chapter 7: travel agents attitude towards online marketing...
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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
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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.
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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.
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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
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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
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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
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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
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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.
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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
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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.
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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
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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.
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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
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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
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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.
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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.
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