隨著智慧型裝置普及以及網際網路蓬勃發展，行動世代之來臨造就新興商業模式及人們不同的生活型態。在眾多種類的App中，購物類型逐漸在這幾年嶄露頭角，在2017年購物型App的成長比例為各類別中幅度最大的，但過去在購物型App的相關研究中，相當多以科技接受模型探討研究，以任務科技適配理論來探討使用者使用行動購物App之行為未見及。
本研究以Goodhue & Thompson(1995)的任務科技適配理論為基礎，探討使用者對購物型App之使用行為，資料蒐集方式為網路問卷，目標對象為有使用過蝦皮購物App並購買過美妝保健類及居家生活類用品的使用者。研究結果發現：(1)任務特性與任務科技配適度無顯著關係，(2)科技特性對任務科技配適度有顯著正向關係(3) 任務科技配適度對於績效表現有顯著正向關係。

The purpose of the study was to investigate if task- technology fit would influence individual performance impact. This model is employed to explore usage performance to use shopping apps from the perspectives of task-technology fit. The first hypotheses: task characteristics positively affect the task technology fit in shopping apps. The second hypotheses: technology characteristics positively affect the task technology fit in shopping apps. The third hypotheses: task-technology fit positively affects the performance to use Shopping apps. The fourth hypotheses: task characteristics positively affects the performance to use Shopping apps. The fifth hypotheses: technology characteristics positively affects the performance to use Shopping apps. An internet survey was adopted to collect the data. The results revealed that the higher task-technology fit of shopping apps are, the higher performance impact toward those apps are. Based on 5-point Likert Scale, the survey concluded: (a)among the valid respondents, 90% were female, and (b) most of survey respondents, their experience of using smart phone is over 5 years. To sum up my research: (a) There is no significant relationship between task characteristics and task-technology fit, and (b) Technology Characteristics would positively affect task-technology fit and performance. (c) Task Characteristics would positively affect performance.

