臺灣博碩士論文加值系統

群眾委外(Crowdsourcing, CS)，透過網路平台雲端服務，使委外企業得以利用最低的成本與來自各地的專業技術工作者進行媒合配對。然而，委外企業需求者與群眾委外工作者配對成功的情況下，其工作者的技術與專業是否達到需求，反而增加了群眾委外工作績效及滿意度的不確定性，包括了任務上的不適配及學習上的合作行為。綜觀過去的研究，群眾委外由於透過平台媒合，缺乏長期合作行為，委外的技術無法適配委外企業需求者工作流程及其缺乏對委外技術的了解與深入學習。本研究根據社會認知理論，發展出一個新的模型驗證，來探討如何有效提高群眾委外的工作績效及其滿意度。本研究利用任務科技適配度、職場彈性及團隊學習氛圍等前因部份來反映委外任務本身的特性，利用職場情境因子來對社會自我調節學習的影響，而透過自我學習來強化學習流程的知識需求與合作行為。而在職場產出部份，則由工作績效及群眾委外滿意度兩個因子來衡量。本研究針對具有委外經驗的填答者為對象，其有效問卷總計380份，採用Smart PLS進行驗證結果。由研究結果顯示職場彈性及團隊學習氛圍對社會自我調節學習有正面的影響，而任務科技適配度對社會自我調節學習有間接影響。也驗證了社會自我調節學習正面影響委外的工作績效及滿意度。

Crowdsourcing (CS), through the cloud service online platform, enables companies to match the technicians from all over the world at the lowest cost. However, when the company and the masses of social workers are successfully paired, whether the technology and professionalism of workers meet the demand, but it increases the uncertainty of the performance and satisfaction of the outsourcing work, it includes the mismatch of tasks and cooperative behaviors in learning. In the past research, due to the lack of long-term cooperative, the outsourcing technology could not adapt to the work process of the enterprise and the lack of understanding of the outsourcing technology and the influence of in-depth learning. Based on the theory of social cognition, this study develops a new model validation to explore how to effectively improve the performance and satisfaction of the crowdsourcing. This study uses the antecedents such as task technology adaptation, workplace flexibility and team learning atmosphere to reflect the characteristics of the external task itself, using the workplace context factor to influence the social self-regulated learning, and conceptualize the knowledge needs and cooperative behaviors of social cognition through self-learning. In the workplace output, it is measured by two factors: Job performance and crowdsourcing satisfaction. This study was conducted for respondents with crowdsourcing experience. A total of 380 valid questionnaires were validated using Smart PLS. The results show that workplace flexibility and team learning climate have a positive impact on social self-regulated learning, while task-technology fit adaptation has an indirect impact. It also verifies that social self-regulation learning positively affects job performance and satisfaction.

摘要 i
Abstract ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
壹、緒論 1
一、研究背景 1
二、研究動機 7
三、研究目的 10
四、研究範圍 11
五、論文架構 12
六、研究程序 13
貳、文獻探討 15
一、雲端服務(Cloud Services) 16
二、群眾委外 (Crowdsourcing) 18
(一) 群眾委外平台 21
(二) 群眾委外現況 25
三、群眾委外績效 25
(一) 工作績效 (Job performance) 26
(二) 群眾委外滿意度 (Crowdsourcing satisfaction) 27
四、自我調節 (Self-Regulation) 29
(一) 社會自我調節學習 (Social self-regulated learning) 30
五、社會認知理論 (Social Cognitive Theory) 30
(一) 任務科技適配度 (Task-technology fit) 33
(二) 職場彈性 (Workplace flexibility) 34
(三) 職場團隊學習氛圍 (Workplace team learning climate) 35
參、研究方法與設計 37
一、研究模型及各變數說明 37
(一) 自我調節學習流程與群眾委外績效之間的關係 38
(二) 社會認知與自我調節學習流程之間的關係 39
(三) 群眾委外績效與對群眾委外工作者之間的關係 39
二、研究假說與模式推導 43
(一) 「社會自我調節學習」對於「工作績效」之間的關係 43
(二) 「社會自我調節學習」對於「群眾委外滿意度」之間的關係 44
(三) 「任務科技適配度」對於「社會自我調節學習」之間的關係 45
(四) 「職場彈性」對於「社會自我調節學習」之間的關係 45
(五) 「職場團隊學習氛圍」對於「社會自我調節學習」之間的關係 46
三、操作型定義與衡量 46
(一) 工作績效(Job performance) 47
