CHAPTER 5: CONSUMERS ATTITUDE TOWARDS ONLINE MARKETING OF INDIAN RAILWAYS

CHAPTER 5: CONSUMERS ATTITUDE TOWARDS ONLINE MARKETING OF INDIAN RAILWAYS 5.1 Introduction This chapter presents the findings of research objectives dealing, with consumers attitude towards online marketing of Indian Railways. It deals with six aspects, namely measuring consumers perception, beliefs and attitude towards online marketing of Indian Railways. Furthermore, it advances the understanding of affect of different demographic variables on attitude, behavioral intention and actual usage. Second part is devoted to identify opportunities offered by online marketing and challenges posed by it. Third part pertains to measure the consumers attitude towards the various online tourism and information gathering services. Fourth section identifies the factors affecting consumers perception of online marketing service quality of Indian Railways. Fifth section is devoted to measure consumers attitude towards Indian Railways website. Last section examines the factors resisting the nonusers to adopt online marketing of Indian Railways. 5.2 Findings pertaining to measure consumers perception, beliefs and attitude towards the online marketing of Indian Railways: 5.2.1 Demographic Profile of the Consumers The final sample size to measure consumers perception, beliefs and attitude towards online marketing of Indian Railways is 767. The sample is considered to represent the IRCTC s website users in India to reserve a train ticket through internet. The profile of the respondents is shown in table 5.1. 122

Age Gender Marital Status Education Occupation Monthly Income Weekly Internet Use Length of Online service Usage Table: 5.1: Demographic Profile of the Consumers Variable Frequency Percent 17-20 years 32 4.2 21-30 years 322 42 31-40 years 216 28.2 Above 40 years 197 25.7 Total 767 100 Male Female Total Married Unmarried Default Total Under Graduate Graduate Post-Graduate Ph.D Other Total Service Business/ self employed Student/Research scholar Retired Professional Other Total Up to Rs. 10,000/- Rs. 10,001/- to Rs. 20,000/- Rs. 20,001/- to 30,000/- Above Rs.30,000/- Total Less than 10 hours 10 to 20 Hours 20 to 30 hours More than 30 hours Total Less than One Year 1 Years to 2 Years 2 Years to 3 Years More than 3 Years Total Source: Primary Data 700 67 767 488 262 17 767 40 362 271 45 49 767 320 93 70 20 181 83 767 107 190 142 328 767 232 174 127 234 767 94 195 159 319 767 91.3 8.7 100 63.6 34.2 2.2 100 5.2 47.2 35.3 5.9 6.4 100 41.7 12.1 9.1 2.6 23.6 10.8 100 14.0 24.8 18.5 42.8 100 30.2 22.7 16.5 30.5 100 12.3 25.4 20.7 41.6 100 As shown in table 5.1 42% of the respondents belong to the age group of 21-30 years old, 28.2% 31-40 years and 25.7% above 40 years of age. It implies that young group is showing more interest in using online services of railways, but at the same time the percentage of old people is not very less. 91.3% of the respondents are male shows 123

very high penetration of online marketing among males as compare to females. Almost half of the respondents are graduate degree holders (47.2%) contrary to only 5.2% under graduates; depicts online marketing is more prevalent among educated people. 41.7% of the respondents belongs to the service occupation and high income group of more than 30,000 (42.8%). As the online marketing is based on internet, so it has been tried to understand respondents internet use. 30.5% of the respondents are using internet for more than 30 hours in a day and 30.2% are using for less than 10 hours. Further, as to the length of online service usage pattern; 41.6% of the respondents have been using online service for more than 3 years, 25.4% from one to two years and 20.7% are using from 2 to 3 years. It shows good usage pattern of online services of Indian Railways. Figure: 5.1 Access to Internet Above figure 5.1 shows that majority of the users have internet access from home and work place. Only 2 respondents have no access and access from cyber café. 5.2.2 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. 5.2.2.1 Measurement Model The measurement model assessed individually with the help of confirmatory factor 124

analysis of all the constructs are presented below. 5.2.2.1.1 Perceived Usefulness GFI=.971 CFI=.965 RMSEA=.163 Cronbach Alpha=.810 The standardized loadings of all the indicators are fairly higher than the acceptable level 0.50. So the convergent validity is considered to be fairly good. Saves time in purchasing the ticket and makes easier to buy a ticket have biggest impact on perceived usefulness; while provides information in time has least impact. 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 (0.971 and 0.965) represents a good fit model. On the other hand the value of RMSEA is.163 which is above the acceptable range of 0.80. To assess the construct reliability cronbach alpha (0.810) 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. 5.2.2.1.2 Perceived Ease of Use GFI=.840 CFI=.894 RMSEA=.285 Cronbach Alpha=.913 All the indicators of perceived ease of use are showing strong standardized loadings 125

on the relative construct more than 0.70. Easy to learn and easy to understand have substantial impact on perceived ease of use. On the other hand last three indicators are also showing biggest impact. So it could be inferred that all the indicators are explaining perceived ease of use very well. The values of CFI (.894) and GFI (.840) are slightly below the acceptable level of.9 are not signifying a very good fit model. The cronbach s alpha value (.913) 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. 5.2.2.1.3 Trust GFI=1 CFI= 1 RMSEA=0 Cronbach Alpha=.869 All the indicators of relative construct Trust are showing very high factor loadings greater than.75. Reliable is a very strong indicator with factor loading of 1.02, while trustworthy is somewhat less. 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 (.869) is also good. So the above measurement model is a perfect good fit model. 5.2.2.1.4 Perceived Enjoyment GFI=1 RMSEA=0 CFI=1 Cronbach Alpha=.922 126

All the indicators of relative construct Perceived enjoyment are showing very high factor loadings greater than.85. It shows that all the three indicators have biggest impact on the construct. 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 (.922) is also very high. So it could be easily concluded that the above measurement model is a reliable and a good fit model. 5.2.2.1.5 Image GFI=1 CFI=1 RMSEA=0 Cronbach Alpha=.826 First two indicators are showing high factor loadings of.93 and.94 except one fits into my life style, which is just equal to.50. It implies that status symbol and improves image are outstanding indicators of image. The goodness-of-fit indices (GFI=.953 and CFI=.946) also confirm it as a good fit model. But badness of fit model is not meeting the requirement as the RMSEA (.112) value is above the cut of value 0.8. The Cronbach alpha s (.826) value is well above the threshold value. So in summary it could be inferred that the above model is a good-fit and a reliable model. 5.2.2.1.6 Subjective Norm GFI=1 CFI=1 RMSEA=0 Cronbach Alpha=.874 127

Both the indicators of relative construct subjective norm are showing very high factor loadings greater than.75. Out of the two indicators second one has substantial impact with a very high factor loading of 1.02. 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 (.874) is also good. So the above measurement model is a perfect good fit model. 5.2.2.1.7 Facilitating Condition GFI=1 CFI=1 RMSEA=0 Cronbach Alpha=.837 All the indicators of relative construct Facilitating condition are showing very high factor loadings greater than.70. This shows that all the three variables are good indicators of facilitating condition. 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 (.837) is also high. So it could be easily concluded that the above measurement model is a reliable and a good fit model. 5.2.2.1.8 Perceived Risk GFI=.999 CFI=1 RMSEA=.000 Cronbach Alpha=.779 First three indicators of perceived risk are showing high factor loadings greater than 128

0.75. But last two indicators are having below average loadings of.42 and.39. it implies that first three variables are better indicators of perceived risk as compare to last two variables. On the other hand the goodness of fit indices (GFI=.999 and CFI=1) and badness of fit index (RMSEA=0) are perfect. The cronbach s alpha value (.779) is also considerable. 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. 5.2.2.1.9 Attitude GFI=.992 CFI=.994 RMSEA=.085 Cronbach Alpha=.886 All the indicators are showing high factor loadings greater than.75. First two indicators have equal loadings of.88 showing substantial impact on attitude while last two indicators are not least one. The goodness-of-fit indices (GFI=.992 and CFI=.994) also confirm it as a very good fit model. But badness of fit model is not meeting the requirement as the RMSEA (.085) value is slightly above the cut of value 0.8. The construct reliability is also satisfactory (Cronbach alpha=.886). So in summary it could be inferred that the above model is a good-fit and a reliable model. 5.2.2.1.10 Behavioral Intention GFI=1 CFI= 1 RMSEA=0 Cronbach Alpha=.879 129

All the indicators of relative construct Behavioral Intention are showing very high factor loadings greater than.75. I will strongly recommend others to use it indicator is remarkably explaining the construct Behavioral intention. The remaining two variables are also very good indicators of it. 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 (.879) is also good. So the above measurement model is a perfect good fit model. 5.2.2.1.11 Actual Usage GFI= 1 CFI=1 RMSEA=0 Cronbach Alpha=.726 Actual usage have only two indicators out of which I will use it frequently is showing a very strong factor loading of.97 and I will use it on a regular basis has a moderate factor loading of.60. 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 (.726) is more than its cut off value 0.6. So above model could be easily considered as reliable and a valid model. 5.2.2.2 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 constructs reliability. As can be seen from the below table 5.2 that all the constructs cronbach s alpha values are greater than the threshold value 0.6 adequate reliability except. The internal consistency of all the constructs included in the model ranged from.726 to.922. This showed all the constructs have very strong and adequate construct reliability. 130

Table 5.2: Assessment of Consumers Constructs Reliability Research Construct Number of Items Cronbach s Alpha Perceive Usefulness 4.810 Perceived Ease of Use 5.913 Trust 2.869 Perceived Enjoyment 3.922 Image 3.826 Subjective Norm 2.874 Facilitating Condition 3.837 Perceived Risk 5.779 Attitude 4.886 Behavioral Intention 3.879 Actual Usage 2.726 5.2.2.3 Assessment of convergent Validity 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 5.3 depicts that all loadings are greater than 0.5 except two PR4 and PR5 and significant at.001 level of significance. The loading of TR1 is not a significant loading. Table 5.3: Assessment of Consumers convergent Validity Construct Indicator Loading Perceived Usefulness PU1 PU2 PU3 PU4.76.82.80.57 Perceived Ease of Use PEOU1 PEOU2.89.90 131

