博碩士論文 105460009 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:7 、訪客IP:3.80.223.123
姓名 詹以如(I-Ju Jan)  查詢紙本館藏   畢業系所 會計研究所企業資源規劃會計碩士在職專班
論文名稱 團隊的結構和特徵對大數據分析能力的影響
(The Impact of Team Structure and Characteristics on Big Data Analytical Capability)
相關論文
★ 行動應用程式產業生態系之研究★ 設計思維應用於醫療器材組裝之研究
★ 台灣中小型家族企業傳承─交班困難因素之探討★ 電動機車共享經濟營運模式之研究
★ 區塊鏈運作原理與應用之研究★ P2P網路信用借貸營運模式之研究
★ 線上交友平台商業模式之研究─以亞洲領導品牌為例★ 買賣雙方依賴關係與影響策略對採購績效之影響
★ 以第四方物流經營模式分析博連資訊科技股份有限公司★ 探討虛擬環境下隱性協調在新產品導入之作用--以電子組裝業為例
★ 動態能力機會擷取機制之研究-以A公司為例★ 探討以價值驅動之商業模式創新-以D公司為例
★ 物聯網行動支付之探討-以Apple Pay與支付寶錢包為例★ B2C網路黏著度之探討-以博客來為例
★ 組織機制與吸收能力關係之研究-以新產品開發專案為例★ Revisit the Concept of Exploration and Exploitation
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2022-6-20以後開放)
摘要(中) 大數據是當今討論話題的中心,所有行業的組織都在大量投資大數據行動。大數據分析已經成為學術界和業界的重要研究領域,因為它大大改變了訊息的產生和用於決策的方式。儘管如此,這種新興科技在技術方面過於關注,並且對其他相關組織元素的關注有限。過去以資源基礎論或資訊科技發展為觀點的研究表明,組織要重新整合各項資源,這些資源組合建立了大數據分析能力。然而,基於組織能力的運作必定由組織來進行,產生這個能力的前因,必定和組織本身有關。在組織中,大數據分析團隊會來執行這個任務。團隊的結構、團隊的特徵和組織的慣例會影響組織能力的使用。本研究利用Garner的四層級資料分析作為測量大數據分析的階段,以文獻分析法推導團隊結構和團隊特徵,在組織慣例的運作下,如何影響各階層的大數據分析能力。
摘要(英) Big data is the most intensively discussed topics today, organizations in all industries are large investing in big data initiatives. Big data analytics has become an important area of research in academia and industry because it has dramatically changed the way information are generated and used in decision making. Nevertheless, this emerging technology takes too much attention to technological aspects and has limited focus on other relevant organizational elements. Previous studies based on resource-based theory or information technology development has shown that organizations need to re-integrate resources that combine to build big data analytics. However, the operation based on organizational capabilities must be carried out by the organization, and the antecedent of this ability must be related to the nature of the organization. In the organization, the big data analytics team will perform this task. The structure and characteristics of the team, and the organization routine will influence the use of organizational capabilities. This study (1) uses Garner′s four-level data analysis as a stage for measuring big data analysis, (2) identifies team structure, team characteristics and organization routine that in combination build a big data analytical capability.
