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題名 重新想像職場：探討員工與人資對設計AI驅動人力資源代理人的觀點。
Reimagining the Workplace: Exploring Employee and HR Perspectives on Designing an AI-Enabled HR Agent.
作者 韓可琳
Roquefeuil, Colombine Bonhomme -- de
貢獻者 侯宗佑
Hou, Yoyo Tsung-Yu
韓可琳
Colombine Bonhomme -- de Roquefeuil
關鍵詞 人工智慧（AI）
人力資源管理（HRM）
AI人資代理人
扎根理論
AI設計
Artificial intelligence (AI)
Human resource management (HRM)
AI HR agent
Grounded theory
AI design
日期 2025
上傳時間 4-Aug-2025 15:41:51 (UTC+8)
摘要 隨著人工智慧（AI）日益整合至職場組織中，其在人力資源管理（HRM）中的應用既帶來機會，也伴隨挑戰。儘管組織對於AI應用的興趣日益增加，現有文獻中仍明顯缺乏對最終使用者——包括員工與人資專業人士——如何看待AI代理人於人力資源管理整合與設計的研究。本研究針對此一缺口，探討員工與人資專業人士如何認知AI代理人在HR情境中的角色與設計。同時，本研究運用UTAUT與TTF理論架構，分析AI在HRM功能中有效且適切的採用方式。本研究採用九位受訪者進行半結構式訪談，並以扎根理論為基礎進行資料分析，聚焦於以下三個面向：對AI整合的期望與疑慮、促進信任與接受度的AI設計特徵、以及情感與人際需求如何影響AI代理人執行特定HR任務的適切性。研究結果顯示，參與者普遍支持在例行性、重複性或資料密集型的人資工作中導入AI，例如招募、排程與員工諮詢。然而，涉及同理心、倫理判斷或情感敏感度的任務，如績效評估或裁員，則被認為不適合完全由AI代理人執行。參與者強調在此類情境中人類介入的重要性，並指出AI設計應注重清晰溝通、降低擬人化程度，以及設定適當的使用限制與使用者控制權。本研究結論指出，儘管AI代理人可提升日常人資工作的效率，其導入過程仍須謹慎規劃。使用者偏好明確非擬人化、文字為主且透明的介面設計，這顯示過度擬人化在部分工作場域應用中可能削弱信任與清晰度。資料隱私與可解釋性亦為關鍵考量，且有效整合仰賴人資專業人員保有決策權，並接受技術與批判性思維相關訓練。值得注意的是，研究結果指出AI並非取代人資角色，而是重新塑造其職能，使其更聚焦於策略性與人際互動層面的工作。此研究期望能為組織如何負責任地導入AI提供實務建議，亦對人力資源管理與AI代理人相關文獻作出理論貢獻。
As artificial intelligence (AI) becomes increasingly integrated into organizational functions, its ap-plication in human resource management (HRM) presents both opportunities and challenges. De-spite the increasing interest in AI applications within organizations, there remains a notable gap in the literature regarding how end users, both employees and HR professionals, may perceive the integration and design of AI-enabled agents in human resource management (HRM). This study addresses this gap by exploring how employees and HR professionals perceive the role and design of AI-enabled agents in HR contexts. This study also applies the UTAUT and TTF frameworks to investigate effective and appropriate AI adoption in HRM functions. Using semi-structured inter-views with nine interviewees and a grounded theory-based analysis, the research aims to examine the following three areas: the expectations and concerns regarding AI integration, the preferred de-sign features that foster trust and acceptance, and the influence of emotional and relational demands on the suitability of AI agents for specific HR tasks. Findings reveal strong support for the use of AI in routine, repetitive, or data-intensive tasks such as recruitment, scheduling, and employee que-ries. However, tasks requiring empathy, ethical judgment, or emotional sensitivity, such as perfor-mance reviews or layoffs, were deemed unsuitable for full AI delegation. Interviewees vouched for the need for human involvement in these contexts and highlighted the importance of clear commu-nication, minimal anthropomorphism, and appropriate limits and user control in AI design. The study concludes that while AI-enabled agents can enhance efficiency in routine HR tasks, their adoption must be approached thoughtfully. Users preferred designs that were clearly artificial, text-based, and transparent, indicating that anthropomorphic features may reduce trust and clarity in cer-tain work setting contexts. Data privacy and explainability also emerged as critical concerns, and effective integration was deemed to depend on HR professionals retaining decision-making au-thority and receiving both technical and critical thinking training. Importantly, the findings suggest that AI is not replacing HR roles but reshaping them, shifting the focus toward more strategic and interpersonal responsibilities. This research provides practical implications for organizations look-ing to adopt AI responsibly and offers theoretical contributions to the HRM and AI agent literature.
