LAY THEORIES OF NETWORKING

Transcription

1 LAY THEORIES OF NETWORKING What we believe about networks and why it matters Ko Kuwabara INSEAD

2 Bill Clinton What makes a good networker? Lay Theory of Social Intelligence Fixed theory Personality Natural talent Charisma Growth theory Effort Practice Experience Networking is Unfair Useless

3 Lay theories of cognitive intelligence You must have worked hard You must be smart

4 Lay theories of cognitive intelligence You must have worked hard You must be smart

5 Lay theories of cognitive intelligence You must have worked hard You must be smart

6 Why lay theories matter Lay theories affect how people view things in terms of Nature vs. nurture Talent vs. effort Fixed traits vs. growth

7 Why lay theories matter Lay theories affect motivation Growth theory effortful learning Fixed theory effortless performance I don t need to study Studying is for stupid kids.

8 Why lay theories matter Lay theories work in implicit ways Hence, counterintuitive and persistent I don t need to study Studying is for stupid kids.

9 Why lay theories matter Lay theories can be changed Unlike personality traits or talent

10 Lay Theories of Networking Beliefs: Fixed vs. growth Attitudes: Utility & morality Consequences: Disengagement Network structure

11 Lay Theories of Social Intelligence Beliefs: Fixed vs. growth Beliefs about social intelligence Fixed belief Personality Charisma Growth belief Skills and tactics Effort Attitudes: Utility & morality Attitudes toward search Networking is Unfair Useless Consequences: Disengagement Network structure Search Size Disengagement from search Smaller networks

12 Lay Theories of Networking Beliefs: Fixed vs. growth Beliefs about social intelligence Beliefs about social relations Beliefs about social capital Attitudes: Utility & morality Attitudes toward search Attitudes toward maintenance Attitudes toward leverage Consequences: Disengagement Network structure Search Size Maintenance Diversity Leverage Cohesiveness

13 Why this is novel Networking is typically explained in terms of Human capital (skills, resources, personality traits) (Casciaro 1998) Rational pursuit of opportunities (Jackson 2008) Accurate perception of networks (Krackhardt & Kilduff 1999) Missing from these views Beliefs: What people think and feel about networking Motivation: Why some people try harder than others

14 Networking as a motivational problem Most people know networking is important. They already know how. Yet they still resent it.

15 Paper 1: Lay theories of social intelligence Goal 1: Scale construction & validation Goal 2: Predicting engagement in networking Goal 3: Manipulating lay theories

16 Paper 1: Lay theories of social intelligence Goal 1: Scale construction & validation Goal 2: Predicting engagement in networking Goal 3: Manipulating lay theories

17 LaySI Networking Scale Higher score Stronger fixed theory Distinct from Big 5 personality traits and selfmonitoring Reliable: alpha= across 10 studies

18 Paper 1: Lay theories of social intelligence Goal 1: Scale construction & validation Goal 2: Predicting engagement in networking Goal 3: Manipulating lay theories

19 Design Participants: 131 MBAs enrolled in a core course Short survey at the beginning of semester LaySI Self-ratings of social intelligence Demographics: age, gender, country of origin, Big 5 personality 6 weeks later, self-reports on No. of networking events attended

20 Model 1 Model 2 1. LANA (fixed theory) -.46* -.50* (.22) (.22) 2. Networking ability.01 (.01) 4. Female (.39) (.38) 5. Age (.13) (.12) 6. Big 5: Extraversion (.01) (.02) 7. Big 5: Agreeableness (.02) (.02) 8. Big 5: Conscientiousness (.01) (.01) 9. Big 5: Neuroticism (.01) (.01) 10. Big 5: Openness (.01) (.01) 11. Managerial skills (.01) (.01) 12. Intelligence (.01) (.01) 13. Physical attractiveness.02*.02* (.01) (.01) 14. No. of friends (.01) (.01) 15. No. of club memberships.09*.08* (.04) (.04)

21 Paper 1: Lay theories of social intelligence Goal 1: Scale construction & validation Goal 2: Predicting engagement in networking Goal 3: Manipulating lay theories

