Here - Beedie School of Business - Simon Fraser University

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sectors and 1,680 industry-year observations. Models 1 and 2 use employment-based measures for IJV presence, WOS presence, and SOE weight, whereas models 3 and 4 use capital-based measures. Models 2 and 4 consistently show that IJV presence has an inverted U-shape relationship with innovative capabilities of indigenous firms. Therefore, H1 is strongly supported; that is, there is likely an optimal level of IJV presence, and a moderate level of IJV presence has a higher effect on innovative capabilities than either a low or a high level of IJV presence. To investigate the moderating effect of technology gap, we implemented subsample tests and summarized the results in Tables 4 and 5. We used the median of technology gap (median = 1.951) to split the whole sample into one with high technology gap (324 industries and 840 observations) and the other with low technology gap (320 industries and 840 observations). We derived the results in Table 4 using employment measures of IJV presence, WOS presence, and SOE weight, whereas those in Table 5 use capital measures. In both tables, we based models 1 and 2 on the sample with high technology gap and models 3 and 4 on the sample with low technology gap. Both tables show that in the low-technology-gap sample, IJV presence has an inverted U-shape effect on innovative capabilities, while it does not have any significant effect in the high-technology-gap sample. These results strongly support H2; that is, an inverted U-shape impact of IJV presence is more salient in industries with low technology gap. Tables 3, 4, and 5 also show some interesting results on the control variables. Table 3 shows that WOS presence has a positive effect on innovative capabilities of indigenous firms. This is consistent with the literature, which emphasizes that WOS presence benefits 19
indigenous firms through knowledge spillovers and the enhancement of market competition (Caves, 1996). As suggested in Tables 4 and 5, these benefits of WOS presence are more salient in industries with low technology gap, probably because indigenous firms in these industries are more capable of competing with foreign companies and can thus benefit more from WOS presence. Table 3 also indicates several important points: 1) Market concentration has no effect on innovative capabilities, probably because its positive and negative effects, as specified earlier, offset each other. 2) Market growth has a positive effect on innovative capabilities, which is consistent with our prediction. 3) Unexpectedly, we did not find a significant positive effect of IJV age on innovative capabilities of indigenous Chinese firms. A plausible explanation is that, although engaging in IJVs for a longer time period may contribute to more knowledge transfer, a long-term association with foreign partners may also facilitate technology dependency upon foreign firms, which in turn limits the learning incentives of the Chinese partners. 4) IJV state equity does not have a negative effect on indigenous capabilities, as we have expected. Some plausible explanations include that SOEs may have improved their ability to absorb knowledge from their foreign partners, as well as their incentives to conduct risky innovative activities over time, due to the SOE reforms carried out by the Chinese government (Tan & Tan, 2005). 5) Consistent with the results for IJV state equity, we did not find a negative effect of SOE weight on innovative capabilities of firms, either; instead, SOE weight has a significant positive effect, which further strengthens our speculations that SOEs in China may have improved their incentives or ability to become innovative. 6) Tables 4 and 5 suggest that Labor quality has no effect 20
Page 1 and 2: Dual-Edged Tools of Trade: How Inte
Page 3 and 4: Innovative capabilities—the capab
Page 5 and 6: turn leads to reduced incentives to
Page 7 and 8: from foreign partners, domestic Hun
Page 9 and 10: and newly established automakers su
Page 11 and 12: slowdown in the increase of additio
Page 13 and 14: which suggests that the second half
Page 15 and 16: an imbalanced panel dataset of 474
Page 17 and 18: Control Variables WOS Presence. Thi
Page 19: in an industry; firm size is the lo
Page 23 and 24: we particularly view the use of IJV
Page 25 and 26: Our findings have important implica
Page 27 and 28: (technology-generating stage), whic
Page 29 and 30: take advantage of knowledge transfe
Page 31 and 32: References Amsden, A. (2001). The r
Page 33 and 34: Lall, S. (1987). Learning to indust
Page 35 and 36: Appendix: Reliability Check of the
Page 37 and 38: Table 2. Correlation Matrix and Des
Page 39 and 40: Table 4. Results of Fixed-Effects G
Page 41: Table 6. Results of Fixed-Effects G

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