شرح تلاش های خلاقانه در شرکت های کوچک و متوسط: مطالعه اکتشافی در بخش مهندسی مکانیک و برق در میان شرکتهای کوچک و متوسط هلند
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|13576||2002||13 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Technovation, Volume 22, Issue 1, January 2002, Pages 1–13
Innovations are among the most important means through which small and medium sized enterprises contribute to increased employment, economic growth and economic dynamics. A lot of research has been carried out to determine which factors enhance innovative efforts of SMEs. This study uses a regression-based methodology to examine the importance of each factor, controlling for the other factors. The study is based on data collected through telephone interviews with managers of Dutch SMEs in the metal-electro-sector. In the analyses innovative efforts are used as the dependent variable. Out of 14 potentially independent variables, three appear to contribute significantly to innovative efforts: using innovation subsidies, having links with knowledge centres, and the percentage of turnover invested in R&D. This article suggests that innovativeness is the result of a deliberately chosen and pursued policy. If governmental and or sectoral institutions want to stimulate SMEs to become and remain innovative, they should encourage these companies to implement an innovation directed policy. Without such a policy, SMEs seem unable to digest successfully stimulating measures and subsidy schemes.
Small and medium sized enterprises (SMEs) have a reputation as boosters of employment, economic growth and economic dynamics. One of the most important means through which SMEs are able to make these contributions is their capability to realise innovations. Therefore, in both developed and developing countries and regions, many efforts have been made during the last few decades to stimulate SMEs to realise innovations. SMEs have been encouraged to make use of funding schemes and to utilise the services of knowledge centres. However, in spite of these efforts there still is a lack of knowledge about the nature and extent of SME support needs and the mechanisms for delivering it effectively. The result is that the policy environment is characterised by a wide range of experimentation (Bessant, 1999). In recent years a lot of research has been done to find out which factors contribute to innovation efforts by SMEs, to build a more thorough theoretical foundation for the mechanisms behind innovations and to substantiate practical interventions. These studies revealed that activities directed to innovation correlate with a considerable number of variables. An important characteristic of these studies is that so far, little or no attention has been focused on uncovering possible interactions between variables. From a theoretical as well as from a managerial perspective, it seems to be relevant to know which variables contribute most to innovation efforts. In this paper, the results of an exploratory survey among managers of SMEs are presented. The aim of the survey was to find a relatively small set of variables within a larger number that are reported to be important for innovation, which suffice to “explain” the differences between SMEs being involved in innovative efforts and others that are not. First, the conceptual background will be described. It includes a literature review of recent publications about variables contributing to innovation efforts of SMEs. Next the survey's design and methodological set-up are explained. After that, the results of the statistical analysis and the interpretation of the results are presented. Finally, we discuss the major findings.
نتیجه گیری انگلیسی
The objective of this paper was to determine which variables from a list of variables are related to innovative efforts, controlling for other variables in the analysis. The study focuses on expanding our knowledge of why some firms are innovative and others are not. Governmental and sectoral institutions meant to stimulate SMEs to become and remain innovative can learn what the target variables are from the study. The study reveals that the most innovative SMEs have three basic characteristics in common: links with knowledge centres, entries to governmental innovation subsidy schemes, and a relatively high R&D budget. The outcomes of the study suggest that innovativeness is the result of a deliberately chosen and pursued policy. If governmental and/or sectoral institutions want to stimulate SMEs to become and remain innovative, they should encourage management to implement and maintain an innovation-directed policy. Without such a policy, SMEs seem unable to effectively digest stimulating measures and subsidy schemes. Small organisation size, low influence and scarce resources are the inherent consequences of the small firm. These limitations can lead to the choice of strategic decisions that fit the small firm's character. The implementation of the combination of the three basic characteristics found in our analyses is postulated as fostering the innovativeness of these companies. At this point we can look again at a few controversies which we have found in the innovation literature. Birchall et al. (1996), Le Blanc et al. (1997) and Hoffman et al. (1998) have noticed different findings with respect to the importance of links to sources of knowledge. They found that there are sectoral and geographical differences in the impact of this factor on innovativeness. Our study reveals that having links to external knowledge centers is one of the few really critical factors contributing to innovative efforts of SMEs in the mechanical and electronical engineering sector. Birchall et al. (1996), Le Blanc et al. (1997) and Hoffman et al. (1998) also pointed at different findings regarding the role of financial funding. Some studies show evidence that financial resources are key for innovativeness, others do not. In our study collaboration stimulating subsidies and the use of financial support regulations did not come out as significant predictors of innovativeness. Also, with respect to the proportion of turnover spent on R&D, the literature shows different observations. Oerlemans et al. (1998) suggested a direct positive relationship between R&D spending and innovations, while Birchall et al. (1996) suggest a more complex indirect relationship. Our findings support the suggestion about a direct positive relationship. One critical question is whether non-innovative firms can and should be persuaded to develop such a profile of key characteristics. An important issue here is to what extent SMEs can be seen as a group with basically