GREAT LAKES INSTITUTE OF MANAGEMENT

RADIO TAXI SERVICES “The success factors”

[Report for Services Management]

Abhimanyu Sharma(FT11203), Atul Shivnani(FT11215), Meenal Sharma(FT 11236),

Kunal Kaul(FT 11233), Nitin Pahuja(FT11242) and Ujjawal Jain(FT11171)

[November 20, 2010]

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CONTENTS

ABSTRACT ................................................................................................................................... 3

INTRODUCTION......................................................................................................................... 3

LITERATURE REVIEW ............................................................................................................ 4

RESEARCH METHODOLOGY ................................................................................................ 7

QUALITATIVE RESEARCH ............................................................................................................. 7

QUANTITATIVE RESEARCH ........................................................................................................... 8

Regression Analysis ................................................................................................................. 8

Factor Analysis ........................................................................................................................ 9

RESPONDENT PROFILES ...................................................................................................... 10

Gender ................................................................................................................................... 10

Age ......................................................................................................................................... 10

QUESTIONNAIRE DESIGN .................................................................................................... 11

DATA COLLECTION ............................................................................................................... 12

DATA INTERPRETATION & ANALYSIS ............................................................................ 12

PEARSON- CORRELATION ANALYSIS ........................................................................................... 12

FACTOR ANALYSIS ..................................................................................................................... 12

REGRESSION ANALYSIS .............................................................................................................. 13

LIMITATIONS IN OUR RESEARCH .................................................................................... 14

RECOMMENDATIONS ............................................................................................................ 15

CONCLUSION ........................................................................................................................... 15

REFERENCES ............................................................................................................................ 17

APPENDIX .................................................................................................................................. 18

QUESTIONNAIRE ......................................................................................................................... 18

SPSS OUTPUT ............................................................................................................................ 19

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ABSTRACT The organized Radio Cabs industry in India has seen phenomenal growth over the last few years.

Spreading its business across metro and other urban centres of the country, the industry stands at

a crucial point. With competition rising, tapping into and delivering superior customer value is

the key to further growth. This paper has attempted to capture the key service dimensions of the

Radio Cabs industry and trace their impact on the overall satisfaction of consumers reflected by

the sample used in the study. A detailed statistical analysis carried out on data collected through

a questionnaire using a Likert’s Scale of 5 has revealed key relationships between key service

dimensions and the overall satisfaction. The paper derives key inputs for corporate players in the

industry to shape their consumer service strategy in order to raise customer lifetime value and

fight competition.

INTRODUCTION The objective of this paper is to understand and examine the factors affecting success of radio

taxi services in India. Radio Taxi services as a business is still new in India, but is growing at a

rapid pace owing to the reliability and ease of commute. Naturally, the competition is heating up

and many players are now vying for the same pie. The only way a company can differentiate and

deliver in an upcoming market is by adding intangibles to the tangibles, by aiming at customer

delight and by making every effort to increase responsiveness.

Service excellence is both unobtrusive and elusive. We know when we have received it and we

know when we have not. Service, both poor and outstanding, has a strong emotional impact upon

the customers, creating intense feelings about the organization, its staff and its services, and

influencing our loyalty to it. Yet, many organizations seem to find service excellence elusive,

hard to grasp and often difficult, if not impossible to deliver. Paradoxically consumers

instinctively know what it is and how simple it can be.

The role of culture and background in the service encounter evaluations cannot be ignored.

Because culture provides the framework for social interactions, the social rules and customer

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expectations that are related to service encounters are likely to vary from culture to culture

(Pucik and Katz, 1986). For example, the international travelers least satisfied with airline in-

flight service are likely to be Japanese, as indicated by customer satisfaction surveys conducted

by international airlines (Zeithaml and Bitner, 1996)

Because service delivery inherently includes customer contact and interaction with employees,

cultural factors may exert greater influence on consumers' evaluations of services than on their

evaluations of tangible goods. For high-contact services especially, good employee-customer

interactions are key to successful relationship building (Chase and Tansik, 1983), and a better

understanding of how to adapt service delivery behavior to the values of major cultural groups

would be highly beneficial to service managers.

The centrality of customers to every marketing concept is more relevant in service industry as it

requires frequent customer interactions. A business like radio taxi service in which customers

experience a high level of contact with the front-line personnel would do well to imbibe peter

drucker’s management philosophy – “Customer is King” – in their business model.

