由於資訊科技的進步，網頁資訊更豐富與複雜，本研究從兩方面，一方面找出複雜的網頁是如何被使用者所認知？另一方面網頁介面設計的好不好？該如何設計才能降低網頁資訊的複雜度使其更易讀的方法。若網站介面設計能搭配使用者的認知，將有助網站設計出能降低複雜度又易讀的網頁，幫助使用者完成任務。因此，研究目的為瀏覽者是如何知覺與認知複雜的網頁？網頁介面的版面配置設計的好不好？研究問題為腦中模版是否影響網頁瀏覽？找出降低資訊複雜度的螢幕設計的原則？ 本研究分別設計四個實驗驗證四個假設，實驗一的目的是驗證使用者腦中模板對資訊選擇有影響。方法是比較實驗未進行前，瀏覽者大腦中所認知的電子商務網頁配置概圖，得到瀏覽者腦中既有的模板是什麼，再進行選擇注意實驗，比較瀏覽者畫出來電子商務網站的回憶圖與未進行實驗前的模板間的一致性。結論為模板與實際組的卡方值均為不顯著，因此，可以得知瀏覽者瀏覽電子商務網站時腦中的模板確實影響網頁選擇注意。 實驗二的目的是驗證使用者因為任務需要不同資訊的選擇策略對造成其網頁的認知不同；方法是實驗中透過使用者進行搜尋的任務中，根據其瀏覽策略將搜尋任務第一頁的瀏覽與最後一頁不同的瀏覽目標，因要達成的選擇策略不同所以其選擇注意不同。結論為第一頁與最後一頁的的瀏覽分析可以得知不同資訊的選擇策略將影響網頁介面的螢幕配置認知。 實驗三的目的螢幕配置會影響選擇注意。方法計算文獻整理的六個指標的理論值之後，實証為讓使用者到四個國家的yahoo網頁進行任務，會因網頁配置不同，影響使用者對四個不同國家的yahoo網頁的認知選擇注意不同。結論為四個國家卡方值為顯著，表示四個國家的介面配置不同確實會影響不同的選擇注意。 實驗四的目的証明網頁設計易讀性理論值與實證的順序是相同的。方法計算文獻整理的六個指標的理論值之後，實証為將四個日韓台美不同的yahoo網頁的配置，兩個為一組，分六組進行實驗，以了解四個國家網頁哪個配置讓瀏覽者較容易閱讀。結論為理論的順序：日(0.099)>美(0.095)>台(0.086)>韓(0.078)；實證的順序：美>日>台>韓；日美優於台韓故順序一致。 根據實驗的結論，我們知道使用者面對複雜的網頁資訊時，會根據腦中的模版來分析與過濾網頁上面的資訊，而且瀏覽者對於資訊的選擇確實會影響網頁螢幕的配置像對空白與色彩..等的差異，並且根據文獻找出的指標確實與實際狀況符合。所以，建議設計師在網頁layout設計，要按照使用者的工作任務經驗，及各工作任務的可能模版特質來設計資訊呈現內容。並根據六個整理出來的指標列為參考的網頁設計元素，將元素差異及元素關係的因素，如資訊在畫面上的位置、形成區塊的空白、框線、對比顏色、文字大小，以及關鍵詞等因素善加運用，按照工作任務性質，做適當安排。將有效降低使用者認知複雜度達成任務。 / Due to the fast growth of information technology, website information has become richer and more complex, and this piece of research will target on two main related areas. One is to find out how those complicated webpage were cognized by their users? The other is to discuss whether if the existing webpage interface design is good or not, and how webpage information should be designed in order to reduce its complicity and make it easier to read. If the webpage interface is able to collocate with users’ cognition, it will help websites to create webpage with low complicity and ease of read, and thereby assisting users to complete their missions. Therefore, the research aim would be to understand how browsers sense and perceive webpage and its complicity. Is the interface and layout design suitable for such webpage? In addition, the key question is to ask if internet browsing would be affected by our brain templates, and to further find out the principles for low information complicity screen design. The research body consists of four experiments which are set to prove 4 hypotheses. First experiment is to prove that users’ brain templates have effects on the choices of information. The main method is to obtain the e-commerce webpage layout from browsers’ brain before carrying out any further tasks, and see what the initial templates look like. And then by taking the selective attention tests, to compare the similarity between the memorial mappings of the e-commerce website with the initial template. The result has shown that the chi-square values between template and real group are both not significant, therefore, it is predicted that when users are browsing e-commerce websites, the brain templates do have effects on webpage selective attention. Experiment two aims to examine if differences in webpage cognition occur as users could have various choices of information due to different needs. When users browse the internet, because of the targets change, the strategies used in first page and last page also differs, and so does the selective attention. To conclude, from the first and last pages browsing analysis we can know that different information choice strategies will have impacts on the webpage interface layout. Third experiment focuses on the display layout which affects selective attentions. By calculating six theoretical values from indicators in literature reviews, and in order to prove the theory users are asked to carry out missions on Yahoo! websites in four countries, and because the webpage layouts are different, the users’ selective attentions also varies in these four countries. The chi-square values of the four countries are significant, which means different webpage layout will alter selective attentions. The last one proves that the theoretical values of legibility in webpage design are proportional to the case studies. In order to understand which Yahoo! website is the easiest to read out of these four countries, they were divided into groups of two and carried out six sets of experiments. Theoretical result shows that Japan (0.099)>US (0.095)>Taiwan (0.086)>Korea (0.078), where practical result shows US>Japan>Taiwan>Korea, and therefore both proved that US and Japan are better than Taiwan and Korea. According to the results of experiments, we can know that when users face complex web information, they tend to use the templates created in their brain to analyze and filter those information, and the choices of information that browsers made do affect webpage and screen layout, such as different spacing and coloring; which just corresponds with our literature indications. Hence, when designing webpage layout, designers are recommended to demonstrate the content by following users’ actual experiences, plus some possible behaviors under different work and mission conditions. In addition, the six indicators should also be used as design references, and key factors such as the positioning of information, spacing, bordering, contrast, font sizes and keywords should also be taken into consideration in order to make appropriate arrangement to suit different work properties, which will lower the cognition complicity for users effectively, and thereby fulfill its tasks.

