Rotman School of Management PhD, UC Berkeley MA, UC Berkeley SM, MIT AB, Harvard

1. Technological Resources and the Direction of Corporate Diversification: Toward an Integration of the Resource-based View and Transaction Cost Economics Rotman School of Management PhD, UC Berkeley MA, UC Berkeley SM, MIT AB, Harvard Silverman, B. S. 1999. Management Science, 45(8): 1109-1124. Presented by Jae Kyun Yoo

2. Overview • A study of how a firm’s resource base affects the choice of industries into which the firm diversifies. • Empirically, operationalizes technological resources at a more fine-grained level than has been done in prior resource-based research. • Theoretically, examines and tests the assumption that rent-generating resources are necessarily too asset specific to allow contracting (link between RBV and TCE).

3. RBV and Diversification • RBV describes the firm as a “collection of sticky and imperfectly imitable resources or capabilities that enable it to successfully compete against other firms” (see Penrose, 1959). • These same characteristics prevent firms from “transplanting” resources into new contexts. • It is assumed that more “related” diversification supports more extensive exploitation of resources. • Studies use SIC system to measure degree of industry relatedness. • R&D intensity, advertising intensity, and other such investments serve as proxies for underlying resources and that firms will diversify into industries with relative intensities.

4. RBV and Diversification • However, current studies depend on strong assumptions regarding the ordering and applicability of the SIC system as well as the fungibility of R&D and advertising intensity. • Popular studies on diversification using RBV characterize resources at the industry level, and leave open the effects of firms’ repositories of expertise or technology. • Identification of individual firms’ resources allows for greater insights into the role of resources in diversification.

5. Hypotheses • Hypothesis 1:Ceteris paribus, a firm is more likely to diversify into a business the more applicable its existing technological resources are to that business (in absolute terms). • Hypothesis 2: Ceteris paribus, a firm is more likely to diversify into a business the more applicable its existing technological resources are to that business, relative to other opportunities facing the firm. • Hypothesis 3. Ceteris paribus, a firm is more likely to diversify in to a business the more likely that contracting out its technological resources in that business is subject to high contractual hazards. • A: A firm is more likely to diversify into a business as the feasibility of licensing its technological resources in that business decreases. • B: A firm is more likely to diversify into a business as the need for secrecy to appropriate returns to its technological resources in that business increases. • C: A firm is more likely to diversify into a business as the degree of tacit knowledge associated with its technological resources in that business increase.

6. Methodology • The empirical test of the hypotheses entailed estimating the entry of existing firms into new SICs during the three-year window 1982-1985 as a function of firm, industry, and resource characteristics in 1981. • Each firm’s resource base was determined through the use of patent data. • Issues arise where firm knowledge is not patented due to ineligibility or firm choice. • It should also be noted that differences in the comprehensiveness of patenting may exist across firms, industries, and time .

7. Variables • Dependent Variable • Divij = 1 where firm (i) enters industry (j) during allotted time • Independent variables • AbsTechij = absolute level of firm (i) patent portfolio applicable to industry (j) • RelTechij = applicability of firm (i) patent portfolio to industry (j) relative to other industries • Royaltyj = the feasibility of licensing innovations in industry (j) • Secrecyj = the importance of secrecy to appropriating returns to innovation in industry (j) • Learningj = the importance of learning curve advantages to appropriating returns to innovation in industry (j)

8. Variables

9. MODEL SPECIFICATION

10. Correlation Table

11. Results Hypothesis 1 supported. Hypothesis 2 supported. Hypothesis 3a, 3c supported.

12. Contributions • This paper has many firsts: • Measures effects of firm heterogeneous technological resources via patent data on diversification. • Examines empirically the hypothesis that firms prioritize their diversification options according to the relative applicability of their resources. • Examines empirically the role of transaction costs on diversification in an RBV context.

13. Contributions • The results of this study suggest that a firm’s technological resource base significantly influences its diversification decisions (as seen through a patent portfolio lens). • Firms elect to enter markets where it can exploit its existing technological resources and in which its existing technological resource base is strongest. • Firms’ diversification decisions are influenced by the severity of hazards surrounding contractual alternatives • The source of innovation in an industry indicates the direction of likely diversifying entry into that industry. (Pavitt et al., 1989) • This study integrates TCE with RBV and suggests that “while conflicts between the two theories exist, the strong complementarities between them should not be ignored”.