1. NCCU Academic Hub
  2. Publications
  3. College of Commerce
  4. Theses
  5. Item

Publications-Theses

Article View/Open

Publication Export

Google ScholarTM

NCCU Library

Citation Infomation

Related Publications in TAIR

題名 「精實創新」觀念應用天使投資決策可行性之研究：以W行銷科技創投公司為例
A Feasibility Study on Applying the Concept of Lean Innovation to Angel Investment Decision-Making: A Case Study of W Marketing Technology Venture Capital Company
作者 石佳音
Shih, Chia-Ying
貢獻者 彭金隆
Peng, Jin-Lung
石佳音
Shih, Chia-Ying
關鍵詞 精實創新
天使投資
行銷科技
投資決策流程
動態驗證
風險識別
Lean Innovation
Angel Investment
Marketing Technology
Investment Decision Process
Dynamic Validation
Risk Learning
日期 2025
上傳時間 4-Aug-2025 12:59:19 (UTC+8)
摘要 本研究旨在探究「精實創新」（Lean Innovation）理念在天使投資決策中的應用可行性與實施效益，並以W行銷科技創投公司作為研究個案。在當前新創生態系中，天使投資人普遍面臨高風險與資訊不對稱問題，傳統投資評估多仰賴主觀經驗與直覺，欠缺系統化分析與持續驗證，致使投資失敗率居高不下。有鑒於此，本研究聚焦三大議題：（1）「精實創新」在天使投資情境之適用性；（2）其對投資決策流程之影響機制與實施路徑；（3）導入後對投資績效之實質效益。旨在回應現今創新創業環境中投資人所面臨之高不確定性挑戰，協助天使投資人提升投資判斷品質、降低失誤風險，並創造長期穩健的投資績效。 研究採個案研究法，解構W行銷科技創投公司的投資流程，分析「精實創新」導入前後於標的篩選效率、市場驗證能力、風險辨識與資源配置四面向之影響。 研究結果顯示，「精實創新」不僅適用於新創產品開發，亦高度適配天使投資場景，為投資決策提供以數據驅動、市場為導向之架構。基此，提出一套具備高度適應性的投資決策架構－「精實創新 × 動態投資」決策模型，透過四個階段的投資流程設計，建構出一套可動態調整、風險可控且具策略彈性的早期投資決策架構，並且進一步對天使投資人提出具體可行的建議。 本研究嘗試系統性探討「精實創新」應用於天使投資決策之機制與效益，為投資實務提供新理論視角與可驗證、數據驅動且動態調整的投資管理框架，有助於提升早期投資成功率，具重要實務價值。未來研究可延伸至不同產業與階段之適用性探討及量化模型開發。
This study aims to explore the feasibility and effectiveness of applying the concept of Lean Innovation in angel investment decision-making, using W Marketing Technology Venture Capital Company (hereafter referred to as W Company) as a case study. In today’s startup ecosystem, angel investors often face high levels of risk and information asymmetry. Traditional investment evaluations heavily rely on subjective experience and intuition, lacking systematic analysis and continuous validation, which contributes to persistently high failure rates in early-stage investments. In response to this issue, this research focuses on three core topics: (1) the applicability of Lean Innovation in the context of angel investing; (2) its influence mechanisms and implementation pathways within the investment decision-making process; and (3) the practical benefits brought by its adoption in enhancing investment performance. The overarching goal is to address the challenges of high uncertainty in the innovation and entrepreneurship environment and to assist angel investors in improving decision quality, reducing the risk of misjudgment, and achieving long-term, sustainable investment returns. A case study approach is adopted to deconstruct the investment process of W Company and analyze the effects of Lean Innovation implementation across four dimensions: target screening efficiency, market validation capability, risk identification, and resource allocation. The findings reveal that Lean Innovation is not only applicable to startup product development but is also highly compatible with the context of angel investing. It provides a data-driven and market-oriented framework for investment decision-making. Based on these findings, this study proposes an adaptive