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KNOWLEDGE POTENTIAL: MAIN AGGREGATED ASSESSMENT PRINCIPLES

Year 2013, Volume: 8 Issue: 1, 63 - 86, 01.06.2013

Abstract

The growth of economic competitiveness as well as effective use of knowledge potential (KP) is of foremost
importance for sustainable development of the newly EU countries. Though scientists have studied the country‘s
KP assessing components, a single quantitative potential assessment technique has not been accepted.
The authors of the performed study provided a theoretical framework and empirical viewing for the complex
evaluation of the KP determinants based on multiple criteria assessment methodology. The essence of the
principal approach lies in quantitative measure of KP level, i.e. determination of general relative level index. The
formulated main multiple criteria evaluation principles are focused on the formalization of an investigated
system describing knowledge components independencies with adequate composite determinants and primary
indicators, i.e. background evaluation models. Thus, the direct and indirect influence of primary criteria is taken
into account; application of different significances of determinants is provided. The proposed technique is
oriented towards incorporation into multicriteria decision making system and may be used for the reasoning of
strategic decisions in the KP development. When applying the Simple Additive Weighting \method, which is
especially applicable for the aggregate evaluation of substantially different criteria having both quantitative and
qualitative expression, the general KP level index has been established.
The idiosyncratic components revealed with account of preliminary situation analysis in newly EU countries and
classification of international institutions are as follows: innovative capacity, use of information technologies
and quality of primary & secondary education. Those components may be described by adequate primary
indicator system formulated in the study. The proposed methodology was approbated by evaluation of the KP
level in Lithuania and by forecasting its prospective situation.