參考文獻 Abaza, W. A., & Eltobgy, A. E. (2025). Examining the intention to use artificial intelligence in re-cruitment and selection within human resource management: A case study of Egyptian tourism and travel agencies. Journal of Association of Arab Universities for Tourism and Hospitality, 28(1), 83–105. Available at: https://jaauth.journals.ekb.eg/ Adeoye-Olatunde, O. A., & Olenik, N. L. (2021). Research and scholarly methods: Semi-structured interviews. Journal of the American College of Clinical Pharmacy, 4, 1358–1367. Available at: https://doi.org/10.1002/jac5.1441 Adobe Stock. (n.d). Cute robot which symbolizes online chatbot or voice support service bot for artificial intelligence or virtual assistant concept. Modern flat style cartoon character for simple logo or icon [Digital image]. Pinterest. Available at: https://in.pinterest.com/pin/411868328442500824/ Ahmad, M., & Wilkins, S. (2024). Purposive sampling in qualitative research: A framework for the entire journey. Quality & Quantity. 1-19. Available at: https://doi.org/10.1007/s11135-024-02022-5 Akbarighatar, P. (2024). Operationalizing responsible AI principles through responsible AI capa-bilities. AI and Ethic, 5, 1787-1801. Available at: https://doi.org/10.1007/s43681-024-00524-4 Alabed, A., Javornik, A., & Gregory-Smith, D. (2022). AI anthropomorphism and its effect on users' self-congruence and self–AI integration: A theoretical framework and research agen-da. Technological Forecasting and Social Change, 182, 1-19. Available at: https://doi.org/10.1016/j.techfore.2022.121786 Almeida, F. et al. (2025). Understanding recruiters’ acceptance of artificial intelligence: Insights from the technology acceptance model. Applied Sciences, 15(746), 1-26. Available at: https://doi.org/10.3390/app15020746 Altemeyer, B. (2019). Making the business case for AI in HR: Two case studies. Strategic HR Re-view, 18(2), 66–70. Available at: https://doi.org/10.1108/SHR-12-2018-0101 Basnet, S. (2024). The impact of AI-driven predictive analytics on employee retention strate-gies. International Journal of Research and Review, 11(9), 50–58. Available at: https://doi.org/10.52403/ijrr.20240906 Bharti, M. (2025). AI agents: A systematic review of architectures, components, and evolutionary trajectories in autonomous digital systems. International Journal of Computer Engineering and Technology (IJCET), 16(1), 809–820. Available at: https://doi.org/10.34218/IJCET_16_01_065 Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. Available at: https://doi.org/10.1191/1478088706qp063oa Castillo-Montoya, M. (2016). Preparing for interview research: The interview protocol refinement framework. The Qualitative Report, 21(5), 811–831. Available at: https://doi.org/10.46743/2160-3715/2016.2337 Clarke, R. (2019). Principles and business processes for responsible AI. Computer Law & Security Review, 35, 410–422. Available at: https://doi.org/10.1016/j.clsr.2019.04.007 Dennis, A. R., Lakhiwal, A., & Sachdeva, A. (2023). AI agents as team members: Effects on satis-faction, conflict, trustworthiness, and willingness to work with. Journal of Management In-formation Systems, 40(2), 307–337. Available at: https://doi.org/10.1080/07421222.2023.2196773 Dima, J. et al. (2024). The effects of artificial intelligence on human resource activities and the roles of the human resource triad: opportunities and challenges. Frontiers in Psychology, 15(1360401), 1-15. Available at: https://doi.org/10.3389/fpsyg.2024.1360401 3DPT.ru. (n.d.). Robot Sophia 3D model [Product image]. Available at: https://3dpt.ru/product/robot-sophia Du, J. (2024). Unlocking the potential: Literature review on the evolving role of AI in HRM. Frontiers in Management Science, 3(1), 28–33. Available at: https://doi.org/10.56397/FMS.2024.02.05 Dutta, D., & Mishra, S. K. (2024). Artificial intelligence-based virtual assistant and employee en-gagement: An empirical investigation. Personnel Review, 54(3), 913-934. Available at: https://doi.org/10.1108/PR-03-2023-0263 Garg, N., Jain, R., & Khera, S. N. (2022). Adoption of AI-enabled tools in social development or-ganizations in India: An extension of UTAUT model. Frontiers in Psychology, 13, 1-17. Available at: https://doi.org/10.3389/fpsyg.2022.893691 Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236. Available at: https://www.jstor.org/stable/249689 Goswami, M. et al. (2023). Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector. Frontiers in Pharmacology, 14, 1215706, 1-14. Available at: https://doi.org/10.3389/fphar.2023.1215706 Huang, Y. (2024). Levels of AI agents: From rules to large language models. arXiv, Cornell Uni-versity, 1-11. Available at:https://arxiv.org/abs/2405.06643 Idrus, S. et al. (2023). THE EVOLUTION OF HUMAN RESOURCE MANAGEMENT: THE NECESSITY OF FACING THE INDUSTRIAL REVOLUTION 4.0. Komitmen: Jurnal Ilmiah Manajemen, 4(1), 91–97. DOI:10.15575/jim.v4i1.23726 Inzlicht, M. et al. (2024). In praise of empathic AI. Trends in Cognitive Sciences, 28(2), 89-91. Available at: https://www.cell.com/trends/cognitive-sciences/abstract/S1364-6613(23)00289-9 Jaikum, T. (n.d.). AI generated 3D cartoon man in white suit. Businessman character on transparent background Pro PNG [Digital image]. Vecteezy. Available at: https://www.vecteezy.com/png/35914354-ai-generated-3d-cartoon-man-in-white-suit-businessman-character-on-transparent-background Jatobá, M. et al. (2019). Evolution of artificial intelligence research in human resources. Procedia Computer Science, 164, 137–142. Available at: https://doi.org/10.1016/j.procs.2019.12.165 Kota, S. K. (2025). The evolution of AI agents: From rule-based systems to autonomous intelligence – A comprehensive review. Journal of Artificial Intelligence & Cloud Computing, 4(2), 1–5. Available at: https://doi.org/10.47363/JAICC/2025(4)433 Kusal, S. et al. (2022). AI-based conversational agents: A scoping review from technologies to fu-ture directions. IEEE Access, 10, 92337–92356. Available at: https://doi.org/10.1109/ACCESS.2022.3201144 Latin Satelital. [n.d.] AGIBOT A2 Humanoid Robot [Digital image]. Available at: https://www.latinsatelital.com/AGIBOT-A2.htm Lippert, I. (2024). Artificial intelligence and the future of managerial roles: A theoretical review. In Proceedings of the Thirty-Second European Conference on Information Systems (ECIS 2024),1-17. Available at: https://www.researchgate.net/publication/379999788 Narimisaei, J. et al. (2024). Exploring emotional intelligence in artificial intelligence systems: A comprehensive analysis of emotion recognition and response mechanisms. Annals of Medicine and Surgery, 86(8), 4657–4663. Available at: https://doi.org/10.1097/MS9.0000000000002315 Nyathani, R. (2023). AI in performance management: Redefining performance appraisals in the digital age. Journal of Artificial Intelligence & Cloud Computing, 2(4), 1–5. Available at: https://doi.org/10.47363/JAICC/2023(2)134 Parasa, S. K. (2024). Impact of AI in compensation management in HR digital transfor-mation. International Journal of Science and Research, 13(6), 1391–1392. DOI: 10.21275/sr24621182320 Placani, A. (2024). Anthropomorphism in AI: hype and fallacy. AI and Ethics, 4, 691–698. Availa-ble at: https://doi.org/10.1007/s43681-024-00419-4 Prikshat, V. et al. (2022). A multi-stakeholder ethical framework for AI-augmented HRM. International Journal of Manpower, 43(1), 226–250. Available at: https://doi.org/10.1108/IJM-03-2021-0118 Przegalinska, A. et al. (2024). Collaborative AI in the workplace: Enhancing organizational perfor-mance through resource-based and task-technology fit perspectives. International Journal of Information Management, 81, 1-24. Available at: https://doi.org/10.1016/j.ijinfomgt.2024.102853 Saputra, F. E. et al. (2024). Anthropomorphism-based artificial intelligence (AI) robots typology in hospitality and tourism. Journal of Hospitality and Tourism Technology, 15(5), 790–807. Available at: https://doi.org/10.1108/JHTT-03-2024-0171 Savoie, L. (2025). Roborock Saros Z70 [Digital picture]. Business Insider. Available