CHAPTER 7: TRAVEL AGENTS ATTITUDE TOWARDS ONLINE MARKETING OF INDIAN RAILWAYS

Transcription

1 CHAPTER 7: TRAVEL AGENTS ATTITUDE TOWARDS ONLINE MARKETING OF INDIAN RAILWAYS 7.1 Introduction This 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 Agents It shows the penetration of small travel agencies 55.7% of the agents belongs to the turnover up to 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 Percent Annual Turnover Up to and above Total No. Of Computers and above Total Internet Usage In a Less than 50 hours Week hours hours hours and above Total Year of Establishment Before

2 Length of Online marketing Usage Source: Primary Data Total Less than two year 2 Years 3 Years More Than 3 Years Total Findings Pertaining To Measure Travel Agents Perception, Belief and Attitude towards the Online Marketing of Indian Railways: 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 Measurement Model The measurement model assessed individually with the help of confirmatory factor analysis of all the constructs are presented below 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 All the variables are outstanding indicators of perceived usefulness as compare to the second indicator increases the productivity. So the convergent validity 206

3 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 To assess the construct reliability cronbach alpha (0.922) is calculated which is fairly above the minimum value of Finally, it may be concluded that perceived usefulness measurement model is reliable and valid 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 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 Trust GFI=1 CFI= 1 RMSEA=0 Cronbach Alpha=

4 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 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 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. 208

5 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 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. 209

6 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 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 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. 210

7 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 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 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 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 211

8 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 Perceived Ease of Use Trust Perceived Enjoyment Image Subjective Norm Facilitating Condition Perceived Risk Attitude Behavioral Intention Actual Usage 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 PU

9 Perceived Ease of Use Trust Perceived Enjoyment Image Subjective Norm Facilitating Condition Perceived Risk Attitude Behavioral Intention Actual Usage PEOU1 PEOU2 PEOU3 PEOU4 PEOU5 PEOU6 TR1 TR2 PE1 PE2 PE3 IM1 IM2 IM3 SN1 SN2 FC1 FC2 FC3 FC4 PR1 PR2 PR3 ATT1 ATT2 ATT3 ATT4 BI1 BI2 BI3 AU1 AU

10 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 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 (R 2 ) R-square Squared multiple correlation (R 2 ) 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 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 percent. The structural model results are summarized in figure 7.1 and table 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 214

11 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 R 2 :.562 PU * R 2 : 1.00 R 2 :.556 R 2 : ATT.096 BI.997 AU PEOU TR *.097*.337 PE IM -.521* SN FC PR Note: - Path Coefficients with * symbol are not supporting the hypothesis 215

12 Table 7.5: Hypothesis Testing for Travel Agents Hypothesis Effects Path p-value Remarks coefficients H1 PU ATT Not Supported H2 PU BI Supported H3 PEOU PU Supported H4 PEOU ATT Supported H5 TR ATT Supported H6 PE ATT Supported H7 IM ATT Not Supported H8 SN ATT Not Supported H9 FC ATT Supported H10 PR ATT Not Supported H11 ATT BI Supported H12 BI AU Supported 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 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 216

13 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) 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 217

14 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 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 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 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

15 Figure 7.2: - Complete Model for Travel Agents with all Indicators PU1 PU2 PU3 PU4.99 PEOU1 PU.85 PEOU2.92 PEOU PEOU4 PEOU.72 PEOU PEOU * TR1 TR TR2.78 PE1.251 BI1 BI2 BI PE2 PE PE3 ATT.096 BI AU AU IM1 IM2 IM3 IM -.521* -.027* ATT1.94 ATT2.91 ATT3.98 ATT AU SN1 SN SN2.89 FC * 1.01 FC2 FC.95 FC3.83 FC4.95 PR PR3 PR4 PR Note: - Path Coefficients with * symbol are not supporting the hypothesis 219

16 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 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. 220

17 Table 7.6 Descriptive Statistics regarding the opportunities For Travel Agents Offered by online marketing Opportunities Helps in handling large volume of sales Mean Std. Deviation Possibility of reduced costs Reaching for new markets Possibility of improved customer services Easy access to information Possibility of improved profitability Increase in customer base Possibility of improvement in the organization s image Possibility of shortening of supply chain Impetus for new product development 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 Iterations stopped because S-stress improvement is less than For matrix Stress = RSQ =

