Competitive success now is based less on the strategic allocation of physical and financial resources, and more on the strategic management of intellectual capital. Although intellectual capital is intangible and cannot be accurately measured, companies must develop methods of increasing corporate value by proactively focusing on intellectual capital management. This study examines the relationship between intellectual capital and corporate value in an emerging economy. This study employs an intellectual capital perspective, resource‐based view and a financial perspective, and investigates how to apply the concept of intellectual capital to value creation. After reviewing the relevant literature, this study identifies human capital, organizational capital, innovation capital and relationship capital as four constructs of intellectual capital. Corporate value is measured using three selection methods: (1) Market/Book value, (2) Tobin'Q and (3) Value Added Intellectual Coefficient (VAIC™). Through a questionnaire survey and secondary data collection, this study applies the Structure Equation Model to analyze the relationships among four constructs of intellectual capital, as well as the relationship between intellectual capital and corporate value. From the empirical findings, for Taiwanese manufacturers, a positive relationship exists between intellectual capital and corporate value. This study visualizes and mobilizes intellectual capital to articulate eight value creation paths.

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As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

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