目前全球正興起另一波企業購併的風潮，其中更以大企業為主軸，使得大企業愈來愈走向大型化的趨勢。而當企業購併時，不僅需睜大眼睛慎選購併對象，更需進一步做好於購併後關於被購併公司策略調整之工作，以免造成時間與資金的浪費，因此本研究即在探討企業在購併後該如何選擇策略調整的方向，及有哪些因素會影響組策略調整的改變幅度與調整時間。 因此，本研究首先針對過去文獻與過去實際進行跨國購併的案例中，購併公司的實際作法提出一關於購併後人事調整、組織文化調整、行銷策略調整與研究發展策略的調整等工作之策略調整方向選擇參考架構，藉以提供一般企業在面臨國際購併時的參考，同時亦作為本研究關於探討影響改變幅度與調整時間因素的基礎架構。 本研究根據過去實際跨國購併案例中，購併公司對於策略調整之改變幅度與調整時間所考慮的因素，同時對五家個案公司分別驗證該影響因素是否適用，及究竟有哪些因素對於購併公司而言，在選擇改變幅度與調整時間的策略上較具影響力。 最後根據上述驗證結果，提出幾項有關影響改變幅度與調整時間因素的命題。命題包括：購併公司對於被購併公司之未來定位與被購併公司原來定位之差異程度及被購併公司的原有經營體質等兩因素會影響購併公司對被購併公司的改變幅度。至於影響調整時間的因素，包括：市場競爭激烈程度、被購併公司原有的經營體質、購併公司本身的目標、購併公司對於被購併公司之未來定位被購併公司原有定位之差異程度，其中後兩項因素對於調整時間影響較大。 根據本研究所提出之策略調整參考架構，對於即將進行購併之企業或對於已經在從事購併的企業而言，可以提供該企業對於進行策略調整一個值得參考的架構；同時企業在作策略調整的工作時，對於改變幅度與調整時間的決定上，可以考慮本研究所提出的八項命題，藉以使其節省在作決策上的所需思考的範圍與時間，使其增加購併成功的機會。 / Recently, the cases of M&A are rapidly growing. M&A makes them grow strongly and globally. When the mother company merges or acquires the target company, she should notice not only the selection of the target company, but also the integration between the mother company and target company. The importance of the post-merged integration is evident, according to the paper and thesis discussion. Therefore, this research will focus on discussing the main factors of influenced post-----merged strategy changes. The research will acquire the conclusions by examining five experienced Taiwan enterprises. According to this research, we find the factors of influenced scale of changes are as follows: 1. The foreseen position of the target company should achieve. 2. Both the advantages and the disadvantages of the target company. According to this research, we find the factors of influenced integrated time are as follows: 1. The foreseen position of the target company should achieve. 2. Both the advantages and the disadvantages of the target company. 3. The motivation of the mother company. 4. The degree of market competition. Eventually, this research advise the enterprise who are planing to merge or acquire another enterprise should consider of. Also, this research summarize a reference structure of post-merged strategy changes.

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.