Power, status, and learning in organizations

This paper reviews the scholarly literature on the effects of social hierarchy—differences in power and status among organizational actors—on collective learning in organizations and groups. We begin with the observation that theories of organization and group learning have tended to adopt a rational system model, a model that emphasizes goal-directed and cooperative interactions between and among actors who may differ in knowledge and expertise but are undifferentiated with respect to power and status. Our review of the theoretical and empirical literatures on power, status, and learning suggests that social hierarchy can complicate a rational system model of collective learning by disrupting three critical learning-related processes: anchoring on shared goals, risk taking and experimentation, and knowledge sharing. We also find evidence to suggest that the stifling effects of power and status differences on collective learning can be mitigated when advantaged actors are collectively oriented. Indeed, our review suggests that higher-ranking actors who use their power and status in more “socialized” ways can play critical roles in stimulating collective learning behavior. We conclude by articulating several promising directions for future research that were suggested by our review

Forgotten Third Parties: Analyzing the Contingent Association Between Unshared Third Parties, Knowledge Overlap, and Knowledge Transfer Relationships with Outsiders

Third parties play a prominent role in network-based explanations for successful knowledge transfer. Third parties can be either shared or unshared. Shared third parties signal insider status and have a predictable positive effect on knowledge transfer. Unshared third parties, however, signal outsider status and are believed to undermine knowledge transfer. Surprisingly, unshared third parties have been ignored in empirical analysis, and so we do not know if or how much unshared third parties contribute to the process. Using knowledge transfer data from an online technical forum, we illustrate how unshared third parties affect the rate at which individuals initiate and sustain knowledge transfer relationships. Empirical results indicate that unshared third parties undermine knowledge sharing, and they also indicate that the magnitude of the negative unshared-third-party effect declines the more unshared third parties overlap in what they know. Our results provide a more complete view of how third parties contribute to knowledge sharing. The results also advance our understanding of network-based dynamics defined more broadly. By documenting how knowledge overlap among unshared third parties moderates their negative influence, our results show when the benefits provided by third parties and by bridges (i.e., relationships with outsiders) will be opposed versus when both can be enjoyed

Knowledge Utilization, Coordination, and Team Performance

Considerable research has established the superior performance of teams on which team members utilize specialized knowledge and also develop transactive processes that promote coordination. Less is known, however, about the consequences for team performance when team members only possess one of the two productivity factors. We develop and test a framework highlighting the distinct challenges these teams will face. In particular, our results show that each productivity factor contributed significantly more to team performance when the other factor was present. And our findings also illustrate a potential failure mode for knowledge utilization. If team members could not coordinate their collective efforts, utilizing knowledge undermined team performance. Our framework outlines a similar risk for too much coordination, if team members cannot utilize their specialized knowledge and are asked to perform a task with a “rugged” performance landscape. We discuss the implications of our framework and results for theory and practice

Communities, Knowledge Creation, and Information Diffusion

In this paper, we examine how patterns of scientific collaboration contribute to knowledge creation. Recent studies have shown that scientists can benefit from their position within collaborative networks by being able to receive more information of better quality in a timely fashion, and by presiding over communication between collaborators. Here we focus on the tendency of scientists to cluster into tightly-knit communities, and discuss the implications of this tendency for scientific performance. We begin by reviewing a new method for finding communities, and we then assess its benefits in terms of computation time and accuracy. While communities often serve as a taxonomic scheme to map knowledge domains, they also affect how successfully scientists engage in the creation of new knowledge. By drawing on the longstanding debate on the relative benefits of social cohesion and brokerage, we discuss the conditions that facilitate collaborations among scientists within or across communities. We show that successful scientific production occurs within communities when scientists have cohesive collaborations with others from the same knowledge domain, and across communities when scientists intermediate among otherwise disconnected collaborators from different knowledge domains. We also discuss the implications of communities for information diffusion, and show how traditional epidemiological approaches need to be refined to take knowledge heterogeneity into account and preserve the system's ability to promote creative processes of novel recombinations of idea

Social Cohesion, Structural Holes, and a Tale of Two Measures

EMBARGOED - author can archive pre-print or post-print on any open access repository after 12 months from publication. Publication date is May 2013 so embargoed until May 2014.This is an author’s accepted manuscript (deposited at arXiv arXiv:1211.0719v2 [physics.soc-ph] ), which was subsequently published in Journal of Statistical Physics May 2013, Volume 151, Issue 3-4, pp 745-764. The final publication is available at link.springer.com http://link.springer.com/article/10.1007/s10955-013-0722-

