Taishin International Bank Pilot Study (N= 47) Sample
The empirical study for this research was conducted in a leading financial institution in Taiwan. This institution is among of one of the most competitive banks in Taiwan banking industry, with a rapid growth pace, as well as the good reputation in the industry by winning numerous awards in both banking practices as well as in the human resource field, in particular the innovation are the key for banks to stand out in the industry. There are eighty questionnaire sent out to the employees, with response rate of 70%, further after deleting the questionnaire which is not valid, left out forty-seven valid sample for this study examination.
Measures
The instrument consisted of five variables with total amount of 82 questions. All items in the questionnaires were measured on a five-point Likert scale (1 = Strongly Disagree; 5 = Strongly Agree). Cronbach’s alpha and internal consistency was used to test the validity and reliability of the instrument in this study, with the acceptance level of 0.7.
Partial Least Square (PLS)
Partial Least Square (PLS) is a type of structural equation modeling (SEM) technique described as a second generation of a multivariate analysis (Fornell et al., 1990). Instead of SEM technique, PLS allows researchers to replicate and examine a series of relationships. In terms of hypothetical constructs that are difficult to be measured theoretically, the estimation of PLS has no assumptions of normality and independence from observation.
Originated from the path analysis, PLS is utilized to work with small samples and exploratory studies. It has been regarded as an authoritative method on the theories of social science; the writer in the research adopts Smart PLS 3.2 as the major tool to scrutinize the relationships of hypotheses within all the variables. The concept of PLS is an iterative combination of component analysis relating measures to constructs. The significance of relationships among the constructs is measured as an indicator of the model performance. Therefore, by using PLS the techniques of bootstrapping, R-square, path coefficients and t-value are implemented to examine the structural model.
Bootstrapping
Bootstrapping is a way to duplicate the sample in a logical manner to retrieve the t-value to simulate the test with larger sample, to see once the sample size has expand, weather the sample can perhaps have a significant results, such method is used in this study. The bootstrapping assumes that sample is reasonably repeated of the assumed population distribution, and therefore the estimated coefficient in the PLS-SEM can be tested for their significance (Henseler, Ringle, and Sinkovics, 2009). The PLS algorithm uses bootstrapping samples to estimate the result, such as the t-value and the path coefficient. Hair, Sarstedt, Ringle, and Mena (2012) also stated that critical t-values for the two-tailed test are 1.65, 1.96 and 2.58, which represented weak or one star, moderate or two stars, and strong relations which represent three stars in significance. Significance of relationship between variables can thus be assessed.
Pilot Study’s Research Framework
Figure 4.1. KIPs research model; analysis by PLS (Pilot study, N= 47) Note. * p < .1. ** p < .05. *** p < .01.
Based on the previous research, this proposed model are consists of knowledge management strategies, knowledge enabler, knowledge creation process, intellectual capital, and business performance. We provide an integrated intellectual capital model named as the KIPs Model that developed by Mingchu Aibo Tsai and Cheng-Ping Shih to measure the effect of each dimension on one another by the statistical parameter. The model was based on several previous researchers study, such as knowledge management strategies of Choi and Lee (2002), knowledge enabler of Lee and Choi (2003), knowledge creation process of (Nonaka et al., 2000), intellectual capital of Bassi and Van Buren (1999) and business performance of Chou (2012) shown in Figure 4.1.
Hypothesis
Based on literature reviews and structured research framework, six null-hypotheses were formulated as follows from H1 to H6.
𝐇𝟏: Knowledge Management Strategies have no effect on Knowledge Enabler.
𝐇𝟐: Knowledge Management Strategies have no effect on Knowledge Creation Process.
𝐇𝟑: Knowledge Enabler has no effect on Knowledge Creation Process.
𝐇𝟒: Knowledge Enabler has no effect on Intellectual Capital.
𝐇𝟓: Knowledge Creation Process has no effect on Intellectual Capital.
𝐇𝟔: Intellectual Capital has no effect on Business Performance.
Result of Hypothesis and Discussion
Most of the variables show a significant result in the Table 4.1., however, the Centralization in the Knowledge Enabler construct has the lowest factor loading score of 0.382, the questions is a reverse coding, therefore, the lower the score, meaning the higher the centralized it is for the company. Therefore, the study confirms that this bank operate in a highly centralized manner, which may be due to the financial institution are a highly regulated industry by the government, which the information and data security is extremely important, therefore, the bank must comply with the standard.