investment decision-making framework—the Lean Innovation × Dynamic Investment model. This four-stage model includes: pre-investment observation, market validation, evidence-based decision-making, and post-investment governance. Together, these stages form a dynamic, risk-controllable, and strategically flexible structure for early-stage investments. Moreover, the study offers specific, actionable recommendations for angel investors seeking to adopt this approach. By systematically investigating the mechanisms and benefits of integrating Lean Innovation into angel investment decision-making, this research contributes a new theoretical perspective and proposes a verifiable, data-driven, and adaptable investment management framework. The findings have significant practical implications for enhancing the success rate of early-stage investments. Future research could extend this framework to explore its applicability across different industries and stages of investment, as well as to develop supporting quantitative models
參考文獻 一、 中文文獻： 王建偉 (2021)。實證決策對早期投資風險控制的影響。《投資管理研究》，14(2)，68-85。 王德華 (2023)。投資決策的新典範：從靜態評估到動態驗證。《投資學刊》，25(1)，38-59。 艾瑞諮詢 (2023)。2022-2023中國行銷科技市場研究報告。 李家瑋 (2020)。精實創新理論在創業投資中的應用研究。《創新管理學報》，12(4)，45-58。 李振華 (2022)。資訊不對稱情境下天使投資決策的困境與突破。《財經評論》，42(4)，112-127。 李文浩 (2019)。天使投資者價值創造機制研究。《管理評論》，31(4)，89-102。 李文誠與陳志強 (2022)。新創投資的數據決策模式。《投資學刊》，23(4)，187-206。 林建煌 (2018)。天使投資者特徵與投資行為研究。《管理學報》，35(2)，157-178。 林育成 (2023)。精實創新與天使投資的協同效應分析。《創新管理與創業》，22(1)，34-49。 林佳蓉 (2023)。快速試錯理念在投資決策中的應用探討。《創新管理學刊》，18(2)，56-71。 周俊吉 (2022)。精實創新與天使投資決策整合框架。《企業管理學報》，25(3)，89-103。 陳志偉與林明德 (2022)。精實發現：市場驗證中的機會識別機制。《創業理論與實踐》，17(3)，167-184。 陳明達 (2020)。創業融資策略與實務。台北：五南圖書出版公司。 郭明仁 (2022)。實證導向投資法：新創投資的嶄新視角。《創業管理研究》，17(3)，45-68。 張世敏 (2020)。天使投資決策過程與風險管理研究。《風險管理》，28(5)，78-92。 張世豪 (2021)。精實創新與天使投資整合實踐案例分析。《創業實務研究》，16(3)，67-81。 黃正中與劉曉芬 (2022)。精實創新數據在投資決策中的應用困境。《管理學報》，39(3)，322-341。 黃明志與王建民 (2023)。MVP策略在新創企業市場驗證中的應用效果分析。《創業實踐研究》，18(3)，56-72。 趙明德與林佳蓉 (2023)。跨功能團隊協作在科技新創企業的應用與挑戰。《組織與管理》，42(1)，78-96。 劉伯遠 (2022)。精實創新在不同商業模式中的應用效果比較。《商業模式創新》，14(2)，67-82。 劉曼紅 (2012)。風險投資決策與管理。上海：上海財經大學出版社。 謝文雄 (2022)。行銷科技創新特性與投資風險管理。《風險管理學報》，17(1)，34-47。 謝明宏 (2023)。天使投資者對精實創新價值認知與實踐差距之研究。《創業投資季刊》，20(4)，88-107。 二、 英文文獻： Accenture. (2024). Reinventing Enterprise Operations with Gen AI. Anderson, K., & Lee, S. (2023). Quantifying the impact of data-driven marketing on operational efficiency. Marketing Analytics Journal, 28(2), 145-162. Angel Capital Association. (2023). Angel investing statistics and market analysis 2023. Angel Capital Association. Altman, S. (2021). How to succeed with a startup. Y Combinator Library. Anderson, D. J. (2012). Kanban: Successful evolutionary change for your technology business. Blue Hole Press. Angel Capital Association. (2023). Angel investing overview: Trends and returns. Balke, G., Oser, P., & Zheng, Z. (2019). Accelerating lean startup validation through marketing automation integration. Journal of Business Venturing, 34(2),234-251. Bernstein, E., Bunch, J., Canner, N., & Lee, M. (2016). Beyond the holacracy hype. Harvard Business Review, 94(7-8), 38-49. Bernstein, P. L. (1996). Against