References

  • Berger, F. (2012). Measuring the KE – Intangible Spending and Investment in Germany. Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), 232(1): 12-30.
  • Buligescu B., Hollanders H. (Maastricht Economic and Social Research Institute on Innovation and Technology). Presentations in: Proceedings of the technical Workshop on EPSIS–10 May 2012. Retrieved from: http://ec.europa.eu/enterprise/policies/ innovation/ files/epsis/workshop-presentation - preliminary- results_en.pdf.
  • Brannstrom, D; Catasus, B.; Giuliani, M.; Grojer, J.E. (2009). Construction of intellectual capital – the case of purchase analysis. Journal of Human Resource Costing and Accounting, 13: 61-76.
  • Buracas, A. (2007). The Competitiveness of the EU in the context of the intellectual capital development. Intellectual Economics, 1(1):19–28.
  • Burinskiene, M.; Rudzkiene, V. (2009). Future insights, scenarios and expert method application in sustainable territorial planning. Technological and Economic Development of Economy, 15 (1): 10-25.
  • Challenges and Opportunities for a European Strategy in Support of Innovation in Services. Fostering New Markets and Jobs through Innovation (2009). The European Union, Luxembourg. Retrieved from: http://ec.europa.eu/enterprise/policies/innovation/files/ swd_services_en.pdf>
  • Chan, P. Ch.; Lee, W. B. (2011). Knowledge Audit with Intellectual Capital in the Quality Management Process: An Empirical Study in Electronics Company. The Electronic Journal of Knowledge Management, 9(2): 98 – 116.
  • Chu, M. T., Shyu, J., Tzeng, G. H.; Khosla, R. (2007). Comparison among three analytical methods for knowledge communities group-decision analysis. Expert systems with applications, 33(4), 1011-1024.
  • Cooke, P. (2001). Regional innovative systems, clusters and the knowledge economy. Industrial and Corporate Change, 10(4): 945 – 974.doi:10.1093/icc/10.4.945.
  • Choong, K. K. (2008). Intellectual capital: Definitions, categorization and reporting models. Journal of Intellectual Capital, 9: 609-638.
  • Digital Lithuania (2009). Retrieved from: http://www.infobalt.lt/sl/add/sl_2009_300.pdf
  • Dombi, J.; Zsiros, A. (2005). Learning multicriteria classification models from examples: Decision rules in continuous space. European Journal of Operational Research, 160(3): 663-675.
  • Dreher, A. (2010). KOF Index of Globalisation, Zurich. Retrieved from: http://globalization.kof.ethz.ch.
  • Europe 2030 (2010), ed. D. Benjamin, Brookings Institution Press.
  • European Innovation Scoreboard (2009). Comparative analysis of innovation performance. ProInno Europe Paper No. 15. European Commission, Enterprise and Industry. Available at: http:www.proinno- europe.eu/sites/default/files/page/10/0371981-DG ENTR-Report EIS.pdf, September 2011.
  • The European Union Strategy for the Baltic Sea Region. Background and analysis [on-line] Available at: http://ec.europa.eu/regional_policy/ cooperation/baltic/pdf/2010_baltic.pdf.
  • Ginevicius, R.; Podvezko, V. (2004). Complex evaluation of the use of information technologies in the countries of Eastern and Central Europe. Journal of Business Economics and Management, 5(4): 183–192.
  • Ginevicius, R.; Podvezko, V. (2009). Evaluating the changes in economic and social development of Lithuanian counties by multiple criteria methods. Technological and Economic Development of Economy, 15 (3): 418- 436. The Global
  • Competitiveness Report (2010-2011). Ed. by Klaus Schwab. Retrieved from:
  • http://www.weforum.org/en/media/publications/CompetitivenessReports/index.htm. Eurostat. Globalisation Indicators (2010). Retrieved from: eu/
  • portal/page/portal/globalisation/indicators.
  • Global Governance 2025: At a Critical Juncture (2010). Retrieved from: /2025_Global_Governance.pdf> Global World Trends 2025: A Transformed (2008). Retrieved from: http://www.dni.gov/
  • nic/PDF_2025/2025_Global_Trends_Final_Report.pdf>
  • Human Development Report, 2010. Retrieved from: http://hdr.undp.org/en/media/ HDR_2010_EN_ Table1_reprint.pdf> for Indicators the Information http://www.ssb.no/ikt/rapp_ict_ baltic /rapp_ictbaltic.pdf> Society in the Baltic Region (2005). Retrieved from:
  • Intellectual Capital for Communities in the Knowledge Economy: Nations, Regions, Cities and Emerging Communities (2005, 2006). World Bank Conferences.
  • Intellectual Capital Services (2011). Retrieved from: http://www.intcap.com/our_ approach.php.
  • Kaufmann, L.; Schneider, Y. (2004). Intangibles: A synthesis of current research. Journal of Intellectual Capital, 5: 366-388.
  • Knowledge for Development (K4D), The World Bank Group (2011). Retrieved from: http://info.worldbank.org/ etools/kam2/KAM_page1.asp; http://info.worldbank.org/ etools/kam2/KAM_page5.asp.
  • Lopes, I. T. (2010). Towards a complementary intangibles reporting approach. Measuring Business Excellence, 14(4): 24 – 34.
  • Lopes, I. T. (2011). The Boundaries of Intellectual Property Valuation: Cost, Market, Income Based Approaches and Innovation Turnover. Intellectual Economics, 1 (9): 99-116.
