Chapter 4 Research Analysis and Results
4.1 Reliability Analysis and Validity Analysis
After the data was obtained and stored, this study conducted a factor analysis of the variables of all the concepts. To realize whether the data are appropriate to execute factor analysis, this study examined and judged data by using the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy. The KMO measure indicates the comparable value between all interrelated coefficients and net interrelated coefficients (Hair, Black, Babin, & Anderson, 2009). KMO values of greater than 0.6, as recommended by Hair, Black, Babin, & Anderson (2009), are deemed acceptable measures of fitness for the variables in factor analysis. Therefore, according to Table 19, the KMO measures of sampling adequacy indicated that the KMO measure were all greater than 0.6, showing that the variables are appropriate for conducting factor analysis.
Table 19 KMO value of All Dimensions
Dimensions KMO
Service Innovation .914
Innovation Level .811
Improvement on Service .751
Firm Performance .848
All of the variables were based on a six-point Likert-type scale rating from
―strongly disagree‖ (1) to ―strongly agree‖ (6), except for background information. This study focused on the four dimensions of ―Service Innovation‖, ―Service Types‖,
―Reverse Product Cycle‖ and ―Firm Performance‖ to conduct exploratory factor analysis, via the principal component and varimax methods of factor analysis to examine variables. Moreover, this study adopted three principles to extract criterion; (1) the criterion of factor loading in empirical research is required to exceed 0.5. (2) the eigenvalue from Kaiser is required to achieve at least 1. (3) the cumulative % of variance in empirical research is required to exceed 60% and (4) Cronbach‘s Alpha of the factor must achieve 0.7 (Hair et al., 2009). Four of the fundamental factors were extracted from the data set, shown at Table 20 to Table 23.
Table 20 shows the concept of service innovation, including three items related to new service conception, new service delivery and new service interface for customers.
The factor loadings all exceeded 0.5 and the eigenvalue reached 1.465. Moreover, the cumulative percent age of explanatory variance reached 78%. And the Crobach‘s alpha of this dimension achieved 0.93, which meets the standard as suggested by Hair et al.
(2009).
Table 20 Reliability Analysis and Validity Analysis of Service Innovation Involvement Dimension resource to develop the new service.
.849 2. Compare with competitions, our firm provides variety
new service to customers in the past 5 years.
.855 3. Compare with competitions, our firm is first brings the
new service to the market usually.
.886 4. Compare with competitions, our firm always try to our
best to provide different service for different customers.
.822 New
Service Delivery
1. Compare with competitions, our firm give the promise to customers for the better ways to deliver the service.
.888 2. Compare with competitions, our firm try to our best to
provide the special service delivery for customers.
.830 3. Compare with competitions, our firm try to our best to
provide the special service delivery for customers.
.915
customers for knowing the customer‗s demands.
.621 3. Our services are requires the interaction from customers
with staff.
.519
Eigenvalue 1.465
Cumulate % of Variance 78.079%
Cronbach‘s Alpha .932
The concept of service type is comprised from two items related to disruptive innovation and sustaining innovation. Table 21 shows that the factor loadings all exceeded 0.6 and the eigenvalue reached 4.343. Moreover, the cumulative percent age of explanatory variance reached 62%. And the Crobach‘s alpha of this dimension achieved 0.89, which meets the standard as suggested by Hair et al. (2009).
Table 21 Reliability Analysis and Validity Analysis of Innovation Level Dimension financial products or financial services is cheaper in the market during the past 5 years.
.680
2. The financial products or financial service of our firm launched during the past 5 years attracted customers who had not used this product or service before.
.832
1. The financial products or financial service of our firm launched in the past 5 years mostly targeted on customers preferring mature professional knowledge.
.736
2. The financial products or financial service of our firm launched in the past 5 years satisfied mainstream customers after improvement for a period of time.
.833
3. Our firm usually relies on present products or service to develop the new products or new service to satisfy with customer‗s demands during the past 5 years.
