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Technological diversification, technology portfolio properties, and R&D productivity

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Abstract

This study aims to examine the differential effect of technological diversification on research and development (R&D) productivity based on the qualitative properties of technology portfolios (i.e., the direction of technological diversification). Using the U.S. patent database from 1980 to 2010, we divided overall technological diversification into related and unrelated technological diversification; furthermore, the two potential moderating factors of technology portfolio centrality and R&D consistency were tested across the different types of technological diversification. The notable findings are as follows: First, technology portfolio centrality has a positive moderating effect that is more pronounced as the degree of technological diversification increases. Second, R&D consistency has a positive and linear moderating effect. Third, the positive (negative) effect of technological diversification is more pronounced under related (unrelated) technological diversification. Consequently, firms can better utilize the R&D-productivity-enhancing effect of technological diversification by considering both the current degree of technological diversification and the properties of their technology portfolios.

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Data availability

The datasets generated and analyzed during the current study are available in ‘https://wrds-web.wharton.upenn.edu/’.

Code availability

Available upon request.

Notes

  1. Technological diversification at the firm level is defined as the expansion in the diversity (or breadth) of firm-specific technological knowledge base and competence into a wider range of technological fields (Granstrand, 1998; Granstrand & Oskarsson, 1994).

  2. Focusing on firm-specific innovative conditions, Choi and Lee (2021) found a U-shaped relationship between technological diversification and R&D productivity with positive moderating effects of firm-specific core-technology competence and pool of knowledge spillovers.

  3. Technological relatedness (or coherence) refers to the degree to which technologies constituting a technology portfolio are technologically related, sharing a similar technological knowledge base and common scientific principles (Breschi et al., 2003; Kim et al., 2016; Leten et al., 2007; Nesta & Saviotti, 2005).

  4. The criteria are based on the indicators for the technological importance (quality) of patents; this includes the degree of the technological impact and diffusion into other technological fields (Squicciarini et al., 2013), and the additional adjustment required for reflecting the fast-changing technological environment. This study employs the patent class-level citation network and centrality (i.e., PageRank), which are widely used in social network analysis, to measure the proxy variable. See Sects. 2.2 and 4.2 for further details.

  5. Dindaroğlu (2018) found that technological diversification and R&D productivity have an S-shaped cubic relationship, however, the coefficients of the polynomial terms are consistent with that of a nearly U-shaped relationship.

  6. Among various measurements for the centrality index, we employed Google’s PageRank centrality. The detailed description and the deriving procedures are provided in Sect. 4.2.

  7. The robustness check for this conjecture proceeds in Sect. 5.2.

  8. According to Klevorick et al. (1995), technological advances occurring within a specific technological field and from technologically applicable external fields expand the pool of technological opportunities, offsetting diminishing returns to R&D.

  9. Dechezleprêtre et al. (2014) used a centrality index from a patent-level citation network as a proxy variable of the intensity of knowledge spillovers among patents.

  10. The hypothesis is likely to be valid at least in the short run. However, in the long run, consistent R&D efforts in previously active technological fields may deplete technological opportunities (Klevorick et al., 1995) and diminish marginal returns to R&D, as ideas are getting hard to find (Bloom et al., 2020). The long-run effect is not explored as this study mainly focuses on a short-run base (e.g., after two years).

  11. Using the property of entropy method, technological diversification can be divided into two parts of related and unrelated technological diversification, based on technological relatedness. See Sect. 4.3 for the detailed derivation process. For simplicity, we use technological relatedness as a term indicating the proportion of related technological diversification out of overall technological diversification.

  12. Both technological relatedness and R&D consistency are related to innovation strategies managing the balance between technological exploration and exploitation. While R&D consistency reflects the dynamic nature of technological consistency between the current technology portfolio and the accumulated technological knowledge base, technological relatedness concentrates on the technological coherence among technologies constituting the current technology portfolio (i.e., static nature).

