臺灣博碩士論文加值系統

在生活步調快速的現代，人們借助科技提高生活效率，於是各式各樣的平台紛紛興起，美食外送即是一個顯而易見的例子。消費習慣的改變，加上疫情的影響，根據資策會產業情報研究所(MIC) 2020年的調查，69%的消費者已經習慣從網路上購買日常用品、生鮮雜貨、冷凍食品等商品，為了增加服務的多樣性以及競爭力，外送平台業者亦紛紛於近兩年內推出生鮮雜貨外送服務，滿足消費者即時性需求。

本研究目的在探討任務特性(消費者生鮮雜貨消費需求)、科技特性(生鮮雜貨外送平台系統功能)與電腦自我效能對任務科技配適度的相對影響力，以及任務科技配適度與個人績效表現(生活效率的提升)的關係。此外，本研究亦探討周圍環境條件是否會對個人績效表現有調節作用，期望透過研究成果提供外送平台業者瞭解消費者對生鮮雜貨外送服務之需求。

本研究透過問卷調查法，以曾經使用過foodpanda生鮮雜貨外送服務者為研究對象，回收有效問卷288份，並運用SPSS Statistics統計軟體進行分析，研究結果發現，除了任務特性外，科技特性、電腦自我效能皆對任務科技配適度有顯著影響，其中又以電腦自我效能的影響最為顯著，然而周圍環境條件對個人績效表現並無調節作用。

根據本文研究結果，電腦自我效能是用來衡量使用者是否有能力使用該科技去達成任務的重要變數，因此本研究建議外送平台業者在系統介面功能設計上，可以針對使用者的年齡、消費習慣等設計不同預設介面，或是加強客服訓練，在突發狀況發生時，能夠盡快提供解決方案，提升消費者的電腦自我效能，協助消費者更快搜尋到其所想要購買的商品。當消費者感知使用生鮮雜貨外送服務可以促使生活變得更加便利，則其將更有動機採用這項新服務，如此一來，消費者也會提高對生鮮雜貨外送服務的黏著性。

In the rapid pace of modern life, people tend to use technology to improve the efficiency of their life, so a wide variety of platforms have emerged one after another, food delivery is an obvious example. With changes in consumption habits and the impact of Covid-19, according to a survey of Market Intelligence & Consulting Institute (MIC) in 2020, 69% of consumers have become accustomed to buying daily necessities, fresh and groceries, frozen food and other commodities from the Internet. In order to increase the diversity and competitiveness of services, the delivery platforms have launched fresh and groceries delivery services in the past two years to meet the immediate needs of consumers.

The purpose of this study is to explore the impact of task characteristics (consumer demand for fresh and groceries), technology characteristics (delivery platform system function) and computer self-efficacy on task-technology fit, and the relationship between task-technology fit and performance impacts (improvement of life efficiency). In addition, this study also discusses whether the surrounding environmental conditions have moderating effect on individual performance impacts, expecting to provide delivery platform with the research results to find out consumers’ demand for fresh and groceries delivery service.

The results show that in addition to task characteristics, both technology characteristics and computer self-efficacy have significant positive effects on task-technology fit, among which computer self-efficacy has the most significant effect. Besides, computer self-efficacy and task-technology fit also have significant positive effects on performance. However, the surrounding environmental conditions has no moderating effect on individual performance impacts.

According to the research results, computer self-efficacy is an important variable to measure whether the users have the ability to use the technology to accomplish the task. Therefore, this study suggests that the delivery platform could focus on simplicity and ease of use in the design of platform system interface or strengthen customer service training to provide solutions as soon as possible. Through these ways, the computer self-efficacy of consumers’ can be improved.

