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研究生: 邱偉寧
Chiu, Wei-Ning
論文名稱: 探討任務科技適配度與績效影響的關聯-以虛擬社群意識為中介變項
A Relationship between Task-Technology-Fit and Performance Impacts ─ Using Sense of Virtual Community as Mediating Variable
指導教授: 蕭顯勝
Hsiao, Hsien-Sheng
口試委員: 張奕華 張義雄 蕭顯勝
口試日期: 2022/01/12
學位類別: 碩士
Master
系所名稱: 科技應用與人力資源發展學系
Department of Technology Application and Human Resource Development
論文出版年: 2022
畢業學年度: 110
語文別: 中文
論文頁數: 95
中文關鍵詞: 虛擬實務社群虛擬社群意識科技特性任務特性任務科技適配模型績效影響
英文關鍵詞: Virtual Community of Practice, Sense of Virtual Community, Technology Characteristics, Task Characteristics, Task-Technology-Fit Model, Performance Impact
研究方法: 調查研究
DOI URL: http://doi.org/10.6345/NTNU202200210
論文種類: 學術論文
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  • 在疫情時代,虛擬社群代表著社會中的溝通型態的轉變,虛擬社群取代了傳統實體社群,本研究旨在探討使用社群軟體的企業員工,其感知任務科技適配與虛擬社群意識,是否能影響其績效影響,並以「任務科技適配模型」為基礎,加入虛擬社群中的虛擬社群意識做為變項,來探討架構中的任務特性及科技特性,如何對任務科技適配度造成影響,以及虛擬社群意識對於任務科技適配度及績效影響之中介效果。
    本研究採用問卷調查法,以有在企業組織中創建或參與虛擬實務社群之個人進行問卷投放,有效樣本314份。研究結果發現:(1)「任務特性」、「科技特性」正向影響「任務科技適配度」;(2)「任務科技適配度」正向影響「虛擬社群意識」;(3)「任務科技適配度」、「虛擬社群意識」正向影響「績效影響」;(4)「虛擬社群意識」部分中介「任務科技適配度」及「績效影響」兩者之間之關係。本研究根據結果提出建議,認為應著重任務科技適配度,強化工作任務與科技特性之間的符合程度,以提升績效影響,相關研究結果可供研究之企業,以及後續研究者作為相關研究參考。

    During the COVID-19 pandemic, virtual communities represent the change of our communication patterns and replaced traditional communities. This research aims to explore individuals using social media within the enterprise, perceiving Task-Technology-Fit and Sense of Virtual Community, that affect performance impact. Through the Task-Technology-Fit Model mix in new variables- the sense of virtual community, exploring the relation of Task Characteristics and Technology Characteristics. Moreover, we discuss the sense of virtual community for the mediating effect of Task-Technology-Fit and performance impact.
    In this study, the questionnaire survey method was used on the employees using social media within the enterprise. It resulted in 314 valid samples. The results of this study show that: (1) Both Task Characteristics and Technology Characteristics have significant influences on Task-Technology-Fit. (2) Task-Technology-Fit has significant influences on Sense of Virtual Community. (3) Both Sense of Virtual Community and Task-Technology-Fit has significant influences on performance impact. (4) Sense of Virtual Community has the partial mediating effect of Task-Technology-Fit and performance impact. According to the results, this study suggests that it should increase the Task-Technology-Fit to enhance the adaptability of technology and work requirements. The results of the study can be referenced by the enterprises which use social media, as well as follow-up researchers.

