Development of Short-form Knowledge-based Economy Scorecards

Development of Short-form Knowledge-based Economy Scorecards Dr. Chih-Kai Chen, Chung Shan Medical University, Taiwan ABSTRACT Research on knowledge-based economy (KBE) has increased noticeably in recent years. Notably, how to evaluate the KBE competitiveness has already attracted a lot of attention worldwide. However, some complex scorecards provide abundant information, but restricted to improper construct factors and weight allocation and cost and fit considerations, few scorecards can meet the requirement of conciseness, accuracy, and efficiency. Thus, this work used the Knowledge-based Economy Scorecards (KES) of World Bank, applying the linear structural relation model to develop the short-form Knowledge-based Economy Scorecards (S-KES) and identify the cross-validation of the S-KES model. Overall, this finding reveals that the KES model is not satisfactory. In contrast, the reliability and validation of the S-KES model are improved although the observed variables in number of the S-KES model decrease. Moreover, the cross-validation test of the S-KES model was accepted not only the loose replication strategy but the tight replication strategy. Overall, the S-KES model seems a concise, efficient, and substitutive scorecard for KES model. Keywords: Knowledge-based Economy Scorecards, Short-form, Cross-validation. INTRODUCTION As noted by OECD (1996), because of the innovative applications of information communication and technology (ICT), knowledge has become the key to national competitiveness instead of land and capital in the past decades. Notably, after the new economy paradigm the United States created the longest economic expansion in the 1990s, some countries have drafted various knowledge-based economy (KBE) development plans to improve the national competitiveness (OECD, 1999; APEC, 2000; WB, 2002). In any case, various indicators were different from one another to date. Some complex scorecards provide abundant information, but restricted to improper construct factors and weight allocation and cost and fit considerations, few scorecards can meet the requirement of conciseness, accuracy, and efficiency. Among different KEB scorecards, this work found that the Knowledge-based Economy Scorecard (KES) of World Bank (WB) contributing with implications for theory and practice is a popular measurement on the KBE competitiveness. However, this work found that the KES model comprising seventy-two variables under five measured categories suffered from the asymmetric allocation and high overlap of some measured indicators. Thus, how to simplify indicators was important to the conciseness and effectiveness of measurement. Moreover, this work used the linear structural relation (LISREL) approach to propose the short-form KES (S-KES) model and to identify the goodness-of-fit of the KES and S-KES model, and then conduct the cross-validation test of the S-KES model. Finally, this work aims to propose a concise, effective, and substitutive scorecard for KES model. The KBE index THEORY AND HYPOTHESE Recently the KBE issue has attracted a lot of attentions worldwide. As OECD (1996) described the knowledge economy that the economic activities and systems were directly established in creation, circulation, and application of the knowledge and information. Put differently, one country whether move toward the KBE should possess the following endowment: First, the vehicle of excellent human resources with innovative knowledge ability helps the innovation, circulation, and application of the knowledge and information. Then, the device of effective information

infrastructure helps the communication and proliferation of knowledge. Next, the incentives of powerful external economic environment helps foster the innovation environment, that is, the rule of law met with the economic environment, the sound protection system of intellectual property, the government effectiveness. Finally, not only these vehicles, tool, and incentive, but also the national innovation system can improve knowledge learning, proliferation, and innovation integration. Put differently, the national innovation system helps transform knowledge into economic advantages. Moreover, WB (2005) indicated that the KBE competitiveness meant the overall national competitiveness based on the creation, circulation, and application of knowledge and information, that is, not only the overall productivity but the sustainable capability of improving economic development and standard of living. In theory, the abundant concept of the KBE narrative are available to date; In practice, some indicators on knowledge competitiveness and innovation proliferation have already proposed by different countries and global and regional institutes in recent years. However these indicators were different from one another in the description of indices, and measured categories and variables. For example, some indicators emphasize specific construct, whereas some highlights the large-scale and complex indicators. In the description of indices and measured categories, some indicators focused on the capability of knowledge innovation and proliferation such as Progressive Policy Institute (PPI), European Union (EU), Singapore Ministry of Trade and Industry (MTI), and OECD. In contrast, some indicators were enlarged to political economic institutional environment such as Asia-Pacific Economic Community (APEC), International Data