With the purpose of presenting the results of this study, there are two main findings.
First section provides the overview of analyzing the data and sample characteristics by descriptive statistics included the reliability and validity, mean, and standard deviation.
Secondly, the correlation analyses are conducted among all the variables. The mentioned results and findings are further explicated.
Sample Characteristics
The characteristics of the participants described in Table 4.1 consisted of their age, education, related-working experience, and working location. Totaling 152 respondents, the largest group was between the age of 26 to 30 is up to 64.05%. There was nearly 25% from 31 to 35; 8.5% from 20 to 25. Interestingly, only about 1.96% of the respondents were above 36 to 40. The remaining 1.31% was over 40.
Regarding their educational level, up to 88.24% of the participatory engineers in TSMC were masters, while merely 5.23% held bachelor degrees. The result corresponds to the situation of employment especially in Taiwan. The percentage of doctor degrees was identical to that of bachelors’ which also shows 5.23%. Only 1.31% of the employees graduated from high schools.
Concerning their related-working experience, up to 40% of the engineers have been working as relevant positions for 1 or 2 years. About 30% of them have been engaged in related industries for 2 to 3 years. Approximately 17% have been employed for over 3 but fewer than 4 years of working experience. 9.15% and 2.61% are for those who have had related working experience for about 4 to 5 years and above 5 years, respectively.
55
About the variable of location, over half or 54% of the respondents were working in the TSMC’s headquarter, Hsinchu, while the other or 45% were employed in Taichung and Tainan then. The interviewees said most of research and development still gather in headquarter, and the accountabilities of Taichung and Tainan are responsible to keep the high performance especially for output of high quality.
Table 4.1.
Distribution of Research Respondents based on Demographic Variables (N= 152)
Variables Entries Percentage (%)
Age (years) 20-25
56
Table 4.1. (continued)
Variables Entries Percentage (%)
Related-working experience
The reliability and validity were the two internal consistency measures calculated to assess the scales of the whole variables in this study. According to Gay (1987), the reliability is the extent to determine whether all the items within each construct are suitable. For that reason, the Cronbach's alpha was adopted to analyze the reliability of all of the variables.
Theoretically, the validity of the instrument is determined by the face validity. The face validity in essence shows the degree of the measurement items when providing adequate assessments for each investigative question to determine whether all the questionnaires are appropriate or not. In this study, the face validity was made by the professors from National Taiwan Normal University (NTNU) and one of the senior engineer managers in TSMC before conducting the pilot test.
57
The final research sample regardless of the five ineffective respondents totaled 147.
Through the reliability test of Statistical Package for the Social Sciences (SPSS) PC 19.0, the coefficient values on each construct were in Table 4.2 as follows: Knowledge Management Strategies (0.831); Knowledge Assets (0.870); Knowledge Creation Process (0.888); Knowledge Enabler (0.798); and Performance (0.863). The Cronbach alpha for all dimensions in this study indicates that they all are higher that the acceptable level of 0.70 (Nunnally, 1978;
Sekaran, 2003). Therefore, it can be concluded that the scales for all constructs have a high level of internal consistency and reliability.
Table 4.2.
Cronbach’s Alpha results for the all Constructs Using SPSS
Constructs Number of items Cronbach’s Alpha
Knowledge Management Strategies 8 0.831
Knowledge Assets 12 0.870
Knowledge Creation Process 12 0.888
Knowledge Enablers 12 0.798
Performance 9 0.863
Table 4.3 illustrates the cronbach alpha reliability test shows coefficient value for each variable and detailed in this study. 0.797 for Knowledge Management Strategies of Human, 0.805 for Knowledge Management Strategies of System. 0.746 for Experiential Knowledge Asset, 0.787 for Conceptual Knowledge Asset, 0.633 for Systemic Knowledge Asset, 0.749
58
for Routine Knowledge Asset, 0.675 for Socialization of Knowledge Creation Process, 0.711 for Externalization of Knowledge Creation Process, 0.728 for Combination of Knowledge Creation Process, 0.691 for Internalization of Knowledge Creation Process, 0.546 for Trust of Knowledge Enabler, 0.760 for Centralization of Knowledge Enabler, 0.726 for T-shaped Skills of Knowledge Enabler, 0.702 for IT of Knowledge Enabler, 0.846 for Performance, 0.808 for Performance of Innovation, 0.803 for Performance of Finance, and 0.648 for Performance of Customer Satisfaction .
