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    题名: 人資與善的距離？勞工績效改善計畫之研究 - 以閉源型生成式AI驗證我國法院判決
    How Far is HR from ";Goodness";? A Study of Performance Improvement Plans for Incompetent Employees Using Closed - Source Generative AI to Analyze Taiwanese Court Judgments
    作者: 陳詩吟
    Chen, Shih-Yin
    贡献者: 劉梅君
    Liu, Mei-Chun
    陳詩吟
    Chen, Shih-Yin
    关键词: 績效改善計畫
    PIP
    S.M.A.R.T.
    漸進式懲戒
    閉源型生成式AI
    performance improvement plan
    PIP
    S.M.A.R.T.
    progressive discipline
    closed-source generative AI
    日期: 2025
    上传时间: 2025-08-04 15:42:17 (UTC+8)
    摘要: 　　本文題目「人資與善的距離」，以人資依據解僱最後手段性原則的觀點，思考企業面對不能勝任工作的勞工時，究竟採取多少善意提供勞工改善機會？由於，我國面臨中高齡化的勞動力市場，企業存在勞工高離職率，本研究思考如何留任在職勞工，茲從法理解釋、法院判決與學者論述，觀察前人研究無法改變勞工被資遣，進而關注「績效改善計畫」（簡稱：PIP）輔導不能勝任工作之勞工。企業重視法官的PIP見解，另發現神經網路能將文字轉成數字進行演算，冀望人資不倉促資遣勞工，運用人工智慧（簡稱：AI）分類判決並制定PIP。

    　　本研究從收集西文期刊彙整PIP制定過程，另從司法院裁判系統檢索判決全文，關鍵字為PIP、績效改善、績效改進、績效輔導等任何一項，裁判期間設定截至2025年03月31日共498篇PIP判決。其次，採取ChatGPT-4o、Gemini 2.5 Pro、Claude 3.7與Grok 3等四家閉源型生成式AI分類PIP判決，執行描述性統計、分類準確度與卡方獨立性檢定，嘗試找出準確度相對較佳的AI工具；再者，歸納法官肯認企業對不能勝任勞工採取PIP措施且判決資方勝訴的要件。

    　　研究發現，西文期刊與我國法院判決在PIP皆運用S.M.A.R.T管理不能勝任勞工，歸納S具體的指「佐證客觀疏失與評估主觀疏失」、M可測量的指「具體指標與量化或順位」、A可達成的指「比較勞工及同事」、R相關的指「達到僱用之客觀合理經濟目的」，T時效的指「改善、追蹤與通知，普遍輔導三個月」；此外，人資在PIP實施漸進式懲戒應落實企業內部辦法，訪談過程採取尊重勞工措施，總之，人資妥善制定PIP能夠降低資方訴訟敗訴，建議人資在PIP訪談前充分瞭解勞工個案且訪談時察言觀色。其次，在AI運用檢索增強生成（簡稱：RAG）有助於分類法院判決，Claude 3.7分類判決準確度為92%與83%，高於ChatGPT-4o準確度為87%與61%，因此，閉源型生成式AI達到快速分類PIP判決，建議人資學習AI提問技巧，遇到勞資糾紛時有能力使用AI整理法官見解。
    　　This study, from a Human Resources (HR) perspective, examines the extent of goodwill and opportunities for improvement that companies should offer to employees deemed incompetent, especially when considering the ultima ratio doctrine of dismissal. Given Taiwan’s aging workforce and high employee turnover rates, this study focuses on strategies for employee retention. While previous legal interpretations, court judgments, and academic literature haven't fully succeeded in preventing terminations, this research highlights the critical role of Performance Improvement Plan(PIP) to guide employees who are incompetent. Recognizing the growing importance of judicial interpretations of PIP for businesses, and the potential of neural networks to process legal texts, the present study advocates for HR professionals to move beyond hasty terminations by leveraging Artificial Intelligence (AI) to classify court judgments and formulate more effective PIP.

    　　This study first reviewed the process of designing PIP based on western academic literature. Subsequently, 498 court judgments concerning PIP were gathered from Judicial Yuan’s Judgment System up to March 31, 2025, using keywords such as “PIP”, “performance improvement”, or “performance coaching”. Furthermore, four closed-source generative AI models (i.e., ChatGPT-4o, Gemini 2.5 Pro, Claude 3.7, and Grok 3) were employed to classify these PIP judgments. Descriptive statistics, classification accuracy analysis, and chi-square tests of independence were then performed to identify the AI tool with relatively superior classification accuracy for legal texts. Finally, the judicial arguments that recognize the adoption of PIP by employers as a significant factor for incompetent were also summarized.

