Marcus Bellamy

Alun Jones

Session 6: Knowledge & Collaboration

Networks

Big Picture Keywords

Knowledge Networks, Path Dependence, Economic Geography, Centrality, Collaboration Modes/Networks, Resource-Based View

Intraorganizatoinal Networks, Interorganizational Networks

Central Themes Network-based mechanisms are used to overcome

problems of bounded rationality, uncertainty, and incomplete information

Studies of innovation should take into account the dual importance of physical location and institutional anchors

Drawing on statistical methods to validate network-centric arguments focused on network structure, dynamics, and nodal properties

Nerkar & Paruchuri (2005)Evolution of R&D Capabilities: The Role of Knowledge Networks Within a Firm

Key Questions Are patents among the most valid indicators of technological

competence? Are there other concrete/proxy indicators of technological competence that are supplemental, just as useful, or better?

Key Perspectives (Intraorganizational View) R&D: main function is to generate new knowledge by recombining

existing knowledge Structural characteristics of members: indicators of quality (fitness

for use) and richness (diversity of content)

Nerkar & Paruchuri (2005)Evolution of R&D Capabilities: The Role of Knowledge Networks Within a Firm

Key Contributions Centrality: a signal of quality; leads to greater reach to other parts

of network; positively associated with likelihood of knowledge selection

Spanning of structural holes: signal of richness; enjoy efficiency and control benefits (knowledge broker); unique rather than redundant information; positively associated with likelihood of knowledge selection

Inventors in strategic positions within a network of inventors influence the processes of selection in their favor Incorporates sociological view into economic-oriented (socioeconomic)

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Owen-Smith & Powell (2004)Knowledge Networks as Channels and Conduits: The Effectsof Spillovers in the Boston Biotechnology Community

Key Questions What measure(s) of centrality best fits your analysis? Does the notion of channels and conduits clash with that of measures

of direct and indirect links? e.g. Interorganizational alliances functioning more as channels than as

conduits Key Perspectives (Interorganizational View)

Strucutral Features: informal ties, board interlocks, strategic alliances

Nonstrucutral Features: characteristics of the organizations, geographic location, institutional underpinnings of the larger structure

Networks as both “open” Channels and “closed” Conduits

Owen-Smith & Powell (2004)Knowledge Networks as Channels and Conduits: The Effectsof Spillovers in the Boston Biotechnology Community

Key Contributions Integrated geographic and network explanations for innovation

rates of biotechnology firms (as well as for firms in other high-technology sectors)

Offered notable propositions about the relationship between propinquity, institutional diversity, network position, and innovation

Considered not only networks structural features, but also node characteristics

Pisano & Verganti (2008)Which Kind of Collaboration is Right for You?

Key Questions Given your strategy, how open or closed should your firm’s network of collaborators be? Who should decide which problems the network will tackle and which solutions will be

adopted? Is it possible to use a combination of collaboration modes simultaneously to support

one’s strategies? Does the same, four-mode framework still apply? Should some form of “industry weight” be incorporated into this framework?

Key Perspectives Collaboration Architecture: a firm’s structure and organizing principles Open Networks: support from an unlimited number of problem solvers Closed Networks: “private clubs” Hierarchical: “kingpins” control the direction and value of innovation Flat: players are equal partners in the process and share the power to decide

key issues

Pisano & Verganti (2008)Which Kind of Collaboration is Right for You?

Key Contributions Firms that choose the most suitable collaboration mode will have the edge in

the relentless race to develop new technologies designs, products, and services

Open: Sample selection problem; Intangible properties difficult to screen Closed: Very risky if you don’t know where to look or who the key players are Two different collaboration modes can be just as effective by firms serving the

same industry Due to differences in strategy and capabilities

Rijnsoever et al. (2008) A resource-based view on the interactions ofuniversity researchers

Key Questions In what ways does a well-kept network become a valuable resource for a

scientist? In light of the limited, localized survey responses, to what extent can these

research findings be used? (i.e. is it valid to generalize any portion of these findings to academia in general?)

Key Perspectives Unit of analysis: individual researcher Independent Variables: Global Innovativeness, Work Experience, Dynamics

of the Scientific Field, Control Variables (Dept, Sex) Dependent Variables: Academic Rank, Network Activity (Network) Affiliation Types: faculty, university, external, industry

Rijnsoever et al. (2008) A resource-based view on the interactions ofuniversity researchers

Key Contributions Supplements the current research on collaborations and networking in

science by Researching at a micro perspective Investigating the factors that influence the intensity of interactions Apply a resource-based view as an explanatory mechanism

The competitive advantage of an individual researcher has several dimensions: Successful cooperation can lead to a larger number of publications Advancement in academic rank Strengthened position in competition for research grants

Reasons for collaboration very often relate to the individual’s own resource stock which can be used to gain a competitive advantage

Innovativeness does not have any effect on industry network activity

Extension Including different types of ties for more

comprehensive findings e.g. Going beyond only considering copatenting

Extending some of the studies to include a broader base e.g. surveying more universities, different regions for

understanding the interactions of university researchers