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A structural equation model of the impact of the “fuzzy front end” on the success of new product development Export

Research Policy (28 October 2009)

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In this study of new product development projects, the fuzzy front end of innovation was explored. The new product development process has multiple stages, so two types of fuzzy front end impacts on the success of new product development were examined: (1) a direct impact and (2) an indirect impact by influencing the next stage of the new product development process, i.e. project execution. Furthermore, the degree of project newness, in accordance with contingency theory, was considered. We developed and tested a conceptual model of relationships among key variables related to the fuzzy front end, project execution, and project success. The structural equation model was tested with AMOS using information from 144 projects completed by German measurement and control technique firms. For the most part, the responses from these firms supported the hypothesized relationships and the frequently claimed importance of the fuzzy front end was confirmed. The results offer strong support for the importance of the early involvement of all departments in new product development to enhance communication and, ultimately, project success. This process can be advanced by initial planning prior to development. Furthermore, the firms’ responses highlighted the importance of reducing market and technical uncertainty during the fuzzy front end, as both were found to negatively influence communication and increase deviations during project execution. The technical uncertainty remaining at the start of a project had a direct, negative influence on project efficiency and the most far-reaching implications regarding the success or failure of the project. With regard to contingency theory, the results indicated that efforts spent on the reduction of uncertainty to improve project execution and success may be influenced by the degree of newness of new product concepts. The degree of newness was found to influence the reduction of technical uncertainty, deviations from specifications, and efficiency.


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