پارتیشن بندی کار در تیم های توسعه محصول جدید : دیدگاه دانش و یادگیری
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|2660||2005||24 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Engineering and Technology Management, Volume 22, Issue 4, December 2005, Pages 291–314
R&D alliances and outsourcing elements of the new product development process are now commonplace practices among many firms. However, little previous work has examined how these organizational choices influence project knowledge and learning. Based on a comparison of three new product development projects in the software industry, this paper examines how task partitioning in the project influences learning and knowledge development within the firm. The paper suggests that internal development projects encourage synthetic learning and development of architectural and tacit knowledge; in contrast, outsourcing and joint ventures encourage analytic learning and development of component and explicit knowledge.
Firms are increasingly using organizational forms such as outsourcing and alliances for product development in addition to the conventional approach of developing the product entirely in-house. Some factors driving this increase that have previously been suggested include discontinuous technological change, increasing cost of R&D, globalization and lower cost of production in less-developed countries (Campione, 2003, Kakabadse and Kakabadse, 2000 and Lambe and Spekman, 1997). Most research to date has focused on the economic benefits and risks of such arrangements (e.g. Williamson, 1975, Pfeffer and Salancik, 1978, Provan, 1983 and Jarillo, 1988). This paper focuses on the learning implications of such arrangements. As Hitt et al. (2000) note, ‘in contrast, the learning and process issues related to innovation have received scant scholarly attention.’ This is surprising since learning lies at the heart of much technological innovation activities (e.g. Carayannis and Alexander, 2002, Akgün et al., 2002, Mohrman et al., 2003, Molleman and Broekhuis, 2003 and Polley and van de Ven, 1996). In particular, little research has been done specifically on how the project organization facilitates or hinders different types of learning in the product development context, although a few studies suggest that there may be a link. For example, Kazanjian et al. (2000) has shown a link between project organizational structure and the technological learning and creativity that occurs within product development projects. Meanwhile Takeishi (2002) examined outsourcing in product development projects in the automobile industry in Japan and found that the type of knowledge gained by the firm (architectural versus component specific) varied according to the type of technology involved and the organizational mechanisms used to transfer knowledge. Therefore, the research question that this paper examines is ‘how does the project organization influence the learning process and the different types of knowledge that are developed?’ I begin by reviewing some of the literature on different organizational approaches to new product development and team learning. I then review different typologies of learning and knowledge and discuss why different project organizational architectures may be expected to lead to different learning and knowledge development. The following sections then outline the key findings of a study that compares the learning process and knowledge developed in three companies that adopted different organizational arrangements for new product development. I end by discussing the implications of these findings for technology management theory and practice in innovation projects.
نتیجه گیری انگلیسی
5.1. Discussion The findings that information and knowledge flows varied in different project designs is consistent with research in the information processing perspective that shows how the information flow depends on communication within the project (Cooper, 1986, Eisenhardt and Tabrizi, 1995, Griffin and Hauser, 1992 and Hoegl and Gemuenden, 2001), boundary spanning personnel (Conway, 1995, Tushman, 1977, Ianisiti, 1995 and Nobel and Birkinshaw, 1998) and external structures that facilitate collaboration with users (Gales and Mansour-Cole, 1995). However, this paper suggests that project organization not only determines how the project team processes information about the environment but also how it learns and the type of knowledge that is developed. A prerequisite of synthetic learning is that areas of knowledge from different specialties are combined. This requires, first, access to expertise in different areas and, second, a means to combine inputs from these different specialisms. Therefore, the project design must achieve these two things—first, enable team members to access sources of specialist knowledge and facilitate exchange between subject experts within the project. In contrast, analytic learning requires detailed knowledge of a particular discipline and so information exchange with other specialisms is generally not required. Therefore, there is less need for information exchange between experts in the project design. The cases illustrate two reasons why synthetic learning may occur. First, as in the case of Elite, synthetic learning may occur because the type of product requires expert input from different disciplines; in this case, subject matter experts for the text, designers and programmers for the software interface and project managers for the production process. Second, in the case of Wild, synthetic learning may be a company objective. In this case, the impact extends beyond project design to organizational design and organizational culture. The level at which learning occurs or is desired is, therefore, also a factor that needs to be considered. Similarly development of component knowledge requires different inputs and knowledge processing as opposed to development of architectural knowledge. Architectural knowledge requires exchange of knowledge about how the different components fit together, a process that entails communication and coordination between individuals knowledgeable about the various product components and how they relate to each other. Therefore, development of such knowledge should