    　　The findings indicate that both Western academic literature and Taiwanese court judgments apply the S.M.A.R.T. principle when managing incompetent employees through PIP. S (Specific) involves “substantiating objective misconduct and evaluating subjective deficiencies.” M (Measurable) entails “setting concrete indicators and quantifying performance, or ranking.” A (Attainable) means “comparing the employee’s performance with that of colleagues.” R (Relevant) represents “achieving the objective and reasonable economic purpose of employment.” Lastly, T (Timely) denotes “improvement, monitoring, and notification, typically involving a three-month coaching period.” Beyond the S.M.A.R.T. framework, HR professionals should implement PIP in alignment with the principle of progressive discipline and ensure the enforcement of internal company policies, while adopting a respectful approach during employee interviews. A well-designed PIP by HR can significantly reduce the likelihood of employers losing lawsuits. To that end, it’s recommended that HR professionals thoroughly understand each employee’s case before PIP interviews and remain attentive to non-verbal cues during these discussions. Furthermore, the study found that AI tools leveraging Retrieval-Augmented Generation (RAG) can effectively enhance court judgment classification. Among the tools, Claude 3.7 achieved classification accuracies of 92% and 83%, outperforming ChatGPT-4o’s 87% and 61%. This demonstrates the effectiveness of closed-source generative AI in rapidly categorizing PIP-related judgments. Consequently, it is advised that HR professionals learn AI prompting techniques to productively organize judicial decisions when faced with labor-management disputes.
    參考文獻: 一、中文

    (一)期刊

    1.李婉維（2023）。績效改善計畫應用於解僱最後手段性原則之研究。全國律師，27(5)，60-82。

    2.林更盛（1998）。論作為解雇事由之「勞工確不能勝任工作」。中原財經法學，4，93-111。

    3.張義德（2018）。對於績效考核之司法審查。月旦會計實務研究，12，43-49。

    4.陳建文（2009）。業績未達標準作為終止事由之合法性判斷問題／高雄地院九七勞訴三○。臺灣法學雜誌，128，247-250。

    5.傅柏翔、王惠玲（2010）。企業績效評估制度對勞動權益之衝擊研究。政大勞動學報，26(12)，91-146。

    6.黃程貫（1990）。企業懲罰權。台灣社會研究季刊，2(3&4)，9-73。https://doi.org/10.29816/TARQSS.199012.0001。

    7.焦興鎧（1988）。美國法上不當解雇之概念及其救濟之道。美國研究，18(2)，35-131。https://doi.org/10.7015/AS.198806.0035。

    (二)書籍

    1.三津村直貴、温政堯（2023）。圖解AI人工智慧 = Artificial intelligence。碁峰資訊。97。

    2.上野千鶴子著（2021）。如何做好研究論文：成為知識生產者，從提問到輸出的18個步驟(涂紋凰譯)。三采文化。173-174。

    3.柯克、陳葵懋、Ryan Chung（2024）。極速ChatGPT開發者兵器指南：跨界整合Prompt Flow、LangChain與Semantic Kernel框架。博碩。2-5與2-6。

    4.張永健（2019）。法實證研究：原理、方法與應用。新學林。9與36。

    5.陳弘儒（2020）。初探目的解釋在法律人工智慧系統之運用可能。載於李建良(編)，法律思維與制度的智慧轉型。元照。225-299。

    6.黃劍青（1985）。勞動基準法詳解。三民。154。

    7.蔡炎龍、林澤佑、黃瑜萍、焉然（2024）。少年Py的大冒險：成為Python AI深度學習達人的第一門課(修訂版)。全華圖書。1-32。

    (三)政府出版品

    1.行政院主計總處（2025年5月），2025年3月底工業及服務業受僱員工人數之表1工業及服務業受僱員工薪資調查統計指標，https://www.dgbas.gov.tw/News_Content.aspx?n=3602&s=234880。

    2.國家發展委員會（2024年），中華民國人口推估(2024年至2070年)， https://pop-proj.ndc.gov.tw。

    3.勞動部勞動力發展署，就業市場情勢分析2023年11月至2025年2月月報，https://www.wda.gov.tw/News.aspx?n=33&sms=10307&_CSN=25。

