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CHAPTER 7: TRAVEL AGENTS ATTITUDE TOWARDSONLINE MARKETING OF INDIAN RAILWAYS

7.1 IntroductionThis chapter seeks to measure perception, belief and attitude of travel agents towards

online marketing of Indian Railways. Further, the opportunities provided by online

marketing and challenges that have arisen because of it have also been identified. It

also focuses on the issue of criticality of online marketing of Indian Railways for

travel agents. The business performance of travel agencies after the adoption of online

marketing of Indian Railways have also been appraised on various parameters namely

Sales revenue Cost of sale, Market share and organizational image. The effect of this

new mode of business on the number of levels of distribution has also been

determined. At last it also addresses the issue of different reasons of growth of online

marketing.

7.2 Profile of the Travel AgentsIt shows the penetration of small travel agencies 55.7% of the agents belongs to the

turnover up to 500000. Majority of the respondents (70.5%) having only 1-5

computers. A substantial number of agencies are newly established just after the

introduction of online ticket reservation. 41% of the respondents are using internet for

less than 50 hours in a week and 55.7% are using online services from last 2 – 3 years.

All descriptive analysis has been shown in table 7.1.

Table 7.1: Profile of the Travel Agents

Variable Frequency PercentAnnual Turnover Up to 500000

500000-10000001000000 and aboveTotal

34171061

55.727.916.4100

No. Of Computers 1 – 56 – 1010 and aboveTotal

439961

70.514.814.8100

Internet Usage In aWeek

Less than 50 hours50-100 hours100 - 150 hours150 hours and aboveTotal

25198961

4131.213.114.8100

Year of Establishment Before 1991 8 13.1

206

1992 – 20012002 – 2010Total

173661

27.959100

Length of Onlinemarketing Usage

Less than two year2 Years – 3 YearsMore Than 3 YearsTotal

9341861

14.855.729.5100

Source: Primary Data

7.3 Findings Pertaining To Measure Travel Agents’

Perception, Belief and Attitude towards the Online

Marketing of Indian Railways:

7.3.1 Model Evaluation

In order to achieve the objective first, the measurement model through confirmatory

factor analysis and statistical tests to establish the validity and reliability of the survey

are performed. Second, the structural model is analyzed to test the hypothesized

relationship among different factors presented in the model.

7.3.1.1 Measurement Model

The measurement model assessed individually with the help of confirmatory factor

analysis of all the constructs are presented below.

7.3.1.1.1 Perceived Usefulness

GFI=.934 CFI=.965 RMSEA=.266 Cronbach Alpha=.922

The standardized loadings of all the indicators are fairly higher than the acceptable

level 0.50. All the variables are outstanding indicators of perceived usefulness as

compare to the second indicator increases the productivity. So the convergent validity

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is considered to be fairly good. As far as model fit is considered the values of

goodness-of-fit indices i.e. GFI and CFI are higher than the acceptable threshold 0.90

(0.934 and 0.965) represents a good fit model. On the other hand the value of RMSEA

is .266 which is above the acceptable range of 0.80. To assess the construct reliability

cronbach alpha (0.922) is calculated which is fairly above the minimum value of 0.70.

Finally, it may be concluded that perceived usefulness measurement model is reliable

and valid.

7.3.1.1.2 Perceived Ease of Use

GFI=.813 CFI=.920 RMSEA=.292 Cronbach Alpha=.962

All the indicators of perceived ease of use are showing very strong standardized

loadings on the relative construct more than 0.70. It reflects all the variables are very

good indicators of perceived ease of use. The value of CFI (.920) is acceptable but

GFI (.813) is slightly below the acceptable level of .9. The cronbach’s alpha value

(.962) depicts high construct reliability. On the other hand RMSEA value is above the

level of 0.8 shows that model is not a good fit model. But on the basis of Cronbach

alpha and high loadings; the model could be considered as reliable and valid.

7.3.1.1.3 Trust

GFI=1 CFI= 1 RMSEA=0 Cronbach Alpha=.760

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All the indicators of relative construct Trust are showing very high factor loadings

greater than .70. Both trustworthy and provides reliable information have substantial

impact on trust. On the other hand the goodness of fit indices (GFI=1 and CFI=1) and

badness of fit index (RMSEA=0) are perfect. The cronbach’s alpha value (.760) is

also good. So the above measurement model is a perfect good fit model.

7.3.1.1.4 Perceived Enjoyment

GFI=1 RMSEA=0 CFI=1 Cronbach Alpha=.919

All the indicators of relative construct Perceived enjoyment are showing high factor

loadings greater than .75. It reflects that all the three variables are very good

indicators of perceived enjoyment. On the other hand the goodness of fit indices

(GFI=1 and CFI=1) and badness of fit index (RMSEA=0) are perfect. The cronbach’s

alpha value (.919) is also very high. So it could be easily concluded that the above

measurement model is a reliable and a good fit model.

