal of Service Industry Management, 16(4), pp 357‐372.

15. Directorate General of Civil Aviation (DGCA), Airports Authority of India (AAI), Interim Union Budget 2019-20

16. Dolnicar, S., Grabler, K., Grün, B. and Kulnig, A. (2011), Key drivers of airline loyalty, Tourism Management, 32(5), pp 1020‐1026.

17. Elliott, K. M. and Roach, D. W. (1993), Service quality in the airline industry: Are carriers getting an unbiased evaluation from consumers?, Journal of Professional Services Marketing, 9(2), pp 71-‐82.

18. Eshghi, A., Roy, S. K., and Ganguli, S. (2008). Service quality and customer satisfaction: An empirical investigation in Indian mobile Telecommunications services, Marketing Management Journal, 18 (2), pp 119-144.

19. Evangelho, F., Huse, C. and Linhares, A. (2005), Market entry of a low cost airline and impacts on the Brazilian business travellers, Journal of Air Transport Management, 11(2), pp 99‐105.

20. Fabrigar, L., Wegener, D.T., MacCallum, R. C. and Strahan, E. J. (1999), Evaluating the ise of exploratory factor analysis in psychological research, Psychological Methods (American psychological association inc.), Vol. 4 No. 3, pp 272-299.

21. Fen, Y. S., and Meillian, K. (2005). Service quality and customer satisfaction: Antecedents of customer’s re-patronage, Sunway Academic Journal, 4, pp 60-73.

22. Fourie, C. and Lubbe, B. (2006), Determinants of selection of full‐service airlines and low‐cost carriers—A note on business travellers in South Africa, Journal of Air Transport Management,12(2), pp 98‐102.

23. Gardial, S. F., Clemons, D. S., Woodruff, R. B., Schumann, D. W. and Burns, M. J. (1994), Comparing consumers' recall of pre-purchase and post-purchase product evaluation experiences, Journal of Consumer Research, 20(4), pp 548-560.

24. Gilbert, D. and Wong, R. K. (2003), Passenger expectations and airline services: A Hong Kong based study, Tourism Management, 24(5), pp 519-532.

25. Grace, D. and O’Cass, A. (2005), Service branding: Consumer verdicts on service brands, Journal of Retailing and Consumer Services,12(2), pp 125-139.

26. Gursoy, D., Chen, M. H. and Kim, H. J. (2005), The US airlines relative positioning based on attributes of service quality, Tourism Management, 26(1), pp 57-67.

27. Häubl, G. and Trifts, V. (2000), Consumer decision making in online shopping environments: The effects of interactive decision aids, Marketing Science, 19(1), pp 4-21.

28. https://www.ibef.org/industry/indian-aviation.aspx (accessed on 02.06.2019)

29. Jamal, A. and Goode, M. (2001), Consumers' product evaluation: A study of the primary evaluative criteria in the precious jewellery market in the UK, Journal of Consumer Behaviour, 1(2), pp 140-155.

30. Jin-Woo Park, Rodger Robertson, Cheng-Lung Wu (2005), “Investigating the effects of Airline Service Quality on Airline Image and Passengers’ Future Behavioral Intentions: Findings from Australian international air passengers” The journal of tourism studies Vol. 16, No. 1

31. Keller, K. L. (1993), Conceptualizing, measuring and managing customer‐based brand equity, The Journal of Marketing, 57(1), pp 1‐22.

32. Kotler, P. (1994), Marketing management, analysis, planning, implementation and control, Upper Saddle River, NJ: Prentice‐Hall.

33. Laroche, M. and Brisoux, J. E. (1989), Incorporating competition into consumer behaviour models: The case of the attitude‐intention relationship, Journal of Economic Psychology, 10(3), pp 343‐362.

34. Laroche, M., Kim, C. and Zhou, L. (1995), Direct and indirect effects of confidence on purchase intention, Advances in Consumer Research, 22(1), pp 333‐339.

35. Leick, R, (2007), Building airline passenger loyalty through an understanding of customer value: A relationship segmentation of airline passengers, Cranfield University, England.

36. Lim, S (2013), Airline service quality expectations and price importance: A study of international students, The University of Queensland, Brisbane, Australia.

37. Mason, K. J. (2000), The propensity of business travellers to use low cost airlines, Journal of Transport Geography, 8(2), pp 107‐119.

38. Maxham, J. G. and Netemeyer, R. G. (2002), Modeling customer perceptions of complaint handling over time: The effects of perceived justice on satisfaction and intent, Journal of Retailing, 78(4), pp 239‐252.

39. Nadiri, H., Hussain, K., Haktan Ekiz, E., and Erdogan, S. (2008), An investigation on the factors influencing passengers' loyalty in the North Cyprus national airline, The TQM Journal, 20(3), pp 265‐280.

40. Nejati, M. and Shafaei, A. (2009), Ranking airlines' service quality factors using a fuzzy approach: Study of the Iranian society, International Journal of Quality and Reliability Management, 26(3), pp 247-260.

41. Ostrowski, P. L., O'Brien, T. V., and Gordon, G. L. (1993), Service quality and customer loyalty in the commercial airline industry, Journal of Travel Research, 32(2), pp 16‐24.

42. Pakdil, F. and Aydın, Ö. (2007), Expectations and perceptions in airline services: An analysis using weighted SERVQUAL scores, Journal of Air Transport Management, 13(4), pp 229-237.

43. Parasuraman A, Zeithaml, VA, Berry LL (1994). Reassessment of expectations as a comparison standard in measuring service quality: implications for future research. J Mark., 58(1), pp 111–124.

44. Parasuraman, A., Berry, L. L. and Zeithaml, V. A. (1991), Refinement and reassessment of the SERVQUAL scale, Journal of Retailing, 67(4), pp 420‐450.

