| Abstract: | 配適為策略研究之一重要議題，然而實證研究對配適類型，包括包括內部配適、外 部配適及整體配適，與績效間之關係尚未有獲致一致性的結論。本計劃的目的在於探討 不同策略配適對績效的影響。本計劃之假說為：（1）高內部配適與績效有正相關；（2） 高外部配適與績效有正相關；（3）整體配適與績效有正相關。至於高內部配適、高外部 配適與整體配適究竟何者對績效的影響較高，則期望能透過個案訪談與實證進一步探 索。此外，在研究方法方面，Venkatraman(1989)提出六個主要的配適方法，然而過去研 究少有同時針對這些方法進行實證分析，因此本計劃希望藉由環境、策略與資源三個構 念進行驗證。最後，本計劃將採用類神經模糊邏輯此一人工智慧工具進行分析，試圖以 其規則庫驗證配適的非線性現象，並提供配適內容的說明。因此，本計劃在學術界的貢 獻可分為兩個方面：第一，透過配適方法的實證分析與比較，提供學術界在進行相關研 究時有關研究方法上的參考；第二，藉由內部配適，外部配適與整體配適間的比較，說 明三者對於績效的影響，並透過類神經模糊邏輯提供配適內容說明，有助於釐清配適與 績效間之關係。對於實務界之參考價值為，提供企業在環境變動時擬定策略之參考。
Fit is an important topic in strategy research. However, empirical research of the types of fit, including internal fit, external fit, and integrated fit, and its relationship with performance have yet to reach congruent conclusions. The objective of this proposal lies in the discussion of different strategies on performance. The hypotheses are: (1) internal fit focus is positively related to performance; (2) external fit focus is positively related to performance; (3) integrated focus is positively related to performance. As for which has a higher influence on performance—internal fit focus, external fit focus, or integrated focus—this research expects to explore further via case studies and empirical studies. With regard to research method, Venkatraman (1989) proposed six ways for explaining fit. However, previous studies have rarely simultaneously tested them. Hence, this proposal hopes to verify his suggestions via three constructs: environment, strategy, and resources. Finally, this proposal adopts artificial intelligence analysis tool—Neuro Fuzzy logic—to verify the nonlinear phenomena of fit and explain the content of fit via its rule database (IF-THEN). Thus, the contributions of the proposal are twofold: first, providing researches with reference material of research method for related researches through empirical analyses and comparison of fit; second, explaining the influence internal fit, external fit, and integrated fit on performance and providing an explanation in the content of fit through Neuro Fuzzy logic, and future clarifying the relationship between fit and performance. The expected outputs would have implications to practitioners for designing better fit among environment, strategy and resource. |
