Short- and long-term stability in organizational networks: Temporal structures of project teams

Quintane, Eric; Pattison, Philippa; Robins, Garry; Mol, Joeri M.

doi:10.1016/j.socnet.2013.07.001

Cited by 66 publications

(62 citation statements)

References 81 publications

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“…Research on "swift trust" (Meyerson, Weick, & Kramer, 1996) and "swift psychological safety" (Dufresne, 2007) suggests that when constructs rapidly emerge, they may be conceptually different from their more gradual counterparts (see also transactive memory systems; Kanawattanachai & Yoo, 2007). If swift cohesion emerges under different conditions or has different effects than "traditional" cohesion, expanding on this concept may enable more reliable indices of cohesion at the early phases of team development (Quintane, Pattison, Robins, & Mol, 2013). Furthermore, empirical evidence suggests that cohesion measured later in the team life cycle, as opposed to during its formation or early phases of development, demonstrates stronger links to improved performance (Bradley, Baur, Banford, & Postlethwaite, 2013;Siebold, 2006).…”

Section: Temporal Issuesmentioning

confidence: 99%

Measuring Team Cohesion

et al. 2015

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Objective: The aim of this study was to review literature relevant to cohesion measurement, explore developing measurement approaches, and provide theoretical and practical recommendations for optimizing cohesion measurement.Background: Cohesion is essential for team effectiveness and performance, leading researchers to focus attention on understanding how to enhance it. However, cohesion is inconsistently defined and measured, making it difficult to compare findings across studies and limiting the ability to advance science and practice.Method: We reviewed empirical research through which we uncovered specific information about cohesion's conceptualization, measurement, and relationships with performance, culminating in a set of current trends from which we provide suggestions and possible solutions to guide future efforts and help the field converge toward greater consistency.Results: Cohesion demonstrates more significant relationships with performance when conceptualized using social and task (but not other) dimensions and when analyses are performed at the team level. Cohesion is inherently temporal, yet researchers rarely measure cohesion at multiple points during the life of a team. Finally, cohesion matters in large, dynamic collectives, complicating measurement. However, innovative and unobtrusive methodologies are being used, which we highlight.Conclusion: Practitioners and researchers are encouraged to define cohesion with task and social subdimensions and to measure with behavioral and attitudinal operationalizations. Individual and team-oriented items are recommended, though teamlevel analyses are most effective. Innovative/unobtrusive methods should be further researched to enable cohesion measurement longitudinally and in large, dynamic collectives.Application: By applying our findings and conclusions, researchers and practitioners will be more likely to find consistent, reliable, and significant cohesion-to-performance relationships.

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Section: Temporal Issuesmentioning

confidence: 99%

Measuring Team Cohesion

et al. 2015

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“…This is a well-known problem for exponential random graph models (Snijders et al, 2006), which also occurs for stochastic actor-based models (Steglich, 2006). To sidestep this issue, the notion of “short-term dynamic equilibrium” is used here (see also Quintane et al, 2011). The short term is operationalized as λ = 20 changes on average by each actor.…”

Section: The Actor-based Model and Global Network Structurementioning

confidence: 99%

Representing Micro–Macro Linkages by Actor-based Dynamic Network Models

Snijders

Steglich

2013

Sociological Methods & Research

107

103

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Stochastic actor-based models for network dynamics have the primary aim of statistical inference about processes of network change, but may be regarded as a kind of agent-based models. Similar to many other agent-based models, they are based on local rules for actor behavior. Different from many other agent-based models, by including elements of generalized linear statistical models they aim to be realistic detailed representations of network dynamics in empirical data sets. Statistical parallels to micro-macro considerations can be found in the estimation of parameters determining local actor behavior from empirical data, and the assessment of goodness of fit from the correspondence with network-level descriptives. This article studies several network-level consequences of dynamic actor-based models applied to represent cross-sectional network data. Two examples illustrate how network-level characteristics can be obtained as emergent features implied by micro-specifications of actor-based models.

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“…It is not yet academically established whether the resulting network graph is actually suitable for interpretations that are informed by 'offline' theories (Howison et al, 2011). At the same time, there is no reason to believe that these relationship types are stable over time or independent of each other (Quintane et al, 2013).…”

Section: Structural Analysis Of Networkmentioning

confidence: 99%

The deep structure of organizational online networking – an actor‐oriented case study

Trier

Richter

2014

Information Systems Journal

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While research on organizational online networking recently increased significantly, most studies adopt quantitative research designs with a focus on the consequences of social network configurations. Very limited attention is paid to comprehensive theoretical conceptions of the complex phenomenon of organizational online networking. We address this gap by adopting a theoretical framework of the deep structure of organizational online networking with a focus on their emerging meaning for the employees. We apply and assess the framework in a qualitative case study of a large-scale implementation of a corporate social network site (SNS) in a global organization. We reveal organizational online networking as a multi-dimensional phenomenon with multiplex relationships that are unbalanced, primarily consist of weak ties and are subject to temporal change. Further, we identify discourse drivers and information retrievers as two mutually interdependent actor roles as an explanation for uneven levels of user contributions to the SNS. Based on our analysis, we elicit abstract order principles, such as topical discourses, and identify transactive memory theory as a potent explanation of the evolving interaction structures. We finally discuss how the deep structure framework can contribute to future research on organizational networks.

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Short- and long-term stability in organizational networks: Temporal structures of project teams

Cited by 66 publications

References 81 publications

Measuring Team Cohesion

Measuring Team Cohesion

Representing Micro–Macro Linkages by Actor-based Dynamic Network Models

The deep structure of organizational online networking – an actor‐oriented case study

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