軟體元件電子市集突現：以代理人為基礎之計算經濟研究途徑 摘 要 軟體發展與演進過程中，產生軟體危機問題，而軟體再用是解決軟體危機的重要因應之道。軟體元件電子市集的興起是軟體演進史上一個重要里程碑，提供軟體再用的核心基礎建設。 本文探討軟體元件電子市集突現的本質原因和信任關係的發展過程，以遺傳規劃法(Genetic Programming, GP)為主的代理人基礎的計算經濟 (Agent-based Computational Economics, ACE) 研究途徑，整合軟體元件特性、交易成本、滿意和信任關係建立模擬模式。藉以觀察和分析底層買賣雙方連續滿意交易與信任關係發展，和上層軟體元件電子市集行為突現(emerge)動態過程。 結果顯示：在市場力量下，具標準化軟體元件，電子市集行為突現過程中，謹慎型交易策略將會勝出，進而主導整個市場。當元件功能特殊性程度低時，電子市集行為的購買率將比元件功能特殊性程度高者更為顯著。如果考慮交易態度滿意與否，記憶型滿意者市集行為的購買率將顯著低於高滿意型，而顯著高於低滿意型。若考慮不同信任程度函數，高信任型電子市集購買率顯著高於低信任型，低信任型其電子市集購買率顯著高於不信任型，對於目錄型市集行為和忠誠目錄型市集行為，上述信任函數的形態亦依序顯著影響購買率的高低。 同時，在不同信任型之間，高信任型大多數有連續累積交易行為；而低信任型則同時採用連續和臨時交易行為；不信任型大多數是臨時交易行為，要花費更多時間的演化，以建立彼此信任關係才會出現連續交易乃至於連續累積交易行為。 關鍵字：軟體元件電子市集、交易成本、遺傳規劃法、代理人基礎計算經濟、信任、突現 / The Emergence of Software Component Electronic Marketplaces: Through An Agent-based Computing Economics Approach Abstract Software reuse plays a vital role in response to software crises in software evolution. An emergence of software component e-marketplace is one of the great milestones providing a core infrastructure for software reuse. The objective of this study involving features of s/w components, transaction costs and satisfaction-trust relations intends to understand why s/w component e-marketplaces emerge as well as demonstrate how they do. The model allows agents to develop their trust in the market as a function of continuation of a satisfied relation through an agent-based computational economics approach with genetic programming. The findings show that the agents with prudent strategies tend to dominate the market in evolution of e-marketplaces under the market power. In addition, the lower level the functional particularity of component is, the higher the buying rate is. As the satisfaction attitude is taken into consideration, the buying rate of recall-satisfied agents lies between that of low-satisfied agents and that of high-satisfied agents. Moreover, when the comparisons are made among the three types of trust function, the buying rate of the high-trust agent is higher than that of low-trust agents. And the buying rate of the low-trust agent is bigger than that of not-trust agents. Similarly, the sequences of the buying rate are strongly influenced by different type of trust function at the catalog market and the loyal catalog market. Meanwhile, almost all high-trust agents have continuous and loyal trade behavior. Either continuous or temporal trade behavior is usually found in the low-trust agents. The tentative trade behavior is seen among almost every not-trust agents. In another words, it is well obvious that it takes more time for the not-trust agents to accumulate trust from their possible trade partners. Keywords: Software component electronic marketplaces; Transaction costs; Genetic programming (GP); Agent-based computational economics (ACE); Trust, Emergence