跳到主要內容

臺灣博碩士論文加值系統

(100.28.0.143) 您好!臺灣時間:2024/07/14 23:33
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:褚富宥
研究生(外文):CHU, FU-YU
論文名稱:人工智能之運用對企業品牌績效的影響之研究
論文名稱(外文):A Research on the Impact of the Application of Artificial Intelligence on Corporate Brand Performance
指導教授:王信文王信文引用關係蕭輔力
指導教授(外文):WANG, XIN-WENHSIAO,FU-LI
口試委員:王信文蕭輔力施碧渶程立民
口試委員(外文):WANG,HSING-WENHSIAO,FU-LIShih, Bi-YingCHENG, LI-MIN
口試日期:2024-06-25
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:光電科技研究所科技應用與管理碩士在職專班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:中文
論文頁數:79
中文關鍵詞:人工智能企業績效組織敏捷性顧客體驗顧客關係
外文關鍵詞:Artificial IntelligenceBrand PerformanceOrganizational AgilityCustomer ExperienceCustomer Relationship
相關次數:
  • 被引用被引用:0
  • 點閱點閱:6
  • 評分評分:
  • 下載下載:1
  • 收藏至我的研究室書目清單書目收藏:0
本研究探討人工智能技術在台灣企業中的應用對品牌績效的影響。隨著科技的迅速發展,人工智能在全球各行各業的應用日益普及。台灣作為重要的科技樞紐,企業品牌對人工智能技術的採用逐步增長。研究旨在通過實證分析,探討人工智能技術如何通過提升員工效率、改善客戶服務質量,從而提升企業品牌績效。本研究以問卷調查法收集數據,並利用SPSS進行分析,結果顯示人工智能同化對組織敏捷性、顧客體驗、顧客關係品質和企業品牌績效有顯著的正向影響。此外,研究發現人工智能技術能夠有效提升企業品牌的市場競爭力和應對變化的能力,並在不同產業中展現出廣泛的應用潛力。本文最後提出實務建議,幫助企業在數位轉型過程中充分利用人工智能技術提升品牌績效,實現持續的競爭優勢,並促進企業長期的可持續發展。
This study explores the impact of artificial intelligence (AI) technology applications on brand performance in Taiwanese enterprises. With the rapid development of technology, the application of AI has become increasingly widespread across various industries globally. Taiwan, as a significant technology hub, is seeing a gradual increase in the adoption of AI technology by corporate brands. The research aims to empirically analyze how AI technology enhances brand performance by improving employee efficiency and customer service quality. Data were collected through a questionnaire survey and analyzed using SPSS. The results show that AI assimilation has a significant positive impact on organizational agility, customer experience, customer relationship quality, and brand performance. Additionally, the study finds that AI technology effectively enhances the market competitiveness of corporate brands and their ability to adapt to changes, demonstrating broad application potential across different industries. The paper concludes with practical recommendations to help enterprises leverage AI technology during digital transformation to enhance brand performance, achieve sustained competitive advantages, and promote long-term sustainable development.
摘要I
AbstractII
誌謝III
目錄IV
表目錄VII
圖目錄IX
第一章 緒論1
第一節 研究背景與動機1
第二節 研究目的3
第三節 研究流程5
第四節 研究範圍8
第二章 文獻探討9
第一節 人工智能同化9
第二節 組織與客戶敏捷性10
第三節 顧客體驗12
第四節 顧客關係品質13
第五節 企業績效14
第三章 研究方法15
第一節 研究設計15
第二節 研究架構15
第三節 研究假設16
第四節 問卷設計19
第五節 操作性定義與衡量20
第六節 研究對象24
第七節 資料分析方法25
第四章 研究結果26
第一節 敘述性分析26
第二節 信度分析40
第三節 效度分析48
第四節 相關性分析50
第五節 線性迴歸分析53
第六節 研究結果58
第五章 結論與建議62
第一節 實證結果分析62
第二節 研究結論63
第三節 研究貢獻與意涵64
第四節 研究限制與建議65
參考文獻68
附錄73
表目錄
表3-1人工智能同化之問項20
表3-2組織與顧客敏捷性之問項21
表3-3顧客體驗之問項22
表3-4顧客關係品質之問項23
表3-5企業績效之問項24
表4-1受訪者性別之分析27
表4-2受訪者年齡分析27
表4-3受訪者居住縣市29
表4-4受訪者教育程度30
表4-5受訪者年收入31
表4-6受訪者職業32
表4-7受訪者職位34
表4-8受訪者所待的公司規模(資本額)34
表4-9描述統計結果(n=364)35
表4-10人工智能同化量表之信度分析41
表4-11組織與顧客敏捷性量表之信度分析42
表4-12顧客體驗量表之信度分析43
表4-13顧客關係品質量表之信度分析44
表4-14企業績效量表之信度分析46
表4-15可靠性統計47
表4-16總體信度分析47
表4-17KMO 和Bartlett球形檢驗48
表4-18因素分析表49
表4-19皮爾森(Pearson)相關係數51
表4-20人工智能同化對組織與顧客敏捷性迴歸分析表(n=364)53
表4-21人工智能同化對顧客體驗迴歸分析表(n=364)54
表4-22人工智能同化對顧客關係品質迴歸分析表(n=364)55
表4-23人工智能同化對企業績效迴歸分析表(n=364)56
表4-24組織與顧客敏捷性、顧客體驗、顧客關係品質對企業績效迴歸分析表(n=364)58
表4-25研究假設驗證結果61
圖目錄
圖1-1本研究之研究流程7
圖3-1研究架構16
一、中文部份
美通社. (2022).IBM 發布《2022 年全球 AI 科技使用現況》. https://www.digitimes.com.tw/tech/dt/n/shwnws.asp?id=0000639197_PVR0HRF4LDC6OH6XZY29V
微軟新聞中心. (2023).《趨勢名人堂》實現可信賴的 AI 應用願景:淺談負責任 AI. https://news.microsoft.com/zh-tw/features/responsible-ai/
二、英文部份
PwC. (2024). AI Jobs Barometer reveals AI’s impact on jobs, wages, skills, and productivity. https://pwc.com/aijobsbarometer
