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Driven by views of teams as dynamic systems with permeable boundaries, scholars are increasingly seeking to better understand how team membership changes (i.e., team members joining and/or leaving) shape the functioning and performance of organizational teams. However, empirical studies of team membership change appear to be progressing in three largely independent directions as researchers consider: (a) how newcomers impact and are impacted by the teams they join; (b) how teams adapt to member departures; or (c) how teams function under conditions of high membership fluidity, with little theoretical integration or consensus across these three areas. To accelerate an integrative stream of research on team membership change, we advance a conceptual framework which depicts each team membership change as a discrete team-level “event” which shapes team functioning to the extent to which it is “novel,” “disruptive,” and “critical” for the team. We use this framework to guide our review and synthesis of empirical studies of team membership change published over the past 20 years. Our review reveals numerous factors, across conceptual levels of the organization, that determine the strength (i.e., novelty, disruptiveness, criticality) of a team membership change event and, consequently, its impact on team functioning and performance. In closing, we provide propositions for future research that integrate a multilevel, event-based perspective of team membership change and demonstrate how team membership change events may impact organizational systems over time and across levels of observation.
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The Illusion of Change: Correcting for Biases in Change Inference for Sparse, Societal-Scale Data
Societal-scale data is playing an increasingly prominent role in social science research; examples from research on geopolitical events include questions on how emergency events impact the diffusion of information or how new policies change patterns of social interaction. Such research often draws critical inferences from observing how an exogenous event changes meaningful metrics like network degree or network entropy. However, as we show in this work, standard estimation methodologies make systematically incorrect inferences when the event also changes the sparsity of the data. To address this issue, we provide a general framework for inferring changes in social metrics when dealing with non-stationary sparsity. We propose a plug-in correction that can be applied to any estimator, including several recently proposed procedures. Using both simulated and real data, we demonstrate that the correction significantly improves the accuracy of the estimated change under a variety of plausible data generating processes. In particular, using a large dataset of calls from Afghanistan, we show that whereas traditional methods substantially overestimate the impact of a violent event on social diversity, the plug-in correction reveals the true response to be much more modest.
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- Award ID(s):
- 1637360
- PAR ID:
- 10104824
- Date Published:
- Journal Name:
- The Illusion of Change: Correcting for Biases in Change Inference for Sparse, Societal-Scale Data
- Page Range / eLocation ID:
- 2608 to 2615
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3308558.3313722