    (四)碩士論文

    1.林武順（1984）。勞工法上解僱問題之研究究〔未出版之碩士論文〕。國立政治大學法律研究所。

    2.劉育承（2021）。勞動基準法第十一條第五款解僱事由之研究──以「不能勝任工作」之判斷標準為中心究〔未出版之碩士論文〕。國立臺灣大學法律學系。

    (五)雜誌新聞

    1.王振容、李瑋（2004年05月25日）。追求績效的人才管理。經濟日報，副刊企管。

    2.管婺媛(2024年06月27日)。企業缺工必學、賺錢力高4成秘密，蘋果、台積都在拚DEI。商業周刊，1911期。取自：商業周刊知識庫。

    3.蔡茹涵(2023年04月06日)。離職、出缺、找人累壞了？金色三麥讓老鳥全職陪伴菜鳥，保住新人黃金3個月。商業周刊，1847期。取自：商業周刊知識庫。

    4.韓化宇(2023年06月08日)。抗缺工》如何讓熟齡夥伴更快上手？養「肌肉記憶」、別一次塞太多東西。商業周刊，1856期。取自：商業周刊知識庫。

    二、英文

    (一)期刊

    1.Alawwad, H. A., Alhothali, A., Naseem, U., Alkhathlan, A., & Jamal, A. (2025). Enhancing textual textbook question answering with large language models and retrieval augmented generation. Pattern Recognition, 162, 111332-. https://doi.org/10.1016/j.patcog.2024.111332.

    2.Bailey, M. K., Crolla, L. J., & Sturtevant, A. (2007). Management Q&A. Medical Laboratory Observer, 39(9), 40-41.

    3.Beecher, D. D. (2003). The Next Wave of Civil Service Reform. Public Personnel Management, 32(4), 457-474. https://doi.org/10.1177/009102600303200401.

    4.Bergman, S., Ji, Z., Kermarrec, A.-M., Petrescu, D., Pires, R., Randl, M., & de Vos, M. (2025). Leveraging Approximate Caching for Faster Retrieval-Augmented Generation. https://doi.org/10.48550/arxiv.2503.05530.

    5.Cho, K., Park, Y., Kim, J., Kim, B., & Jeong, D. (2025). Conversational AI forensics: A case study on ChatGPT, Gemini, Copilot, and Claude. Forensic Science International. Digital Investigation (Online), 52, 301855-301865. https://doi.org/10.1016/j.fsidi.2024.301855.

    6.Costakis, H. R., & Pickern, J. S. (2022). Managing Human Capital Through the Use of Performance Improvement Plans. The Journal of Applied Business and Economics, 24(6), 216-222. https://doi.org/10.33423/jabe.v24i6.5749.

    7.Daley, D. M. (2008). The Burden of Dealing with Poor Performers: Wear and Tear on Supervisory Organizational Engagement. Review of Public Personnel Administration, 28(1), 44-59. https://doi.org/10.1177/0734371X07311253.

    8.David, E. M. (2013). Examining the Role of Narrative Performance Appraisal Comments on Performance. Human Performance, 26(5), 430-450. https://doi.org/10.1080/08959285.2013.836197.

    9.Falcone, P. (2019). Don’t Sugarcoat Performance Reviews. HRNews.

    10.Friel, B. (2005). Red Tape or Redemption. Government
    Executive, 37(12), 88-88.

    11.Goncharsky, A., & Iverson, A. (2018). Human Resource Hot Topics for Closely Held Businesses. Journal of Pension Benefits, 25(3), 22-24.

    12.Hawkins, K. (2021). How to Deal with Underperforming Employees. The Lane Report, 36(9), 56-56.

    13.Janove, J. (2018). Putting Humanity into HR Compliance: Down with Documentation. HRNews.

    14.Janove, J. (2019). Putting Humanity into HR Compliance: Fire Progressive Discipline. HRNews.

    15.Janove, J. (2022).How to Maximize the Same Day Summary. HRNews.

    16.Jones, M. E. (2018). No. 3 Think Before You Fire. The Army Lawyer, 40-45.

    17.Kasson, E. G. (2016). Plan your exits. In HR Magazine ,61(5),110-117.

    18.Ladika, S. (2022). Termination Tips for HR Practitioners. HRMagazine, 1-1.

    19.Lapi, N. (2018). Performance management in the Vanuatu public service: foundations, achievements and challenges. Asia Pacific Journal of Public Administration = Ya Tai Gong Gong Xing Zheng Xue, 40(4), 245-251. https://doi.org/10.1080/23276665.2018.1543096.

    20.Lee, H.-W., & Rhee, D.-Y. (2020). The practices of performance management and low performers in the US Federal Government. International Journal of Manpower, 41(4), 417-433. https://doi.org/10.1108/IJM-12-2018-0404.

    21.Leonard, E. (2020). Career Conversations: Progressive Discipline the Right Way. Reference and User Services Quarterly, 59(2), 92–95. https://doi.org/10.5860/rusq.59.2.7272.

    22.Martin, J. (2021). It’s time to change how we think about our employees. Strategic HR Review, 20(2), 55–59. https://doi.org/10.1108/SHR-01-2021-0002.