Although some variables of Cronbach’s alpha were lower than 0.70, comprising Systemic Knowledge Assets (0.633), Socialization of Knowledge Creation Process (0.675), Internalization of Knowledge Creation Process (0.691), Centralization of Knowledge Enablers (0.546), the average results of the whole variables were higher than 0.70. Shown in Table 4.3, such an indicator made the total variables acceptable. Therefore, it is concluded that the scales for the entire variables had a high level of internal consistency and reliability.
59
Table 4.3.
Results of Cronbach’s alpha for all Variables in This Study
Dimensions Variables No. of Items Cronbach's alpha
Knowledge Management Strategies
Human Strategy 4 0.797
System Strategy 4 0.805
Total 8 0.831
Knowledge Assets
Experiential Knowledge Assets 3 0.746
Conceptual Knowledge Assets 3 0.787
Systemic Knowledge Assets 3 0.633
Routine Knowledge Assets 3 0.749
Total 12 0.870
Knowledge Creation Process
Socialization 3 0.675
Externalization 3 0.711
Combination 3 0.728
Internalization 3 0.691
Total 12 0.888
(continued)
60
Table 4.3. (continued)
The test of sphericity by Kaiser-Meyer-Olkin (KMO) and Barlett was used to examine whether the sample was adequate. The range of KMO’s values had to be between 0.5 and 1.0 that means the factor analysis would be appropriate (Krizman, 2009). In Table 4.4, The Eigen values were measured to evaluate the appropriateness of factor loadings. The indicators of Eigen values were more than one so the value would be retained for further analysis (Hair et al., 2006).
Dimensions Variables No. of Items Cronbach's alpha
Knowledge
Finance performance 3 0.803
Customer Satisfaction 3 0.648
Total 9 0.863
61
The KMO and Eigen values for each construct were correspondingly elicited as follows: .500 and 1.469 for Knowledge Management Strategies, .773 and 2.550 for Knowledge Assets, .794 and 2.867 for Knowledge Creation Process, .668 and 2.112 for Knowledge Enablers, and .725 and 2.295 for Performance. All the variances could be explained that the constructs were over 50% of the indicators.
Table 4.4.
Results of Factor Analysis for All of the Constructs
Constructs
Number of items
KMO
Eigen value
Variance explained
Barlett’s tests of Sphericity
KMS 8 0.500 1.469 73.435% 35.844;p=0.000
KA 12 0.773 2.550 63.752% 189.621;p=0.000
KCP 12 0.794 2.867 71.668% 284.664;p=0.000
KE 12 0.668 2.112 52.798% 119.456;p=0.000
P 9 0.725 2.295 76.499% 182.842;p=0.000
Note: KMS= knowledge management strategies, KA= knowledge assets, KCP=knowledge creation process, KE=knowledge enablers, P= performance
62
Descriptive Statistics Analysis
Out of the 147 responses notwithstanding the five invalid questionnaires, the response rate was still up to 90%. Included are the indicators of the survey questionnaires: min, max, mean, and standard deviation measured by a 5-point Likert scale. The participants were then asked to express their opinions ranging from “strongly disagree= 1” to “strongly agree= 5”.
In table 4.5, the average responses for each variable were greater than the “mid-point= 3”
of the Likert scale except for the negative questions pertaining to T3 of Knowledge Enablers revealed in Table 4.8. The results showed that all the respondents’ answers were positive varying from “neutral” to “strongly agree”. More details of the descriptive statistics for all the independent variables and the dependent variables are correspondingly presented as below.
Knowledge Management Strategies
Concerning the constructs of Knowledge Management Strategies, the results of the two constructs or Human and System were higher than the average or over 3 of Mean. First, the four variables in Human showed that a range of 3.87 to 4.10 of Mean was calculated, implying the respondents agreed to learn new knowledge from experts especially by one-to-one mentoring. However, among the four variables in System the employees relied more on the codified forms in delivering their knowledge than on their diary duties by manuals and documents. The figure of Mean for the former was from 3.69 to 3.78, while that figure for the latter, 3.71.
63
Table 4.5.
Knowledge Management Strategies via a 5-point Likert scale (N=147)
Survey Questionnaires Min. Max. Mean Std. Deviation
H1
Informal dialogues and meetings are used for knowledge sharing.
Knowledge (know-how, technical skills, or problem solving methods) is well codified.