common characteristics. Some scepticism seems to be justified. Vos (2000) shows that there is a crucial difference between companies that produce and deliver self-specified products and companies that make their capability available for production according to specifications of their customers. One company, for instance, describes its capability as “making precision stampings from ferro and non-ferro metals, within very tight tolerances, followed by forming, hardening and surface treatment to customer specifications”. Our study does not have the intention to highlight this distinction. Further research is needed to find out whether product delivering and capability delivering companies show the same innovation profile and whether their innovation policies are identical. The implication is that innovativeness can at least partially be controlled by management, policy-directed action. A special point of interest is the variable “payback period”, one of the three variables that were near to significance in this study. The role of this variable is mentioned in a report on a global survey on innovation from a leading consultant firm specialised in technological innovation (Little, 1997). The survey reveals that “increasing the number of new products” and “reducing the payback period of these new products” is seen as a much more critical business success factor by innovating companies than it used to be 5 years before. The negative sign of key variable 13 (applying a shorter payback period) in our study is consistent with the general findings made by Little (1997). If a company is heading for sequential new products, they note that a short payback period for each new product is required. A delicate problem in this paper is the role of the manager's educational level. We found a weak but not significant negative relationship with innovation activities. This does not sound reasonable. We suggest two possible solutions. First, there is no relationship at all. Both the weak overall positive correlation under pairwise deletion and the weakly negative correlations under listwise deletion (neither of them significant in this paper) really have no meaning. Secondly, there is a weakly positive relationship. However, as we described earlier, an accidentally strong positive association in the groups of respondents that are excluded from the analysis in our paper, suppresses this. As a complement, negative correlations appear in Table A3. Further research is needed to clear up this issue. Another question concerns the robustness of the results. It was our ambition to find out which variables really matter to innovativeness. The choice was made to gather data through telephone interviews. The advantage was that the data could be collected in a short time and that the response would probably be higher than could have been achieved with a mailed questionnaire. A disadvantage was that the items in the questionnaire had to be formulated according to the prerequisites of a survey by telephone. We decided on two simultaneous approaches. Logistic regression is statistically healthy but it does not allow for causal interpretations. Linear regression, however, is appropriate in this respect, but in our case it is limited by severe violations of its model assumptions. In retrospect, this concurrent design was nevertheless fruitful because both approaches could be used complementarily. Our analysis could even have been refined by first selecting input variables that hold, controlling for the remaining input variables, and secondly, examining which throughput variables hold, together with the selected input variables. We believe that this would be more appropriate in a project that is explicitly designed for this purpose, and in which no assumptions of the linear regression model are violated. The agreement between the results can be understood, starting from their fundamentally common objective. Apart from all differences in symbols, values and probabilities, both methods aim at the identification of variables that cause systematic, significant changes in a dependent variable. Finally, there is a relevant question concerning the effectiveness of the innovation efforts. To what extent may innovation activities undertaken by a specific SME be considered successful or not? In this study, the involvement in innovation projects was taken as the output variable of innovative efforts. The study did not focus on the success rate (the percentage of products meeting the firm's innovation criteria) of these efforts. In an extensive analysis of 195 new product cases from 125 industrial product firms, Kleinschmidt and Cooper (1991) investigated the relationship between product innovativeness and success rate. According to the level of market newness and technological newness, Kleinschmidt and Cooper divided the product cases into three categories: highly innovative products, moderately innovative products, and low innovativeness products. A U-shaped relationship between product innovativeness and success rate became evident. The success rate was greatest for highly innovative products (78% successful), and almost as high for low innovative products (68%), but dropped dramatically to 51% for the middle group. In our survey, no classification was used to categorise the type of innovation efforts. An exploratory policy study of the Dutch Ministry of Economic Affairs, to uncover the number and nature of product innovations in The Netherlands (Kleinknecht et al., 1992), showed that 43.9% of all product innovations may be classified as low innovative, 52.3% as moderate and only 3.8% as highly innovative products. If the findings of Kleinschmidt and Cooper are projected on the classification data of the Dutch Ministry of Economic Affairs, one may assume that a lot of innovative efforts in The Netherlands are focused on product innovations with a relatively lower chance of succeeding in the market. Several studies have been carried out to find out which variables are associated with innovative efforts of SMEs. The aim of this study was to find which variables suffice to “explain” the differences between SMEs carrying out innovation projects and others that do not, within the total domain of variables reported in the literature as being important for innovation. Finally, four variables remained as independent predictors of innovation activities. We would like to remind the reader that our exploratory study is based on a relatively small sample of one kind of firm, within a particular geographic area of The Netherlands. Further research is needed to determine whether the outcomes hold for innovative efforts in other sectors of industry.