132 PEOU3 PEOU4 PEOU5.79.81.73 Trust TR1 TR2.75 1.02 Perceived Enjoyment PE1 PE2 PE3.86.94.88 Image IM1 IM2 IM3.93.94.50 Subjective Norm SN1 SN2.76 1.02 Facilitating Condition FC1 FC2 FC3.82.86.72 Perceived Risk PR1 PR2 PR3 PR4 PR5.76.87.77.42.39 Attitude ATT1 ATT2 ATT3 ATT4.88.88.75.76 Behavioral Intention BI1 BI2 BI3.79.93.81 Actual Usage AU1 AU2.60.97

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. 5.2.2.4 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 (R 2 ). 5.2.2.4.1 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 5.4: R-square for endogenous constructs for Consumers Construct R-square Perceived Usefulness.371 Attitude.331 Behavioral Intention.500 Actual Usage.564 In this study perceived usefulness explains 37.1 percent of average variation. Perceived usefulness and perceived ease of use explains 33.1 percent of attitude. But attitude explains 50 percent of behavioral intention which is above average. On the other hand behavioral intention explains good variation of actual usage i.e. 56.4 percent. The structural model results are summarized in figure 5.2 and table 5.5. 133

5.2.2.4.2 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 appropriateness of TAM for its applicability in adoption of online marketing in Indian Railways. It could be seen that perceived usefulness is predicted by perceived ease of use ( =.609, p=.000). Furthermore, Attitude is based on perceived usefulness ( =.282, p=.000), perceived ease of use ( =.110, p=.018), perceived enjoyment ( =.254, p=.000), subjective norm ( =.950, p=.044) and facilitating condition ( =.352, p=.000). It has also been verified that Trust ( =.011, p=.649), image ( =.028, p=.408) and perceived risk ( = -.056, p=.120) have insignificant path coefficients. Subsequently behavioral intention is determined by perceived usefulness ( =.182, p=.000) and attitude ( =.623, p=.000). Finally, Actual usage behavior is predicted very strongly by behavioral intention ( =.751, p=.000). At last it could be concluded that H1, H2, H3, H4, H6, H8, H9, H11 and H12 are supported and remaining H5, H7 and H10 has not been supported. The hypothesis testing results are summarized in table 5.5. Table 5.5: Hypothesis Testing for Consumers Hypothesis Effects Path p-value Remarks coefficients H1 PU ATT.282.000 Supported H2 PU BI.182.000 Supported H3 PEOU PU.609.000 Supported H4 PEOU ATT.110.018 Supported H5 TR ATT.011.649 Not Supported H6 PE ATT.254.000 Supported H7 IM ATT.028.408 Not Supported H8 SN ATT.950.044 Supported H9 FC ATT.352.000 Supported H10 PR ATT -.056.120 Not supported H11 ATT BI.623.000 Supported H12 BI AU.751.000 Supported 134

Figure 5.2: Results of testing the Hypothesized links for Consumers R 2 :.371 PU.182.282 R 2 :.331 R 2 :.500 R 2 :.564.609 ATT.623 BI.751 AU PEOU TR.110.011*.254.950 -.056*.352 PE IM.028* SN FC PR Note: - Path Coefficients with * symbol are not significant and there by not supporting the hypothesis 5.2.2.5 Model s Overall Goodness of fit Finally, model s overall goodness of fit has been presented in table. The GFI (0.751) value is less than the acceptable level and CFI (0.837) is above the cutoff value. The value of RMSEA is equal to 0.081 slightly above minimum criteria of badness of fit indices. It implies that overall model is not a bad fit model. The following results indicate that the model is marginally a good fit model in Indian context. TAM model could be applied to measure consumers attitude and adoption level of online marketing of Indian railways but not in isolation. It should be used with some other models or techniques in order to get the better results. 135

Figure 5.3: Complete Model for consumers with all the indicators.76.82.80.57 PU1 PU2 PU3 PU4.89.90.79.81 PEOU1 PEOU2 PEOU3 PEOU4 PU.609 PEOU.73 PEOU5.282.182.75 TR1 TR.110.79.93.81 1.02.86 TR2 PE1.011* BI1 BI2 BI3.60.94.88 PE2 PE3 PE.254 ATT.623 BI AU AU1.93.94.50 IM1 IM2 IM3 IM.028*.950 ATT1 ATT2 ATT3 ATT4.88.88.75.76.751 AU2.97.76 SN1 SN.352 1.02 SN2.82 FC1 -.056*.86 FC2 FC.72 FC3.76 PR1.87 PR2.77 PR3 PR.42 PR4.39 PR5 136

Table 5.6: Consumers Model s Overall Goodness of fit Item Measured Value Recommended value Goodness of fit Index (GFI) 0.751 0.80 Comparative fit index (CFI) 0.837 0.80 Root mean square error of approximation (RMSEA) 0.081 0.08 5.2.3 Explaining Antecedents of consumers 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 consumers. Attitude of consumers is jointly predicted by perceived usefulness ( =.282), perceived ease of use ( =.110), trust ( =.011), perceived enjoyment ( ==.254), image ( =.028), subjective norm ( =.950), facilitating condition ( =.352) and perceived risk ( = -.056). In fact, all the constructs are explaining a 33.1% of variance in attitude. This is an indication of worthy explanatory power of the model in explaining the attitude of the consumers towards online marketing in Indian Railways. Among the relationships facilitating condition and subjective norm are two major determinants of consumers attitude towards online marketing of Indian railways. 5.2.3.1 Significant results Subjective norm as social effect (path coefficient=.95 and p=.044) has a significant and very strong impact on consumers attitude towards online marketing of Indian Railways and supporting hypothesis 8. This relationship has most important influence on attitude so there is need to find the factors affecting social influence. It implies that positive reports of important and influencing social group will increase the attitude of the consumers. The same findings have also been reported by yu et al. (2004) and karami (2006). Facilitating condition has a significant and strong impact on consumers attitude (path coefficient=.352 and p=.000) and supports hypothesis 9. It implies that consumers 137

have required resources or ability 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). Perceived usefulness has significant impact on attitude of consumers (path coefficient=.282 and p=.000) and supports hypothesis 1. It indicates that consumers will have positive attitude if they find it useful, saving time, provides information in time and made easier to buy a ticket. The findings are consistent with Dehbashi (2007), karami (2006), Taylor and Todd (1995) and Yu et al., (2004) who reported a significant and positive relationship between perceived usefulness and attitude. Consumers attitude is positively affected by perceived enjoyment (path coefficient=.254 and p=.000) thereby supporting hypothesis 6. It indicates that consumers attitude will positively increase if they perceive that using online marketing is interesting, joyful activity and enjoyable. Perceived ease of use has significant positive effect on driving the consumers attitude (path coefficient=.110 and p=.018) and supporting hypothesis 4. It indicates that if consumers perceive that service is easy to use, learns, and understand, simple and interaction is clear; it will increase their attitude. The results have also been verified by Taylor and Todd (1995) and Karami (2006). 5.2.3.2 Insignificant Results Perceived risk has negative influence on attitude (path coefficient= -.056 and p=.120) but it is insignificant and thereby not supporting hypothesis 10. This study shows that perceived risk reduces attitude but it does not plays a very important role. 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. The results indicate that consumer s attitude will not decrease if they perceive using online marketing of Indian Railways is risky. It also implies that they do not consider any kind of financial and privacy risk with online marketing. Trust has insignificant (path coefficient=.011 and p=.649) impact on attitude towards online marketing and not supporting hypothesis 5. It implies that reliability and trustworthiness of online marketing of Indian Railways does not affect attitude of consumers. 138

Image has also insignificant (path coefficient=.028 and p=.408) impact on attitude and not supporting hypothesis 7. It implies that consumers do not consider that the use of online marketing is a status symbol, improves image and fits into their lifestyle. 5.2.4 Explaining Antecedents of consumers 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 =.182 and =.623 respectively. Therefore, the results are supporting hypothesis 2 two and hypothesis 11. The effect of these two constructs perceived usefulness and attitude is accounted for substantial variance of 50% 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 attitude is a strongest predictor of behavioral intention. So it is advisable to work on the constructs which are important in shaping the attitude of consumers. These factors have been discussed earlier in detail. It implies that if employees perceive online marketing useful, they will be likely to show behavioral intention to use it. 5.2.5 Explaining Antecedents of Consumers 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 0.751. Marjan Ghamatrasa (2006) also reported a significant positive relation between intention and actual usage. There is a good effect of intention on actual use accounted for 56.4% 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. 5.2.6 Equation to Measure Consumer Attitude Path analysis has provided estimates for each relationship in the model shown in figure. These estimates could be used to measure the consumers attitude, behavioral intention and actual use (adoption). In the consumers model for any observed values of perceived usefulness, perceived ease of use, perceived enjoyment, subjective norm and facilitating condition; consumers attitude could be measured by using the following equation: ATT =.282(PU) +.110(PEOU) +.254(PE) +.950(SN) +.352(FC) 139

Similarly, estimated value for Behavioral Intention and Actual Use can be obtained: BI =.182(PU) +.623(ATT) AU =.751(BI) 5.3 Findings Pertaining to Examine Differences between Demographic Variables and Attitude, Behavioral intention & Actual Use 5.3.1 Examining the Differences between Age and Attitude, Behavioral intention & Actual Use H1: There is no significant difference in consumers attitude towards online marketing of Indian Railways among different age groups. H2: There is no significant difference in consumers Behavioral Intention towards online marketing of Indian Railways among different age groups. H3: There is no significant difference in consumers actual use of online marketing of Indian Railways among different age groups. Table 5.7: ANOVA Result Examining the Differences between Age and Attitude, Age Group Behavioral intention & Actual Use N Attitude Behavioral Intention Actual use Mean S.D Mean S.D Mean S.D 0 20 Years 32 4.04.68 4.15.76 3.95.84 21 30 Years 322 4.22.71 4.31.77 4.14.85 31 40 Years 216 4.34.63 4.38.77 4.22.80 Above 40 Years 197 4.33.59 4.39.70 4.16.77 Total 767 4.27.66 4.34.75 4.16.82 F Value 3.231 1.376 1.237 Sig..022.249.295 * Bold values indicate that the mean difference is significant at the.05 level. 140