However, it also pose the potential hazard that an organization is not stretched well enough, which the personnel within the organization may potentially missed out the innovation transition turning point. In comparison, the T-shaped skills stands out among all, due to the fact that T-shaped skills is the powerful facilitator inside an organization on reinforce the knowledge flows, as well as, to take advantage of its breadth and depth of knowledge to push the knowledge boundary into the next level.
Combination is another important factor in the Knowledge Creation Process, which shows within an organization, there’s an intense completion among acquiring the knowledge. In the Intellectual Capital construct, particularly, the Human Capital sub-dimension stands out among all, with the factor loading score of 0.946; is exceptionally high in term of the factor loading. As Human Capital is the most important and strategic factor for an organization’s success, in addition, it is the driven force for delivering the market leadership as well as the financial performance. The acceptable level for Cronbach’s Alpha is 0.7.
Table 4.1. Factor Loading and Internal Consistency & Reliability Analysis via PLS Factor Loading and Internal Consistency & Reliability Analysis via PLS
Constructs Items
Table 4.1. (continued) Intellectual
Capital
IC_H 0.946 0.951 0.922 0.865
IC_I 0.932
IC_RC 0.912
Business Performance
BP_ML 0.960 0.957 0.909 0.917
BP_FP 0.955
Note. KMS_H= Human Strategy; KMS_S= System Strategy, KE_TS= T-Shaped Skills;
IT= IT-Support; KE_L= Learning; KE_C= Centralization, KCP_C= Combination;
KCP_I= Internalization; KCP_E= Externalization; KCP_S= Socialization, IC_H=
Human Capital; IC_I= Innovation Capital; IC_RC= Relational Capital, BP_ML=
Market Leadership; BP_FP= Financial Performance.
From the holistic perspective to view the financial institution, this pilot study found that other than Knowledge Management Strategies constructs, all other constructs are significant in Cronbach’s Alpha. However, the internal consistency all shows a good result, especially the Intellectual Capital and Business Performance constructs has the highest internal consistency among all, with score of 0.951 and 0.957 respectively. This pilot study believes that the low Cronbach’s Alpha score of Knowledge Enabler are primarily due to the insufficient sample of the pilot study by its nature, hence, once the sample are expanded, the Cronbach’s Alpha would be improved, the result of an analysis can be found in the Table 4.2.
Table 4.2. PLS Cronbach’s Alpha, Internal Consistency and 𝑅2 PLS Cronbach’s Alpha, Internal Consistency and 𝑅2
Constructs N. of Items Cronbach’s Alpha Internal Consistency 𝑅2
KMS 8 0.581 0.827
KE 19 0.798 0.870 0.297
KCP 20 0.944 0.959 0.723
IC 22 0.922 0.951 0.831
BP 10 0.909 0.957 0.456
Note. KMS= Knowledge Management Strategies, KE= Knowledge Enabler, KCP=
Knowledge Creation Process, IC= Intellectual Capital, BP= Business Performance.
By examined the path, this pilot study found that Knowledge Management Strategies have no direct effect on Knowledge Creation Process. However, Knowledge Management Strategies do have an indirect effect on Knowledge Creation Process by the path of Knowledge Management Strategies to Knowledge Enabler then to Knowledge Creation Process. In addition, six out of four hypotheses were rejected and the remaining two are supported, which provides a clear view on the major and a glance look of determinant factor for the success of the study sample in the banking industry as shown in Table 4.3.
Table 4.3. Empirical result of PLS Path Analysis (Hypothesis, Standardized Beta Coefficients and Adjusted T-values)
Table 4.3. (continued)
KCP→IC H5 0.752 4.768 *** Rejected +
IC→BP H6 0.675 10.354 *** Rejected +
Note. KMS= Knowledge Management Strategies, KE= Knowledge Enabler, KCP=
Knowledge Creation Process, IC= Intellectual Capital, BP= Business Performance.
*p<.10. ** p<.05. *** p<.001.
Results show this study rejects most of the hypotheses and significant in terms of t-value and path. This study provides useful implications for both academic and practice on analyzing the Intellectual Capital for financial institutions. Despite of the construct in Intellectual Capital may still not be very clear in terms of the definition. However, this study aims to make contributions to the academic research regarding Intellectual Capital in Taiwan as well as the research regards financial institution in Taiwan. Further research is warranted to collect a larger sample size, as well as conduct intra/inter industry-wide research to provide a more holistic view on Taiwan Intellectual Capital status quo, also a more in-depth study regarding various Knowledge Management Strategies combinations for the financial institutions in Taiwan, as well as try to establish a KPI measurement for Intellectual Capital.