  13. The observation year is restricted to 2010 owing to the truncation issue of forward citations that citations for recent patents after the end of the sample are not considered, thereby distorted downwardly (Hall et al., 2001).

  14. Degree centrality and closeness centrality measures cannot capture the indirect interactions among actors in a network.

  15. It considers not the total number of citations but the relative ratio, normalizing the absolute size of patent citations caused by the technology-specific citation trend.

  16. Chen et al. (2007) provided the conjecture and empirical evidence for the usage of the value 0.5 in the citation network. First, entries in the citation list, on average, are collected within two searches following the citation links. Second, the portion of the followed citation (e.g., B in an A to B, A to C, and B to C citation loop) is approximately 50%, implying the probability of following this indirect citation path (i.e., \(1-d\)) is close to 0.5, assuming that A followed the citation path of B.

  17. The value is set to one if there is no forward citation and added one for each additional forward citation.

  18. The derivation of the decomposition process is described in Palepu (1985).

  19. Refer to Sect. 4.2 for the detailed derivation process and notational information of the PageRank centrality (\(TECHCE{N}_{jt}\)).

  20. The usage of a fixed-effect model rather than a random-effect model is justified following the Hausman test.

  21. The sample is constructed with 19,778 observations out of the total 69,051 observations. While the mean R&D expenditures (total sales) of innovative firms in the total sample is 75.98 (1,987.76), the mean value in the matched sample is 191.16 (4,405.00).

  22. The upper bound for the positive effect of technological diversification (8.15) exceeds the largest sample level (4.65). Figure 4 illustrates the result.

  23. The negative moderating effect is related to Hypotheses 2 and 3 that firms at an early stage of exploratory technological diversification are more likely to suffer from technological competence-destroying effects. Hence, unless R&D consistency is sufficiently large to offset the negative moderating effect, firms at an early stage of technological diversification cannot effectively manage the positive moderating effects of technology portfolio centrality and are more likely to be dominated by harmful effects, such as R&D competition from the active R&D activities around technological fields with a high centrality index. The moderating effect of technology portfolio centrality in Model (6) of Table 2 is as follows: \(\frac{\partial lnRDPROD}{\partial TD\partial lnPORTCEN}=-0.182+0.224TD\).

  24. The positive moderating effect of R&D consistency on related technological diversification vanishes in Models (4) and (5) of Table 3. The results are explained as follows: First, the high correlation between two variables in that both are associated with exploitative R&D activities can boost the standard errors. Second, whereas R&D consistency attenuates harmful effects of technological diversification on R&D productivity, related technological diversification generates no significant harmful effects.

  25. The variable of technological relatedness (\(TECHREL\)), distributed over values between 0 and 1, is constructed as follows: \(TECHREL=\frac{TD\_R}{TD}\). It is not defined when the degree of overall technological diversification equals zero. The value of technological relatedness is set to one for those firms with zero degrees of technological diversification, given that concentrating on a single technological field implies the common usage of technological knowledge base across the R&D projects.

  26. All of the alternative variables are normalized by dividing them with mean values for each cohort (i.e., by patent class and year) in consideration of the class- and time-specific trend of patent applications and citation activities (Hall et al., 2001).

  27. For instance, R&D consistency of firm \(i\) at the observation year \(t\) with the 3-year moving average patent counts \((RDCON{S}_{it}^{3YMA})\) is measured by using a vector of the current technology portfolio where each column contains a 3-year average of the number of patents (i.e., the average value of \({P}_{ijt}\), \({P}_{ijt-1}\), and \({P}_{ijt-2}\)) and a vector of the knowledge stock where each column contains the accumulated stock of technological knowledge until the observation year \(t-3\).

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    Seh-Hyun Yoo & Chang-Yang Lee

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Yoo, SH., Lee, CY. Technological diversification, technology portfolio properties, and R&D productivity. J Technol Transf 48, 2074–2105 (2023). https://doi.org/10.1007/s10961-022-09953-x

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