LITERATURE REVIEW Unconditional service guarantees: Use and scope in Indian market

Service guarantees have become an important and effective means to signal quality (Ostrom &

Lacobucci, 1995), attract and retain customers (Evans, Clark, & Knuston, 1996), and gain market

share through differentiation. In addition, firms use service guarantees to learn about customer

needs (Hart, 1988)and fine-tune internal processes to respond to service failure. In general, firms

report significant gains such as premium prices, positive customer attitudes and behaviors, and

increased revenues from implementing service guarantees (Hart 1993)(Kashyap, 2001).

A strong service guarantee that puts the customer first doesn’t necessarily lead to chaos. If

designed and implemented properly, it enables you to get control over your organization- with

clear goals and an information network that gives you the data to help you improve performance.

Companies have proved that service guarantees can be a boon to performance and profits and

can be a vehicle to market dominance. (Hart, 1988)

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Potential benefits from guarantees

The effects on consumer behavior can be grouped into impacts on: (1) Potential customers

during the decision-making process, such as reducing perceived risk; (2) current customers, for

example increasing brand loyalty and positive word-of-mouth; (3) dissatisfied customers, e.g.,

increasing their propensity to complain when dissatisfied and thereby creating opportunities for

the firm to recover the service, to lower dissatisfaction and switching propensity, as well as to

reduce negative word-of-mouth.

It is suggested that there are two amplifiers of the impact of guarantees on consumer behavior.

These are perceived risk and uniqueness of the guarantee. The higher the risk perceived by

consumers, and the higher the degree of uniqueness of the guarantee (i.e., better than

competitors’ guarantees), the higher is the potential impact of a guarantee. It is noteworthy, that

these amplifiers are proposed to mediate the impact of guarantees on consumer behavior only,

and not on operations and service quality. In other words, the beneficial impacts on operations

and service quality can be achieved even when consumers perceive little risk or other firms offer

similar guarantees. (Wirtz, 1998)

Customers have difficulty evaluating service quality prior to consumption since most services are

high in experience or credence attributes. Even when search attributes can be used to distinguish

between firms, customers may not have access to full information about the quality of competing

services. Hence, service guarantees serve as useful signals of service quality. Consequently,

service guarantees that successfully signal high service quality also serve to reduce customer

costs of search and information. Service guarantees may communicate higher service quality

either directly or indirectly by conveying lower risk (Kashyap, 2001).

Cultural implication in service delivery & role of front line staff in Indian service business

context

Research has shown that culture plays an important role in determining how customers expect

services to be delivered, (Tansik & Chase, 1988)it is important that today’s service managers

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should be aware both of their ability to leverage culture-driven expectations and of the costs of

ignoring cultural norms.

A study by Matilla (1990) revealed that that business travelers tend to focus on the output, not

the style, of the service delivery. For this target market in both Asian and western context,

efficiency (including the speed of service) is more crucial than the functional quality of the

interaction. The study also indicated which part of the service delivery is influenced by the

customer’s cultural background. The individual customer's sensitivity to culture-based norms in

service encounters, however, might be mediated by that person's purchase motivation.

It is interesting to note that, Asian and Western leisure travelers had highly different perceptions

of the quality of the focal service encounter, but Asian and Western business travelers showed

only insignificant differences in these perceptions.

Service marketing is different from FMCG marketing

The biggest challenge in the service industry is that there has to be consistency. Buying a product

is very different from buying service. When you buy a piece of soap, it’s just a piece of soap

manufactured by the same process day in and day out. A service is more human, with the

potential for inconsistency. Price based promotions, such as price deals, coupons and refunds

offers seem to dominate the marketing literature because they are the most commonly used and

are applied mostly to consumer goods. (Donnelley, 1991) US survey of promotional practice

showed that showed that price coupons were the favourite promotional tool (with 95 per cent of

marketers planning to use them in the next year). Price promotions and quantity-based

promotions offering “10 per cent extra free” or banded packs popular commonly in consumer

goods arena manipulate the quantity/price equation to increase the value of the product offering

to consumers. Such “value-increasing” promotions cannot easily work for services by an

increase in physical quantity, and therefore can only work through potentially dangerous,

margin- and image-eroding, price reductions. (Doyle, 1990) has identified four possible

dimensions of strong branding. Although they are not intentionally directed at any particular

sector, their relevance to service marketing is not in doubt.

