本研究旨在編製「知識創新學習環境量表」，以瞭解學習環境中知識創新氛圍的程度。透過三個獨立樣本A、B及C，分別進行探索性因素分析、驗證性因素分析及複核效化分析。樣本A（332人）以探索性因素分析獲得因素成份，結果顯示此份量表有三個因素，分別命名為「想法因素」、「自主學習者因素」及「社群因素」。其次，透過建立本量表的一系列競爭模式，以樣本B（536人）進行驗證性因素分析之評鑑，結果顯示二階單因素模式為最簡效模式，並且量表具有良好之信、效度。而樣本C（536人）則作為複核效化之分析，結果顯示二階單因素模式具有穩定性與預測力。希冀本量表能提供相關單位做為教學及研究之應用。 / The purpose of this study was to develop the Knowledge Building Environment Scale (KBES). Three independent samples was used to validte the reliability and validity of the scale. Firstly, sample A (n=332) was used to generate the factors through exploratory factor analysis. It resulted in a scale of three factors which contains ‘idea’ factor, ‘agent’ factor and ‘community’ factor. Secondly, a series of competing models was established and evaluated by confirmatory factor analysis through sample B (n=536). Comparing with several competing models, hierarchical model was found to be the most efficient model with good reliability and validity. Finally, the cross-validation was tested by sample C (n=536) for hierarchical model to confirm the stability and predictive power of this model. The KBES can provide relevant institutions as a tool for evaluating learning environments.

台灣半導體業自1970年代發展至今，垂直分工完整，從上游的IC設計、製造，到下游的封測與配銷，都有眾多企業投入。本研究著眼於台灣半導體產業中，最上游的IC設計公司之競爭策略與經營模式，主要有幾個原因： 1.台灣有超過300間IC設計公司，吸納了眾多理工相關人才。 2.在全球範圍內，近年的產業結構劇變導致IC設計公司消長迅速。 3.IC設計可靠著正確的產品崛起，但也容易後續乏力，是高風險高獲利的產業。 4.經20餘年發展，世界前20大IC設計公司中，台灣企業已佔到5席。此時回顧IC設計公司的發展歷程，探究其成長策略，當可做為未來其他新進業者參考。 企業成長的策略模式是多樣化的，本研究整理相關文獻與個案發展歷程，針對四家台灣不同領域的IC設計公司所做分析，提出IC設計公司成長策略的擬定，可以由「市場面」、「技術面」，和「策略面」出發。目的在於，探討台灣IC設計業在成長過程中，面對外部競爭環境如何擬定競爭策略以建立、維持競爭優勢。 本研究實證結果摘要以下幾點： 1.技術面：新創IC設計公司資源不足，專攻特定技術可盡速累積經驗與未來發展資源。為了補足資源缺口，可以考慮緊跟產業標準制定者，進行產品規劃。 2.市場面：透過挑選指標型客戶，建立穩定的訂單來源，以及市場聲譽。 3.策略面：引進重要客戶或業界龍頭入股，加深合作關係。 關鍵字：IC設計、競爭模式、創新策略、成長策略、競爭優勢。 / Since 1970s, the semiconductor industry in Taiwan has formed a complete vertical specialized system which consisted of numerous companies from the upstream IC design, manufacturing, assembly & testing to the downstream IC distribution. The reasons why this study focused on the competitive patterns and business models of IC design includes: 1.There are more than 300 fabless IC design house in Taiwan and the whole industry attracted lots of young engineering talents and became remarkable to Taiwan economy. 2.Significant structural transformations changed the whole industry recently. 3.IC design house can rise rapidly by just one right product and fall even faster if they missed the right timing. So the industry is about high operation risks with high return on investments. 4.To learn the successful stories of Taiwanese IC design could be reference classes for the coming start-ups. The growth strategies of corporations are quite diversified, therefore, this study induced the related documents and generalized the history of four case companies to find how they develop growth strategies. This study proposed the growth strategies could be formulated out of three dimensions: the market orientation, the technology orientation and the strategy orientation as below: 1.Technology orientation: In order to accumulate necessary resources for future growth, IC design start-ups may concentrate on core technologies and keep in step with the industry standard setters to plan their product line. 2.Market orientation: Collaborate with key clients to gain stable purchasing orders and reputations. 3.Strategy orientation: Leverage key clients or industry leaders to do market development. Keywords: Fabless IC Design, Competitive Patterns, Innovation Strategy, Growth Strategy, Competitive Advantage.