45. Parasuraman, A., Zeithaml, V. A. and Berry, L. L. (1988), SERVQUAL, Journal of Retailing, 64(1), pp 12-40.

46. Park, J. W., Robertson, R. and Wu, C. L. (2006), Modelling the impact of airline service quality and marketing variables on passengers’ future behavioural intentions, Transportation Planning and Technology, 29(5), pp 359-381.

47. Richards, L. G. and Jacobson, I. D. (1977), Ride quality assessment III: Questionnaire results of a second flight programme. Ergonomics, 20(5), pp 499‐519.

48. Sarabia, F. and Ostrovskaya, L. (2014), A redefined measure of the tendency to use brand name in purchasing decisions, ESIC Market Economics and Business Journal, 45(2), pp 201‐219.

49. Schneider, B. and White S. S. (2004). Service quality: Research perspectives. USA. Sage Publication Ltd.

50. Sebastianelli, R. and Tamimi, N. (2002), How product quality dimensions relate to defining quality, International Journal of Quality and Reliability Management, 19 (4), pp 442-453.

51. Smith, A. K. and Bolton, R. N. (1998), An experimental investigation of customer reactions to service failure and recovery encounters paradox or peril?, Journal of Service Research, 1(1), pp 65‐81.

52. Sultan, F. and Simpson, M. C. (2000). International service variants: Airline passenger expectations and perceptions of service quality, Journal of Services Marketing, 14(3), pp. 188-‐216.

53. Teas, R. K. (1993), Consumer expectations and the measurement of perceived service quality, Journal of Professional Services Marketing, 8(2), pp 33‐54.

54. Teikake, A. (2012), Customer satisfaction with air service delivery within Kiribati, Massey University, Palmerston North, New Zealand.

55. Tsaur, S. H., Chang, T. Y. and Yen, C. H. (2002), The evaluation of airline service quality by fuzzy MCDM, Tourism Management, 23(2), pp 107-115.

56. Vink, P., Bazley, C., Kamp, I. and Blok, M. (2012), Possibilities to improve the aircraft interior comfort experience, Applied Ergonomics, 43(2), pp 354‐359.

57. Wong, J. Y. and Chung, P. H. (2007), Managing valuable Taiwanese airline passengers using knowledge discovery in database techniques, Journal of Air Transport Management,13(6), pp 362‐370.

58. Wong, K. M. and Musa, G. (2011), Branding satisfaction in the airline industry: A comparative study of Malaysia Airlines and Air Asia, African Journal of Business Management, 5(8), pp 3410-3423.

Received on 05.06.2019 Modified on 10.07.2019

Asian Journal of Management. 2019; 10(3):167-175.

DOI: 10.5958/2321-5763.2019.00027.1

AQRs	Referenced journals
Ease of convenience of flight schedule	Chang and Yeh, 2002; Chen and Chang, 2005
No-changeover of flights	Leick, 2007; Lim, 2013; Pakdil and Aydin, 2007
Price of ticket	Aksoy et. al., 2003; Teikake, 2012; Lim, 2013
Convenience of ticket reservations	Leick, 2007; Lim, 2013; Ostrowski et. al., 1993; Pakdil and Aydin, 2007
Ticket flexibility	Leick, 2007; Lim, 2013
Baggage Allowance	Archana and Subha, 2012; Lim, 2013
Frequent Flyer Program	Gilbert and Wong, 2003; Leick, 2007
Availability of alliance partner	Gilbert and Wong, 2003; Pakdil and Aydin, 2007
Appealing advertising	Aksoy et. al., 2003; Bowen and Headley, 2000
Country of origin	Aksoy et. al., 2003,Grace and O’Cass, 2005
Brand Image	Jamal and Goode, 2001; Grace and O’Cass, 2005
Safety	Wong and Chung, 2007
Check-in counter operations	Aksoy et. al., 2003; Bowen and Headley, 2000; Chang and Yeh, 2002; Chen and Chang, 2005
Punctuality of on-board services	Tsaur, Chang and Yen, 2002; Wong and Musa, 2011
Resolving issues related to flight delays	Chang and Yeh, 2002; Pakdil and Aydın, 2007
Courtesy of frontline employees	Chen and Chang, 2005; Chou et. al., 2011; Sultan and Simpson, 2000
Frontline employee service rendering efficiency	Aksoy et. al., 2003; Chang and Yeh, 2002; Chen and Chang, 2005; Chou et. al., 2011; Lim, 2013; Ostrowski et. al., 1993; Teikake, 2012
Frontline employee problem solving skills	Chen and Chang, 2005; Chou et. al., 2011; Lim, 2013; Pakdil and Aydın, 2007
Physical appearance of frontline employee	Chang and Yeh, 2002; Chen and Chang, 2005
Multi-lingual crew	Aksoy et al., 2003; Lim, 2013; Pakdil and Aydın, 2007; Tsaur et al., 2002
Physical appearance of the aircraft interior	Aksoy et. al., 2003; Chang and Yeh, 2002; Chen and Chang, 2005; Gilbert and Wong, 2003; Lim, 2013; Ostrowski et. al., 1993
Comfort level of seats	Lim, 2013; Nejati and Shafaei, 2009
Leg-room space	Leick, 2007; Elliott and Roach, 1993
On-board food and beverage services	Aksoy et. al., 2003; Archana and Subha, 2012
On-board Entertainment Services	Pakdil and Aydın, 2007; Tsaur et al., 2002
Baggage handling	Gursoy, Chen and Kim, 2005; Lim, 2013; Ostrowski et. al., 1993; Teikake, 2012; Wong and Chung, 2007
Lounge services	Chen and Chang, 2005; Gilbert and Wong, 2003; Lim, 2013; Park, Robertson and Wu, 2006; Wong and Musa, 2011