the gods: The remarkable story of risk. New York, NY: John Wiley & Sons. Box, G. E., Hunter, J. S., & Hunter, W. G. (2005). Statistics for experimenters: Design, innovation, and discovery (2nd ed.). Hoboken, NJ: Wiley-Interscience. Brown, T. (2008). Design thinking. Harvard Business Review, 86(6), 84-92. Brown, T. (2009). Change by design: How design thinking transforms organizations and inspires innovation. Harper Business. CB Insights. (2023). Global marketing technology market forecast 2023-2028. Gartner Research. Chen, L., & Smith, K. (2020). Evidence-based angel investing: A new paradigm for early- stage investment. Journal of Venture Capital, 22(3), 245-268. Chen, L., & Williams, R. (2023). Decision-making patterns in angel investing: Balancing data and intuition. Journal of Venture Capital, 18(3), 112-128. Chen, L., & Rodriguez, M. (2023). Data-driven marketing culture: The role of technology dashboards in decision making. Journal of Marketing Technology, 15(4), 234-251. Chen, Y. L., Wang, M. H., Li, J. K., & Zhang, P. Q. (2023). The cold start problem in Marketing technology adoption: Challenges for early-stage startups. Entrepreneurship & Marketing Technology Review, 12(2), 89-104. Christensen, C. M., & Kaufman, S. P. (2008). Assessing your organization's Capabilities: Resources, Processes and Priorities. Boston, MA: Harvard Business School. Christensen, C. M., Kaufman, S. P., & Shih, W. C. (2008). Innovation killers: How financial tools destroy your capacity to do new things. Harvard Business Review, 86(1), 98-105. Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative,and mixed methods approaches (5th ed.). Thousand Oaks, CA: Sage Publications. Deloitte. (2024). Revenue growth and data analytics insights using data-driven strategies. Dweck, C. S. (2006). Mindset: The new psychology of success. New York, NY: Random House. Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: What are they? Strategic Management Journal, 21(10-11), 1105-1121. European Angel Investment Association. (2023). Angel investment trends and validation challenges. Angel Investment Quarterly, 15(2), 23-35. EY. (2021). 5 ways CMOs can create an agile operating model. Furr, N., & Dyer, J. (2014). The innovator's method: Bringing the lean startup into your organization. Harvard Business Review Press. Gartner. (2023). Global marketing technology market forecast 2023-2028. Gartner Research. Gomes, G. (2024). Seven attributes that define the data?driven enterprise in 2025. CTO Magazine. Heimbach, I., Kostyra, D. S., & Hinz, O. (2015). Marketing automation efficiency and effectiveness in digital marketing campaigns. Journal of Business Research, 68(9), 1998-2005. IDC Research. (2023). Worldwide marketing technology spending forecast 2023-2027. International Data Corporation. Kauffman Foundation. (2021). Staged investment strategies in venture capital: A comprehensive analysis of risk management and returns. Kim, S., & Johnson, R. (2023). Synergistic effects of lean innovation and marketing technology on validation cycles. Journal of Innovation Management, 31(4), 156-173. Kumar, V., & Reinartz, W. (2023). Marketing technology evolution: AI and IoT integration in customer analytics. Harvard Business Review, 101(4), 112-128. Lee, S. (2025). 