Corporation (IDC), Michael Porter and S. Stern (MPSS), and World Bank. In the measured variable, some indicators were consistent with popular international datasets, such as International Management Development (IMD), World Economic Forum (WEF), and United Nations Education Scientific and Cultural Organization (UNESCO), whereas some indicators comprised a lot of interviews and qualitative questionnaire, such as PPI and MTI. In the surveyed object, some indicators comprised more objects of countries or economies, such as OECD, APEC, WB, EU, IDC, and MPSS. In contrast, some indicators focused on specific country, city, and state. Moreover, in the measured categories, for example, OECD comprised five pillars such as knowledge input, knowledge output; the stock and flow of knowledge, knowledge network, and knowledge learning. APEC involved not only these categories but some economic environment factors, such as the transparency of government and openness of economy. PPI comprised more innovation characteristic of availability of venture capital. As regard WB, the measured categories were extended to five pillars. In any case, the measured categories of different KBE indicators were convergent, that is, the KBE indicators could be divided into the following five measured categories: business environment, innovation system, human resources, information infrastructure, and performance indictors. Overall, the composite KBE indicators were shown in Table 1. Economic Environment The Competitive Advantage of Nations in Porter (1990) proposed the diamond model, emphasizing that the national competitiveness depends on the production structure, demand condition, relative supportive industry, and business strategy that help improve the competitiveness advantage of individual business. Above all, the government and opportunity play an important supportive role in this diamond model. Moreover, Coase (1988) noted that the economic regime seems the key to the innovation of individual business, and the excellent economic environment helps improve the sharing and proliferation of knowledge. In detail, an excellent economic environment helps improve the innovation capability of individual business, stimulate the entrepreneurship, and improve the overall economic effectiveness. Thus, evaluating the quality of economic environment should consider on not only the foundation of knowledge-based industry, but also the overall national institutions such as the transparency of government policy and finance institutions, competitive policy and internationalization of a country etc. In any case, the empirical study on economic environment has increased noticeably in recent years, but the measured indicators for evaluating national environment endowment should be further developed. To date some research (OECD, 1999;APEC, 2000) proposed the measured pillars as follows: First, the soundness of economic regime such as the transparency of government policy and finance banking, rule of competitive law, and internationalization of a country etc; Second, the extent of knowledge-based industry development such as the ratio of knowledge intensive industry, and export of service industry and hi-tech product etc.

Human Resources Retrospect to the economic theory, the founder of economics Adam Smith in the eighteenth century, the German scholar Von Thunen in nineteenth century, the American scholar Irving Fisher, and the Nobel Prizes winner Schultz (1960) and Becker (1975) in the twentieth century, all emphasized that the human resources investment and education training (knowledge application) was important to the economic development. Notably in the KBE era, the technology innovation and productivity effectiveness was greatly affected by the level of labor education. In detail, this investment process of education and training implied the improvement of human capital. Similarly, Schultz (1960) indicated that the natural resources and physical capital did not completely explain the productivity unless considered the human resources. Moreover, Harbison (1973) suggested that the human resource is the core foundation of national wealth, and highlighted that the education investment was important to economic productivity. More recently, some information showed, in the KBE competitiveness, the disparity of wealth and digital gap enlarged by the disparity of information; as a result, the decreasing social cohesion and the distortion of social opportunity were unfavorable to the sustainable economic development. Thus, how to narrow the digital gap and improve the digital opportunity seems an important issue for developing a prominent KBE society. In any case, the empirical study on human resources has increased noticeably in recent years, but the measured indicators for evaluating human resources endowment should be further developed. To date some research (OECD, 1999) proposed the measured pillars as follows: First, the average quality of labors such as the secondary enrollment and college enrollment etc; Second, the supply of professional manpower such as the professional technical and knowledge worker rate percent of total labor force, and the extent of well educated people emigrating abroad etc. Information Infrastructure The information technology was increasingly important to the KBE development, because the application and development of different tools of information technology helps the accumulation, recombination, and innovation of knowledge capital. The