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The paradox of technological capabilities: a study of knowledge sourcing from host countries of overseas R&D operations

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Abstract

We investigate the factors that influence the extent to which a multinational corporation's headquarters (MNC-HQ) sources knowledge from the host countries of its R&D labs. We propose that the technological capabilities held by MNC-HQs present a paradox. On the one hand, they enhance MNC-HQs' learning capabilities. On the other hand, they reduce MNC-HQs' motivations to outsource knowledge from host countries. We also argue that it is important to consider both relative and absolute levels of technological capabilities, because relative levels can influence MNC-HQs' motivations to source knowledge from host countries. Statistical findings generally support our arguments.

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Notes

  1. The fact that knowledge spills over more efficiently in clusters can also have a negative impact on a firm's incentives to locate within a cluster, as rivals can benefit from a focal firm's R&D efforts (Flyer & Shaver, 2003).

  2. The diminished motivation may derive from the path-dependent nature of search behaviors, lower economic incentives due to overlaps in knowledge bases, or the difficulty associated with creating new routines to incorporate geographically dispersed knowledge.

  3. Semiconductor-related technology classes are 29, 156, 257, 326, 327, 357, 364, 365, 395, and 437.

  4. In our sampling scheme we focused exclusively on host countries where overseas R&D labs had already been set up by MNCs. We chose this sampling scheme because our main research question was to investigate under what conditions MNC-HQs source knowledge more actively from the host countries of their R&D labs. In other words, in this study we examined how variations in technological capabilities affect the level of knowledge sourcing from host locations.

  5. Some recent studies (e.g., Chung & Alcacer, 2002; Chung & Song, 2004) have been conducted at a smaller regional level of analysis such as the state, although much of the prior empirical research in this area has been conducted at the country level. We also conducted a multi-country study, mainly because of technical difficulties in classifying regions in systematic and comparable ways for the purposes of empirical research, especially in the international setting. For example, it would be difficult to say that states in the United States are comparable to provinces in China, or that Silicon Valley is comparable to the Shinju Science Park in Taiwan.

  6. The Poisson regression model is a special case of the negative binomial: it corresponds to α=0. In order to test α=0 STATA performs a likelihood test, as shown at the bottom of Table 4. In our full model the probability that we would observe this data conditional on α=0 is virtually zero, thereby suggesting a significant over-dispersion problem in the data.

  7. Two popular distance measures for multivariate situations are Euclidean distance and Mahalanobis distance. The latter is preferred when the variables under consideration have different scales. We measured similarity or dissimilarity of technological profiles between an MNC and a host country by Euclidean distance as follows:

    where

    and

  8. We did not include firm size as a control variable, mainly because of the multicollinearity problem between firm size and the number of a firm's patents (Ahuja, 2000: 329). In addition, we had difficulties extracting the figures of sales or employees of the multidivisional firms in our internationally dispersed samples.

  9. We conducted sensitivity tests using different time frames such as 1994–1999 and 1996–1999, but the statistical results did not change much, thereby showing the robustness of our findings.

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Acknowledgements

We appreciate comments from Richard Nelson, Atul Nerkar, Kazuhiro Asakawa, John Lafkas, and seminar participants at Columbia Business School on the earlier versions of this paper. We also appreciate comments from three JIBS reviewers and Departmental Editor Professor J. Myles Shaver at University of Minnesota. This research was supported by grants from the Korean Research Foundation (KRF-2002-003-B00092). We also appreciate support from the Institute of Management Research at Seoul National University.

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Authors and Affiliations

  1. Graduate School of Business, Seoul National University, Seoul, Korea

    Jaeyong Song

  2. College of Management, Long Island University, Greenvale, USA

    Jongtae Shin

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  1. Jaeyong Song
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Correspondence to Jaeyong Song.

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Accepted by J Myles Shaver, Departmental Editor, 9 July 2007. This paper has been with the authors for three revisions.

Appendix

Appendix

Pros and Cons of Using Patent (Citation) Data to Trace Knowledge Flows

Patents represent externally validated measures of innovative success and can therefore be interpreted as signals of technological competence (Narin, Noma, & Perry, 1987; Ahuja, 2000). Past research established that the patenting record of firms is closely related to their stature in the technological arena (Jaffe & Trajtenberg, 2002). Patent data have received so much attention because they are systematically compiled, have detailed information, and are available continuously across time.

However, using patent data as a proxy for technological capabilities may have some drawbacks. First, the propensity of a firm to patent its innovations varies substantially across industries (Mowery et al., 1998; Kortum & Lerner, 1999). The semiconductor industry that we investigate was found to be one of the most patent-intensive industries (Kortum & Lerner, 1999). Second, because a patent itself represents codified knowledge, there is some difficulty in capturing the tacit knowledge of a firm using patent data. However, Mowery et al. (1998) suggest that knowledge flows are closely linked between codified knowledge and tacit knowledge because they are not substitutes, but complements. Third, according to Hall and Ziedonis (2001), a pro-patent legal shift in the 1980s encouraged capital-intensive firms to increase the number of patents substantially for strategic reasons, even if they continue to rely on other mechanisms for appropriating returns to R&D investments, such as lead time and superior manufacturing and design capabilities rather than patents.

Although patent citations have been used widely in research as a way to capture knowledge flows, they also have some drawbacks as an accurate measure for capturing knowledge flows (Singh, 2005: 759). First, patent citations might be included by the inventor for strategic reasons such as avoidance of litigation. Second, patent citations could be added by patent examiners as well. Nevertheless, recent studies that compared patent citation data with direct surveys of inventors found that the correlation between patent citations and actual knowledge flow is high, thereby justifying the use of patent citation as a proxy measure of knowledge flows (Jaffe & Trajtenberg, 2002). Thus we also believe that, in spite of some disadvantages, patent citations are probably the best proxy measures of knowledge flows available for empirical studies. Given the high propensity of patenting in the semiconductor industry, patent citations could be effective in capturing knowledge flows in the industry, as shown by such studies as Almeida (1996), Almeida et al. (2002), Song et al. (2003), and Ziedonis (2004). For example, Ziedonis (2004) suggested that innovative activities in the semiconductor industry are highly cumulative because they often build on a large stock of prior inventions.

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Song, J., Shin, J. The paradox of technological capabilities: a study of knowledge sourcing from host countries of overseas R&D operations. J Int Bus Stud 39, 291–303 (2008). https://doi.org/10.1057/palgrave.jibs.8400348

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