CHAPTER 5 Data Analysis Results and Discussion................................... 115-174
5.5 Validation of TRI drivers with Interpersonal Service Encounter
146 I keep up with the latest technological development in my area of interest
.58 Discomfort Technical support lines are not helpful because
they don’t explain things in terms I understand Sometimes, I think that technology systems are not designed for use by ordinary people Use manuals for a high-tech product or service are not written in plain language
Technology always seems to fail at the worst possible time
.79 .80 .75 .64
.98 .91
Insecure People are too dependent on technology to do things for them
Too much technology distracts people to a point that is harmful.
Technology lowers the quality of relationships by reducing personal interaction
.75 .81 .71
.97 .90
Satisfaction Overall I am satisfied with Internet trading I am happy about my decision to choose Internet trading
I believe I did the right thing when I used Internet trading
.81 .85 .80
.99 .97
CR = Construct Reliability; AVE = Average Variance Extracted
147 Table 5.29: Principal Factor Analysis
Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of
Variance
Cumulative
%
1 4.893 30.579 30.579 4.893 30.579 30.579
2 3.052 19.077 49.656 3.052 19.077 49.656
3 1.449 9.059 58.714 1.449 9.059 58.714
4 1.030 6.435 65.150 1.030 6.435 65.150
5 .832 5.198 70.348
6 .731 4.567 74.915
7 .705 4.408 79.323
8 .509 3.183 82.506
9 .483 3.020 85.525
10 .471 2.947 88.472
11 .405 2.534 91.006
12 .367 2.294 93.300
13 .303 1.894 95.194
14 .288 1.802 96.996
15 .262 1.635 98.631
16 .219 1.369 100.000
Extraction Method: Principal Component Analysis.
Table 5.30: Exploratory Factor Analysis (EFA)
Rotated Component Matrixa Component
1 2 3 4
Item37 .845 Item36 .811 Item38 .802 Item39 .774
Item41 .632 .531
Item46 .854
Item45 .790
Item47 .637
Item50 .615
Item51 .515 .510
Item42 .827
Item40 .768
Item43 .729
Item49 .822
Item48 .416 .671
Item44 .543
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Table 5.31: Alpha, Mean, Standard Deviation, and Loadings of TRI construct with ISE
Factor Item Load
ings
Mean SD KMO Alpha
OPT New technologies contribute to a better quality of life.
Technology gives me more freedom of mobility Technology gives people more control over their daily lives
Technology makes me more productive in my personal life
I prefer to use the most advanced technology available 0.81 0.85 0.80 0.77 0.63
4.28 4.22 4.10 4.24 4.04
0.943 0.835 0.868 0.838 1.102
0.860 / 68%
vari
0.879
INN Other people think I have better knowledge on new technologies
I can usually figure out new high-tech products and services without help from others
I keep up with the latest technological development in my area of interest
0.77 0.83 0.73
3.66 3.51 3.71
0.998 1.146 1.012
0.697 /70%
0.777
DISC Technical support lines are not helpful because they don’t explain things in terms I understand
Sometimes, I think that technology systems are not designed for use by ordinary people
Use manuals for a high-tech product or service are not written in plain language
Technology always seems to fail at the worst possible time
0.79 0.86 0.63 0.62 0.52
3.09 3.22 2.94 3.43 3.21
1.117 1.168 1.099 1.214 1.220
0.780 /55%
0.791
INS People are too dependent on technology to do things for them
Too much technology distracts people to a point that is harmful.
Technology lowers the quality of relationships by reducing personal interaction
I do not feel confident doing business with a place that can only be reached online
0.54 0.67 0.82
2.95 3.57 3.42
1.113 1.076 1.158
0.583 /57%
0.616
Figure 5.7: Confirmatory Factor Analysis (CFA)
149 Table 5.32: Estimated Model Fit Values
Measurement Calculated Recommended
CMIM/P value GFI
AGFI RMR CFI NFI RMSEA
2.326/0.001 0.911 0.872 0.076 0.930 0.880 0.076
< 3.00
<0.050
>0.900
>0.800
>0.900
>0.900 <0.080
Table 5.33: Measurement Model: Construct Reliability, Average Variance Extracted, and Correlation Matrix
Construct
Construct Reliability
(CR)
Average Variance Extracted (AVE)
Correlation Matrix
1 2 3 4 5
Optimism .99 .95 .97
Innovative .96 .88 .69 .94
Discomfort .95 .83 .22 .14 .91
Insecurity .93 .82 .21 .03 .76 .90
[
Diagonal with italicized values shows the discriminant validity of the four constructs]
Figure 5.8: TRI with Interpersonal Encounter Model Fit
150
[CMIM/P Value: 2.326/0.001; GFI= 0.911; AGFI= 0.872; RMR= 0.076; CFI= 0.930;
NFI= 0.880; RMSEA= 0.076]
Table 5.34: Estimated Path Analysis
Estimate S.E. C.R. P Label
Optimism <--- TRI .194 .056 3.444 *** D
Innovative <--- TRI .126 .066 1.898 .058 C Discomfort <--- TRI .758 .127 5.981 *** B Insecurity <--- TRI .791 .126 6.259 *** A Table 5.35: TRI items with Interpersonal Service Encounter
Factor Item Loadings
Optimism New technologies contribute to a better quality of life.