Table 5.7 clearly reveals that null hypothesis (1) was rejected, hence it can be said that there is a significant difference between age and attitude towards online marketing. On the other hand hypothesis 2 and 3 have been accepted. So it could be inferred that there is no significant difference among different age groups regarding behavioral intention and actual use. (I) 0 20 Years 21 30 Years Table 5.8: Post-Hoc Analysis: Age and Attitude Age (J) Mean Differenc e Std. Error Sig. 21 30 Years -.18143.12161.136 31 40 Years.30122.12428.016 Above 40 years -.28708.12505.022 31 40 Years -.11978.05770.038 Above 40 years -.10565.05935.075 31 40 Years Above 40 years -.01414.06464.827 * Bold values indicate that the mean difference is significant at the.05 level. Post hoc test (table 5.8) reveals that there is difference of attitude between age group 0 30 years and above 30 years. It could be easily inferred that old age people are more positive in attitude as compare to the young generation. 5.3.2 Examining the Differences between Gender and Attitude, Behavioral intention & Actual Use H4: There is no significant difference between gender and consumers attitude towards online marketing of Indian Railways. H5: There is no significant difference between gender and consumers Behavioral Intention towards online marketing of Indian Railways. H6: There is no significant difference between gender consumers actual use of online marketing of Indian Railways. 141

Table 5.9: T-test Result Examining the Differences between Gender and Gender Attitude, Behavioral intention & Actual Use N Attitude Behavioral Intention Actual use Mean S.D Mean S.D Mean S.D Male 700 4.2807.65957 4.3400.75581 4.1621.81811 Female 67 4.2015.65314 4.3930.71991 4.1269.79935 Total 767 4.2738.65897 4.3446.75244 4.1591.81604 F Value.884.304.114 Sig..348.582.736 Above table 5.9 clearly shows that there is no impact of gender on attitude, Behavioral intention and actual use. Thereby, hypothesis 4, 5 and 6 could be easily rejected. 5.3.3 Examining the Differences between Marital Status and Attitude, Behavioral intention & Actual Use H7: There is no significant difference in consumers attitude towards online marketing of Indian Railways among different marital status. H8: There is no significant difference in consumers Behavioral Intention towards online marketing of Indian Railways among different marital status. H9: There is no significant difference in consumers actual use of online marketing of Indian Railways among different marital status. Table 5.10: ANOVA Result Examining the Differences between Marital Status Marital Status and Attitude, Behavioral intention & Actual Use N Attitude Behavioral Intention Actual use Mean S.D Mean S.D Mean S.D Married 488 4.3243 4.3243 4.4010.72360 4.2234.78507 Unmarried 262 4.1823 4.1823 4.2443.80264 4.0344.85923 Default 17 4.2353 4.2353 4.2745.62622 4.2353.83137 Total 767 4.2738 4.2738 4.3446.75244 4.1591.81604 F Value 4.021 3.799 4.693 Sig..018.023.009 142

Table 5.10 reflects that there is a significant difference between marital status and attitude, behavioral intention and actual use; thereby rejecting hypothesis 7, 8 and 9. Table 5.11: Post-Hoc Analysis: Marital Status and Dependent variable Dependent Variable Marital Status (I) (J) Mean Difference Std. Error Sig. Attitude Married Unmarried.14203 *.05027.005 Default.08899.16195.583 Behavioral Married Unmarried.15668 *.05742.007 Intention Default.12645.18497.494 Actual use Married Unmarried.18901 *.06220.002 Default -.01193.20037.953 * Bold values indicate that the mean difference is significant at the.05 level. Further results of post hoc analysis depicts that there is difference between married and unmarried respondents regarding attitude, behavioral intention and actual use of online marketing of Indian Railways. Married respondents are more positive towards online marketing as compare to unmarried ones. 5.3.4 Examining the Differences between Education Level and Attitude, Behavioral intention & Actual Use H10: There is no significant difference in consumers attitude towards online marketing of Indian Railways among different education level. H11: There is no significant difference in consumers Behavioral Intention towards online marketing of Indian Railways among different education level. H12: There is no significant difference in consumers actual use of online marketing of Indian Railways among different education level. Table 5.12: ANOVA Result Examining the Differences between Education Level Education Level and Attitude, Behavioral intention & Actual Use N Attitude Behavioral Intention Actual use Mean S.D Mean S.D Mean S.D Under graduate 40 4.1438.65995 4.1667.74344 4.2125.77532 Graduate 362 4.2956.68497 4.3794.75359 4.1436.81881 143

Post graduate 271 4.2537.64333 4.3210.76056 4.1605.83836 Higher education 45 4.2722.59070 4.3630.71711 4.1889.79978 Other 49 4.3316.61320 4.3469.74212 4.1939.74173 Total 767 4.2738.65897 4.3446.75244 4.1591.81604 F Value.645.825.112 Sig..631.509.978 Above table 5.12 clearly shows that there is no impact of education level on attitude, Behavioral intention and actual use. Thereby, hypothesis 10, 11 and 12 could be easily rejected. 5.3.5 Examining the Differences between Different Occupation and Attitude, Behavioral intention & Actual Use H13: There is no significant difference in consumers attitude towards online marketing of Indian Railways among different occupation. H14: There is no significant difference in consumers Behavioral Intention towards online marketing of Indian Railways among different occupation. H15: There is no significant difference in consumers actual use of online marketing of Indian Railways among different occupation. Table 5.13: ANOVA Result Examining the Differences between Occupation and Occupation Attitude, Behavioral intention & Actual Use N Attitude Behavioral Intention Actual use Mean S.D Mean S.D Mean S.D Service 320 4.2797.61516 4.3708.71115 4.1578.80512 Business 93 4.2634.72078 4.2760.84930 4.0914.80739 Student 70 4.0821.75748 4.1810.78776 3.9714.91242 Retired 20 4.4250.57411 4.5333.55567 4.2000.71451 Professional 181 4.3508.66237 4.3867.76914 4.2735.80696 Other 83 4.2199.65262 4.3213.75877 4.1386.80892 Total 767 4.2738.65897 4.3446.75244 4.1591.81604 F Value 2.024 1.278 1.607 Sig..073.271.156 * Bold values indicate that the mean difference is significant at the.05 level. 144

Table 5.13 clearly reveals that null hypothesis (13) has been rejected, hence it can be said that there is a significant difference between occupation and attitude towards online marketing. On the other hand hypothesis 14 and 15 have been accepted. So it could be inferred that there is no significant difference among different occupation regarding behavioral intention and actual use. Table 5.14: Post-Hoc Analysis: Occupation and Attitude (I) Occupation (J) Mean Differenc e Std. Error Sig. Student Service -.19754 *.08666.023 Business -.18130.10393.081 Retired -.34286 *.16652.040 Professional -.26869 *.09244.004 Other -.13774.10658.197 * Bold values indicate that the mean difference is significant at the.05 level. Post hoc test (table 5.14) reveals that there is difference of attitude between student and other occupation like service, retired and professional. It could be easily inferred that students are not positive in attitude as compare to the other occupations. 5.3.6 Examining the Differences between Different Income levels and Attitude, Behavioral intention & Actual Use H16: There is no significant difference in consumers attitude towards online marketing of Indian Railways among different income levels. H17: There is no significant difference in consumers Behavioral Intention towards online marketing of Indian Railways among different income levels. H18: There is no significant difference in consumers actual use of online marketing of Indian Railways among different income levels. Table 5.15: ANOVA Result Examining the Differences between Income Levels Income Levels and Attitude, Behavioral intention & Actual Use N Attitude 145 Behavioral Intention Actual use Mean S.D Mean S.D Mean S.D Less than 10000 107 3.9556.84173 4.0779.92890 3.8925.99771

10001-20000 190 4.2605.61980 4.3404.70935 4.1105.78908 20001-30000 142 4.3627.62921 4.3850.73126 4.2218.78443 Above 30000 328 4.3468.59397 4.4167.70422 4.2470.76024 Total 767 4.2738.65897 4.3446.75244 4.1591.81604 F Value 10.955 5.727 5.680 Sig..000.001.001 * Bold values indicate that the mean difference is significant at the.05 level. Table 5.15 depicts that income has significant impact on attitude, behavioral intention and actual use; thereby rejecting null hypothesis 16, 17 and 18. Table 5.16: Post-Hoc Analysis: Income Level and Attitude (I) Income Level (J) Mean Differenc e Std. Error Sig. Less than 10000 10001-20000 -.30492 *.07814.000 20001-30000 -.40707 *.08276.000 above 30000 -.39119 *.07197.000 * Bold values indicate that the mean difference is significant at the.05 level. A post hoc analysis result reveals that respondents with income less than Rs 10,000 are not so positive in attitude towards online marketing in contrast to the other income groups. It could be concluded that there is positive relation between income and attitude towards online marketing. Table 5.17: Post-Hoc Analysis: Income Level and Behavioral Intention (I) Income Level (J) Mean Differenc e Std. Error Sig. Less than 10000 10001-20000 -.26247 *.09011.004 20001-30000 -.30709 *.09544.001 above 30000 -.33879 *.08300.000 A post hoc analysis of income level and behavioral intention result reveals that respondents with income less than Rs 10,000 are not intended to use online marketing in contrast to the other income groups. It could be inferred that with increase in income behavioral intention also increases. 146

