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Silverman – 1999, MS TECHNOLOGICAL RESOURCES AND THE DIRECTION OF CORPORATE DIVERSIFICATION: TOWARD AN INTEGRATION OF THE RESOURCE-BASED VIEW AND TRANSACTION.

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Presentation on theme: "Silverman – 1999, MS TECHNOLOGICAL RESOURCES AND THE DIRECTION OF CORPORATE DIVERSIFICATION: TOWARD AN INTEGRATION OF THE RESOURCE-BASED VIEW AND TRANSACTION."— Presentation transcript:

1 Silverman – 1999, MS TECHNOLOGICAL RESOURCES AND THE DIRECTION OF CORPORATE DIVERSIFICATION: TOWARD AN INTEGRATION OF THE RESOURCE-BASED VIEW AND TRANSACTION COST ECONOMICS

2  A study of how a firm’s resource base affects the choice of industries into which the firm diversifies  Silverman (1999) integrates principles of both the Resource-based View (RBV) and Transaction Cost Economics (TCE) to develop a theory of firm diversification  Looks beyond methodologies used at the time to develop new sources of insight into firm behavior OVERVIEW

3  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  It is also assumed that R&D intensity, advertising intensity, etc. serve as proxies for underlying resources and that firms will diversify into industries with relative intensities RBV AND DIVERSIFICATION

4  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 RBV AND DIVERSIFICATION

5  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 HYPOTHESES

6  The empirical test of the hypotheses entailed estimating the entry of existing firms into new SICs during the three-year window 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 METHODOLOGY

7  Dependent Variable  Div ij = 1 where firm (i) enters industry (j) during allotted time  Independent variables  AbsTech ij = absolute level of firm (i) patent portfolio applicable to industry (j)  RelTech ij = applicability of firm (i) patent portfolio to industry (j) relative to other industries  Royalty j = the feasibility of licensing innovations in industry (j)  Secrecy j = the importance of secrecy to appropriating returns to innovation in industry (j)  Learning j = the importance of learning curve advantages to appropriating returns to innovation in industry (j) VARIABLES

8

9 THE MODEL SPECIFICATION

10 CORRELATION TABLE

11  Hypothesis 1 was corroborated (AbsTech = significant & positive)  Hypothesis 2 was corroborated (RelTech = significant & positive)  Hypothesis 3a was corroborated (Royalty = significant & negative)  Hypothesis 3b was not supported (Secrecy = not statistically significant)  Hypothesis 3c was corroborated (Learning = significant & positive) RESULTS

12  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  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” CONTRIBUTIONS

13  The measure developed by this study links a firm’s position to product markets where its technological strength is likely to offer commercial advantage  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 to the strongest extent  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 CONTRIBUTIONS


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