請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/84571完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃明蕙 | zh_TW |
| dc.contributor.advisor | Ming-Hui Huang | en |
| dc.contributor.author | 胡非凡 | zh_TW |
| dc.contributor.author | Mark van Huijkelom | en |
| dc.date.accessioned | 2023-03-19T22:16:04Z | - |
| dc.date.available | 2023-11-10 | - |
| dc.date.copyright | 2022-09-23 | - |
| dc.date.issued | 2022 | - |
| dc.date.submitted | 2002-01-01 | - |
| dc.identifier.citation | Afiouni-Monla, R. (2019). Organizational Learning in the Rise of Machine Learning. ICIS 2019 Proceedings, 2. https://aisel.aisnet.org/icis2019/business_models/business_models/2
Al-Ahmad, W., Al-Fagih, K., Khanfar, K., Alsamara, K., Abuleil, S., & Abu-Salem, H. (2009). A Taxonomy of an IT Project Failure: Root Causes. International Management Review, 5. https://www.researchgate.net/publication/282135018_A_Taxonomy_of_an_IT_Project_Failure_Root_Causes Ammanath, B., Mittal, N., Saif, I., & Anderson, S. (2021). Becoming an AI-fueled organization (Deloitte's State of AI in the Enterprise, Issue. https://www2.deloitte.com/content/dam/insights/articles/US144384_CIR-State-of-AI-4th-edition/DI_CIR-State-of-AI-4th-edition.pdf Anyoha, R. (2017, 11 January). The History of Artificial Intelligence. https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/ Argote, L., & Miron-Spektor, E. (2011). Organizational Learning: From Experience to Knowledge. Organization Science, 22(5), 1123-1137. https://doi.org/10.1287/orsc.1100.0621 Baier, L., Jöhren, F., & Seebacher, S. (2019). CHALLENGES IN THE DEPLOYMENT AND OPERATION OF MACHINE LEARNING IN PRACTICE (In Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm & Uppsala, Sweden, June 8-14, 2019, Issue ISBN 978-1-7336325-0-8 Research Papers). https://aisel.aisnet.org/ecis2019_rp/163/ Bedeian, A. G. (1986). Contemporary Challenges in the Study of Organizations. Journal of Management, 12(2), 185-201. https://doi.org/10.1177/014920638601200204 Bharadwaj, A. S. (2000). A Resource-Based Perspective on Information Technology Capability and Firm Performance: An Empirical Investigation. MIS Quarterly, 24(1). https://doi.org/10.2307/3250983 Boin, A. (2004). Lessons from Crisis Research. International Studies Review, 6(1), 165-194. https://doi.org/10.1111/j.1521-9488.2004.393_2.x Catalan, C., & Robert, B. (2011). Evaluation of organizational resilience: application in Quebec. https://www.resilience-engineering-association.org/download/resources/symposium/symposium-2011/43.pdf Chakravarthy, B. S. (1986). Measuring strategic performance. Strategic Management Journal, 7(5), 437-458. https://doi.org/10.1002/smj.4250070505 Dellermann, D., Lipusch, N., Ebel, P., Popp, K. M., & Leimeister, J. M. (2017). Finding the Unicorn: Predicting Early Stage Startup Success Through a Hybrid Intelligence Method. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3159123 Duchek, S. (2019). Organizational resilience: a capability-based conceptualization. Business Research, 13(1), 215-246. https://doi.org/10.1007/s40685-019-0085-7 Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Medaglia, R., Le Meunier-FitzHugh, K., Le Meunier-FitzHugh, L. C., Misra, S., Mogaji, E., Sharma, S. K., Singh, J. B., Raghavan, V., Raman, R., Rana, N. P., Samothrakis, S., Spencer, J., Tamilmani, K., Tubadji, A., Walton, P., & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2021). Artificial Intelligence and Business Value: a Literature Review. Information Systems Frontiers. https://doi.org/10.1007/s10796-021-10186-w Eriksson, T., Bigi, A., & Bonera, M. (2020). Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation. The TQM Journal, 32(4), 795-814. https://doi.org/10.1108/tqm-12-2019-0303 Farnese, M. L., Barbieri, B., Chirumbolo, A., & Patriotta, G. (2019). Managing Knowledge in Organizations: A Nonaka’s SECI Model Operationalization. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.02730 Ferràs-Hernández, X. (2017). The Future of Management in a World of Electronic Brains. Journal of Management Inquiry, 27(2), 260-263. https://doi.org/10.1177/1056492617724973 Gittell, J. H., Cameron, K., Lim, S., & Rivas, V. (2016). Relationships, Layoffs, and Organizational Resilience. The Journal of Applied Behavioral Science, 42(3), 300-329. https://doi.org/10.1177/0021886306286466 Gressgård, L. J., & Hansen, K. (2015). Knowledge exchange and learning from failures in distributed environments: The role of contractor relationship management and work characteristics. Reliability Engineering & System Safety, 133, 167-175. https://doi.org/10.1016/j.ress.2014.09.010 Grønhaug, K., & Falkenberg, J. (1990). Organizational success and success criteria: Conceptual issues and an empirical illustration. Scandinavian Journal of Management, 6(4), 267-284. https://doi.org/10.1016/0956-5221(90)90029-g Hive Ventures. (2021). State of Taiwan Enterprise AI Report. https://www.hiveventures.io/sotea Huang, M.