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CREATIVE ENVIRONMENT AND YOUNG RESEARCHERS´ PERFORMANCE: THE KEYS TO PHD STUDENTS` SUCCESS

CREATIVE ENVIRONMENT AND YOUNG RESEARCHERS´ PERFORMANCE: THE KEYS TO PHD STUDENTS` SUCCESS. Anuška Ferligoj Hajdeja Iglič , Franc Mali, Uroš Matelič, Petra Zihe rl, Tina Kogovšek, Valentina Hlebec University of Ljubljana, VALICON d.d., Bank of Slovenia. OUTLINE.

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CREATIVE ENVIRONMENT AND YOUNG RESEARCHERS´ PERFORMANCE: THE KEYS TO PHD STUDENTS` SUCCESS

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  1. CREATIVE ENVIRONMENT AND YOUNG RESEARCHERS´ PERFORMANCE:THE KEYS TO PHD STUDENTS` SUCCESS Anuška Ferligoj Hajdeja Iglič, Franc Mali, Uroš Matelič, Petra Ziherl, Tina Kogovšek, Valentina Hlebec University of Ljubljana, VALICON d.d., Bank of Slovenia

  2. OUTLINE • About the project • The aim of the Slovenian study • Theoretical background • Methodology • Analysis of dyads (mentors-young researchers) • Analysis of whole networks (research groups) • Conclusion

  3. ABOUT THE PROJECT • an international research project (INSOC) • Belgium, Germany, Slovenia, and Spain • doctoral students involved in research groups • academic performance of young researchers • social networks of research groups

  4. THE AIM OF THE SLOVENIAN STUDY • The first goal is to study the effect of the relationship between the young researcher and his/her mentor, together with the mentor´s and the young researcher´s characteristics, on the young researcher´s performance. • The second goal is to study the characteristics of the networks of young researchers which stimulate their academic success.

  5. THEORETICAL BACKGROUND The hypotheses are based • on the theory of the creative knowledge environments (Hemlin, Allwood, Martin, 2004) and • on the theories of the social capital (Granovetter, 1973; Coleman, 1988; Burt, 1983, 1992).

  6. METHODOLOGY The methodology was discussed and sinhronized at twelve meetings of the INSOC group.

  7. DATA COLLECTION IN SLOVENIA 1 • The unit of the analysis is a young researcher in the third year of the graduate study (2002/03). The names of the young researchers and their mentors (236) were obtained at the Ministry of Higher Education and Technology in Slovenia.

  8. DATA COLLECTION IN SLOVENIA 2 • The members of the young researcher´s research group was defined by his/her mentor. • A focus group consisted by selected mentors from University of Ljubljana was organized in May 2003 to discuss the questionnes to determine the boundaries of the research groups.

  9. DATA COLLECTION IN SLOVENIA 3 • From June to September 2003 each mentor was contacted by phone (204) and later a face-to-face interview was organized where the mentor named the members of his/her research group. • 190 mentors gave the names and the e-mail addresses of the members of the research groups (all together 1355 members).

  10. DATA COLLECTION IN SLOVENIA 4 • WEB survey was performed from January to April 2004. • After 2 reminders 711 questionnaires were answered (117 of young researchers, 99 of mentors, 495 of other members).

  11. DATA SETS • the data set of 117 egocentric networks of young researchers, • the data set of 60 dyads (mentor-young researcher) and • the data set of 23 whole networks. There were no statisticaly significant differences in the composition between these samples and the sample of non-respondents.

  12. MEASURED RELATIONS • Advice (work related problems), • Cooperation (e.g., on a project), • Technical(e.g., regarding data, software), • Socializing(outside work context, e.g., doing sports), • Emotional (e.g., lack of motivation).

  13. ANALYSIS OF DYADS MENTOR – YOUNG RESEARCHER The aim of the analysis of the dyads is to detect young researcher’s and his/her mentor’s characteristics and characteristics of the tie beetween mentor and young researcher that effect young researcher’s academic performance.

  14. CREATIVE ENVIRONMENT IN THE MICRO LEVEL(based on Hemlin, Allwood, Martin, 2004) Creativity (and so research performance) is not only individual characteristics but it is influenced also by creative knowledge environments in which they work (autonomy, flexibility, cooperation,...). Relationships among the researchers, especially between young researcher and his/her mentor, play an important role for the scientific performance.

