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Market Vision and Market Visioning Competence: Impact on Early Performance for Radically New, High‐Tech Products *
Author(s) -
Reid Susan E.,
de Brentani Ulrike
Publication year - 2010
Publication title -
journal of product innovation management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.646
H-Index - 144
eISSN - 1540-5885
pISSN - 0737-6782
DOI - 10.1111/j.1540-5885.2010.00732.x
Subject(s) - clarity , market orientation , competence (human resources) , high tech , new product development , business , competitive advantage , structural equation modeling , empirical research , marketing , industrial organization , knowledge management , economics , computer science , management , biochemistry , chemistry , philosophy , epistemology , machine learning , political science , law
Having the “right” market vision (MV) in new product scenarios involving high degrees of uncertainty has been shown to help firms achieve a significant competitive advantage, which can ultimately lead to superior financial results. Despite today's increased rate of radical innovation, and hence the importance of effective vision, relatively little research has been undertaken to improve our understanding of this phenomenon. The exploratory and empirical investigation undertaken herewith responds to this research gap by focusing on MV and its precursor, market visioning competence (MVC), for radically new, high‐tech products. MV is a clear and specific mental model/image that organizational members have of a desired and important product‐market for a new advanced technology, and MVC is a set of individual and organizational capabilities that enable the linking of advanced technologies to a future market opportunity. Based on samples of high‐tech firms involved in early technology developments, the measurement study indicates that five factors comprise MV (i.e., clarity, magnetism, specificity, form, and scope) and that four factors underlie MVC (i.e., networking, idea driving, proactive market orientation, and market learning tools). Structural equation modeling is used to demonstrate that MVC significantly and positively impacts MV and that each of these constructs significantly and positively influences certain aspects of early performance (EP) in new product development. This is the first empirical study to develop a comprehensive set of scales to measure these constructs and then to combine them in a model by which to examine their interrelationships.