CHAPTER 5: CONSUMERS ATTITUDE TOWARDS ONLINE MARKETING OF INDIAN RAILWAYS

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1 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: 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

2 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 years years years Above 40 years Total 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 As shown in table % of the respondents belong to the age group of years old, 28.2% 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

3 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 Model Evaluation In order to achieve the objective first, the measurement model through confirmatory factor analysis and statistical tests to establish the validity and reliability of the survey are performed. Second, the structural model is analyzed to test the hypothesized relationship among different factors presented in the model Measurement Model The measurement model assessed individually with the help of confirmatory factor 124

4 analysis of all the constructs are presented below 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 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 To assess the construct reliability cronbach alpha (0.810) is calculated which is fairly above the minimum value of Finally, it may be concluded that perceived usefulness measurement model is reliable and valid Perceived Ease of Use GFI=.840 CFI=.894 RMSEA=.285 Cronbach Alpha=.913 All the indicators of perceived ease of use are showing strong standardized loadings 125

5 on the relative construct more than 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 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 Perceived Enjoyment GFI=1 RMSEA=0 CFI=1 Cronbach Alpha=

6 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 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 Subjective Norm GFI=1 CFI=1 RMSEA=0 Cronbach Alpha=

7 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 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 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 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

8 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 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 Behavioral Intention GFI=1 CFI= 1 RMSEA=0 Cronbach Alpha=

9 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 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 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

10 Table 5.2: Assessment of Consumers Constructs Reliability Research Construct Number of Items Cronbach s Alpha Perceive Usefulness Perceived Ease of Use Trust Perceived Enjoyment Image Subjective Norm Facilitating Condition Perceived Risk Attitude Behavioral Intention Actual Usage Assessment of convergent Validity 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 PU Perceived Ease of Use PEOU1 PEOU

11 132 PEOU3 PEOU4 PEOU Trust TR1 TR Perceived Enjoyment PE1 PE2 PE Image IM1 IM2 IM Subjective Norm SN1 SN Facilitating Condition FC1 FC2 FC Perceived Risk PR1 PR2 PR3 PR4 PR Attitude ATT1 ATT2 ATT3 ATT Behavioral Intention BI1 BI2 BI Actual Usage AU1 AU

12 It could be inferred from the above measurement model validity and reliability examination that the instrument used to measure attitude, Behavioral intention and Actual usage individually is adequate and reliable Structural Model After successful validation and reliability testing of measurement models, the structural model can be analyzed. Structural model will be evaluated by using R- square for dependent constructs, path coefficients and significant level of structural path coefficient. First of structural equation model will be analyzed on the basis of squared multiple correlation (R 2 ) R-square Squared multiple correlation (R 2 ) for each endogenous construct is used to measure the percentage of construct variation explained by the exogenous construct. The values should be sufficiently high for the model to have a minimum level of explanatory power. Chin (1998b) considers values of approximately.670 substantial, values around.333 average, and values of.190 and lower weak. Table 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 percent. The structural model results are summarized in figure 5.2 and table

13 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 Supported H2 PU BI Supported H3 PEOU PU Supported H4 PEOU ATT Supported H5 TR ATT Not Supported H6 PE ATT Supported H7 IM ATT Not Supported H8 SN ATT Supported H9 FC ATT Supported H10 PR ATT Not supported H11 ATT BI Supported H12 BI AU Supported 134

14 Figure 5.2: Results of testing the Hypothesized links for Consumers R 2 :.371 PU R 2 :.331 R 2 :.500 R 2 : ATT.623 BI.751 AU PEOU TR * *.352 PE IM.028* SN FC PR Note: - Path Coefficients with * symbol are not significant and there by not supporting the hypothesis 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 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

15 Figure 5.3: Complete Model for consumers with all the indicators PU1 PU2 PU3 PU PEOU1 PEOU2 PEOU3 PEOU4 PU.609 PEOU.73 PEOU TR1 TR TR2 PE1.011* BI1 BI2 BI PE2 PE3 PE.254 ATT.623 BI AU AU IM1 IM2 IM3 IM.028*.950 ATT1 ATT2 ATT3 ATT AU SN1 SN SN2.82 FC *.86 FC2 FC.72 FC3.76 PR1.87 PR2.77 PR3 PR.42 PR4.39 PR5 136