  • Marr, B. (2008). Disclosing the invisible: Publishing intellectual capital statements. CMA Management, August/September, p. 35-49.
  • Marr, B.; Moustaghfir, K. (2005). Defining intellectual capital: A three-dimensional approach. Management Decision, 43: 1114-1128.
  • Mazumdar, A., Datta, S., & Mahapatra, S. S. (2010). Multicriteria decision-making models for the evaluation and appraisal of teacher’ performance. International Journal of Productivity and Quality Management, 6(2): 213-230.
  • Measuring Intellectual Capital at Skandia Group (FPM, 1993). Retrieved from: <www.fpm.com/script/UK/ Jun93/930602.htm>
  • Meza, C. J. G. (2011). Measuring knowledge-based development: an overview of models and methodological issues. International Journal of Knowledge-Based Development, 2(3): 251 - 266.
  • Millar, C.J.M.; Choi, Ch. J. (2010). Development and knowledge resources: a conceptual analysis. Journal of Knowledge Management, 14 (5): 759 – 776.
  • Mohamed, M.; Stankosky, M.; Mohamed, M. (2009). An empirical assessment of know- ledge management criticality for sustainable development. Journal of Knowledge Management, 13(5): 271 – 286.
  • Navarro J. L. A. ; López Ruiz, V. R.; Nevado Peña, D. (2011). Estimation of intellectual capital in the European Union using a knowledge model. Proceedings of Rijeka Faculty of Economics: Journal of Economics and Business, 29(1): 109-132.
  • OECD Guide to Measuring the Information Society (2011). Retrieved from: www.oecd.org/sti/measuring- infoeconomy/guide.
  • Parkan, C.; Wu, M. L. (2000). Comparison of three modern multicriteria decision – making tools. International Journal of Systems Science, 31(4): 497-517.
  • Peldschus, F. (2007). The effectiveness of assessment in multiple criteria decisions. International Journal of Management and Decision Making, 8( 5-6): 519 – 526.
  • Podvezko, V. (2007). Determining the level of agreement of expert estimates. International Journal of Management and Decision Making, 8(5/6): 586-600.
  • Project Europe 2030 (2010). Challenges and Opportunities. A report to the European Council. Retrieved from: http://www.consilium.europa.eu/uedocs/cmsUpload /en_web.pdf>
  • RICARDIS: Reporting Intellectual Capital to Augment Research, Development and Innovation in SMEs (June 2006). European Commission. Retrieved from: http://ec.europa.eu/invest-in-research/pdf/download_en/2006- 2977_web1.pdf>
  • Shapira, P.; Youtie, J. (2006). Measures for Knowledge-Based Economic Development: Introducing Data Mining Techniques to Economic Developers in the State of Georgia and the US South. Technological Forecasting and Social Change, 73(8): 950-965.
  • Shapira, P.; Youtie, J.; Yogeesvaran, K; Jaafer, Z. (2006). Knowledge Economy Measurement: Methods, Results and Insights from the Malaysian Knowledge Content Study. Research Policy, 35(10): 1522-1537.
  • Shiu, H.-J. (2006). The Application of the Value Added Intellectual Coefficient to Measure Corporate Performance: Evidence from Technological Firms. International Journal of Measurement. Retrieved from: http://findarticles.com/p/articles/mi_qa5440/is_200606/ai_n21393124/.
  • Stam, C.; Andriessen, D. (2009). Intellectual Capital of the European Union 2008. European Conference of Intellectual Capital. Netherlands.
  • Sweeney T. (2012) Five Tech Trends Impacting Business Innovation in 2012. Retrieved from: http://www.innovationexcellence.com/blog/2012/01/11/five-tech-trends-impacting-business-innovation-in- 2012
  • Tervonen, T.; Figueira, J. R. (2008). A survey on stochastic multicriteria acceptability analysis methods. Journal of Multi-Criteria Decision Analysis, 15(1-2): 1–14.
  • Weziak, D. (2007). Measurement of national intellectual capital application to EU countries. - An Integrated
  • Socio-economic Research
  • http://iriss.ceps.lu/documents/ irisswp81.pdf. in the Sciences, Nr.13. Retrieved from:
  • The World Bank Group (Oct. 2009).
  • Knowledge in Development Notes, Retrieved from:
  • http://siteresources.worldbank.org/INTRES/Resources/KinD2009_impact_evaluation.pdf>
  • World Economic Forum 2009 Report. Retrieved from: http://www.weforum.org/pdf/> FinancialDevelopment Report/Report2009.pdf>
  • World Telecommunication/ICT Indicators Database (2011). Retrieved from: http://www.itu.int/ITU- D/ict/statistics/.
  • Zapounidis, C. ; Doumpos, M. (2002). Multi-criteria decision aid in financial decision making: methodologies and literature review. Journal of Multi-Criteria Decision Analysis, 11 (4-5): 167-186.
  • Zvirblis, A.; Buracas, A. (2010). The consolidated measurement of the financial markets development: the case of transitional economies. Technological and economic development of economy, 16(2): 266-279.
  • Zvirblis, A.; Buracas, A. (2011). Multicriteria evaluation of national entrepreneurship in newly EU countries. International Journal of Economic Sciences and Applied Research, 4(1): 79-94.
  • Zvirblis, A.; Buracas, A. (2012). Multiple criteria evaluation of entrepreneurship development in newly EU countries. Scientific study. Lambert Academic Publishing, Saarbrücken, Germany.