8 surprising analytics stats for boosting data?driven decision making. NumberAnalytics. Martinez, L., & Chen, W. (2023). Marketing technology impact on customer acquisition costs in lean startups. Entrepreneurship & Technology Journal, 28(3), 112-129. MarTech Alliance. (2023). Global marketing technology landscape report 2023. MarTech Alliance Publications. Maurya, A. (2012). Running lean: Iterate from plan A to a plan that works. O'Reilly Media. McKinsey. (2018). The five trademarks of agile organizations. Ohno, T. (1988). Toyota production system: Beyond large-scale production. Productivity Press. Pauwels, K., & Neslin, S. A. (2015). Building with bricks and mortar: The revenue impact of opening physical stores in a multichannel environment. Journal of Retailing, 91(2), 182-197. Peterson, K., & Anderson, M. (2023). Adaptive investment strategies during market volatility: Performance comparison analysis. Journal of Portfolio Management,49(3), 78-95. Popper, K. (1959). The logic of scientific discovery. Routledge. Ries, E. (2011). The lean startup: How today's entrepreneurs use continuous innovation to create radically successful businesses. Crown Business. Senge, P. M. (1990). The fifth discipline: The art and practice of the learning organization. Doubleday/Currency. Smith, A., & Johnson, B. (2023). Portfolio diversification and risk-adjusted returns. Harvard Business Review, 101(4), 45-52. Smith, A., & Johnson, B. (2023). Technical debt and iteration cycles in lean startup methodology. Journal of Software Engineering and Entrepreneurship, 18(4), 234-251. Startup Genome. (2023). Global startup ecosystem report 2023. Startup Genome LLC. Thomke, S. (2003). Experimentation matters: Unlocking the potential of new technologies for innovation. Harvard Business School Press. Thompson, M. J., Chen, L. K., & Rodriguez, A. P. (2023). Marketing technology adoption and market responsiveness: An empirical study of digital transformation impact. Journal of Marketing Technology, 15(3), 78-95. Trigeorgis, L. (1996). Real options: Managerial flexibility and strategy in resource allocation. MIT Press. Wang, S. H., & Ahmed, K. M. (2021). Lean innovation versus marketing technology: Methodological approaches and integration challenges in digital transformation. Strategic Management Journal, 42(8), 1456-1478. Williams, R., & Zhang, L. (2023). Lean innovation methodology in angel investment: Performance analysis and risk reduction. Venture Capital Review, 31(4), 156-173. Wilson, A., & Park, S. (2023). Accelerating product-market fit through integrated lean innovation and marketing technology approaches. Strategic Innovation Journal, 15(2), 89-106. Womack, J. P., & Jones, D. T. (1996). Lean thinking: Banish waste and create wealth in your corporation. Simon & Schuster. Womack, J. P., & Jones, D. T. (2003). Lean thinking: Banish waste and create wealth in your corporation (2nd ed.). Free Press.