    23.McCrea, B. (2021). How to prepare for personnel issues. Independent Banker, 71(5), 22–24.

    24.Nagele, L. (2018). 12 Tips for Handling Employee Terminations and Disciplinary Actions. HRNews.

    25.Panchal, D., Gole, A., Narute, V., & Joshi, R. (2025). LawPal : A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India. https://doi.org/10.48550/arxiv.2502.16573.

    26.Rosemarie Lally, J.D. (2021). Agency Must Justify PIP When Employee Challenges Removal. HRNews.

    27.Russell, K. (2011). Addressing work performance at an individual level. Strategic HR Review, 10(2), 51-53. https://doi.org/10.1108/shr.2011.37210bab.007.

    28.Ryan Scheinkman, Ricardo Cooke, Alexia Vignau, Daniel Green, Philippe Jean-Pierre, & Keyvan Nouri. (2025). Comparing Treatment Recommendations for Ten Dermatological Conditions Using ChatGPT, Claude, and PI AI Models. International Journal of Medical Students, 12-12.

    29.Sahoo, C. K., & Mishra, S. (2012). Performance management benefits organizations and their employees. Human Resource Management International Digest, 20(6), 3-5. https://doi.org/10.1108/09670731211260771.

    30.Simoneaux, S. L., & Stroud, C. L. (2012). BUSINESS BEST PRACTICES: Great Expectations: Performance Management and Development Strategies. Journal of Pension Benefits, 19(2), 74-76.

    31.Smith, J. R. (2020). Performance improvement plan not constructive dismissal. Canadian Employment Law Today, 6-7.

    32.Stephens, S. (2018). How to properly fire a practice employee. Medical Economics, 95(8), 31-32.

    33.Tolan, T. (2015). Consequences of Performance Improvement Plans. Healthcare Informatics, 32(6), 48-48.

    34.Wangsa, K., Karim, S., Gide, E., & Elkhodr, M. (2024). A Systematic Review and Comprehensive Analysis of Pioneering AI Chatbot Models from Education to Healthcare: ChatGPT, Bard, Llama, Ernie and Grok. Future Internet, 16(7), 219-242. https://doi.org/10.3390/fi16070219.

    35.West, S. (2016). RE-BALANCING THE PENDULUM: A RECOMMENDATION FOR CIVIL SERVICE REFORM. Administrative Law Review, 68(2), 359–394.

    36.Yan, Y., Wang, K., Feng, B., Yao, J., Jiang, T., Jin, Z., Zheng, Y., Zhou, Y., Chen, C., Sui, L., Chen, X., Du, Y., Yang, J., Pan, Q., Zhou, L., Wang, V. Y., Liang, P., & Xu, D. (2025). The use of large language models in detecting Chinese ultrasound report errors. NPJ Digital Medicine, 8(1), 66-79. https://doi.org/10.1038/s41746-025-01468-7.

    37.Georgia Institute of Technology, PERFORMANCE IMPROVEMENT PLAN GUIDELINES/PROCESSES, GEORGIA INSTITUTE OF TECHNOLOGY, https://ohr.gatech.edu/sites/default/files/documents/pip_guidelines_process.pdf.

    38.Tschohl, J. (2016). When You Have To Say “YOU’RE FIRED.” American Fastener Journal, 32(5), 82–83.

    (二)書籍

    1.Kirkpatrick, D. L. (2006). The Performance Improvement Plan. In Improving Employee Performance Through Appraisal and Coaching,69-79.AMACOM.

    2.Nair, V. K., Lavanya, B., Biju, A., Alareeni, B., & Hamdan, A. (2024). Generative AI in Cosmetics Regulations: A Comparison Between ChatGPT, Bard, and Claude. In Navigating the Technological Tide: The Evolution and Challenges of Business Model Innovation ,1081, 82-91. Springer. https://doi.org/10.1007/978-3-031-67437-29.

    (三)政府出版品

    1.美國法規。《美國法典》第43章與第75章，https://www.govregs.com/uscode/title5_partIII。

    2.美國政府出版品(1889)。績效管理與獎勵制度重新授權法。https://www.govinfo.gov/content/pkg/STATUTE-103/pdf/STATUTE-103-Pg670.pdf.

    (四)雜誌

    1.Alaniz, R. (2021). Problem Employees – Develop or Dismiss? In Roofing contractor. BNP Media. 14-14.

    2.Prencipe, L. (1997). When firing, become a documentarian. In Network world,14(18),93-93.

    3.Zavitz, P., & Means, R. (2014). Why Progressive Discipline Systems Often Fail. In Law and order, 62(10),16-20.
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    勞工研究所
    108262006
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    数据类型: thesis
    显示于类别:[勞工研究所] 學位論文

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