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研究生: 陳明均
Ming-Chun Chen
論文名稱: 從動態能力探索人和機器的雙歧靈巧性
Ambidexterity of Man & Machine from Dynamic Capabilities
指導教授: 張譯尹
Yi-Ying Chang
口試委員: 王孔政
Kung-Jeng Wang
謝亦泰
Yi-Tai Seih
學位類別: 碩士
Master
系所名稱: 管理學院 - 管理研究所
Graduate Institute of Management
論文出版年: 2022
畢業學年度: 110
語文別: 英文
論文頁數: 50
中文關鍵詞: 人工智能雙岐靈巧性人與機器動態能力協作增強自動化聯合代理
外文關鍵詞: Artificial Intelligence, Ambidexterity, Man & Machine, Dynamic Capabilities, Collaboration, Augmentation, Automation, Conjoin Agency
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為響應人工智能研究應發展有意義的理論,並為實踐提供合理建議的呼籲(Raisch 和Krakowski 2021),這項研究的重點是在人類和智能型機器之間建立一個具雙岐靈巧性框架,利用採取動態能力促進人類和機器設備之間的協作。以一家原型製作公司的單一案例研究中提取了九個半結構式訪談的資料。此研究通過利用聯合代理準則調查了探索和利用的模式。建立了一個綜合的概念框架,該框架的三個主要部分是聯合’感知/感測器’代理(攫取技術)、聯合’把握/模擬’代理(增強技術)、聯合’重新配置/自我優化’代理(增強技術)。用以表明人和機器的雙歧靈巧性可來自於動態能力的養成。


To reflect the call for AI research “to develop meaningful theory and provide practice with sound advice.” (Raisch and Krakowski 2021) This research focused on creating an ambidextrous framework between humans and intelligent machines to facilitate collaboration between man and machine by adopting dynamic capabilities. It has extracted nine semi-structured interview data from single-case study research at a prototyping-making company. The study investigated patterns of exploration and exploitation by leveraging conjoined agency guidelines. It has identified an integrated conceptual framework with Conjoined Sensing / Sensor agent (Arresting technology), Conjoined Seizing / Simulation agent (Augmenting technology), and Conjoined Reconfiguring / Self-optimization agent (Augmenting technology) as three major portions of this framework. It shows the ambidexterity of Man & Machine comes from Dynamic Capabilities.

1. INTRODUCTION 1 1.1. PURPOSE OF STUDY & RESEARCH QUESTIONS 1 1.2. STATEMENT OF PROBLEM 2 1.3. PREVIEW OF THE REST OF THE THESIS 3 2. LITERATURE REVIEW 4 2.1. AMBIDEXTERITY 4 2.2. DYNAMIC CAPABILITIES 5 2.3. AGENT-BASED MODELING IN CYBER-PHYSICAL SYSTEM 7 3. METHOD & RESEARCH DESIGN 9 3.1. RESEARCH QUESTIONS 9 3.2. RESEARCH PROPOSITIONS 10 3.3. THE 'CASE' AND DATA COLLECTION 11 3.4. DATA PROCESSING 15 4. DATA ANALYSIS AND FINDINGS 20 4.1. CONJOINED SENSING / SENSOR AGENT (ARRESTING) 21 4.2. CONJOINED SEIZING / SIMULATION AGENT (AUGMENTING) 22 4.3. CONJOINED RECONFIGURING / SELF-OPTIMIZATION AGENT (AUGMENTING) 23 5. DISCUSSION 25 5.1. INTEGRATED CONCEPTUAL FRAMEWORK 25 5.1.1. EXPLORATION THROUGH AUGMENTING SEIZING-SIMULATION 26 5.1.2. EXPLOITATION THROUGH AUGMENTING RECONFIGURING-SELF_OPTIMIZATION 27 5.1.3. LEARNING SYSTEM THROUGH AUTOMATING SIMULATION-SELF_OPTIMIZATION 28 5.2. PRACTICAL IMPLICATION 29 5.3. LIMITATIONS AND FUTURE RESEARCH 33 BIBLIOGRAPHY 34 APPENDIX A: INTERVIEW QUESTIONS 37 APPENDIX B: EMAIL MEMO OF THE INTERVIEWEES' ARRANGEMENT 40 APPENDIX C: RESEARCH PARTICIPANT CONSENT FORM 41

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