描述 碩士
國立政治大學
經營管理碩士學程(EMBA)
111932068
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111932068
資料類型 thesis
dc.contributor.advisor 彭金隆zh_TW
dc.contributor.advisor Peng, Jin-Lungen_US
dc.contributor.author (Authors) 石佳音zh_TW
dc.contributor.author (Authors) Shih, Chia-Yingen_US
dc.creator (作者) 石佳音zh_TW
dc.creator (作者) Shih, Chia-Yingen_US
dc.date (日期) 2025en_US
dc.date.accessioned 4-Aug-2025 12:59:19 (UTC+8)-
dc.date.available 4-Aug-2025 12:59:19 (UTC+8)-
dc.date.issued (上傳時間) 4-Aug-2025 12:59:19 (UTC+8)-
dc.identifier (Other Identifiers) G0111932068en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158311-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 經營管理碩士學程(EMBA)zh_TW
dc.description (描述) 111932068zh_TW
dc.description.abstract (摘要) 本研究旨在探究「精實創新」（Lean Innovation）理念在天使投資決策中的應用可行性與實施效益，並以W行銷科技創投公司作為研究個案。在當前新創生態系中，天使投資人普遍面臨高風險與資訊不對稱問題，傳統投資評估多仰賴主觀經驗與直覺，欠缺系統化分析與持續驗證，致使投資失敗率居高不下。有鑒於此，本研究聚焦三大議題：（1）「精實創新」在天使投資情境之適用性；（2）其對投資決策流程之影響機制與實施路徑；（3）導入後對投資績效之實質效益。旨在回應現今創新創業環境中投資人所面臨之高不確定性挑戰，協助天使投資人提升投資判斷品質、降低失誤風險，並創造長期穩健的投資績效。 研究採個案研究法，解構W行銷科技創投公司的投資流程，分析「精實創新」導入前後於標的篩選效率、市場驗證能力、風險辨識與資源配置四面向之影響。 研究結果顯示，「精實創新」不僅適用於新創產品開發，亦高度適配天使投資場景，為投資決策提供以數據驅動、市場為導向之架構。基此，提出一套具備高度適應性的投資決策架構－「精實創新 × 動態投資」決策模型，透過四個階段的投資流程設計，建構出一套可動態調整、風險可控且具策略彈性的早期投資決策架構，並且進一步對天使投資人提出具體可行的建議。 本研究嘗試系統性探討「精實創新」應用於天使投資決策之機制與效益，為投資實務提供新理論視角與可驗證、數據驅動且動態調整的投資管理框架，有助於提升早期投資成功率，具重要實務價值。未來研究可延伸至不同產業與階段之適用性探討及量化模型開發。zh_TW
dc.description.abstract (摘要) This study aims to explore the feasibility and effectiveness of applying the concept of Lean Innovation in angel investment decision-making, using W Marketing Technology Venture Capital Company (hereafter referred to as W Company) as a case study. In today’s startup ecosystem, angel investors often face high levels of risk and information asymmetry. Traditional investment evaluations heavily rely on subjective experience and intuition, lacking systematic analysis and continuous validation, which contributes to persistently high failure rates in early-stage investments. In response to this issue, this research focuses on three core topics: (1) the applicability of Lean Innovation in the context of angel investing; (2) its influence mechanisms and implementation pathways within the investment decision-making process; and (3) the practical benefits brought by its adoption in enhancing investment performance. The overarching goal is to address the challenges of high uncertainty in the innovation and entrepreneurship environment and to assist angel investors in improving decision quality, reducing the risk of misjudgment, and achieving long-term, sustainable investment returns. A case study approach is adopted to deconstruct the investment process of W Company and analyze the effects of Lean Innovation implementation across four dimensions: target screening efficiency, market validation capability, risk identification, and resource allocation. The findings reveal that Lean Innovation is not only applicable to startup product development but is also highly compatible with the context of angel investing. It provides a data-driven and market-oriented framework for investment decision-making. Based on these findings, this study proposes an adaptive investment decision-making framework—the Lean Innovation × Dynamic Investment model. This four-stage model includes: pre-investment observation, market validation, evidence-based decision-making, and post-investment