2. Networks and job satisfaction 2 Can network ties increase job satisfaction? And if so, how? Affective ties (trust, friendship) Instrumental ties (communication)

3. General Mechanisms 3 Centrality effect Contagion effect

4. 4 Overview hypotheses Popularity/Centrality effects Advice (weak, instrumental tie) - Hyp. 1 Trust (strong, affective tie) - Hyp. 2 Contagion effects Information contagion - Hyp. 3 Affective contagion - Hyp. 4

5. Centrality mechanism 5 Centrality effect Social Capital Makes Us Feel Good social networks serve as a social resource which affects job satisfaction through the provision of social support (Hurlbert, 1991) => Effects of FRIENDSHIP: Baldwin et al. (1997) two ways: 1) important resource for psychosocial support (buffer work problems) 2) important for access to crucial resources (i.c., information)

6. Centrality advice 6 Centrality effect Instrumental support: Communication network important for access to crucial resources (i.e. information) cf. Performance literature (e.g. Sparrowe et al., 2001) Hyp 1: The higher the number of interpersonal advice ties of a focal actor (outdegree centrality), the more likely it is that the job satisfaction of the focal actor will increase over time

7. Centrality personal trust 7 Centrality effect Affective support: Affective (friendship/personal trust) ties: buffer work problems (Baldwin et al., 1997; Morrison, 2004) -Trust facilitates collaboration and exchange of information Hyp 2: The higher the number of interpersonal trust choices received by a focal actor (indegree centrality), the more likely it is that the job satisfaction of the focal actor will increase over time

8. Contagion Mechanisms 8 Contagion effect Ties as transmitters of - information about the job - feelings, moods

9. Contagion advice 9 Contagion effect Contagion based social capital: -Social information process theory(Salancik and Pfeffer, 1977; Festinger, 1954): Evaluation of own situation based on others perception of situation, etc. => Employees vision about their job is based on information from his/her colleagues…

10. Contagion advice 10 Contagion effect Hyp 3: The higher (lower) the mean job satisfaction of those colleagues whom a focal actor asks advice from, the more likely it will be that the job satisfaction of the focal actor will be high (low). Contagion based social capital: -Social information process theory(Salancik and Pfeffer, 1977; Festinger, 1954): Evaluation of own situation based on others perception of situation, etc. => Employees vision about their job is based on information from his/her colleagues…

11. Contagion personal trust 11 Contagion effect Peoples mood is influenced by others they are emotionally connected with -Mood linkage theory: unconscious mimicking => emotional contagion (cf. Cote, 2005)

12. Contagion personal trust 12 Contagion effect Hyp 4: The higher (lower) the mean job satisfaction of those colleagues whom a focal actor trusts, the more likely it will be that the job satisfaction of the focal actor will be high (low). Peoples mood is influenced by others they are emotionally connected with -Mood linkage theory: unconscious mimicking => emotional contagion (cf. Cote, 2005)

13. 13 Data and method DATA 30 teams in 2 knowledge-intensive organizations Teams between 5 and 12 members Job satisfaction Different items: income, job security, autonomy, etc... Background characteristics: Age, gender, hierarchy, size of team,...

14. 14 Data and method Missing Missing data imputed with existing data (from other actors). Missing network data randomly imputed by given density Done multiple times Reports average of different imputations (and have a look at variation)

15. 15 Data and method Method Network centrality Indegree (trust received/advice received) Outdegree (trust in many others/advice in others) Method: Regression and spatial regression Y=b*X + rho*W*Y + e

16. Results ADVICE 16 Centrality effect bs.e.t p Intercept7.570.6611.43 *** hierarch0.680.312.23* sizetcon-0.100.05-2.14* gender-0.180.22-0.83 age0.020.011.35 ind0.020.050.32 outd0.04 0.88 Global Moran's I for regression residuals Moran I statistic standard deviate = 3.1869 - 4.1075, p-value = 0.0007191 – 0.000002 alternative hypothesis: greater sample estimates: Observed Moran's I Expectation Variance 0.146031106 -0.011125034 0.001463894 0.109657376 -0.011566099 0.001446935 0.136249899 -0.011280247 0.001456123

17. bs.e.t p Intercept7.370.6411.58 *** hierarch0.740.292.53 * sizetcon-0.070.05-1.44 gender-0.190.21-0.89 age0.01 1.03 ind0.010.050.24 outd-0.310.14-2.22 °/* Results ADVICE 17 Rho: 0.045722 - 0.052735 LR test value: 7.7238 - 11.708 p-value: 0.0054498 - 0.00062222 Asymptotic standard error: 0.017986 - 0.019132 p-value: 0.0033672 - 0.016856 */*** Contagion effect

18. bs.e.tP Intercept7.8820.62312.654*** hierarch0.6700.2762.428* sizetcon-0.1410.032-4.397*** gender-0.1410.206-0.687 age0.0060.0110.592 ind0.1310.0612.137°/* outd0.1450.0354.199*** Results TRUST 18 Centrality effect Global Moran's I for regression residuals Moran I statistic standard deviate = 0.8797-1.5514, p-value = 0.1895-0.06041 alternative hypothesis: greater sample estimates: Observed Moran's I Expectation Variance 0.061405456 -0.013524389 0.002332866 0.042599368 -0.013784516 0.002385922 0.029185915 -0.013648903 0.002370762

19. Results TRUST 19 bs.e.t p Intercept7.760.6112.69 *** hierarch0.680.272.52 * sizetcon-0.120.03-3.75 ** gender-0.130.2-0.64 age0.01 0.62 ind0.10.061.56 outd-0.190.21-0.9 Rho: 0.041156 - 0.050424 LR test value: 2.7963 - 4.8523 p-value: 0.094482 - 0.027609 Asymptotic standard error: 0.027771 - 0.028047 p-value: 0.14227 – 0.070874 Contagion effect

20. 20 Summary results Mechanism Hyp. Theory Popularity/centrality – Advice Social capital/knowledge transfer – Trust Affective social support Contagion – Advice Social information process theory – Trust Affect contagion/mood

21. 21 Limitations Causality? longitudinal analysis (SIENA) on other datasets do support influence rather than selection (Agneessens and Wittek, 2008) Job satisfaction distinction between intrinsic and extrinsic aspects? (Flap and Volker, 2001; Agneessens and Wittek = longitudinal) More complex network effects? Types of ego-networks?