Article2010

Statistical analysis on the intellectual capital statement

86 citations0/0 files storedJournal of Intellectual Capital

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Intellectual Capital and Performance Analysis Accounting Theory and Financial Reporting Corporate Social Responsibility Reporting

Abstract

Purpose The intellectual capital (IC) can be divided into three categories, i.e. human, structural, and relationship capitals. The purpose of this paper is to investigate the correlation among those capitals to their indicators, particularly for intellectual capital statement made in Germany and intellectual capital statement made in Europe models. Design/methodology/approach In these two models, each capital has four, six, and five indicators, respectively. So totally, there are 15 indicators. Structural equation modeling and its sensitivity analysis are utilized for measuring the correlation among those capitals to their indicators. Findings Among those 15 indicators, 14 indicators have strong correlation with their respective capitals. Moreover, there exist strong correlation in a similar weight among those capitals, i.e. the correlations between human (HC) and structural capital (SC) is 0.88, SC and relationship capital (RC) is 0.87 and HC to RC is 0.81. Originality/value So far, the data collected from the IC projects are presented and analyzed through descriptive statistics and statistics summaries, e.g. mean and standard deviation. This paper offers other statistical tools for exposing valuable information such as the correlation among each capital to its indicators in IC model.

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References

1998Principles and Practice of Structural Equation Modeling 1998Working knowledge: how organizations manage what they know 1996Power analysis and determination of sample size for covariance structure modeling.2007Global Sensitivity Analysis. The Primer 2001Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates 1997The New Organizational Wealth: Managing and Measuring Knowledge-Based Assets 1998The Balanced Scorecard 2004Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models

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