關鍵字(中) ★ 大數據
★ 大數據分析能力
★ 團隊結構
★ 團對特徵
★ 組織慣例
關鍵字(英) ★ Big data
★ Big data analytical capability
★ team structure
★ team characteristics
★ organization routine
論文目次 中文摘要 i
英文摘要 ii
目錄 iii
圖目錄 v
表目錄 vi
一 、緒論 1
1-1 研究背景 1
1-2 研究動機及問題 2
二 、文獻回顧 4
2-1 核心文獻探討 4
2-1-1 25 Years of Team Effectiveness in Organizations: Research Themes and Emerging Needs (Salas, Stagl, & Burke, 2005) 4
2-1-2 Transferring, Translating, and Transforming: An Integrative Framework for Managing Knowledge Across Boundaries(Carlile, 2004) 5
2-1-3 Team-Level Predictors of Innovation at Work: A Comprehensive Meta-Analysis Spanning Three Decades of Research(Hülsheger, Anderson, & Salgado, 2009) 6
2-1-4 Translating Team Creativity to Innovation Implementation The Role of Team Composition and Climate for Innovation(Somech & Drach-Zahavy, 2013) 7
2-1-5 A dynamic perspective on diverse teams: Moving from the dual-process model to a dynamic coordination-based model of diverse team performance(Srikanth, Harvey, & Peterson, 2016) 7
2-1-6 Big Data : The Management Revolution(McAfee & Brynjolfsson, 2012) 8
2-1-7 Predicts 2013: Information Innovation(Bitterer, Sallam, & Kart, 2012) 8
2-2定義 12
2-2-1團隊結構Team Structure 12
2-2-2 成員多元性Diversity of team members 14
2-2-3 權力分佈Power distribution 14
2-2-4 創新氛圍Climate for innovation 15
2-2-5 收集資料 Collect Data 16
2-2-6 以數據為主的決策文化 Data-Driven decision making-culture 17
2-2-7 溝通 Communication 18
2-2-8 協調 Coordination 19
三、 研究方法及架構 21
3-1文獻分析法 21
3-2研究架構 23
3-3研究命題 24
四、 研究討論 25
五、 結論與建議 30
5-1 結論 30
5-2 研究限制與未來建議 30
六、參考文獻 31
參考文獻 Anderson, N. R., & West, M. A. (1998). Measuring climate for work group innovation: development and validation of the team climate inventory. Journal of Organizational Behavior, 19(3), 235-258. doi: doi:10.1002/(SICI)1099-1379(199805)19:3<235::AID-JOB837>3.0.CO;2-C
Barker, J. R. (1993). Tightening the Iron Cage: Concertive Control in Self-Managing Teams. Administrative Science Quarterly, 38(3), 408-437. doi: 10.2307/2393374
Bitterer, D. L. A., Sallam, R. L., & Kart, L. (2012). Predicts 2013: Information Innovation. Gartner (G00246040).
Brown, J. S., & Duguid, P. (2001). Knowledge and Organization: A Social-Practice Perspective. Organization Science, 12(2), 198-213. doi: 10.1287/orsc.12.2.198.10116
Burke, S., C Stagl, K., Salas, E., Pierce, L., & Kendall, D. (2006). Understanding Team Adaptation: A Conceptual Analysis and Model (Vol. 91).
Campion, M. A., Medsker, G. J., & Higgs, A. C. (1993). Relations Between Work Group Characteristics and Effectiveness: Implications for Designing Effective Work Groups (Vol. 46): Purdue University, Krannert Graduate School of Management.
Carlile, P. R. (2004). Transferring, Translating, and Transforming: An Integrative Framework for Managing Knowledge Across Boundaries. Organization Science, 15(5), 555-568. doi: 10.1287/orsc.1040.0094
Carlile, P. R., & Rebentisch, E. S. (2003). Into the Black Box: The Knowledge Transformation Cycle. Management Science, 49(9), 1180-1195. doi: 10.1287/mnsc.49.9.1180.16564
Correa, T., Hinsley, A. W., & de Zúñiga, H. G. (2010). Who interacts on the Web?: The intersection of users’ personality and social media use. Computers in Human Behavior, 26(2), 247-253. doi: https://doi.org/10.1016/j.chb.2009.09.003
Couldry, N. (2012). Media, society, world: Social theory and digital media practice: Polity.
Delen, D., & Demirkan, H. (2013). Data, information and analytics as services. Decision Support Systems, 55(1), 359-363. doi: https://doi.org/10.1016/j.dss.2012.05.044
Edmondson, A. (1999). Psychological Safety and Learning Behavior in Work Teams. Administrative Science Quarterly, 44(2), 350-383. doi: 10.2307/2666999
Evans, J. R., & Lindner, C. H. (2012). Business analytics: the next frontier for decision sciences. Decision Line, 43(2), 4-6.