    第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的與待答問題 6 第三節 重要名詞釋義 8 第二章 文獻探討 11 第一節 任務科技適配模型 11 第二節 虛擬社群意識 18 第三節 績效影響 26 第四節 不同背景變項之研究 28 第五節 文獻評析 30 第六節 SDGs指標 31 第三章 研究方法 35 第一節 研究架構與假設 35 第二節 研究步驟與流程 37 第三節 研究對象 38 第四節 研究方法與工具 39 第五節 資料分析 44 第六節 問卷量表預試分析 46 第四章 研究結果與討論 49 第一節 樣本資料分析 49 第二節 結構方程模型分析 54 第三節 中介效果之分析 60 第四節 重要性績效矩陣分析(Importance-Performance Matrix Analysis, IPMA) 63 第五章 結論與建議 67 第一節 研究結論 67 第二節 研究貢獻 72 第三節 研究建議 74 參考文獻 79 附 錄 89

    王舒民、林娟娟、簡子晴、鄒仁淳(2016)。由任務科技配適度觀點探討司法人員資訊系統使用與工作績效之影響因子。臺大管理論叢,26(2),273-302。
    古永嘉、楊雪蘭(2009)。企業研究方法第十版。臺北市:華泰文化。
    吳明隆、張毓仁(2014)。SPSS(PASW)與統計應用分析。台北市:五南圖書。
    李國瑋、張詠盛、陳宜辰、林東緯(2017)。行動銀行使用意圖之關聯性探討-整合科技接受模式與任務-科技適配模式之觀點。當代商管論叢,2(1),61-86。
    李承傑、董旭英(2017)。偏最小平方法結構方程模型。科學發展,539,20-25。
    林婉婷(2009)。文本電腦輔助溝通對於團隊任務績效之影響研究(未出版之碩士論文)。國立中央大學,桃園縣。
    林洲安、陳岳陽、侯凱中(2018)。社群網站之認知有趣性對使用者持續使用意圖之影響-虛擬社群意識之中介角色。工業科技教育學刊,11,1-20
    范懿文、方毓賢、吳政杰、劉昌輝(2011)。虛擬社群持續參與因素之探討。電子商務學報,13(2),413-434。
    張原誠(2012)。虛擬社群意識與社群忠誠度對持續使用行為及推薦傾向影響之研究-以Facebook為例。創新與管理,9(4),85-111。
    陳筱華、李佩貞(2011)。探討社群意識在虛擬環境下對凝聚力與忠誠度的影響。行銷評論,8(4),519-537。
    陳至柔、吳如娟、林松江(2016)。雲端 CRM 系統持續使用意圖之實證研究: 整合任務-科技適配模式與體制理論。電子商務學報,18(1),1-40。
    翟本瑞(2001)。網路文化。台北市:楊智出版社。
    葉連祺(2020)。IPMA-RG在教育研究之應用。教育研究與發展期刊,16(4),71-108。
    鄭淑華、許哲瀚、龔建吉、張馨云、趙建蕾(2019)。結合科技接受模型及任務科技配適度探討護理人員使用社群媒體對工作績效之影響。醫務管理期刊,20(4),267-287。
    蔡宏進 (1996)。社區原理。台北:三民書局。
    Abfalter, D., Zaglia, M. E., & Mueller, J. (2012). Sense of virtual community: A follow up on its measurement. Computers in Human Behavior, 28(2), 400-404.
    Anderson, P. J., & Piatt, J. F. (1999). Community reorganization in the Gulf of Alaska following ocean climate regime shift. Marine Ecology Progress Series, 189, 117-123.
    Armstrong, A. & Hagel, J. (1996). The real value of on-line communities, Harvard Business Review, 74(3), 134-141.
    Armstrong, A., & Hagel, J. (1997). Net gain: Expanding markets through virtual communities, Harvard Business School.
    Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8(4), 244-254.
    B.J. Felton & M. Shinn. (1992). Social integration and social support: Moving ‘social support’ beyond the individual level. Journal of Community Psychology, 20 (1), 103-115.
    Beamish, A. (1995). Communities on-line: Community-based computer networks. Cambridge, MA: Massachusetts Institute of Technology.
    Blanchard, A. and Horan, T. (2000). Virtual Communities and Social Capital, in E. Lesser (Ed), Knowledge and Social Capital, Oxford and Boston: Butterworth-Heinemann, 159-178.
    Blanchard, A. (2004) Virtual behavior settings: An application of behavior setting theories to virtual communities. Journal of Computer Mediated Communication, 9(2), 924-928.
    Blanchard, A. L. (2007) Developing a Sense of Virtual Community Measure. Cyber Psychology & Behavior, 10(6), 827-830.
    Bressler, S. E., & Grantham, C. (2000). Communities of commerce: Building internet business communities to accelerate growth, minimize risk, and increase customer loyalty. McGraw-Hill.
    Byars, L. L., & Rue, L. W. (2001). Manajemen Sumber Daya Manusia, PT. Andi Offset: Yogyakarta.
    Bond, M. A., & Lockee, B. B. (2014). Building virtual communities of practice for distance educators. Springer International Publishing.
    Chow, W.C. and Chan, L.S. (2008) Social network, social trust and shared goals in organizational knowledge sharing. Information & Management, 45(7), 458-465.