  15. “INNER” vs “EXTERNAL” MOTIVATION • People are most creative when they are motivated primary by interest, enjoyment, satisfaction, and the challenge of the work itself (Cole and Cole, 1973)(inner motivation). • Expectations of financial and similar rewards has a negative influence on research performance (Hennesey and Amabile, 1988; Spangenberg et al., 1990) (external motivation).

  16. RESEARCH AUTONOMY vs CONTROL • Autonomy is a fundamental prerequisite for creative work (Tardif and Sternmerg, 1988; Hennesey and Amabile, 1988). • Autonomy is a basic characteristic of a good research unit, frequently in addition to a loose organizational structure (Pelz and Andrews 1976; Premfors, 1986). • One of its central element is tolerance. “If people feel insecure, won’t be creative” (Gulbrandsen in Elgar, 2004).

  17. MODEL Dependent variable • young researcher ’s performance Independent variables • Mentor’s advice • Mentor’s control • Research autonomy • “Inner” motivation • “External” motivation • Integration of PhD into traditional research in the research group

  18. INDEX OF PERFORMANCE(Coenders and Coromina, 2004) 2*int_art + 2*pub_rev + pub_norm + pap_conf int_art - article in an international journal (with/without reviewers), book/chapter - with reviewers pub_rev - article, paper in proceedings - with reviewers pub_norm - article, book/chapter, paper in proceedings, internal research - without reviewers pap_conf - international/national conference – with/without presentation

  19. ADVICE Consider all work related problems you had during the past year (that is, since 1 November 2002) and for which you couldn’t find a solution yourself. How often have you asked for advice to each of your colleagues on the following list? On scale from 1 to 8 where 1 – not in past year to 8 – daily

  20. CONTROL • I have enough freedom at my PhD research • My mentor is too often insisting on his/her ideas • My PhD is too strict controlled by my mentor On scale from 1 to 7, where 1 – not important at all and 7 great importance

  21. RESEARCH INTEREST AND RESEARCH AUTONOMY • My great interest for this subject • Opportunity to lead my own research • Opportunity of specialization on my own research field • Research autonomy • Intellectual freedom • My great interest for research On scale from 1 to 7, where 1 – not important at all and 7 great importance

  22. CAREER ADVANTAGE • To achieve PhD • Reputation, which young researcher has • Better chances for employment with PhD On scale from 1 to 7, where 1 – not important at all and 7 great importance

  23. INTEGRATION OF PhD INTO REASEARCH TRADITION OF RESEARCH GROUP • My PhD is integrated into research tradition of my work group On scale from 1 to 7, where 1 – completely disagree and 7 completely agree

  24. MENTOR’S ADVICE MENTOR’S NOT TOO HARSH CONTROL ,249** ,461*** RESEARCH INTEREST AND AUTHONOMY OF YOUNG R. ,227* E CAREER ADVANTAGES SEEN BY YOUNG RESEARCHER -,222* INTEGRATION OF PhD INTO REASEARCH TRADITION OF RESEARCH GROUP ,127 YOUNG RESEARCHER’S PERFORMANCE ,247*** MENTOR’S PERFORMANCE -,030 ,204* AGE - MENTOR ,135 GENDER - MENTOR ,182 • R2 = .344 • F = 3,829 (p=.001) SIZE OF RESEARCH GROUP INSTITUTION (university vs institutes)

  25. RESULTS 1 • Control has the most important effect on the young researchers´ performance: mentor should leave to young researcher enough freedom, which means that (s)he should not intrude his/her ideas to the young researcher or direct him/her to much with his/her own ideas. • Mentor’s advice has positive effect on the young researchers´ performance.

  26. RESULTS 2 • Inner motivation has positive influence: high inner motivation like showing great interest for research, desire to lead his/her own research, opportunity to get specialization on his/her research area, work autonomy, intelectual freedom leads to better young reseracher’s performance. • External motivation has negative effect: getting PhD degree just because of reputation or just for getting better job position has negative influence on young researcher’s performance.

  27. ANALYSIS OF WHOLE NETWORKSRESEARCH GROUPS The goal is to study the effect of the social relationships of young researchers with their collegues in their research groups (the network characteristics of their research group) on their academic performance.

  28. THEORETICAL BACKGROUND • Complex knowledge transfer process The importance of strong ties between youngresearcher and older researchers for theknowledge transfer • Complex knowledge creation process I The importance of cohesive research group which is able to offer support in the time of uncertainty • Complex knowledge creation process II The importance of diversity in the research groupin order to bring in innovation and use diversechannels of publication.