1 5 3.69 0.874
Note: H= Human; S= System; Min= 1; Max= 5; SD= Standard deviation
64
Knowledge Assets
In the dimension of Knowledge Assets detailed in Table 4.6, the first construct with 4.01 of Mean was higher than the others, indicating that the employees were encouraged to share their experience in the company. In comparison with the experiential (E) construct, the conceptual (C) and systematic (S) constructs indicated that the company had to establish the symbols or design criteria for demonstrating their product characteristics. Although they were allowed to use the internal intellectual assets, the employees still followed the standard operation.
Table 4.6.
Knowledge Assets via a 5-point Likert Scale (N=147)
Survey Questionnaires Min. Max Mean Std. Deviation
E1
Employees are encouraged to share their hands-on experience.
3 5 4.01 0.659
E2
Employees are encouraged to express their emotional knowledge such as carefulness.
1 5 3.82 0.844
E3
Employees are encouraged to demonstrate their enthusiasm.
2 5 3.86 0.773
C1
Firms demonstrate product characteristics by adopting images, symbols, and language.
1 5 3.67 0.855
(continued)
65
Table 4.6. (continued)
Survey Questionnaires Min. Max Mean Std. Deviation
C2
Employees are encouraged to interact with other organizations, e.g., partners,
customers, to establish design criteria.
1 5 3.67 0.915
C3
Firms demonstrate brand equity by adopting images, symbols, and language.
1 5 3.65 0.942
S1
The provision of well-organized product documents is needed.
1 5 3.63 0.884
S2
The provision of easy access to product database or catalog is required.
1 5 3.69 0.896
S3
The firm’s intellectual property with authorization is adopted.
1 5 3.97 0.921
R1
Employees realize the importance of knowledge in routine operations.
2 5 3.75 0.810
R2
High levels of participation are expected in capturing and transferring knowledge.
2 5 3.94 0.761
R3
Overall organizational culture and objectives are clearly stated.
2 5 3.84 0.783
Note: E= Experiential; C= Conceptual; S= Systematic; R= Routine; Min= 1; Max= 5; SD= Standard deviation
66
Knowledge Creation Process
In the construct of Knowledge Creation Process exposed in Table 4.7, the respondents had less chance of interacting with their suppliers and customers. What is more, they had difficulties in planning strategies or developing new opportunities. Since most of them had to focus on their departmental tasks, the variables for externalization and combination were still emphatic on systematically creating knowledge. It helped the organization accumulate its core knowledge assets.
Table 4.7.
Knowledge Creation Process via a 5-point Likert scale (N=147)
Survey Questionnaires Min. Max. Mean Std. Deviation
S1
My company stresses sharing experience with suppliers and customers.
1 5 3.56 0.877
S2
My company stresses finding new strategies and market opportunities by wandering inside the firm.
1 5 3.34 0.877
S3
My company stresses creating a work
environment that allows peers to understand the craftsmanship and expertise.
1 5 3.69 0.818
E1
My company stresses the use of deductive and inductive thinking.
1 5 3.90 0.788
(Continued)
67
Table 4.7. (continued)
Survey Questionnaires Min. Max. Mean Std. Deviation
E2
My company stresses exchanging various ideas and dialogues
2 5 3.70 0.831
E3
My company stresses the use of metaphors in dialogue for concept creation.
1 5 3.93 0.782
C1
My company stresses planning strategies by using published literature and forecasting.
1 5 3.83 0.788
C2
My company stresses building up materials by gathering management figures and technical information.
1 5 3.79 0.752
C3
My company stresses creating manuals and documents on product and services.
1 5 3.61 0.840
I1
My company stresses forming teams as a model, conducting experiments, and sharing results with entire departments.
1 5 3.77 0.828
I2
My company stresses searching and sharing new values and thoughts.
1 5 3.62 0.847
I3 My company stresses benchmarking and test marketing.
1 5 3.59 0.792
Note: S= Socialization; E= Externalization; C= Combination; I= Internalization; Min= 1; Max= 5; SD=
Standard deviation
68
Knowledge Enablers
When it comes to the issue of building culture of mutual trust in an organization, it is definitely one of the most difficult parts. And the respondents in this research also agreed to this argument. The construct of Centralization (C) showed that the company seldom agreed with its employees in making their own decisions. After interviewing with some respondents, the engineers said they have to be responsible for making decisions by themselves. Owing to ensuring for the products of quality, the supervisors will discuss and make decisions with all the engineers together. This is the explanation for the questionnaires from C1 to C3. 2.53 of Mean in C3 highlighted most of the tasks had to be discussed directly to their supervisors.