    Chen, C. J., & Hung, S. W. (2010). To give or to receive? Factors influencing members’ knowledge sharing and community promotion in professional virtual communities. Information & Management, 47(4), 226-236.
    Chang, I.C., Chang, C.H., Wu, J.W. & Huang, T.C.K. (2015). Assessing the performance of long-term care information. Telematics and Informatics, 32(2), 273-281.
    Cohen, J. (1988). Set correlation and contingency tables. Applied Psychological Measurement, 12(4), 425-434.
    Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
    Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions, and behavioral impacts. International Journal of Man Machine Studies, 1(38), 475-487.
    D.C. Yen, C.H. Wu, F.F. Cheng, Y.W. Huang. (2010). Determinants of users’intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(4), 906-915.
    D'Ambra, J., Wilson, C.S. & Akrer, S. (2013). Application of the task-technology fit model to structure and evaluate the adoption of e-books by academics. Journal of the American Society for Information Science and Technology, 64(1), 48-64.
    DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9-30.
    Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information & Management, 36(1), 9-21.
    Dong, L., Huang, L., Hou, J. J., & Liu, Y. (2020). Continuous content contribution in virtual community: The role of status-standing on motivational mechanisms. Decision Support Systems, 132, 113-283.
    Farkas, M. G. (2007). Social software in libraries: building collaboration, communication, and community online. Information Today.
    Fornell & Larcker, D. F. C. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
    Gefen, D., Karahanna, E. & Staub, D.W. (2003). Trust and TAM in Online Shopping: An Integrated Model. MIS Quarterly, 27(1), 51-90.
    Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236.
    Gu, M.L., Chi, M. & Han, J.P. (2019). Research on the impact of open innovative community governance mechanism on user knowledge contribution behavior – based on the mediating effect of virtual community sense. Science and Technology Progress and Policy, 36(20), 30-37.
    Hair, F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L.J. (2006). Multivariate Data Analysis (6th ed.). Prentice-Hall.
    Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1-2), 1-12.
    Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications
    Hou, C. K. (2012). Examining the effect of user satisfaction on system usage and individual performance with business intelligence systems: An empirical study of Taiwan's electronics industry. International Journal of Information Management, 32(6), 560-573.
    Howard, M. C., & Rose, J. C. (2019). Refining and extending task–technology fit theory: Creation of two task–technology fit scales and empirical clarification of the construct. Information & Management, 56(6), 103-134.
    Harrati, N., Bouchrika, I., & Mahfouf, Z. (2017). Investigating the uptake of educational systems by academics using the technology to performance chain model. Library Hi Tech, 35(4), 629-648
    Im, G. (2014). Effects of cognitive and social factors on system utilization and performance outcomes. Information & Management, 51(1), 129-137.
    Isaac, M. E., & Matous, P. (2017). Social network ties predict land use diversity and land use change: a case study in Ghana. Regional Environmental Change, 17(6), 1823-1833.
    Jing, G. and Seon, Y.I. (2013). An empirical study on the effect of e-service quality to satisfaction. International Journal of Management Sciences and Business Research, 2(10), 25-31.
    Koh, J., Kim, Y. G., & Kim, Y. G. (2003). Sense of virtual community: A conceptual framework and empirical validation. International Journal of Electronic Commerce, 8(2), 75-94.
    Koh, J., & Y.G. Kim. (2004). Sense of Virtual Community: a Conceptual Framework and Empirical Validation. International Journal of Electronic Commerce, 8(2), 75-93.
    Kim, W.G., Lee, C., & Hiemstra, S. J. (2004). Effects of an Online Virtual Community on Customer Loyalty and Travel Product Purchases. Tourism Management, 25(3), 343-355.
    Lee, F. S., Vogel, D., & Limayem, M. (2003). Virtual community informatics: A review and research agenda. Journal of Information Technology Theory and Application (JITTA), 5(1), 47-61.
    Liao, G. Y., Pham, T. T. L., Cheng, T. C. E., & Teng, C. I. (2020). Impacts of real-world need satisfaction on online gamer loyalty: Perspective of self-affirmation theory. Computers in Human Behavior, 103, 91-100.
    Lin, K. Y., & Lu, H. P. (2011). Intention to continue using Facebook fan pages from the perspective of social capital theory. Cyberpsychology, Behavior, and Social Networking, 14(10), 565-570.
    Lin, X., & Wang, X. (2020). Examining gender differences in people’s information-sharing decisions on social networking sites. International Journal of Information Management, 50, 45-56.