at: https://www.businessinsider.com/guides/home/roborock-saros-z70-preview-2025-4 Siau, K., & Wang, W. (2020). Artificial intelligence (AI) ethics: Ethics of AI and ethical AI. Journal of Database Management, 31(2), 74–86. Available at: https://doi.org/10.4018/JDM.2020040105 Solwen.ai. (2024). ChatBot [Digital image].. Available at: https://solwen.ai/posts/ai-chatbot Sposato, M. (2025). The future of AI in HR: A speculative review. Strategic HR Review, 24(1), 1–3. Available at: https://doi.org/10.1108/SHR-02-2025-0015 Terblanche, N. H. D. (2024). Artificial intelligence (AI) coaching: Redefining people development and organizational performance. The Journal of Applied Behavioral Science, 60(40), 1-8. Available at: https://doi.org/10.1177/00218863241283919 Uneeq. (2024). Sophie, a digital human from UneeQ will take the stage at SIGGRAPH 2024. Pow-ered by Gen AI, She'll answer questions in real-time [Digital image]. PRWeb. Available at: https://www.prweb.com/releases/uneeq-holds-first-ever-speaking-session-co-presented-by-an-ai-powered-digital-human-sophie-and-showcases-next-generation-digital-humans-at-siggraph-2024-302208329.html University of Edinburgh. (2013). Interview consent form. School of GeoSciences – Ethics Commit-tee. Available at: https://edwebcontent.ed.ac.uk/sites/default/files/imports/fileManager/Interview_Consent_Form.pdf Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0 – A glimpse. Procedia Manufacturing, 20, 233–238. Available at: https://doi.org/10.1016/j.promfg.2018.02.034ScienceDirect+2Stume Jour-nals+2ScienceDirect+2 Venkatesh, V. et al. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. Available at: https://www.researchgate.net/publication/220259897 Vicci, H. (2024). Emotional intelligence in AI systems: An exploratory review. The IUP Journal of Soft Skills, 18(2), 5–20. Available at: https://www.proquest.com/openview/75b60b22771831e4d2fd4af2aa9f8a7b/1.pdf?cbl=2029989&pq-origsite=gscholar Watling, C. et al. (2017). Necessary Groundwork: Planning a Strong Grounded Theory Study. Journal of Graduate Medical Education, 9(1), 129-130. doi: 10.4300/JGME-D-16-00693.1
描述 碩士
國立政治大學
國際傳播英語碩士學位學程(IMICS)
112461015
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112461015
資料類型 thesis
dc.contributor.advisor 侯宗佑zh_TW
dc.contributor.advisor Hou, Yoyo Tsung-Yuen_US
dc.contributor.author (Authors) 韓可琳zh_TW
dc.contributor.author (Authors) Colombine Bonhomme -- de Roquefeuilen_US
dc.creator (作者) 韓可琳zh_TW
dc.creator (作者) Roquefeuil, Colombine Bonhomme -- deen_US
dc.date (日期) 2025en_US
dc.date.accessioned 4-Aug-2025 15:41:51 (UTC+8)-
dc.date.available 4-Aug-2025 15:41:51 (UTC+8)-
dc.date.issued (上傳時間) 4-Aug-2025 15:41:51 (UTC+8)-
dc.identifier (Other Identifiers) G0112461015en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158778-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 國際傳播英語碩士學位學程(IMICS)zh_TW
dc.description (描述) 112461015zh_TW
dc.description.abstract (摘要) 隨著人工智慧（AI）日益整合至職場組織中，其在人力資源管理（HRM）中的應用既帶來機會，也伴隨挑戰。儘管組織對於AI應用的興趣日益增加，現有文獻中仍明顯缺乏對最終使用者——包括員工與人資專業人士——如何看待AI代理人於人力資源管理整合與設計的研究。本研究針對此一缺口，探討員工與人資專業人士如何認知AI代理人在HR情境中的角色與設計。同時，本研究運用UTAUT與TTF理論架構，分析AI在HRM功能中有效且適切的採用方式。本研究採用九位受訪者進行半結構式訪談，並以扎根理論為基礎進行資料分析，聚焦於以下三個面向：對AI整合的期望與疑慮、促進信任與接受度的AI設計特徵、以及情感與人際需求如何影響AI代理人執行特定HR任務的適切性。研究結果顯示，參與者普遍支持在例行性、重複性或資料密集型的人資工作中導入AI，例如招募、排程與員工諮詢。然而，涉及同理心、倫理判斷或情感敏感度的任務，如績效評估或裁員，則被認為不適合完全由AI代理人執行。參與者強調在此類情境中人類介入的重要性，並指出AI設計應注重清晰溝通、降低擬人化程度，以及設定適當的使用限制與使用者控制權。本研究結論指出，儘管AI代理人可提升日常人資工作的效率，其導入過程仍須謹慎規劃。使用者偏好明確非擬人化、文字為主且透明的介面設計，這顯示過度擬人化在部分工作場域應用中可能削弱信任與清晰度。資料隱私與可解釋性亦為關鍵考量，且有效整合仰賴人資專業人員保有決策權，並接受技術與批判性思維相關訓練。值得注意的是，研究結果指出AI並非取代人資角色，而是重新塑造其職能，使其更聚焦於策略性與人際互動層面的工作。此研究期望能為組織如何負責任地導入AI提供實務建議，亦對人力資源管理與AI代理人相關文獻作出理論貢獻。zh_TW