16 Table 5.6: Consumers Model s Overall Goodness of fit Item Measured Value Recommended value Goodness of fit Index (GFI) Comparative fit index (CFI) Root mean square error of approximation (RMSEA) 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 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

17 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) Insignificant Results Perceived risk has negative influence on attitude (path coefficient= 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

18 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 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 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 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 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

19 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 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 Years Years Above 40 Years Total F Value Sig * Bold values indicate that the mean difference is significant at the.05 level. 140

20 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 Years Table 5.8: Post-Hoc Analysis: Age and Attitude Age (J) Mean Differenc e Std. Error Sig Years Years Above 40 years Years Above 40 years Years Above 40 years * 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 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

21 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 Female Total F Value Sig 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 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 Unmarried Default Total F Value Sig

22 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 * Default Behavioral Married Unmarried * Intention Default Actual use Married Unmarried * Default * 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 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 Graduate

23 Post graduate Higher education Other Total F Value Sig 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 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 Business Student Retired Professional Other Total F Value Sig * Bold values indicate that the mean difference is significant at the.05 level. 144

24 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 * Business Retired * Professional * Other * 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 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

25 Above Total F Value Sig * 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 * * above * * 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 * * above * 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

26 Table 5.18: Post-Hoc Analysis: Income Level and Actual use (I) Income Level (J) Mean Differenc e Std. Error Sig. Less than * * above * * 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 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 hours hours More than 30 hours Total F Value Sig

27 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 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 Year - 2 year years - 3 Years More Than 3 Years Total F Value Sig * 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

28 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 * Year - 2 year * years - 3 Years * 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 * Year - 2 year * years - 3 Years * * 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 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

29 Table 5.23: Descriptive Statistics regarding the opportunities offered by online marketing N Mean Std. Deviation Convenient Time saving No long queues Buying tickets 24/7 (at any time & from anywhere) Price saving Easy access to information New technology experience Multidimensional scaling (MDS): In order to perform MDS ALSCAL procedure with the help of SPSS 16 is being used. MDS yields to perceptual mapping which explains the relative position of various opportunities on a 2 X 2 matrix. Before performing MDS there is a need to check its suitability. Iteration history for the 2 dimensional solutions (in squared distances) Young's S-stress formula 1 is used. Iteration S-stress Improvement Iterations stopped because S-stress improvement is less than For matrix Stress = RSQ =.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

30 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 = RSQ = Number Stimulus Name Dimension Convenient Time Saving Price Saving Buying tickets 24/7 (at any time & from anywhere) 5 No long queues New technology experience Easy access to information 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

31 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 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 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 Risky to use credit card Difficulty in cancellation or refund Lack of online payment facility Lack of privacy of personal information Risk of wrong ticket Expensive Complex system Don t know how to use

32 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 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 Iterations stopped because S-stress improvement is less than For matrix Stress = RSQ = The fit of an MDS solution is commonly assessed by the stress measure. Stress is a lack of fit measure; higher values of stress indicate poorer fits. R-square is a measure of goodness of fit. Although higher values of R-square are desirable, values of 0.60 or higher are considered acceptable (Malhotra 2008). In this case, the value of RSQ is which is very high with fairly low value of stress (.03230) indicates goodness of MDS. Configuration derived in 2 dimensions Table 5.26: Stimulus Coordinates of Consumers Challenges Stress = RSQ = Number Stimulus Name Dimension Risky to Use Credit Card Very Busy Network

33 3 Difficulty in Cancellation or Refund Lack of Online Payment Facility Risk of wrong Ticket Lack of Privacy of Personal Information 7 Don t Know How to Use Complex system Expensive 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

34 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 Row % % Car Rental Count Row % % Tour packages Count Row % % Seat Availability Status Count Row % % Train arrival and Count departure time Row % % Online Ticket reservation Count Row % % Fare Enquiry Count Row % % Train Schedule Count

35 Row % % Frequently Asked Count Questions Row % % Loyalty Programs Count Row % % 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

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