Domain-Relevant Commitment and Individual Technical Innovation Performance.
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| Title: | Domain-Relevant Commitment and Individual Technical Innovation Performance. |
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| Authors: | Bettencourt, Lance A., Bond, Edward U., Cole, Michael S., Houston, Mark B. mhouston@mays.tamu.edu |
| Source: | Journal of Product Innovation Management. Mar2017, Vol. 34 Issue 2, p159-180. 22p. 1 Diagram, 14 Charts. |
| Subjects: | Change agents, Idea (Philosophy), Innovations in business, Commitment (Psychology), Technological innovations |
| Abstract: | Individual innovators play a critical organizational role in that they generate and often champion technology and product ideas. Amidst an ongoing stream of organizational and team innovation research, few empirical studies focus on differences in individual innovation performance despite the importance of the individual innovator to a firm's innovation efforts. Based on goal commitment theory, we introduce a new domain-relevant commitment construct and develop a conceptualization of conditional indirect effects. Our model suggests that relevant individual abilities enhance commitment to technical innovation and innovation performance while also insulating against the impact of situational variables, making employees' commitment to innovation performance less dependent upon context. Hypotheses are tested using two sources of data and a sample of 339 R&D professionals from a Fortune 100 industrial firm. Results suggest that commitment to innovation is a key motivational factor in explaining individual technical innovation performance. Situational characteristics impact motivation differently for individuals with lower vs. higher ability levels, even in this context in which truly low-ability individuals, in the absolute sense, have been screened out by the employment selection process. The relationship between commitment and innovation performance is strengthened by higher levels of individual ability. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Product Innovation Management is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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