dc.description.abstract (摘要) As artificial intelligence (AI) becomes increasingly integrated into organizational functions, its ap-plication in human resource management (HRM) presents both opportunities and challenges. De-spite the increasing interest in AI applications within organizations, there remains a notable gap in the literature regarding how end users, both employees and HR professionals, may perceive the integration and design of AI-enabled agents in human resource management (HRM). This study addresses this gap by exploring how employees and HR professionals perceive the role and design of AI-enabled agents in HR contexts. This study also applies the UTAUT and TTF frameworks to investigate effective and appropriate AI adoption in HRM functions. Using semi-structured inter-views with nine interviewees and a grounded theory-based analysis, the research aims to examine the following three areas: the expectations and concerns regarding AI integration, the preferred de-sign features that foster trust and acceptance, and the influence of emotional and relational demands on the suitability of AI agents for specific HR tasks. Findings reveal strong support for the use of AI in routine, repetitive, or data-intensive tasks such as recruitment, scheduling, and employee que-ries. However, tasks requiring empathy, ethical judgment, or emotional sensitivity, such as perfor-mance reviews or layoffs, were deemed unsuitable for full AI delegation. Interviewees vouched for the need for human involvement in these contexts and highlighted the importance of clear commu-nication, minimal anthropomorphism, and appropriate limits and user control in AI design. The study concludes that while AI-enabled agents can enhance efficiency in routine HR tasks, their adoption must be approached thoughtfully. Users preferred designs that were clearly artificial, text-based, and transparent, indicating that anthropomorphic features may reduce trust and clarity in cer-tain work setting contexts. Data privacy and explainability also emerged as critical concerns, and effective integration was deemed to depend on HR professionals retaining decision-making au-thority and receiving both technical and critical thinking training. Importantly, the findings suggest that AI is not replacing HR roles but reshaping them, shifting the focus toward more strategic and interpersonal responsibilities. This research provides practical implications for organizations look-ing to adopt AI responsibly and offers theoretical contributions to the HRM and AI agent literature.en_US
dc.description.tableofcontents Acknowledgements iii 摘要 iv Abstract v List of Figures and Tables ix Introduction 1 Chapter 1: Literature Review 3 1 HRM and AI 3 1.1 The evolution of HRM 3 1.2 Smart human resource management 4.0 (Smart HRM 4.0) 4 2 AI agents and HRM 6 2.1 Understanding AI agents 6 2.2 Key ideas in designing AI agents for HRM. 7 3 Theories of AI adoption in HRM 14 4 Risks of AI-driven HRM 15 Chapter 2: Methodology 18 1 Research design 18 2 Recruiting process and interviewees 19 3 Data collection 20 5 Ethical considerations 22 Chapter 3: Results 22 Section 1: Work background and context 26 Theme 1.1 Organizational structure shapes the scope of HR roles 26 Theme 1.2 HR functions reflect a balance between administration and strategy 27 Theme 1.3 HR tasks tend to be logistically demanding 31 Section 2: Attitudes towards AI agents in HRM 31 Theme 2.1 General openness towards AI Agents implementation in HRM 31 Theme 2.2 Conceptual interest, practical inexperience 32 Section 3: Expectations and concerns (RQ1) 32 Theme 3.1 Repetitive tasks: ideal candidates for AI delegation 33 Theme 3.2 Boundaries of AI’s reach 37 Theme 3.3 AI can help, but it’s not always safe 40 Section 4: Agentic design preferences (RQ2) 44 Theme 4.1 Text-based and clear is best 45 Theme 4.2 Keep it machine-like 47 Theme 4.3 AI should support, not replace 50 Section 5: Emotional and relational considerations (RQ3) 52 Theme 5.1 Some tasks still need human touch 53 Theme 5.2 AI still cannot handle emotions well 54 Section 6: Potential AI integration support strategies 55 Theme 6.1 How to build trust in AI 56 Theme 6.2 To integrate AI in HRM, start small and train people well 58 Theme 6.3 AI frees HR to focus on what really matters 61 Chapter 4: Discussion 62 1 Summary and interpretation of key