چیزی را که به خوبی نسنجیده باشید نمیتوانید به درستی مدیریت کنید: کارایی نوآوری تکنولوژیکی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|4761||2013||12 صفحه PDF||37 صفحه WORD|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Research Policy, Available online 22 April 2013
- چهارچوب نظری
- مفهوم کارایی نوآوری تکنولوژیکی
- جدول 1: بررسی منابع مربوط به کارایی نوآوری.
- اثر تعدیلکنندهی سطح شدت تکنولوژیکی و اندازه شرکت
- دادهها و نمونه
- سنجش کارایی نوآوری تکنولوژیکی
- انتخاب ورودی و خروجی
- نتایج تجربی رویکرد دو مرحلهای
- مرحله 1: کارایی نوآوری تکنولوژیکی
- جدول 2: میانگین، انحراف معیار و همبستگیها
- جدول 3:امتیازهای کارامدی خودراهانداز DEA زمانی و مالمکوئیست عمومی
- مرحله 2: اثر کارامدی نوآوری تکنولوژیکی روی عملکرد شرکت
- جدول 4: برآوردهای کارایی نوآوری تکنولوژیکی
- جدول 5: برآوردهای مالمکوئیست عمومی
- جدول 6: برآوردهای ورودیهای نوآوری
- جدول 7: برآوردهای خروجیهای نوآوری
- بحث و نتیجهگیری
- پیوست الف
This paper proposes a new approach to tackle the innovation–performance relationship. It addresses the, so far, mixed and inconclusive results of studies analyzing this relationship. We argue that the undifferentiated use of innovation inputs and outputs to measure firm innovativeness is not without problems, and that, from a productive perspective, they should be simultaneously analyzed. This study follows a two-stage empirical analysis using a sample of Spanish manufacturing firms for the period 1992–2005. By examining two inputs and two outputs of the innovation process in the first stage, we estimate technological innovation efficiency by means of an intertemporal data envelopment analysis (DEA) bootstrap and also observe the yearly efficiency changes based on a global Malmquist index. In the second stage we analyze the effect of technological innovation efficiency on firm performance through a generalized method of moments (GMM) system. The results support our arguments that the best measurement of outcomes of technological innovations is through the efficiency with which they are developed. In addition, we test the moderating effect of technological intensity level and firm size on the efficiency–performance relationship.
While most of the literature in the innovation field argues that technological innovations are central to business success, empirical results are inconclusive as they have reported positive, negative or no effects of innovations on firm performance. We believe that this controversy might have its origins in the measurement of innovation. Thus far, it has variously been measured as innovation inputs (O’Regan et al., 2006) or as innovation outputs (Akgün et al., 2009). Additionally, there is a lack of agreement among authors about how to measure the effect of innovation on firm performance. This paper differs from previous studies and proposes a new approach to measuring the effects of technological innovation activities on firm performance. Tidd and Bessant (2009) stress that innovation is a complex process and that it should be evaluated as such, not as a single input or output activity. Therefore, we defend the idea that innovation inputs produce innovation outputs and the key to increasing firm performance is the efficiency with which the technological innovation process is undertaken. Moreover, we argue that directly linking innovation inputs to firm performance would generate misleading results since innovation inputs (e.g. R&D expenditure) could not improve firm performance by themselves because they involve short-term costs and those investments that do not result in innovations are sunk costs that will not improve firm performance (Koellinger, 2008). Finally, linking innovation outputs to firm performance without considering the effort – innovation inputs – needed to achieve those innovation outputs leads to a skewed perspective. Based on the previous discussion, this paper aims to contribute to the innovation–performance literature by proposing a new approach to measuring the effect of the technological innovation process on firm performance. Moreover, we assess the moderating effect of technological intensity level and firm size on the relationship between technological innovation efficiency and firm performance. The methodological strategy is executed in two stages. In the first stage, taking into account the causal and lagged effect of innovation inputs upon innovation outputs, we estimate technological innovation efficiency for each firm based on an intertemporal output-oriented data envelopment analysis (DEA) bootstrap. Looking for more robust results we estimate the global Malmquist index in order to observe the dynamics of the technological innovation efficiency. In the second stage, we take the estimated technological innovation efficiency as the explanatory variable of firm performance through the estimation of a dynamic panel data model. To verify the consistency of our arguments, we also estimate two models that include innovation inputs and innovation outputs instead of technological innovation efficiency as explanatory variables of firm performance. In order to achieve the second objective, we also test for the moderating effect of technological intensity level and firm size in these models. Few studies have endeavored to measure technological innovation efficiency and most of them have mixed innovation inputs or outputs beyond the innovation process (Zhong et al., 2011) while others have disregarded the lag effect of R&D on innovation outputs (Guan et al., 2006) or have used macro-level data (Lee et al., 2010). Moreover, the linkage between technological innovation efficiency and firm performance is almost non-existent in the literature. In this context, this paper contributes to the literature by estimating a technological innovation efficiency measure using only innovation inputs and outputs in the analysis, which allows an objective evaluation of the technological innovation process and by linking the estimated efficiency with firm performance. In addition, the nature of our sample allows us to obtain more robust results since we are able to correct for endogeneity and autocorrelation at the second stage of the analysis. This paper proceeds as follows. Section two presents the theoretical framework the hypotheses. Data and methods used are described in the third section. Results from the first- and second-stage estimations are shown in the fourth section, while the fifth is reserved for discussion and conclusions.