governance. Together, these stages form a dynamic, risk-controllable, and strategically flexible structure for early-stage investments. Moreover, the study offers specific, actionable recommendations for angel investors seeking to adopt this approach. By systematically investigating the mechanisms and benefits of integrating Lean Innovation into angel investment decision-making, this research contributes a new theoretical perspective and proposes a verifiable, data-driven, and adaptable investment management framework. The findings have significant practical implications for enhancing the success rate of early-stage investments. Future research could extend this framework to explore its applicability across different industries and stages of investment, as well as to develop supporting quantitative modelsen_US
dc.description.tableofcontents 第一章 緒論 6 第一節 研究背景與動機 6 第二節 研究問題 9 第三節 研究範圍 10 第四節 研究方法與限制 11 第二章 文獻探討 14 第一節 「精實創新」的理論基礎 14 第二節 天使投資的決策模式 21 第三節 文獻研究的空白 26 第三章 「精實創新」應用於新創天使投資的可行性 29 第一節 「精實創新」與天使投資決策流程的相容性與挑戰 29 第二節 「精實創新」與行銷科技的適配性與挑戰 37 第四章 個案分析 44 第一節 個案公司背景與概況 44 第二節 「精實創新」應用案例實踐說明 49 第五章 結論 54 第一節 研究問題與實證發現之對照分析 54 第二節 研究貢獻 58 第三節 對天使投資人的建議 61 第四節 未來研究範圍 62 參考文獻 64zh_TW
dc.format.extent 1011960 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111932068en_US
dc.subject (關鍵詞) 精實創新zh_TW
dc.subject (關鍵詞) 天使投資zh_TW
dc.subject (關鍵詞) 行銷科技zh_TW
dc.subject (關鍵詞) 投資決策流程zh_TW
dc.subject (關鍵詞) 動態驗證zh_TW
dc.subject (關鍵詞) 風險識別zh_TW
dc.subject (關鍵詞) Lean Innovationen_US
dc.subject (關鍵詞) Angel Investmenten_US
dc.subject (關鍵詞) Marketing Technologyen_US
dc.subject (關鍵詞) Investment Decision Processen_US
dc.subject (關鍵詞) Dynamic Validationen_US
dc.subject (關鍵詞) Risk Learningen_US
dc.title (題名) 「精實創新」觀念應用天使投資決策可行性之研究：以W行銷科技創投公司為例zh_TW
dc.title (題名) A Feasibility Study on Applying the Concept of Lean Innovation to Angel Investment Decision-Making: A Case Study of W Marketing Technology Venture Capital Companyen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 一、 中文文獻： 王建偉 (2021)。實證決策對早期投資風險控制的影響。《投資管理研究》，14(2)，68-85。 王德華 (2023)。投資決策的新典範：從靜態評估到動態驗證。《投資學刊》，25(1)，38-59。 艾瑞諮詢 (2023)。2022-2023中國行銷科技市場研究報告。 李家瑋 (2020)。精實創新理論在創業投資中的應用研究。《創新管理學報》，12(4)，45-58。 李振華 (2022)。資訊不對稱情境下天使投資決策的困境與突破。《財經評論》，42(4)，112-127。 李文浩 (2019)。天使投資者價值創造機制研究。《管理評論》，31(4)，89-102。 李文誠與陳志強 (2022)。新創投資的數據決策模式。《投資學刊》，23(4)，187-206。 林建煌 (2018)。天使投資者特徵與投資行為研究。《管理學報》，35(2)，157-178。 林育成 (2023)。精實創新與天使投資的協同效應分析。《創新管理與創業》，22(1)，34-49。 林佳蓉 (2023)。快速試錯理念在投資決策中的應用探討。《創新管理學刊》，18(2)，56-71。 周俊吉 (2022)。精實創新與天使投資決策整合框架。《企業管理學報》，25(3)，89-103。 陳志偉與林明德 (2022)。精實發現：市場驗證中的機會識別機制。《創業理論與實踐》，17(3)，167-184。 陳明達 (2020)。創業融資策略與實務。台北：五南圖書出版公司。 郭明仁 (2022)。實證導向投資法：新創投資的嶄新視角。《創業管理研究》，17(3)，45-68。 張世敏 (2020)。天使投資決策過程與風險管理研究。《風險管理》，28(5)，78-92。 張世豪 (2021)。精實創新與天使投資整合實踐案例分析。《創業實務研究》，16(3)，67-81。 黃正中與劉曉芬 (2022)。精實創新數據在投資決策中的應用困境。《管理學報》，39(3)，322-341。 黃明志與王建民 (2023)。MVP策略在新創企業市場驗證中的應用效果分析。《創業實踐研究》，18(3)，56-72。 趙明德與林佳蓉 (2023)。跨功能團隊協作在科技新創企業的應用與挑戰。《組織與管理》，42(1)，78-96。 劉伯遠 (2022)。精實創新在不同商業模式中的應用效果比較。《商業模式創新》，14(2)，67-82。 劉曼紅 (2012)。風險投資決策與管理。上海：上海財經大學出版社。 謝文雄 (2022)。行銷科技創新特性與投資風險管理。《風險管理學報》，17(1)，34-47。 謝明宏 (2023)。天使投資者對精實創新價值認知與實踐差距之研究。《創業投資季刊》，20(4)，88-107。 