Creation Performance . - Measurement Results for Relevant Research Variables \

CHAPTER 4 DATA ANALYSIS AND RESULTS

4.2 Measurement Results for Relevant Research Variables \

4.2.3 Creation Performance .

With regard to the factor analysis for creation performance, the results are obtained in Table 4-12. It is found that Cronbach’s α for each factor is between 0.91 and 0.94.

Factors that can be confirmed are product creation performance (PCP), manufacturing creation performance (MFCP), and management creation performance (MGCP). Moreover, the total variance explanation for these factors is 69.595%. Also, the result obtained for test model fit is shown in Table 4-13. It is found that χ²(195) is 493.607 and p-value is 0.000, indicating not acceptable. However, the χ²/df is 2.531 that is less than the ideal value, indicating acceptable. The value of GFI is 0.807 and NFI is 0.880, indicating close to the ideal value. Other parameters are all acceptable.

Therefore, the model fit for the variable of creation performance is acceptable. For the investigation of reliability and validation, it is found from Table 4-14 and 4-15 that factor loading is significant and CR and AVE are more than the ideal value.

Moreover, all AVE of potential factors are greater than the square of related coefficient of the paired factors. Thus, reliability and validation of the variable of knowledge creation modes are acceptable.

Table 4-12: Factor Analysis and Reliability Test for Creation Performance Variable Item Mean Factor

Item to

Total Eigenvalues Explained

Variance Cronbach’s α

Note: the total variance explanation is 69.595%

Table 4-13: Model Fit for the Creation Performance Model Fit Indicators Ideal

Value Value

χ² p>0.05 p=0.000

χ²/df <3. 2.

GFI (goodness of fit index) >0.90 0.807 Absolute Fit

Measures

RMR (root mean square residual) <0.08 0.0652 NFI (normed fit index) >0.90 0.880

00 531

NNFI (non-normed fit index) >0.90 0.900 CFI (comparative fit index) >0.90 0.923 Incremental

Fit Measures

IFI (incremental fit index) >0.90 0.924 PNFI (parsimonious normed fit index) >0.50 0.679 Parsimonious

Fit Measures PGFI (parsimonious goodness of fit index) >0.50 0.571

Table 4-14: Scale Properties for the Measurement Model of Creation Performance Variable Item Standardized Loading T-value Composite

Reliability

Table 4-15: AVE of Measured Factors of Creation Performance

Mean Std. Deviation PCP MFCP MGCP

PCP 5.279 0.926 0.783^a 0.711 0.478

MFCP 5.451 0.855 0.711 0.775 0.580

MGCP 5.211 0.929 0.478 0.580 0.801

a. Diagonal elements (in bold and italic) in correlation matrix represented the Average Variance Extracted (=

2 2

(Σλ )

[(Σλ )+Σ(θ)]；λ= Factor Loading；θ= Measured Error), while off-diagonal elements were represented by the square of correlation among variables.

4.2.4 Research Model

For the entire research model fit, the confirmatory factor analysis is used to test the causal relationships between independent variables and dependent variable. The results obtained in Table 4-16 indicate that χ²(32) is 130.763 and p-value is 0.000, which is not acceptable. However, the χ²/df is 2.255, showing acceptable because it is less than the ideal value. By this, it is found that the whole research model fit is acceptable. In addition, the results shown in Table 4-17 and 4-18 indicate that based on the test criteria of reliability and validation, the factor loading of each item is significant. The value of both CR and AVE are greater than the ideal value. Moreover, all AVE of potential factors are greater than the sum of related coefficient of the paired factors. In short, the reliability and validation of the whole research model is acceptable.

Table 4-16: The Fit of the Research Model

GFI(goodness of fit index) >0.90 0.922 Absolute Fit

Measures

RMR(root mean square residual) <0.08 0.0558 NFI(normed fit index) >0.90 0.927

0 5

NNFI(non-normed fit index) >0.90 0.936 CFI(comparative fit index) >0.90 0.957 Incremental

Fit Measures

IFI(incremental fit index) >0.90 0.958 PNFI(parsimonious normed fit index) >0.50 0.618 Parsimonious

Fit Measures PGFI(parsimonious goodness of fit index) >0.50 0.503

Table 4-17: Scale Properties of the Research Model Construct/Variable Standardized

Loading T-value Composite Reliability

Table 4-18: AVE for Research Variables

Mean Std. Deviation OC KCM CP

OC 5.245 0.794 0.903^a 0.561 0.722

KCM 10.817 1.728 0.561 0.795 0.419

CP 5.314 0.769 0.722 0.419 0.859

a. Diagonal elements (in bold and italic) in correlation matrix represented the Average Variance Extracted (=

2 2

(Σλ )

[(Σλ )+Σ(θ)]；λ= Factor Loading；θ= Measured Error), while off-diagonal elements were represented by the square of correlation among variables.