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1. Prioritizing quality- Evidence suggests that prioritizing quality improves margin by

helping to create a competitive edge, thus increasing market share, which in turn leads to

scale economies (Buzzell & Gale, 1987).

2. Offer Superior Services- (Doyle, 1990) highlights research, which demonstrates high rate

if brand switching among customers dissatisfied with service levels. This despite a

relatively low willingness to complain to the service provider.

3. Get there first- The basic principle of product life cycle clearly emphasizes the

importance of capturing a market prior to the entry of large numbers of competitors. In

branding terms it is being first into the consumer’s mind which is all important.

4. Be different – The importance of differentiation particularly In mature markets is well

recognized. In the service sector generally, the effective control of differentiation is

particularly important.

RESEARCH METHODOLOGY

QUALITATIVE RESEARCH Qualitative researchers aim to gather an in-depth understanding of human behaviour and the

reasons that govern such behaviour. The qualitative method investigates the why and how of

decision making, not just what, where, when. Hence, smaller but focused samples are more often

needed, rather than large samples.

In order to explore the attributes on which the research has to be focused, we did a qualitative

survey. A mixed lot of 10 students were selected on the basis of demographics such as gender,

age, city etc.. Since the topic demanded discussing personal views and feedback, in-depth

interviews were conducted with them. This ascertained that there are no bias in their opinions. In

the interview which lasted for 10-12 minutes each, respondents were probed to comprehend on

what all parameters they judge are relevant for measuring the overall satisfaction levels of

availing services of a radio taxi. The interviews were recorded for later review by the team. Data

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obtained during this exercise was later structured to extract factors on which quantitative

research was to be performed.

Based on the analysis of the responses and doing secondary research we finalised our

parameters. On triangulating the information from qualitative survey, literature review and

secondary research we decided to use the SERVQUAL scale.

SERVQUAL was developed in the mid eighties by Zeithaml, Parasuraman & Berry and is used

to determine the gap between quality expectations and perceptions. It is an operational

instrument that is widely used to measure the service quality.

QUANTITATIVE RESEARCH

REGRESSION ANALYSIS

In statistical analysis, regression analysis comprises any techniques for modelling and analyzing

multi variables, the entire spotlight is on the association between dependent variable and one or

more independent variables. Regression analysis helps us to understand the phenomenon how a

changed in dependent variable is carried out when there is a change in one independent variable

keeping the other independent variable constant. More specifically it provides the conditional

expectation of non-independent variable with the given independent variable - i.e. the average

value of non-independent variable when the independent variables are kept constant. The less

common focus is on location parameter of conditional distribution of the dependent variable

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given the independent variables. The regression function is the function of independent variables

which determines the estimation target. In regression analysis, probability distribution can be

used to characterize the variation of dependent variable with respect to regression function. Also

for prediction and forecasting regression analysis is widely used. The relation of a particular

independent variable and their extent of relationship or forms can also be studied using

regression analysis. In restricted circumstances, regression analysis can be used to infer causal

relationships between the independent and dependent variables.

FACTOR ANALYSIS

Factor analysis is a statistical approach that can be used to analyze interrelationships among a

large number of variables and to explain these variables in terms of their common underlying

dimensions (factors). The statistical approach involving finding a way of condensing the

information contained in a number of original variables into a smaller set of dimensions (factors)

with a minimum loss of information (Hair et al., 1992).

In other words, it is a method used to describe variability among observed variables in terms of a

potentially lower number of unobserved variables called factors. For example, those variations in

three or four observed variables mainly reflect the variations in a single unobserved variable, or

in a reduced number of unobserved variables. Factor analysis searches for such joint variations in

response to unobserved latent variables. The observed variables are modelled as linear

combinations of the potential factors, plus "error" terms. The information gained about the

interdependencies between observed variables can be used later to reduce the set of variables in a

dataset.

Factor analysis is also called data reduction or structure detection method. The only requirement

of factor analysis is to have data in the form of correlations.