  29. DIVERSITY IN RESEARCH GROUP RANGE (Ronald Burt, 1983; 1992): - size of a network (number of researchers in a research group, number of contacts outside the primary research group) - diversity of people in the network (researchers coming from different institutions) - structure of social ties in the network (structural holes - student’s brokering position between unconnected parts in hisnetwork)

  30. THEORETICAL MODEL

  31. DATA • Excluded all research groups in which mentor or PhD student did not respond • Excluded all research groups which did not attain response rate over 60% • Excluded all missing members with low frequencies of cooperation • Other missing members were included, where the ties from him/her to the others were estimated with the values of answers given by the respondents to him/her • Thus, 23 research groups remain for the analysis

  32. Representativity of sample 1

  33. Representativity of sample 2

  34. COOPERATION RELATION Consider all situations of the past year (that is, since 1 November 2002) in which you cooperated with your colleagues, e.g., working on the same project, solving problems together and so on. Minor advice do not belong to this type of cooperation. How often have you been cooperating with each of your colleagues on the list? On scale from 1 (not in the past year) to 8 (every day), or 0, if the respondent does not know the person

  35. VARIABLES USED IN ANALYSES Tie strength average frequency of cooperation between PhD student and other members in research group Cohesion average frequency of cooperation between all members of research group Group Diversity • Size of a research group (original one) • Number of different institutions that people from research group are employed in • Others:number of people with who PhD student cooperates outside the research group defined by mentor • Burt's measure of constraints for PhD students

  36. Burt’s measure of constraints where zij is the frequency of interaction between person i and person j

  37. CLUSTER ANALYSIS The goal is to obtain clusters of the research groups according to the network characteristics • Standardized variables • Euclidean distance (between two research groups) • Hierarchical clustering • Ward method

  38. RESULTS 1

  39. RESULTS 2

  40. CLUSTER 1 – WEAK SOCIAL CAPITAL • Small research groups • Rare cooperation between members of research group • Rare cooperation between PhD students and other members • PhD students do not search for cooperation with people outside their “primary” research groups • Members of research group are from the same institution

  41. CLUSTER 2 – BONDING SOCIAL CAPITAL • Small research group • Developed cooperation • The highest average strength of ties between PhD students and others • Some cooperation of PhD students with others outside “primary” research group • Members of research group are from the same institution

  42. CLUSTER 3 – BRIDGING SOCIAL CAPITAL • Large networks • Different institutions • PhD students have numerous cooperation ties with people outside the original group • Moderate strength of ties and cohesion • Network structure shows structural holes

  43. Clusters Index of performance Mean 6,63 1 – Weak social capital cluster S td.dev. 5,65 Mean 9,29 2 – Bonding social capital cluster Std.dev. 4,07 Mean 22,13 3 – Bridging social capital cluster Std.dev. 8,87 Mean 12,83 Total Std.dev. 9,44 COMPARISON OF INDEX OF PERFORMANCE BETWEEN CLUSTERS

  44. SUMMARY OF CLUSTERING RESULTS • Three clusters according to social capital variables were obtained: weak social capital, bonding social capital, and bridging social capital • Average strength of ties and cohesion in three clusters have non-linear influence on PhD students’ performance • Most successful PhD students are included in large, diverse research groups with network structure that is characterized by structural holes

  45. STRUCTURAL EQUATION MODEL dependent variable: index of performance independent variables: strength of ties: average frequency of cooperation between PhD student and other members in research group – the linear and quadratic terms were centralized cohesion: average frequency of cooperation between all members of research group range: size of a network + number of different institution + Burt's measure of constraints for PhD students (-) others: number of people with who PhD student cooperates outside the research group defined by mentor control variables: work centrality mentor’s performance

  46. WORK CENTRALITY • I'll do overtime to finish my job, even if I'm not paid for it. • The major satisfaction in my life comes from my job. • The most important things that happen to me involve my work. • Some activities are more important to me than work. (-) On scale from 1 to 7

  47. SEM MODEL

  48. RESULTS • Strong effect of range on performance - size of the networks, the number of others - the number of different institution from which people in research group - PhD student’s brokering position between unconnected parts in hisnetwork • Showing some effect of quadratic term of strength of ties on performance

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