George, G., Haas, M. R., & Pentland, A. (2014). Big data and management: Academy of Management Briarcliff Manor, NY.
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049-1064. doi: https://doi.org/10.1016/j.im.2016.07.004
Hülsheger, U. R., Anderson, N., & Salgado, J. F. (2009). Team-level predictors of innovation at work: a comprehensive meta-analysis spanning three decades of research. Journal of Applied Psychology, 94(5), 1128-1145.
Hahn, G. J., & Packowski, J. (2015). A perspective on applications of in-memory analytics in supply chain management. Decision Support Systems, 76, 45-52.
Homan, A. C., Knippenberg, D. v., Kleef, G. A. V., & Dreu, C. K. W. D. (2006). Bridging Faultlines by Valuing Diversity: Diversity Beliefs, Information Elaboration, and Performance in Diverse Work Groups. Journal of Applied Psychology, 92, 1189-1199. doi: 10.1037/0021-9010.92.5.1189
Johnson, M. W., Christensen, C. M., & Kagermann., H. (2008). Reinventing Your Business Model (Vol. 87).
Kakhani, M. K., Kakhani, S., & Biradar, S. (2013). Research issues in big data analytics. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 2(8), 228-232.
Keller, R. T. (2001). Cross-functional project groups in research and new product development: Diversity, communications, job stress, and outcomes. Academy of management journal, 44(3), 547-555.
Kitchenham, B. (2007). Guidelines for Performing Systematic Literature Reviews in Software Engineering, Version 2.3 EBSE Technical Report EBSE-2007-01.
Lavalle, S., Lesser, E., Shockley, R., S. Hopkins, M., & Kruschwitz, N. (2011). Big Data, Analytics and the Path From Insights to Value (Vol. 52).
Leonard-Barton, D. (1995). Wellsprings of knowledge: Building and sustaining the sources of innovation.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.
Manz, C. C. (1992). Self-leading work teams: Moving beyond self-management myths. Human relations, 45(11), 1119-1140.
Markus, M. L. (1983). Power, politics, and MIS implementation. Communications of the ACM, 26(6), 430-444.
McAfee, A., & Brynjolfsson, E. (2012). Big data: the management revolution. Harvard Business Review, 90(10), 60-68.
McDonald, R. (2011). Inside P&G’s digital revolution. McKinsey Quarterly.
Menon, S. (2001). Employee empowerment: An integrative psychological approach. Applied psychology, 50(1), 153-180.
Milliken, F. J., & Martins, L. L. (1996). Searching for common threads: Understanding the multiple effects of diversity in organizational groups. Academy of management review, 21(2), 402-433.
Mohammed, S., & Nadkarni, S. (2011). Temporal Diversity and Team Performance: The Moderating Role of Team Temporal Leadership. Academy of management journal, 54(3), 489-508. doi: 10.5465/amj.2011.61967991
Nonaka, I. (1994). A Dynamic Theory of Organizational Knowledge Creation. Organization Science, 5(1), 14-37.
Payne, R. (1990). The effectiveness of research teams: A review: John Wiley & Sons.
Perry-Smith, J. E., & Shalley, C. E. (2003). The social side of creativity: A static and dynamic social network perspective. Academy of management review, 28(1), 89-106.
Pfeffer, J., & Pfeffer, J. (1981). Power in organizations (Vol. 33): Pitman Marshfield, MA.
Rico, R., Sánchez-Manzanares, M., Antino, M., & Lau, D. C. (2012). Bridging team faultlines by combining task role assignment and goal structure strategies. Journal of Applied Psychology, 97(2), 407-420. doi: 10.1037/a0025231
Ross, J. W., Beath, C. M., & Quaadgras, A. (2013). You may not need big data after all. Harvard Business Review, 91(12), 90-+.
Salas, E., Stagl, K. C., & Burke, C. S. (2005). 25 Years of Team Effectiveness in Organizations: Research Themes and Emerging Needs. In C. L. Cooper & I. T. Robertson (Eds.), International Review of Industrial and Organizational Psychology 2004 (Vol. 19).