-H., & Rust, R. T. (2020). Engaged to a Robot? The Role of AI in Service. Journal of Service Research, 24(1), 30-41. https://doi.org/10.1177/1094670520902266 Hughes, D. L., Rana, N. P., & Simintiras, A. C. (2017). The changing landscape of IS project failure: an examination of the key factors. Journal of Enterprise Information Management, 30(1), 142-165. https://doi.org/10.1108/jeim-01-2016-0029 IBM, & Morning Consult. (2022). IBM Global AI Adoption Index 2022. https://www.ibm.com/downloads/cas/GVAGA3JP Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586. https://doi.org/10.1016/j.bushor.2018.03.007 Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25. https://doi.org/10.1016/j.bushor.2018.08.004 Karman, A. (2020). Flexibility, coping capacity and resilience of organizations: between synergy and support. Journal of Organizational Change Management, 33(5), 883-907. https://doi.org/10.1108/jocm-10-2019-0305 Keding, C. (2020). Understanding the interplay of artificial intelligence and strategic management: four decades of research in review. Management Review Quarterly, 71(1), 91-134. https://doi.org/10.1007/s11301-020-00181-x Kim, J., Egan, T., & Tolson, H. (2015). Examining the Dimensions of the Learning Organization Questionnaire. Human Resource Development Review, 14(1), 91-112. https://doi.org/10.1177/1534484314555402 Knowles, E. (2006). The Oxford Dictionary of Phrase and Fable. https://doi.org/10.1093/acref/9780198609810.001.0001 Krakowski, S., Luger, J., & Raisch, S. (2022). Artificial intelligence and the changing sources of competitive advantage. Strategic Management Journal. https://doi.org/10.1002/smj.3387 Kulkov, I. (2021). The role of artificial intelligence in business transformation: A case of pharmaceutical companies. Technology in Society, 66. https://doi.org/10.1016/j.techsoc.2021.101629 Lee, J., Suh, T., Roy, D., & Baucus, M. (2019). Emerging Technology and Business Model Innovation: The Case of Artificial Intelligence. Journal of Open Innovation: Technology, Market, and Complexity, 5(3). https://doi.org/10.3390/joitmc5030044 Lengnick-Hall, C. A., Beck, T. E., & Lengnick-Hall, M. L. (2011). Developing a capacity for organizational resilience through strategic human resource management. Human Resource Management Review, 21(3), 243-255. https://doi.org/10.1016/j.hrmr.2010.07.001 Linnenluecke, M. K. (2017). Resilience in Business and Management Research: A Review of Influential Publications and a Research Agenda. International Journal of Management Reviews, 19(1), 4-30. https://doi.org/10.1111/ijmr.12076 Luthans, F., Avolio, B. J., Avey, J. B., & Norman, S. M. (2007). Positive Psychological Capital: Measurement and Relationship with Performance and Satisfaction. Personnel Psychology, 60(3), 541-572. https://doi.org/10.1111/j.1744-6570.2007.00083.x Madsen, P. M. (2009). These Lives Will Not Be Lost in Vain: Organizational Learning from Disaster in U.S. Coal Mining. Organization Science, 20(5), 861-875. https://doi.org/10.1287/orsc.1080.0396 Maltz, A. C., Shenhar, A. J., & Reilly, R. R. (2003). Beyond the Balanced Scorecard. Long Range Planning, 36(2), 187-204. https://doi.org/10.1016/s0024-6301(02)00165-6 McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955. AI Magazine, 27(4). https://doi.org/https://doi.org/10.1609/aimag.v27i4.1904 Meyer, A. D. (1982). Adapting to Environmental Jolts. Administrative Science Quarterly, 27(4). https://doi.org/10.2307/2392528 Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3). https://doi.org/10.1016/j.im.2021.103434 Nelson, R. R. (2008). IT Project Management: Infamous Failures, Classic Mistakes, and Best Practices. MIS Quarterly Executive, 6(2). https://aisel.aisnet.org/misqe/vol6/iss2/4 Nonaka, I. (1994). A Dynamic Theory of Organizational Knowledge Creation. Organization Science, 5(1), 14-37. https://doi.org/10.1287/orsc.5.1.14 Otioma, C. (2022). IT Capability, Organisational Learning and Innovation Performance of Firms in Kenya. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-021-00886-8 Pinto, J. K., & Mantel, S. J. (1990). The causes of project failure. IEEE Transactions on Engineering Management, 37(4), 269-276. https://doi.org/10.1109/17.62322 Prabhakar, G. P. (2009). What is Project Success: A Literature Review. International Journal of Business and Management, 3(9). https://doi.org/10.5539/ijbm.v3n9p3 Prem, E. (2019). Artificial Intelligence for Innovation in Austria. Technology Innovation Management Review, 9(12), 5-15. https://doi.org/10.22215/timreview/1287 Rana, S. (2021). Creating ‘PROP’ to Achieve Organizational Success: A Future Research Agenda. FIIB Business Review, 10(1), 1-4. https://doi.org/10.1177/23197145211006218 Ransbotham, S., Khodabandeh, S., Kiron, D., Candelon, F., Chu, M., & Lafountain, B. (2020). Expanding AI's Impact With Organizational Learning. MIT Sloan Management Review and Boston Consulting Group. https://sloanreview.mit.edu/projects/expanding-ais-impact-with-organizational-learning/ Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping Business With Artificial Intelligence. MIT Sloan Management Review and Boston Consulting Group. http://sloanreview.mit.edu/AI2017 Ruiz-Real, J. L., Uribe-Toril, J., Torres, J. A., & De Pablo, J. (2020). Artificial Intelligence in Business and Economics Research: Trends and Future. Journal of Business Economics and Management, 22(1), 98-117. https://doi.org/10.3846/jbem.2020.13641 Sitkin, S. B., & Pablo, A. L. (1992). Reconceptualizing the Determinants of Risk Behavior. The Academy of Management Review, 17(1). https://doi.org/10.2307/258646 Staw, B. M., Sandelands, L. E., & Dutton, J. E. (1981). Threat Rigidity Effects in Organizational Behavior: A Multilevel Analysis. Administrative Science Quarterly, 26(4). https://doi.org/10.2307/2392337 Sutcliffe, K. M., & Vogus, T. J. (2003). Organizing for Resilience. https://www.researchgate.net/publication/255853978_Organizing_for_Resilience Thia, C.-W. (2020). Return on Investment (ROI) of Artificial Intelligence (AI) - Impact of AI on Business Key Performance Indicators (KPIs) National Taiwan University]. Taiwan. von Krogh, G. (2018). Artificial Intelligence in Organizations: New Opportunities for Phenomenon-Based Theorizing. Academy of Management Discoveries, 4(4), 404-409. https://doi.org/10.5465/amd.2018.0084 Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924. https://doi.org/10.1108/bpmj-10-2019-0411 Westenberger, J., Schuler, K., & Schlegel, D. (2022). Failure of AI projects: understanding the critical factors. Procedia Computer Science, 196, 69-76. https://doi.org/10.1016/j.procs.2021.11.074 Williams, T. A., Gruber, D. A., Sutcliffe, K. M., Shepherd, D. A., & Zhao, E. Y. (2017). Organizational Response to Adversity: Fusing Crisis Management and Resilience Research Streams. Academy of Management Annals, 11(2), 733-769. https://doi.org/10.5465/annals.2015.0134 Wood Daudelin, M. (1996). Learning from experience through reflection. Organizational Dynamics, 24(3), 36-48. https://doi.org/10.1016/s0090-2616(96)90004-2 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/84571 | - |
| dc.description.abstract | Artificial intelligence as a research topic is prevalent, and interest from the corporate world is increasing, with organizations looking into how they can incorporate AI technology. However, applying AI within companies doesn’t go with much ease. Many AI projects tend to fail, either because they are terminated early or because they didn’t meet the expectations of the decision-makers. For that reason, researchers and organizations are asking themselves whether there are ways to prevent failure or improve the odds of AI projects.
Research explores important antecedents, enablers, inhibitors, and AI capabilities. There is also much research on organizational learning, project, and organizational success. With all that, one question seems to remain, what if an AI project isn’t going well and an organization is facing adversity in its undertaking? With that question, this research steers in the direction of organizational resilience and, more precisely, adaptation, anticipation, and coping capabilities. The research focused on conceptualizing this research subject and the essential related variables to answer the main research question. A theoretical framework and a measurement scale were developed to hypothesize how adaptation, specific AI-anticipation and coping capabilities can help an organization face adversity during its AI projects. At the end of this research, the measurement tool is still unvalidated. But the implication of both the framework and measurement scale is that a new research field suddenly reveals itself, providing a better understanding of potential future research. Furthermore, it led to several suggestions and advice for organizations wanting to or deploying AI. | zh_TW |
| dc.description.abstract | Artificial intelligence as a research topic is prevalent, and interest from the corporate world is increasing, with organizations looking into how they can incorporate AI technology. However, applying AI within companies doesn’t go with much ease. Many AI projects tend to fail, either because they are terminated early or because they didn’t meet the expectations of the decision-makers. For that reason, researchers and organizations are asking themselves whether there are ways to prevent failure or improve the odds of AI projects.
