S. Bogaert, C. Boone, & A. van Witteloostuijn

1. The impact of demographic distance and network ties on individual turnover of professional employees S. Bogaert, C. Boone, & A. van Witteloostuijn QMSS2 Seminar on ‘Networks, Markets and Organizations’ Groningen, 27-29 August 2009

2. Research Question Why do people leave their employing organization?

3. Theoretical framework • Organizational demography (e.g., Pfeffer, ’83) • Demographic diversity increases the hazard of turnover • Similarity-Attraction (e.g., Reagans et al., ’04) • Social identification (e.g., Tsui et al., ’92) • Demography and network ties equivalent? (cf., Lawrence, ’97; Reagans et al., ’04)

4. Theoretical framework What is the joint impact of demographic distance and network ties on the likelihood of turnover op professional employees?

5. Network research • Research exists on • Network ties and turnover (e.g., Feeley & co, ’97, ’00, ’08) • Demography and networks (e.g., Ibarra, ’93; Mehra et al., ’98) • No integrated approach in demographic research on turnover

6. Contribution • Overarching framework on the joint impact of demography and network ties • Insight into how both theories combine / interact • Deeper insight into the complex process of turnover

7. Demography & Turnover • ‘Atypical’ (isolated) people are more inclined to leave than ‘typical’ people (e.g., Popielarz & McPherson, ’95; Tsui et al., ’92; Jackson et al., ’91) • Principle of homophily • Equivalence assumption valid?  3 possible pathways

8. Path 2 Internal Professional Ties & External Professional Ties Demographic Distance Path 1 Turnover Path 3 Model

9. Path 2 Internal Professional Ties & External Professional Ties Demographic Distance Path 1 Turnover Path 3 Model

10. Path 1 • Indirect effect of demographic dissimilarity mediated by network ties • Similarity-Attraction (cf. Byrne, ’69) • Consistent with network research showing that internal ties reduce turnover (cf. Brass et al., ’04; Feeley & co, ’08, ’00, ’97)  H1a. Internal ties mediate relation between demographic distance and turnover.

11. Path 1 • Indirect effect of demographic dissimilarity mediated by network ties • Demographic space (cf. Popielarz & McPherson, ’95)

12. Demographic Space (cf. McPherson, Blau,…) ° Age ° ° ° ° ° ° ° ° ° ° + + ° + + + + + ° ° ° ° ° * * * * * * * * ° ° ° Education

13. Path 1 • Indirect effect of demographic dissimilarity mediated by network ties • Demographic space (cf. Popielarz & McPherson, ’95)  H1b. External ties mediate relation between demographic distance and turnover.

14. Continued Theory Building • Equivalence in professional context? • Career & professional goals • Inter-organizational collaborations • Self-categorization theory & self-identity • Alternative relations

15. Path 2 Internal Professional Ties & External Professional Ties Demographic Distance Path 1 Turnover Path 3 Model

16. Path 2 • Direct effect of demographic dissimilarity & network ties • H2a. Positive relation between demographic distance and turnover • H2b. Negative relation between internal ties and turnover • H2c. Positive relation between external ties and turnover.

17. Path 2 Internal Professional Ties & External Professional Ties Demographic Distance Path 1 Turnover Path 3 Model

18. Path 3 • Moderating effects • Extent to which demographic dissimilarity triggers turnover depends on network ties / opportunity structure • H3a. Positive relation between demographic distance and turnover when # internal ties is low • H3b. Positive relation between demographic distance and turnover when # external ties is high

19. Methodology • Data • Sample: all academics of one faculty of a medium-sized university  134 respondents • Window of observation : 1994-2004 • Monthly observations (n=6475) • Data collection • Personnel lists, CV’s, personnel files • Career information; dates of entry and exit, position, fte • Lists of publication output • Network ties through co-autorship

20. Methodology • Event history analysis • Event: voluntary turnover • Clock: functional tenure • 134 respondents, 35 exits, 6475 spells • Main independent variables • Dissimilarity indices • Network ties

21. Dissimilarity indices • Demographic distance from colleagues in the department • Continuous variables (e.g., age, tenure) : • Mean squared euclidean distance √ [ ∑ (X i – X j )2 / (n-1) ] • Categorical variables (e.g., gender, education) : • Squared proportion 1 – Pi2

22. Dissimilarity indices ~ Example • Group of 3 people • Age Dissimilarity (A) √[(30-35)2 + (30-40)2 /2] • Gender Dissimilarity Person A, B: 1-(2/3)2 Person C: 1- (1/3)2

23. One Dissimilarity index • Four Dissimilarity Indices • Functional Tenure • Age • Nationality • Educational background • Mean Dissimilarity = average of the four standardized dissimilarity scores

24. Network ties • Co-autorship • Ties in one specific year: internal – external • 3-year window of observation: • Weak / new ties • Semi-strong ties • Strong ties

25. Network Ties ~ Example

26. Network Ties ~ Example

27. Network Ties ~ Example

28. Controls • Dummy September • Faculty & Department size • Promotion opportunities • Rate of turnover in department • Mean tenure of departmental members • Individual-level controls: age, gender, position, ..., publication output

29. Findings

30. Baseline model • U-shaped relation between tenure and exit • Dummy September • Lack of promotion opp. • Nationality • Organizational tenure at entry • Publication output

31. Results demographic dissimilarity • Tenure dissimilarity increases turnover • Overall dissimilarity increases turnover

32. Results network ties • Only # strong external network ties increases turnover

33. Results joint effects

34. Results joint effects  No evidence of mediation

35. Results joint effects  Some support for interactions

36. Discussion • Tenure is important • Cohort-differences associated with different perceptions, routines, norms? • Strong external ties are important • Enduring collaborative ties associated with trust & reciprocity  pull-effect? • Signal of person’s visibility & reputation  opportunities? • Internal ties: professional vs. friendship?

37. Discussion • Interactions • Compensate demographic dissimilarity by strong internal network ties • Enhance effect of demographic distance by semi-strong external ties

38. Discussion • No support for equivalence model • Boundaries of pure network-based homophily model • Context? • Need for simultaneous study of attribute and relational influences (cf. Balkundi & Harrison, ’06)

39. Limitations • Single case • Demographic information about external ties • External tie network based on similarity? • Moderating role of HRM policy • Identification based on demograhics vs. organizational membership

40. Questions? Suggestions? Remarks?