Filip Agneessens, VU University, Amsterdam “The importance of advice and trust on individual performance in teams: the effect of individual position in a network or group structure?” Friday, April 16th, 2010.

Both the way in which trust and advice relations influence the performance of employees in organizations, and the level at which these networks might have an impact on individual performance have been a major topic of discussion. This paper simultaneously investigates the impact of (1) trust as an independent effect next to advice, (2) trust as an underlying source for the emergence of advice, as well as (3) the importance of multiplex advice-trust ties for performance.  Moreover a multilevel analysis is used to simultaneously study the position of members in a team, and the impact of the structural characteristics of the team as a whole for individual performance. The results at the individual level show that the impact of frequent advice giving on performance is only partially mediated by the level to which the person is considered more trustworthy, while the effect of advice relations embedded in a trust-relation (i.e. advice-trust multiplexity) is not important. However, at the team level results show not only that a centralized advice structure (one, or a few persons being asked a lot for advice) and a decentralized trust network (members being more similar in the level they trust others) both have a positive effect on performance, but also that having more advice relations which are combined with trust in the team as a whole, have an impact on the performance of all its members.

Filip Agneessens’ research centers on network formation within organizations and their impact on attitudes and behavior of individual employees, with a particular focus on how networks might impact job satisfaction and performance. He has also been working on the development of random and biased networks, the building of social support typologies and how they might impact an individual’s wellbeing. In recent work he has also adapted exponential random graph models (p*models) to study cultural participation and (co) sponsorship among senators as two-mode networks. He teaches courses on network theory, social network methods and social network analysis applied to organizations.