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RESPONDENT PROFILES

GENDER Gender Size Frequency

Male 30 69%

Female 14 31%

Total 44

AGE Age Groups Size

From 22 to 24 9

From 25 to 27 26

Over 28 9

Total 44

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QUESTIONNAIRE DESIGN The questionnaire used closed (or multiple choice) questions and asked the respondent to choose

among a possible set of answers. The response reflects the closest representation his/her

viewpoint. Questions of this kind may offer simple choice such as Likert scale. They may also

require that the respondent chooses among several answer categories, or that he/she uses a Likert

scale to indicate preferences. The questions were short and unambiguous to avoid difficulty in

answering the same. In addition, the problem of incomplete entries was avoided by making all

questions in the survey mandatory.

The main advantages of closed questions are:

a) The respondent is restricted to a finite (and therefore more manageable) set of responses,

b) They are easy and quick to answer,

c) They have response categories that are easy to code, and

d) They permit the inclusion of more variables in a research study because the format

enables the respondent to answer more questions in the same time required to answer

fewer open-ended questions.

The main disadvantages with closed questions are:

a) They can introduce bias, either by forcing the respondent to choose between given

alternatives or by offering alternatives that otherwise would not have come to mind,

b) They do not allow for creativity or for the respondent to develop ideas,

c) They do not permit the respondent to qualify the chosen response or express a more

complex or subtle meaning.

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DATA COLLECTION We used an online survey (refer appendix for questionnaire) to collect data. The respondents

were asked to fill a questionnaire based on the different service dimensions as elaborated in the

discussion above. A group of randomly selected participants from various cities were asked to

fill the surveys. All the respondents have used Radio Cabs service before filling the

questionnaire.

DATA INTERPRETATION & ANALYSIS The data collected from 44 respondents from online survey measured each independent variable

hypothesized by three to four questions. The responses for the questions trying to measure the

same independent variable were analyzed for reliability with cronbach’s alpha method. The

results yielded a satisfactory reliability score of 0.769 above the threshold value of 0.6. Exhibit 1

of the Appendix has the detailed list of cronbach scores for all the factors.

PEARSON- CORRELATION ANALYSIS The survey responses were collated to represent mean score for every independent variable.

This score representing each independent variable was analyzed for correlation. Correlation in

the independent variable scores reduces the credibility of inferences obtained from further

analysis. The correlation scores detailed in exhibit 2 of Appendix show that there is high

correlation among the independent variables (Reliability, Responsiveness, Empathy, Tangibility

& Assurance). This suggested the presence of common underlying factors that drive two or

more of the independent variables.

FACTOR ANALYSIS The data was analyzed with factor method to reduce the dimensions (independent variables) to

eliminate drivers that caused high correlation between the independent variables. Initial analysis

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of factor reduction resulted in unclear grouping of the factors and hence the real drivers

impacting the dependent variable could not be found. For further details refer to Exhibit 3 of

Appendix.

The factor analysis was repeated with quartimax rotation to enhance the grouping of factors

based on Eigen scores and component matrix. This resulted in clear grouping among the factors

we had hypothesized, thereby explaining the correlation discovered in the Pearson’s correlation

analysis.

Based on the loading results from the rotated factor analysis we have grouped the factors under

the following categories

Factor loading and their naming

Factor 1 Factor 2 Factor 3 Factor 4

Reliability Front line performance Tangibility Availability

REGRESSION ANALYSIS The list of independent variables inferred from the factor analysis was further used to understand

the relation between the independent variables and the overall satisfaction of the consumers from

the radio cab services. The comprehensive results of the regression analysis are presented in the

Exhibit 4 of Appendix.

The regression model yielded a significance of 0.00 demonstrating strong correlation between

the dependent variable and the independent factors identified by factor analysis.

The coefficients of the independent factors can be obtained from the co-efficient table of the

regression results in Exhibit 4 of Appendix.

The final model constructed by this research project is as follows

Satisfaction from service quality of cab services = 0.321 FAC1_Reliability + 0.311

FAC2_Front line performance + FAC3_0.066 Tangibility + 0.257 FAC4_Availability

The model has a variance explanation score of 0.194.

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The significance of the hypothesis is proven by observing the significance of the factors

comprising of the independent variables hypothesized. The summary report of the hypothesis is

as follows

Hypothesis Beta-Value p-value Results

Hypothesis 1: Delivering on promised

attributes has a high impact on overall

service satisfaction

0.321 .026 Supported

Hypothesis 2: Well trained cab drivers,

call centre executives have a high impact

on overall service satisfaction

0.311 .031 Supported

Hypothesis 3: Safety and value for money

have a high impact on overall consumer

satisfaction

0.066 .638 Not Supported

Hypothesis 4: Cab availability has a high

impact on overall consumer satisfaction 0.257 .071 Not Supported

LIMITATIONS IN OUR RESEARCH There were certain limitations in our research which is given below:

1. The study was confined to a limited set of respondents from only few cities. Therefore

the results cannot be generalized and may not be applicable to pan India.