Abousaber, I., & Abdalla, H. (2023). Review of Using Technologies of Artificial Intelligence in Companies. . International Journal of Communication Networks and Information Security (IJCNIS). https://doi.org/10.17762/ijcnis.v15i1.5743
Agarwal, A., Shankar, R., & Tiwari, M. (2007). Modeling agility of supply chain. Industrial Marketing Management, 36, 443-457. https://doi.org/https://doi.org/10.1016/J.INDMARMAN.2005.12.004.
Agarwal, S., Krishna Erramilli, M., & Dev, C. S. (2003). Market orientation and performance in service firms: role of innovation. Journal of Services Marketing, 17(1), 68-82. https://doi.org/10.1108/08876040310461282
Alshahrani, A., Dennehy, D., & Mäntymäki, M. (2022). An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia. Government Information Quarterly, 39(4), 101617. https://doi.org/https://doi.org/10.1016/j.giq.2021.101617
An, L. (2022). Research on Short Video Publishing Algorithm and Recommendation Mechanism Based on Artificial Intelligence [Video recommendation algorithm; Double layer feature representation; Multi head self attention]. 2022, 3(2), 6. https://doi.org/10.47738/jads.v3i2.59
Andrade, I. M. D., & Tumelero, C. (2022). Increasing customer service efficiency through artificial intelligence chatbot. Revista de Gestão, 29(3), 238-251. https://doi.org/10.1108/REGE-07-2021-0120
Bondan Seno Aji, U. U., Mulyanto Nugroho. (2021). Relationship of Customer Relationship Learning to Service Quality of Regional Development Banks. Asian Multicultural Research Study, 2(2), 40-45. https://doi.org/10.47616/JAMREMS.V2I2.108
da Costa, R. L., Gupta, V., Gonçalves, R., Dias, Á., Pereira, L., & Gupta, C. (2022). Artificial Intelligence and Cognitive Computing in Companies in Portugal: An Outcome of Partial Least Squares—Structural Equations Modeling. Mathematics, 10(22), 4358. https://www.mdpi.com/2227-7390/10/22/4358
Fadli. (2023). The effect of customer experience on customer loyalty through customer satisfaction to users of transportation services online in medan city. . International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration, 3(4), 1090-1094. https://doi.org/10.54443/ijebas.v3i4.981
Fosso Wamba, S. (2022). Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility. International Journal of Information Management, 67, 102544. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2022.102544
Haenlein, M., & Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5-14. https://doi.org/10.1177/0008125619864925
Kalaignanam, K., Tuli, K. R., Kushwaha, T, & Lee, L., & Gal, D (2021). Marketing Agility: The Concept, Antecedents, and a Research Agenda. Journal of Marketing, 85(1), 35-58. https://doi.org/https://doi.org/10.1177/0022242920952760
Kunal, K., Ramprakash, K. R., Arun, C. J., & Xavier, M. J. (2023). A behavioural study on the impact of artificial intelligence on customer services retention in telecom industry. Russian Law Journal, 11(5s). https://doi.org/https://doi.org/10.52783/rlj.v11i5s.960
Lee, H., & Choi, B. (2003). Knowledge Management Enablers, Processes, and Organizational Performance: An Integrative View and Empirical Examination. Journal of Management Information Systems, 20(1), 179-228. https://doi.org/10.1080/07421222.2003.11045756
Li, Y., Zhang, Y., Xu, J., & Feng, T. (2020). The impacts of customer involvement on the relationship between relationship quality and performance. Journal of Business & Industrial Marketing, 35(2), 270-283. https://doi.org/10.1108/JBIM-04-2018-0131
Lu, Y., & Ramamurthy, K. (2011). Understanding the link between information technology capability and organizational agility: An empirical examination. MIS quarterly, 931-954.
