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Conceptualizing, assessing, and managing front-end fuzziness in innovation/NPD projects
Sheng-Li Chang1, Chih-Yuan Chen2 and Shyh-Chyi Wey3
Department of Business Administration, National Yunlin University of Science and Technology, No. 135, Section 3, Taichung Kwang Road, Taichung, Taiwan 407, Republic of China. shenglichang@gmail.com 2 Department of Business Administration, National YunlinUniversity of Science and Technology, No. 123, Section 3, University Road, Touliu, Taiwan 640, Republic of China. chency@yuntech.edu.tw 3 Department of Foreign Language, National Yunlin University of Science and Technology, No. 123, Section 3, University Road, Touliu, Taiwan 640, Republic of China. wey@yuntech.edu.tw
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Front-end fuzziness (FEF) within innovation/NPD projects remains unclear andunderexplored. In this research, FEF is clarified to have three change patterns of dynamic fuzziness levels and to have both positive and negative effects on the success of an innovation/NPD project. In this context, FEF sources are categorized into front-end environment, means, and goals, and FEF dimensions are extended to include uncertainty, equivocality, complexity, and variability.Accordingly, a management template is developed to help innovators track specific FEF to relative sources, assess FEF quantitatively, and manage both the positive and the negative effects of FEF. Finally, the article concludes with suggestions of the applications of the FEF management template.

1. Introduction

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lthough there have been several models developed to define, assess, and manage front-endfuzziness (FEF) (Doll and Zhang, 2001; Zhang and Doll, 2001; Kim and Wilemon, 2002), it has been incompletely conceptualized in three main ways that could impede fuzzy frontend management and performance. Firstly, the term FEF has been used confusedly and interchangeably to refer to some terms such as uncertainty, ambiguity, variability, equivocality, complexity, and so on (e.g., Doll and Zhang,2001; Kim and Wilemon, 2002). Previous researchers have identified FEF to be composed of different components. Zhang and Doll (2001) defined FEF to include technology, consumer, and competitor

fuzziness. Doll and Zhang (2001) proposed FEF to include uncertainty and equivocality. Kim and Wilemon (2002) defined FEF in terms of both exogenous and endogenous uncertainties. Two distinct dimensions of FEFas identified by other authors are ignored, including complexity (e.g., Dosi, 1988; Pich et al., 2002) and variability (e.g., Dess and Beard, 1984; Correa, 1994). ˆ Secondly, FEF has been considered to have merely negative effects on the success of innovation/NPD, which needs to be reduced or avoided (e.g., Moenaert et al., 1995). However, some literature has indicated that FEF has positiveeffects on the success of innovation/NPD through inspiring innovators’ creativity and learning (e.g., Ogilvie, 1998; Alves et al., 2005). It is therefore critical for FEF assessment and management to 469

R&D Management 37, 5, 2007. r 2007 The Authors. Journal compilation r 2007 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford, OX4 2DQ, UK and 350 Main St, Malden, MA, 02148, USA

Sheng-LiChang, Chih-Yuan Chen and Shyh-Chyi Wey consider both the positive and the negative effects of FEF. Thirdly, the traditional classifications of FEF sources have weak casual relevance to FEF activities and goals (e.g., Khurana and Rosenthal, 1997; Zhang and Doll, 2001). As noted by Kim and Wilemon (2002, 2003), both identifying the scope of fuzzy front-end activities and setting fuzzy front-endperformance priorities are related to idea generation and fuzzy front-end performance. It is therefore important to build a causal relationship between FEF sources and FEF assessment and management. Incomplete conceptualizations make FEF hard to understand, assess, and manage. Therefore, the purpose of this research is to conceptualize FEF and develop a management template to help innovators assess and...