Conference Agenda

In this study we ask how individuals search for experts at networking events. Building on the intuition that individuals’ propensities to engage in certain search actions, as well as their effectiveness in locating experts, will depend on the quality and salience of the metaknowledge they have about others, we conducted an expert search game as a field experiment in which we randomly assigned participants – researchers in a multinational corporation – to one of three treatment conditions, reflecting varying degrees of search planning. Based on data from sociometric badges, we derive a taxonomy of the micro-decisions individuals make at events. We find that letting others approach yields more referrals than taking the initiative in starting conversations, and that planning increases the tendency to maintain such initiative even when doing so is ineffective – a possible manifestation of the Einstellung effect.

To further bolster our two main results, we plan to run two additional experiments using sociometric badges. The first will focus on the effect of reciprocity on referral yield, and the second on how the Einstellung effect may distort rational search efforts.

Recruitment and mobility are generally seen as individual phenomena, driven by individual factors, especially human capital. In the classical labor market framework, the exit option reveals the individual market value of a worker. What if labor mobility is not only individual, but could have a collective dimension? Indeed, this is what happens when a firm poaches a pre-formed team from another firm. In such a case, the output of the whole counts more than the sum of the human capital of the individuals. Therefore, team movements challenge the classical individualistic approach to the labor market.

Previous work (Lazega, 2001, Groysberg, 2010, Godechot, 2017) has shown that team moves seem to be crucial for the functioning of the labor market in a number of upper-class occupations, such as law firms and finance. However, beyond this seminal research on team moves, we don’t know much about their magnitude and underlying mechanisms. When and where do we find such team moves? Who are the leaders? How are they connected to their followers? How profitable are these moves?

Previous qualitative research in finance suggests that such moves are more likely to occur in immaterial industries, where firm boundaries are not well guarded and immaterial assets are easier to move. The leadership of such teams is likely to depend on a combination of formal hierarchy, seniority, and proximity to team members. Long experience of working together and homophily may be at the core of these close-knit teams. The moves should be profitable not only for the leader who organizes them, but also for other team members.

To test preliminary hypotheses emerging from field observations, we rely on interviews and three data sets: UK financial market based on the register of Financial Service Authorities (n=300,000); Linked-in profiles of lawyers in the top 300 French law firms (n=30,000); and the Paris region labor market for managers and professionals based on the exhaustive French social security wage dataset (DADS n=17,000,000 worker*year observations).

The data also allow us to test the effects of team moves. Using staggered diff-in-diff and first-difference methods, we show that team moves lead to significant wage increases. Team movers enjoy a 10-12% wage premium over stayers and a 3-5% wage premium over solo movers. The team move premium is even larger in the financial sector, with a 20% wage increase relative to stayers and a 10% premium relative to solo movers. Thus, team moves play a role in the reconfiguration of capitalism by allowing groups of (highly skilled) workers to use their collective power to undermine the boundaries of the firm and appropriate some of the capital for their own interests.

The question of how innovation diffuses across networks has been a driving force in research on organizational networks for decades, with much research being done on the interorganizational level and through assessing the role of specific network structures. Yet, multiple facets of this process have remained largely unexplored: (i) the cadence and frequency at which innovation diffuses, (ii) the role of culture, or (iii) how innovation may overcome regional boundaries. Using a unique dataset of time-stamped data that depicts lyrical references between rap songs, we tackle these research gaps. More precisely, we interpret genre adoption in music as evidence of innovation diffusion and model this process via a bipartite DyNAM that spans the 100 most popular rap songs each year across two decades.

In so doing, our study contributes to research on innovation diffusion in multiple ways: First, we explore how the success of innovations – i.e., commercial success as indicated by billboard charts – influences the speed at which innovation diffuses. This is particularly relevant because music is a fast-moving space with potentially infinite competing products. Second, we highlight the role of cultural proximity in innovation diffusion, which has so far been virtually missing from extant literature. And third, given that many musical genres start – and often stay - in geographical pockets, we leverage our dataset to investigate the role of geographical proximity in innovation diffusion and how an innovation’s success moderates the likelihood of said innovation becoming a supra-regional phenomenon.

Many forms of economic exchange don't take place in 'pure' markets, but instead are socially embedded into communities where they are guided by social logics of exchange in addition to economic ones. In this presentation, we investigate the degree to which the reliance on such social or community logics increases in times of crisis. In crisis, social embedding can be argued to serve both as a mechanism for uncertainty reduction as well as a selection mechanism when previous levels of transactional activity cannot be maintained. Our empirical case consists of a large scale mutual currency system in Italy with a dataset of more than 2 million transactions that have occurred before, during, and after the covid-19 pandemic. Based on this dataset, we use relational event models to study the degree to which exchange structure varies in line with social and community-based logics of exchange between periods of normality and periods of crisis.

Purpose. We assessed the relationship between social support networks in primary care clinics and clinician and staff job outcomes (i.e., job satisfaction, burnout, and turnover intention).

Methods. We conducted a social network survey (2021-2022) in 23 primary care clinics in two U.S. states—New York (14) and Pennsylvania (9). All clinicians (e.g., physicians and nurse practitioners) and staff (e.g., nurses, social workers, administrators) in each clinic received an online survey, in which the respondents identified their team members and reported who they seek out for work-related support. They also answered questions about job outcomes.

In total, 626 respondents completed the survey with a response rate of 52% (range: 21-82%). Social network analyses (SNA) evaluated respondent-level degree centrality and betweenness centrality, and clinic-level attributes, such as density. Regression models examined the relationships between support network SNA metrics and job outcomes.

Results. Respondents from clinics with denser support networks, indicating higher levels of interaction, reported lower burnout (B=-0.12, p-value=0.028) and had lower odds of turnover intention (OR=0.53, p-value=0.004). Respondents with greater betweenness, indicating they more often served as a bridge between colleagues, were more likely to report greater job satisfaction (cumulative OR=1.09, p-value=0.09) and lower burnout (B=-0.06, p-value=0.07) with marginal significance.

Contribution. The U.S. is facing challenges to primary care delivery and workforce shortages driven by burnout, job dissatisfaction, and turnover. Our findings indicate that when team members are connected and can seek out work-related support, they report better job outcomes. Clinic-level interventions to promote teamwork and support sharing are needed.