| 摘要: | 組織群聚可視為巢結組織系統，組織鑲嵌於所屬的群聚單位，而群聚單位又鑲嵌於更高 層次的結構單位，彼此相互影響。許多學者強調組織地理聚集可以活絡創新活動，並促進技術與知識升級。觀察群聚研究領域，微觀與巨觀研究已分別蓬勃發展，然而卻缺乏連結微觀 及巨觀層次的研究架構來分析多層次群聚系統中組織創新行為。本研究將藉由多層級理論與研究設計，以多元組織理論的觀點，探討組織與其所屬群聚單位，或更高層次的群聚單位間的互動關係對組織或群聚單位創新行為的影響。 研究目有三項：一、回顧國內外巨觀與微觀層次群聚創新之研究，以群聚經濟、學習理論、路徑相依與社會網絡等理論為基礎，提出影響各層級群聚創新之構念，並發展衡量指標。 二、提出多層級群聚創新假設模式，探討跨層級構念與創新間的關係。三、進行實證研究驗證多層級群聚創新模式。 本研究擬分三年執行，第一年進行文獻回顧與整理，並利用 meta-analysis 找出各層級的共同構面，與辨認群聚理論關係之一致性；第二年選擇特定產業，蒐集與建置產業創新與研 發資料庫，為次年實證研究之基礎；第三年建立組織地理向量資料劃分群聚範圍、建立組織與群聚特徵資料，並結合創新研發資料進行多層級群聚創新假設模式之實證研究。
Agglomerated organizations can be regarded as a system of nested levels. These organizations are embedded in a specific cluster unit embedded in the higher level unit. Many scholars emphasized that geographic agglomeration can active innovation, and advance knowledge and technology. Micro-level and macro-level studies have been developed; however, the meso framework that links micro and macro views to study innovative behaviors in a multi-level clustering system is still missing. Based on the multi-level theory, this study will explore the direct and interaction effects of across the organizations, cluster units, and higher cluster units in the some research constructs on organizational and cluster’s innovation. Research purposes have three. First, reviewing the literatures on macro-level and micro-level researches for agglomeration innovation; proposing and developing the research constructs, based on agglomeration economics, learning theory, path dependence, and social network theory, which affect all levels of cluster innovation,. Second, proposing a multilevel hypothetical model for agglomeration innovation; and exploring the cross-level relationships between organization-level and cluster-level constructs on innovation. Third, conducting an empirical research and validating the multilevel cluster innovation model. This research, as a three year project, will adopt both meta research and quantitative research design. In the first year, I’ll plan to collect and review relevant literature to find the common constructs and to identify the constant theoretical relationship across macro and micro levels, using a meta-analysis. In the second year, choosing specific industries, I collect and build the industry innovation and R&D database for the basis of the empirical study. In the third year, I build the geographic vector data of firm for defining geographic scope of agglomeration, firm and cluster characteristics database, and combined with innovation database to verify the multilevel agglomeration innovation model. |
