Chapter 4 Results
4.3 Data analysis
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indicated that according to past experience, seeking information from weak ties yielded less effective results.
4.3 Data analysis
The data from questionnaire were entered into SPSS software for analysis. The following tests were conducted:
Reliability analysis--to examine if scales are consistent in measuring a particular concept.
Paired-samples t-test--to find out if there is significant difference on two variables by respective respondents.
Correlate bivariate analysis--to find out if there is correlation between variables and the direction of the relations.
Multiple linear regressions--to find out which predictors or independent variable carry higher predicting value on dependent variable.
Hypothesis 1: The importance of weak ties as source of information is significantly greater
than that of strong ties.
Paired sample t-test was conducted to test H1. Contrary to the weak ties theory, respondents attributed higher importance to strong ties as a source of new information in comparison with weak ties. The differences are significant (paired sample t-test, t=3.15, d.f. =25, p<.01, table 1). Thus H1 is not supported.
Table 3 Paired sample t-test results on information source and tie strength Mean (n=249) Standard
deviation
t-value
Strong ties as information source 3.39 2.46 3.15**
Weak ties as information source 2.87 1.76
**p<.01
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Paired sample t-test was used to find out if there are significant differences on the importance of strong ties vs. that of weak ties in exchanging different types of information. Results showed significant differences between the importance of strong ties vs. weak ties in providing information about leisure activities (t-value=3.75, p < .001), with strong ties as the more important sources of information. Yet for job-related information, the difference was insignificant. The lack of statistical significance for the importance of strong ties vs. weak ties in providing job-related information might be explained by respondents' lack of interest or experiences in seeking such information, as most of them were still students. However, it is worthy of notice that the pattern of response remained the same, with greater importance attached to strong ties, rather than weak ties, in information provision.
Table 4 Paired sample t-test results on information types and tie strength Information
types
Strong ties Weak ties
mean SD mean SD t-value
Leisure 1.91 1.42 1.55 1.02 3.75***
Job related 1.47 1.21 1.32 .90 1.88
*** p<.001
Hypothesis 2: There is significant difference between respondents‟ guanxi with strong ties
and their guanxi with weak ties.
Paired sample t-test was performed to compare guanxi closeness between strong and weak ties. The results shown the differences are significant. Five items of four-point Likert scale were used to build the guanxi index in this hypothesis. The higher numbers indicated a closer guanxi. Guanxi on strong ties (mean =2.27, SD= 1.52) indicated closer guanxi between respondents and the strong tie they identified; while guanxi on weak ties (mean= 1.69, SD=.96) showed a more distant guanxi between respondents and the weak tie they identified.
Thus H2 is supported.
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Mean (n=249) Standard deviation
t-value
Guanxi on strong ties 2.27 1.52 5.53***
Guanxi on weak ties 1.69 .96
***p<.001
H3: Strong guanxi positively predicts respondents‟ selection of strong ties in exchanging information.
H4: Weak guanxi negatively predicts respondents‟ selection of weak ties in exchanging information.
Based on the properties of guanxi as discussed earlier, including transferability and reciprocity, a series of items were developed to find out to what extent does guanxi accounts for variance in respondents‟ selection of information source. Reliability analysis was conducted respectively for the guanxi scale on strong ties (mean=2.27, SD=1.52, Cronbach‟s alpha=.971) and weak ties (mean=1.69, SD=.96, Cronbach‟s alpha=.914). Both shown high internal consistency.
Multiple linear regression analysis was performed for strong and weak ties to test the hypothesis. The first block is demographics (gender, age, education background and economic status); the second block is Facebook usage (number of Facebook contacts and time spent on Facebook); and third block, guanxi.
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Table 6 Multiple linear regression analysis results of demographics, Facebook usage, and guanxi on information source
Strong ties Weak ties
Dependent variable Strong ties as
information source
Weak ties as information source First block (demographics)
Gender -.01 -.01
Age -.04 -.04
Education background .12*** .06
Economic status -.04 .00
R Square .133 .29
∆ Adjusted R2 .12 .01
Second block (Facebook usage)
Time spent on Facebook .01 .02
Number of Facebook friends .02 .02
R Square .08 .03
∆ Adjusted R2 .08 .02
Third block (Guanxi)
Guanxi .83*** .76***
R Square .55 .54
∆ Adjusted R2 .56 .55
***p<.001
The multiple linear regression results has shown that for strong ties, guanxi positively predicted strong ties as information source (Beta=.83, p<.001). Education level (Beta=.12, p<.01) also carried significant predicting values. Guanxi is also the most distinctive predictor (Beta=.76, p<.001) for weak ties as information sources. Positive relations of strong guanxi prediction on strong ties as information source indicated the closer the guanxi with their contact, the more respondent relied on them to receive guanxi. Thus H3 was supported. The
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fact that the more distant guanxi was to be associated with WT indicated respondents were less likely to rely on weak for information. Thus H4 was also supported.
Overall, the data analysis showed that Taiwanese Facebook users relied on strong ties as their information sources rather than weak ties as predicted by weak ties theory. Strong ties are the primary information sources for the respondents and their guanxi with strong ties are also closer. This can be seen from the several features of guanxi—mutual help, trust, face to face social interactions, gift-giving and reciprocity. Further discussion of the implications of the results will be presented in next chapter.
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