CHAPTER I CHAPTER I INTRODUCTION
CHAPTER 3 METHODOLOGY
S. D. Cronbach's a Items Factor
4.2.3 Assumptions in ANOV A to Test H 7
Box-plot of the sample distribution was examined to meet the assumption of ANOV A, i.e. absence of outliers. Since it is a useful standard in data interpretation, reveals data symmetry, skewness and the presence of outliers. Moreover, it also facilitates in comparing more than one population without knowing anything about the underlying statistical distributions of those populations.
4.2.3.1 Box-and-Whisker Plot (H7)
In the case of satisfaction level with existing OT As, respondents who reported that they 'can compromise' on SQAs of the airline have a median at 2 (black line) as shown in Figure 4.4. This represents high satisfaction level and at the same time indicates 50% of the data is greater than this value. Users' with any lesser satisfaction with existing OT As are represented everything above median black line, while the users with higher satisfaction are represented everything below median black line. As shown by the top 'whisker', this group has greatest values but no outliers. Hence the data is normally distributed.
0
40~ 35
.,. ~
·B a,a .:; i 25
] 2.0
·c g
~ 15 lii
Sati<;faction Level with existing Online Travel Agencies (OT As)
I • Higly Satisfied, S -lh!#lly DISsatisfied
"' 1.0
N11: <2 83 fJI5
Can Comprorruse May Compromise Cannot Compromise Users' flexibolity in compromosong on sc:rvoce quabty attnbutes
Figure 4.4: Box Plot on Satisfaction Level with Existing OT As
In the case of satisfaction level with existing OT As, respondents who reported that they 'may compromise' on SQAs of the airline have a median at 2 (black line). This
represents neutral satisfaction level and at the same time indicates 50% of the data is greater than this value. Users' with any lesser satisfaction with existing OTAs are represented everything above median black line, while the users with higher satisfaction are represented everything below median black line. As shown by the top 'whisker', this group has greatest values but no outliers. Hence the data is normally distributed.
In the case of satisfaction level with existing OT As, respondents who reported that they 'cannot compromise' on SQAs of the airline have a median at 3 (black line). This represents high satisfaction level and at the same time indicates 50% of the data is greater than this value. Users' with any lesser satisfaction with existing OTAs are represented everything above median black line, while the users with higher satisfaction are represented everything below median black line. As shown by the top 'whisker', this group has greatest values but no outliers. Hence the data is normally distributed.
4.2.3.2 Means Plot (H7)
The means plot is shown in Figure 4.5.
Satisfaction Level with existing Online Travel Agencies (OTAs)
I- Highly Satisfied, 5-Hi~hly Dissattsfied
2 . 7 , - - - ,
2.6
0
~ 2519~---4
Can Compronuse May Compromise Cannot Compromise
User's flexibility in compromising on semce quality attributes
Figure 4.5: Means Plot on Satisfaction Level with Existing OT As
The means plot shows that their apparently enormous difference between the satisfaction level of the three respondents groups, which may appear not to be actual case. Therefore as a follow-up and to backup this. we will analyze same results in a different chart to see the difference between the groups.
4.2.3.3 T-Test (H7)
In this case the three groups are significantly different using a !-test (t =35.509, df = 169, p = 0.000) as shown in Table 4.6. 95% Confidence interval is probability that the interval contains the true mean. CI of the groups is closely related to the results of the analysis of variance for these groups. The confidence interval for each graph below shows a linear pattern of the sample distribution which otherwise appeared to be showing huge variations in the means plot.
Table 4.6: T-Test on User's Flexibility with Existing OTAs Test Value= 0
- - - · - - - : : - - - - : : - : : -
df Sig. (2- Mean 95% Confidence Interval of the ---~ta=iii!IJLDifference _______ _ ____ Differen=-=c:c:e _ _ _ t
How
flexible 35.509 169 you are
4.2.3.4 Error Bars (H7)
.000 2.14 Lower
2 02
Upper 2.25
The same results are now reproduced in the error bars as shown in Figure 4.6, with 95% confidence intervals to have an idea of the variation in sample distribution.
In error bars we intend to see if the mean of one group is included in the confidence interval of the other two groups - if so then there is likely no difference among the groups. Moreover, it is not relevant whether the error bars 'overlap' but whether the mean of one group 'overlaps' with the error bars of the other. The confidence intervals can overlap by as much as 25 percent of their total length and still show a significant difference between the means for each group. Any more overlap and the results will not be significant.
Error Bar: Satisfaction Level with existing Online Travel Agencies
1-Highly Satisfied, 5- Highly Dissatisfied
3 . 0 , - - - ,
~ 0 28
"
·~ 0 2.6 0
" "
"
2.4.§
"
.g
1l 22
·~
2.0 , 8
'.