.719
4. Our firm usually relies on market changes to improve financial products and financial service than create revolutionary innovation products and service during the past 5 years.
.803
Eigenvalue 4.343
Cumulate % of Variance 62.408%
Cronbach‘s Alpha .896
The concept of reverse product cycle identified two items, including service efficiency and new service. Table 22 shows that the factor loadings all exceeded 0.6 and the eigenvalue reached 3.214. Moreover, the cumulative percent age of explanatory variance reached 64%. And the Crobach‘s alpha of this dimension achieved 0.83, which meets the standard as suggested by Hair et al. (2009).
Table 22 Reliability Analysis and Validity Analysis of Degree of Service Improvement Research
Conceptions
Operational Questions Factor
Loadings Service
Efficiency
1. Generally, our firm provides financial products or services is based on the present market to gives more service efficiency to customers in the past 5 years.
.870
2. Generally, although our firm provides financial products or services are not create new market, but provides more service efficiency than ever in the past 5 years.
.857
3. Generally, our firm‘s operational policy is more focus on improve service efficiency than seek the new market in the past 5 years.
.676
New Service
1. Generally, our firm plays the leader role form all competition to seek and create new market in the past 5 years.
.894 2. Generally, our firm provides financial products and services are the new goods which all have not the similar goods in the past 5 years.
.920
Eigenvalue 3.214
Cumulate % of Variance 64.271%
Cronbach‘s Alpha .834
Ultimately, Table 23 shows the details of the concept of firm performance derived from sale performance, profitability and enhanced opportunities. The factor loadings were all exceeded 0.7 and the eigenvalue was reached 6.006. Moreover, the cumulative percent of explanatory variance reached 66%. And the Crobach‘s alpha of this dimension achieved 0.93, which meets the standard as suggested by Hair et al. (2009).
Table 23 Reliability Analysis and Validity Analysis of Firm Performance after we develop the service innovation.
.717 2. Generally, our firm‘s market share could increase after
we develop the service innovation.
.790 3. Generally, service innovation assists our firm to
enhance the profits after we develop the service innovation.
.820
Profitability 1. Generally, service innovation assists our firm‘s sales performance to reach our goals after we develop the performance of other financial products or financial service after we develop the service innovation.
.788
increase positive image from customers.
.783
Eigenvalue 6.006
Cumulate % of Variance 66.729%
Cronbach‘s Alpha .937
4.2 Correlation Analysis
As follows, this study discusses the correlation analysis of the dimensions of service innovation, innovation level, improvement on service and firm performance.
This study conducted a Person correlation analysis to shed light on all four dimensions.
Moreover, collinearity tests of the variables were made to delete the less important explanatory variables which were replaced by other equivalent variables if the variance inflation factor (VIF) exceeded 10 (Hair et al., 2009). Therefore, the results of variance inflation factor of independent variables (service innovation, service types and reverse product cycle) in this study were 2.19, 3.22 and 3.37. The numbers of variance inflation factor which means that service innovation, service types and reverse product cycle were uncorrelated with each other. Service innovation was calculated as the average
number from the items of new service conception, new service delivery and new service interface for customers. Service types were calculated as the average number from the two items of disruptive innovation and sustaining innovation. Reverse product cycle was derived from the average amount from the two items of service efficiency and new service. And the dimension of firm performance was derived from the average number from the three items of sale performance, profitability and enhanced opportunities. The results of correlation analysis are shown in Table 24.
Table 24 Means, Standard Deviations, and Correlationsa
Dimensions Mean S.D. 1 2 3
1. Service Innovation Involvement 4.56 .877
2. Innovation Level 4.17 .85 .743**
3. Degree of Service Improvement 4.27 .89 .819** .710**
4. Firm Performance 4.64 .76 .678** .658** .693**
aN=48
*p<.05, ** p<.01; two-tailed tests
As for the concept of service innovation, the average number of service innovations was 4.56 with a standard deviation of 0.887. Positive correlations were shown foe all the variables. Specifically, the correlation coefficients of service types, reverse product cycle and firm performance were 0.743, 0.819 and 0.678 with a statistical significance of positive correlation (p < .05). These numbers indicate that the service innovation dimension was positively related to the service types, reverse product cycle and firm performance. The average number of service types was 4.17 with a standard deviation of 0.85. Positive correlations were shown for all the variables.