二、 英文文獻： Accenture. (2024). Reinventing Enterprise Operations with Gen AI. Anderson, K., & Lee, S. (2023). Quantifying the impact of data-driven marketing on operational efficiency. Marketing Analytics Journal, 28(2), 145-162. Angel Capital Association. (2023). Angel investing statistics and market analysis 2023. Angel Capital Association. Altman, S. (2021). How to succeed with a startup. Y Combinator Library. Anderson, D. J. (2012). Kanban: Successful evolutionary change for your technology business. Blue Hole Press. Angel Capital Association. (2023). Angel investing overview: Trends and returns. Balke, G., Oser, P., & Zheng, Z. (2019). Accelerating lean startup validation through marketing automation integration. Journal of Business Venturing, 34(2),234-251. Bernstein, E., Bunch, J., Canner, N., & Lee, M. (2016). Beyond the holacracy hype. Harvard Business Review, 94(7-8), 38-49. Bernstein, P. L. (1996). Against the gods: The remarkable story of risk. New York, NY: John Wiley & Sons. Box, G. E., Hunter, J. S., & Hunter, W. G. (2005). Statistics for experimenters: Design, innovation, and discovery (2nd ed.). Hoboken, NJ: Wiley-Interscience. Brown, T. (2008). Design thinking. Harvard Business Review, 86(6), 84-92. Brown, T. (2009). Change by design: How design thinking transforms organizations and inspires innovation. Harper Business. CB Insights. (2023). Global marketing technology market forecast 2023-2028. Gartner Research. Chen, L., & Smith, K. (2020). Evidence-based angel investing: A new paradigm for early- stage investment. Journal of Venture Capital, 22(3), 245-268. Chen, L., & Williams, R. (2023). Decision-making patterns in angel investing: Balancing data and intuition. Journal of Venture Capital, 18(3), 112-128. Chen, L., & Rodriguez, M. (2023). Data-driven marketing culture: The role of technology dashboards in decision making. Journal of Marketing Technology, 15(4), 234-251. Chen, Y. L., Wang, M. H., Li, J. K., & Zhang, P. Q. (2023). The cold start problem in Marketing technology adoption: Challenges for early-stage startups. Entrepreneurship & Marketing Technology Review, 12(2), 89-104. Christensen, C. M., & Kaufman, S. P. (2008). Assessing your organization's Capabilities: Resources, Processes and Priorities. Boston, MA: Harvard Business School. Christensen, C. M., Kaufman, S. P., & Shih, W. C. (2008). Innovation killers: How financial tools destroy your capacity to do new things. Harvard Business Review, 86(1), 98-105. Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative,and mixed methods approaches (5th ed.). Thousand Oaks, CA: Sage Publications. Deloitte. (2024). Revenue growth and data analytics insights using data-driven strategies. Dweck, C. S. (2006). Mindset: The new psychology of success. New York, NY: Random House. Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: What are they? Strategic Management Journal, 21(10-11), 1105-1121. European Angel Investment Association. (2023). Angel investment trends and validation challenges. Angel Investment Quarterly, 15(2), 23-35. EY. (2021). 5 ways CMOs can create an agile operating model. Furr, N., & Dyer, J. (2014). The innovator's method: Bringing the lean startup into your organization. Harvard Business Review Press. Gartner. (2023). Global marketing technology market forecast 2023-2028. Gartner Research. Gomes, G. (2024). Seven attributes that define the data?driven enterprise in 2025. CTO Magazine. Heimbach, I., Kostyra, D. S., & Hinz, O. (2015). Marketing automation efficiency and effectiveness in digital marketing campaigns. Journal of Business Research, 68(9), 1998-2005. IDC Research. (2023). Worldwide marketing technology spending forecast 2023-2027. International Data Corporation. Kauffman Foundation. (2021). Staged investment strategies in venture capital: A comprehensive analysis of risk management and returns. Kim, S., & Johnson, R. (2023). Synergistic effects of lean innovation and marketing technology on validation cycles. Journal of Innovation Management, 31(4), 156-173. Kumar, V., & Reinartz, W. (2023). Marketing technology evolution: AI and IoT integration in customer analytics. Harvard Business Review, 101(4), 112-128. Lee, S. (2025). 