本研究旨在了解在不同情境底下的購物任務中，消費者的行為是否具有差異。採用 ZOSTs 搜尋移動概念及 K-means 分群法，並設計具明確購物目標及不具明 確購物目標兩種情境之模擬購物實驗，分析消費者在購物網站上的搜尋瀏覽行為， 並且歸納出消費者不同的搜尋行為模式及特徵，藉此了解購物網站所提供之推薦 功能對購物決策的效益。研究結果發現，在具有明確購物目標的任務之下，消費 者能精準的鎖定在特定類型下的商品，透過網站中的關鍵字搜尋功能來找尋商品; 而在不明確的購物目標之下，消費者仰賴網站的推薦功能或是已有的商品分類來 幫助塑造商品的需求，產生多樣的搜尋瀏覽行為。另根據 20 位受試者的瀏覽數 據分群結果，區分出瀏覽型、明確型及其延伸之搜尋型消費者的存在，兩大類型 之消費者在「瀏覽不同類別次數」、「搜尋比較頁面佔比」、「活動/推薦頁面佔 比」方面具有顯著的差異。根據以上的實證研究結果，提出針對購物網站功能改善之建議，並提供賣家制定更精準行銷策略之參考。

The aim of this research is to understand whether consumers' shopping behavior vary in the context of different shopping tasks. We first designed two types of shopping tasks: goal-oriented shopping and exploratory-based shopping. We then used zero- order state transition matrices (ZOSTs) and K-means clustering algorithm to analyze consumers' online shopping behaviors. Through clustering analysis, we identify different search patterns for two types of consumers with different tasks and examine the effectiveness of the recommendation functions (RFs) offered by the shopping website for online shopping decision making. The results show that the goal-oriented consumers tend to focus on specific type of products and use keyword search to find out what they want; while the exploratory-based consumers rely on the RFs or existing categories to help them clarify needs. In addition, there are significant differences between goal-oriented and exploratory-based consumers in “ category variety measure”, the percentage of pages that were “search result pages” and “RFs pages”. The findings can provide a reference for sellers to develop precision marketing strategies.

李郁雅（2000）。博士論文研究歷程中情境與資訊行為之探索：以植物分類學為例（未出版之碩士論文）。臺灣大學，臺北市。
林珊如、許禎芸（2008）。從國內碩士論文探討資訊行為相關研究。圖書資訊學研究，3(1)，51-74。
邱銘心（2012）。意義建構理論。圖書館學與資訊科學大辭典。檢自：http://terms.naer.edu.tw/detail/1679177/
袁興福、張鵬翼、劉洪蓮、王軍（2015）。基於點擊流的電商用戶會話建模。圖書情報工作，59(1)，119-126。
國家發展委員會（2017）。 歷年數位機會調查報告。檢自：https://www.ndc.gov.tw/cp.aspx?n=55c8164714dfd9e9
張文君、王軍、徐山川（2015）。電商用戶需求狀態的聚類分析——以淘寶網女裝為例。數據分析與知識發現，31(3)，67-74。
資策會產業情報研究所MIC（2017）。雙11前哨戰海內外開打，消費者買氣較去年成長10％。檢自：https://mic.iii.org.tw/IndustryObservations_PressRelease02.aspx?sqno=483
資策會產業情報研究所MIC（2018）。「網購大調查系列一」日常購物頻率 網購已達45％。檢自：https://mic.iii.org.tw/IndustryObservations_PressRelease02.aspx?sqno=488
資策會產業情報研究所MIC（2018）。「網購大調查系列二」行動網購普及率達64.9％。檢自：https://mic.iii.org.tw/IndustryObservations_PressRelease02.aspx?sqno=489
資策會產業情報研究所MIC（2018）。「網購大調查系列三」2017跨境網購年消費額成長5.4％。檢自：https://mic.iii.org.tw/IndustryObservations_PressRelease02.aspx?sqno=490
數位時代（2017）。再刷新紀錄！雙11開跑三分鐘，交易額破百億人民幣。檢自：https://www.bnext.com.tw/article/46989/alibaba-t-mall-double-eleven-festival
Belkin, N. J. (1978). Information concepts for information science. Journal of Documentation, 34(1), 55-85.
Belkin, N. J. (1980). Anomalous states of knowledge as a basis for information retrieval. Canadian Journal of Information Science, 5(1), 133-143.
Belkin, N. J., Oddy, R. N., & Brooks, H. M. (1982). ASK for information retrieval: Part I. Background and theory. Journal of Documentation, 38(2), 61-71.
