دانلود مقاله ISI انگلیسی شماره 20213
عنوان فارسی مقاله

الگوهای جستجو و ظرفیت جذب : بخش های فن آوری سطح پایین و پیشرفته در کشورهای اروپایی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
20213 2009 12 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Search patterns and absorptive capacity : Low- and high-technology sectors in European countries
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Research Policy, Volume 38, Issue 3, April 2009, Pages 495–506

کلمات کلیدی
ظرفیت جذب - استراتژی های جستجو - بخش های فناوری سطح پایین - بخش های​ فن آوری متوسط - بخش های فن آوری پیشرفته -
پیش نمایش مقاله
پیش نمایش مقاله الگوهای جستجو و ظرفیت جذب : بخش های فن آوری سطح پایین و پیشرفته در کشورهای اروپایی

چکیده انگلیسی

Searching for externally available knowledge has been characterised as a vital part of the innovation process. Previous research has, however, almost exclusively focused on high-technology environments, largely ignoring the substantial low- and medium-technology sectors of modern economies. We argue that firms from low- and high-technology sectors differ in their search patterns and that these mediate the relationship between innovation inputs and outputs. Based on a sample of 4500 firms from 13 European countries, we find that search patterns in low-technology industries focus on market knowledge and that they differ from technology sourcing activities in high-technology industries.

مقدمه انگلیسی

Innovation activities have frequently been shown to be a cornerstone for increasing the market share and market value as well as the long-term survival prospects of firms (e.g. Banbury and Mitchell, 1995, Brockhoff, 1997 and Brockhoff, 1999). In order to sustain the ability to introduce new products to the market successfully, many firms have shifted to a model of ‘open innovation’ that exploits the knowledge of a wide range of actors (Chesbrough, 2003). Such innovation inputs from external sources like customers, suppliers, competitors or universities can be conceptualised as the main elements of a firm's search strategy, which has been shown to have a substantial impact on innovative performance ( Katila, 2002, Katila and Ahuja, 2002 and Laursen and Salter, 2006). The search strategy can be defined as an “organisation's problem-solving activities that involve the creation and recombination of technological ideas” ( Katila and Ahuja, 2002, p. 1184). Problem-solving activities hence occur along the spectrum from exploitation to exploration ( March, 1991). The definition of an appropriate search strategy, however, critically depends on an ability to recognise the potential value of external knowledge sources. This ability has been summarised as the absorptive capacity of firms ( Cohen and Levinthal, 1990). Interestingly, there is often an implicit assumption in the literature that search strategies for external knowledge are particularly beneficial for firms operating in those environments where research and development (R&D) is key to overall firm strategy, i.e. in high- or medium-high-technology (HMT) industries. Shan et al. (1994) investigate the relationship between organisational learning through cooperation and innovative output in the biotechnology industry. The relationship between inter-organisational collaboration and innovation in the same industry is studied by Powell et al. (1996). Rosenkopf and Nerkar (2001) focus on the optical disc industry to examine boundary-spanning searches. Katila (2002) and Katila and Ahuja (2002) look into the search strategies of firms in the robotics industry. Generally speaking, the studies substantiate a positive impact of search activities on innovation performance, although there are also hints of possible ‘over-searching’ that could impede innovation. Medium-low-technology and low-technology industries (LMT), however, have been largely ignored so far. Exploring the search strategies of LMT firms seems even more intriguing as firms from these sectors account for by far the largest share of modern manufacturing in terms of value-added and employment (OECD, 2006). Furthermore, research on the nature of these search strategies has largely focused on the dimensions of breadth and depth (see for example Katila and Ahuja, 2002 and Laursen and Salter, 2006), where breadth designates the diversity and depth the intensity of search activities. Very little is known about the complementary or contradictory effects of external knowledge from various sources. This is especially relevant as effective knowledge acquisition depends heavily on a firm's ability to transform new knowledge so that a variety of combinations become possible (Todorova and Durisin, 2007). Hence, we suggest that distinctive search patterns can be identified that reflect a firm's technology and market environment. In that sense, we propose that these search patterns vary between HMT and LMT industries. Moreover, we assume that there is not a single uniform association with innovation success but rather that the search patterns mediate the relationship between innovation input and output. Consequently, there are differences in the extent to which firms can appropriate external innovation inputs and hence generate returns on their absorptive capacities. Our research, therefore, aims at extending the existing literature in two ways. First, we investigate whether different patterns of search strategies exist between HMT and LMT industries. Second, we analyse the link between these search patterns and the payoffs from R&D investments with regard to market success. The empirical part of this research is based on the third Community Innovation Survey (CIS-3), providing insights into the innovation processes of firms from 13 European countries using latent class methodology. This enables us to derive targeted policy recommendations as we obtain fine-grained input–output relationships for different industries (HMT versus LMT) and under different search patterns. Our paper is organised in six further sections. Section 2 provides a brief review of absorptive capacities and search strategies, while Section 3 presents the research questions driving the analysis. Section 4 focuses on our empirical study, outlining data, variable measurements and estimation methodology. Section 5 provides the results of the quantitative analysis. Based on the results, we discuss our findings in Section 6. Section 7 closes with concluding remarks.

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