sociology scholar Daniel Bell (1996) indicated that information like a strategic resource in current society seems an important vehicle toward the post-industrial society. Moreover, Anderson (2001) suggested that the characteristics of post-industrial information society are as follows: the development model of economy has shifted from production economy to service economy; the intellectual technology is greatly improved by the application of computers and other smart machines. Similarly, OECD s (1999) research suggested that the effective information technology helps improve the transmission and proliferation of the accumulated explicit knowledge contributing to the KBE development, decrease the transmission cost, and improve the transmission effectiveness. Further, Mansel and Wehn (1998) noted that the digital convergence of information technology helps the developing countries decrease the technology gap. Constructing a knowledge-based society based on knowledge learning and information technology was the best strategy for solving this digital gap. Overall, the increasing connections between personal computer, websites, and internet hosts worldwide shape the effective global information network. According to the Metcalfe s law, the effectiveness of internet was positively related to the square of internet connection, showing that the importance of excellent information communication infrastructure. In any case, the empirical study on information technology has increased noticeably in recent years, but the measured indicators for evaluating information technology endowment should be further developed. To date some information proposed the measured pillars as follows to date: First, the information hardware such as the telephones, computers, and internet hosts per thousand people etc. (Piazolo, 2001); Second, the information communication application such as the supply of professional manpower, mobile phones per thousand people, e-commerce development, and internet population etc. (OECD, 1999) Innovation System Not only the knowledge creation, but also the industry upgrade of a country greatly depends on innovation. Retrospect to the economic theory development, Schumpeter (1934) firstly proposed the innovation theory, emphasizing that the economic development stemmed from a process of continuous creative destruction. For example, the Classical School of economics thought that the innovation was an exogenous variable, but Schumpeter argued that the innovation

was the key to the economic growth. Afterward, the issue of innovation has been attracted a great deal of attentions worldwide, and extending from individual organization to industry cluster and country. Recently, the concept of national innovation system proposed by Nelson and Rosenberg (1993) highlighted that improving the innovation proliferation between businesses, universities, and research institutes, and the supportive institutions of government system help transform knowledge into economic competitiveness. Moreover, Lundvall (1998) indicated that successfully applying innovation was more important than effectively assigning resources. Meanwhile, he proposed four pillars of innovation system as follows: the time horizon of agent, role of trust, actual mix of rationality, and way of authority. Overall, most scholars agreed that the capability of knowledge of a country depend on the researcher and expenditure in R&D and the proliferation network of knowledge. In any case, the empirical study on innovation system has increased noticeably in recent years, but the measured indicators for evaluating innovation system endowment should be further developed. To date some works (OECD, 1999) proposed the measured pillars as follows: First, the R&D input such as the expenditure and researcher in R&D etc; Second, the innovation capability such as the granted patent applications, and science and technical journal articles per thousand people etc; Third, the knowledge proliferation such as the research cooperation between business and university etc. Performance Indicators In general, no matter what kind of scorecards was applied to the measurement of the KBE development and national competitiveness, the performance capability of creating wealth and sustainable economic development always plays an important role in the measurement. In the national competitiveness, Buckley et al. (1988) noted that the competitiveness comprised efficiency and effectiveness, that is, the means and the end. The measured indicators comprised not only the competitive potential and management process, but the final competitive performances, such as the market share of export product, the balance of export, the foreign direct investment (FDI), and the export growth. However, the traditional economist thought that the endogenous factors of economic development were related to the economic development; however these factors were not the key drivers of economic development. Thus, some scorecards on national competitiveness distinguish the performance indicators from these scorecards. Put differently, the performance indicators can reflect the final effect of economic development, if not contributing to the economic development. In any case, the empirical study on performance indicators has been highlighted recently; however the measured indicators for evaluating performance indicators should be further developed. Some works (OECD, 1999) proposed the measured pillars as follows: First, the