KNOWLEDGE POTENTIAL: MAIN AGGREGATED ASSESSMENT PRINCIPLES

Year 2013, Volume: 8 Issue: 1, 63 - 86, 01.06.2013

Abstract

The growth of economic competitiveness as well as effective use of knowledge potential (KP) is of foremost
importance for sustainable development of the newly EU countries. Though scientists have studied the country‘s
KP assessing components, a single quantitative potential assessment technique has not been accepted.
The authors of the performed study provided a theoretical framework and empirical viewing for the complex
evaluation of the KP determinants based on multiple criteria assessment methodology. The essence of the
principal approach lies in quantitative measure of KP level, i.e. determination of general relative level index. The
formulated main multiple criteria evaluation principles are focused on the formalization of an investigated
system describing knowledge components independencies with adequate composite determinants and primary
indicators, i.e. background evaluation models. Thus, the direct and indirect influence of primary criteria is taken
into account; application of different significances of determinants is provided. The proposed technique is
oriented towards incorporation into multicriteria decision making system and may be used for the reasoning of
strategic decisions in the KP development. When applying the Simple Additive Weighting \method, which is
especially applicable for the aggregate evaluation of substantially different criteria having both quantitative and
qualitative expression, the general KP level index has been established.
The idiosyncratic components revealed with account of preliminary situation analysis in newly EU countries and
classification of international institutions are as follows: innovative capacity, use of information technologies
and quality of primary & secondary education. Those components may be described by adequate primary
indicator system formulated in the study. The proposed methodology was approbated by evaluation of the KP
level in Lithuania and by forecasting its prospective situation. 