7.3.1.1.5 Image

GFI=1 CFI=1 RMSEA=0 Cronbach Alpha=.938

All the indicators are showing high factor loadings more than .85. It implies that all

the three variables are very good indicators of image. On the other hand the goodness

of fit indices (GFI=1 and CFI=1) and badness of fit index (RMSEA=0) are perfect.

The cronbach’s alpha value (.938) is also very high. So it could be easily concluded

that the above measurement model is a reliable and a good fit model.

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7.3.1.1.6 Subjective Norm

GFI=1 CFI=1 RMSEA=0 Cronbach Alpha=.808

Both the indicators of relative construct subjective norm are showing high factor

loadings of .69 and .98. The second variable is a marvelous indicator of subjective

norm because it has a loading of .98. On the other hand the goodness of fit indices

(GFI=1 and CFI=1) and badness of fit index (RMSEA=0) are perfect. The cronbach’s

alpha value (.808) is also good. So the above measurement model is a perfect good fit

model.

7.3.1.1.7 Facilitating Condition

GFI=.874 CFI=.931 RMSEA=.439 Cronbach Alpha=.949

All the indicators of relative construct Facilitating condition are showing very high

factor loadings greater than .80. It implies that these indicators explain facilitating

condition very well. The value of CFI (.931) is acceptable but GFI (.874) is slightly

below the acceptable level of .9. The cronbach’s alpha value (.849) depicts very good

construct reliability. On the other hand RMSEA value is above the level of 0.8 shows

that model is not a good fit model. So on the basis of above values; the model could

be considered as reliable and valid.

210

7.3.1.1.8 Perceived Risk

GFI=1 CFI=1 RMSEA=.000 Cronbach Alpha=.895

All the three indicators of perceived risk are showing high factor loadings greater than

0.70. It could be seen that first two variables are very good indicators as compare to

the last indicator lack of privacy. On the other hand the goodness of fit indices (GFI=1

and CFI=1) and badness of fit index (RMSEA=0) are perfect. The cronbach’s alpha

value (.895) is also high. So it could be easily concluded on the basis of goodness of

fit indices and alpha value that the above measurement model is a reliable and a good

fit model.

7.3.1.1.9 Attitude

GFI=.802 CFI= .901 RMSEA=.559 Cronbach Alpha=.963

All the indicators are showing very high factor loadings greater than .90. it depicts

that all the indicators have substantial impact on attitude. The goodness-of-fit indices

(GFI=.802 and CFI=.901) also confirm it as a good fit model. But badness of fit

model is not meeting the requirement as the RMSEA (.559) value is above the cut of

value 0.8. The construct reliability is also high (Cronbach alpha=.963). So in

summary it could be inferred that the above model is a good-fit and a reliable model.

211

7.3.1.1.10 Behavioral Intention

GFI=1 CFI= 1 RMSEA=0 Cronbach Alpha=.727

First two indicators of relative construct Behavioral Intention are showing satisfactory

factor loadings of .48 and .57. But the last indicator is very strong with the loading of

1.12. It could be inferred that last indicator explain behavioral intention very well,

while first two variables are not good indicators. On the other hand the goodness of fit

indices (GFI=1 and CFI=1) and badness of fit index (RMSEA=0) are perfect. The

cronbach’s alpha value (.727) is also considerable. So the above measurement model

is a reliable and perfect good fit model.

7.3.1.1.11 Actual Usage

GFI= 1 CFI=1 RMSEA=0 Cronbach Alpha=.766

Actual usage have only two indicators out of which I will use it frequently is showing

a very strong factor loading of .96 and I will use it on a regular basis has a factor

loading of .66. On the other hand the goodness of fit indices (GFI=1 and CFI=1) and

badness of fit index (RMSEA=0) are perfect. The cronbach’s alpha value (.766) is

more than its cut off value 0.6. So above model could be easily considered as reliable

and a valid model.

7.3.2 Assessment of Constructs Reliability

Before proceeding to the any research it is very necessary to check the reliability of

the research findings. This study will compute cronbach’s alpha to assess the

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constructs reliability. As can be seen from the below table 7.2 that all the constructs

cronbach’s alpha values are greater than the value 0.70 depicts substantial reliability.

The internal consistency of all the constructs included in the model ranged from .727

to .963. This showed all the constructs have very strong and adequate construct

reliability.