18 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 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 = RSQ = Number Stimulus Name Dimension Helps in handling large volume of sales 2 Possibility of reduced costs Possibility of improved customer services 4 Possibility of improvement in the organization s image 5 Possibility of shortening of supply chain Impetus for new product development Reaching for new markets Increase in customer base Possibility of improved profitability Easy access to information 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. 222

19 Figure 7.3: Opportunities for Travel Agents 7.5 Findings Pertaining To Challenges Posed By Online Marketing of Indian Railways to Travel Agents 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. 223

20 Table 7.8: Descriptive Statistics regarding the challenges for Travel Agents posed by online marketing Mean Std. Deviation Lack of government support Lack of infrastructure Lack of technology Security Resistance from channel members Lack of training Lack of confidence in the benefits of online marketing Difficulty with integrating online marketing and existing system Lack of skilled employees Threat of disintermediation Lack of funds 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) For matrix Young's S-stress formula 1 is used. Iteration S-stress Improvement Iterations stopped because S-stress improvement is less than Stress = RSQ =

21 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 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 = RSQ = Number Stimulus Name Dimension Threat of disintermediation Lack of technology Lack of funds Lack of skilled employees Lack of confidence in the benefits of online marketing 6 Difficulty with integrating online marketing and existing system 7 Lack of infrastructure Resistance from channel members Lack of training Security Lack of government support Source: Primary Data It 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). 225

22 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 Somewhat critical

23 Not at all Don t Know Total 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 Railways It 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 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 Sales Revenue Frequency Percent Strongly Disagree Disagree

24 Undecided Agree Strongly Agree Total Figure 7.6: Impact on Sales Revenue 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 of Sales Frequency Percent Strongly Disagree Disagree Undecided Agree Strongly Agree Total

25 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 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 Market Share Frequency Percent Strongly Disagree Disagree Undecided Agree Strongly Agree Total Figure 7.8: Impact on Market share

26 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 Organizational Image Frequency Percent Strongly Disagree Disagree Undecided Agree Strongly Agree Total 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 230

27 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 of Distribution channel Frequency Percent No Change Increased Reduced 0 0 Total Figure 7.10: Effect of online marketing of Indian Railways on Number of Levels of Distribution Channel 7.9 Reason of Growth of Online Marketing As 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 231

28 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 232

29 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 References Egger, F. N. (1999), Human Factors in Electronic Commerce : Making System Application Pealing, Usable and Trustworthy, Proceedings of twelfth Bled International E-commerce Bled, Slovenia. Badnjevic, Jasmina and Lena Padukova (2006), ICT Awareness in Small Enterprises in the Indian Tourism Branch, Project Report, IT University of Göteborg, Sweden. Grenblad, Daniel and Pernilla, Rosén (1999), Internet A Sales Channel InThe Airline IndustryDecision Situation, Relationships, Added Value,And Financials, Master Thesis in Business Administration and Management, Linköping University, Sweden available at Duncan, Tom and Moriarty, Sandra E. (1998), A Communication-Based Marketing Model for Managing Relationships, Journal of Marketing, Vol. 62, April, p Lewis, Ira Et al (1998), The Impact of Information Technology on Travel Agents, Transportation Journal, Vol. 37, Issue. 4, pp Siguaw, Judy A. et al (1998), Effects of Supplier Market Orientation on Distributor Market Orientation and the Channel Relationship: The Distributor Perspective, Journal of Marketing, Vol. 62, July, p Ghamatrasa, Marjan, Internet Adoption Decision Model among Iranian Small and Medium Enterprises, Master Thesis, Lulea University of Technology retrieved from www. essays.com. Homayooni, Narges, The Impact of the Internet on the distribution Value chain- The Case of the Iranian Tourism Industry, Master Thesis, Lulea University of Technology retrieved from www. essays.com. Bitner, Mary J. and Bernard H. Booms (1982). Trends in Travel and Tourism Marketing: The Changing Structure of Distribution Channels, Journal of Travel Research, (Spring), pp