Table 5.18: Post-Hoc Analysis: Income Level and Actual use (I) Income Level (J) Mean Differenc e Std. Error Sig. Less than 10000 10001-20000 -.21800 *.09774.026 20001-30000 -.32931 *.10352.002 above 30000 -.35443 *.09003.000 * Bold values indicate that the mean difference is significant at the.05 level. A post hoc analysis of income level and actual use result reveals that adoption of online marketing among lower income groups is less as compare to high income groups. Finally it could be inferred that income affects the attitude, behavioral intention and actual use of online marketing. 5.3.7 Examining the Differences between Internet Usage and Attitude, Behavioral intention & Actual Use H19: There is no significant difference in consumers attitude towards online marketing of Indian Railways among different internet usage. H20: There is no significant difference in consumers Behavioral Intention towards online marketing of Indian Railways among different internet usage. H21: There is no significant difference in consumers actual use of online marketing of Indian Railways among different internet usage. Table 5.19: ANOVA Result Examining the Differences between Internet Usage Internet Usage and Attitude, Behavioral intention & Actual Use N Attitude Behavioral Intention Actual use Mean S.D Mean S.D Mean S.D 10 hours 232 4.2134.64193 4.2457.75687 4.0797.82382 11-20 hours 174 4.3075.67437 4.3946.73368 4.2126.76079 21-30 hours 126 4.2738.66063 4.3915.71190 4.1587.82862 More than 30 hours 234 4.3141.65892 4.3860.77427 4.2030.83796 Total 766 4.2755.65779 4.3464.75136 4.1606.81549 F Value 1.096 2.005 1.208 Sig..350.112.306 147

Table 5.19 depicts that use of internet have no significant impact on attitude, behavioral intention and actual use; thereby accepting null hypothesis 19, 20 and 21. 5.3.8 Examining the Differences between Different length of time of using online marketing and Attitude, Behavioral intention & Actual Use H22: There is no significant difference in consumers attitude towards online marketing of Indian Railways among different categories of length of time of using it. H23: There is no significant difference in consumers Behavioral Intention towards online marketing of Indian Railways among different categories of length of time of using it. H24: There is no significant difference in consumers actual use of online marketing of Indian Railways among different categories of length of time of using it. Table 5.20: ANOVA Result Examining the Differences between length of time of length of Time of Usage usage and Attitude, Behavioral intention & Actual Use N Attitude Behavioral Intention Actual use Mean S.D Mean S.D Mean S.D Less than half a year 94 4.1755.65360 4.2128.76490 3.9202.88081 1 Year - 2 year 195 4.2385.67125 4.2650.78203 4.0590.82821 2 years - 3 Years 159 4.2374.65470 4.3753.73848 4.1289.80782 More Than 3 Years 319 4.3425.65131 4.4169.73066 4.3056.76777 Total 767 4.2738.65897 4.3446.75244 4.1591.81604 F Value 2.211 2.780 7.343 Sig..086.040.000 * Bold values indicate that the mean difference is significant at the.05 level. Table 5.20 depicts that length of time of using online marketing of Indian Railways have significant impact on behavioral intention and actual use but it is insignificant for attitude; thereby rejecting null hypothesis 23 & 24 and accepting hypothesis 22. A post hoc analysis result reveals that respondents who are using it for more than three years are more intended to use it in contrast to those who are using it for less than 2 years. It could be concluded that there is positive relation between length of time of using online marketing of Indian Railways and behavioral intention to use it. 148

Table 5.21: Post-Hoc Analysis: Length of Time of Usage and Behavioral Length of Time of Usage (I) (J) Intention Mean Differenc e Std. Error Sig. More Than 3 Years Less than half a year.20416 *.08800.021 1 Year - 2 year.15197 *.06816.026 2 years - 3 Years.04167.07279.567 * Bold values indicate that the mean difference is significant at the.05 level. Table 5.22: Post-Hoc Analysis: Length of Time of Usage and Actual Use Length of Time of Usage (I) (J) Mean Differenc e Std. Error Sig. More Than 3 Years Less than half a year.38543 *.09460.000 1 Year - 2 year.24667 *.07327.001 2 years - 3 Years.17671 *.07825.024 * Bold values indicate that the mean difference is significant at the.05 level. A post hoc analysis result reveals that respondents who are using it for more than three years are more intended to use it in contrast to those who are using it for less than 3 years. Finally it could be inferred that length of time of using online marketing affects the behavioral intention and actual use of online marketing. 5.4 Findings Pertaining To Measure Consumers Attitude towards Opportunities Offered By Online Marketing of Indian Railways 5.4.1 Descriptive Statistical Analysis: Table highlights the importance of each opportunity on the basis of its mean scores. It is evident from the table 5.23 that convenient is a major opportunity followed by time saving and no long queues with mean scores 2.07, 2.09 and 3.05 respectively. In order to draw better results all the responses are further analyzed with the help of Multidimensional scaling. 149

Table 5.23: Descriptive Statistics regarding the opportunities offered by online marketing N Mean Std. Deviation Convenient 767 2.07 1.808 Time saving 767 2.09 1.665 No long queues 767 3.05 2.040 Buying tickets 24/7 (at any time & from anywhere) 767 3.07 2.101 Price saving 767 3.98 2.600 Easy access to information 767 4.28 2.499 New technology experience 767 4.40 2.460 5.4.2 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 1.04035 2.02379.01656 3.01875.00503 4.01569.00306 5.01325.00244 6.01127.00198 7.00967.00160 8.00862.00105 9.00794.00067 Iterations stopped because S-stress improvement is less than.001000 For matrix Stress =.01251 RSQ =.99923The 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.99923 150

which is very high with enough low value of stress (.01251) indicates goodness of MDS Configuration derived in 2 dimensions Table 5.24: Stimulus Coordinates of Consumers Opportunities Stress =.01251 RSQ =.99923 Number Stimulus Name Dimension 1 2 1 Convenient 1.9531 -.1796 2 Time Saving 1.6584 -.0308 3 Price Saving -.9410-1.2607 4 Buying tickets 24/7 (at any time &.1157.3882 from anywhere) 5 No long queues.1424.5666 6 New technology experience -1.4641.2493 7 Easy access to information -1.4645.2671 Source: Primary Data Figure 5.4: Opportunities for consumers It could be easily inferred from the perceptual mapping (Figure 5.4) of consumers attitude that Time saving and convenient are most important and primary opportunity of online marketing of Indian Railways. The number of studies argued that time saving is an important opportunity of online marketing (Ramalingam, 2008; Wigand & Benjamin, 1995; Krause 1998). Price saving and convenience are most important factors influencing consumers intention to use online services (Shim et al, 2001; 151

Kuan-pin et al, 2003; Yu-Bin et al., 2005). On the other hand No long queues and anytime booking are reported as other primary opportunities but these are least important. Furthermore price saving, new technology experience and easy access are considered as secondary opportunity but price saving is reported as most important opportunity. McIvor, O Reilly et al. 2003 in their research found that price saving, time saving and convenient as an important factors of using airlines website to purchase a ticket. Benefits of internet and consequences of purchasing ticket directly from airline give many opportunities to the customers such as 24 hours available (Any time availability) and reducing time loss in queues to receive paper based ticket (Shima Dehbashi 2007). 5.5 Findings pertaining to Measure Consumers Attitude towards challenges posed by Online Marketing of Indian Railways 5.5.1 Descriptive Statistical Analysis: Table 5.25 highlights the importance of each challenge on the basis of its mean scores. Table 5.25 Descriptive Statistics regarding the challenges posed by online marketing N Mean Std. Deviation Very busy network 767 3.07 2.307 Risky to use credit card 767 3.68 2.452 Difficulty in cancellation or refund Lack of online payment facility Lack of privacy of personal information 766 4.61 2.169 766 5.01 1.790 767 5.17 1.652 Risk of wrong ticket 767 5.32 1.833 Expensive 767 5.52 2.561 Complex system 767 5.77 2.078 Don t know how to use 767 6.32 2.058 152

It is evident from the table 5.25 that very busy network is a major challenge followed by risky to use credit card and difficulty in cancellation or refund with mean scores 3.07, 3.68 and 4.61 respectively. In order to draw better results all the responses are further analyzed with the help of Multidimensional scaling. 5.5.2 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 solution (in squared distances) Young's S-stress formula 1 is used. Iteration S-stress Improvement 1.07194 2.05039.02156 3.04424.00614 4.04052.00372 5.03781.00271 6.03583.00198 7.03436.00147 8.03324.00113 9.03230.00094 Iterations stopped because S-stress improvement is less than.001000 For matrix Stress =.03230 RSQ =.99447 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.99447 which is very high with fairly low value of stress (.03230) indicates goodness of MDS. Configuration derived in 2 dimensions Table 5.26: Stimulus Coordinates of Consumers Challenges Stress =.03230 RSQ =.99447 Number Stimulus Name Dimension 1 2 1 Risky to Use Credit Card 1.9125 -.0804 2 Very Busy Network 1.9634.9465 153

3 Difficulty in Cancellation or Refund.7407 -.8422 4 Lack of Online Payment Facility.2040 -.3637 5 Risk of wrong Ticket -.3505 -.6221 6 Lack of Privacy of Personal -.2296.0098 Information 7 Don t Know How to Use -1.5164 -.4130 8 Complex system -1.3309.1943 9 Expensive -1.3931 1.1708 Source: Primary Data Figure 5.5: Challenges for Consumers It could be easily inferred from the perceptual mapping (Figure 5.5) of consumers attitude that Very busy network is a most important and primary challenge of online marketing of Indian Railways. Limyem and Khalifa 2003 also found that speed of the website has significant effect on online shopping. On the other hand Risky to use credit card, Lack of online payment facility and difficulty in cancellation and refund are other primary challenges but these are least important. A number of studies found that common reasons for online purchase reluctance are refund problems, financial security fear (Mayer, 2002; Chen and He, 2003). Furthermore expensive, complex system, Lack of privacy of personal information, don t know how to use and risk of wrong ticket are considered as secondary challenges but after a close examination lack of privacy of personal information is reported as most important challenge. Harvard Business Review, 2000; also reported that technical problem such as 154

complex system is one of the reason of abandoning online purchases. At the end it could be concluded that consumers want to use online services but complex or ineffective system discourage them. 5.6 Findings Pertaining To the Consumers Attitude towards the Various Online Tourism and Information Gathering Services of Indian Railways: Indian Railways website is used not only for ticket reservation but also for information gathering and ordering different tourism services. It is beneficial for one who wants to build a personalized travel package. To investigate the degree of adoption of various online tourism and information gathering services of Indian Railways descriptive statistical analysis is used. Table 5.27: Degree of Adoption of online tourism and information gathering Service Type of Service Adoption Total Users Non- Users Hotel booking Count 228 539 767 Row % 29.73 70.27 100% Car Rental Count 218 549 767 Row % 28.42 71.58 100% Tour packages Count 251 516 767 Row % 32.72 67.28 100% Seat Availability Status Count 684 83 767 Row % 89.18 10.82 100% Train arrival and Count 679 88 767 departure time Row % 88.53 11.47 100% Online Ticket reservation Count 703 64 767 Row % 91.66 8.34 100% Fare Enquiry Count 696 71 767 Row % 90.74 9.26 100% Train Schedule Count 694 73 767 155

