PERSPECTIVE—Shrouded in Structure: Challenges and Opportunities for a Friction-Based View of Network Research

Ghosh, Anindya; Rosenkopf, Lori

doi:10.1287/orsc.2014.0922

Cited by 55 publications

(71 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While individuals function as knowledge pools in these networks, however, they may also function as ‘filters,’ meaning that they may allow some knowledge types to pass through while preventing others (Ghosh and Rosenkopf, ). Specifically, knowledge and information that is highly explicit is relatively easy to pass from one person to another in a network (Hansen, , ; Nonaka, ), and may not even require the willingness of the person who originally possessed the knowledge.…”

Section: Introductionmentioning

confidence: 99%

Pipes, pools, and filters: How collaboration networks affect innovative performance

Singh

Kryscynski

et al. 2015

Strategic Management Journal

View full text Add to dashboard Cite

Research summary: Innovation requires inventors to have both new knowledge and the ability to combine and configure knowledge (i.e., combinatory knowledge), and such knowledge may flow through networks. We argue that both combinatory knowledge and new knowledge are accessed through collaboration networks, but that inventors' abilities to access such knowledge depends on its location in the network. Combinatory knowledge transfers from direct contacts, but not easily from indirect contacts. In contrast, new knowledge transfers from both direct and indirect contacts, but is far more likely to be new and useful when it comes from indirect contacts. Exploring knowledge flows in 69,476 patents and 89,930 unique inventors reveals evidence that combinatory knowledge from direct contacts and new knowledge from indirect contacts significantly affects innovative performance.Managerial summary: Inventors often combine ideas to create innovations. To do this, they need ideas to combine and they need the ability to combine those ideas. Inventors can get ideas to combine as well as the ability to combine ideas through prior co-workers. Prior co-workers can share ideas that may be relevant for the inventor's project and can tell the inventor about other things that other people are working on, especially people the inventor may not know. This can help inventors easily learn about ideas from friends-of-friends. The ability to combine ideas, however, is much harder to pass on. Prior co-workers must carefully work with the inventor to teach him or her the complex processes of combining ideas. This means that it is very hard to learn how to combine knowledge from a friend-of-a-friend, but it may be possible to learn from prior co-workers. We explore this phenomenon in the social relationships of software inventors.

show abstract

Section: Introductionmentioning

confidence: 99%

Pipes, pools, and filters: How collaboration networks affect innovative performance

Singh

Kryscynski

et al. 2015

Strategic Management Journal

View full text Add to dashboard Cite

show abstract

“…Though existing research has already begun to account for such a possibility (Ahuja 2000;Vanhaverbeke et al 2012), it has nevertheless implicitly assumed that firms' learning from indirect partners is purely a function of the number of such partners. More broadly, it has tended to assume that knowledge flows fairly easily beyond individual alliance dyads, for example, between indirectly connected firms (Ghosh and Rosenkopf 2014). However, a firm with five indirect partners may be aware of all five yet have a limited ability to learn from them, while an otherwise identical firm may be aware of only two indirect partners yet have a strong ability to learn from these two.…”

Section: Discussionmentioning

confidence: 99%

“…To account for such heterogeneity, our model formally incorporates as relevant parameters the extent to which firms are aware of and able to learn from their indirect partners. This way, our theory facilitates a refocusing, away from the assumption that firms' learning from indirect partners is a direct function of the actual number of such partners, towards a more nuanced account that considers both firms' cognitive limitations in observing other firms (Westphal 2008) as well as limitations in learning from them (Burt 2010;Ghosh and Rosenkopf 2014).…”