Table 7.2: Assessment of Constructs Reliability for Travel Agents

Research Construct Number of Items Cronbach’s Alpha

Perceive Usefulness 4 .922

Perceived Ease of Use 6 .962

Trust 2 .760

Perceived Enjoyment 3 .919

Image 3 .938

Subjective Norm 2 .808

Facilitating Condition 4 .949

Perceived Risk 3 .895

Attitude 4 .963

Behavioral Intention 3 .727

Actual Usage 2 .766

7.3.3 Assessment of convergent Validity for Travel Agents

The convergent validity of the measurement models of the constructs is assessed by

examining the standardized regression coefficient (loading) between the indicator and

their constructs. High loadings ensure that all indicators are measuring the same

construct. Acceptable loading is 0.5 or higher and should be statistically significant.

The following table 7.3 depicts that all loadings are greater than 0.5 except one BI1

and significant at .001 level of significance.

Table 7.3: Assessment of Convergent Validity for Travel Agents

Construct Indicator Loading

Perceived Usefulness PU1

PU2

PU3

PU4

.92

.59

1.00

.91

213

Perceived Ease of Use PEOU1

PEOU2

PEOU3

PEOU4

PEOU5

PEOU6

.99

.85

.92

.85

.72

1.00

Trust TR1

TR2

.73

1.00

Perceived Enjoyment PE1

PE2

PE3

.78

.94

1.00

Image IM1

IM2

IM3

.92

.95

.88

Subjective Norm SN1

SN2

.69

.98

Facilitating Condition FC1

FC2

FC3

FC4

.89

1.01

.95

.83

Perceived Risk PR1

PR2

PR3

.95

.96

.71

Attitude ATT1

ATT2

ATT3

ATT4

.94

.91

.98

.96

Behavioral Intention BI1

BI2

BI3

.48

.57

1.12

Actual Usage AU1

AU2

.66

.96

214

It could be inferred from the above measurement model validity and reliability

examination that the instrument used to measure attitude, Behavioral intention and

Actual usage individually is adequate and reliable.

7.3.4 Structural Model

After successful validation and reliability testing of measurement models, the

structural model can be analyzed. Structural model will be evaluated by using R-

square for dependent constructs, path coefficients and significant level of structural

path coefficient. First of structural equation model will be analyzed on the basis of

squared multiple correlation (R2).

7.3.4.1 R-square

Squared multiple correlation (R2) for each endogenous construct is used to measure

the percentage of construct variation explained by the exogenous construct. The

values should be sufficiently high for the model to have a minimum level of

explanatory power. Chin (1998b) considers values of approximately .670 substantial,

values around .333 average, and values of .190 and lower weak.

Table 7.4: R-square for endogenous constructs for Travel Agents

Construct R-square

Perceived Usefulness .562

Attitude 1.000

Behavioral Intention .556

Actual Usage .993

In this study perceived usefulness explains 56.2 percent of variation. Perceived

usefulness, perceived ease of use and all other external constructs explains 100

percent variation in attitude. But attitude explains 55.6 percent of behavioral intention.

On the other hand behavioral intention explains almost total variation of actual usage

i.e. 99.3 percent.

The structural model results are summarized in figure 7.1 and table 7.5.

7.3.4.2 Path Analysis

The next step is to evaluate the proposed hypothesis by using the estimated path

coefficients and their significance levels. Path coefficients depict the strength of the

relationship between two constructs. The following results confirm the

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appropriateness of TAM for its applicability in adoption of online marketing in Indian

Railways. All the path coefficients are significant at p-value=.000. It could be seen

that perceived usefulness is predicted by perceived ease of use ( = .750).

Furthermore, Attitude has positive relation with perceived ease of use ( = .001),

perceived enjoyment ( =.734), Trust ( =.251), facilitating condition ( = .337) and

perceived risk ( =.097). It has also been verified that perceived usefulness ( = -.067),

subjective norm ( =-.027) and Image ( =-.521) have negative relationship with

attitude. Subsequently behavioral intention is determined by perceived usefulness ( =

.746) and attitude ( =.096). Finally, Actual usage behavior is predicted very strongly

by behavioral intention ( = .997). At last it could be concluded that H2, H3, H4, H5,

H6, H9, H11 and H12 are supported and remaining H1, H7, H8 and H10 has not been

supported. The hypothesis testing results are summarized in table 7.5.