Row % 90.48 9.52 100% Frequently Asked Count 539 228 767 Questions Row % 70.27 29.73 100% Loyalty Programs Count 421 346 767 Row % 54.89 45.11 100% Figure 5.6: Degree of Adoption of online tourism and information gathering Service Table 5.26 and figure 5.6 depict that online ticket reservation is a largest used service with 91.66% users. It is followed by information gathering facilities like frequently asked questions, train schedule, fare enquiry, train arrival & departure time and seat availability status with 70.27%, 90.48%, 90.74%, 88.53% and 89.18% of users respectively. On the other hand car rental (28.42%), hotel booking (29.73%), tour packages (32.72%) and loyalty programs (54.89%) have comparatively very low percentage of users. This indicates higher adoption of online ticket reservation and information gathering facility as compare to online tourism servcies. This section focuses on analyzing consumers attitude towards the evaluation of different online tourism and information gathering services of railways. 156

Table 5.28: Descriptive Statistics Regarding Consumers Evaluation of Different Online Tourism and Information Gathering Services N Mean Std. Deviation Online ticket reservation 767 3.94 1.461 Fare enquiry 767 3.94 1.477 Train schedule 767 3.82 1.507 Seat availability status 767 3.55 1.593 Train arrival and departure time 767 3.42 1.639 Frequently asked questions 767 2.50 1.886 Loyalty programs 767 1.72 1.815 Tour packages 767 1.05 1.624 Hotel booking 767.92 1.528 Car rental 767.85 1.454 Table 5.28 depicts that mean scores of online ticket reservation and the entire information gathering services are above 3. This indicates good feeling of consumers towards the evaluation of these services. On the other hand mean score of tourism services are below 2 shows very poor performance. Therefore it can be said that online tourism services needs lot of improvements. 5.7 Findings Pertaining To Identify Factors Affecting Consumers Perception of Online Marketing Service Quality of Indian Railways: 5.7.1 Profile of the respondents of online service Quality The final sample size for e-service evaluation is 150. The sample is considered to represent the IRCTC s website users in India to reserve a train ticket through internet. The profile of the respondents is shown in table 5.29. Age Table 5.29: Profile of the respondents of online service quality Variable Frequency Percent 18-28 years 41 27.3 29-39 years 53 35.3 157

Gender Education Occupation Annual Income Length of e-service usage Frequency of use of e-service 40-50 years Above 50 years Total Male Female Total Under Graduate Graduate Post-Graduate Professional Degree Total Government Employee Private Employee Student/Research scholar Own Business/Entrepreneur Professional/ self employed Total Up to Rs. 2, 50,000/- Rs. 2, 50,000/- to Rs. 5, 00,000/- Rs. 5, 00,000/- to 7,50,000/- Above Rs. 7,50,0000/- Total Less than 6 Months 6 to 12 Months 12 to 24 Months More than 24 Months Total Occasionally Moderately Frequently Total 35 21 150 145 5 150 13 53 45 39 150 24 67 20 20 19 150 55 53 22 20 150 11 13 35 91 150 21 54 75 150 23.3 14.0 100.0 96.7 3.3 100.0 8.7 35.3 30.0 26.0 100.0 16.0 44.7 13.3 13.3 12.7 100.0 36.7 35.3 14.7 13.3 100.0 7.3 8.7 23.3 60.7 100.0 14.0 36.0 50.0 100.0 The respondents were at least 18 years old since, by this age, one is allowed to reserve a railway ticket through internet in India. The age profile of the respondents represents most age groups with the majority (35.3%) being in the 29-39 years and (27.3%) in 18-28 years. Majority of the respondents (96.7%) are male and only (3.3%) are female. About 35.3% of the respondents are graduation degree holders and 44.7% are private employee. Seventy two percent of the respondents belongs to low income group up to Rs250000 (36.7%) and Rs250000 to Rs500000 (35.3%). Further, as to the e-service usage pattern 60.7% of the respondents have been using e-service for more than 24 months and 50% uses this service frequently. 5.7.2 DATA ANALYSIS Reliability was tested by using Cronbach s alpha coefficient. The higher score denotes the high reliability of the generated scale i.e. items represent a high degree of 158

inter-correlation. In this study the cronbach s alpha score is 0.945 which is well above the recommended level of 0.70, indicating that the variables are interrelated. In order to express the structure of the original questionnaire with fewer variables and maintain most information provided by the original data factor analysis is performed on 21 items by using SPSS 16.0. The first step is to assess the appropriateness of factor analysis. Measure of sampling adequacy, such as Bartlett s Test of Sphericity (approx. chi square is 1.833E3, degree of freedom is 210 and significance is 0.000) and Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) value is 0.927 shows that data was fit for factor analysis (Table 5.30). Table 5.30 : KMO and Bartlett's Test for ESERVQUAL Kaiser-Meyer-Olkin Measure of Sampling.927 Adequacy. Bartlett's Test of Approx. Chi-Square 1.833E3 Sphericity df 210 Sig..000 Further, factors have been extracted by using Principal Component Analysis extraction method with orthogonal varimax rotation. Factors have been extracted on the basis of Eigen Values. In this approach, factors with Eigen values greater than 1.0 will be retained and other factors will be excluded. In the present study four factors has been extracted explaining 66.797% of total variance (Table 5.31). Component Total Table 5.31: Total Variance Explained by ESERVQUAL Factors Initial Eigen values Rotation Sums of Squared Loadings % of Variance Cumulative % Total % of Variance Cumulative % 1 10.325 49.169 49.169 4.112 19.581 19.581 2 1.339 6.378 55.547 3.620 17.236 36.816 3 1.265 6.025 61.572 3.209 15.281 52.097 4 1.097 5.225 66.797 3.087 14.700 66.797 Extraction Method: Principal Component Analysis. To test the reliability of the variables communalities of the variables has been computed. Variables having value less than 0.4 should be dropped from the further analysis as they are not fit for the factor solution. As it is clear from table 5.30, all the items fit well in factor solutions, as all factors have value more than 0.40. 159

Table 5.32: Communalities of ESERVQUAL Variables The IRCTC site makes it easy to find what I need (with minimum data input). Initial 1.000.774 Simple for me to learn the handling of site. 1.000.786 It loads its page fast 1.000.638 Information at this site is well organized 1.000.788 Overall, the IRCTC s site navigation is consistent and standardized. 1.000.700 The IRCTC s site is always available (i.e. 24*7*365 accessibility). 1.000.716 The IRCTC s site operates without problem. 1.000.719 When the IRCTC s site promise to perform something it does so. 1.000.618 It offers all required products and service I want 1.000.538 Information on the IRCTC s website about the tourism products and services etc. is up-to-date and correct. 1.000.485 The IRCTC s site never shares my personal information with other sites. 1.000.678 The IRCTC s site protects information about my credit card. 1.000.791 The IRCTC s site provides confirmation of executing online transactions. 1.000.696 The IRCTC authority gives me promote and relevant response of my queries and problems. 1.000.583 The IRCTC s site provides a phone number to reach the IRCTC. 1.000.651 The IRCTC s site has online customer service representatives. 1.000.671 The IRCTC s site provides language option so as to personalize the site as per personal requirement. Desired products and services can be easily found using key word search option in the web site The IRCTC site provides comprehensive FAQ section to help/ guide me for my common questions. I don t have to scroll from side to side to adequately see the IRCTC s website page. The website does not contain too many pop-ups and banner advertisement that make it difficult for me to look on the webpage. Extraction Method: Principal Component Analysis. 1.000.665 1.000.713 1.000.765 1.000.625 1.000.427 Extraction 160

Table 5.32: Communalities of ESERVQUAL Variables The IRCTC site makes it easy to find what I need (with minimum data input). table 5.33 and explained below. 161 Table 5.33: Rotated Component Matrix a of ESERVQUAL Initial 1.000.774 Simple for me to learn the handling of site. 1.000.786 It loads its page fast 1.000.638 Information at this site is well organized 1.000.788 Overall, the IRCTC s site navigation is consistent and standardized. 1.000.700 The IRCTC s site is always available (i.e. 24*7*365 accessibility). 1.000.716 The IRCTC s site operates without problem. 1.000.719 When the IRCTC s site promise to perform something it does so. 1.000.618 It offers all required products and service I want 1.000.538 Information on the IRCTC s website about the tourism products and services etc. is up-to-date and correct. 1.000.485 The IRCTC s site never shares my personal information with other sites. 1.000.678 The IRCTC s site protects information about my credit card. 1.000.791 The IRCTC s site provides confirmation of executing online transactions. 1.000.696 The IRCTC authority gives me promote and relevant response of my queries and problems. 1.000.583 The IRCTC s site provides a phone number to reach the IRCTC. 1.000.651 The IRCTC s site has online customer service representatives. 1.000.671 The IRCTC s site provides language option so as to personalize the site as per personal requirement. Desired products and services can be easily found using key word search option in the web site The IRCTC site provides comprehensive FAQ section to help/ guide me for my common questions. I don t have to scroll from side to side to adequately see the IRCTC s website page. The website does not contain too many pop-ups and banner advertisement that make it difficult for me to look on the webpage. 1.000.665 1.000.713 1.000.765 1.000.625 1.000.427 Extraction After the extraction of factors the next task is to interpret and name them. This is done by identifying the items that have high loading on individual factors. An important output, rotated component matrix is used to identify the variables in terms of the factors. The factor loadings represent the correlations between factors and variables. Values close to 1 represent high loadings and those close to 0 represent low loadings. All variables have factor loading greater than.45 are significant contributors and if factor loading is greater than.7 these are supposed to be highly significant. The objective is to find variable which have high loading on one factor, but low loading on other factors. Rotated factor matrix has been depicted in