findings 62 1.1 Expectations and concerns regarding AI agents (RQ1) 63 1.2 Preferred design features for AI-enabled HR agents (RQ2) 64 1.3 The influence of emotional and relational task features on AI design (RQ3) 66 2 Theoretical implications 66 2.1 Task-Technology Fit (TTF) 66 2.2 Unified Theory of Acceptance and Use of Technology (UTAUT) 67 3 Practical implications 68 4 Limitations of the study 70 5 Recommendations for future research 71 Conclusion 72 References 74 APPENDIX A: INTERVIEW CONSENT FORM 81 APPENDIX B: RESEARCH ETHICS CERTIFICATE 85 APPENDIX C: ORIGINAL INTERVIEW PROTOCOL FOR HR PROFESSIONALS 86 APPENDIX D: FIRST INTERVIEW PROTOCOL ITERATION FOR HR PROFESSIONALS 89 APPENDIX E: SECOND INTERVIEW PROTOCOL ITERATION FOR HR PROFESSIONALS 92 APPENDIX F: ORIGINAL INTERVIEW PROTOCOL FOR EMPLOYEES 95 APPENDIX G: FIRST INTERVIEW PROTOCOL ITERATION FOR EMPLOYEES 99 APPENDIX H: SECOND INTERVIEW PROTOCOL ITERATION FOR EMPLOYEES 103 APPENDIX I: THIRD INTERVIEW PROTOCOL ITERATION FOR EMPLOYEES 107 APPENDIX J: FIRST INTERVIEW TRANSCRIPT 111 APPENDIX K: PRELIMINARY CODING PROCESS ON NVIVO 134 APPENDIX L: MODIFIED CODEBOOK EXTRACTED FROM NVIVO. 135 APPENDIX M: DATA CROSS-CHECKING PROCESS USING CHATGPT 141zh_TW
dc.format.extent 4040943 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112461015en_US
dc.subject (關鍵詞) 人工智慧（AI）zh_TW
dc.subject (關鍵詞) 人力資源管理（HRM）zh_TW
dc.subject (關鍵詞) AI人資代理人zh_TW
dc.subject (關鍵詞) 扎根理論zh_TW
dc.subject (關鍵詞) AI設計zh_TW
dc.subject (關鍵詞) Artificial intelligence (AI)en_US
dc.subject (關鍵詞) Human resource management (HRM)en_US
dc.subject (關鍵詞) AI HR agenten_US
dc.subject (關鍵詞) Grounded theoryen_US
dc.subject (關鍵詞) AI designen_US
dc.title (題名) 重新想像職場：探討員工與人資對設計AI驅動人力資源代理人的觀點。zh_TW
dc.title (題名) Reimagining the Workplace: Exploring Employee and HR Perspectives on Designing an AI-Enabled HR Agent.en_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Abaza, W. A., & Eltobgy, A. E. (2025). Examining the intention to use artificial intelligence in re-cruitment and selection within human resource management: A case study of Egyptian tourism and travel agencies. Journal of Association of Arab Universities for Tourism and Hospitality, 28(1), 83–105. Available at: https://jaauth.journals.ekb.eg/ Adeoye-Olatunde, O. A., & Olenik, N. L. (2021). Research and scholarly methods: Semi-structured interviews. Journal of the American College of Clinical Pharmacy, 4, 1358–1367. Available at: https://doi.org/10.1002/jac5.1441 Adobe Stock. (n.d). Cute robot which symbolizes online chatbot or voice support service bot for artificial intelligence or virtual assistant concept. Modern flat style cartoon character for simple logo or icon [Digital image]. Pinterest. Available at: https://in.pinterest.com/pin/411868328442500824/ Ahmad, M., & Wilkins, S. (2024). Purposive sampling in qualitative research: A framework for the entire journey. Quality & Quantity. 1-19. Available at: https://doi.org/10.1007/s11135-024-02022-5 Akbarighatar, P. (2024). Operationalizing responsible AI principles through responsible AI capa-bilities. AI and Ethic, 5, 1787-1801. Available at: https://doi.org/10.1007/s43681-024-00524-4 Alabed, A., Javornik, A., & Gregory-Smith, D. (2022). AI anthropomorphism and its effect on users' self-congruence and self–AI integration: A theoretical framework and research agen-da. Technological Forecasting and Social Change, 182, 1-19. Available at: https://doi.org/10.1016/j.techfore.2022.121786 Almeida, F. et al. (2025). Understanding recruiters’ acceptance of artificial intelligence: Insights from the technology acceptance model. Applied Sciences, 15(746), 1-26. Available at: https://doi.org/10.3390/app15020746 Altemeyer, B. (2019). Making the business case for AI in HR: Two case studies. Strategic HR Re-view, 18(2), 66–70. Available at: https://doi.org/10.1108/SHR-12-2018-0101 Basnet, S. (2024). The impact of AI-driven predictive analytics on employee retention strate-gies. International Journal of Research and Review, 11(9), 50–58. Available at: https://doi.org/10.52403/ijrr.20240906 Bharti, M. (2025). AI agents: A systematic review of architectures, components, and evolutionary trajectories in autonomous digital systems. International Journal of Computer Engineering and Technology (IJCET), 16(1), 809–820. Available at: https://doi.org/10.34218/IJCET_16_01_065 Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. Available at: https://doi.org/10.1191/1478088706qp063oa Castillo-Montoya, M. (2016). Preparing for interview research: The interview protocol refinement framework. The Qualitative Report, 21(5), 811–831. Available at: https://doi.org/10.46743/2160-3715/2016.2337 Clarke, R. (2019). Principles and business processes for responsible AI. Computer Law & Security Review, 35, 410–422. Available at: https://doi.org/10.1016/j.clsr.2019.04.007 Dennis, A. R., Lakhiwal, A., & Sachdeva, A. (2023). AI agents as team members: Effects on satis-faction, conflict, trustworthiness, and willingness to work with. Journal of Management In-formation Systems, 40(2), 307–337. Available at: https://doi.org/10.1080/07421222.2023.2196773 Dima, J. et al. (2024). The effects of artificial intelligence on human resource activities and the roles of the human resource triad: opportunities and challenges. Frontiers in Psychology, 15(1360401), 1-15. Available at: https://doi.org/10.3389/fpsyg.2024.1360401 3DPT.ru. (n.d.). Robot Sophia 3D model [Product image]. Available at: https://3dpt.ru/product/robot-sophia Du, J. (2024). Unlocking the potential: Literature review on the evolving role of AI in HRM. Frontiers in Management Science, 3(1), 28–33. Available at: https://doi.org/10.56397/FMS.2024.02.05 Dutta, D., & Mishra, S. K. (2024). Artificial intelligence-based virtual assistant and employee en-gagement: An empirical investigation. Personnel Review, 54(3), 913-934. Available at: https://doi.org/10.1108/PR-03-2023-0263 Garg, N., Jain, R., & Khera, S. N. (2022). Adoption of AI-enabled tools in social development or-ganizations in India: An extension of UTAUT model. Frontiers in Psychology, 13, 1-17. Available at: https://doi.org/10.3389/fpsyg.2022.893691 Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236. Available at: https://www.jstor.org/stable/249689 Goswami, M. et al. (2023). Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector. Frontiers in Pharmacology, 14, 1215706, 1-14. Available at: https://doi.org/10.3389/fphar.2023.1215706 Huang, Y. (2024). Levels of AI agents: From rules to large language models. arXiv, Cornell Uni-versity, 1-11. Available at:https://arxiv.org/abs/2405.06643 Idrus, S. et al. (2023). THE EVOLUTION OF HUMAN RESOURCE MANAGEMENT: THE NECESSITY OF FACING THE INDUSTRIAL REVOLUTION 4.0. Komitmen: Jurnal Ilmiah Manajemen, 4(1), 91–97. DOI:10.15575/jim.v4i1.23726 Inzlicht, M. et al. (2024). In praise of empathic AI. Trends in Cognitive Sciences, 28(2), 89-91. Available at: https://www.cell.com/trends/cognitive-sciences/abstract/S1364-6613(23)00289-9 Jaikum, T. (n.d.). AI generated 3D cartoon man in white suit. Businessman character on transparent background Pro PNG [Digital image]. Vecteezy. Available at: https://www.vecteezy.com/png/35914354-ai-generated-3d-cartoon-man-in-white-suit-businessman-character-on-transparent-background Jatobá, M. et al. (2019). Evolution of artificial intelligence research in human resources. Procedia Computer Science, 164, 137–142. Available at: https://doi.org/10.1016/j.procs.2019.12.165 Kota, S. K. (2025). The evolution of AI agents: From rule-based systems to autonomous intelligence – A comprehensive review. Journal of Artificial Intelligence & Cloud Computing, 4(2), 1–5. Available at: https://doi.org/10.47363/JAICC/2025(4)433 Kusal, S. et al. (2022). AI-based conversational agents: A scoping review from technologies to fu-ture directions. IEEE Access, 10, 92337–92356. Available at: https://doi.org/10.1109/ACCESS.2022.3201144 Latin Satelital. [n.d.] AGIBOT A2 Humanoid Robot [Digital image]. Available at: https://www.latinsatelital.com/AGIBOT-A2.htm Lippert, I. (2024). Artificial