2. The number of service providers across the country is large so the sample size will not be

predictive of the nature and quality of service across the gamut of cab service providers

3. The scope of operations is different for different players and therefore their

professionalism in terms of their service will also be perceived differently in terms of

expectations.

4. The sample size will not be able to predict the size of operations and the level of service.

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Despite these limitations, a sincere attempt has been made to collect and analyze the data and

present the information as accurately as possible.

RECOMMENDATIONS

This research was based on the goal of identifying key service dimensions that have a high

impact on overall satisfaction of radio cab consumers. The main results of this endeavor have

been summarized below. The present research has allowed us to highlight various factors which

lead to satisfaction from cab services measured on the existing service quality factors (Servqual

method from Valarie A. Zeithaml, A. Parasuraman and Leonard L.Berry) apart from others that

we identified in the form of cab condition, route familiarity etc. This research points towards

significant gaps in the available research, both with regard to the extent to which different

methodological approaches have been used and with regards to relevant factors that have not yet

been investigated. We saw that there are factors like front line performance, availability,

reliability, assurance matters, there are many other factors whose results and consideration of

their implications raise many questions that can and should be addressed in future studies.

CONCLUSION On the basis of statistical analysis of the collected data we could clearly identify the causal

relationship between the hypothesized variables and the dependent variable. Given the high

significance of relationship between promise delivery, well trained staff as expressed by the

regression analysis, both Hypothesis 1 and 2 are accepted. However the significance of factors

like safety, value for money and cab availability was low on the similar count, thereby leading to

the rejection of Hypothesis 3 and 4. We would however like to point out that qualitative

interviews with respondents suggested that they assumed availability a granted factor and hence

its impact as a relevant independent variable cannot be completely ruled out.

We further categorized the overall satisfaction levels of the sample through Fred Reichheld’s

Consumer satisfaction framework. Our findings revealed that over 22% of the respondents fell

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under the Defectors category suggesting a considerable issue with respect to customer life time

value and vulnerability to negative word of mouth. We also found that more than 31% of the

respondents were in the Neutral Category while only 45% respondents were in the Promoter

category thereby indicating a strong need to raising the overall consumer satisfaction level.

The research highlights the importance of strategic focus needed on building reliability through

promised service delivery and highly trained staff. It is a key input for the major players in the

strategic direction as this can help them in achieving higher level of consumer satisfaction going

ahead in the future. This will help the organized players transfer the Defectors and Neutrals to

the Promoters category, critical to their survival and growth in the future. In the context of rising

competition, this facet of their business holds the key to long term sustainability and profitability.

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REFERENCES

Buzzell, R., & Gale, B. (1987). The PIMPS Principles: Linking Strategy to Performance. Collier

Macmillan.

Donnelley, M. (1991). Couponing still top promo tool. DM News , 13 (13), 7.

Doyle, P. (1990). Building Successful Brands: Strategic Options. The Journal of Consumer Marketing ,

7 (2), 5-20.

Evans, M. R., Clark, J. D., & Knuston, B. (1996, December). The 100-Percent Unconditional Money-

Back Guarantee . Cornell Hotel and Restaurant Administration Quarterly , pp. 56-61 .

Hart, C. W. (1988, July-August). The power of Unconditional Service Guarantee. Harvard Business

Review .

Kashyap, R. (2001). The Effects of Service Guarantees on External and Internal Markets. Academy of

Marketing Science Review , 2001 (10).

Mattila, A. S. (1999). The role of culture and purchase motivation in service encounter evaluations.

JOURNAL OF SERVICES MARKETING , 13 (4/5), 376-389.

Ostrom, A., & Lacobucci, A. (1995). Consumer Trade-Offs and the Evaluation of Services. Journal of

Marketing , 59, pp. 17-28.

Tansik, D., & Chase, R. (1988). Effects of customer induced uncertainty on the design of service

systems. Academy of Management Annual Meeting. Anaheim,CA.