22 Design 4 weeks later Lay theory manipulation No. networking events

23 Priming growth theories

24 Priming fixed theories

25 Model 1 Model 2 LANA (fixed theory) -.45* -.46* (.21) (.21) Club membership.38**.39** (.04) (.04) Male (.23) (.24) White -.46* -.42* (.19) (.20) Big 5: Extraversion (.06) (.07) Big 5: Agreeableness (.08) (.08) Big 5: Conscientiousness (.07) (.07) Big 5: Neuroticism (.07) (.07) Big 5: Openness (.08) (.08) Self-monitoring (.16) (.17) Networking skill -.00 (.01) Analytical skill -.00 (.01) Presentation skill.00 (.01) Has summer internship.05 (.21)

26 Paper 1: Lay theories of social intelligence Goal 1: Scale construction & validation Goal 2: Predicting engagement in networking Goal 3: Manipulating lay theories

27 Design IV: Manipulation & Manipulation check Networking evening Event 1: EMBAs Event 2: EMBAs and MBAs

28 Design IV: Manipulation & Manipulation check Networking evening Event 1: EMBAs Event 2: EMBAs and MBAs DV s Engagement scale No. of people met

29 Results Engagement: Did you find networking at the event enjoyable, meaningful, valuable?

30 Summary Lay theories of social intelligence help explain Why people feel so conflicted or ambivalent about networking Lay theories can be Measured Manipulated Lay theories have Immediate and long-term consequences Above and beyond other individual differences

31 Lay Theories of Social Relations Beliefs about social relations Fixed belief Instant chemistry Natural compatibility Attitudes toward maintenance Maintenance is Useless Inauthentic Maintenance Diversity Disengagement from maintenance Greater homophily Lower multiplexity

32 Paper 2: Lay theories of social relations Goal 1: Scale construction & validation Goal 2: Predicting homophily Goal 3: Predicting multiplexity

33 Lay Theory of Social Relations Scale

34 Paper 2: Lay theories of social relations Goal 1: Scale construction & validation Goal 2: Predicting homophily Goal 3: Predicting multiplexity

35 Design Lay theories scale Demographics Name generator

36 Name generator List up to 10 (min 3) people you normally go to for work-related advice, information, resources, or support. Demographics: race, age, gender, education, marital status, political stance How similar are they to you in Hobbies or interests Political or moral values Personality Current lifestyle Upbringing Sociometrics How long have you known them? How often do you go to them? Do they go to each other?

37 Results Fixed theory predicts cultural homophily Hobbies or interests (p=.12) Political or moral values (p=.01) Personality (p=.03) Lifestyle (p=.08) Upbringing (p.=17) Aggregate similarity, p=.02 No support for demographic homophily In race, age, gender, education level, marital status, political stance

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39 Paper 2: Lay theories of social relations Goal 1: Scale construction & validation Goal 2: Predicting homophily Goal 3: Predicting multiplexity

40 Design Sample: 114 undergraduates and 77 MBAs Lay theories scale Partner selection 6-8 weeks later How similar are they to you?

41 Results People with fixed theories were more likely show: Cultural homophily But not demographic homophily

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43 Paper 2: Lay theories of social relations Goal 1: Scale construction & validation Goal 2: Predicting homophily Goal 3: Predicting multiplexity

44 Predicting Multiplexity Sample: 167 mturk participants and 146 MBAs DV: Multiplexity Attitudes: How do you feel about spending time with professional contacts outside of work hours? Instrumental: useless/unnecessary/like a chore Moral: awkward/disingenuous/unnatural Avoidance: To what extent do you try to avoid professional contacts outside of work hours Face-to-face (offline) Facebook (online)

45 Results People with fixed theories were more likely to: Report more negative instrumental and moral attitudes Prefer avoiding multiplex ties both offline and online

46 Implications Lay theories of social relations predict Homophily Multiplexity Lay theories explain Individual differences in homophily and multiplexity

47 Conclusion Beliefs matter Often in surprising ways In ways that directly affect how we form networks More work is needed We know quite a bit about academic theories of networks, but not much about lay theories of networks

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