(二) 群眾委外滿意度(Crowdsourcing satisfaction) 48
(三) 社會自我調節學習(Social Self-Regulated Learning) 49
(四) 任務科技適配度(Task-Technology Fit) 50
(五) 職場彈性(Workplace Flexibility) 51
(六) 職場團隊學習氛圍(Workplace Team Learning Climate) 52
四、問卷設計與資料蒐集 53
五、統計方法 55
肆、研究發現與資料分析 58
一、研究發現 58
二、資料收集 60
三、資料分析 65
(一) 測量模式分析 66
(二) 結構模式分析 79
四、研究結果 91
(一)「社會自我調節學習」對「工作績效」之驗證分析 91
(二)「社會自我調節學習」對「群眾委外滿意度」之驗證分析 91
(三)「任務科技適配度」對「社會自我調節學習」之驗證分析 92
(四)「職場彈性」對「社會自我調節學習」之驗證分析 92
(五)「職場團隊學習氛圍」對「社會自我調節學習」之驗證分析 92
伍、討論 94
一、分析討論 94
二、研究貢獻 95
三、研究限制 97
(一)實務上的限制 97
(二)區域性的限制 97
四、結論 97
參考文獻 99
附錄：研究問卷 105

1.Ahuja, M. K., and Thatcher, J.B. (2005). Moving beyond intention and toward the theory of trying: Effects of work environment and gender on post-adoption information technology use. MIS Quarterly, 29, 3, 427–459.
2.Amabile, T. M., Conti, R., Coon, H., Lazenby, J., & Herron, M. (1996). Assessing the work environment for creativity. Academy of Management Journal, 39, 5, 1154–1184.
3.Bagozzi, R. P., & Yi, Y. (1998). On the evaluation of structural equation models. Journal of the academy of marketing science, 16(1), 74-94.
4.Bandura, A. (1982). Self-Efficacy Mechanism in Human Agency, American Psychologist (37:2), February, pp. 122-147.
5.Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall.
6.Banerjee, P., Friedrich, R., Bash, C., Goldsack, P., Huberman, B., Manley, J., Patel, C., Ranganathan, P., Veitch, A. (2011). Everything as a service: Powering the new information economy. Computer, no. 3, pp. 36–43,.
7.Barclay, D. W., Higgins, C. A., & Thompson, R. (1995). The partial least squares approach to causal modeling: personal computer adoption and use as illustration, Technology Studies (2:2), pp. 285-309.
8.Chin, W. W. (1998). Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), VII-XVI.
9.Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
10.Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo Simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189-217.
11.Chou, S.W., & Chiang, C.H. (2013). Understanding the formation of software-as-a-service (SaaS) satisfaction from the perspective of service quality. Decision Support Systems, 56(0), 148-155.
12.Cohen, J. (1988). Statistical power for the social sciences. Hillsdale, NJ: Laurence Erlbaum and Associates.

13.Corritore, C. L., Kracher, B., & Wiedenbeck, S. (2003). On-Line Trust: Concepts, Evolving Themes, a Model, International Journal of Human-Computer Studies (58:6), pp. 737-758.
14.Deng, X., & Joshi, K.D. (2016). Why Individuals Participate in Micro-task Crowdsourcing Work Environment: Revealing Crowdworkers’ Perceptions. Journal of the Association for Information Systems, 17(10), 648-673.
15.Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information & Management, 36(1), 9-21.16.Edmondson, A.C. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44, 2, 350–383.
17.Fornell, & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of marketing research, 382-388.
18.Gallivan, M. J., Spitler, V. K.; & Koufaris, M. (2005). Does information technology training really matter? A social information processing analysis of coworkers’ influence on IT usage in the workplace. Journal of Management Information Systems, 22, 1 (Summer), 153–192.