Wirtz, J. (1998). Development of a Service Guarantee Model. Asic Pacific Journal of Management , 15

(51-75).

Zeithaml, V. and Bitner, M. (1996), Services Marketing, McGraw-Hill, New York, NY.

Pucik, V. and Katz, J. (1986), ``Information, control and human resource management in

multinational firms'', Human Resource Management, Vol. 25, pp. 121-32.

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APPENDIX

QUESTIONNAIRE Demographic Details

1. Age

2. City where cab services used

a. Metro (includes Hyderabad, Pune, Bangalore)

b. Non Metro

c. Both

3. Gender

Service Quality – On the scale of 1 to 5 please rate the following

1. Cabs are usually in excellent condition.

2. Drivers are well behaved and neatly dressed

3. Whatever promised, is delivered

4. When a passenger has a problem the driver/customer care executive is helpful

5. Excellent cab services deliver great service every time

6. The cab services are punctual

7. The cab services staff is always willing to help the customer

8. As a traveller you feel safe travelling in the cab

9. The cab is always available when I need to travel

10. The cab driver is usually well versed with the routes and roads to the destination

11. The customer care executives are courteous, prompt and responsive

12. Cab services offer great value for money

13. It's very convenient to book a cab

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14. Rate your overall satisfaction with the current radio cab services

15. Facility to book a cab online will increase user convenience

SPSS OUTPUT Data Reliability

Exhibit 1

Reliability Test Results

Case Processing Summary

N %

Cases Valid 43 100.0

Excludeda 0 .0

Total 43 100.0

a. Listwise deletion based on all variables in the procedure.

Reliability Statistics

Cronbach's Alpha N of Items

.769 15

Exhibit 2

Correlations

Condition Behaviour Promisedelivery Problemsolving Ontime

Condition Pearson Correlation 1 .483**

.269 .098 .249

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Sig. (2-tailed) .001 .081 .531 .108