Table 5.32: Communalities of ESERVQUAL Variables The IRCTC site makes it easy to find what I need (with minimum data input). 162 Initial 1.000.774 Simple for me to learn the handling of site. 1.000.786 It loads its page fast 1.000.638 Information at this site is well organized 1.000.788 Overall, the IRCTC s site navigation is consistent and standardized. 1.000.700 The IRCTC s site is always available (i.e. 24*7*365 accessibility). 1.000.716 The IRCTC s site operates without problem. 1.000.719 When the IRCTC s site promise to perform something it does so. 1.000.618 It offers all required products and service I want 1.000.538 Information on the IRCTC s website about the tourism products and services etc. is up-to-date and correct. 1.000.485 The IRCTC s site never shares my personal information with other sites. 1.000.678 The IRCTC s site protects information about my credit card. 1.000.791 The IRCTC s site provides confirmation of executing online transactions. 1.000.696 The IRCTC authority gives me promote and relevant response of my queries and problems. 1.000.583 The IRCTC s site provides a phone number to reach the IRCTC. 1.000.651 The IRCTC s site has online customer service representatives. 1.000.671 The IRCTC s site provides language option so as to personalize the site as per personal requirement. Desired products and services can be easily found using key word search option in the web site The IRCTC site provides comprehensive FAQ section to help/ guide me for my common questions. I don t have to scroll from side to side to adequately see the IRCTC s website page. The website does not contain too many pop-ups and banner advertisement that make it difficult for me to look on the webpage. Component 1.000.665 1.000.713 1.000.765 1.000.625 1.000.427 1 2 3 4 Extraction The IRCTC site makes it easy to find what I need (with minimum.812 data input)..196.231.148 Simple for me to learn the handling of site..830.220.144.166 It loads its page fast.522.125.200.557 Information at this site is well organized.681.388.146.390 Overall, the IRCTC s site navigation is consistent and.653 standardized..144.251.436 The IRCTC s site is always available (i.e. 24*7*365 accessibility)..064.163.147.815

Table 5.32: Communalities of ESERVQUAL Variables The IRCTC site makes it easy to find what I need (with minimum data input). Initial 1.000.774 Simple for me to learn the handling of site. 1.000.786 It loads its page fast 1.000.638 Information at this site is well organized 1.000.788 Overall, the IRCTC s site navigation is consistent and standardized. 1.000.700 The IRCTC s site is always available (i.e. 24*7*365 accessibility). 1.000.716 The IRCTC s site operates without problem. 1.000.719 When the IRCTC s site promise to perform something it does so. 1.000.618 It offers all required products and service I want 1.000.538 Information on the IRCTC s website about the tourism products and services etc. is up-to-date and correct. 1.000.485 The IRCTC s site never shares my personal information with other sites. 1.000.678 The IRCTC s site protects information about my credit card. 1.000.791 The IRCTC s site provides confirmation of executing online transactions. 1.000.696 The IRCTC authority gives me promote and relevant response of my queries and problems. 1.000.583 The IRCTC s site provides a phone number to reach the IRCTC. 1.000.651 The IRCTC s site has online customer service representatives. 1.000.671 The IRCTC s site provides language option so as to personalize the site as per personal requirement. Desired products and services can be easily found using key word search option in the web site The IRCTC site provides comprehensive FAQ section to help/ guide me for my common questions. I don t have to scroll from side to side to adequately see the IRCTC s website page. The website does not contain too many pop-ups and banner advertisement that make it difficult for me to look on the webpage. 1.000.665 1.000.713 1.000.765 1.000.625 1.000.427 Extraction The IRCTC s site operates without problem..358.387.144.648 When the IRCTC s site promise to perform something it does so..303.236.351.590 It offers all required products and service I want.329.383.333.414 Information on the IRCTC s website about the tourism products.136 and services etc. is up-to-date and correct..531.294.313 The IRCTC s site never shares my personal information with other.031 sites..252.745.241 The IRCTC s site protects information about my credit card..134.202.840.162 The IRCTC s site provides confirmation of executing online.498 transactions..236.614.126 163

Table 5.32: Communalities of ESERVQUAL Variables The IRCTC site makes it easy to find what I need (with minimum data input). 164 Initial 1.000.774 Simple for me to learn the handling of site. 1.000.786 It loads its page fast 1.000.638 Information at this site is well organized 1.000.788 Overall, the IRCTC s site navigation is consistent and standardized. 1.000.700 The IRCTC s site is always available (i.e. 24*7*365 accessibility). 1.000.716 The IRCTC s site operates without problem. 1.000.719 When the IRCTC s site promise to perform something it does so. 1.000.618 It offers all required products and service I want 1.000.538 Information on the IRCTC s website about the tourism products and services etc. is up-to-date and correct. 1.000.485 The IRCTC s site never shares my personal information with other sites. 1.000.678 The IRCTC s site protects information about my credit card. 1.000.791 The IRCTC s site provides confirmation of executing online transactions. 1.000.696 The IRCTC authority gives me promote and relevant response of my queries and problems. 1.000.583 The IRCTC s site provides a phone number to reach the IRCTC. 1.000.651 The IRCTC s site has online customer service representatives. 1.000.671 The IRCTC s site provides language option so as to personalize the site as per personal requirement. Desired products and services can be easily found using key word search option in the web site The IRCTC site provides comprehensive FAQ section to help/ guide me for my common questions. I don t have to scroll from side to side to adequately see the IRCTC s website page. The website does not contain too many pop-ups and banner advertisement that make it difficult for me to look on the webpage. 1.000.665 1.000.713 1.000.765 1.000.625 1.000.427 Extraction The IRCTC authority gives me promote and relevant response of my queries and problems..253.453.196.525 The IRCTC s site provides a phone number to reach the IRCTC..465.593.120.260 The IRCTC s site has online customer service representatives..026.750.074.319 The IRCTC s site provides language option so as to personalize the.217 site as per personal requirement..728.258.147 Desired products and services can be easily found using key word.459 search option in the web site.601.375.010 The IRCTC site provides comprehensive FAQ section to help/.438 guide me for my common questions..632.406.097

Table 5.32: Communalities of ESERVQUAL Variables Initial Extraction The IRCTC site makes it easy to find what I need (with minimum data input). 165 1.000.774 Simple for me to learn the handling of site. 1.000.786 It loads its page fast 1.000.638 Information at this site is well organized 1.000.788 Overall, the IRCTC s site navigation is consistent and standardized. 1.000.700 The IRCTC s site is always available (i.e. 24*7*365 accessibility). 1.000.716 The IRCTC s site operates without problem. 1.000.719 When the IRCTC s site promise to perform something it does so. 1.000.618 It offers all required products and service I want 1.000.538 Information on the IRCTC s website about the tourism products and services etc. is up-to-date and correct. 1.000.485 The IRCTC s site never shares my personal information with other sites. 1.000.678 The IRCTC s site protects information about my credit card. 1.000.791 The IRCTC s site provides confirmation of executing online transactions. 1.000.696 The IRCTC authority gives me promote and relevant response of my queries and problems. 1.000.583 The IRCTC s site provides a phone number to reach the IRCTC. 1.000.651 The IRCTC s site has online customer service representatives. 1.000.671 The IRCTC s site provides language option so as to personalize the site as per personal requirement. Desired products and services can be easily found using key word search option in the web site The IRCTC site provides comprehensive FAQ section to help/ guide me for my common questions. I don t have to scroll from side to side to adequately see the IRCTC s website page. The website does not contain too many pop-ups and banner advertisement that make it difficult for me to look on the webpage. I don t have to scroll from side to side to adequately see the IRCTC s website page. The website does not contain too many pop-ups and banner advertisement that make it difficult for me to look on the webpage. Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 7 iterations. FACTOR 1: EFFICIENCY 1.000.665 1.000.713 1.000.765 1.000.625 1.000.427.387.335.590.120.319.032.481.305 This is most significant factor as it explains 49.169% variance of the total variance with Eigen value 10.325. This factor has exhibited heavy loadings for five items out

of 21 items. It includes ease of use, simple, well organized, navigation and speed. After reviewing carefully, it was evident that all of these items related to efficiency of the website. Efficiency refers to the ease and speed of accessing and using the site (Parasuraman et al., 2005). Efficiency could also be defined as user s ability to access the website. So the most appropriate label for this factor is efficiency. It has also been identified as one of the major core dimension of E-SERVQUAL. Several research studies in recent past years (Khan, et al; 2009, Khurana 2009, Parasuraman et al., 2005) have recognized it as a major contributor in measuring e-service quality. FACTOR 2: RESPONSIVENESS It has been found as second largest factor with 6.378% variance explained and Eigen value 1.339. This factor consists of six items pertaining to correct and up-todate information, phone number, availability of customer representative, language option, easy search and FAQs for help in problems. It can be easily observed that the items are revealing the responsiveness of the website. So the researcher named this factor as responsiveness. It could be defined as effective handling of problems with the help of website. In case of e-service quick service to users through website can make users feel more comfortable during purchasing without any interruption. Parsuraman et. al.(2005) have also highlighted it as an important recovery dimension for E-SERVQUAL. Yoo and Donthu,2001; Khan et. al. 2009; Khurana 2009 and Hongxiu et. al. also asserted that prompt response to the problems of the customers largely determines customer evaluation of e-service. FACTOR 3: PRIVACY AND SECURITY This factor explains 6.025% variance of the total variance with Eigen value of 1.265. Five items are loaded to this factor pertains to security of credit card and personal information, confirmation of completed transactions, privacy from pop ups and no need to scroll from side to side. The researcher named this factor as privacy and security as it shows the company s will and ability to maintain the privacy of the user and protects their personal information. The security and privacy dimension used by Zeithmal et al. (2000), involves the degree to which the customer believes the site is safe from intrusion and personal information is protected. This is also an important dimension of E-servqual and etailq e-service evaluation technique. FACTOR 4: RELIABILITY This is the last factor accounts for 5.225% explained variance and have Eigen value 1.097. It consists of five scale items related with the availability of the website, 166

operates without problem, desired products, and response to queries. This Reliability depicts the technical aspect of the website such as its 24 hours availability and functioning without any problem. Since all the items of this factor emphasize on technical part, the researcher decided to name this factor as reliability. According to some empirical studies (Parasuraman et al. 1985, 1988; Dabholkar 1996; Khan et al. 2009 and Khurana 2009) reliability is the most important dimension of e-service quality. Following table depicts the summary of factor analysis. It shows name of factor, factor loadings, mean and cronbach s alpha. Cronbach s alpha is checking the construct reliability. If the value is greater than 0.65 it is reliable. The values of all the factors are greater than 0.65 so the constructs are reliable. Table 5.34: Summary of the Factors Affecting Consumers Perception Of Online Marketing Service Quality Of Indian Railways Facto r No. F1 F2 Factor Interpretation Efficiency Responsiveness Item No. 1 2 3 4 5 10 15 16 17 Variables The IRCTC site makes it easy to find what I need (with minimum data input). Simple for me to learn the handling of site. It loads its page fast Information at this site is well organized Overall, the IRCTC s site navigation is consistent and standardized Information on the IRCTC s website about the tourism products and services etc. is up-to-date and correct. The IRCTC s site provides a phone number to reach the IRCTC The IRCTC s site has online customer service representatives The IRCTC s site provides language option so as to personalize the Factor Loading.812.830.522.681.653.531.593.750.728 Mean 3.72 3.99 4.15 3.01 3.68 3.79 3.43 3.43 3.69 3.28 3.27 Cronbach s Alpha.886.873 167