intelligence and the future of managerial roles: A theoretical review. In Proceedings of the Thirty-Second European Conference on Information Systems (ECIS 2024),1-17. Available at: https://www.researchgate.net/publication/379999788 Narimisaei, J. et al. (2024). Exploring emotional intelligence in artificial intelligence systems: A comprehensive analysis of emotion recognition and response mechanisms. Annals of Medicine and Surgery, 86(8), 4657–4663. Available at: https://doi.org/10.1097/MS9.0000000000002315 Nyathani, R. (2023). AI in performance management: Redefining performance appraisals in the digital age. Journal of Artificial Intelligence & Cloud Computing, 2(4), 1–5. Available at: https://doi.org/10.47363/JAICC/2023(2)134 Parasa, S. K. (2024). Impact of AI in compensation management in HR digital transfor-mation. International Journal of Science and Research, 13(6), 1391–1392. DOI: 10.21275/sr24621182320 Placani, A. (2024). Anthropomorphism in AI: hype and fallacy. AI and Ethics, 4, 691–698. Availa-ble at: https://doi.org/10.1007/s43681-024-00419-4 Prikshat, V. et al. (2022). A multi-stakeholder ethical framework for AI-augmented HRM. International Journal of Manpower, 43(1), 226–250. Available at: https://doi.org/10.1108/IJM-03-2021-0118 Przegalinska, A. et al. (2024). Collaborative AI in the workplace: Enhancing organizational perfor-mance through resource-based and task-technology fit perspectives. International Journal of Information Management, 81, 1-24. Available at: https://doi.org/10.1016/j.ijinfomgt.2024.102853 Saputra, F. E. et al. (2024). Anthropomorphism-based artificial intelligence (AI) robots typology in hospitality and tourism. Journal of Hospitality and Tourism Technology, 15(5), 790–807. Available at: https://doi.org/10.1108/JHTT-03-2024-0171 Savoie, L. (2025). Roborock Saros Z70 [Digital picture]. Business Insider. Available at: https://www.businessinsider.com/guides/home/roborock-saros-z70-preview-2025-4 Siau, K., & Wang, W. (2020). Artificial intelligence (AI) ethics: Ethics of AI and ethical AI. Journal of Database Management, 31(2), 74–86. Available at: https://doi.org/10.4018/JDM.2020040105 Solwen.ai. (2024). ChatBot [Digital image].. Available at: https://solwen.ai/posts/ai-chatbot Sposato, M. (2025). The future of AI in HR: A speculative review. Strategic HR Review, 24(1), 1–3. Available at: https://doi.org/10.1108/SHR-02-2025-0015 Terblanche, N. H. D. (2024). Artificial intelligence (AI) coaching: Redefining people development and organizational performance. The Journal of Applied Behavioral Science, 60(40), 1-8. Available at: https://doi.org/10.1177/00218863241283919 Uneeq. (2024). Sophie, a digital human from UneeQ will take the stage at SIGGRAPH 2024. Pow-ered by Gen AI, She'll answer questions in real-time [Digital image]. PRWeb. Available at: https://www.prweb.com/releases/uneeq-holds-first-ever-speaking-session-co-presented-by-an-ai-powered-digital-human-sophie-and-showcases-next-generation-digital-humans-at-siggraph-2024-302208329.html University of Edinburgh. (2013). Interview consent form. School of GeoSciences – Ethics Commit-tee. Available at: https://edwebcontent.ed.ac.uk/sites/default/files/imports/fileManager/Interview_Consent_Form.pdf Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0 – A glimpse. Procedia Manufacturing, 20, 233–238. Available at: https://doi.org/10.1016/j.promfg.2018.02.034ScienceDirect+2Stume Jour-nals+2ScienceDirect+2 Venkatesh, V. et al. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. Available at: https://www.researchgate.net/publication/220259897 Vicci, H. (2024). Emotional intelligence in AI systems: An exploratory review. The IUP Journal of Soft Skills, 18(2), 5–20. Available at: https://www.proquest.com/openview/75b60b22771831e4d2fd4af2aa9f8a7b/1.pdf?cbl=2029989&pq-origsite=gscholar Watling, C. et al. (2017). Necessary Groundwork: Planning a Strong Grounded Theory Study. Journal of Graduate Medical Education, 9(1), 129-130. doi: 10.4300/JGME-D-16-00693.1zh_TW