8 surprising analytics stats for boosting data?driven decision making. NumberAnalytics. Martinez, L., & Chen, W. (2023). Marketing technology impact on customer acquisition costs in lean startups. Entrepreneurship & Technology Journal, 28(3), 112-129. MarTech Alliance. (2023). Global marketing technology landscape report 2023. MarTech Alliance Publications. Maurya, A. (2012). Running lean: Iterate from plan A to a plan that works. O'Reilly Media. McKinsey. (2018). The five trademarks of agile organizations. Ohno, T. (1988). Toyota production system: Beyond large-scale production. Productivity Press. Pauwels, K., & Neslin, S. A. (2015). Building with bricks and mortar: The revenue impact of opening physical stores in a multichannel environment. Journal of Retailing, 91(2), 182-197. Peterson, K., & Anderson, M. (2023). Adaptive investment strategies during market volatility: Performance comparison analysis. Journal of Portfolio Management,49(3), 78-95. Popper, K. (1959). The logic of scientific discovery. Routledge. Ries, E. (2011). The lean startup: How today's entrepreneurs use continuous innovation to create radically successful businesses. Crown Business. Senge, P. M. (1990). The fifth discipline: The art and practice of the learning organization. Doubleday/Currency. Smith, A., & Johnson, B. (2023). Portfolio diversification and risk-adjusted returns. Harvard Business Review, 101(4), 45-52. Smith, A., & Johnson, B. (2023). Technical debt and iteration cycles in lean startup methodology. Journal of Software Engineering and Entrepreneurship, 18(4), 234-251. Startup Genome. (2023). Global startup ecosystem report 2023. Startup Genome LLC. Thomke, S. (2003). Experimentation matters: Unlocking the potential of new technologies for innovation. Harvard Business School Press. Thompson, M. J., Chen, L. K., & Rodriguez, A. P. (2023). Marketing technology adoption and market responsiveness: An empirical study of digital transformation impact. Journal of Marketing Technology, 15(3), 78-95. Trigeorgis, L. (1996). Real options: Managerial flexibility and strategy in resource allocation. MIT Press. Wang, S. H., & Ahmed, K. M. (2021). Lean innovation versus marketing technology: Methodological approaches and integration challenges in digital transformation. Strategic Management Journal, 42(8), 1456-1478. Williams, R., & Zhang, L. (2023). Lean innovation methodology in angel investment: Performance analysis and risk reduction. Venture Capital Review, 31(4), 156-173. Wilson, A., & Park, S. (2023). Accelerating product-market fit through integrated lean innovation and marketing technology approaches. Strategic Innovation Journal, 15(2), 89-106. Womack, J. P., & Jones, D. T. (1996). Lean thinking: Banish waste and create wealth in your corporation. Simon & Schuster. Womack, J. P., & Jones, D. T. (2003). Lean thinking: Banish waste and create wealth in your corporation (2nd ed.). Free Press.zh_TW