Close together or far apart? The geography of host-country knowledge sourcing and MNCs’ innovation performance

ABSTRACT

We investigate the influence of different host-country knowledge sources on the quality and generality of multinational corporation (MNC) innovation. We suggest that the quality of MNC innovation increases with the geographical distance from local industrial organizations. We also argue that the generality of MNC innovation increases with the distance from local research institutions, but decreases after a certain threshold. We explain these relationships based on the different cognitive and geographical distances separating MNC innovation activities from industrial organizations and research institutions. We test our arguments on a sample of US Patent and Trademark Office (USPTO) patents developed in the United States by foreign semiconductor MNCs.

KEYWORDS:

• multinational corporation (MNC)
• innovation quality and generality
• host-country knowledge sourcing
• geographical distance
• cognitive distance

JEL:

• F23
• O32
• R12

ACKNOWLEDGEMENTS

The authors are grateful to the editor and three anonymous reviewers for their guidance and constructive comments during the review process. They also thank the participants at the Academy of International Business, Dubai, United Arab Emirates (UAE), 2017; Academy of Management, Atlanta, Georgia, USA, 2017; Strategic Management Society, Berlin, Germany, 2016; Academy of Management International Management Division PDW, Anaheim, California, USA, 2016; Uddevalla Symposium, Sweden, 2016; DRUID Conference, Copenhagen, Denmark, 2016; iBEGIN Conference, Philadelphia, Pennsylvania, USA, 2015 and 2016; as well as participants at the seminars held at the Henley Business School and the Department of Economics and Management at the University of Trento.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. We recognize that IOs do perform basic research, but this activity remains residual and unintentional (Rosenberg, 1990).

2. While this is a small number of MNCs, it is in line with the market structure of the semiconductor industry, which is concentrated in all its submarkets (Gruber, 2000), as well as with our objective to study the innovation performance of companies that can afford to develop research and development (R&D) activities in foreign countries.

3. The generality measure differs from other typical indexes used to qualify patents, such as technological relatedness or complexity. In fact, technological relatedness is a relative measure that captures the degree of technological overlap between two patents or patent portfolios, while technological complexity focuses on the technological classes of a specific patent, and is meant to assess the ease with which these classes have been recombined with each other in the past.

4. We also perform a similar test by using the share of scientific citations out of the total number of citations (including both patent and scientific citations), rather than the absolute number of scientific citations, and the results are robust (t = 9.444, p > 0.01). Data on scientific citations come from the ‘USPTO Patent and Citation Data’, published by Sampat Bhaven within the Harvard Dataverse programme (2011, hdl:1902.1/16412, Harvard Dataverse, V4).

5. The US Census defines MSAs as areas composed of a densely populated nucleus, aggregated with those adjacent communities featuring high levels of economic and social linkages with that nucleus (assessed, among other things, via the analysis of commuting patterns). MSAs with a population of at least 1 million inhabitants can be recognized as CMSAs if they demonstrate specific levels of economic and social integration. Our empirical analysis includes 53 MSAs/CMSAs.

6. The data are available at http://www.peterthompson.gatech.edu/.

7. We standardize the independent variables before squaring them.

8. This is the travel time associated with the turning point, which is equal to 1.938 and lies within the range of the variable distribution and within 2 SD above the mean. The travel time associated with the turning point has been computed by accounting for the fact that the independent variable has been standardized and rescaled.