productivity and unemployment such as the annual GDP growth, the unemployment in industry, the composite risk rating etc; Second, the competitive performance such as the market share of export product, the balance of trade, and the export growth etc; Third, the human development such as the human development index, the gender development index, and the poverty index etc. Materials METHODS This work used the 2003-2006 KES comprising a set of seventy-two structural and qualitative variables as shown in Table 1 that benchmark how an economy compares to its neighbors, competitors, or countries. The comparison is undertaken for a group of 132 countries that includes almost all OECD economies and about ninety developing countries. Moreover, the 2003-2004 KES served as the calibration sample, whereas the 2005-2006 KES served as the validation sample. The former was applied to model test and development of the S-KES model, the latter was applied to cross-validation test of the S-KES model. Table 1: The Category and Indicators of the KES Model Business Environment Innovation System Human Resources Information Infrastructure Performance Indicators E1. Gross Capital I1. FDI outflows / GDP H1. Adult literacy rate T1. Total telephones per P1. Average annual formation/gdp I2. FDI inflows/ GDP H2. Average years of 1000 people GDP growth E2. Trade/GDP I3. Royalty and license schooling T2. Main telephones P2. GDP per capita

E3. Tariff / non-tariff fees payments H3. Secondary lines per 1000 people (PPP) barriers I4. Royalty and license enrollment T3. Mobile phones per P3. Human development E4. Intellectual receipts H4. College enrollment 1000 people index property protection I5. Science and H5. Life expectancy at T4. Computers per 1000 P4. Poverty index E5. Soundness of engineering enrollment birth people P5. Composite ICRG banks ratio H6. Internet access in T5. Households with risk rating E6. Service I6. Science enrollment schools television P6. Unemployment rate exports/gdp ratio H7. Expenditure on T6. Daily newspapers % of total labor force E7. Interest rate I7. Researcher in R&D education/gdp per 1000 people P7. Employment in spread I8. Total expenditure for H8. Professional and T7. Internal internet industry E8. Local competitive R&D/GDP technical worker/labor bandwidth P8. Employment in environment I9. Trade manufacturing force T8. Internet users per service E9. Regulatory quality industry / GDP H9. 8th grade 1000 people E10. Rule of law I10. Research cooperation achievement in T9. Price basket for E11. Government between companies and mathematics internet (US per month) effectiveness university H10. 8th grade T10. Availability of E12. Voice and I11. Number of science achievement in science e-government services accountability and technical journal H11. Quality of science T11. Extent of business E13. Political stability articles per million people and math education internet use E14. Control of corrupt I12. Administrative burden H12. Extend of staff T12. ICT E15. Press freedom for star-ups training expenditure/gdp I13. Availability of venture H13. Quality of capital management education I14. Patent applications H14. granted by USPTO per H15. Brain drain Gender development million people index I15. Hi-Tech exports / H16. manufactured industry exports Females in labor force I16. Private sector H17. Seats in parliament spending on R&D held by women I17. Firm-level technology H18. School enrollment, absorption secondary, female I18. Value chain presence H19. School enrollment, tertiary, female Conceptual Model This work used the LISREL model and assumed that both of the KES and S-KES model were the second-order model. Put differently, ξ 1 is the KBE index of higher-order latent exogenous variable; the η 1, η 12, η 3, η 4, and η 5 are the economic environment, innovation system, humane resources, information infrastructure, and performance indicators first-order latent endogenous variables, respectively. Moreover, γ 11, γ 21, γ 31, γ 41, and γ 51 are the regression parameters relating the KBE index ξ 1 to the economic environment η 1, innovation system η 2, human resources η 3, information infrastructure η 4, and performance indicators η 5, respectively; the ζ are the residuals (errors in structural equation); the evaluation of all parameters was the significance of t value at 0.05. In detail, both of the KES and S-KES model were comprised of five first-order factors affected by the KBE index of common higher-order factors. In the KES model, all observed variables of first-order factors were seventy-two items. In the S-KES model, Marsh, Balla, and Grayson (1998) suggested that the optimum number of observed variables should not be greater than twenty and that of latent factors are five or six items, because the adopted variables of the structural equation modeling analysis should not be too much. Thus, this work used the following principles to extract observed variables: First, delete the measures of sampling adequacy (MSA) that were lower than 0.5 by the exploratory factor analysis; Second, delete the factor loadings of insignificant t value by the confirmatory factor analysis; Third, delete the factor loadings that were lower than 0.9. Data Analysis For developing the S-KES model and evaluating the goodness-of-fit of the KES and S-KES model, this work conducted not only the descriptive statistics and correlation analysis by SPSS 10.0, but also the goodness-of-fit and parameter estimation by LISREL 8.7. In general, the goodness-of-fit of the LISREL model can be evaluated by external

