Digital Innovation, Entrepreneurship and New Business Models
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Paper Type
Complete
Paper Number
2088
Description
Acknowledging that valuable ideas can come both from inside and outside the organization, firms have turned to more open models of innovation. However, it remains difficult to measure firms’ open innovation activities, particularly in a quantitative, longitudinal setting. This impedes an adequate assessment of open innovations’ long-term implications for firms’ financial performance. While researchers have devoted a considerable effort to examining the link between open innovation and innovative performance, the link to financial performance is unclear. In this study, we develop and validate a text-based measure for firms’ open innovation activities, and probe related performance implications in a longitudinal, cross-industry setting. Combining machine-learning content analysis to create an open innovation dictionary, we analyze the 10-K annual reports of 9,100 publicly listed firms in the U.S. between 1994 and 2017. Our results support our theorizing that a nonlinear relationship takes an S-shape between open innovation and financial performance.
Recommended Citation
Schaeper, Thomas; Jung, Christopher; Foege, J. Nils; and Nüesch, Stephan, "Measuring Open Innovation through Textual Analysis: An Assessment of Nonlinear Performance Implications" (2020). ICIS 2020 Proceedings. 11.
https://aisel.aisnet.org/icis2020/digital_innovation/digital_innovation/11
Measuring Open Innovation through Textual Analysis: An Assessment of Nonlinear Performance Implications
Acknowledging that valuable ideas can come both from inside and outside the organization, firms have turned to more open models of innovation. However, it remains difficult to measure firms’ open innovation activities, particularly in a quantitative, longitudinal setting. This impedes an adequate assessment of open innovations’ long-term implications for firms’ financial performance. While researchers have devoted a considerable effort to examining the link between open innovation and innovative performance, the link to financial performance is unclear. In this study, we develop and validate a text-based measure for firms’ open innovation activities, and probe related performance implications in a longitudinal, cross-industry setting. Combining machine-learning content analysis to create an open innovation dictionary, we analyze the 10-K annual reports of 9,100 publicly listed firms in the U.S. between 1994 and 2017. Our results support our theorizing that a nonlinear relationship takes an S-shape between open innovation and financial performance.
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