在複雜動態競爭市場中，生產者的價格競爭行為一直是一個研究的重點，相較於生產者動態價格競爭，零售商的價格競爭行為鮮少被探討，因此本研究針對零售商價格競爭行為進行研究。針對零售商之間的價格競爭行為，除了考量零售商與對手零售商的價格互動，不可忽略的是上游供應商的競爭互動與下游消費者的學習行為在市場中與零售商端互動下錯綜複雜的動態影響，緣此，本研究以零售商端的角度，想了解供應商競爭與消費者學習行為對零售商競爭的影響，再以單一零售商角度，分析各情況下所應對的價格調整策略。 本研究將零售商、供應商及消費者互動形成之競爭市場視為一個複雜適應性系統（Complex Adaptive System ，簡稱CAS），應用代理人基塑模與模擬（Agent-based Modeling and Simulation，簡稱ABMS）方式建構考量供應商競爭與消費者學習行為之零售商價格競爭模式，將演化賽局理論應用於價格競爭中，探討不同的消費者學習及供應商價格競爭行為如何動態影響零售商價格競爭型態，以及不同價格調整策略之績效表現。 研究結果發現一，市場中消費者呈現不同的學習行為，對零售商競爭將造成不同的衝擊。「貨比三家無學習」型消費者將造成零售商端低價競爭，使其平均價格最低及獲利最低。「自我式學習」型消費者將造成零售商高價合作，使其平均價格最高及獲利最高。「群體式學習」型消費者同樣使零售商端偏向高價合作，且其平均價格及獲利相當接近自我式學習市場，雖然兩種學習行為具有近似的平均價格與獲利，「群體式學習」卻會導致零售商價格競爭之型態轉為劇烈，包括獲利領先轉換方式由漸進轉為瀑布，領先方式從勢均力敵轉為大幅領先，領先互換的頻率由低轉為高。另外，消費者購買決策之理性程度對零售商端競爭形態有影響，不論在何種供應商行為下，高理性購買決策在群體式學習下將導致零售商端價格競爭較激烈，在自我式學習下卻導致零售商端競爭行為較緩和。 研究發現二，市場中供應商的價格競爭行為會對零售商端的價格、獲利與競爭型態造成衝擊。供應商呈現價格競爭行為下，在「貨比三家無學習」之消費者行為市場中，將減緩零售商價格競爭，使零售商端之平均價格及獲利提高。在「自我」與「群體式」學習消費者市場中，將增強零售商價格競爭強度，使零售商端之平均價格及獲利降低。 研究發現三，不同的競爭市場中，零售商之最佳價格調整策略也將不同。基本上在供應商無競爭行為下，無論消費者呈現何種行為，零售商採取開放式價格調整策略具有明顯優勢。在供應商呈現競爭行為下，開放式價格調整策略在「無學習」及「群體式學習高理性程度」行為市場仍為優勝策略，在「自我式學習」及「群體式學習低理性程度」下，保守型價格調整策略則表現較佳。 在實務意涵上，若零售商可使消費者行為偏向自我或群體式學習，並穩定供應商價格競爭下，整體而言零售商端競爭可獲得最高的獲利，若當此刻競爭零售商採取保守型價格策略，而本身採取開放式價格調整策略，則獲利最大。然而面臨群體式學習消費者，由於競爭強度的增加，需留意市場動態，須隨時靈活調整本身價格策略，避免因價格策略的僵化，而成為虧損之零售商。 / The pricing competitive model traditionally assumes that consumers will buy from the firm selling the homogeneous product at the lowest price, thus discarding any possibility of learning behavior on the demand side. But if, as in real competition, consumers learn adaptively and competition is a dynamic process, then some attention should be paid to consumers' behavior. In a multiple supplier – multiple retailer supply chain, multiple price competitive forces interact to influence firm price decisions. These forces include: (1) the supplier level competition each supplier faces from others producing the same product, (2) the retailer level competition among the retailers selling the same set of goods, and (3) the vertical interaction competition between the retailer and supplier. We are interest in these questions: How does the consumer learning behavior affect the retailer pricing competitive model? How does the competition of supplier affect the retailer pricing competitive model? What is the optimal adaptive pricing strategy for retailer performance in such competitive market including retailers, suppliers and consumers. Therefore, this research study a version of the pricing competitive (Bertrand) model in which consumer exhibit dynamic adaptive learning behavior when deciding from what retailers they will buy. And we consider to join the supplier competitive pricing behavior into the retailer pricing competitive model and formulate their interaction as evolutional game and to analyze the competition of supplier effect and its impact on the pricing competition of retailers. This research uses a complex adaptive system perspective to construct a retailer pricing competitive model which considers both competitive supplier and learning consumer behavior. Using agent-based modeling and simulation (ABMS) to construct the competitive market include retailers, suppliers and consumers, and use the fuzzy logic, genetic algorithms to model the pricing decision and learning behavior of retailers and suppliers, and use reinforcement learning and swarm algorithms to model consumers’ learning behavior. The simulation results demonstrate that: The retailer level obtains the highest profit when the consumer behavior following reinforcement learning. When the consumer behavior displays swarm learning, the retailer level also obtains high profit near the highest profit. However swarm learning increases the competitive intensity on the retailer level. The competitive supplier increases the competitive intensity and decrease profit on the retailer level when the consumer behavior displays reinforcement learning and swarm learning. The performance of retailer following a closed adaptive pricing strategy (high exploitation low exploration) exceeds that of retailer following an open adaptive pricing strategy (low exploitation high exploration) when the consumer behavior displays reinforcement learning and supplier display competitive behavior. However when the consumer behavior displays swarm learning and supplier display competitive behavior, the performance of retailer following an open adaptive pricing strategy exceeds that of retailer following a closed adaptive pricing strategy. The proposed pricing competitive model with adaptive learning of consumer behavior and competition of supplier can help retailers to analyze pricing strategy and further discovery and design the more optimal pricing strategy.