fit and internal fit. The former measures are as follows: The chi-square (χ 2 ) value should be insignificant; the goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), incremental fit index (IFI), comparative fit index (CFI), and relative fit index (RFI) should be greater than 0.9; the standardized root mean square residual (SRMR) should be lower than 0.008; the chi-square ratio should be lower than 3. In contrast, the latter measures are as follows: the square multiple correlations (SMC) of individual observed variables should be greater than 0.5; the composite measurement reliability (CMR) should be greater than 0.6; the average variance extracted (AVE) should be greater than 0.5. Moreover, for evaluating the cross-validation of the S-KES model, this work further divided the overall KES samples into two groups: the 2003-2004 KES served as the calibration sample, and the 2005-2006 KES served as the cross-validation sample. Next, this work conducted the cross-validation test by the following three strategies: the loose replication strategy, moderate replication strategy, and tight replication strategy. In other words, the loose replication strategy meant that the calibration sample with non-invariance parameters setting was applied to the validation sample. The moderate replication strategy meant that the model structure, factor loading, and parameter coefficient were assumed invariant over these two samples. Further, the tight replication strategy meant that the model structure, factor loading, parameter coefficients, and error covariance were assumed invariant. Overall, the cross-validation (stability) of the S-KES model could be accepted, if the comparisons of goodness-of-fit between these three stepwise nested models were satisfactory. Goodness-of-fit Test RESULTS The results of this work testing the proposed model by maximum likelihood (ML) indicated that the solution of the KES model was not convergent. In the external fit, the chi-square value (χ 2 =12159.3) was significant at 0.05, showing that the null hypothesis was rejected. Moreover, other measures such as the GFI, AGFI, IFI, CFI, and RFI were 0.25, 0.20, 0.85, 0.85, and 0.82, respectively, all did not reach 0.9; the SRMR=0.14 was greater than 0.08; the chi-square ratio 78.95 was greater than 3. The detail results shown in Table 2 show that the goodness-of-fit of the KES model was not satisfactory. In the S-KES model, the extracted variables from the KES model were as follows: The economic environment factor comprises five variables, namely, E4, E9, E10, E11, and E14; The innovation system factor comprises two variables, namely, I7 and I11; The human resources factor comprises two variables, namely, H7 and H15; The information technology factor comprises four variables, namely, T2, T4, T7, and T8; The performance indicators factor comprises two variables, namely, P2 and P3. All of factor solutions in the confirmatory factor analysis were convergent. In the external fit, the chi-square value (χ 2 =693.52) was significant at 0.05, showing that the comparison between theory model and empirical data was still significantly different. Moreover, the GFI=0.79 and AGFI=0.69 of the S-KES model were greater than those of the KES model, but these two measures did not reach 0.9. Further, the IFI, CFI, and RFI were 0.92, 0.92, and 0.90, respectively, all reached 0.9; the SRMR=0.0048 was lower than 0.08; the chi-square ratio of 8.159 was greater than 3. In the internal fit as shown in Table 3, the SMC values of all observed variables of the S-KES model were greater than 0.5; all of the CRM and AVE values were greater than 0.6 and 0.5, respectively. In detail, the goodness-of-fit of the S-KES model was satisfactory. Further the comparison between the S-KES and KES model indicated that all measures of the S-KES model were significantly better than those of the KES model. Overall, all factor loadings between higher-order factor, first-order factors, and observed variables in the S-KES model were significant at 0.05 as shown in Figure 1, representing that the S-KES model was a precise, efficient, and substitutive scorecard for the KES model. Table 2: The Comparison of Goodness-of-fit Measures between the KES and S-KES model Model χ 2 χ 2 /df GFI AGFI SRMR IFI CFI RFI KES 12159.30 78.95.25.20.14.85.85.82 S-KES 693.52 8.159.79.69.0081.92.92.90

Table 3: The Internal Fit on the S-KES model Measures Business Environment Innovation System Human Resources Information Infrastructure Performance Indicators E4 E9 E10 E11 E14 I7 I11 H7 H15 T2 T4 T7 T8 P2 P3 SMC.85.88.97.98.97.88.87.84.99.87.83.90.78.80.99 CRM.93.88.92.85.94 AVE.95.91.94.90.96 Validity and Reliability The comparison of mean, standard deviation, and internal consistent reliability between the KES and S-KES Model were shown in Table 4. In the internal consistent reliability, the Cronbach s α value of all factors of the KES model were 0.69-0.96, and that of overall KBE index was 0.96. In contrast, the Cronbach s α value of all factors of the S-KES model were 0.94-0.98, and that of overall KBE index was 0.96. Put differently, the internal consistent reliability of the S-KES model was improved although the variables in number of the S-KES model decreases. The correlation coefficients of corresponding factors between the S-KES and KES model were 0.86-0.98, and that of overall KBE index was 0.97, showing that a higher correlation between these two models.