Sample Characteristics
The characteristics of participants can be portrayed in Table 4.4. The table will provide a general understanding of how participants of this study being distributed, which consists of gender, working experience in financial related industry and their position within an organization. Please be noted this study has sent out 200 questionnaires, however, only 150 sample response are actually retrieved with response rate of 75%, and after deleting the invalid and outlier responses with some questions unanswered or other forms of invalid answers, mostly deleting the outliers, the final valid sample left out 57% of total sample to assure for represent a true and accurate
picture of the analysis in best understanding the winning pattern of intellectual capital initiative in financial institution in Taiwan, which the valid samples are consists of 86 valid respondent’s response to perform for the statistical analysis.
In particular, female is taking account for the major respondents, which consists of 81.9% of the sample with male respondents taking 18.1% of the sample respectively.
The differential in gender proportion may ultimately generate different outcomes toward the analyses and understanding of ability to explain the current industry status by the research framework, which reinforce the importance of understanding gender distribution in this study’s sample, to provide us a more comprehensive view and better understanding.
Particularly, the year of working experience in financial related industry is the most critical demographic data we would like to abstract. Most of the respondent’s in this study, are work in the financial related industry within 6 to 10 years, with portion of 35.6% of entire sample. Second highest group are respondents with 3 to 5 years of experience or 24.8% of total sample. Third place goes to respondents with 11 to 15 years of experience in financial related industry, with 17.4% of entire sample, following are respondents with over 15 years financial related experiences with 16.1% of entire sample, and the least are respondents work in financial related industry with experience fewer than 3 years, taking 6% of the total sample in this study.
Important demographic information in this study is the respondent’s position within an organization. Which provides an understanding of employees within different hierarchy level and position concerned and perspective toward this study’s framework, in helping better explain and understand the structure of an organization? Most of the respondents hold an associate or assistant related position in an organization, taking 68.5% of total sample. In addition, there are 22.8% of total respondents in this sample holds the management trainee or management associate position. Following by the
senior manager and senior management position in an organization, taking account of 6% of the entire same. The position which consists the least portion in this study are respondents who holds an assistant management position or are assistant manager, taking account of 2.7% of entire sample in this study.
Table 4.4. Distribution of Demographic (N=149) Distribution of Demographic (N= 149)
Variable Sample Characteristics Frequency Percentage (%)
Gender Male 27 18.1
Female 122 81.9
Work Exp. Less than 3 Years 9 6
3 to 5 Years 37 24.8
6-10 Years 53 35.6
11-15 Years 26 17.4
Over 15 Years 24 16.1
Position Assoc. /Assistant 102 68.5
Mgmt. Trainee / Mgmt. Assoc. 34 22.8
Sr. Manager / Sr. Management 9 6
Assistant Manager 4 2.7
Note. Assoc.= Associate; Mgmt.= Management; Sr.= Senior.
Reliability and Validity Analysis
To demand the most accurate analysis, many researchers adopted the standardized measures or measurements in their study. Therefore, to best assess and understand the information from the respondent’s response, regardless of their unique characteristics, experience, standpoint, angle and circumstances. The study must have an adequate measurement that allows the construct of responses can best fit into the respondent’s response, as well as having the right assessment tool in hand, which are able to fit various responses into a limited number of predetermined answer categories, also given a specific value assigned correspondingly on each unique responses, consequently, the importance of discussing validity and reliability for the research has been reinforced, also such indicators very often serves as an important benchmark in explaining the value of the research, to allows readers know quickly if the research is worthwhile to invest their time to read and discussed in the future research (Golafshani, 2003).
To better understand why validity and reliability is so important to report in any given research, now take the example of how measurement and assessing tool can guide researchers and readers to the very wrong conclusion, as it is very unlikely for a research to adopt math skills as assessment for verbal skills. To confirm that it is measure what it said to be measured, further giving credit to the data collection process and method it adopted. Once the research can confirm the measure has ability to reflect the reliability and validity of the study in an adequate level, then researchers and readers can now rely on the results generates from the statistical software, to further explain the most fascinating story and other research insight behind the data, to further confirm if the findings do confirm or disconfirm with the current theory or is it counter-intuitive.
Particularly, the predictive validity plays an important role in explaining the validity in the study. As the researcher’s framework built by Tsai and Shih, serve as the human resource / human capital development assessing instrument, to best assess and
explain the current organizational human resource development status on intellectual capital facilitating process and knowledge management strategies, as well as their efforts on enhancing the process of intellectual capital flows within an organization, to provide a comprehensive view on explaining why the market leader in any given industry are and can be the market leaders, also what is the critical math or critical mass behind their success in obtaining large market share in the essence of human resource development as well as business administration perspectives.