4.3 Structural Equation Modeling

In this research, in order to ensure the research model is acceptable, the structural equation model is developed both to examine the model fit and to test the defined hypotheses. For the model fit, it is found from Table 4-19 that χ²(30) is 67.650, indicating not acceptable. However, the value of χ²/df is 2.255, indicating acceptable because it is less than the ideal value. Other parameters are all acceptable. By this, it is found that the whole research model fit is acceptable.

Table 4-19: Model Fit for the Structure Equation Model Model Fit Indicators Ideal

Value

Analysis Value

χ² p>0.05 p=0.000

χ²/df <3.0 2.255

GFI(goodness of fit index) >0.90 0.922 Absolute Fit

Measures

RMR(root mean square residual) <0.08 0.0445 NFI(normed fit index) >0.90 0.927

NNFI(non-normed fit index) >0.90 0.936 CFI(comparative fit index) >0.90 0.957 Incremental

Fit Measures

IFI(incremental fit index) >0.90 0.958 PNFI(parsimonious normed fit index) >0.50 0.618 Parsimonious

Fit Measures PGFI(parsimonious goodness of fit index) >0.50 0.503

For the hypothesis test, structural equation is built to conduct the path analysis to examine whether or not the directions and causal relationships are significantly accepted. The test criteria used include 1) standardized path coefficient which shows causal relationship (direction) between independent variables and dependent variable, and 2) determination coefficient (R²). For the former, the standardized path coefficient measures the path coefficient and regression weight between latent variables. That is, it measures the strong extent of casual relationship between variables. The goal is to check whether or not the path direction of the model is the same as that defined in the research model. If the estimated parameter goes to a positive value, the research model meets the direction. If it shows negative, the predefined path does not meet what the collected data says. Significance is determined by the t value. For example, it is significant if t is greater than or equal to 1.96 under the significance level of 0.05 (α=0.05). For the later, the R² represents the variance of each internal variable. It is the percentage of explanation of causal relationship by its independent variable. The larger the R² is, the better the equation model will be. The path diagram and its parameters are shown in Figure 4-1.

Knowledge Creation Modes R²=0.775

Organizational Culture Creation Performance R²=0.801 0.880***

(4.05)

0.308 (1.33)

Goal-Depended Goal-Driven Goal-Free

0.765*** 2. － represent significant

3. --- represent insignificant 4. ( ) represent t value

Figure 4-1: Path Diagram of SEM Model

It is found from Figure 4-1 that all path directions match those defined in the research model. In addition, parameters of the path from knowledge creation modes to creation performance indicate insignificant (standardized path coefficient = 0.308, t = 1.33) while organizational culture to knowledge creation modes as well as to creation performance shows significant (standardized path coefficient = 0.612 and 0.880, t = 2.552 and 4.05, respectively). Moreover, it is also found that R² of knowledge creation modes is 0.775. This implies that the percentage that the knowledge creation modes can be explained by the organizational culture with respect to its variance is 77.5%.

Also, R² of creation performance is 0.801, indicating that percentage that creation performance can be explained by both organizational culture and knowledge creation modes is 80.1%. Based on these results, it is believed that the research model is an acceptable model to present the causal relationships both between organizational culture and creation performance and knowledge creation modes and creation performance.

4.4 Regression

The results of hypothesis test are summarized in Table 4-20. It is found that 1) organizational culture has significantly influence on creation performance, 2) organizational culture has significantly influence on knowledge creation modes, and 3) knowledge creation modes have not significantly influence on creation performance.

In order to further explore more information among these variables, Multiple Regression Analysis (MRA) is used to examine their causal relationships and to test the predefined hypotheses. The MRA performed is to investigate the significance that effect exists between independent variables and dependent variables. There were three

parts for this: 1) the effect of organizational culture on creation performance, 2) the effect of knowledge creation modes on creation performance, and 3) the effect of organizational culture on knowledge creation modes.

Table 4-20: Summary of Hypotheses Tests

Variable Relationship Coefficient Decision

H1 Organizational culture has significantly influence on creation performance. 0.612* Support H2 Knowledge creation modes has significantly influence on creation

performance. 0.308 Not

Support H3 Organizational culture has significantly influence on knowledge creation

modes. 0.880*** Support

*p < 0.05; **p < 0.01; ***p < 0.001

4.4.1 The Effect of Organizational Culture on Creation Performance

The Hypothesis 1 defined that there is a significant effect of organizational culture on creation performance. Organizational culture, including characteristics of market culture, adhocracy culture, hierarchy culture and market culture are to be the independent variable. The dependent variable, creation performance, includes product creation performance, manufacturing creation performance, and management creation performance. Therefore, there are twelve sub-hypotheses to be tested. Multiple regression analysis is adopted to derive the conclusions and there are three regressions.