From core to periphery and back: A study on the deliberate shaping of knowledge flows in interfirm dyads and networks

We study 892 Italian motorcycle industry projects carried out via 184 different buyer–supplier and supplier-supplier relationships to provide evidence on the knowledge dynamics occurring in dyads and networks and to understand the underexplored but important (perhaps even dominant) leading role that some firms play in the evolution of networks and interfirm learning processes. We develop a multiphase model which, from a multilevel perspective addressing different relational subsets, suggests how firms can best organize to generate and exchange knowledge efficiently. We argue that extant theoretical perspectives can profitably draw on our findings to strengthen their dynamic components and help them explain the widely diffused ‘exploring through partner’ strategies more effectivel

Commitment, Learning, and Alliance Performance: A Formal Analysis Using an Agent-Based Network Formation Model

Current theoretical arguments highlight a dilemma faced by actors who either adopt a weak or strong commitment strategy for managing their alliances and partnerships. Actors who pursue a weak commitment strategy|i.e. immediately abandon current partners when a more pro table alternative is presented|are more likely to identify the most rewarding alliances. On the other hand, actors who enact a strong commitment approach are more likely to take advantage of whatever opportunities can be found in existing partnerships. Using agent-based modeling, we show that actors who adopt a moderate commitment strategy overcome this dilemma and outperform actors who adopt either weak or strong commitment approaches. We also show that avoiding this dilemma rests on experiencing a related tradeo : moderately-committed actors sacri ce short-term performance for the superior knowledge and information that allows them to eventually do better

an agent-based model of optimal exploitation

Using an agent-based simulation, we illustrate how goal-seeking behavior affects network formation, learning, and performance. Our organization has one manager, who decides where to invest financial capital; individual workers, who decide where to work and prefer projects with larger budgets; and projects, which vary in quality. Our manager discovers high-quality projects from interactions with workers and allocates more capital to high-quality projects. When given an opportunity, our workers move to bigger-budget projects. We let our manager vary in terms of how much she exploits what she learns and allow our workers vary in terms of how sensitive they are to differences in capital. Our results highlight a contingency which shapes how goal-seeking behavior affects learning. The contingency is network fragility. Fragile connections decay quickly when individuals are not working together, while robust relationships decay more slowly. When relationships are robust, exploitation by our manager leads to a dense organizational network, improving information quality, and performance. Decisions by self-interested individuals (our manager and our workers) produce a virtuous learning cycle. When relationships are fragile, exploitation by our manager produces a sparse network, reducing information quality, and undermining performance. When network connections are fragile, the manager must find the right balance of exploitation and exploration, a balance which limits the rate at which workers move from one project to the next, allowing the manager to exploit some of what she knows, without undermining the very network which allows for useful information to be obtained.publishersversionpublishe

Community structure and patterns of scientific collaboration in Business and Management

This is the author's accepted version of this article deposited at arXiv (arXiv:1006.1788v2 [physics.soc-ph]) and subsequently published in Scientometrics October 2011, Volume 89, Issue 1, pp 381-396. The final publication is available at link.springer.com http://link.springer.com/article/10.1007%2Fs11192-011-0439-1Author's note: 17 pages. To appear in special edition of Scientometrics. Abstract on arXiv meta-data a shorter version of abstract on actual paper (both in journal and arXiv full pape

From Lurker to Active Participant

The original publication is available from www.springerlink.com. Sloep, P. B., & Kester, L. (2009). From Lurker to Active Participant. In R. Koper (Ed)., Learning Network Services for Professional Development (pp. 17-26). Berlin, Germany: Springer Verlag.In this chapter we will specifically go into the question of how prospective Learning Network users may be convinced of these benefits, for that is likely to be the necessary condition for their active participation in any Learning Network. Their question would be ‘Why should I participate?’, this chapter inventories an-swers to that question, which are then translated into a few guidelines for those contemplating to set up a particular, topic-bound Learning Network. Two kinds of answer are distinguished. Proximate answers, which affect the decision to partici-pate here and now; and ultimate answers, which motivate participation, but only in the long run, after the decision to participate has already been taken. Both are im-portant, the former to persuade people to participate, the latter to persuade people to keep participating. Before going into them, we’ll introduce a concrete example to add some realism to the discussion.The work on this publication has been sponsored in part by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org