Specifically, the correlation coefficients of service innovation, reverse product cycle and firm performance were 0.743, 0.710 and 0.658 with a statistical significance of positive correlation (p < .05). These numbers indicate that the service types dimension was positively related to service innovation, reverse product cycle and firm performance.
Furthermore, as to the concept of improvement on service, the average number of reverse product cycles was 4.27 with a standard deviation of 0.89. Positive correlations were shown for all the variables. Specifically, the correlation coefficients of service innovation, service types and firm performance were 0.89, 0.710 and 0.693 with a statistical significance of positive correlation (p < .05). These numbers indicate that the
reverse product cycle dimension was positively related to service innovation, service types, and firm performance. As for dimension of firm performance, the average number of service types was 4.64 with a standard deviation of 0.76. Positive correlations were shown for all the variables. Specifically, the correlation coefficients of service innovation, service types and reverse product cycle were 0.678, 0.658 and 0.693 with a statistical significance of positive correlation (p < .05). These numbers indicate that the firm performance dimension was positively related to service innovation, service types and reverse product cycle.
4.3 Cluster Analysis
Before clustering all of the samplings, this study first discriminated the firms with low participation in service innovation. All of the variables of the service innovation concept were based on a six-point Likert-type scale rating from ―strongly disagree‖ (1) to ―strongly agree‖ (6). This study first identified the firms which adopted and paid attention to strategies related to service innovation, thus eliminating the financial firms with an average number of service innovation concepts lower than 3 (not includes 3).
The completes results of the average number of service innovation concepts are shown in Appendix 4. Appendix 4 reveals that 1 financial firm (firm 40) was below 3, only having the average number of 2.5, meaning the low operation of service innovation.
Therefore, 47 firms paid attention to strategies related to service innovation, thus, conforming with the purpose of this study.
The major purpose of this study was to classify four service innovation strategies and to further explore the relationship with firm performance. This study used two stages to present the results. In the first stage, the Ward‘s method of hierarchical clustering was applied to consolidate the groups before clustering. Further, in the second stage, the K-means algorithm was selected to conduct cluster analysis due to its efficiency in clustering large data sets and its simple calculation process. The four service innovation strategies, identified in the previous discussion, were used for cluster analysis. For contractors with different backgrounds, their service innovation strategic behaviors are different. Therefore, the contractors were classified into different groups with different strategic orientations based on cluster analysis. The clustering process was conducted as follows.
Figure 9 graphically represented the relationship between types of innovation and improvement of service. It highlights a number of important features. The majority of financial firms cluster toward the high end for the types of innovation and improvement of service- Steady Value-added strategy (i.e. focus on disruptive innovation and service efficiency). Figure 9 also revealed a small number of financial firms that rated low for both types of innovation and improvement of service- Prosperous Business strategy, and another group which rated low for improvement of service and high for types of innovation- Emerging Goal. There were no financial firms that rated very high for improvement on service and low on types of innovation - Satisfactory Efficiency strategy (i.e. stress on sustaining innovation and service efficiency concept).
Figure 9 Plot of Improvement on Service versus Types of Innovation
The graph of Figure 9 suggests the possibility of identifying some meaningful clusters, so this study undertook cluster analysis to facilitate the specification of groups.