First, independent variables (IV) are MC, AC, HC, and CC and dependent variable (DV) is PCP. Second, IVs are MC, AC, HC, and CC and DV is MFCP. Third, IVs are MC, AC, HC, and CC and DV is MGCP. The results are presented in Table 4-21. It was found from Table 4-21 that Adhocracy culture had a significant impact on product creation performance (β = 0.604, p-value = 0.000). Market culture (β = 0.176, p-value = 0.073) and adhocracy culture (β = 0.375, p-value = 0.000) showed a significant impact on manufacturing creation performance. However, hierarchy

culture (β = 0.236, p-value = 0.028) and clan culture (β = 0.389, p-value = 0.000) has significant impact only on management creation performance. Accordingly, we accepted H1-2, H1-5, H1-6, H1-11, and H1-12.

Table 4-21: Effect of Organizational Culture on Creation Performance

Facet Construct/Model CP

PCP MFCP MGCP

MC 0.019 0.176⁺ 0.052 AC 0.604*** 0.375*** 0.126

HC 0.116 0.201 0.236*

CC - 0.082 - 0.075 0.389***

Adjusted R² 0.397 0.351 0.512 F-value 26.038*** 21.543*** 40.874***

N 153 153 153

1. ⁺ p < 0.1; *p < 0.05; ***p < 0.001

2. OC= Organizational Culture; MC= Market Culture; AC= Adhocracy Culture; HC=

Hierarchy Culture; CC= Clan Culture; CP= Creation Performance; PCP= Product Creation Performance; MFCP= Manufacturing Creation Performance; MGCP= Management Creation Performance

4.4.2 The Effect of Knowledge Creation Modes on Creation Performance

In Hypothesis 2, the research problem is that whether or not knowledge creation modes have a significant influence on creation performance. There are three regressions. First, IVs are G-DR, G-FR, and G-DE and DV is PCP. Second, IVs are G-DR, G-FR, and G-DE and DV is MFCP. Third, IVs are G-DR, G-FR, and G-DE and DV is MGCP. From Table 4-22, it was found that goal-free (β = 0.387, p-value=0.000) and goal-depended creation mode (β = 0.164, p-value = 0.050) have a positive and significant influence on product creation performance. Goal-free (β = 0.427, p-value = 0.000) and goal-depended creation mode (β = 0.290, p-value = 0.000)

have a positive and significant influence on manufacturing creation performance.

Particularly, however, goal-driven creation mode (β = -0.173, p-value = 0.013) has a negative and significant effect on manufacturing creation performance. Goal-free (β = 0.279, p-value = 0.000) and goal-depended creation mode (β = 0.505, p-value = 0.000) have a positive and significant influence on management creation performance, while goal-driven creation mode (β = -0.135, p-value = 0.040) has a positive and significant influence on management creation performance. Accordingly, we accepted H2-2, H2-3, H2-4, H2-5, H2-6, H2-7, H2-8, and H2-9.

Table 4-22: Effect of Knowledge Creation Modes on Creation Performance Facet Construct/Model CP

PCP MFCP MGCP

G-DR - 0.017 - 0.173* - 0.135*

G-FR 0.387*** 0.427*** 0.279***

KCM

G-DE 0.164* 0.290*** 0.505***

Adjusted R² 0.217 0.337 0.417

F-value 15.044*** 26.789*** 37.196***

N 153 153 153

1. *p < 0.05; ***p < 0.001

2. KCM= Knowledge Creation Modes; G-DR= Goal-Driven Creation Mode; G-FR=

Goal-Free Creation Mode; G-DE= Goal-Depended Creation Mode; CP= Creation Performance; PCP= Product Creation Performance; MFCP= Manufacturing Creation Performance; MGCP= Management Creation Performance

4.4.3 The Effect of Organizational Culture on Knowledge Creation Modes

There are three regressions. First, IVs are MC, AC, HC, and CC and DV is G-DR.