References

  • Berger, F. (2012). Measuring the KE – Intangible Spending and Investment in Germany. Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), 232(1): 12-30.
  • Buligescu B., Hollanders H. (Maastricht Economic and Social Research Institute on Innovation and Technology). Presentations in: Proceedings of the technical Workshop on EPSIS–10 May 2012. Retrieved from: http://ec.europa.eu/enterprise/policies/ innovation/ files/epsis/workshop-presentation - preliminary- results_en.pdf.
  • Brannstrom, D; Catasus, B.; Giuliani, M.; Grojer, J.E. (2009). Construction of intellectual capital – the case of purchase analysis. Journal of Human Resource Costing and Accounting, 13: 61-76.
  • Buracas, A. (2007). The Competitiveness of the EU in the context of the intellectual capital development. Intellectual Economics, 1(1):19–28.
  • Burinskiene, M.; Rudzkiene, V. (2009). Future insights, scenarios and expert method application in sustainable territorial planning. Technological and Economic Development of Economy, 15 (1): 10-25.
  • Challenges and Opportunities for a European Strategy in Support of Innovation in Services. Fostering New Markets and Jobs through Innovation (2009). The European Union, Luxembourg. Retrieved from: http://ec.europa.eu/enterprise/policies/innovation/files/ swd_services_en.pdf>
  • Chan, P. Ch.; Lee, W. B. (2011). Knowledge Audit with Intellectual Capital in the Quality Management Process: An Empirical Study in Electronics Company. The Electronic Journal of Knowledge Management, 9(2): 98 – 116.
  • Chu, M. T., Shyu, J., Tzeng, G. H.; Khosla, R. (2007). Comparison among three analytical methods for knowledge communities group-decision analysis. Expert systems with applications, 33(4), 1011-1024.
  • Cooke, P. (2001). Regional innovative systems, clusters and the knowledge economy. Industrial and Corporate Change, 10(4): 945 – 974.doi:10.1093/icc/10.4.945.
  • Choong, K. K. (2008). Intellectual capital: Definitions, categorization and reporting models. Journal of Intellectual Capital, 9: 609-638.
  • Digital Lithuania (2009). Retrieved from: http://www.infobalt.lt/sl/add/sl_2009_300.pdf
  • Dombi, J.; Zsiros, A. (2005). Learning multicriteria classification models from examples: Decision rules in continuous space. European Journal of Operational Research, 160(3): 663-675.
  • Dreher, A. (2010). KOF Index of Globalisation, Zurich. Retrieved from: http://globalization.kof.ethz.ch.
  • Europe 2030 (2010), ed. D. Benjamin, Brookings Institution Press.
  • European Innovation Scoreboard (2009). Comparative analysis of innovation performance. ProInno Europe Paper No. 15. European Commission, Enterprise and Industry. Available at: http:www.proinno- europe.eu/sites/default/files/page/10/0371981-DG ENTR-Report EIS.pdf, September 2011.
  • The European Union Strategy for the Baltic Sea Region. Background and analysis [on-line] Available at: http://ec.europa.eu/regional_policy/ cooperation/baltic/pdf/2010_baltic.pdf.
  • Ginevicius, R.; Podvezko, V. (2004). Complex evaluation of the use of information technologies in the countries of Eastern and Central Europe. Journal of Business Economics and Management, 5(4): 183–192.
  • Ginevicius, R.; Podvezko, V. (2009). Evaluating the changes in economic and social development of Lithuanian counties by multiple criteria methods. Technological and Economic Development of Economy, 15 (3): 418- 436. The Global
  • Competitiveness Report (2010-2011). Ed. by Klaus Schwab. Retrieved from:
  • http://www.weforum.org/en/media/publications/CompetitivenessReports/index.htm. Eurostat. Globalisation Indicators (2010). Retrieved from: eu/
  • portal/page/portal/globalisation/indicators.
  • Global Governance 2025: At a Critical Juncture (2010). Retrieved from: /2025_Global_Governance.pdf> Global World Trends 2025: A Transformed (2008). Retrieved from: http://www.dni.gov/
  • nic/PDF_2025/2025_Global_Trends_Final_Report.pdf>
  • Human Development Report, 2010. Retrieved from: http://hdr.undp.org/en/media/ HDR_2010_EN_ Table1_reprint.pdf> for Indicators the Information http://www.ssb.no/ikt/rapp_ict_ baltic /rapp_ictbaltic.pdf> Society in the Baltic Region (2005). Retrieved from:
  • Intellectual Capital for Communities in the Knowledge Economy: Nations, Regions, Cities and Emerging Communities (2005, 2006). World Bank Conferences.
  • Intellectual Capital Services (2011). Retrieved from: http://www.intcap.com/our_ approach.php.
  • Kaufmann, L.; Schneider, Y. (2004). Intangibles: A synthesis of current research. Journal of Intellectual Capital, 5: 366-388.
  • Knowledge for Development (K4D), The World Bank Group (2011). Retrieved from: http://info.worldbank.org/ etools/kam2/KAM_page1.asp; http://info.worldbank.org/ etools/kam2/KAM_page5.asp.
  • Lopes, I. T. (2010). Towards a complementary intangibles reporting approach. Measuring Business Excellence, 14(4): 24 – 34.
  • Lopes, I. T. (2011). The Boundaries of Intellectual Property Valuation: Cost, Market, Income Based Approaches and Innovation Turnover. Intellectual Economics, 1 (9): 99-116.
  • Marr, B. (2008). Disclosing the invisible: Publishing intellectual capital statements. CMA Management, August/September, p. 35-49.
  • Marr, B.; Moustaghfir, K. (2005). Defining intellectual capital: A three-dimensional approach. Management Decision, 43: 1114-1128.