3.80 of Mean in T1 “Our company members are generally trustworthy” and 3.81 of Mean in T2 “Our company members have reciprocal faith in others’ abilities” are full demonstrate the trust culture cultivated in TSMC. About the T-shaped skills (TS), the respondents in TS1 strongly agreed that their members were specialists in manufacturing revealed by 3.85 of Mean. The Mean in IT3 is up to 3.85 which prove the importance of relying on IT support. In general, the company has built up the solid knowledge management system of sharing and delivering new knowledge by referring to the construct of IT.
69
Table 4.8.
Knowledge Enablers via a 5-point Likert scale (N=147)
Survey Questionnaires Min. Max Mean Std. Deviation
C1
Our company members can take action without a supervisor.
1 5 3.30 0.989
C2
Our company members are encouraged to make their own decisions.
1 5 3.26 0.966
C3
Our company members don’t need to refer to someone else.
1 5 2.53 0.981
T1
Our company members are generally trustworthy.
1 5 3.80 0.776
T2
Our company members have reciprocal
faith in others’ abilities. 1 5 3.81 0.762
T3
Our company members have reciprocal faith in others’ decisions toward
organizational interests than individual interests.
1 5 3.77 0.836
TS1
Our company members are specialists in their own parts.
1 5 3.85 0.822
(continued)
70
Table 4.8. (continued)
Survey Questionnaires Min. Max Mean Std. Deviation
TS2
Our company members can communicate well not only with their departmental members but also with members from other departments.
1 5 3.86 0.728
TS3
Our company members can perform their own tasks effectively without regard to environmental changes.
1 5 3.73 0.814
IT1
Our company members provide IT support for collaborative works regardless of time and place.
1 5 3.53 0.931
IT2
Our company members provide IT support for searching for and accessing necessary information.
1 5 3.85 0.762
IT3
Our company members provide IT support for systematic storing.
1 5 3.90 0.779
Note: C= Centralization; T= Trust; TS= T-shaped skills; IT= IT supported; Min= 1; Max= 5;
SD= Standard deviation
71
Performance
The results show that the Finance Performance was higher than Customer Satisfaction.
The construct of the former signified that respective 3.92 and 3.98 of Mean in “reducing the operational cost” and “improving the profit” could be the company’s objective. More to the point, 3.86 and 3.87 of Mean in developing “customer satisfaction” and “service equality”
correspondingly have also become the imperative indicators in the IC industry.
Table 4.9.
Performance via a 5-point Likert scale (N=147)
Survey Questionnaires Min. Max. Mean Std. Deviation
I1
Prompt response to customers’ need
1 Note: I= Innovation; F= Finance performance; CS= Customer Satisfaction.
72
Correlation Analysis
The relationship between all variables in this study was measured by using Pearson coefficient correlation (see Table 4.10). Cohen (1988) suggests guidelines in which the correlation between 0.10/-0.10 to 0.29/-0.29 is low, 0.3/-0.3 to 0.49/-0.49 is moderate and 0.5/-0.5 to 1/-1 is high.
According to these guidelines, Knowledge Management Strategies of Human (X1) has a moderate positive significant correlations with KMS_S (r=.469, p ≤ 0.01) and a construct of Knowledge Assets (KA) which is included KA_E (r= .648, p ≤ 0.01), KA_C (r= .278, p ≤ 0.01), KA_S (r= .363 p ≤ 0.01), and KA_R (r= .618, p ≤ 0.01). In addition, Knowledge Management Strategies of Human has also a high positive significant correlation with the construct of Knowledge Creation Process (KCP) which is included KCP_S (r=.439, p ≤ 0.01), KCP_E (r=.439, p ≤ 0.01), KCP_C (r=.404, p ≤ 0.01),and KCP_I (r=.371, p ≤ 0.01) and a construct of Knowledge Enablers (KE) which are included KE_T (r=.440, p ≤ 0.01), KE_TS (r=.492, p ≤ 0.01), and KE_IT (r=.428, p ≤ 0.01). Except for the value of KE_C (Centralization, r=.124) showing a low relationship with Knowledge Management Strategies of Human, the variables of Performance included (P_I, r=.388, p ≤ 0.01), P_F (r=.312, p ≤ 0.01), and P_CS (r=.317, p ≤ 0.01) also have a significant correlations with Knowledge Management Strategies of Human. From these relationships, it can be assumed that Knowledge Management Strategies of Human is positively related with the Knowledge Assets, Knowledge Creation Process, Knowledge Enablers, and Performance except for the item of KE_C (Centralization, (r=.124 ).
