Shared language in the team network-performance association: Reconciling conflicting views of the network centralization effect on team performance

We reconcile two conflicting views of the network centralization effect on team performance. In one view, a centralized network is problematic because it limits knowledge transfer, making it harder for team members to discover productive combinations of their know-how and expertise. In the alternative view, the limits on knowledge transfer encourage search and experimentation, leading to the discovery of more valuable ideas. We maintain the two sides are not opposed but reflect two distinct ways centralization can affect a team’s shared problem-solving framework. The shared framework in our research is a shared language. We contend that team network centralization affects both how quickly a shared language emerges and the performance implications of the shared language that develops. We analyze the performance of 77 teams working to identify abstract symbols for 15 trials. Teams work under network conditions that vary with respect to centralization. Results indicate that centralized teams take longer to develop a shared language, but centralized teams also create a shared language that is more beneficial for performance. The findings also indicate that the highest performing teams are assigned to networks that combine elements of a centralized and a decentralized network

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

Close encounters: Analyzing how social similarity and propinquity contribute to strong network connections.

Models of network formation emphasize the importance of social similarity and propinquity in producing strong interpersonal connections. The positive effect each factor can have on tie strength has been documented across a number of studies, and yet we know surprisingly very little about how the two factors combine to produce strong ties. Being in close proximity could either amplify or dampen the positive effect that social similarity can have on tie strength. Data on tie strength among teachers working in five public schools were analyzed to shed light on this theoretical question. The empirical results indicate that teachers who were similar in age were more likely to be connected by a strong tie, especially teachers for whom age similarity was more likely to be salient. Moreover, teachers who took breaks at the same time or who had classrooms on the same floor communicated more frequently and felt more emotionally attached. Among the public school teachers, propinquity amplified the positive effect that age similarity had on tie strength. The strongest network connections occurred among age-similar teachers who had classrooms on the same floor. The empirical results illustrate the value of considering how social similarity and propinquity contribute to strong ties independently and when combined with each other

Measuring personal networks and their relationship with scientific production

The analysis of social networks has remained a crucial and yet understudied aspect of the efforts to measure Triple Helix linkages. The Triple Helix model aims to explain, among other aspects of knowledge-based societies, ¿the current research system in its social context. This paper develops a novel approach to study the research system from the perspective of the individual, through the analysis of the relationships among researchers, and between them and other social actors. We develop a new set of techniques and show how they can be applied to the study of a specific case (a group of academics within a university department). We analyse their informal social networks and show how a relationship exists between the characteristics of an individual¿s network of social links and his or her research output

Team talk: Learning, jargon, and structure versus the pulse of the network

We began this work intending to illustrate the network origins of jargon, a signal feature of team learning and the division of labor. In the process, we came to recognize the substantive importance of message timing, which we discuss as the pulse of a network. This paper describes our route to that recognition. We analyze data from a renovated classic network experiment providing empirical support for three hypotheses. The first, and most familiar from past work, is that teams moving down their learning curve to greater efficiency are prone to shared jargon. As a team moves down its learning curve, language drifts away from day-to-day speech, into jargon. The second and third hypotheses concern network correlates of the drift. With respect to network structure, teams are less likely to converge on jargon when communication is concentrated in one teammate. With respect to pulse, teams are more likely to converge on jargon when communication efforts are numerous and crowded in time. The two network predictors overlap conceptually. They both involve learning and access to information, but are distinct in their mechanism: Structure provides access. Pulse creates motivation to access. Teammates keeping up with numerous messages concentrated in time have a shared incentive to find shorthand terms (i.e., jargon) that enable faster exchange of accurate information. Network structure predicts team convergence on jargon, but pulse is a stronger predictor. Directions for new research are discussed

Economic Downturns, Technology Trajectories and the Careers of Scientists

Atlanta Conference on Science and Innovation Policy 2011This paper studies the effect of the burst of the telecom bubble on the trajectory of an emerging technology, and the careers of scientists in that industry. We focus on optoelectronics, a general purpose technology (e.g. Helpman 1998) with applications in energy, biomedical, telecommunications, computing and military. Leveraging USPTO patents, we analyze the relationship between an inventor’s pre-bubble characteristics and his productivity post-burst and thereby the national trend. Past research in technology innovation has used publicly available patent data to measure productivity (Kapoor and Lim, 2007, Fleming 2007), inventor mobility (Song et al, 2003, Rosenkopf and Correidora, 2009, Marx et al 2009), and transfer of knowledge (Almeida and Kogut, 1999, Song et al, 2003, Rosenkopf and Correidora, 2009). In this research we use patent data to estimate inventors’ pre- and post-bubble burst knowledge capital (here, number of patents), pre- and post-bubble burst mobility (i.e. number of assignees), pre- and post-bubble burst technology focus (based on patent classes), and pre-bubble burst career length patenting in optoelectronics (here time from first to last optoelectronic patent, allowing for a period of inactivity based on gaps in one’s patent applications). With respect to technology focus, the dimension on which we differentiate inventors is whether they have pre-bubble patents in “integration” – an emerging optoelectronics technology that facilitates optoelectronics’ application to non-telecommunications applications. We use logit and OLS models to explore potentially interesting relationships between inventors’ pre-burst characteristics (knowledge capital in general or non-integration OE and knowledge capital in integration, career length, and their interactions) and (1) continuing to patent in optoelectronics and (2) productivity post-burst. We estimate simple slopes (Aiken, 1991) using the margins command in Stata 11. Our model finds that on average higher pre-burst knowledge capital and career length predict higher probabilities of continuing to patent in the field; however, the probability that an inventor will continue to patent if he has high pre-burst knowledge capital in integration is higher than if he has pre-burst knowledge capital in general OE. In addition, we find that moves by pre-burst integration inventors who patent in integration post-burst are associated with higher productivity while moves by pre-burst general optoelectronics inventors (without patents in integration) who patent in optoelectronics post-burst are associated with lower productivity. This differentiated role for inventors who have ever patented in integration supports past work suggesting that new technologies can experience disproportionate advancement during economic downturns, and highlights the need for further research into the relationship between sector specific downturns and the diversification of technologies into new market applications.NSF, Schlumberger Foundation, OSA, OIDA, SPI