本論文所定義之「網路群體犯罪」，不同於組織犯罪般有結構的犯罪團體，亦非為了追求共同利益而合作的共犯夥伴，而是網路使用者自發性互動行為下逐漸浮現的群體近似犯罪行為，並且普遍存在於當今各式各樣的網際網路社群，以各種不同的樣貌與形式展現。本研究以Sutherland（1978）提出之差別接觸理論與Bandura（1977）提出之社會學習理論為基礎，運用理論相關的元素與概念作為食材與食譜，以BDI代理人模式為方法來設計網路群體犯罪之模擬模式，透過動態模擬群體犯罪在不同條件下展現不同之面貌。更運用Watts（2003）主張的網路科學概念與分析方法，來分析犯罪關係網絡之特性，本研究藉由控制網路社群之使用者人數（Size）與初始犯罪率（ICR）來觀察不同組合之下所演化的網路結構差異，並從四個衡量指標：犯罪技能平均數、群聚係數、前10%使用者平均連結度、連結度小於10之比率，標示演化之網路結構的特徵。研究結果發現：1. 犯罪技能擴散的速度受到ICR高低的影響，當ICR越高的時候犯罪技能擴散的速度越快，反之，當ICR較低的時候犯罪技能擴散速度隨之減緩。2. 當ICR超越某一特定臨界值之後，使用者擁有的犯罪技能平均數與所屬社群人數成正向關係。3. ICR的高低對於群聚係數的高低有反向關係，當ICR越高則群聚係數越低，反之，當ICR越低時群聚係數越高。4. 社群使用者人數越多的情況下，群聚係數越低。5. 前10%使用者的平均連結度有隨著演化次數逐漸增加的趨勢。6. 初始犯罪率的高低與前10% 使用者的平均連結度成反比關係。7. 不論演化次數、社群人數多寡與初始犯罪率值之高低，均僅有少數犯罪者擁有高度的連結，絕大多數的使用者或犯罪者其連結度數均不高（符合power law分佈）。 / Collective crime is an emerging phenomenon along with collective intelligence in recent years. It is defined as a form of universally distributed crime originated from spontaneous interaction among community users in this paper. The issues that collective crime addresses focus on deviant or criminal behavior existing in common groups or crowds rather than traditional topics at computer crime or cybercrime. The theories, “differential association” proposed by criminologist Sutherland(1978) and “social learning” proposed by sociologist Bandura(1977), underpin the explanation of collective crime phenomena and the model design of agent-based simulation. The detection function of collective crime consists of the evolving network function based on the micro-simulation and an analysis of the function along with four indicators: average amount of crime skills, average cluster coefficient, average degree of top 10% users, and rate of users with degrees smaller than 10. The research findings are: 1. A community with higher initial crime rate (ICR) results in faster spreading of crime skills. 2. A negative relationship between the community size and the average amounts of crime skills exists, as ICR exceeds a threshold. 3. As ICR gets increasing, the average cluster coefficient gets decreasing, and vice versa. 4. The average cluster coefficient gets decreasing along with increasing community size. 5. The average degree of top 10% users gets increasing along time. 6. A negative relationship exists between ICR and the average degree of the top 10% users. 7. The distribution of the degrees of community users follows the scale-free power law distribution – whatever the network evolution times, community size and ICR are, most of the community users have fewer degrees and only few criminals have pretty high degrees relatively.

處於服務大量興起的時代，創新服務成為企業關注的熱門議題且如雨後春筍般發展。然而，一個新服務的成功並不在於它的獨特性，而是取決於它的使用率與普及化。當中，觀察到有趣的現象，人們對於新服務的使用行為取決於自身的認知與社會影響力相互作用而產生；社會影響力在個人行為表現上扮演關鍵的因素，引導個人了解認知差距並提供建議改善認知以促進個人產生使用意圖。在本研究中，整合行為改變模型(TTM)、傳播理論、框架效應而發展出一套理論架構，並應用視覺化方式呈現資訊提供個人快速理解重點，重塑個人對於新服務的認知，且引起他們願意使用新服務的想法。基於認知重塑的目標，本研究貢獻為設計一套能輔助個人進行行為意向改變與加速新服務使用率的系統。 / In the service growing era, service innovation becomes a popular topic on the businesses who want to develop innovative services for people. However, the success factor is not on how special a service is; but rather, depends on how much utility and popularity of the service. This study has observed the interesting phenomenon that people's behavior performance depends on their cognition and the social influence playing an important role on affecting the intention. In order to resolve this condition, this study connects the transtheoretical model (TTM) of behavior change, communication theories, and frame effect model to develop a framework and apply visualization to provide people with the integral information that can reframe their cognition to enhance their intention to accept the new service. Based on the cognition reframing, this study develops an artifact the can assist people to exercise the cognition adjustment that can reinforce the behavior intention change and facilitate the usage of new service.