Figure 7.1: Results of testing the Hypothesized links for Travel Agents

Note: - Path Coefficients with * symbol are not supporting the hypothesis

PU

ATT BI AU

PEOU

R2: .562

R2: 1.00 R2: .556R2: .993-.067*

.001

.096 .997.750

.746

TR

PE

IMSN FC

PR

.097*

.337

-.027*

-.521*

.734

.251

216

Table 7.5: Hypothesis Testing for Travel Agents

Hypothesis Effects Path

coefficients

p-value Remarks

H1 PU ATT -.067 .000 Not Supported

H2 PU BI .746 .000 Supported

H3 PEOU PU .750 .000 Supported

H4 PEOU ATT .001 .000 Supported

H5 TR ATT .251 .000 Supported

H6 PE ATT .734 .000 Supported

H7 IM ATT -.521 .000 Not Supported

H8 SN ATT -.027 .000 Not Supported

H9 FC ATT .337 .000 Supported

H10 PR ATT .097 .000 Not Supported

H11 ATT BI .096 .000 Supported

H12 BI AU .997 .000 Supported

7.3.5 Explaining Antecedents of Travel Agents Attitude

Previous researches on TAM make use of belief about perceived usefulness and

perceived ease of use to explain attitude. These beliefs are usually created from

external information, experiences or self generated. The present study highlights the

significance of these two constructs in addition with various external constructs in

determining the attitude of travel agents. Attitude of travel agents is jointly predicted

by perceived ease of use ( = .001), perceived enjoyment ( =.734), Trust ( =.251),

facilitating condition ( = .337), perceived risk ( =.097), perceived usefulness ( = -

.067), subjective norm ( =-.027) and Image ( =-.521). In fact, all the constructs are

explaining a 100% of variance in attitude. This is an indication of worthy explanatory

power of the model in explaining the attitude of the travel agents towards online

marketing in Indian Railways. Among the relationships facilitating condition and

perceived enjoyment are two major determinants of travel agents attitude towards

online marketing of Indian railways.

7.3.5.1 Positive antecedents of attitude

Travel agents attitude is positively and strongly affected by perceived enjoyment

(path coefficient= .734) thereby supporting hypothesis 6. It indicates that travel agents

217

attitude will positively increase if they perceive that using online marketing is

interesting, joyful activity and enjoyable.

Facilitating condition is a second strong positive antecedent of travel agents attitude

(path coefficient= .337) and supports hypothesis 9. It implies that travel agents

sufficient funds, appropriate technology, training and help to use online marketing

and it plays a very important role in determining the attitude. The results are

consistent with the findings of venkatesh (2000).

Trust (path coefficient= .251) also has a positive impact on attitude towards online

marketing and supporting hypothesis 5. It implies that travel agents consider online

marketing of Indian Railways reliable and trustworthy and it positively affects their

attitude.

Surprisingly perceived risk has positive influence on attitude (path coefficient= .097)

although it is very less thereby not supporting hypothesis 10. This study shows that

travel agents think that online transactions are secure. It also provides safe monetary

transactions and privacy. The results are not consistent with the findings of Ruyter et.

al (2000), Changa et. al. (2004) who found that that risk perception has significant

negative impact on attitude towards e-service adoption. Manzari (2008) reported in

his research that perceived risk has insignificant negative impact on intention to use

online reservation system.

Perceived ease of use has positive effect on driving the travel agents attitude (path

coefficient= .001) and supporting hypothesis 4. It indicates that if travel agents

perceive that service is easy to use, learns, and understand, simple and interaction is

clear; it will increase their attitude. But it has negligible effect on attitude as path

coefficient is very less. The results have also been verified by Taylor and Todd (1995)

and Karami (2006).

7.3.5.2 Negative antecedents of attitude

Image has strong negative (path coefficient= -.521) impact on attitude and not

supporting hypothesis 7. It implies that travel agents do not consider that the use of

online marketing is a status symbol, prestigious and improves image of their business.

Perceived usefulness also has negative impact on attitude of travel agents (path

coefficient= -.067) and does not supports hypothesis 1. It indicates that agents’ think

that online marketing of Indian Railways does improves their performance and

productivity. It is not useful in making their business easy and fast. The findings are

not consistent with Dehbashi (2007), karami (2006), Taylor and Todd (1995) and Yu

218

et al., (2004) who reported a significant and positive relationship between perceived

usefulness and attitude.

Subjective norm as social effect (path coefficient= -.027) has negative impact on

attitude towards online marketing of Indian Railways and not supporting hypothesis 8.

It implies that positive reports of important and influencing social group will not

increase the attitude of the agents. The reverse findings have been reported by yu et

al. (2004) and karami (2006). They have verified positive impact of subjective norm

on attitude.

7.3.6 Explaining Antecedents of Behavioral Intention

In the present study behavioral intention to adopt online marketing is jointly predicted

by perceived usefulness and attitude with significant path coefficients of = .746 and

=.096 respectively. Therefore, the results are supporting hypothesis 2 and

hypothesis 11. The effect of these two constructs perceived usefulness and attitude is

accounted for substantial variance of 55.6% on behavioral intention. Dehbashi (2007),

Yu et. al. (2004) and Karami (2006) also verified the existence of direct and positive

effect of perceived usefulness and attitude on intention towards acceptance of e-

ticketing. Out of these two determinants perceived is a strongest predictor of

behavioral intention. So it is advisable to work on the constructs which are important

in making the online services useful. But earlier it has been discussed that agents do

not consider it as useful. It suggests efforts should be made to make the online

services useful so that it can improve their performance and productivity. Also it

should to do business more conveniently and easily.

