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This study highlights the effects of industry collaboration in enhancing academic innovation output. We exploit a unique longitudinal dataset on the 110 top U.S. research universities for the last 19 years. Our empirical findings confirm that the relationship between the number of industry collaborations and academic innovation is curvilinear. Moreover, we hypothesized and found that university contribution, collaboration breadth and knowledge capacity moderate the curvilinear relationship between the number of industry collaborations and academic innovation. Poisson, negative binomial and generalized negative binomial regressions are used to test the hypotheses in a panel data of 2090 university-year cases. Our results are robust to the three econometric methods, measures of variety of academic innovation and the findings support our prediction. [ABSTRACT FROM AUTHOR]

Abstract: This study conducts a 9-year longitudinal analysis on the effects of diversity of R&D sources, diversity of human capital, innovation infrastructure and academic knowledge on industrial performance. Here, I suggest there is an inverse U-shaped relationship between diversity of R&D sources and industrial performance. Industrial performance is also related to diversity of human capital in a curvilinear (inverted U-shaped) manner. Moreover, innovation infrastructure negatively moderates the effect of diversity of R&D sources on industrial performance, while academic knowledge also negatively moderates the effect of diversity of R&D human capital on industrial performance. The fixed- and random-effects regressions are used to test the hypotheses in a panel data of 315 industry-year cases and the findings support our prediction. The results of this study can help reconciling contradictory findings from previous studies by demonstrating the potential impact of diversity on industrial performance. [Copyright &y& Elsevier]