19.George, J. F., Iacono, S., & Kling, R. (1995). Learning in context: Extensively computerized work groups as communities of practice. Accounting, Management and Information Technology, 5, 3–4, 185–202.
20.Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236.
21.Gravill, J., and Compeau, D. (2008) Self-regulated learning strategies and software training. Information & Management, 45, 5, 288–296.
22.Hackman, J. R., & Oldham, G. R. (1976). Motivation through the design of work: Test of a theory. Organizational Behavior and Human Performance, 16(2), 250-279.
23.Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis(7th ed.). Englewood Cliffs: Prentice Hall.
24.Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19(2), 139-152.
25.Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2013). A primer on partial least squares structural equation modeling (PLS-SEM): Sage Publications.
26.Hammon D. K. L., & Hippner H. (2012). Crowdsourcing. Business & Information Sys. Eng., V. 4, I. 3, pp. 163–166.
27.Howe, J. (2006). The rise of crowdsourcing. Wired Magazine, 14(6). Retrieved from http://www.wired.com/wired/archive/14.06/crowds_pr.html.
28.Howe, J. (2008). Crowdsourcing: Why the power of the crowd is driving the future of business. NY: Crown Business.
29.Jöreskog, K. G., & Wold, H. O. (1982). Systems under indirect observation: Causality, structure, prediction (Vol. 139): North Holland.
30.Kaganer, E., Carmel, E., Hirschheim, R. & Olsen, T. (2013). Managing the human cloud. MIT Sloan Management Review, 54(2), 22-32.
31.Kittur, A., Nickerson, J. V., Bernstein, M. S., Gerber, E. M., Shaw, A., Zimmerman, J., Lease, M., & Horton, J. J. (2013). The future of crowd work. In Proceedings of the 16th ACM Conference on Computer Supported Coooperative Work.
32.Kirkpatrick, S. A., & Locke, E. A. (1996). Direct and indirect effects of three core charismatic leadership components on performance and attitudes. Journal of Applied Psychology, 81(1), 36-51.
33.Kraiger, K. (2008). Third-generation instructional models: More about guiding development and design than selecting training methods. Industrial and Organizational Psychology, 1, 4, 501–507.
34.Lee, E. A-L., Wong, K. W., & Fung, C. C. (2010). How does desktop virtual reality enhance learning outcomes? A structural equation modeling approach. Computers and Education, 55(4), 1424-1442.
35.Lohmöller, J.-B. (1982). An overview of latent variables path analysis.
36.Lenk, A., Klems, M., Nimis, J., Tai, S., & Sandholm, T. (2009) What’s inside the Cloud? An architectural map of the Cloud landscape. in Proceedings of the ICSE, pp. 23–31.
37.Maruping, L. M. & Magni, M. (2012). What’s the Weather Like? The Effect of Team Learning Climate, Empowerment Climate, and Gender on Individuals’ Technology Exploration and Use. Journal of Management Information System, 29(1), 79-113.
38.Mather, T., Kumaraswamy, S., & Latif, S. (2009). Cloud security and privacy: An enterprise perspective on risks and compliance. O’Reilly Media, Inc.
39.Mitchell, T., & Daniels, D. (2003). Motivation, handbook of psychology: Industrial psychology. New York: LLC Group.
40.Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior, MIS Quarterly, 30(1), 115-143.
41.Peng, J., Zhang, X., Lei, Z., Zhang, B., Zhang, W., & Li Q. (2009) Comparison of several cloud computing platforms. in (ISISE), pp. 23–27.
42.Pinder, C.C. (1997). Work Motivation in Organizational Behavior. Upper Saddle River, NJ:Prentice Hall.
43.Poltrock, S., & Handel, M. (2010) Models of collaboration as the foundation for collaboration technologies. Journal of Management Information Systems, 27, 1 (Summer), 97–122.
44.Rajan, S., & Jairath, A. (2006). Cloud computing: The fifth generation of computing. in Communication Systems and Network Technologies (CSNT), pp. 665–667.