Technology gives me more freedom of mobility
Technology gives people more control over their daily lives Technology makes me more productive in my personal life
0.77 0.78 0.89 0.72 Innovative
ness
Other people think I have better knowledge on new technologies
I can usually figure out new high-tech products and services without help from others
I keep up with the latest technological development in my area of interest
0.75 0.78 0.68 Discomfort Technical support lines are not helpful because they don’t explain things in
terms I understand
Sometimes, I think that technology systems are not designed for use by ordinary people
Use manuals for a high-tech product or service are not written in plain language
Technology always seems to fail at the worst possible time
0.89 0.70 0.60 0.65 Insecurity People are too dependent on technology to do things for them
Too much technology distracts people to a point that is harmful.
Technology lowers the quality of relationships by reducing personal interaction
0.54 0.82 0.56
Rapid expansion of the use of information technology in day to day life and its perceived functional and emotional benefits have been improving individuals’ attitude toward technology encounter. The present study was an initiative to measure the respondents’
attitude toward adopting technology in solving their day to day activities provided that Bangladeshi consumers are relatively new in information technology encounters. The present study revealed some important insights those are consistent with the findings of previous studies. People in developing countries like Bangladesh hold positive attitude toward technology uses irrespective of technology users as well as non-users. For instance, the mean value of attitude (Optimism) toward technology of online traders (4.25 at 5.0 scale) and offline traders (4.21) present that they are very positive regarding benefits of
151
using technology and the insignificant paired t-test value (t = .634; p = .527) of their mean difference posits that both the groups are equally positive toward technology encounter.
The moderate mean value of technology users (3.74) and non-users (3.62) on innovation dimension demonstrate that sample respondents are not early movers or adopters of new technology even not thought leaders but both are equally positive and late adopter to new technology. On the other hand, both the respondent groups exhibited moderate level of attitudes to discomfort and insecurity to use technology. It found that technology users have relatively low level of discomfort (M=3.08) and insecurity (3.11) compare to non- technology users’ level of discomfort (M = 3.18) and insecurity (3.31). Significant paired t test value for insecurity (P = .000) posits that though non- technology users have favorable attitude toward the benefits of using technology, but due to high level of insecurity feelings they mistrust technology and remain reluctant to go for technology encounter in receiving service. These findings are supported by the previous findings in different cultural settings. For example, in a study in Thailand and a cross cultural study in between USA and Chile revealed high level of optimism i.e., 4.21, 4.59, and 4.25 respectively and moderate level of innovativeness i.e., 3.59, 3.85, and 3.89 respectively.
High insecurity feelings of Bangladeshi consumers in technology encounter might be the causes of relatively new in use, culturally reluctant to use etc., but rapid and pervasive use of technology, development of systems, ICT policies, promptness of monitoring agencies, govt. policy of digitalization will help to reduce the fear of insecurity of technological encounter. Users’ learning curve will contribute much provided that service providers will not make deceptive practices with technology.
As technology readiness construct refers to people’s propensity to embrace and use technologies for accomplishing goals in home and at work, as there are many types of adopter categories to innovation, five types of technology customers – explorers, pioneers, skeptics, paranoids, and laggards- can be identified according to the TRI score (Lee et al., 2009). Explorers are high optimism and innovativeness and low in discomfort and insecurity. Pioneers are high in optimism, innovativeness, discomfort and insecurity.
Skeptics are low in optimism, innovativeness, discomfort and insecurity. Paranoids are high in optimism, discomfort and insecurity, but low in innovativeness. Finally, laggards score low in optimism and insecurity, but high in discomfort and insecurity.
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Table 35.a: Comparison between Online and Offline Users Behavior
Measuring the Impact of TR on Customer Satisfaction