F3 F4 Privacy And Security Reliability 18 19 11 12 13 20 21 6 7 8 9 14 site as per personal requirement. Desired products and services can be easily found using key word search option in the web site The IRCTC site provides comprehensive FAQ section to help/ guide me for my common questions. The IRCTC s site never shares my personal information with other sites. The IRCTC s site protects information about my credit card. The IRCTC s site provides confirmation of executing online transactions. I don t have to scroll from side to side to adequately see the IRCTC s website page. The website does not contain too many pop-ups and banner advertisement that make it difficult for me to look on the webpage. The IRCTC s site is always available (i.e. 24*7*365 accessibility). The IRCTC s site operates without problem. When the IRCTC s site promise to perform something it does so. It offers all required products and service I want The IRCTC authority gives me promote and relevant response of my queries and problems..601.632.745.840.614.590.481.815.648.590.414.525 3.40 3.49 3.85 3.79 3.92 4.19 3.62 3.71 3.19 2.89 2.86 3.41 3.37 3.44.830.846 168

5.7.3 Level of customers perceived feeling of satisfaction with regard to e-service quality dimensions The figure 5.7 below, displays mean scores of customers perceived satisfaction regarding derived dimensions: Figure 5.7: Mean Scores of E-service Quality Dimensions It can be observed from the figure 5.7 that privacy and security dimension scores the highest score of 3.85. It indicates that the users do not recognize considerable risk in online environment of e-service stemming from the possibility of misuse of their financial information and personal information. It is followed by efficiency, responsiveness and reliability with mean scores of 3.72, 3.43 and 3.19 respectively. All the mean scores of the dimensions are rated by the users between Neutral and Agree so the railways should pay attention for further improvement of these dimensions. Specially, railway should rectify operating problems of the website and it should run all the time 24*7*365 as the users tend to have low mean score of 2.86 and 2.89 respectively. In e-service, if site operates without any problem and 24*7*365 access is offered than it helps in improving the image of the e-service quality. It is good indicator that users found it very simple to learn the handling of website and satisfied with confirmation of executing online transactions. 5.8 Findings Pertaining To the Consumers Attitude towards Indian Railways Website 169

5.8.1 Profile of the respondents of Feedback Form Table 5.35: Profile of the respondents of Feedback Form Variable Frequency Percent Age 18-28 years 29-39 years 40-50 years Above 50 years Total 848 845 422 334 2449 34.6 34.5 17.2 13.6 100.0 Gender Occupation Marital Status Male Female Total Government Employee Public Employee Private Employee Professional self employed Student Other Total Married Unmarried Total 2284 165 2449 316 151 802 495 225 287 173 2449 1497 952 2449 93.3 6.7 100.0 12.9 6.2 32.7 20.2 9.2 11.7 7 100.0 61.1 38.9 100.0 The respondents were at least 18 years old since, by this age, one is allowed to reserve a railway ticket through internet in India. The age profile of the respondents represents most age groups with the majority (34.5%) being in the 29-39 years and (34.6%) in 18-28 years. Majority of the respondents (93.3%) are male and only (6.7%) are female. Majority of the respondents are private employee (32.7%) followed by professionals (20.2). Substantial number of respondents is married (61.1%). 5.8.2 Data Analysis Reliability was tested by using Cronbach s alpha coefficient. The higher score denotes the high reliability of the generated scale i.e. items represent a high degree of intercorrelation. In this study the cronbach s alpha score is 0.922 which is well above the recommended level of 0.70, indicating that the variables are interrelated. Initially factors analysis was performed to identify the main factors for the evaluation of an e-commerce website. To examine the appropriateness of the data for factor analysis Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett s test of Sphericity is calculated. 170

Table 5.36: KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling.903 Adequacy. Bartlett's Test of Approx. Chi-Square 22074.989 Sphericity df 78 Sig..000 In this study, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy score (.903) is well above the recommended 0.5 level (Malhotra, 2008). It implies that the sample is large adequate. The Bartlett s Test of Sphericity gives approximate chisquare statistic 22074.989 with 78 degrees of freedom, which is significant at the.05 level. Thus factor analysis may be considered as an appropriate technique for analyzing the given data. High chi-square value implies that the data is normally distributed. To obtain the initial factor solution principal component analysis extraction method with orthogonal varimax rotation is used. Now the next step is to identify the number of factors to be extracted. A typical approach is to extract the factors with eigen values greater than 1. But lower eigen values has also been considered for the extraction of factors in the literature to increase the variance. So in this study approach of eigne values greater than 1 has not been followed as only two factors will be extracted and only 64.319% variance will be explained. Thus in order to increase the explained variance factors with eigen values greater than 0.6 have been extracted. Four factors has been extracted explaining 75.36% variance. Compone nt Total Table 5.37 : Total Variance Explained Initial Eigen values % of Variance Cumulative % Total Rotation Sums of Squared Loadings % of Variance Cumulative % 1 6.752 51.940 51.940 3.451 26.546 26.546 2 1.609 12.379 64.319 3.323 25.565 52.111 3.809 6.221 70.541 1.537 11.822 63.933 4.627 4.820 75.360 1.486 11.427 75.360 171

After the extraction of factors the next task is to interpret and name them. This is done by identifying the items that have high loading on individual factors. An important output, rotated component matrix is used to identify the variables in terms of the factors. The factor loadings represent the correlations between factors and variables. Values close to 1 represent high loadings and those close to 0, low loadings. All variables have factor loading greater than.45 are significant contributors and if factor loading is greater than.7 these are supposed to be highly significant. The objective is to find variable which have high loading on one factor, but low loading on other factors. Rotated factor matrix has been depicted in table 5.38 and explained below. It is clear from the table that all the variables except e-ticket have factor loading greater than 0.7. It implies that all the variables are significant contributors. Table 5.38 : Rotated Component Matrix a Component 1 2 3 4 Registration.788.143.181.180 navigation.781.247.151.153 Login.759.192.126.156 LOok and Feel.728.249.201.170 online Booking.702.229.055.288 Phone Promptness.207.881.135.193 Phone quality.216.879.129.177 Email Promptness.272.795.293.105 Email quality.274.790.290.110 Refund.150.393.786.105 cancellation.323.223.731.305 I-ticket.249.248.195.794 E-ticket.441.138.162.683 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 6 iterations. Factor-1 Website Service Quality This is most significant factor as it explains 51.94% variance of the total variance with Eigen value 6.752. This factor has exhibited heavy loadings for five variables out of 13 variables. It includes navigation, registration, login, online booking and look and feel. All these variables seem to represent the service quality of the website. So the most appropriate label for this factor is website service quality. A number of 172

studies identified website service quality as an important factor (Liu and Arnett., 2000 and Lu et al.., 2001) Factor-2 Customer Care This is the second important factor explaining 12.379% of variance with Eigen Value 1.609. It consists of four variables email quality, phone quality, email promptness and phone promptness. All these variables pertaining to the customer care service. So the researcher has labeled this factor as customer care. Elliot (2000) also extracted customer care as an important category for a website functionality evaluation framework. Factor-3 Refund Process This is the third significant factors accounts for 6.221% variance. Two items were loaded on to this factor cancellation and refund. This is the process of money refund after the cancellation of the order so the researchers have named this factor as refund process. Schubert and Dettling (2002) also discovered after sale phase as a crucial phase in its four phased model for the evaluation of the website. Factor-4 Product This factor accounts for 4.820% variance of the total variance. This factor has high loading for two items e-ticket and i-ticket. These are the two main products which are being sold by the website. So the researchers have decided to name this factor as product. Several research studies conducted in recent past years (Elliot, 2000; Lu et al,. 2001; Schubert and Dettling, 2002) applauded that product information and its transaction processing through internet significantly affect the website evaluation. After identifying the main factors there is need to evaluate all these factors. In order to know the better and clear image of the performance of an e-commerce website Multidimensional scaling is used. 5.8.3 Multidimensional Scaling (MDS) The data has been analyzed by using SPSS 17 version to perform ALSCAL MDS. Before performing MDS there is need to assess the suitability of MDS. Iteration history for the 2 dimensional solutions (in squared distances) Young's S-stress formula 1 is used. Iteration S-stress Improvement 1.28328 2.23618.04710 173