7.3.7 Explaining Antecedents of Actual Use Behavior

Behavioral intention to use online marketing is significantly positively related with

the actual usage behavior of the consumers with an extremely high path coefficient of

0.997. Marjan Ghamatrasa (2006) also reported a significant positive relation between

intention and actual usage. There is a substantial effect of intention on actual use

accounted for 99.3% of the variance in this construct. It indicates a very good

explanatory power of the model for adoption of online marketing in Indian Railways.

The results also supports hypothesis 12.

219

Figure 7.2: - Complete Model for Travel Agents with all Indicators

Note: - Path Coefficients with * symbol are not supporting the hypothesis

1.00 .91.59.92

PU4PU3PU2PU1

.99 PEOU1

.85 PEOU2

PU

.750.92 PEOU3

.85 PEOU4 PEOU

.72 PEOU5

1.00 -.067* .746PEOU6

1.12.001

.57.48.73 TR1

TR

.251

.96

.66

AU2

AU1

AU

BI2BI1

BI

BI31.00 TR2

PE.94

.78

PE2

PE1

1.00 .096

.734

ATTPE3

.997-.521*.92

IM2

IM3

IM1

.88

.95IM

-.027*

.98

ATT4ATT3ATT2ATT1

.96.91

.337

.94

.69

SN2

SN1SN

.98

-.097*

.95

.89

FC2

FC3

FC1

FC1.01

.83 FC4

.71

.96

.95 PR2

PR4

PR3 PR

220

7.3.8 Equation to Measure Travel Agents Attitude

Path analysis has provided estimates for each relationship in the model shown in

figure. These estimates could be used to measure the travel agents attitude, behavioral

intention and actual use (adoption).

In the travel agents model for any observed values of perceived usefulness, perceived

ease of use, perceived enjoyment, image, trust, subjective norm, facilitating condition

and perceived risk; their attitude could be measured by using the following equation:

ATT = .734(PE) + .251(TR) + .337(FC) + .001(PEOU) + .097(PR) - .067(PU) -

.521(IM) - .027(SN)

Similarly, estimated value for Behavioral Intention and Actual Use can be obtained:

BI = .746(PU) + .096(ATT)

AU = .997(BI)

7.4 Findings Pertaining To Opportunities Offered By Online

Marketing of Indian Railways to Travel Agents

7.4.1 Descriptive Statistical Analysis:

Table7.6 highlights the importance of each opportunity on the basis of its mean

scores.

It is evident from the table 7.6 that Helps in handling large volume of sales and

possibility of reduced costs are the major opportunities with mean scores 2.87and 3.02

respectively. On the other hand respondents have ascribed impetus for new product

development as a least preferred opportunity. In order to draw better results all the

responses are further analyzed with the help of Multidimensional scaling.

221

Table 7.6 Descriptive Statistics regarding the opportunities

For Travel Agents Offered by online marketing

OpportunitiesMean

Std.Deviation

Helps in handling large volume ofsales

2.87 3.069

Possibility of reduced costs 3.02 2.668Reaching for new markets 5.25 1.738Possibility of improved customerservices

5.75 2.300

Easy access to information 5.92 3.556

Possibility of improved profitability 6.10 2.694Increase in customer base 6.18 2.924Possibility of improvement in theorganization’s image

6.46 2.579

Possibility of shortening of supplychain

6.54 1.385

Impetus for new productdevelopment

6.92 1.865

7.4.2 Multidimensional scaling (MDS): In order to perform MDS ALSCAL

procedure with the help of SPSS 16 is being used. MDS yields to perceptual mapping

which explains the relative position of various opportunities on a 2 X 2 matrix. Before

performing MDS there is a need to check its suitability.

Iteration history for the 2 dimensional solutions (in squared distances)

Young's S-stress formula 1 is used. Iteration S-stress Improvement

1 .09694 2 .08138 .01557 3 .07942 .00196 4 .07828 .00114 5 .07717 .00111 6 .07650 .00067 Iterations stopped because S-stress improvement is less than .001000 For matrix Stress = .08158 RSQ = .96582

222

The fit of an MDS solution is commonly assessed by the stress measure. Stress is a

lack of fit measure; higher values of stress indicate poorer fits. R-square is a measure

of goodness of fit. Although higher values of R-square are desirable, values of 0.60 or

higher are considered acceptable (Malhotra 2008). In this case, the value of RSQ is

.96582 which is very high with fairly low value of stress (.08158) indicates goodness

of MDS.