Reliability is used to measure the internal consistency of a test and is crucially important, majority of the researchers report Cronbach’s Alpha as their representation for the reliability when adopting the Likert-type scales for the study, therefore, the Cronbach’s Alpha was adopted in this research to represent the reliability of the study (Gliem and Gliem, 2003). Let’s further take example to explain how survey or observations can vary from time to time causing inconsistent, not reliable data to researchers or readers. Now, imagine if a person weighted 40 kilogram to see if the workout done by the last hour did help him or her lose some weight, to keep his or her in a good shape. However an hour later the same person weighted 55 kilogram, and two hours after weighted 35 kilogram respectively, the observation shows a clear inconsistency between the measurement, consider any research rely on such measurement will be absolutely not reliable / trustworthy, the readers as well as other researchers will definitely challenge the research findings and methodologies.
Accordingly, it imposes the importance on understanding reliability of the data, as well as the measurement. Therefore, the data can be both consistent and reliable, the outcome from the research can hence be persuasive and worth reading.
For quantitative research, the accuracy of the constructs as well as each sub-dimensions are the primary concern for reliability, with that being said, according to the rule of thumb, Cronbach’s alpha value are suggested to be critically above the 0.7, to
be well represent the internal consistency and reliability of the study itself (Nunnally, 1978). In Table 4.5., approximately 80% of all constructs in this study are above 0.9 and beyond, which shows that most of the constructs in this study are consider as extremely reliable.
Given the fact that there is one construct although below 0.9 level, however, the value is not anywhere far away from most of other constructs. In addition, the Knowledge Enabler constructs are particularly below 0.9, with value of .867 are still far beyond the 0.7 acceptance level. Again, the value of Cronbach’s alpha in all constructs reflects the constructs are strongly reliable. As what this study is focused on, the intellectual capital construct is with the highest Cronbach’s alpha .961, which the empirical data reassure the intellectual capital is trust the most critical factor in determining the success of a financial institution, especially in the 21st century highly competitive FinTech Era.
Table 4.5. Reliability Analysis on All Constructs by SPSS Reliability Analysis on All Constructs by SPSS
Constructs Cronbach’s Alpha N of Items
Knowledge Management Strategies .900 2
Knowledge Enabler .867 4
Intellectual Capital .961 4
Business Performance .922 2
Given the general understanding of Cronbach’s alpha for all constructs tested by SPSS, the study confirm that all the constructs exceeds the requirement of reliability and internal consistency for the empirical research. Now, this study wishes to be more specific about the reliability and internal consistency, in order to provide a more comprehensive picture about the entire study reliability and internal consistency.
Therefore, the study tested the Cronbach’s alpha by each different sub-dimension within various constructs.
In Table 4.6., serve the purpose to best provide an overall stability outlook of an entire research, perspectives from the bottom to the top and vice versa. For Knowledge Management Strategies, both Human and System sub-dimensions are exceeding the 0.7 acceptance level to reconfirm with the previous reliability test run by constructs. In the Knowledge Enabler, study found that IT-support has the highest Cronbach’s alpha value, with respect to .930.
Most critical constructs in this study are doubtlessly the Intellectual Capital, which with the highest Cronbach’s alpha value overall in comparison with other constructs.
Notably, the Innovation Capital with value of .943, surpass the rest, each sub-dimensions in this construct has further strengthen the total Cronbach’s alpha with value of .974, which indeed are nearly the perfect score in reliability. Last but not least, the Business Performance’s Financial Performance sub-dimension with the highest Cronbach’s alpha value of .937 within, to highlight the importance of financial performance once again.
Table 4.6. Reliability Analysis on All Dimensions by SPSS Reliability Analysis on All Dimensions by SPSS
Dimensions Sub-dimension Cronbach’s Alpha N of Items Knowledge
Relational Capital .901 4
Innovation Capital .943 6
Total .974 20
Business Performance
Market Leadership .924 5
Financial Performance .927 4
Total .955 9
The construct of validity in this study, can be well-explained by the KMO, which is the representation of the sample adequacy. The KMO is an abbreviation for Kaiser-Meyer-Olkin (KMO), which is one of the important parameter in determine and
The construct of validity in this study, can be well-explained by the KMO, which is the representation of the sample adequacy. The KMO is an abbreviation for Kaiser-Meyer-Olkin (KMO), which is one of the important parameter in determine and