P10 (00 441 P10)
Analysis of longitudinal personal and small social network analysis
Form of presentation:
Bargaining Power and Network Dynamics
Felix Bader | University of Mannheim | Germany
The last decades have seen many theoretical and experimental contributions on bargaining power in exchange networks. The debate about the empirical fit of the proposed models to explain structural power is still ongoing. The Network Control Bargaining model by Norman Braun and Thomas Gautschi, based on non-cooperative game theory, is one promising candidate. It can be used to predict - for rational individuals - not only profit splits but also network breaks, i.e. frequent non-exchange in available bargaining ties. A network is said to be non-robust if an actor decides, comparing bargaining outcomes in his ties, about the non-usage of a certain tie. He will only break the network if this increases his profits using the remaining relations.
We tested this assumption experimentally by assigning each participant to a random position in a T-Shape, 4-Line, or a novel, more complex network we call V-Box-X-Box. As bargaining and exchange runs over 20 rounds participants have the opportunity to learn to use the power of their specific position. Our longitudinal data allows the analysis of network and behavioral dynamics: In which round, who uses which ties and gets how much of the profit? Timestamps in the data allow to look even further into details of network dynamics: Network members who have fulfilled their desired number of exchanges in one round are thus not available anymore for their remaining bargaining partners. Therefore, we can see the network gradually falling apart over the duration of one round. This changes the restrictions and externalities for the remaining participants for this specific round. Every time an exchange happens, the remaining structure that emerges needs to be reanalyzed.
According to our expectations, the T-Shape and V-Box-X-Box decay (i.e. permanent breaks occur) and the 4-Line turns out to be robust. Rare empirical occurrences of theoretically unexpected exchanges in the V-Box-X-Box can be explained by network dynamics.
New Kids on the Job - Analysing the emerging intra-organizational networks of newcomers and the social capital they provide.
Sabine Matejek | Radboud University | Netherlands
This paper analyses personal networks of organizational newcomers from the perspective of social capital theory. It strives to increase our understanding of the variance of network resources which newcomers (need to) draw from their emerging ties during organizational socialization. While extant research has focused primarily on factors influencing the degree to which newcomers are successfully socialized, this paper asks as its guiding question how a social capital perspective can help us to assess the quality of newcomer socialization.
In order to address this question, the paper’s structure follows three steps: First, the concept of organizational socialization is framed in terms of the challenges entailed for newcomers as they enter a new job environment. Faced with the need to acquire task mastery, social inclusion, and role clarity, newcomers often lack the resources to meet these needs on their own. Rather, they must find their place in resource exchange relations within the organization. This invites a connection to be made between the research literatures on socialization and social network analysis. Drawing on a viable framework of intra-organizational social capital, the paper identifies task-performance support, socio-emotional integration and initial career promotion as valuable resources helping newcomers to tackle the respective challenges they face as “new kids on the job”:
In its second step, the paper therefore reviews extant research on how the particular configuration of personal networks will affect the kinds of resources which newcomers will be able to draw from their emerging ties. Three propositions are derived, specifying which kinds of ties to which kinds of organizational insiders are considered particularly apt as conduits to provide newcomers with access to each type of relevant intra-organizational social capital.
As a third step towards answering its research question, the paper then illustrates how the configurations of individual newcomer networks as well as the network resources actually derived from them can be systematically mapped for research purposes by means of clustered graphs. This visualization approach not only facilitates the comparative analysis of small groups of personal networks with regard to the formulated propositions. It further enables researchers to track developments in the network configurations and derived social capital over time. To investigate in how far changes in either or both are related and how they evolve from a newcomer’s initial onboarding to the point when they achieve insider status is considered highly informative both from a socialization and social capital perspective.
For this paper, an initial data set comprising the first wave of data collected on the personal networks of a panel of 25 newcomers in 9 organizations is visualized as clustered graphs and analysed with regard to the formulated propositions. The paper concludes with an outlook on applying its approach to the remaining panel data, collected in two more consecutive waves covering three years in total. It is delineated how a longitudinal analysis is to help refine the propositions for future research and how its initial findings can inform organizational onboarding practices and HR support during newcomer socialization.
Personal network dynamics in reference to physical activity behaviours
Matthew Sitch | University of Chichester | United Kingdom
To enhance understanding regarding social influence on Physical Activity (PA) by examining personal network structure over time.
Participants (N = 20) were recruited from office based organisations. Personal network data was collected over three time points, one year apart, using a pen and paper method (Hogan, Carrasco and Wellman 2007) to create personal network diagrams. Multiple name generators were utilised and no limit was placed on network size. Personal network diagrams were used to explore network structure and analysed using social network software Visone (Brandes & Wagner, 2004). Numeric measures of network structure examined for change were network mass, size, homophily, weak components, brokerage, average degree, and effective size. A visual analysis was also conducted examining network change in relation to specific alters, such as those rated as “very close” or “very physically active”. Measures were examined case by case over time and then comparisons were drawn between participants to identify trends.
Five personal network types were identified which were characterised with certain network structures. Network homophily tended to change in line with Ego’s change in behaviour suggesting that the network operated to inhibit or promote physical activity. Visual analysis indicated that changes in particular social relationships were indicative of a change in ego’s behaviours.
The ways in which personal networks structures changed were multiple sometimes dramatic. The finding that changes in the network were associated with changes in Ego’s behaviour is insightful in attempting understanding how personal networks operate. However, the method of analysis was relatively novel and exploratory, attempting to examine in-depth personal network data over time presented many challenges which are discussed.