Bucklin, R. E., & Sismeiro, C. (2003). A model of web site browsing behavior estimated on clickstream data. Journal of Marketing Research, 40(3), 249-267.
Byström, K., & Järvelin, K. (1995). Task complexity affects information seeking and use. Information Processing & Management, 31(2), 191-213.
Chiang, R. D., Wang, Y. H., & Chu, H. C. (2013). Prediction of members’ return visit rates using a time factor. Electronic Commerce Research and Applications, 12(5), 362-371.
da Silva, J. M. M. (2014). The Road to Enlightenment: Generating Insight and Predicting Consumer Actions in Digital Markets. Dissertation Research Submitted to University of Porto.
Dervin, B. (1983). An overview of sense‐making research: concepts, methods, and results to date. In International Communication Association Annual Meeting, 1-14.
Dervin, B. (1992). From the mind's eye of the user: The Sense-making qualitative-quantitative methodology. In J. D. Glazier, & R. R. Powell (Eds), Qualitative Research in Information Management, 61-84.
Ellis, D. (1989). A behavioural model for information retrieval system design. Journal of Information Science, 15(4-5), 237-247.
Ingwersen, P., & Järvelin, K. (2005). The development of information seeking research. The Turn: Integration of Information Seeking and Retrieval in Context, 55-109.
Kuhlthau, C. C. (1991). Inside the search process: Information seeking from the user's perspective. Journal of the American Society for Information Science, 42(5), 361-371.
Kukar-Kinney, M., Ridgway, N. M., & Monroe, K. B. (2012). The role of price in the behavior and purchase decisions of compulsive buyers. Journal of Retailing, 88(1), 63-71.
Li, Y., Lu, L., & Xuefeng, L. (2005). A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce. Expert Systems with Applications, 28(1), 67-77.
Liu, Y., Li, H., Peng, G., Lv, B., & Zhang, C. (2015). Online purchaser segmentation and promotion strategy selection: evidence from Chinese E-commerce market. Annals of Operations Research, 233(1), 263-279.
Mitsui, M., Liu, J., Belkin, N. J., & Shah, C. (2017). Predicting information seeking intentions from search behaviors. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1121-1124.
Moe, W. W. (2003). Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology, 13(1-2), 29-39.
Moe, W. W., & Fader, P. S. (2004). Capturing evolving visit behavior in clickstream data. Journal of Interactive Marketing, 18(1), 5-19.
Niu, X., Li, C., & Yu, X. (2017). Predictive analytics of e-commerce search behavior for conversion. In Proceedings of American Conference on Information Systems, 775-784.
Pallant, J. I., Danaher, P. J., Sands, S. J., & Danaher, T. S. (2017). An empirical analysis of factors that influence retail website visit types. Journal of Retailing and Consumer Services, 39, 62-70.
Park, Y. J., & Chang, K. N. (2009). Individual and group behavior-based customer profile model for personalized product recommendation. Expert Systems with Applications, 36(2), 1932-1939.
Siochi, A. C., & Ehrich, R. W. (1991). Computer analysis of user interfaces based on repetition in transcripts of user sessions. ACM Transactions on Information Systems (TOIS), 9(4), 309-335.
Sismeiro, C., & Bucklin, R. E. (2004). Modeling purchase behavior at an e-commerce web site: A task-completion approach. Journal of Marketing Research, 41(3), 306-323.
Taylor, R.S. (1968). Question-negotiation and information seeking in libraries. College & Research Libraries, 29(3), 178-194.
Wang, Y., Liu, J., Mandal, S., & Shah, C. (2017). Search successes and failures in query segments and search tasks: A field study. Proceedings of the Association for Information Science and Technology, 54(1), 436-445.
Wildemuth, B. M. (2004). The effects of domain knowledge on search tactic formulation. Journal of the American Society for Information Science and Technology, 55(3), 246-258.
Wilson, T. D. (1981). On user studies and information needs. Journal of Documentation, 37(1), 3-15.
Wilson, T. D. (1997). Information behaviour: an interdisciplinary perspective. Information Processing & Management, 33(4), 551-572.
Wu, R. S., & Chou, P. H. (2011). Customer segmentation of multiple category data in e-commerce using a soft-clustering approach. Electronic Commerce Research and Applications, 10(3), 331-341.
Zhao, X., Niu, Z., & Chen, W. (2013). Interest before liking: Two-step recommendation approaches. Knowledge-Based Systems, 48, 46-56.
Zheng, L., Cui, S., Yue, D., & Zhao, X. (2010). User interest modeling based on browsing behavior. In Proceedings of 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), V5-455-V5-458.