Secondly, Knowledge Management Strategies of System (X2) has a positive significant correlations with the construct of Knowledge Assets (KA) which are included KA_E (r= .541, p ≤ 0.01), KA_C (r= .466, p ≤ 0.01), KA_S (r= .558 p ≤ 0.01), and KA_R (r= .591,
73
p ≤ 0.01). In addition, Knowledge Management Strategies of System also has a high positive significant correlation with the construct of Knowledge Creation Process (KCP) which is included KCP_S (r=.360, p ≤ 0.01), KCP_E (r=.465, p ≤ 0.01), KCP_C (r=.441, p ≤ 0.01), and KCP_I (r=.438, p ≤ 0.01) and the construct of Knowledge Enablers (KE) which are included KE_T (r=.225, p ≤ 0.01), KE_TS (r=.245, p ≤ 0.01), and KE_IT (r=.465, p ≤ 0.01).
Except for the value of KE_C (Centralization, r=.157) which shows a low relationship with Knowledge Management Strategies of System, the variables of Performance included P_I (r=.309, p ≤ 0.01), P_F (r=.356, p ≤ 0.01), and P_CS (r=.417, p ≤ 0.01) also have a significant correlations with Knowledge Management Strategies of System. From these relationships, it can be assumed that Knowledge Management Strategies of System is positively related with the Knowledge Assets, Knowledge Creation Process, Knowledge Enablers, and Performance except for the item of KE_C (Centralization, (r=.157).
Thirdly, the correlation analysis of the variables of Knowledge Assets (KA) which are included Knowledge Assets of Experiential (KA of E, X3), Knowledge Assets of Conceptual (KA_C,X4), Knowledge Assets of Systemic (KA_S, X5), and Knowledge Assets of Routine (KA_R, X6) have a positive significant correlations with the variables of Knowledge Creation Process (KCP) which are included the variables of Knowledge Creation Process of Socialization (KCP_S), Knowledge Creation Process of Externalization (KCP_E), Knowledge Creation Process of Combination (KCP_C), and Knowledge Creation Process of Internalization (KCP_I) and the variables of Knowledge Enablers (KE) which are included Knowledge Enablers of Trust (KE_T), Knowledge Enablers of T-shaped skills (KE_TS), and Knowledge Enablers of IT supported (KE_IT). Except for the value of KE_C (Centralization, r=.116, r=.190, r=.209) which shows a low relationship with Knowledge Assets of Experiential (KA_E), Knowledge Assets of Conceptual (KA _C) , and Knowledge Assets of
74
Routine (KA _R). The relationship between Knowledge Assets and the variables of Performance (P) which are included Performance of Innovation (P_I), Performance of Finance (P_F), and Performance of Customer Satisfaction (P_CS) also have a significant correlations with Knowledge Assets (KA). From these relationships, it can be supposed that Knowledge Assets are positively related with Knowledge Creation Process, Knowledge Enablers, and Performance except for the item of KE_C (Centralization).
Fourthly, the correlation analysis of the variables of Knowledge Creation Process (KCP) included Knowledge Creation Process of Socialization (KCP_S, X7), Knowledge Creation Process of Externalization (KCP_E, X8), Knowledge Creation Process of Combination (KCP_C, X9), and Knowledge Creation Process of Internalization (KCP_I, X10) have a positive significant correlation with the variables of Knowledge Enablers (KE) which are included the variables of Knowledge Enablers of Trust (KE_T), Knowledge Enablers of T-shaped skills (KE_TS), Knowledge Enablers of IT supported (KE_IT) and the variables of variables of Performance (P) which are included Performance of Innovation (P_I), Performance of Finance (P_F), and Performance of Customer Satisfaction (P_CS) except for the value of Centralization (KE_C, r=.146, r=.110, r=.207,and r=.089 ) which shows a low relationship with Knowledge Creation Process of Socialization (KCP_S), Knowledge Creation Process of Externalization (KCP_E), Knowledge Creation Process of Combination (KCP_C),and Knowledge Creation Process of Internalization (KCP_I). Therefore, it can be supposed that Knowledge Creation Process is positively related with Knowledge Enablers, and Performance except for the item of KE_C (Centralization).