Key Words: Task-Technology Fit model, foodpanda, fresh groceries delivery service

摘要 II
ABSTRACT III
誌謝 VI
表目錄 IX
圖目錄 X
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 5
第三節 研究目的與研究問題 7
第四節 研究流程 7
第二章 文獻回顧 9
第一節 任務科技配適模型 9
第二節 電腦自我效能 15
第三節 環境條件 18
第三章 研究方法 21
第一節 研究架構 21
第二節 研究變數之操作型定義與衡量 22
第三節 研究對象與資料蒐集 28
第四節 資料分析方法 28
第四章 研究結果 31
第一節 敘述性統計分析 31
第二節 因素分析與信效度分析 35
第三節 Pearson相關分析 40
第四節 迴歸分析與假說驗證 41
第五章 結論與建議 48
第一節 研究結論 48
第二節 研究貢獻 48
第三節 研究限制與未來研究建議 49
參考文獻 51
附錄 58

中文文獻
王舒民、林娟娟、簡子晴、鄒仁淳，2016年，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，第26卷第2期，第273-302頁。
白凢芸、葉子明、蕭鈺錦，2020年，第三方餐飲外送平台價值分析: 餐飲供應合作夥伴之觀點，電子商務學報，第22卷第2期，第213-238頁。
吳文雄(2002)。電腦技能學習者過去的績效、目標認同、電腦自我效能及電腦績效因果關係之驗證—社會認知理論與目標設定理論的整合，師大學報：科學教育類，第47卷第1期，第39-54頁。
張芳瑜、謝碧容，2020年，延伸任務科技配適理論以探討疾病分類人員對於ICD-10-CM/PCS 登錄系統之滿意度與績效，醫務管理期刊，第21卷第4期，第293-313頁。
賀靖雅(2019)。以任務科技配適度觀點探討顧客採用行動APP購物之行為–以蝦皮購物為例。國立成功大學企業管理學系暨研究所碩士論文，台南市。
葉逸萱(2011)。企業採用資訊科技之配適-可行性延伸模式。國立中山大學資訊管理學系博士論文，高雄市。
顏奕仁、周惠文、林裕勛，2009年，個人工作績效之探討—整合任務科技配適模式與社會認知理論，中原企管評論，第8卷第1期，第143-166頁。
羅仕順、陳棟樑、林建志、陳俐文，2020年，運用科技接受模式探討縣政府公文系統使用行為與使用滿意度之研究，管理資訊計算，第9卷，第112-126頁。
 