N 43 43 43 43 43

Behaviour Pearson Correlation .483**

1 .431**

.156 .280

Sig. (2-tailed) .001 .004 .318 .069

N 43 43 43 43 43

Promisedelivery Pearson Correlation .269 .431**

1 .365* .457

**

Sig. (2-tailed) .081 .004 .016 .002

N 43 43 43 43 43

Problemsolving Pearson Correlation .098 .156 .365* 1 .240

Sig. (2-tailed) .531 .318 .016 .120

N 43 43 43 43 43

Ontime Pearson Correlation .249 .280 .457**

.240 1

Sig. (2-tailed) .108 .069 .002 .120

N 43 43 43 43 43

Punctual Pearson Correlation .389**

.326* .347

* .185 .319

*

Sig. (2-tailed) .010 .033 .023 .234 .037

N 43 43 43 43 43

Helpfulstaff Pearson Correlation .195 .176 .260 .248 .426**

Sig. (2-tailed) .209 .259 .093 .109 .004

N 43 43 43 43 43

Safety Pearson Correlation .263 .205 .194 .004 .171

Sig. (2-tailed) .088 .187 .212 .980 .273

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N 43 43 43 43 43

Availability Pearson Correlation .288 -.098 .152 .232 .106

Sig. (2-tailed) .061 .534 .330 .135 .498

N 43 43 43 43 43

Destinationknowledge Pearson Correlation .546**

.439**

.225 .035 .000

Sig. (2-tailed) .000 .003 .147 .822 1.000

N 43 43 43 43 43

Promptnesscourtsy Pearson Correlation .251 .226 .417**

.474**

.404**

Sig. (2-tailed) .105 .145 .005 .001 .007

N 43 43 43 43 43

Valueformoney Pearson Correlation .341* .087 .124 -.186 .196

Sig. (2-tailed) .025 .577 .427 .232 .208

N 43 43 43 43 43

Bookingconvenience Pearson Correlation .296 .038 .118 .128 .204

Sig. (2-tailed) .054 .807 .451 .414 .190

N 43 43 43 43 43

Internetbooking Pearson Correlation .207 -.176 .174 .005 -.027

Sig. (2-tailed) .182 .260 .265 .973 .864

N 43 43 43 43 43

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

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Correlations

Punctual Helpfulstaff Safety Availability

Destinationknowle

dge

Condition Pearson Correlation .389**

.195 .263 .288 .546**

Sig. (2-tailed) .010 .209 .088 .061 .000

N 43 43 43 43 43

Behaviour Pearson Correlation .326* .176 .205 -.098 .439

**

Sig. (2-tailed) .033 .259 .187 .534 .003

N 43 43 43 43 43

Promisedelivery Pearson Correlation .347* .260 .194 .152 .225

Sig. (2-tailed) .023 .093 .212 .330 .147

N 43 43 43 43 43

Problemsolving Pearson Correlation .185 .248 .004 .232 .035

Sig. (2-tailed) .234 .109 .980 .135 .822

N 43 43 43 43 43

Ontime Pearson Correlation .319* .426

** .171 .106 .000

Sig. (2-tailed) .037 .004 .273 .498 1.000

N 43 43 43 43 43

Punctual Pearson Correlation 1 .396**

.190 .145 .228

Sig. (2-tailed) .009 .223 .352 .142

N 43 43 43 43 43

Helpfulstaff Pearson Correlation .396**

1 .261 .011 -.105

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Sig. (2-tailed) .009 .091 .947 .503

N 43 43 43 43 43

Safety Pearson Correlation .190 .261 1 .116 -.004

Sig. (2-tailed) .223 .091 .458 .978

N 43 43 43 43 43

Availability Pearson Correlation .145 .011 .116 1 .161

Sig. (2-tailed) .352 .947 .458 .304

N 43 43 43 43 43

Destinationknowledge Pearson Correlation .228 -.105 -.004 .161 1

Sig. (2-tailed) .142 .503 .978 .304

N 43 43 43 43 43

Promptnesscourtsy Pearson Correlation .471**

.180 .175 .267 .094

Sig. (2-tailed) .001 .247 .262 .083 .550

N 43 43 43 43 43

Valueformoney Pearson Correlation -.052 -.093 .280 .304* .126

Sig. (2-tailed) .738 .553 .069 .047 .421

N 43 43 43 43 43

Bookingconvenience Pearson Correlation .231 .250 .219 .145 -.233

Sig. (2-tailed) .136 .106 .159 .354 .132

N 43 43 43 43 43

Internetbooking Pearson Correlation -.129 -.029 .106 .240 .111

Sig. (2-tailed) .410 .854 .499 .122 .480

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N 43 43 43 43 43

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Correlations

Promptnesscourts

y Valueformoney

Bookingconvenie

nce Internetbooking

Condition Pearson Correlation .251 .341* .296 .207

Sig. (2-tailed) .105 .025 .054 .182

N 43 43 43 43

Behaviour Pearson Correlation .226 .087 .038 -.176

Sig. (2-tailed) .145 .577 .807 .260

N 43 43 43 43

Promisedelivery Pearson Correlation .417**

.124 .118 .174

Sig. (2-tailed) .005 .427 .451 .265

N 43 43 43 43

Problemsolving Pearson Correlation .474**

-.186 .128 .005

Sig. (2-tailed) .001 .232 .414 .973

N 43 43 43 43

Ontime Pearson Correlation .404**

.196 .204 -.027

Sig. (2-tailed) .007 .208 .190 .864

N 43 43 43 43

Punctual Pearson Correlation .471**

-.052 .231 -.129

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Sig. (2-tailed) .001 .738 .136 .410

