Teachers College of Columbia University, United States
Although job satisfaction has received vast attention in management science, little research has examined how a wide spectrum of social network actors impact employee satisfaction outcomes. Drawing from Dynamic Network Theory (DNT), we first hypothesized that network motivation (a higher-order network factor composed of three positive subnetwork ties) and network resistance (composed of three negative subnetwork ties) would positively and negatively predict traditional job satisfaction, as well as satisfaction with one’s network. Using a time-lagged design, we collected data from a national sample of full-time employees (n=310) using an egocentric name-generator which allowed for unique listings of social network actors. Path analysis first confirmed that network motivation and resistance predicted both job satisfaction and network satisfaction, as hypothesized. Regarding social actor effects, as hypothesized, the network motivation and resistance from supervisors had significant effects on both forms of satisfaction. Although coworkers were descriptively rated as providing the most help and the most resistance, neither their motivation or resistance ties had independent effects on satisfaction levels, against predictions. Going beyond past theory, other actors in the wider network produced effects on both types of satisfaction, especially direct reports. Our results add new insights into how network dynamics impact workplace satisfaction.