Configuration derived in 2 dimensions

Table 7.7: Stimulus Coordinates for Travel Agents Stress = .08158 RSQ = .96582

DimensionNumber Stimulus Name1 2

1 Helps in handling large volume ofsales

2.1334 -.6484

2 Possibility of reduced costs 1.8831 -.58063 Possibility of improved customer

services.6439 .4077

4 Possibility of improvement in theorganization’s image

.1945 1.3803

5 Possibility of shortening of supplychain

-.0954 .7291

6 Impetus for new product development -.5472 .90067 Reaching for new markets -.2630 -.27708 Increase in customer base -1.1704 -.67509 Possibility of improved profitability -1.2008 -.229910 Easy access to information -1.5780 -1.0068

Source: Primary Data

It is evident from the perceptual mapping (Figure 7.3) of travel agents attitude that

Helps in handling large volume of sales, Possibility of reduced costs, Possibility of

improved customer service and Possibility of improvement in the organization’s

image are the primary opportunities. On the basis of closer examination it could be

seen that sales volume and reduce cost are more skewed to the positive axis, so these

could be reported as main primary opportunity. On the other hand impetus for new

product development and possibility of shortening of supply chain are the most

important secondary opportunities. Rests of the opportunities are cited as secondary

least important opportunities.

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Figure 7.3: Opportunities for Travel Agents

7.5 Findings Pertaining To Challenges Posed By Online

Marketing of Indian Railways to Travel Agents

7.5.1 Descriptive Statistical Analysis: Table 7.8 highlights the importance of each

challenge on the basis of its mean scores.

It is evident from the table 7.8 that Lack of government support is a major challenge

followed by Lack of infrastructure and Lack of technology with mean scores 3.34,

5.39 and 5.46 respectively. In order to draw better results all the responses are further

analyzed with the help of Multidimensional scaling.

224

Table 7.8: Descriptive Statistics regarding the challenges for

Travel Agents posed by online marketing

MeanStd.

Deviation

Lack of government support 3.34 3.600Lack of infrastructure 5.39 2.347Lack of technology 5.46 4.064

Security 5.79 3.560Resistance from channelmembers

6.28 2.450

Lack of training 6.38 3.489Lack of confidence in thebenefits of online marketing

6.41 1.465

Difficulty with integrating onlinemarketing and existing system

6.41 1.736

Lack of skilled employees 6.46 3.058

Threat of disintermediation 6.92 3.814Lack of funds 7.16 2.458

7.5.2 Multidimensional scaling (MDS): In order to perform MDS ALSCAL

procedure with the help of SPSS 16 is being used. MDS yields to perceptual mapping

which explains the relative position of various challenges on a 2 X 2 matrix. Before

performing MDS there is a need to check its suitability.

Iteration history for the 2 dimensional solutions (in squared distances)

Young's S-stress formula 1 is used.

Iteration S-stress Improvement

1 .18168 2 .13895 .04273 3 .12690 .01205 4 .12115 .00575 5 .11904 .00211 6 .11834 .00070 Iterations stopped because

S-stress improvement is less than .001000

For matrix

Stress = .10931 RSQ = .93442

225

The fit of an MDS solution is commonly assessed by the stress measure. Stress is a

lack of fit measure; higher values of stress indicate poorer fits. R-square is a measure

of goodness of fit. Although higher values of R-square are desirable, values of 0.60 or

higher are considered acceptable (Malhotra 2008). In this case, the value of RSQ is

.93442 which is very high with fairly low value of stress (.10931) indicates goodness

of MDS

Configuration derived in 2 dimensions

Table 7.9: Stimulus Coordinates for Travel Agents Challenges

Stress = .10931 RSQ = .93442DimensionNumber Stimulus Name1 2

1 Threat of disintermediation 1.7903 -.11672 Lack of technology 1.7222 .91683 Lack of funds .8202 -.73694 Lack of skilled employees .1.1786 -.54055 Lack of confidence in the benefits of online

marketing.1934 .0989

6 Difficulty with integrating online marketing andexisting system

-.4075 -.4647

7 Lack of infrastructure .0399 -.84188 Resistance from channel members -.8675 -.12379 Lack of training -.8201 1.1946

10 Security -1.5737 -.433611 Lack of government support -2.0758 1.0475

Source: Primary DataIt could be easily conclude from the perceptual mapping (Figure 7.4) of travel agents

attitude that Lack of technology is a most important and primary challenge of online

marketing of Indian Railways followed by Lack of confidence in the benefits of

online marketing . On the other hand Threat of disintermediation, Lack of skilled

employees, Lack of funds and Lack of infrastructure are other primary challenges but

these are least important. Furthermore Lack of government support and Lack of

training are considered as most important secondary challenges. But after a close

examination lack of technology is reported as most important challenge. The number

of studies also identified fear of technology, problems about disintermediation,

Privacy and security problems, high costs of entering e-business, changes between the

telecommunication infrastructures etc. as major challenges of entering into an online

business (Paul, 1996; Rosen and Howard, 2000).