Under the K-means algorithm, Appendix 5 shows the types of innovation and improvement of service scores for the four clusters centers. The steady value-added strategy of group 1 consisted of 25 financial firms, which had higher ratings on types of innovation (i.e. focus on disruptive innovation) and improvement on service (i.e. focus on service efficiency). The emerging goal strategy of group 2 consisted of 15 financial firms, with higher ratings for types of innovation (i.e. focus on disruptive innovation) and lower ratings for improvement on service (i.e. focus on new service). The prosperous business strategy of group 3 consisted of 7 financial firms with lower average ratings for both types of innovation and improvement on service concepts (i.e.
focus on sustaining innovation and new service). And finally, for the satisfactory efficiency strategy, none of the respondents of financial firms focused on sustaining innovation and service efficiency.
4.4 Analysis of Variance (ANOVA)
The respondents were grouped into Steady Value-Added Strategy, Emerging Goal Strategy and Prosperous Business Strategy. Appendix 5 also shows a summary of the grouping. It indicates that 25 Steady Value-Added Strategy firms constitute the largest group (53%), followed by 15 Emerging Goal Strategy (32%) firms, 7 Prosperous Business Strategy (15%) firms and 0 Satisfactory Efficiency Strategy firms. Table 28 indicates the firm performance of different strategies, including Steady Value-Added Strategy, Emerging Goal Strategy and Prosperous Business Strategy. This study showed that Prosperous Business Strategy created better firm performance (M = 5.47) than that of Steady Value-Added Strategy (M = 4.85) and Emerging Goal Strategy (M = 4.00).
The ANOVA test showed that the groups of strategy differences in firm performance were significant (p < .001). By conducting Scheff‘s test, this study confirmed that Prosperous Business Strategy had significantly higher firm performance than the Steady Value-Added Strategy, Emerging Goal Strategy, and the Emerging Goal Strategy. The results reveal that the financial firms which adopted both the concepts of sustaining innovation and new service of Prosperous Business Strategy had higher firm performance in Taiwan‘s financial environment. In contrast, the profile of firm performance for the Emerging Goal Strategy had the lowest rating performance among three service innovation strategies.
Table 25 Service Innovation Strategy on Firm Performance
Subgroup (N) Mean S.D. F-value Scheff’s
test 1. Steady Value-Added Strategy (25) 4.85 .54 21.66*** (1) > (2)
(1) < (3) 2. Emerging Goal Strategy (15) 4.00 .57 (2) < (1) (2) < (3) 3. Prosperous Business Strategy (7) 5.47 .34 (3) > (1) (3) > (2)
4. Satisfactory Efficiency (0) - - -
*** p < .001
Furthermore, this study also examined the profile of firm performance conceptions for different service innovation strategies. Table 25 and Table 26 show that Prosperous Business Strategy create prominent firm performance for financial firms (M = 5.47), and the conception of enhanced opportunities provides more support (M = 5.71) than sale performance and profitability. Besides, although Steady Value-Added Strategy was not the highest profile firm performance among all of the strategies, it also provided excellent profiles for financial firms (M = 5.71), especially for enhanced opportunities (M = 4.93). And, finally, the Emerging Goal Strategy provided the lowest firm performance among all three service innovation strategies, revealing that service innovation strategy could contributed to the firm performance of financial firms (M = 4.00). Among all of three service innovation strategies, Table 26 shows that the enhanced opportunities conception led to significantly higher performance excellence than other firm performance conceptions, meaning that service innovation strategy could create more opportunities for the financial industry.
Table 26 The Profile of Firm Performance in Service Innovation Strategy Service Innovation
Strategy
Conceptions of Firm Performance
Mean
Steady Value-Added Strategy
(1) Sale Performance 4.74
(2) Profitability 4.89
(3) Enhanced Opportunities 4.93 Emerging Goal Strategy (1) Sale Performance 3.57
(2) Profitability 4.2
(3) Enhanced Opportunities 4.2 Prosperous Business
Strategy
(1) Sale Performance 5.38
(2) Profitability 5.33
(3) Enhanced Opportunities 5.71 Satisfactory Efficiency (1) Sale Performance -
(2) Profitability -
(3) Enhanced Opportunities -