Moreover, the comparison of correlation coefficients matrix between the KES and S-KES model was shown in Table 5. In the KES model, the correlation coefficients between different factors were 0.75-0.89, and those relating different factors to overall KBE index were 0.91-0.96. In the S-KES model, the correlation coefficients between different factors were 0.79-0.94, and those relating different factors to overall index were 0.90-0.98. Overall, the proposed S-KES model consisted in the KES model; consequently the correlation coefficients of corresponding factor and overall KBE index between the KES and S-KES model were significant. Moreover, the correlation coefficients between each factor and overall KBE index within the S-KES model showed that the validity of the S-KES model was acceptable. Table 4: The Comparison of Internal Consistency Reliability between the KES and S-KES Model Category KES S-KES Correlation Coefficient Item M-SD α Item M-SD α between the KES and S-KES Model Business 15 6.89(.28).96 5 7.24(.31).98.98** Environment Innovation 18 7.08(.26).94 2 6.99(.34).94.86** System Human 19 6.86(.24).94 2 7.42(.31).95.89** Resources Information 12 7.28(.22).93 4 7.31(.27).95.97** Infrastructure Performance 8 6.28(.18).69 2 7.61(.26).94.90** Indicators The KBE Index 72 6.88(.22).96 15 7.32(.28).96.97** Note: ** p<.01 Table 5: The Correlation Coefficient Matrix on the KES and S-KES Model S-KES KES Business Environment Innovation System Human Resources Information Infrastructure Performance Indicators The KBE Index Business 1.00.79.79.82.80.90 Environment Innovation.81 1.00.79.92.89.93 System Human.81.85 1.00.89.94.92 Resources Information.86.88.89 1.00.94.98 Infrastructure Performance.75.82.88.86 1.00.98 Indicators The KBE.91.94.95.96.91 1.00 Index Note: the correlations of the KES model was shown in the left triangular district; the correlations of the S-KES model was shown in the right triangular district; all of p values of correlations in table are lower than 0.01. Cross-validation Test As this method has shown, this work divided the KES sample into the following two samples, that is, the 2003-2004 calibration sample and the 2005-2006 validation sample. This work applied the proposed S-KES model derived from the calibration sample to whether the test of the validation sample was accepted or not. Table 6 showed that the chi-square value and degree of freedom were equal to the sum of these two samples, because of the non-invariance setting of the loose replication strategy. Put differently, the loose replication strategy aims to serve as the benchmarking for cross-validation test. Next, comparing the moderate replication strategy to the loose replication strategy showed that χ 2 =1412.7, df=15 (p>0.05), that is, the difference between this two strategies was not significant, implying that the cross-validation of the loose replication strategy could be acceptable. Moreover, comparing the tight

replication strategy to the moderate replication strategy showed that χ 2 =1422.3, df=5 (p>0.05), that is, the difference between these two strategies was also not significant, implying that the cross-validation of the tight replication strategy could be accepted. Overall, the result of cross-validation test between the calibration sample and the validation sample on the S-KES model was acceptable. Table 6: The Cross-Validation Test on the S-KES Model Sample χ 2 Df GFI AGFI SRMR IFI CFI ECVI Calibration 693.52* 85.79.69.0081.92.92 2.71 Sample Validation 699.49* 85.80.71.0079.91.91 2.79 Sample Loose 1393.01* 170.80.71.0079.92.92 2.80 Replication Strategy Moderate Replication Strategy 1412.70* ( χ 2 =19.69) 185 ( df=15).80.71.0082.90.90 2.82 Tight Replication Strategy Note: * p<.05 1422.30* ( χ 2 =9.60) 190 ( df=5).80.71.0085.90.90 2.84 DISCUSSION The main purpose of this work was to propose the S-KES model, evaluate the goodness-of-fit of the KES and S-KES model, and identify the reliability, validation, and cross-validation of the proposed S-KES model. This work hypothesized that the S-KBE model should be a precise and efficient scorecard for the KES model. To conclude, the results support these hypotheses and important conclusions are as follows: Conclusions The Goodness-of-fit of the KES and S-KES model: This work used the LISREL model and then simplified the KES model of seventy-two variables into the S-KES model of fifteen variables. As a result, the findings of conducting confirmatory factor analysis not only support the five-factor structure of the KES model, but also echo the proposition of multi-dimension KBE measurement either OECD or APEC. However, the goodness-of-fit of the S-KES model was better than that of the KES model. In detail, all of the factor loadings between higher-order factor, first-order factors, and observed variables were significant, implying that the S-KES model was a precise, efficient, and substitute scorecard for the KES model. The Reliability and Validity of the S-KES model: the reliability, validity, and cross-validation of the proposed S-KES model in this work