  • Mazumdar, A., Datta, S., & Mahapatra, S. S. (2010). Multicriteria decision-making models for the evaluation and appraisal of teacher’ performance. International Journal of Productivity and Quality Management, 6(2): 213-230.
  • Measuring Intellectual Capital at Skandia Group (FPM, 1993). Retrieved from: <www.fpm.com/script/UK/ Jun93/930602.htm>
  • Meza, C. J. G. (2011). Measuring knowledge-based development: an overview of models and methodological issues. International Journal of Knowledge-Based Development, 2(3): 251 - 266.
  • Millar, C.J.M.; Choi, Ch. J. (2010). Development and knowledge resources: a conceptual analysis. Journal of Knowledge Management, 14 (5): 759 – 776.
  • Mohamed, M.; Stankosky, M.; Mohamed, M. (2009). An empirical assessment of know- ledge management criticality for sustainable development. Journal of Knowledge Management, 13(5): 271 – 286.
  • Navarro J. L. A. ; López Ruiz, V. R.; Nevado Peña, D. (2011). Estimation of intellectual capital in the European Union using a knowledge model. Proceedings of Rijeka Faculty of Economics: Journal of Economics and Business, 29(1): 109-132.
  • OECD Guide to Measuring the Information Society (2011). Retrieved from: www.oecd.org/sti/measuring- infoeconomy/guide.
  • Parkan, C.; Wu, M. L. (2000). Comparison of three modern multicriteria decision – making tools. International Journal of Systems Science, 31(4): 497-517.
  • Peldschus, F. (2007). The effectiveness of assessment in multiple criteria decisions. International Journal of Management and Decision Making, 8( 5-6): 519 – 526.
  • Podvezko, V. (2007). Determining the level of agreement of expert estimates. International Journal of Management and Decision Making, 8(5/6): 586-600.
  • Project Europe 2030 (2010). Challenges and Opportunities. A report to the European Council. Retrieved from: http://www.consilium.europa.eu/uedocs/cmsUpload /en_web.pdf>
  • RICARDIS: Reporting Intellectual Capital to Augment Research, Development and Innovation in SMEs (June 2006). European Commission. Retrieved from: http://ec.europa.eu/invest-in-research/pdf/download_en/2006- 2977_web1.pdf>
  • Shapira, P.; Youtie, J. (2006). Measures for Knowledge-Based Economic Development: Introducing Data Mining Techniques to Economic Developers in the State of Georgia and the US South. Technological Forecasting and Social Change, 73(8): 950-965.
  • Shapira, P.; Youtie, J.; Yogeesvaran, K; Jaafer, Z. (2006). Knowledge Economy Measurement: Methods, Results and Insights from the Malaysian Knowledge Content Study. Research Policy, 35(10): 1522-1537.
  • Shiu, H.-J. (2006). The Application of the Value Added Intellectual Coefficient to Measure Corporate Performance: Evidence from Technological Firms. International Journal of Measurement. Retrieved from: http://findarticles.com/p/articles/mi_qa5440/is_200606/ai_n21393124/.
  • Stam, C.; Andriessen, D. (2009). Intellectual Capital of the European Union 2008. European Conference of Intellectual Capital. Netherlands.
  • Sweeney T. (2012) Five Tech Trends Impacting Business Innovation in 2012. Retrieved from: http://www.innovationexcellence.com/blog/2012/01/11/five-tech-trends-impacting-business-innovation-in- 2012
  • Tervonen, T.; Figueira, J. R. (2008). A survey on stochastic multicriteria acceptability analysis methods. Journal of Multi-Criteria Decision Analysis, 15(1-2): 1–14.
  • Weziak, D. (2007). Measurement of national intellectual capital application to EU countries. - An Integrated
  • Socio-economic Research
  • http://iriss.ceps.lu/documents/ irisswp81.pdf. in the Sciences, Nr.13. Retrieved from:
  • The World Bank Group (Oct. 2009).
  • Knowledge in Development Notes, Retrieved from:
  • http://siteresources.worldbank.org/INTRES/Resources/KinD2009_impact_evaluation.pdf>
  • World Economic Forum 2009 Report. Retrieved from: http://www.weforum.org/pdf/> FinancialDevelopment Report/Report2009.pdf>
  • World Telecommunication/ICT Indicators Database (2011). Retrieved from: http://www.itu.int/ITU- D/ict/statistics/.
  • Zapounidis, C. ; Doumpos, M. (2002). Multi-criteria decision aid in financial decision making: methodologies and literature review. Journal of Multi-Criteria Decision Analysis, 11 (4-5): 167-186.
  • Zvirblis, A.; Buracas, A. (2010). The consolidated measurement of the financial markets development: the case of transitional economies. Technological and economic development of economy, 16(2): 266-279.
  • Zvirblis, A.; Buracas, A. (2011). Multicriteria evaluation of national entrepreneurship in newly EU countries. International Journal of Economic Sciences and Applied Research, 4(1): 79-94.
  • Zvirblis, A.; Buracas, A. (2012). Multiple criteria evaluation of entrepreneurship development in newly EU countries. Scientific study. Lambert Academic Publishing, Saarbrücken, Germany.
There are 62 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Antanas Buracas – Vytas Navıckas This is me

Antanas Buracas This is me

Algis Zvırblıs This is me

Vytas Navickas This is me

Algis Zvirblis This is me

Publication Date June 1, 2013
Published in Issue Year 2013 Volume: 8 Issue: 1

Cite

APA Navıckas, A. B. –. V., Buracas, A., Zvırblıs, A., Navickas, V., et al. (2013). KNOWLEDGE POTENTIAL: MAIN AGGREGATED ASSESSMENT PRINCIPLES. Bilgi Ekonomisi Ve Yönetimi Dergisi, 8(1), 63-86.