Henderson, R. & Cockburn, I. (1994). Measuring Competence? Exploring Firm Effects in Pharmaceutical Research. Strategic Management Journal, 15: Seung Hoon Lee

Empirical work on Resource Based View so far… Firm effects account for diversification strategy (Hitt & Ireland, 1985; Montgomery & Wernerfelt, 1988) and performance (Cool & Schendel, 1988; Rumelt, 1991). But… Few studies considered particular competences (had to rely on aggregate level measures of competence) And more importantly, a puzzle remained from their own recent research (using the same data set). - A more direct motivation of the study Overview

Henderson, R. & Cockburn, I. (1994). Scale, Scope and Spillovers: The Determinants of Research Productivity in Drug Discovery. NBER Working Paper. (Later published in Rand J Econ, 1996) Originally about firm size (economy of scope) and efficiency in management of research. But also found, 1) Variance in research productivity explained by firm fixed effects after controlling for firm size, scope, program size, etc. 2) Despite the fact that differences in the structure of the research portfolio (e.g., industry) had significant effects on research productivity, variations in portfolio structure across firms were persistent (e.g., firm specific competence). Guided by the resource-based view lens, the authors attempt to explain these finings.

Why (Pharmaceutical) Research? Successful research efforts take many years to build and often rely on idiosyncratic search routines that may be difficult to transfer across organizations (Nelson, 1991). More Generally, Knowledge as exemplar of valuable, rare, imperfectly imitable, non-substitutable resource. In particular, Absorptive Capacity (Cohen & Levinthal, 1990) Combinative Capability (Kogut & Zander, 1992) Dynamic Capability (Teece, Pisano & Shuen, 1997). Architectural Competence (Henderson & Cockburn, 1994) A Proliferation of Concepts?

Architectural Competence Component Competence - Abilities or knowledge specific to particular local activities within the firm (e.g., expertise in particular disciplinary areas) - Fundamental to day-to-day problem solving Architectural Competence - Ability to use component competence - Integrate them effectively and to develop fresh component competences as they are required

Hypotheses Component Competence Hypothesis1: Drug discovery productivity is an increasing function of firm specific expertise in particular disciplinary areas (e.g., molecular biology, physiology, biochemistry, etc.) Hypothesis2: in particular disease areas (e.g., diabetics, anxiety) Architectural Competence Hypothesis3: Firms with the ability to encourage and maintain an extensive flow of information across the boundaries of the firm will have significantly more productive drug discovery efforts, all other things equal. Hypothesis4: Firms that encourage and maintain an extensive flow of information across the boundaries between scientific disciplines and therapeutic classes within the firm will have significantly more productive drug discovery efforts, all other things equal.

Sample and Data, Measures of Variables Population: Firms in pharmaceutical industry Sample: 10 major European and American firms - Data collection at research project level observations (research project * year) - Data source: archival for drug discovery productivity; internal for R&D input; qualitative for Architectural Competence Variables - Productivity: Counts of “Important” patent grants. (Important, if granted in at least two out of America, EU, Japan) - Size (R&D input): Annual expenditures on exploratory research and clinical development by research program - Other controls: shape, scope of research portfolio, internal and external spillovers, therapeutic class dummies

Measures of Variables Variables - Component Competence 1) firm specific expertise in particular disciplinary areas: not measured, not tested 2) firm specific expertise in particular disease areas: the stock of patents obtained in each program (stock calculated by assuming 20% depreciation rate for knowledge) - Architectural Competence: 3) Information flow between the boundaries of firms: the degree to which reputation in wider community is the dominant criterion for promotion of scientific personnel 4) Information flow within firms (between scientific disciplines and therapeutic classes): the degree of exchange of rich information; the degree of regional integration; the degree of centralized resource allocation (inverse)

Model Specification Poisson Process 1) Poisson Distribution (mean=variance for d.v.) if violated, estimators consistent but standard errors underestimated 2) Negative Binominal (gamma distribution for residual term) if violated, estimators inconsistent 3) Nonlinear least-square estimates 4) quasi-generalized pseudo-maximum likelihood (or weighted 3)) For robustness check Assumption check: ANOVA Most of the variance for Architectural Competence variables are accounted by between firms rather than within firms

Results