N 43 43 43 43

Helpfulstaff Pearson Correlation .180 -.093 .250 -.029

Sig. (2-tailed) .247 .553 .106 .854

N 43 43 43 43

Safety Pearson Correlation .175 .280 .219 .106

Sig. (2-tailed) .262 .069 .159 .499

N 43 43 43 43

Availability Pearson Correlation .267 .304* .145 .240

Sig. (2-tailed) .083 .047 .354 .122

N 43 43 43 43

Destinationknowledge Pearson Correlation .094 .126 -.233 .111

Sig. (2-tailed) .550 .421 .132 .480

N 43 43 43 43

Promptnesscourtsy Pearson Correlation 1 .064 .147 -.063

Sig. (2-tailed) .681 .347 .690

N 43 43 43 43

Valueformoney Pearson Correlation .064 1 .227 .248

Sig. (2-tailed) .681 .143 .109

N 43 43 43 43

Bookingconvenience Pearson Correlation .147 .227 1 -.228

Sig. (2-tailed) .347 .143 .141

26 | P a g e

Exhibit 3

Total Variance Explained

Component Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

dimen

sion0

1 3.657 28.134 28.134 3.657 28.134 28.134

2 1.724 13.261 41.396 1.724 13.261 41.396

3 1.532 11.783 53.179 1.532 11.783 53.179

4 1.256 9.662 62.841 1.256 9.662 62.841

5 .923 7.102 69.943

6 .772 5.938 75.881

7 .724 5.566 81.447

8 .675 5.190 86.637

9 .506 3.891 90.528

10 .366 2.812 93.340

N 43 43 43 43

Internetbooking Pearson Correlation -.063 .248 -.228 1

Sig. (2-tailed) .690 .109 .141

N 43 43 43 43

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

27 | P a g e

11 .353 2.718 96.058

12 .287 2.211 98.270

13 .225 1.730 100.000

Extraction Method: Principal Component Analysis.

Total Variance Explained

Component Rotation Sums of Squared Loadings

Total % of Variance Cumulative %

dimen

sion0

1 3.083 23.713 23.713

2 1.976 15.198 38.910

3 1.760 13.541 52.452

4 1.351 10.389 62.841

5

6

7

8

9

10

11

12

13

28 | P a g e

Total Variance Explained

Component Rotation Sums of Squared Loadings

Total % of Variance Cumulative %

dimen

sion0

1 3.083 23.713 23.713

2 1.976 15.198 38.910

3 1.760 13.541 52.452

4 1.351 10.389 62.841

5

6

7

8

9

10

11

12

13

Extraction Method: Principal Component Analysis.

Component Matrixa

Component

29 | P a g e

1 2 3 4

Condition .680 .497 .079 -.072

Behaviour .593 .307 -.365 -.370

Promisedelivery .686 -.080 -.200 .043

Problemsolving .456 -.431 -.282 .452

Ontime .634 -.272 .077 -.107

Punctual .667 -.122 -.154 -.120

Helpfulstaff .498 -.476 .061 -.379

Safety .418 .057 .432 -.305

Availability .352 .163 .328 .700

Destinationknowledge .363 .706 -.438 .073

Promptnesscourtsy .655 -.248 -.114 .364

Valueformoney .272 .485 .625 .087

Bookingconvenience .371 -.226 .591 -.102

Extraction Method: Principal Component Analysis.

a. 4 components extracted.

Rotated Component Matrixa

Component

1 2 3 4

Condition .306 .649 .420 .166

30 | P a g e

Behaviour .401 .678 .092 -.294

Promisedelivery .662 .274 .041 .056

Problemsolving .687 -.127 -.322 .292

Ontime .643 .008 .277 -.053

Punctual .646 .236 .126 -.093

Helpfulstaff .613 -.168 .280 -.373

Safety .211 .079 .633 -.081

Availability .195 .042 .170 .824

Destinationknowledge .058 .884 -.141 .152

Promptnesscourtsy .723 .046 -.046 .331

Valueformoney -.137 .242 .677 .416

Bookingconvenience .290 -.262 .624 .079

Extraction Method: Principal Component Analysis.

Rotation Method: Quartimax with Kaiser Normalization.

a. Rotation converged in 11 iterations.

Exhibit 4

Model Summary

Model

R R Square Adjusted R Square

Std. Error of the

Estimate

31 | P a g e

d

i

m

e

n

s

i

o

n

0

1 .520a .271 .194 .76522

a. Predictors: (Constant), REGR factor score 4 for analysis 1, REGR factor score 3

for analysis 1, REGR factor score 2 for analysis 1, REGR factor score 1 for analysis

1

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 8.260 4 2.065 3.526 .015a

Residual 22.252 38 .586

Total 30.512 42

a. Predictors: (Constant), REGR factor score 4 for analysis 1, REGR factor score 3 for analysis 1, REGR factor score 2 for analysis 1,

REGR factor score 1 for analysis 1

b. Dependent Variable: Overallsatisfaction

32 | P a g e

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 3.186 .117 27.302 .000

REGR factor score 1 for analysis

1

.274 .118 .321 2.320 .026

REGR factor score 2 for analysis

1

.265 .118 .311 2.247 .031

REGR factor score 3 for analysis

1

.056 .118 .066 .474 .638

REGR factor score 4 for analysis

1

.219 .118 .257 1.857 .071

a. Dependent Variable: Overallsatisfaction