    Lu, H. P., & Yang, Y. W. (2014). Toward an understanding of the behavioral intention to use a social networking site: An extension of task-technology fit to social-technology fit. Computers in Human Behavior, 34, 323-332.
    Luo, N., Zhang, M., & Liu, W. (2015). The effects of value co-creation practices on building harmonious brand community and achieving brand loyalty on social media in China. Computers in Human Behavior, 48, 492-499.
    Martilla, J. A., & James, J. C. (1977). Importance-performance analysis. The Journal of Marketing, 41(1), 77-79.
    Martensen, A., & Grønholdt, L. (2003). Improving library users’ perceived quality, satisfaction and loyalty: An integrated measurement and management system. The Journal of Academic Librarianship, 29(3), 140-147.
    Mansell, P., Philbin, S. P., & Konstantinou, E. (2020). Redefining the use of sustainable development goals at the organisation and project levels—A survey of engineers. Administrative Sciences, 10(3), 55.
    McMillan, D.W., & Chavis, D.M. (1986). Sense of Community: A Definition and Theory. Journal of Community Psychology, 14(1), 6-23.
    McKeen, J. D., Guimaraes, T., & Wetherbe, J. C. (1994). The relationship between user participation and user satisfaction: an investigation of four contingency factors. MIS Quarterly, 18(4), 427-451.
    O'Leary, L., Erikainen, S., Peltonen, L. M., Ahmed, W., Thelwall, M., & O'Connor, S. (2021). Exploring nurses’ online perspectives and social networks during a global pandemic COVID‐19. Public Health Nursing, 1-15.
    Park, H. S. (2015). A Longitudinal Study of Adolescents’ Sense of Community over Three Years. Studies in Humanities and Social Sciences, 48, 5-25.
    Park, J., & Gabbard, J. L. (2018). Factors that affect scientists' knowledge sharing behavior in health and life sciences research communities: differences between explicit and implicit knowledge. Computers in Human Behavior, 78, 326-335.
    Porter, M. E., & Kramer, M. R. (2011). The Big Idea: Creating Shared Value Rethinking Capitalism Creating Shared Value & Developing countries. Harvard Business Review, 89, 2-17.
    Rheingold, H. (2000). The Virtual Community Homesteading on the Electronic Frontier. MIT press.
    Scheyvens, R., Banks, G., & Hughes, E. (2016). The private sector and the SDGs: The need to move beyond ‘business as usual’. Sustainable Development, 24(6), 371-382.
    Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99-118.
    Sullivan, K., Thomas, S., & Rosano, M. (2018). Using industrial ecology and strategic management concepts to pursue the Sustainable Development Goals. Journal of Cleaner Production, 174, 237-246.
    Tajvidi, M., Richard, M. O., Wang, Y., & Hajli, N. (2018). Brand co-creation through social commerce information sharing: The role of social media. Journal of Business Research, 121(1), 476-486.
    Torkzadeh, G., & Doll, W. J. (1999). The development of a tool for measuring the perceived impact of information technology on work. Omega, 27(3), 327-339.
    T. Zhou, Y. Lu, B. Wang. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26 (4), 760-767.
    Wenger, E. C., & Snyder, W. M. (2000). Communities of practice: The organizational frontier. Harvard Business Review, 78(1), 139-146.
    Wenger, E. (2010). Communities of practice and social learning systems: the career of a concept. Social Learning Systems and Communities of Practice, 179-198.
    Wu, Y. L., & Li, E. Y. (2018). Marketing mix, customer value, and customer loyalty in social commerce: A stimulus-organism-response perspective. Internet Research, 28(1), 74-104.
    Yang, Z., Algesheimer, R., & Tessone, C. J. (2016). A comparative analysis of community detection algorithms on artificial networks. Scientific Reports, 6(1), 1-18.
    Yen, D. C., Wu, C. S., Cheng, F. F., & Huang, Y. W. (2010). Determinants of users’intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(5), 906-915.
    Yu, T.K. & Yu, T.Y. (2010). Modelling the factors that affect individual’s utilisation of online learning systems: an empirical study combining the task technology fit model with the theory of planned behavior. British Journal of Educational Technology, 41(6), 1003-1017.
    Zhang, C. & Zhou, Q. (2018). Online investigation of users’ attitudes using automatic question answering. Online Information Review, 42(3), 419-435.
    Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760-767.

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