【摘要】：在供給側(cè)改革政策的推動下,近年來我國的并購活動十分活躍。隨著并購交易數(shù)量逐漸增多,市場出現(xiàn)連續(xù)并購的案例。然而,以往多數(shù)文獻(xiàn)研究發(fā)現(xiàn)連續(xù)并購的并購績效呈下降趨勢,大部分并購交易以失敗告終。并購是企業(yè)戰(zhàn)略管理不可忽視的重要手段,為了實(shí)現(xiàn)企業(yè)內(nèi)外部資源整合的目標(biāo),就要在連續(xù)并購中不斷積累并購經(jīng)驗(yàn),增強(qiáng)并購能力,因此,并購中的組織學(xué)習(xí),對企業(yè)來說是至關(guān)重要的。本文通過分析組織學(xué)習(xí)對并購決策的影響,探究并購中組織學(xué)習(xí)的學(xué)習(xí)機(jī)制,對企業(yè)在連續(xù)并購中實(shí)現(xiàn)有效學(xué)習(xí),獲取并購收益具有理論指導(dǎo)意義�；诮M織學(xué)習(xí)理論以及企業(yè)戰(zhàn)略管理理論,本文按照組織學(xué)習(xí)對象的不同將組織學(xué)習(xí)分為組織內(nèi)學(xué)習(xí)和組織間學(xué)習(xí)兩個部分,研究組織學(xué)習(xí)對并購決策概率的影響。在分析的過程中,通過將學(xué)習(xí)內(nèi)容分為并購經(jīng)驗(yàn)慣例和并購績效反饋兩個方面,分層次探討組織學(xué)習(xí)行為對并購決策的影響。并且在此基礎(chǔ)上,考察了管理者特征對組織學(xué)習(xí)影響并購決策的調(diào)節(jié)作用。企業(yè)是否決定進(jìn)行并購,是未來發(fā)生的不確定性事件,但根據(jù)大數(shù)定理,其概率分布具有規(guī)律可循,利用回歸分析歷史數(shù)據(jù),可以預(yù)測某個企業(yè)是否會在未來作出并購決策。本文選擇了生存分析中的Cox回歸模型作為實(shí)證分析方法,使用我國上市公司2011年至2015年的并購交易數(shù)據(jù)作為研究樣本,驗(yàn)證組織學(xué)習(xí)中的并購經(jīng)驗(yàn)慣例和并購績效反饋對并購決策效率產(chǎn)生的影響,并與國外已有并購中組織學(xué)習(xí)的實(shí)證研究結(jié)果相對比,探究中國并購市場具有的特殊性。實(shí)證結(jié)果表明,中國企業(yè)在并購中的組織學(xué)習(xí)行為是并購經(jīng)驗(yàn)不斷積累,并購能力逐漸形成的過程,具體表現(xiàn)為,與國外關(guān)于學(xué)習(xí)曲線呈現(xiàn)“倒U”型的預(yù)測結(jié)果不同,自身并購經(jīng)驗(yàn)慣例對并購決策概率的影響呈現(xiàn)線性關(guān)系,企業(yè)的并購決策中并未體現(xiàn)并購序列前后期吸收能力的不同所帶來的影響;自身并購績效反饋越好,企業(yè)做出連續(xù)并購決策的可能性越大;他人并購經(jīng)驗(yàn)慣例對并購決策概率的影響顯著地呈線性關(guān)系;對并購績效反饋較好的同行業(yè)的學(xué)習(xí),提高了企業(yè)的并購決策效率;并且,他人并購經(jīng)驗(yàn)慣例與并購績效反饋的交互作用結(jié)果顯著,該作用有效的提高了并購決策效率。管理者特征在學(xué)習(xí)過程中的調(diào)節(jié)作用也得到了驗(yàn)證,結(jié)果顯示管理者任職時間越長、管理層兼職比率越高、管理者學(xué)習(xí)能力越強(qiáng),學(xué)習(xí)效率越高,組織學(xué)習(xí)對并購決策概率的影響越強(qiáng)。因此,企業(yè)在連續(xù)并購中,應(yīng)重視組織學(xué)習(xí),通過學(xué)習(xí)提高并購成功率,同時,應(yīng)對決策制定者加強(qiáng)監(jiān)控,防止出現(xiàn)過度并購行為。
[Abstract]:Driven by the supply-side reform policy, M & A activities in China are very active in recent years. With the increasing number of M & A transactions, the market appears continuous M & A cases. However, most previous literature studies found that the M & A performance of successive mergers and acquisitions declined, and most of the M & A transactions ended in failure. M & A is an important means of enterprise strategic management. In order to realize the goal of integration of internal and external resources, it is necessary to continuously accumulate experiences and strengthen the ability of M & A in continuous M & A. Therefore, organizational learning in M & A is an important way to achieve the goal of integration of internal and external resources. It is vital to the enterprise. By analyzing the influence of organizational learning on M & A decision, this paper probes into the learning mechanism of organizational learning in M & A, which is of theoretical significance for enterprises to realize effective learning in continuous M & A and to obtain M & A earnings. Based on the organizational learning theory and the enterprise strategic management theory, this paper divides organizational learning into two parts: intraorganizational learning and inter-organizational learning according to the different organizational learning objects, and studies the influence of organizational learning on the probability of M & A decision. In the process of analysis, the influence of organizational learning behavior on M & A decision is discussed by dividing the learning content into two aspects: M A experience convention and M A performance feedback. On this basis, the regulatory effect of managerial characteristics on organizational learning affecting M & A decision is investigated. Whether an enterprise decides to make a merger or not is an uncertain event in the future, but according to the theorem of large numbers, its probability distribution can be followed. By using the regression analysis of historical data, we can predict whether an enterprise will make a decision on M & A in the future. This paper chooses the Cox regression model in the survival analysis as the empirical analysis method, using the M & A transaction data of listed companies from 2011 to 2015 as the research sample. In order to explore the particularity of M & A market in China, this paper verifies the influence of M & A experience convention and M A performance feedback on M & A decision-making efficiency in organizational learning, and compares with the empirical results of organizational learning in M & A in foreign countries in order to explore the particularity of M & A market in China. The empirical results show that the organizational learning behavior of Chinese enterprises in M & A is a process in which the experience of M & A is accumulated and the ability of M & A is gradually formed. There is a linear relationship between the influence of their own experience and practice on the probability of M & A decision, and the influence of different absorptive capacity of M & A sequence is not reflected in the decision of M & A. The better the performance feedback of M & A is, the better the performance feedback of M & A is. The possibility of enterprise making continuous merger decision is greater; the influence of others' experience and practice on the probability of M & A decision is linearly significant; the learning of the same industry with better feedback on M & A performance improves the efficiency of M & A decision; The result of interaction between other people's experience practice and performance feedback is significant, which can effectively improve the efficiency of M & A decision. The regulatory effect of managerial characteristics in learning process is also verified. The results show that the longer the manager holds office, the higher the part-time ratio of management is, the stronger the learning ability of manager is, and the higher the learning efficiency is. The influence of organizational learning on M & A decision probability is stronger. Therefore, enterprises should pay attention to organizational learning, improve the success rate of M & A through learning, at the same time, the decision makers should strengthen the monitoring to prevent excessive M & A behavior.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F271

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