Saltz, J. (2017). Acceptance Factors for Using a Big Data Capability and Maturity Model. In Proceedings of the 25th European Conference on Information Systems (ECIS).
Shalley, C. E., Zhou, J., & Oldham, G. R. (2004). The effects of personal and contextual characteristics on creativity: Where should we go from here? Journal of management, 30(6), 933-958.
Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423. doi: doi:10.1002/j.1538-7305.1948.tb01338.x
Sillince, J. A. A., & Mouakket, S. (1997). Varieties of Political Process During Systems Development. Information Systems Research, 8(4), 368-397.
Somech, A., & Drach-Zahavy, A. (2013). Translating Team Creativity to Innovation Implementation:The Role of Team Composition and Climate for Innovation. Journal of management, 39(3), 684-708. doi: 10.1177/0149206310394187
Spreitzer, G. M. (1995). Psychological empowerment in the workplace: Dimensions, measurement, and validation. Academy of management journal, 38(5), 1442-1465.
Srikanth, K., Harvey, S., & Peterson, R. (2016). A Dynamic Perspective on Diverse Teams: Moving from the Dual-Process Model to a Dynamic Coordination-based Model of Diverse Team Performance. The Academy of Management Annals, 10(1), 453-493. doi: 10.1080/19416520.2016.1120973
Tannenbaum, S. I., Beard, R. L., & Salas, E. (1992). Chapter 5 Team Building and its Influence on Team Effectiveness: an Examination of Conceptual and Empirical Developments. In K. Kelley (Ed.), Advances in Psychology (Vol. 82, pp. 117-153): North-Holland.
Tasa, K., & Whyte, G. (2005). Collective efficacy and vigilant problem solving in group decision making: A non-linear model. Organizational Behavior and Human Decision Processes, 96(2), 119-129. doi: https://doi.org/10.1016/j.obhdp.2005.01.002
Thompson, J. D. (1967). Organizations in action: social science bases of administrative theory: McGraw-Hill.
Turner, D., Schroeck, M., & Shockley, R. (2013). Analytics: The real-world use of big data in financial services. IBM Global Business Services, 27.
Van der Vegt, G., & Van de Vliert, E. (2002). Intragroup interdependence and effectiveness: Review and proposed directions for theory and practice (Vol. 17).
Vegt, G. S. V. d., & Janssen, O. (2003). Joint Impact of Interdependence and Group Diversity on Innovation. Journal of management, 29(5), 729-751. doi: 10.1016/s0149-2063_03_00033-3
Webber, S. S., & Donahue, L. M. (2001). Impact of highly and less job-related diversity on work group cohesion and performance: a meta-analysis. Journal of management, 27(2), 141-162. doi: 10.1177/014920630102700202
Wenger, E. (1998). Communities of Practice: Learning, Meaning and Identity: Cambridge University Press, Cambridge, U.K.
West, M. (1990). The Social Psychology of Innovation in Groups.
West, M. A. (2002). Sparkling Fountains or Stagnant Ponds: An Integrative Model of Creativity and Innovation Implementation in Work Groups. Applied psychology, 51(3), 355-387. doi: doi:10.1111/1464-0597.00951
West, M. A., & Anderson, N. R. (1996). Innovation in top management teams. Journal of Applied Psychology, 81(6), 680-693. doi: 10.1037/0021-9010.81.6.680
Woodman, R. W., Sawyer, J. E., & Griffin, R. W. (1993). Toward a Theory of Organizational Creativity. Academy of management review, 18(2), 293-321. doi: 10.5465/amr.1993.3997517
Zhao, J. L., Fan, S., & Hu, D. (2014). Business challenges and research directions of management analytics in the big data era. Journal of Management Analytics, 1(3), 169-174.
指導教授 陳炫碩(Shiuann-Shuoh Chen) 審核日期 2018-7-9
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明