| 摘要: | 創新行為是二十一世紀廠商致勝的重要關鍵，許多學者嘗試對廠商的研發過程有更多 的了解和分析。即使有許多創新指標，例如創新密度、多角化、創新質度等被廣泛的 應用，目前既有文獻卻鮮少從動態角度來探討創新活動。創新的持續性即是廠商於某 段期間從事創新活動且持續不斷地在下一期間進行創新的行為，因此它是具有時間持 續性的創新活動，且被視為廠商創新的累積。然而何種因素會減損廠商持續創新的能 耐？而隨著時間改變，我們如何衡量廠商的創新能力？且持續創新的廠商有何種特 徵？以及最理想的持續期間為何？這些都是本研究亟欲進一步深入探討的主要議題。 本研究擬應用廠商的專利資料、財務資料與銷售資料，來進行廠商持續創新的實證分 析。由於廠商持續創新的行為會隨著時間和廠商的特性而改變，因此要如何衡量廠商 持續創新的能力，將成為本研究的一大挑戰。過去的研究有利用非參數的TPMs 法 (Transition Probabilities Metrics)來計算廠商在兩個不同的時間點上，創新行為是否持續 的機率(Cefis & Orsenigo, 2001; Cefis, 2003; Jang, et. al., 2008; Roper & Dundas, 2008; Peters 2007)，以及廠商從事創新活動所持續的時間長短(Alfranca, Rama & Tunzelmann 2004; Geroski, Van Reenen, and Walters, 1997; Chen, Jang and Young, 2006)。由於上述研究 都有許多的缺點，本研究將整合以上方法更進一步提出新的衡量指標。 了解廠商持續創新的動態過程和內涵之後，我們不僅可以啟發後續學者的研究方向， 還可以提供給廠商應如何決定未來的研發策略以及有效配置研發資源，來提升其競爭 力。本計劃完成後，預計至少有一篇論文發表於SSCI 期刊。
It is widely recognized that innovation has become an imperative routine for 21st-centry business; many researchers have therefore, taken a stab at understanding and evaluating the innovation process. While various dimensions of innovation, such as intensity, diversity, and quality, have been more widely studied (Coombs and Bierly, 2006; Garcia-Vega, 2006), the dynamic aspect of innovative activities receive little attention in the current literature. Innovative persistence is a temporal pattern of conducting innovative activities. It occurs when we observe a firm which has innovated in one period innovates once again in the subsequent period, showing a pattern of creative accumulation as opposed to bursts of innovations. What undermines the duration of innovative initiative, and how do we measure the resulted innovations over time? What performance implication would innovative persistence bring? What might be an optimum level of such persistence? These are the core questions raised in this proposed study, with the aim of contributing improved indicators and analysis. The study attempts to build up a proprietary database by linking innovative output data to relevant financial and marketing data at the firm-level. Because there are different ways to examine patterns of innovation over time for particular firms, there remain considerable challenges as to how innovative persistence be conceptualized, operationalize and hence, measured. Extant studies have typically captured innovation persistence in descriptive states by using a non-parametric approach based on Transition Probabilities Metrics (TPMs) to compare innovative output during the current period, with that of the subsequent period (Cefis & Orsenigo, 2001; Cefis, 2003; Jang, et. al., 2008; Roper & Dundas, 2008; Peters 2007). Alternatively, persistence could be interpreted as a period, or turn, of innovative work (Alfranca, Rama & Tunzelmann 2004; Geroski, Van Reenen, and Walters, 1997; Chen, Jang and Young, 2006). These measures have some shortcomings and limitations. The study will provide a consolidation of these different approaches, in addition to proposing alternative indices. Unveiling the dynamic of innovative activities and the subsequent performance implication not only provide a means to examine the underlying process that drive innovation, but also a tool to assess different growth models. Better metrics or indices would enable companies to benchmark where they stand vis-à-vis their competition, to allocate resources more productively, and to determine future strategic options. |