226

Figure 7.4: Challenges for Travel Agents

7.6 Criticality of Online Marketing of Indian Railways

As is clearly reflected by the table 7.10 and figure 7.5 that majority of the agents

(31%) agreed that online marketing of Indian Railways plays a very critical part in

their marketing strategies. Out of the rest of the respondents only 13% claimed it as

somewhat critical. An analysis of these findings shows that travel agents in India are

recognizing the growing importance of online marketing. But the percentage of

agents assigning it the status of “Not at all critical” and “Don’t know” is similar

(28%) implies that some of the agents who had adopted online marketing are still not

serious about it.

Table 7.10: Criticality of online marketing of Indian Railways

Extent of Criticality Frequency Percent

Very critical 19 31.1

Somewhat critical 8 13.1

227

Not at all 17 27.9

Don’t Know 17 27.9

Total 61 100.0

Figure 7.5: Extent of Criticality of Online Marketing

7.7 Appraising the Business Performance of the Travel

Agencies after the Adoption of Online Marketing of Indian

RailwaysIt is to be noted here that these findings are indicating only the directions of the

performance not the quantum since these were not supported with the actual data.

7.7.1 Increase in Sales Revenue: A majority of the respondents (49.18%) were

agreeing about the increase in the sales revenue after the adoption of online

marketing. A sizable number of them (22.95%) strongly agreed that their revenue has

increased. But handful of the respondents reported their strong disagreement and

disagreement (8.2% and 6.56%, respectively) with the parameter that their revenue

had increased. However rest of the respondents (13.11%) was undecided about the

impact on sales revenue. It could be easily inferred that online marketing has a

positive impact on the sales revenue.

Table 7.11: Impact on Sales Revenue

Increase in SalesRevenue Frequency Percent

Strongly Disagree 5 8.2Disagree 4 6.56

228

UndecidedAgree

814

13.1122.95

Strongly AgreeTotal

3061

49.18100

Figure 7.6: Impact on Sales Revenue

7.7.2 Increase in cost of sales:

The largest group of the respondents recorded their agreement and strong agreement

(44.26% and 27.87%) that with the introduction of Online marketing their cost of

sales has increased. A handful of the respondents were undecided (11.48%) about the

contention. However a small group of respondents disagreed and strongly disagreed

(9.8% and 6.56%, respectively) with the fact that their cost has improved.

Table 7.12: Impact on Cost of Sales

Increase in Cost ofSales Frequency Percent

Strongly Disagree 4 6.56

Disagree 6 9.8

UndecidedAgree

727

11.4844.26

Strongly AgreeTotal

1761

27.87100

229

Figure 7.7: Impact on Cost of Sales

The reason of increase in cost of sales may be the installation of expensive computers,

hiring of trained and skilled employees etc. No doubt internet is a new mode of doing

business. It may cut the operational cost but the installation costs are quite high in the

beginning.

7.7.3 Increase in market Share : As regards to the increase in market share a large

number of respondents confirmed their agreement and strong agreement (50.82% and

27.87%, respectively) that the market share has substantially increased with the

introduction of Online Marketing.

Table 7.13: Impact on Market Share

Increase in MarketShare Frequency Percent

Strongly Disagree 3 4.92

Disagree 5 8.2

UndecidedAgree

531

8.250.82

Strongly AgreeTotal

1761

27.87100

Figure 7.8: Impact on Market share

230

7.7.4 Improvement in Organizational Image: An overwhelming majority of the

respondents confirmed (Agree = 36.07% and strongly agree = 32.79%) that there is an

improvement in the organizational image after the introduction of online marketing.

Around 18.03% were undecided, however only 8.2% respondents denied the fact of

improvement in image.

Table 7.14: Improvement in Organizational Image

Improvement in OrganizationalImage Frequency Percent

Strongly Disagree 3 4.92

Disagree 5 8.2

UndecidedAgree

1122

18.0336.07

Strongly AgreeTotal

2061

32.79100

Figure 7.9: Improvement in Organizational Image

It has also come into sight as one of the significant inspirational factor to adopt online

marketing in their organization.