3.23143.00475 4.23091.00052 Iterations stopped because S-stress improvement is less than.001000. Stress values are Kruskal's stress formula 1. For matrix Stress =.22349 RSQ =.86987 Kruskal s formula 1 has been used to compute the stress values. Stress values indicate the quality of MDS solution; low values of stress indicate good fit. RSQ are the proportion of variance of the scaled data (disparities) in the partition (row, matrix, or entire data) which is accounted for by their corresponding distances. R-square value indicates goodness of fit, values higher than 0.60 are desirable for good fit. In this case the value of RSQ is.86987 well above the recommended level and the stress value is.22349 suggests goodness of MDS. On the basis of the perceptual mapping of user s feedback of the factors affecting the evaluation of railways website following conclusion can be withdrawn. Website Service quality: It is one of the most important determinants in the evaluation of a website. As it could be observed it is performing well especially login. But still some elements like look and feel etc need some improvement. Customer Care: As it is clear from the map that this is the poorest one among all the factors. Users are not at all satisfied with the email and phone service of the organization. Refund Process: This is an after sale service provided to the customers. Cancellation is still above average but refund is very close to the poor value. Product: Website is offering mainly two types of products that is I-ticket and E- ticket. Consumers gave high rate to the E-ticket as compare to the I-ticket. E-ticket is most preferred over the purchase of an I-ticket. 174

Figure 5.8: Website Evaluation 5.9 Findings Pertaining To Examine Factors Resisting the Non-Users to Adopt Online Marketing of Indian Railways 5.9.1 Profile of the Non- Users Table 5.39: Profile of the Non-Users Variable Frequency Percent Age Gender Education Occupation 18-28 years 29-39 years 40-50 years Above 50 years Total Male Female Total Under Graduate Graduate Post-Graduate Professional Degree Total Government Employee Private Employee Student/Research scholar Own Business/Entrepreneur Professional/ self employed Other Total 175 77 11 32 24 144 66 78 144 9 24 85 26 144 15 26 57 16 20 10 144 53.5 7.6 22.2 16.7 100 45.8 54.2 100 6.2 16.7 59 18.1 100 10.4 18.1 39.6 11.1 13.9 6.9 100

Annual Income No Income Up to Rs. 2, 50,000/- Rs. 2, 50,000/- to Rs. 5, 00,000/- Rs. 5, 00,000/- to 7,50,000/- Above Rs. 7,50,0000/- Total 25 77 18 6 18 144 17.4 53.5 12.5 4.2 12.5 100 Figure 5.9: Non Users Access to Internet Above figure 5.9 shows that majority of the respondents have access to internet from their home. It could be noted that 10 respondents do not have any access to internet and 39 have access from cyber café. So it could be concluded that non accessibility to internet could be a reason of not using the online marketing of Indian Railways. But there is need to find out the reasons of non users abstinence from using online marketing that have access to internet. A fairly large majority of 34 respondents have internet usage less than 5 hours and 13 have no use of internet. However, number of respondents using internet 5 to 1o hours and more than 10 hours is 25 and 28 respectively which shows high potential of adoption of online marketing. Figure: 5.10: Internet Usage in a week in Hours by Non- users 176

Figure 5.11: Level of awareness among Non-Users An overwhelming majority of the respondents (137) reported that they are aware with the online marketing of Indian railways. On the other hand only a few respondents (7) are not aware with the online marketing of Indian Railways. The results reflect that awareness is not an issue of non adoption of online marketing of Indian railways, as majority of respondents (95%) is aware with it. So there is need to examine the factors resisting adopting the online marketing of Indian Railways. 5.9.2 Examining factors resisting the Non-Users to adopt online marketing of Indian Railways As it has been reflected by the results that majority of the respondents who claimed that have heard about the online marketing of Indian railways, have not opted for it. So it becomes necessary to identify the reasons resisting them to adopt online 177

marketing. In this line the non users were asked to bring out the reasons of non usage on 5-point Likert scale. Responses to the statements are first analyzed with the help of descriptive statistics followed by the inferential statistics to better understand the phenomenon. Table 5.40: Descriptive Statistics Regarding the factors resisting the use of online marketing of Indian Railways Reasons Do not have adequate knowledge of using computer and internet Lack of awareness about the benefits of online services of Indian Railways Lack of sufficient information about how to make use of online services (online reservation) Fear of Security and Privacy of your monetary transactions Mean Std. Deviation 3.04 1.332 3.146 1.5643 3.47 1.467 3.18 1.277 Fear of making error while feeding information 3.08 1.249 Online reservation system would be too complicated to operate Lack of interest in using internet for ticket reservation 2.58.957 3.06 1.570 No access to internet at home or office 2.15 1.308 Not reliable to reserve a ticket through internet 3.04 1.438 May not receive correct information over the internet 2.89 1.375 Using online services is time consuming 2.40 1.099 It is expensive to use online services of Indian railways 2.51 1.003 Source: Primary data It could be easily inferred from the above table 5.40 that lack of sufficient information is a leading de-motivating factor with the highest mean score of 3.47. The next in the line is fear of security and privacy followed by lack of awareness about the benefits, fear of making error, Lack of interest, not reliable and do not have adequate knowledge with the mean scores being 3.18, 3.146, 3.08, 3.06, 3.04 and 3.04. No access to internet is reported as the weakest hurdle to the adoption of online marketing of Indian railways. It has also been observed earlier that a very large majority of the respondents have access to the internet. Inferential Statistical Analysis: One way ANOVA (Analysis of Variance) is 178

performed to analyze differences among three groups of non-adopters. Laukkanen et.at. (2008) segmented non adopters into three segments as: those who intend to use within next 12 months, Postponers; those intend to use but not decided, Opponents; and those who do not intend to use at all, Rejecters. The following table shows how resistant factors differ among the three segments. Table 5.41: Descriptive Statistics Regarding the factors resisting the use of online marketing of Indian Railways Reasons Do not have adequate knowledge of using computer and internet Lack of awareness about the benefits of online services of Indian Railways Lack of sufficient information about how to make use of online services (online reservation) Fear of Security and Privacy of your monetary transactions Fear of making error while feeding information Online reservation system would be too complicated to operate Lack of interest in using internet for ticket reservation No access to internet at home or office Not reliable to reserve a ticket through internet Postponers N=40 Opponents N=87 Rejecters N=17 Mean S.D Mean S.D Mean S.D F Sig 2.58 1.299 3.23 1.361 3.18 1.015 3.531.032 * 53.529.000* 1.550.5038 3.897 1.4145 3.059 1.0290 1.92.997 4.13 1.237 3.76.437 54.649.000 * 2.80 1.324 3.57 1.117 2.06 1.029 14.898.000 * 2.87 1.305 3.29 1.247 2.53.874 3.509.033 * 2.18.594 2.55.949 3.71.849 19.321.000 * 1.45.677 3.67 1.387 3.76 1.251 48.788.000 * 2.55 1.467 2.20 1.237 1.00.000 9.504.000 * 2.53 1.414 3.34 1.445 2.71 1.047 5.276.006 * 179

May not receive correct information over the internet Using online services is time consuming It is expensive to use online services of Indian railways Source: Primary Data 1.70.758 3.51 1.293 2.53.874 36.297.000 * 1.85 1.167 2.59.983 2.76 1.091 7.890.001 * 2.55 1.300 2.39.688 3.00 1.414 2.737.068 Note:* Denotes Significant at 5% level of significance Above table 5.41 depicts that except expensive to use online services, there are significant differences among three different segments of non-adopters with respect to all the other factors resisting them to adopt online Marketing of Indian Railways. A closer examination of the results reveals that resistance to online marketing on the part of postponers is not so intense as compare to opponents and rejecters. As the mean scores of all the reasons of postponers are less than 3. After analyzing the results of opponents it could be seen that lack of sufficient information prevents them to adopt online marketing. At the same time they have shown lack of awareness about the benefits of online marketing. An examination of rejecters results reveals that in addition to sharing same reasons of opponents they have no interest in using internet for ticket reservation in addition to this they have found it too complicated to operate. At the same time they do not have adequate knowledge of using computer and internet which plays as a very significant barrier in adopting online marketing of Indian Railways. Finally it may be concluded that rejecters have claimed very intense resistance for adopting online marketing of Indian Railways in contrast to postponers and opponents. It could be that the mean score of most of the reasons are above two and close to 3 shows a high potential of adoption of online marketing. An implementation of effective marketing strategies to provide sufficient information about the use and by creating confidence in the benefits of online marketing of Indian Railways may help in persuading non-adopters to adopt it efficiently. 5.10 Conclusion The results of this research shows that majority of the respondents are male and belongs to the young age group. Online marketing is more prevalent among educated 180

people. Service class people with high income are the frequent users of online services of Indian Railways. Majority of the respondents are using this facility for more than 3 years. The result confirms the appropriateness of TAM for its applicability in adoption of online marketing in Indian Railways. It could be seen that perceived usefulness, perceived ease of use, perceived enjoyment, subjective norm and facilitating condition have significant positive effect on consumers attitude. Trust and image are insignificant factors of attitude. Furthermore perceived risk has negative effect on attitude but it is not significant. Subsequently behavioral intention is determined by perceived usefulness and attitude. Finally, Actual usage behavior is predicted very strongly by behavioral intention. An analysis of the responses according to the respondents demographics indicated that old age people are more positive in attitude as compare to the young generation. Married respondents are more positive towards online marketing in contrast to unmarried ones. It could be easily inferred that students are not positive in attitude as compare to the other occupations. The result reveals that respondents who are using it for more than three years are more intended to use it in contrast to those who are using it for less than 3 years. Finally it could be inferred that income positively affects the attitude, behavioral intention and actual use of online marketing. There is no impact of gender, education level and internet usage on attitude, Behavioral intention and actual use. The main opportunities, which prompted consumers to go for online marketing, are time saving and convenience. While majority of the respondents felt that busy network is a most important and primary challenge of online marketing of Indian Railways. It can also be extracted that online ticket reservation and information gathering facility is being used by the large section of society. However, online tourism services are in initial stage, as the use of these sophisticated online services is confined to small proportion of customers. This indicates higher adoption of online ticket reservation and information gathering facility as compare to online tourism servcies. Furthermore, the study has identified four factors affecting consumers perception of online marketing service quality of Indian Railways namely; efficiency, responsiveness, privacy and security and reliability. Surprisingly consumers felt that the Indian railway s website is providing sufficient privacy and security. The examination of the factors resisting consumers from using online marketing shows that lack of sufficient information is a leading de-motivating factor among the non- 181

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