Section: Discussionmentioning

confidence: 99%

“…For example, firms vary greatly in their number of indirect partners for a given number of direct partners (e.g., Iyer et al 2006), 1 while 1 For example, in the pharmaceutical biotechnology industry during 1990-1994, The Upjohn Company and Sepracor Inc. both had two direct partners, though these connected Upjohn to only two indirect partners while connecting Sepracor to well over twenty (Roijakkers and Hagedoorn 2006, p. 439). As another example, in the global semiconductor manufacturing industry during 1990-1996, both Matsushita Electric Industrial Co. Ltd. and Motorola Inc. had one direct partner, but Matsushita had two indirect Learning horizon and optimal alliance formation 213 they may also differ in the extent to which they are aware of (e.g., Lhuillery and Pfister 2011) and benefit from (e.g., Boyd and Spekman 2008;Ghosh and Rosenkopf 2014) such indirect partners. Motivated by these observations, and following a call to begin to consider the role of indirect partners in optimal alliance portfolio design (Lavie 2006, p. 651), we complement the study of optimal alliance formation under technological uncertainty with a systematic theory of how learning from indirect partners shapes a firm's optimal alliance portfolio size.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Learning horizon and optimal alliance formation

Frankort

Hagedoorn

Letterie

2015

Comput Math Organ Theory

View full text Add to dashboard Cite

We develop a theoretical Bayesian learning model to examine how a firm's learning horizon, defined as the maximum distance in a network of alliances across which the firm learns from other firms, conditions its optimal number of direct alliance partners under technological uncertainty. We compare theoretical optima for a 'close' learning horizon, where a firm learns only from direct alliance partners, and a 'distant' learning horizon, where a firm learns both from direct and indirect alliance partners. Our theory implies that in high tech industries, a distant learning horizon allows a firm to substitute indirect for direct partners, while in low tech industries indirect partners complement direct partners. Moreover, in high tech industries, optimal alliance formation is less sensitive to changes in structural model parameters when a firm's learning horizon is distant rather than close. Our contribution lies in offering a formal theory of the role of indirect partners in optimal alliance portfolio design that generates normative propositions amenable to future empirical refutation.

show abstract

“…A heavier load in terms of a more collaborative and complex mode of entry with a more integrated joint workflow is expected to result in greater cultural friction. The surface exposed to cultural friction reflects the number, type, and connectedness of the parties involved in the interaction and the distribution of knowledge among essential decision makers (Ghosh & Rosenkopf, 2014). A larger surface is expected to produce more cultural friction than a smaller surface.…”

Section: Theory and Hypothesesmentioning

confidence: 99%

Language friction and partner selection in cross-border R&D alliance formation

Joshi

Lahiri

2014

J Int Bus Stud

View full text Add to dashboard Cite

How does language friction affect alliance formation? Language friction is a form of cultural friction arising from structural differences in the respective languages used by potential partners to reason and solve problems together. A little language friction may prompt partners to rethink solutions, thereby enhancing collaboration, but excessive friction may impede collaboration. We develop a Language Friction Index (LFI) to quantify relative differences in linguistic structure for any language pair. Utilizing a unique data set of semiconductor design activities (1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001), our empirical analysis finds an inverted U-shaped relationship between partners' LFI and the likelihood of cross-border research and development (R&D) alliance formation. This relationship is further moderated by prior ties and technological distance. Our findings have several important implications, including: (1) language differences are a measurable and discernible source of cultural friction; (2) the effects of language friction are economically significant and strategically consequential; (3) certain aspects of language friction occur independent of language proficiency and persist despite the use of lingua franca to reduce language barriers; (4) linguistic diversity is an indirect marker of cognitive diversity, which is useful in boosting creativity, especially in first-time collaborations; (5) beyond R&D alliances, language friction may also influence other types of strategic interactions and organizational processes.

show abstract

PERSPECTIVE—Shrouded in Structure: Challenges and Opportunities for a Friction-Based View of Network Research

Cited by 55 publications

References 80 publications

Pipes, pools, and filters: How collaboration networks affect innovative performance

Pipes, pools, and filters: How collaboration networks affect innovative performance

Learning horizon and optimal alliance formation

Language friction and partner selection in cross-border R&D alliance formation

Contact Info

Product

Resources

About