Can Compromise May Compromise Cannot Compromise Users' flexibility m compromising on service quality allributes
Figure 4.6: Error Bar on Satisfaction Level with Existing OTAs
In Figure 4.6, 95% CI tells us that the satisfaction level of existing OT As for users who "can compromise" on SQAs of the airline is probably between 1.98 and 2.42, with group mean of 2.2. Likewise, for users who "may compromise" it is probably between 2.03 and 2.44, with group mean of 2.28, and for users who "cannot compromise" it is probably between 2.3 and 2.8, with group mean of2.6.
The group mean of users' who 'can compromise' shares a certain degree of confidence interval overlap with the error bars for users who 'may compromise', thus the two groups may not necessarily be different from one another. Moreover, the group mean of users' who 'cannot compromise' does not share any degree of confidence interval overlap with either of the two groups, therefore, this particular group appears to be significantly different from the rest of the sample population.
However, only with our post-hoc tests, this can be confirmed.
4.2.4 Hypothesis Testing H7
H1: The level of satisfaction with existing OTAs will be different for respondents with different attitudes towards Users' Flexibility in compromising on SQAs of the airline.
-1.2.41 One-way Analysis of Variance (if)
To test this hypothesis, one-way analysis of vanancc was used to determine satisfaction mean of users with existing online travel agencies of the airline and at the same time report their flexibility level in terms of compromising on SQAs of the airline. The respondents had to select from given three options of, ( 1) Can compromise on SQAs, (2) May compromise on SQAs, (3) Cannot compromise on SQAs as shown in Table 4.7.
The analysis showed significant differences among satisfaction mean of the three user groups with existing online travel agencies (F (2,169) = 6.728, p = .002 < .01).
Table 4.7: ANOVA on Satisfaction Level with Existing OTAs
Sum of df Mean F
s·
S S Ig .
. . ___ quar~--- quare
_,B:::.:e~tw:.:.e::.:e:.::n'-'G"'r'-'o:.::uorp.-_s _ _ -"9_,_.4c.::2:.:_9 __ 2 _ _ .. _4.7_1_5 _ . _ _ 6._7_28 _ _ .00_2 __
Within Grouj!s 117.Ql8__ 167 .701
Total 126.447 169
The respondents who indicated their flexible attitude as "can compromise" on SQAs of the airline, depicted highest level of satisfaction with existing online travel agencies (M = 2. SO = .625). This was closely followed by satisfaction of the respondents who indicated their flexible attitude as "may compromise" on SQAs of the airline (M = 2.17, SO= .794). The respondents who reported their flexible attitude as "cannot compromise" on SQAs of the airline, depicted least level of satisfaction with existing online travel agencies (M = 2.57, SO = .984). Since the three user groups differed significantly on satisfaction mean level of existing online travel agencies, therefore, hypothesis H7 is accepted
-1.2.4.2 Post-hoc Sche[[e Tests (H7)
Post-hoc Scheffe tests in Table 4.8 showed that there is a significant difference between the pair of means of the respondents who reported their flexible attitude as
"Cannot compromise" on SQAs of the airline with those who "Can compromise"; p = .000 ( < .00 I). The same group of respondents also differed significantly from the
group of respondents who reported their flex1ble attitude as "May compromise" on SQAs of the airline, p=.031 ( < .05).
Table 4.8: Multiple Comparisons on Satisfaction Level with Existing OT As
Dependent Variable: Satisfaction with Existing OTAs
fl
(I~bhlow
(J) how flexible D:wf ... ean Std.s·
C 9fisdo/oext e you 1 .erenc E tg. on 1 ence
you are rror
, _ _ are . - .... --~--~·-- ~~J)i _________ --~nterval_~
Lower Upper Bound Bound
~---~--
Can May Compromise
Compromise ______ .. ---~ -.17 .167 .579 -.59 .24
Cannot
-.57(*) .166 .003 -.98 -.16 _ ~omjlromise - ; - - - - - - - -
May Can Compromise -.
Comjlromise _____
~·
- - - - ___ .1_7--·~16~7~---·5_79-~-
24 --- .59 Cannot_ _ _ _ _ Compromise -.39(*) .148 .031 -.76 -.03
···-~·---·---· - - - - -
Cannot Can Compromise
Comprom_is_e_~_ . - - - ; - - - · - - · · · - - - : : c ; c - -
MayCompromise .39(*) .148 .031 .03 .76
.57(*) .166 .003 .16 .98
*
The mean difference is significant at the .05 level.4.2.4.3 Effect Size for One-way ANOVA
From our hypothesis testing we know that the three groups are different, but this does not confer the strength or the magnitude of this effect. Effect size is measure of the strength of an effect. And since we have already rejected the null-hypothesis in the both of the above cases, therefore it makes sense to calculate effect-size to determine the size of the effect. The size of the effect is 7.5% (1]2 = 0.0745).
4.3 Phase 1: Users' Satisfaction with SBTs against their rated OTA Feature