be facilitated by a project structure that encourages knowledge exchange about the different components are related in the production process. In contrast, development of component-level knowledge requires detailed knowledge about the component but not about other components and how they relate. Therefore, there is less need for the project design to cater for knowledge exchange about different components. Architectural knowledge is more closely related to synthetic learning since it requires more knowledge of the entire production process and so is more likely to require synthesis of knowledge from different areas. This was evident in the cases examined, where Elite and Wild both showed high levels of synthetic learning and architectural knowledge compared with Global which focused more on analytic learning and component knowledge. The finding that the pattern of communication and the tacitness of knowledge are related is consistent with findings in the psychology and linguistics literature. As discussed earlier, knowledge can be tacit for two reasons—either it cannot be easily verbalized or it need not be. Where communications about a particular topic are frequent or commonly understood, there will be less need to verbalize certain things since individuals can refer to previous communications or common knowledge, known in the linguistics literature as ‘implicatures’ (Sperber and Wilson, 2002). For instance, where conversants have previously discussed something, they may simply refer to the discussion as “what we talked about yesterday”, without the need to repeat the details again. Similarly where over time individuals come to share assumptions about certain things there may be less need to verbalize since the meaning is already understood and unambiguous (Hymes, 1972). Thus, one reason why the knowledge developed in Wild was tacit may be that, given the close proximity and daily social contact in the workplace, through unconscious observation the team members developed a mutual understanding of work-related activities that did not require explanation. In contrast, given the less frequent and less intensive contacts between members in the Elite and Global teams, there was less opportunity to develop such tacit understandings. For purposes of highlighting the importance of project design, the discussion has highlighted the principal differences in knowledge and learning in each project. However, this should not be taken to mean that a particular project design only results in a particular type of learning or knowledge. Different mixes of synthetic and analytic learning, architectural knowledge and component knowledge and tacit and explicit knowledge may occur within each project. The point made here is that although a variety of learning and knowledge types may be found in a project, particular project designs seem to favor particular types of learning and knowledge. A practical implication of this research is that project design can influence the type of knowledge exchange and learning within product development teams. Depending on whether the objective is to develop tacit or explicit knowledge, to develop architectural or component knowledge or to encourage synthetic or analytic learning, managers should choose different project designs. Conversely choosing an inappropriate design can result in an undesired learning outcome such as only component level learning when architectural knowledge was desired. 5.2. Limitations and further research The aim of this study was exploratory and so the findings clearly have limitations. Since only three projects were studied and the data collected was mainly qualitative, it is not possible to conduct formal, statistical tests to determine the generalizability of the patterns found. Future research should aim to overcome these limitations by testing with a larger sample of projects that allows the strength of relationships between factors and generalizability of findings to be tested. The study also relied on free-form interviews to surface individuals’ recall and perception of learning that occurred. This method was chosen because at the outset it was unclear exactly what the learning effects would be in the different projects. However, as in all personal accounts of events, such accounts could be subject to errors such as forgetfulness, selective recall and post-rationalization. Future research could attempt to more objectively measure the communication and learning that occurs in projects, possibly testing before and after the project. Some areas that could be furthered explored using more rigorous methods, for example, are the frequency and type of communications about various aspects of the project and the extent of different types of learning. Secondly, although attempts were made to select projects that were as alike as possible in terms of product type, project length and team size, the effect of other project- or organization-specific factors cannot be entirely ruled out such as the international spread of the project team, the age of the company, organizational culture and internal politics. From the interviews within the companies examined, these factors were reported to have been explicitly considered in the project design rather than factors that directly affected learning independently of project design. However, further research could examine possible effects of these other factors on learning, for example, by examining projects within the same organization in order to control for organization-specific factors. Finally, although it was not specifically examined in this research, this research suggests that the project structure is aligned or misaligned with the knowledge or learning desired should have performance implications. Other researchers from an information processing perspective (e.g. Daft and Lengel, 1986 and Galbraith, 1977) have made the argument that project structure influences information processing and consequently organizational performance. The cases examined here suggest that adopting a knowledge and learning perspective could also be useful and that the structure–performance relationship is mediated by the specific type of knowledge and learning involved. Further research could explore this in more detail.