本研究目的在從混沌理論的觀點探討產業演化的動力。混沌理論隱含著產業演化的
動力是非線性、對初始條件敏感的、路徑依賴和不可預測的。因此，本研究認為混沌理
論是正確理解產業動態的理論觀點。為了研究這個問題，在本文中將研究設計為一種混
合方法的研究方式，同時採用質化與量化研究產業演化的過程。我們選擇了五個數學量
數 (BDS、Hurst 指數、相關維度、李雅普諾夫指數、局部李雅普諾夫指數)來分析半導
體產業與生物科技產業的時間序列數據。而後，我們用質化的方式說明和解釋這五個數
學量詞的結果。最後，我們發現這兩個產業表現出不同程度的混亂，並且我們推斷半導
體產業更容易受到制度壓力，導致半導體產業的混沌程度高於生物科技產業。我們的研
究有助於自然科學方法在管理中的應用，我們也試圖對策略管理和創新過程產生一些啟
示。

This research aims to study the dynamics of industry evolution through the perspective of chaos theory. Chaos implies that the dynamics of industry evolution is featured by structural order out of disorder, sensitivity to the initial condition, path dependence, and unpredictability. Thus, we argue that chaos theory is a proper theoretical view to comprehend the dynamics of industry. In order to address this issue, we design our article herein as a mixed-method research, employing both quantitative and qualitative sequentially to study how industry evolves. Five mathematical quantifiers (i.e., BDS, Hurst exponent, correlation dimension, Lyapunov exponent, and local Lyapunov exponent) are chosen to analyze the time series data drawn from the semiconductor and biotechnology industry. Then, we qualitatively illustrate and interpret the results of the five quantifiers. In the end, we find these two industries demonstrate different degree of chaos, and we infer that the semiconductor industry is more subject to institutional pressure, leading to its low degree of the biotechnology. Our research contributes to the application of natural science’s approaches in management and we also try to give some implications to strategic management and innovation process.

1. INTRODUCTION 1
2. LITERATURE 3
2.1 Industrial Evolution 3
2.2 Chaos Theory and Management 5
3. RESEARCH DESIGN 8
3.1 Case Selection 8
3.2 Mathematical Approaches 9
3.2.1 BDS test 10
3.2.2 Hurst exponent 11
3.2.3 Correlation dimension 12
3.2.4 Lyapunov exponent 14
3.2.5 Local Lyapunov exponent 15
3.3 Data Collection 16
4. ANALYSIS 20
4.1 ARIMA-GARCH 20
4.2 Computations of Mathematical Approaches 24
4.2.1 BDS test 24
4.2.2 Hurst exponent 25
4.2.3 Correlation dimension 26
4.2.4 Lyapunov exponent 27
4.3 Case Study 31
4.3.1. Why does the semiconductor industry have higher long-term memory? 31
4.3.2. Why does the biotechnology industry have higher dimension chaos than that of semiconductor industry? 34
4.3.3. Why is the semiconductor conductor industry more sensitive to initial conditions? 36
4.3.4. Why can the semiconductor industry distinguish chaotic intervals, but the biotechnology industry cannot? 39
5. CONCLUSION AND DISCUSSION 43
5.1 Theoretical Implications 43
5.2 Practical Implications 44
5.3 Limitations and Future Study 45
REFERENCES 46

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