Fifthly, the correlation analysis of the variables of Knowledge Enablers (KE) which are included Knowledge Enablers of Centralization (KE_C, X11), Knowledge Enablers of Trust (KE_T, X12), Knowledge Enablers of T shaped skills (KE_TS, X13), and Knowledge
75
Enablers of IT supported (KE_IT, X14) have a positive significant correlation with the variables of Performance (P) which are included the Performance of Innovation (P_I), Performance of Finance (P_F), and Performance of Customer Satisfaction (P_CS) except for the item of Centralization (KE_C, X11). The value of Knowledge Enablers of Centralization (KE_C) shows the low related with Knowledge Enablers of T shaped skills (KE_TS, r=.204*), Knowledge Enablers of IT supported (KE_IT, r=.112), Performance of Innovation (P_I, r=.115), Performance of Finance (P_F, r=.076), and Performance of Customer Satisfaction (P_CS, r=.121) expect for KE_T (r=0.375**, p ≤ 0.01). The result demonstrates that Knowledge Enablers of Centralization (KE_C) affect calculations regarding individual predictors within the sample data themselves. Although the correlation values of Knowledge Enablers of Centralization (KE_C) have the problem of multicollinearity, the result doesn’t reduce the predictive power or reliability in this model as a whole.
Lastly, Performance of Innovation (P_I, X15) shows the positive significant correlation between Performance of Finance (P_F) and Performance of Customer Satisfaction (P_CS).
However, Performance of Finance (P_F) has a low relationship with Performance of Customer Satisfaction (P_CS) which value is (r=0642). In general, the result of correlation analysis of Knowledge Management Strategies, Knowledge Assets, Knowledge Creation Process, and Knowledge Enablers are significantly correlated with Performance, and the correlation values of them are between 0.10 and 0.75 except for the item of Knowledge Enablers of Centralization (KE_C).
76
Table 4.10.
Correlations among all the variables
Constructs X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17
1. KMS_H 1
2. KMS_S 0.469** 1
3. KA_E 0.648** 0.541** 1
4. KA_C 0.278** 0.466** 0.405** 1
5. KA_S 0.363** 0.558** 0.503** 0.574** 1
6. KA_R 0.618** 0.591** 0.587** 0.474** 0.552** 1
7. KCP_S 0.439** 0.360** 0.437** 0.487** 0.532** 0.408** 1
8. KCP_E 0.439** 0.465** 0.499** 0.523** 0.586** 0.516** 0.535** 1
9. KCP_C 0.404** 0.441** 0.396** 0.496** 0.569** 0.478** 0.696** 0.556** 1
10. KCP_I 0.371** 0.438** 0.400** 0.551** 0.546** 0.454** 0.679** 0.652** 0.611** 1
11. KE_C 0.124 .157 .116 0.190* 0.209* 0.237** .146 .110 0.207* .089 1
12. KE_T 0.440** 0.255** 0.397* 0.318** 0.416** 0.471** 0.256** 0.408** 0.220** 0.317** 0.375** 1
13. KE_TS 0.492** 0.245** 0.405** 0.333** 0.378** 0.422** 0.459** 0.353** 0.360** 0.488** 0.204* 0.589** 1 14. KE_IT 0.428** 0.465** 0.354** 0.393** 0.413** 0.335** 0.495** 0.481** 0.422** 0.559** .122 0.411** 0.429** 1 15. P_I 0.388** 0.309** 0.452** 0.419** 0.468** 0.481** 0.597** 0.516** 0.474** 0.624** .115 0.357** 0.489** 0.509** 1 16. P_F 0.312** 0.356** 0.328** 0.384** 0.468** 0.419** 0.477** 0.470** 0.494** 0.539** .067 0.220** 0.401** 0.376** 0.615** 1 17. P_CS 0.317** 0.417** 0.436** 0.409** 0.495** 0.459** 0.626** 0.503** 0.54** 0.602** .121 0.254** 0.411** 0.465** 0.685** .642 1 Note: N=90; Diagonal elements represent the square root of AVE values which are greater than exhibit acceptable discriminant valid
* p < 0.1. **p <0.05. *** p <0.001.
77