45.Roberts, J. A., Hann, I.-H., & Slaughter, S. A. (2006). Understanding the motivations, participation, and performance of open source software developers: A longitudinal study of the Apache projects. Management Science, 52(7), 984-999.
46.Salehan, M., Kim, D., & Kin, C. (2017). Use of Online Social Networking Services from a Theoretical Perspective of the Motivation-Participation-Performance Framework. Journal of the Association for Information Systems, 18(2), 141-172.
47.Sambamurthy, V., & Chin, W. W. (1994). The Effects of Group Attitudes Toward Alternative GDSS Designs on the Decision-making Performance of Computer-Supported Groups. Decision Sciences, Volume 25 Number2
48.Santhanam, R., Sasidharan, S., and Webster, J. (2008) Using self-regulatory learning to enhance e‑learning-based information technology training. Information Systems Research, 19, 1, 26–47.
49.Sarstedt, M., Ringle, C. M., Raithel, S., & Gudergan, S. (2012). In pursuit of understanding what drives fan satisfaction. Hamburg University of Technology (TUHH), Management@ TUHH Research Paper Series(7).
50.Schein, E. H. (1985). Career anchors. San Diego: University Associates.
51.Schunk, D. H. (1989) Self-Efficacy and Achievement Behaviors. Educational Psychology Review(1), pp. 173-208
52.Schunk, D.H., and Ertmer, P. (1999) Self-regulatory processes during computer skill acquisition: Goal and self-evaluative influences. Journal of Educational Psychology, 91, 2, 251–260.
53.Schunk, D.H. (2004) Learning Theories: An Educational Perspective. Upper Saddle River, NJ: Pearson/Merrill/Prentice Hall.
54.Shiau, W. L., & Luo, M. M. (2013). Continuance intention of blog users: the impact of perceived enjoyment, habit, user involvement and blogging time. Behaviour and Information Technology, 32(6), 570-583.
55.Snow, R. E. (1989). Aptitude–treatment interaction as a framework for research on individual differences in learning. In P.L. Ackerman, R.J. Sternberg, and R. Glaser (eds.), Learning and Individual Differences: Advances in Theory and Research. New York: Freeman, pp. 13–59.
56.Sun, H., Fang, Y., & Zou, H.(2016). Choosing a Fit Technology: Understanding Mindfulness in Technology Adoption and Continuance. Journal of the Association for Information Systems, 17(6), 377-412.
57.Tabachnick, B. G., & Fidell, L. S. (2011). Using multivariate statistics (4th ed.): Allyn & Bacon.
58.Wan, Z., Compeau, D., & Chau, P. (2012). The Effects of Self-Regulated Learning Processes on E-Learning Outcomes in Organizational Settings. Journal of Management Information Systems, 29(1), 307-339.
59.Wasko, M. M., Faraj, S., & Teigland, R. (2004). Collective action and knowledge contribution in electronic networks of practice. Journal of the Association for Information Systems, 5(11), 493-513.
60.Wageman, R. (1995). Interdependence and group effectiveness. Administrative Science Quarterly, 40. 1 (1995), 145-180.
61.Wang, Y., and Haggerty, N. (2009) Knowledge transfer in virtual settings: The role of individual virtual competency. Information Systems Journal, 19, 6, 571–593.
62.Ye, H., & Kankanhalli, A. (2016) Solvers’ participation in crowdsourcing platforms: Examining the impacts of trust, and benefit and cost factors. Journal of the Association for Information Systems, Volume 17, PP618-673 October.
63.Zimmerman, B.J.; Bandura, A.; and Martinez-Pons, M. (1992) Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting. American Educational Research Journal, 29, 3, 663–676.
64.Zimmerman, B.J., and Schunk, D.H. (2001) Self-Regulated Learning and Academic Achievement: Theory, Research, and Practice. Mahwah, NJ: Lawrence Erlbaum.
65.Zimmerman, B.J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45, 1, 166–183.
66.Zimmerman, B.J., & Martinez-Pons, M. (1998). Construct validation of a strategy model of student self-regulated learning. Journal of Educational Psychology, 80, 3, 284–290.