參考文獻
中文文獻
田巧宜（2013）。影響社交型App採用行為之因素探討。國立中山大學資訊管理學系研究所碩士論文，高雄市。 取自https://hdl.handle.net/11296/2faqy8
楊王鴻（2014）。行動購物特性、網站互動性、消費者購物行為與網站購物-行動版App忠誠度之研究_以網站購物-行動板實證。國立臺北科技大學經營管理系碩士班碩士論文，台北市。 取自https://hdl.handle.net/11296/9ws634
蘇芳亭（2013）。運用科技接受模型(TAM)與任務科技配適度(TTF)之整合探討消費者對平板電腦接受度之研究。國立臺北科技大學工業工程與管理系碩士班碩士論文，台北市。 取自https://hdl.handle.net/11296/7rnavs
張榮森（2015）。行動購物APP服務品質、顧客滿意度與顧客忠誠度關係之研究。國立中山大學企業管理學系研究所碩士論文，高雄市。 取自https://hdl.handle.net/11296/e2djyg
李炯廷（2016）。探討系統相關品質對消費者使用購物型APP意圖之影響: 以整合性科技接受模型為基礎。國立成功大學工業與資訊管理學系碩士在職專班碩士論文，台南市。 取自https://hdl.handle.net/11296/426mvx
賴宥豪（2015）。以科技接受模型探討 行動購物APP使用之意願。國立高雄應用科技大學資訊管理研究所碩士班碩士論文，高雄市。 取自https://hdl.handle.net/11296/4dd5zf
英文文獻
Agrebi, S., &Jallais, J. (2015). Explain the intention to use smartphones for mobile shopping. Journal of Retailing and Consumer Services, 22, 16–23. https://doi.org/10.1016/J.JRETCONSER.2014.09.003
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Coursaris, Constantinos & Hassanein, Khaled & G Degroote, M. (2002). Understanding m-commerce: A consumer-centric model. Retrieved from https://www.researchgate.net/publication/228936046_Understanding_m-commerce_A_consumer-centric_model
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555
Davis, F. D., &D., F. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
DeLone, W. H., &McLean, E. R. (1992). Information Systems Success: The Quest for the Dependent Variable. Information Systems Research, 3(1), 60–95. https://doi.org/10.1287/isre.3.1.60
Dishaw, M. T., &Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information & Management, 36(1), 9–21. https://doi.org/10.1016/S0378-7206(98)00101-3
Fishbein, M., &Ajzen, I. (1977). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Retrieved from https://philpapers.org/rec/FISBAI
Gebauer, J., Shaw, M. J., &Gribbins, M. L. (2010). Task-Technology Fit for Mobile Information Systems. Journal of Information Technology, 25(3), 259–272. https://doi.org/10.1057/jit.2010.10
Goodhue, D. L., &Thompson, R. L. (1995). Task-Technology Fit and Individual Performance. MIS Quarterly, 19(2), 213. https://doi.org/10.2307/249689
Groß, M. (2015). Exploring the acceptance of technology for mobile shopping: an empirical investigation among Smartphone users. The International Review of Retail, Distribution and Consumer Research, 25(3), 215–235. https://doi.org/10.1080/09593969.2014.988280
Inge Klopping, &Earl McKinney. (2004). Extending the Technology Acceptance Model Extending the Technology Acceptance Model and the Task and the Task--Technology Fit Model to Technology Fit Model to Consumer E Consumer E- Commerce Commerce | Request PDF. Retrieved May29, 2019, from https://www.researchgate.net/publication/237251740_Extending_the_Technology_Acceptance_Model_Extending_the_Technology_Acceptance_Model_and_the_Task_and_the_Task--Technology_Fit_Model_to_Technology_Fit_Model_to_Consumer_E_Consumer_E-_Commerce_Commerce
Kang, S., &Seok. (2014). Factors influencing intention of mobile application use. International Journal of Mobile Communications, 12(4), 360. https://doi.org/10.1504/IJMC.2014.063653
Klopping, Inge & McKinney, E. (2004). Extending the Technology Acceptance Model Extending the Technology Acceptance Model and the Task and the Task--Technology Fit Model to Technology Fit Model to Consumer E Consumer E- Commerce Commerce | Request PDF. Retrieved from https://www.researchgate.net/publication/237251740_Extending_the_Technology_Acceptance_Model_Extending_the_Technology_Acceptance_Model_and_the_Task_and_the_Task--Technology_Fit_Model_to_Technology_Fit_Model_to_Consumer_E_Consumer_E-_Commerce_Commerce
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. https://doi.org/10.1016/j.dss.2005.05.008
Lee, K. C., Lee, S., &Kim, J. S. (2004). Analysis of Mobile Commerce Performance by Using the Task-Technology Fit. In Mobile Information Systems (pp. 135–153). https://doi.org/10.1007/0-387-22874-8_10
Liang, T.-P., &Wei, C.-P. (2004). Introduction to the Special Issue: Mobile Commerce Applications. International Journal of Electronic Commerce, 8(3), 7–17. https://doi.org/10.1080/10864415.2004.11044303
Lim, H., &Dubinsky, A. J. (2004). Consumers’ perceptions of e‐shopping characteristics: an expectancy‐value approach. Journal of Services Marketing, 18(7), 500–513. https://doi.org/10.1108/08876040410561839
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. https://doi.org/10.1016/J.IM.2008.06.004
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. https://doi.org/10.1016/j.chb.2013.10.020
Natarajan, T., Balasubramanian, S. A., &Kasilingam, D. L. (2017). Understanding the intention to use mobile shopping applications and its influence on price sensitivity. Journal of Retailing and Consumer Services, 37, 8–22. https://doi.org/10.1016/J.JRETCONSER.2017.02.010
Online, R., Ambra, J. D. ’, Wilson, C. S., Akter, S., Ambra, D. ’, Wilson, J., &Akter, C. &. (2013). Application of the task-technology fit model to structure and evaluate the adoption of e-books by academics Publication Details. Retrieved from http://ro.uow.edu.au/commpapers/3189
Tam, C., &Oliveira, T. (2016). Performance impact of mobile banking: using the task-technology fit (TTF) approach. International Journal of Bank Marketing, 34(4), 434–457. https://doi.org/10.1108/IJBM-11-2014-0169
Taylor, D. G., Voelker, T. A., &Pentina, I. (2011). Mobile Application Adoption by Young Adults: A Social Network Perspective (Vol. 6). Retrieved from http://digitalcommons.sacredheart.edu/wcob_fac/1
Turban, E., &King, D. (2003). Introduction to e-commerce. Prentice Hall.
Venkatesh, Morris, Davis, &Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540
Vessey, I. (1991). Cognitive Fit: A Theory-Based Analysis of the Graphs Versus Tables Literature. Decision Sciences, 22(2), 219–240. https://doi.org/10.1111/j.1540-5915.1991.tb00344.x
Zhou, S., Chen, D., Cai, W., Luo, L., Yoke, M., Low, H., …Hamilton, B. D. (2010). Crowd Modeling and Simulation Technologies. ACM Transactions on Modeling and Computer Simulation. https://doi.org/10.1145/1842722.1842725

網路資料
1. Digitalreport：https://digitalreport.wearesocial.com/
2. Statista：https://www.statista.com/statistics/251096/fastest-growing-shopping-app-categories/
3. Inside：https://www.hksilicon.com/articles/1207073
4. Deloitte. (2017, December) Global mobile consumer survey 2017: Trends from around the world. Deloitte. [Online]. Available: https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Technology-Media-Telecommunications/gx-deloitte-tmt-global-mobile-consumer-trends-report-first-edition.pdf
5. 資策會產業情報研究所（MIC）(2016):行動購物消費者調查。台北市：資策會產業情報研究所（MIC）https://mic.iii.org.tw/IndustryObservations_PressRelease02.aspx?sqno=489
6. Upwork：https://www.upwork.com/hiring/for-clients/technology-needed-to-build-shopping-app/
7. Designhu：https://www.design-hu.com/web-news/moblie-website.html
8. App Annie：https://www.appannie.com/en/
9. Sea：https://www.seagroup.com/home