Second, IVs are MC, AC, HC, and CC and DV is G-FR. Third, IVs are MC, AC, HC, and CC and DV is G-DE. For hypothesis 3, Table 4-23 indicated that adhocracy culture has a positive and significant influence on goal-free creation modes (β = 0.182,

p-value = 0.072). Market culture (β = 0.032, p-value = 0.032), adhocracy culture (β =

0.166, p-value = 0.041), hierarchy culture (β = 0.247, p-value = 0.024), and clan culture (β = 0.213, p-value = 0.035) have a positive and significant influence on goal-depended creation modes. Accordingly, we accepted H3-6, H3-9, H3-10, H3-11, and H3-12.

Table 4-23: Effect of Organizational Culture on Knowledge Creation Modes KCM Facet Construct/Model

G-DR G-FR G-DE

MC 0.184 0.139 0.186*

AC -0.057 0.182⁺ 0.166*

HC -0.049 0.050 0.247*

CC 0.133 0.196 0.213*

Adjusted R² 0.022 0.222 0.495 F-value 1.836 11.838*** 38.191***

N 153 153 153

1. ⁺ p < 0.1; *p < 0.05; ***p < 0.001

2. OC= Organizational Culture; MC= Market Culture; AC= Adhocracy Culture; HC= Hierarchy Culture; CC= Clan Culture; KCM= Knowledge Creation Modes; G-DE = Goal-Depended Creation Mode; G-DR = Goal-Driven Creation Mode; G-FR= Goal-Free Creation Mode

4.5 A Summary of Hypotheses Testing

The results of hypothesis test were listed in Table 4-24 in a concise manner. It was found that there were 33 hypotheses that were examined, and of which 18 hypotheses were supported.

Table 4-24: A Summary of Hypotheses Testing

H 1-1: Market culture has significant influence on product creation performance. Not support H 1-2: Adhocracy has significant influence on product creation performance. Support H 1-3 Hierarchy culture has significant influence on product creation performance. Not support H 1-4: Clan culture has significant influence on product creation performance. Not support H 1-5: Market culture has significant influence on manufacturing creation performance. Support H 1-6: Adhocracy has significant influence on manufacturing creation performance. Support H 1-7: Hierarchy culture has significant influence on manufacturing creation performance. Not support H 1-8: Clan culture has significant influence on manufacturing creation performance. Not support H 1-9: Market culture has significant influence on management creation performance. Not support H 1-10: Adhocracy has significant influence on management creation performance. Not support H 1-11: Hierarchy culture has significant influence on management creation performance. Support H 1-12: Clan culture has significant influence on management creation performance. Support H 2-1: Goal-driven creation mode has significant influence on product creation performance. Not support H 2-2: Goal-free creation mode has significant influence on product creation performance. Support H 2-3 Goal-driven creation mode has significant influence on manufacturing creation performance. Support H 2-4: Goal-free creation mode has significant influence on manufacturing creation performance. Support H 2-5: Goal-driven creation mode has significant influence on management creation performance. Support H 2-6: Goal-free creation mode has significant influence on management creation performance. Support H 2-7: Goal-depended creation mode has significant influence on product creation performance. Support H 2-8: Goal-depended creation mode has significant influence on manufacturing creation Support H 2-9: Goal-depended creation mode has significant influence on management creation performance. Support H 3-1: Market culture has significant influence on goal-driven creation mode. Not support H 3-2: Adhocracy culture has significant influence on goal-driven creation mode. Not support H 3-3: Hierarchy culture has significant influence on goal-driven creation mode. Not support H 3-4: Clan culture has significant influence on goal-driven creation mode. Not support H 3-5: Market culture has significant influence on goal-free creation mode. Not support H 3-6: Adhocracy culture has significant influence on goal-free creation mode. Support H 3-7: Hierarchy culture has significant influence on goal-free creation mode. Not support H 3-8: Clan culture has significant influence on goal-free creation mode. Not support H 3-9: Market culture has significant influence on goal-depended creation mode. Support H 3-10: Adhocracy culture has significant influence on goal-depended creation mode. Support H 3-11: Hierarchy culture has significant influence on goal-depended creation mode. Support H 3-12: Clan culture has significant influence on goal-depended creation mode. Support