Research explores important antecedents, enablers, inhibitors, and AI capabilities. There is also much research on organizational learning, project, and organizational success. With all that, one question seems to remain, what if an AI project isn’t going well and an organization is facing adversity in its undertaking? With that question, this research steers in the direction of organizational resilience and, more precisely, adaptation, anticipation, and coping capabilities. The research focused on conceptualizing this research subject and the essential related variables to answer the main research question. A theoretical framework and a measurement scale were developed to hypothesize how adaptation, specific AI-anticipation and coping capabilities can help an organization face adversity during its AI projects. At the end of this research, the measurement tool is still unvalidated. But the implication of both the framework and measurement scale is that a new research field suddenly reveals itself, providing a better understanding of potential future research. Furthermore, it led to several suggestions and advice for organizations wanting to or deploying AI. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T22:16:04Z (GMT). No. of bitstreams: 1 U0001-1909202210132600.pdf: 2293684 bytes, checksum: 26cfb0e63f46dbc58b4aa82214e6182d (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | Acknowledgments i
Abstract ii Contents iii List of Tables and Figures v List of Abbreviations vi 1. Introduction 1 1.1. Research Problem 3 1.2. Research Methodology 3 1.3. Structure of Thesis 3 2. Literature Review 5 2.1. Artificial Intelligence 5 2.2. AI Capability 8 2.3. Organizational Learning Capabilities 11 2.4. Organizational Resilience 14 2.5. Adaptation Capabilities 18 2.6. Organizational AI Anticipation and Coping Capabilities 21 2.7. AI Project Failures 23 2.8. Factors Leading to AI Project Failure 26 2.9. Organizational Success 28 3. Theoretical Framework 33 3.1. Example of an Organization Facing Difficulties 34 3.2. Organizational Learning and Adaptation Capabilities 36 3.3. Organizational AI Anticipation and Coping Capabilities 38 3.4. AI Project Failure 39 3.5. Organizational Success 41 4. Measurement 43 5. Discussion and Directions for Future Research 50 5.1. Validity Measurement Scale 53 5.2. Measuring the Relationships of the AI-Resilience Capabilities 54 5.3. Impact of AI-Resilience Capabilities on AI-Project and Organizational Success 54 5.4. Impact of AI-Project Success on Organizational Success 55 5.5. Bigger Context: AI-Resilience Capabilities vs. AI-Capability 56 5.6. Bigger Context: AI Projects in their Environment 56 5.7. Bigger Context: AI supporting organizational resilience 57 5.8. Uncharted Territory: Out-of-the-Box (Research) Questions 58 6. Suggestions for Managers Intending to Deploy AI in their Organization 61 6.1. Embrace Uncertainty 61 6.2. Flexibility in Action 62 6.3. Build Social Capital 63 6.4. Self-Assessment 64 6.5. Find your AI-Path 64 6.5.1. Starting the First AI Project 65 6.5.2. Terminating or Finishing an AI Project 68 6.5.3. Executing Several AI Projects 69 6.5.4. Having Several Years of Experience Executing AI Projects 70 6.5.5. Reviewing an Organization’s AI path 70 Bibliography 71 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 人工智慧 | zh_TW |
| dc.subject | 應對 | zh_TW |
| dc.subject | 預期 | zh_TW |
| dc.subject | 適應 | zh_TW |
| dc.subject | 組織成功 | zh_TW |
| dc.subject | 項目失敗 | zh_TW |
| dc.subject | Anticipation | en |
| dc.subject | Coping | en |
| dc.subject | Adaptation | en |
| dc.subject | Artificial Intelligence | en |
| dc.subject | Project Failure | en |
| dc.subject | Organizational Success | en |
| dc.title | 企業環境下人工智慧應用之評估與策略 | zh_TW |
| dc.title | Developing Strategies for the Successful Application of Artificial Intelligence in Corporate Environments | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 110-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 許文馨;堯里昂 | zh_TW |
| dc.contributor.oralexamcommittee | Audrey Hsu;Leon van Jaarsveldt | en |
| dc.subject.keyword | 人工智慧,項目失敗,組織成功,適應,預期,應對, | zh_TW |
| dc.subject.keyword | Artificial Intelligence,Project Failure,Organizational Success,Adaptation,Anticipation,Coping, | en |
| dc.relation.page | 78 | - |
| dc.identifier.doi | 10.6342/NTU202203550 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2022-09-22 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 企業管理碩士專班 | - |
| dc.date.embargo-lift | 2022-09-23 | - |
| 顯示於系所單位： | 管理學院企業管理專班(Global MBA) | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-110-2.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 2.24 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。