M.I. Idian, A. S. T. (2023). Artificial Intelligence, Blockchain, Machine Learning, and Customer Relationship Management. Journal of Management Information Systems, 2(1), 16-20. https://doi.org/10.56741/bst.v2i01.276
Maddy, E. S., & Boukabara, S. A. (2021). MIIDAPS-AI: An Explainable Machine-Learning Algorithm for Infrared and Microwave Remote Sensing and Data Assimilation Preprocessing - Application to LEO and GEO Sensors. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 8566-8576. https://doi.org/10.1109/JSTARS.2021.3104389
Overby, E., Bharadwaj, A., & Sambamurthy, V. (2006). Enterprise Agility and the Enabling Role of Information Technology. EJIS, 15, 120-131. https://doi.org/10.1057/palgrave.ejis.3000600
Prentice, C., & Nguyen, M. (2020). Engaging and retaining customers with AI and employee service. . Journal of Retailing and Consumer Services, 56, 102186. https://doi.org/ https://doi.org/10.1016/j.jretconser.2020.102186
Prentice, C., Dominique-Ferreira, S., & Wang, X. (2020). The impact of artificial intelligence and employee service quality on customer satisfaction and loyalty. Journal of Hospitality Marketing & Management, 29. https://doi.org/10.1080/19368623.2020.1722304
Prikshat, V., Malik, A., & Budhwar, P. (2023). AI-augmented HRM: Antecedents, assimilation and multilevel consequences. Human Resource Management Review, 33(1), 100860. https://doi.org/https://doi.org/10.1016/j.hrmr.2021.100860
Queiroz, M., Tallon, P. P., Sharma, R., & Coltman, T. (2018). The role of IT application orchestration capability in improving agility and performance. The Journal of Strategic Information Systems, 27(1), 4-21. https://doi.org/https://doi.org/10.1016/j.jsis.2017.10.002
Ravichandran, T. (2018). Exploring the relationships between IT competence, innovation capacity and organizational agility. The Journal of Strategic Information Systems, 27(1), 22-42. https://doi.org/https://doi.org/10.1016/j.jsis.2017.07.002
Readman, J., Squire, B., Bessant, J., & Brown, S. (2006). The application of agile manufacturing for customer value. Journal of financial transformation, 18, 133-141.
Roberts, N., & Grover, V. (2012). Leveraging Information Technology Infrastructure to Facilitate a Firm's Customer Agility and Competitive Activity: An Empirical Investigation. . Journal of Management Information Systems, 28, 231 - 270. https://doi.org/ https://doi.org/10.2753/MIS0742-1222280409.
Ruangkanjanasesb, T. H. A. (2024). Assessing the impact of artificial intelligence on customer performance: A quantitative study using partial least squares methodology. Data Science and Management, 7, 155-163. https://doi.org/10.1016/j.dsm.2024.01.001
Sambamurthy, V., Bharadwaj, A., & Grover, V. (2003). Shaping Agility Through Digital Options: Reconceptualizing the Role of Information Technology in Contemporary Firms. MIS quarterly, 27, 237-263. https://doi.org/10.2307/30036530
Schein, E. H. (1989). Corporate culture and organizational effectiveness, by D. R. Denison. New York, NY: John Wiley & Sons, Inc., 1990, 267 pp. $39.95. Human Resource Management, 28(4), 557-561. https://doi.org/https://doi.org/10.1002/hrm.3930280408
Sciascia, I. A. (2023). From Market Segmentation to Customer Loyalty. International journal of business and management, 18(4), 192-199. https://doi.org/ 10.5539/ijbm.v18n4p192
Seth, M. (2019). The Four Drivers of Artificial Intelligence. https://www.peoplematters.in/article/technology/the-four-drivers-of-artificial-intelligence-22858.
Sidaoui, K., Jaakkola, M., & Burton, J. (2020). AI feel you: customer experience assessment via chatbot interviews. Journal of Service Management, 31(4), 745-766. https://doi.org/10.1108/JOSM-11-2019-0341
StandfordUniversity. (2024). Measuring trends in AI.
Suraj Pal, S. H., A. D. Talwar. (2023). Artificial Intelligence Tools for Enhancing Customer Experience. International Journal For Science Technology And Engineering, 11(5), 7040-7047. https://doi.org/10.22214/ijraset.2023.53360
Venkatraman, N., & Ramanujam, V. (1986). Measurement of Business Performance in Strategy Research: A Comparison of Approaches. The Academy of Management Review, 11(4), 801-814. https://doi.org/10.2307/258398
Zheng, H., Jiang, L., Lou, H., Hu, Y., Kong, X., & Lu, H. (2011). Application of Artificial Neural Network (ANN) and Partial Least-Squares Regression (PLSR) to Predict the Changes of Anthocyanins, Ascorbic Acid, Total Phenols, Flavonoids, and Antioxidant Activity during Storage of Red Bayberry Juice Based on Fractal Analysis and Red, Green, and Blue (RGB) Intensity Values. Journal of Agricultural and Food Chemistry, 59(2), 592-600. https://doi.org/10.1021/jf1032476
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top