英文文獻
Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in human behavior, 63, 75-90.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.
Ball, D. M., & Levy, Y. (2008). Emerging educational technology: Assessing the factors that influence instructors’ acceptance in information systems and other classrooms. Journal of Information Systems Education, 19(4), 431.
Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ, 1986(23-28).
Chen, I. S. (2017). Computer self-efficacy, learning performance, and the mediating role of learning engagement. Computers in Human Behavior, 72, 362-370.
Cho, M., Bonn, M. A., & Li, J. J. (2019). Differences in perceptions about food delivery apps between single-person and multi-person households. International Journal of Hospitality Management, 77, 108-116.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 189-211.
Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS quarterly, 145-158.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Gebauer, J., & Shaw, M. J. (2004). Success factors and impacts of mobile business applications: results from a mobile e-procurement study. International Journal of Electronic Commerce, 8(3), 19-41.
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS quarterly, 213-236.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1992). Multivariate Data Analysis with Readings, 3-rd edition.
Hasan, B. (2007). Examining the effects of computer self-efficacy and system complexity on technology acceptance. Information Resources Management Journal (IRMJ), 20(3), 76-88.
Hsia, J. W., Chang, C. C., & Tseng, A. H. (2014). Effects of individuals' locus of control and computer self-efficacy on their e-learning acceptance in high-tech companies. Behaviour & Information Technology, 33(1), 51-64.
Jia, D., Bhatti, A., & Nahavandi, S. (2014). The impact of self-efficacy and perceived system efficacy on effectiveness of virtual training systems. Behaviour & Information Technology, 33(1), 16-35.
John, S. P. (2013). Influence of computer self-efficacy on information technology adoption. International Journal of Information Technology, 19(1), 1-13.
Jöreskog, K. G., & Sörbom, D. (1989). LISREL 7: A guide to the program and applications. Spss.
Lee, C. C., Cheng, H. K., & Cheng, H. H. (2007). An empirical study of mobile commerce in insurance industry: Task–technology fit and individual differences. Decision support systems, 43(1), 95-110.
Lee, S. W., Sung, H. J., & Jeon, H. M. (2019). Determinants of continuous intention on food delivery apps: extending UTAUT2 with information quality. Sustainability, 11(11), 3141.
Liang, T. P., & Wei, C. P. (2004). Introduction to the special issue: Mobile commerce applications. International journal of electronic commerce, 8(3), 7-17.
Liang, T. P., Huang, C. W., Yeh, Y. H., & Lin, B. (2007). Adoption of mobile technology in business: a fit‐viability model. Industrial management & data systems.
Lin, T. C., & Huang, C. C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & management, 45(6), 410-417.
Ma, Q., & Liu, L. (2005). The role of Internet self-efficacy in the acceptance of web-based electronic medical records. Journal of Organizational and End User Computing (JOEUC), 17(1), 38-57.
Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56-73.
Roh, M., & Park, K. (2019). Adoption of O2O food delivery services in South Korea: The moderating role of moral obligation in meal preparation. International Journal of Information Management, 47, 262-273.
Salloum, S. A., & Shaalan, K. (2018). Adoption of e-book for university students. In International Conference on Advanced Intelligent Systems and Informatics (pp. 481-494). Springer, Cham.
Sam, H. K., Othman, A. E. A., & Nordin, Z. S. (2005). Computer self-efficacy, computer anxiety, and attitudes toward the Internet: A study among undergraduates in Unimas. Journal of Educational Technology & Society, 8(4), 205-219.
Schlebusch, C. L. (2018). Computer anxiety, computer self-efficacy and attitudes towards the internet of first year students at a South African university of technology. Africa Education Review, 15(3), 72-90.
Shih, Y. Y. (2006). The effect of computer self-efficacy on enterprise resource planning usage. Behaviour & Information Technology, 25(5), 407-411.
Simpson, G. G., Gerard, R. W., Goodenough, W. H., & Inkeles, A. (1961). Comments on cultural evolution. Daedalus, 90(3), 514-533.
Tam, C., & Oliveira, T. (2016). Performance impact of mobile banking: using the task-technology fit (TTF) approach. International Journal of Bank Marketing.
Teo, T. S., & Yu, Y. (2005). Online buying behavior: a transaction cost economics perspective. Omega, 33(5), 451-465.
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS quarterly, 125-143.
Triandis, H. C. (1979). Values, attitudes, and interpersonal behavior. In Nebraska symposium on motivation. University of Nebraska Press.
Tshabalala, M., Ndeya-Ndereya, C., & Van der Merwe, T. (2013, June). Academic staff's challenges in adopting blended learning: reality at a developing university. In International Conference on e-Learning (p. 396). Academic Conferences International Limited.
Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: the competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS quarterly, 483-502.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
Williams, L. J., & Hazer, J. T. (1986). Antecedents and consequences of satisfaction and commitment in turnover models: A reanalysis using latent variable structural equation methods. Journal of applied psychology, 71(2), 219.
Williamson, O. E. (1975). Markets and hierarchies: analysis and antitrust implications: a study in the economics of internal organization. University of Illinois at Urbana-Champaign's Academy for Entrepreneurial Leadership Historical Research Reference in Entrepreneurship.
Williamson, O. E. (1985). The economic institutions of capitalism. New York: Free Press.
Wolverton, C. C., Hollier, B. N. G., & Lanier, P. A. (2020). The Impact of Computer Self Efficacy on Student Engagement and Group Satisfaction in Online Business Courses. Electronic Journal of e-Learning, 18(2), pp175-188.
Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232.
Yeo, V. C. S., Goh, S. K., & Rezaei, S. (2017). Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services. Journal of Retailing and Consumer Services, 35, 150-162.
Yuan, S., Liu, Y., Yao, R., & Liu, J. (2016). An investigation of users’ continuance intention towards mobile banking in China. Information Development, 32(1), 20-34.
Zhao, Y., & Bacao, F. (2020). What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period?. International journal of hospitality management, 91, 102683.
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.
Zmud, R. W. (1979). Individual differences and MIS success: A review of the empirical literature. Management science, 25(10), 966-979.