4.6 Discussion and Implications

4.6.1 Organizational Culture to Creation Performance

In Table 4-21, the organizational culture is seen to be a significant predictor of creation performance. This result is consistent with those by Damanpour (1991), Deshpandé et al. (1993), Syrett and Lammiman (1997), Tushman and O’Reilly (1997), Chandler et al. (2000), and Martins and Terblanche (2003). This implies that companies in the manufacturing industry should be aware of the characteristics of organizational culture to improve their creation performance. Among culture characteristics, culture with characteristic of adhocracy has a positive impact on product creation performance (Lock and Kirkpatrick, 1995; Hauser, 1998; Mauriel et al., 2000; Martins and Terblanche, 2003). Moreover, market culture and adhocracy culture shows a positive and significant impact on creation performance in manufacturing. The result of impact of adhocracy culture on creation performance is consistent with Lock and Kirkpatrick (1995), Hauser (1998), Mauriel et al. (2000), Ogboma and Harris (2000) and Martins and Terblanche (2003). This indicates that an organizational culture with characteristics of market competition is more likely to emphasize goal achievement with respect to creation in manufacturing. In addition, organizational culture with characteristics of creation is likely willing to accentuate the entrepreneurship and likely willing to be a risk taker, such as the frequent adjustment of the internal and external requirements, the improvement of manufacturing processes and technologies, the manufacturing process reengineering, and the capability of order delivery.

In addition, organizational culture with characteristics of both hierarchy and clan is

more likely to positively enhancing the management creation performance. The result of the influence of clan culture on management creation performance is consistent with Angle (1989), Ogboma and Harris (2000), and Martins and Terblanche (2003).

However, Hauser (1998) argued that organizational culture with characteristics of clan would have negative influence on creation performance. Ogboma and Harris (2000) also proposed that hierarchy culture has negative influence on organizational performance. However, the participants selected by Ogboma’s and Harris’s research are from companies in United Kingdom. With other variables fixed, according to the standardized coefficient, it can be seen that clan (β = 0.389) have more contribution than hierarchy (β = 0.236). This implies that creation performance in management can be improved with an emphasis on organizational cohesion and loyalty, decision participation, and teamwork. The research suggestion is therefore that a company implementing a formal management process regulation and policy will stimulate employees to come up with creative ideas, and thereafter to improve management performance.

4.6.2 Knowledge Creation Modes to Creation Performance

First, although it is found from the SEM, the knowledge creation modes have not significantly influence on the creation performance. However, Deshpandé et al. (1993) proposed that the more knowledge creation modes adopt, the better creation performance is. Therefore, in order to explore more information between knowledge creation modes and creation performance, this research conducted the investigation of about the effects between sub-factors of knowledge creation modes and sub-factor of creation performance. In Table 4-22 it is found that both G-FR and G-DE positively and significantly influence the creation performance in product. The result of the

impact of G-FR on CP is consistent with Tesluk (1997) and Ayres (1999). However, G-DR does not show the same effect. With other variables fixed, the standardized coefficient of G-FR creation mode（β = 0.387）is larger than that of G-DE (β = 0.164) with respect to the contribution of product creation performance. This implies that in a creative company management with both G-FR and G-DE creation modes are more likely willing to frame the strategic decision (or goals) and then let their employees freely look for creative ways to reach the goals. By this, new products are more likely to meet the organizational strategy and characteristics of the creative products can be more diversified. Moreover, in the process of new product creation the goals can be slightly adjusted to relevantly meet the realistic requirements without putting the original goal(s) aside.

Second, with regards to the impact of knowledge creation modes it is found that, G-DR creation mode, G-FR creation mode, and G-DE creation mode have all significantly influence on the creation performance in manufacturing. Particularly, G-FR and G-DE have a positive influence while G-DR has negative. Based on the standardized coefficient, it is found that the G-FR presents the most contribution (β = 0.427) in affecting creation performance, and G-DE shows the middle (β = 0.290), and G-DR the last (β = -0.173). This implies that creation mode using goal-free is most likely to support creative idea generation in creation performance in manufacturing. In other words, normally G-FR creation mode gives employees more thinking space within a specific manufacturing technology or a specific manufacturing process to increase its performance. Besides, within a specific manufacturing requirement (or a goal) the G-DE creation mode may firstly set up a fundamental goal to frame the employees’ thinking space, and then shift to the G-FR

improvement. On the other hand, however, in adopting the G-DR creation mode, it can be seen from the obtained result that to highly limit thinking space may greatly weaken the creation performance in manufacturing.

Finally, three knowledge creation modes are all significantly related to the creation performance in management. However, similar to the creation performance in manufacturing, G-FR and G-DE has a positive impact while G-DR has negative.

Again, based on the standardized coefficient, it is found that the G-DE presents the most contribution (β = 0.505) in affecting creation performance, and G-FR shows the middle (β = 0.279) while G.-DR the last (β = -0.135). This implies that creation mode using goal-depended is most likely to support creative idea generation in creation performance in management. The reason is that a management strategy normally is made by top decision makers. Within a defined strategy, G-DE allows a free space to generate creative ideas for its implementation. On the other hand, G-DR creation mode does not have more rooms in raising ideas in creation of management. In consequence, adopting G-DR may lessen the performance of new management

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