7.8 Effect on Number of Levels of Distribution Channel

Surprisingly a very large majority of respondents (74%) stated that the number of

intermediaries has increased after the implementation of online marketing of Indian

Railways. Rest of the respondents (26%) claimed that there is no change in the levels

231

of distribution. No respondent claimed that there is any kind of reduction and

elimination in the number of levels. This seems rather surprising that instead of

disintermediation of intermediaries the number has increased. This increase in the

level may be because online facility requires facility of other services like Banks for

payment gateways etc. At the same time it has become very easy and economical to

become an online travel agent.

Table 7.15: Effect of online marketing of Indian Railways

on Number of Levels of Distribution Channel

Effect on Levels ofDistribution channel Frequency Percent

No Change 16 26

Increased 45 74

Reduced 0 0

Total 61 100.0

Figure 7.10: Effect of online marketing of Indian Railways

on Number of Levels of Distribution Channel

7.9 Reason of Growth of Online MarketingAs reflected by the above figure7.11 a very large majority (53) of the respondents

ticked on the reason Internet and mobile users are growing. A sizable number (43) of

respondents opted for easy accessibility to products from any part of the world. A

very small number has gone for other options. A look at the chart reveals that no

232

respondent selected the option television will be internet based. So on the basis of

above findings it may be concluded that Internet and mobile users are growing and

easy accessibility to products from any part of the world are the two most important

reasons of growth of online marketing.

Figure 7.11: Reason of Growth of Online Marketing

7.10 Conclusion

The overall result shows that Technology Acceptance Model provides good

understanding to measure perception, belief and attitude of travel agents. The result

show the strong support for the positive effect of perceived enjoyment, facilitating

condition, trust and perceived ease of use on attitude. The constructs that have

negative effect on attitude are perceived usefulness, image and subjective norm.

These factors explain 100% of the variance of attitude towards online marketing of

Indian Railways. The result shows significant support for impact of attitude on

behavioral intention to use online marketing. Finally, Actual usage behavior is

predicted very strongly by behavioral intention.

The main opportunity, which prompted travel agents to go in for online marketing of

Indian Railways, are the ease of handling large volume of sales and possibility of

reduced cost. The main challenge that these travel agents are facing while

implementing online marketing is the lack of technology and lack of confidence in the

benefits of online marketing. Approximately one third of the respondents felt that

online marketing is a critical part of their marketing strategy and 28% of them do not

233

think so. On the basis of self assessment of their performances after the execution of

online marketing, a greater part of respondents agreed that they have improved than

before on various parameters. Approximately fifty percent of the respondents reported

an increase in sales revenue and market share. While a majority of them experienced a

cutback in cost of sales. Similarly, as regards the change in organizational image, a

considerable number of respondents experienced enrichment. Around three fourth of

the respondents cited an increase in the levels of distribution channel, a few reported

no change in the latter. A very large number of travel agents claimed that increase in

internet and mobile users and easy accessibility of products from ant part of the world

are the main reasons of growth in online marketing.

7.11 ReferencesEgger, F. N. (1999), “Human Factors in Electronic Commerce : Making System

Application Pealing, Usable and Trustworthy”, Proceedings of twelfth BledInternational E-commerce Bled, Slovenia.

Badnjevic, Jasmina and Lena Padukova (2006), “ICT Awareness in Small Enterprisesin the Indian Tourism Branch”, Project Report, IT University of Göteborg,Sweden.

Grenblad, Daniel and Pernilla, Rosén (1999), “Internet – A Sales Channel InTheAirline IndustryDecision Situation, Relationships, Added Value,AndFinancials”, Master Thesis in Business Administration and Management,Linköping University, Sweden available athttp://www.ep.liu.se/exjobb/eki/1999/040/

Duncan, Tom and Moriarty, Sandra E. (1998), “A Communication-Based MarketingModel for Managing Relationships”, Journal of Marketing, Vol. 62, April, p1-13.

Lewis, Ira Et al (1998), “The Impact of Information Technology on Travel Agents”,Transportation Journal, Vol. 37, Issue. 4, pp. 20-25.

Siguaw, Judy A. et al (1998), “Effects of Supplier Market Orientation on DistributorMarket Orientation and the Channel Relationship: The DistributorPerspective”, Journal of Marketing, Vol. 62, July, p 99-111.

Ghamatrasa, Marjan, “Internet Adoption Decision Model among Iranian Small andMedium Enterprises”, Master Thesis, Lulea University of Technologyretrieved from www. essays.com.

Homayooni, Narges, “The Impact of the Internet on the distribution Value chain- TheCase of the Iranian Tourism Industry”, Master Thesis, Lulea University ofTechnology retrieved from www. essays.com.

Bitner, Mary J. and Bernard H. Booms (1982). “Trends in Travel and TourismMarketing: The Changing Structure of Distribution Channels,” Journal ofTravel Research, (Spring), pp.39-44.