were acceptable. In the internal consistent reliability, the reliability of the S-KES model was better than that of the KES model although the variables in number of the S-KES model decreases. In the validity, the correlation coefficients of corresponding factors and overall KBE index between the KES and S-KES model and those relating each factor to overall KBE index within the S-KES model were significant, representing that the validity of the S-KES model was acceptable. Moreover, in the cross-validation, not only the loose replication strategy, but also the tight replication strategy was accepted. This result significantly suggests that the cross-validation test was satisfactory. The Selection Principles of the KBE indicators: Overall, the results show that the solution of the KES model was not convergent, implying that the goodness-of-fit of this model was not acceptable. In contrast, the reliability, validity, and cross-validation of the S-KES model were satisfactory. In detail, the S-KES model seems a precise, effective, and substitutive short-form indicators for the KES model. In any case, the complex and large-scale indicators could provide a lot of information, but the accuracy of large-scale indicators seems not to be better than that of the

short-form indicators because of the improper construct factors and weight allocation. Thus, while selecting the KBE indicators based on the following factors such as cost, application, and soundness, the short-form indicators may be more precise, efficient, and substitutive than the large-scale indicators. Recommendations In any case, the development of the S-KES model was not a short-term and single research but a continuous evaluation process. Thus, this work proposed the following suggestions for future studies: Apply the Cross Group Analysis: This work addresses the cross-validation test based on cross time (2003-2006) approach. However, the future study could be considered based on cross group approach, such as the economic development (e.g. the developed, developing, and third world countries), and different income level (e.g., high income and low income), thereby comparing with the results of this work. Add the Criterion Related Validity: This work applied the KES model of WB to the construct validation test of the S-KES model. However, the criterion related validity of future study could be pursued based on different KBE scorecards, such as OECD and APEC, thereby comparing with the results of this work. Adopt the Comparative Model: This work addressed the construct validation of the KES and S-KES model by the correlation approach. However, the future study could be developed by other comparative approach, such as the multitrait-multimethod (MTMM) matrix and direct product model (DPM), thereby comparing with the results of this work. REFERENCES Anderson (2001). The information of global society, http://www.worldpaper.com. APEC. (2000). Towards Knowledge Based Economy in APEC. Becker, G.. (1975). Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education (3 rd edition). Chicago: University of Chicago Press. Coase, R. H. (1988). The Nature of the Firm: The Firm, the Market and the Law. Chicago and London: The University of Chicago Press, 35-40. Daniel Bell (1996). The cultural contradictions of capitalism. HarperCollins Publishers; 20th Anniv edition. Harbison, F. H. (1973). Human Resources as the Wealth of Nations. Oxford University Press, 3. International Data Corporation. (1999). IDC/World Times Information Society Index. Lundvall (1998). Why Study National Systems and National Styles of Innovation? Technovation, 11 (8), 457-473. Mansel, R., & Wehn U. (Ed.). (1998). Knowledge societies: information technology for sustainable development. Oxford University Press. Marsh, H. W., Hau, K. T., Balla, J. R., & Grayson, D. (1998). Is more ever too much? The number of indicators per factor in confirmatory. Multivariate Behavior Research, 33(1), 181-220. Marshall, A. (1890). Principles of Economics. London: Macmillan. Nelson, R. R. (Ed.). (1993). National Innovation System: A Comparative Analysis. Oxford University Press. OECD. (1996). The Knowledge-Based Economy, Paris. OECD. (1999). OECD Science, Technology, and Industry Scoreboard 1999: Benchmarking Knowledge-Based Economies, Paris. Piazolo, D. (2001). The New Economy and the International Regulatory Framework, Kiel Working Paper, No, 1030. Porter, Michael E. (1990). The Competitive Advantage of Nations. New York: Free Press Romer, P. M. (1990). Endogenous Technological Change. Journal of political Economy, Vol. 98, No. 5, Part 2, 71-102. Schultz, T. W. (1960). Capital formation by education. Journal of Political Economy, 68 (6): 571-583. Schumpeter, J.A. (1934). The Theory of Economic Development, Tran. R. Opie, Cambridge, Mass: Harvard University Press. Solow, Robert M. (1956). A contribution to the theory of economic Growth. Quarterly Journal of Economics, 70:1, 65-94. World Bank. (2005). Knowledge Assessment Methodology, Retrieved from http://info.worldbank.org/etools/kam2/kam_page1.asp.