隨著服務體驗經濟時代的來臨，服務提供商所面臨到的環境是愈來愈競爭及逐漸是轉由消費者所主導的型式，因此服務提供商如何去滿足顧客的需求並且達成更高的滿意度便是主要的目標之一。特別是在發展型服務提供者的環境下，他們需要去考量自已本身較不充足的服務能力及資源來設計及提供服務給顧客，而這樣的條件之下，他們也很難在短時間及時的去改善並且提供穩定的服務品質。因此，本研究提出一套是基於顧客期望理論及粒子群演算法的架構下所發展的協同式互動設計機制，希望協助發展型服務提供商解決他們所面臨的問題及創造出更大的服務價值。 本研究將協同式互動設計機制應用在會展產業的服務環境底下，並利用模擬實驗的方式去驗證此機制的有效性及鞏固性。協同式互動設計機制共有四大模組：(1)顧客偏好識別模組 (2) 粒子群期望因子選擇模組 (3) 情境式旅程抉擇模組 及(4) 服務執行模組。本研究設計此機制時考量了加入顧客間互動的能力來幫助發展型服務提供商進行更有效的服務互動並且執有效的顧客群體期望控制的目標，以便在減輕服務提供商所付出的成本之下，還能達成良好的顧客滿意度。而本研究的研究貢獻為幫助發展型服務提供商解決他們所面臨的挑戰，並且在有限的資源和能力底下，仍然可以使得他們保持與高能力服務廠商之間的競爭;而另一貢獻為在整體的服務環境底下，能讓所有的參與角色都能夠得到最大的價值，而形成一個高效能的服務生態系統。 / With the progressive advancement of the technology and fiercely-competitive environment in recent years, customers have paid more attention to the issue that how diversity and rich the service experience could satisfy their needs; in other words, the service providers must acquire the competitive advantage among other service competitors by pondering on that how to deliver the qualified service offerings in every service encounter to achieve the objective of customer satisfaction. On the other hand, many research findings noted that customers’ service quality evaluation in service encounter were influenced by the comparison between the customer expectation toward service and the service performance that they perceived; therefore, managing the customer expectation becomes the vital part concerning the customer satisfaction. Furthermore, the shortcomings of the low-moderate competence service providers is that they could not provide the constant qualified service offerings to customers in each service interaction in terms of the reason for lesser service capability and resource. Consequently, this study propose the collaborative interaction design approach which based on the Particle Swarm Optimization(PSO) algorithm to generate the dynamical service interaction among the service providers and customers for the low-moderate competence service providers and aids them to control their group customers’ expectation by collaborating with customers; in other words, the service effort of the service provider could be lightened by engaging the customer capability and the service offerings could be enhanced to provider for customers. Therefore, this study utilizes the four modules in the research framework to achieve the aforementioned objective. Ultimately, the expected contributions of this study are two-folds: (1) Aid the low-moderate competence service providers to improve the service experience for customers on the restriction of lesser service capability. (2) Utilize the PSO algorithm to decide the determinants that effectively influence on customers’ expectation considering the whole benefits among stakeholders. Hence, the collaborative interaction design proposed in this study has conspicuous benefits for the low-moderate competence service providers to preserve the competitive advantage by providing the well-design exemplar to let them follow.

在科技持續進步的時代，金融市場發展隨著社會的演進不斷地成長與活絡，金融商品也從原本單純的本國存放款、外幣投資衍生出票券、債券等利率投資工具；除此之外，隨著資本市場的擴張，股票、基金、期貨與選擇權等投資標的更是琳瑯滿目。 而後產生了許多人使用資料探勘工具預測市場的買賣時機。如Baba N., Asakawa H. and Sato K.(1999)使用倒傳遞類神經網路來預測到股市未來的漲跌，而後又在2000年研究當中加入基因演算法來求得倒傳遞類神經網路的權重，得到最後的類神經網路模型。 在做資料探勘的同時，我們得在希望預測目標(Target)上事先定義好一套固定規則，這會使得模型的彈性與可預測度降低，本研究希望能透過資料探勘工具增加預測目標規則的彈性，增加模型最後的預測準確度。 本研究樣本區間選用2010年到2015年的台指期貨數據做為資料，並結合羅吉斯回歸與粒子群演算法建構更加有彈性的預測模型結果，最後發現在未來10分鐘，若漲幅超過0.1114%做為買進訊號的話，其建立出的模型可達到84%的預測準確度。