نتیجه گیری انگلیسی
Throughout this paper we have argued that the lack of clarity in relationship between innovation and firm performance might be due to the absence of agreement on how to measure innovation and how to link innovation with firm performance. Most of the studies in this field have not distinguished between innovation inputs and outputs as a comprehensive measure of innovativeness and linked it, directly or indirectly, to firm performance. In order to respond to this theoretical and empirical problem, we have proposed a new approach to measuring the effects of the technological innovation activities on firm performance. This new approach consists of a two-stage analysis. In the first stage we estimate an intertemporal DEA bootstrap to obtain the efficiency coefficient of the technological innovation activities and the yearly efficiency change based on a global Malmquist index. In the second stage analysis we consider the technological innovation efficiency and the efficiency change as explanatory variables of firm performance. Furthermore, we analyze the moderating effect of technological intensity level and firm size on the relationship between technological innovation efficiency and firm performance. The findings support our arguments. The effect of technological innovation efficiency on firm performance emerged as positive, while R&D capital stock and the number of patents produced negative effects; product innovations had no effect on performance and only high-skill staff positively influences firm performance. These results clearly show that what really increases firm performance is the efficiency with which innovation inputs are transformed into innovation outputs. In other words, it does not matter how much you invest, rather, what you obtain with that investment. Furthermore, the results shed light on the fact that positive change in technological innovation efficiency from the previous year also has a positive effect on firm performance. Our analysis also shows the moderating role of technological intensity level and firm size in the efficiency-performance relationship. The effect of the technological innovation efficiency on firm performance is positive for HTs, suggesting that these firms, immersed in turbulent environments, base their competitive advantages on technological innovations, while for LMTs technological innovation efficiency is tangential. As for firm size, it is evident that large firms, due to their larger resources and economies of scale, can overcome potential inefficiency without affecting their performance, whereas SMEs – lacking physical resources – might make the most of their limited resources. The results of the input/output performance relationship highlight two points. First, the mere inclusion of R&D capital stock or innovation outputs would have misleading consequences for the results. Second, the patent rate had a negative impact on firm performance for LMTs, illustrating that the pay-off for patenting in LMTs is so low that it does not compensate for the significant cost needed to protect the invention. In the first stage it was observed that there is great scope for Spanish manufacturing firms to improve their technological innovation efficiency. However, something encouraging for the Spanish manufacturing industries is the fact that most of them are moving in the correct direction and are improving their efficiency rates. The contribution of our study to the state of the art is twofold. First, although measuring technological innovation efficiency is not a novel concept, the empirical evidence is limited and most studies have taken a cross-sectional sample of a single industry and have mixed innovation inputs and innovation outputs with other variables unrelated to the innovation process, thus, contaminating the technological innovation efficiency scores. In addition, contrary to most of the studies estimating technological innovation efficiency using firm-level data, we consider the imperative lagged effect of innovation inputs for creating innovation outputs. Second, this study is pioneering in its empirical examination of the relationship between technological innovation efficiency and firm performance and in considering the moderating effect of technological intensity level and firm size. This methodology has allowed us to observe the complete process of technological innovations, thereby improving the inference of causal effects. For policy makers and practitioners alike, it is of major value to know the importance of measuring technological innovation efficiency in order to evaluate how firms are developing one of the most important activities central to business success – technological innovations. Further, results of the global Malmquist index show that the reward of an increase in efficiency produces a positive effect on firm performance, indicating the firms should continuously try to foster their technological innovation process in order to be more profitable. This research is not free of limitations and these could be addressed in future research. First, due to the lack of information in the SBS, we were not able to include process innovations as a third input in the intertemporal DEA bootstrap model. Second, some previous studies (Cruz-Cázares et al., 2010) have demonstrated that the source of R&D activities, internal and/or external, might produce different levels of innovation outcomes and, as a consequence, could produce higher or lower levels of inefficiency. Therefore, splitting the RDCS input into different sources of R&D activities might provide interesting results.