General information
Code: J5-3101
Period: 1.10.2021 - 30.9.2024
Range on year: 0.39 FTE | 2021
Project leader at FDV: doc.dr. Luka Kronegger
Co-financiers: ARIS
Research activity: Social sciences
Abstract
BACKGROUND: In contemporary science, the interactions among scientists and their scientific collaboration (SC) are critical processes for sharing knowledge, developing human creativity and creating new ideas, which propel social and scientific innovations. This project focuses on a specific form of SC embedded in the higher education context – the mentorship relationship between two or more researchers (i.e. mentor and mentee) and the interaction of social context and knowledge creation.
PROBLEM DEFINITION: The concepts of SC and the mentorship relationship in the higher education setting are well studied as separate phenomena. However, such efforts have neglected the important fact that the mentor–mentee relationship in the higher education context is one of the first and most crucial SC processes of great importance for the further scientific work, performance and experiences of the mentee. This project builds on the premise that the mentorship relationship is a fundamental scientific collaboration process whose substantial purpose provides a platform for the generation of new knowledge.
RESEARCH OBJECTIVES:
(1) To design and evaluate measurement instruments to guage individual and institutional factors that affect the nature of the mentorship relationship in the higher education context.
(2) To identify different career paths of mentees through time and analyse the mentorship relationship on different social levels (micro, mezzo, macro).
(3) To investigate the impact of personal characteristics as well as system and legal regulations that could benefit/hinder the mentorship relationship and assocated collaboration
(4) To study the phenomenon through the lens of the production of ideas, their transfer across generations and to position them within the disciplinary knowledge space to thereby provide insights into the scientific knowledge production process.
METHODS: The project’s main goals will be pursued using a mixed methods approach, with a palette of qualitative and quantitative methodological approaches and thus through a combination of different data collection techniques (e.g., in-depth interviews, survey, retrieving data from bibliographic information systems) and data analysis methods (e.g., inductive thematic analysis, multivariate analysis, blockmodeling of dynamic networks, multilevel structural equation modelling). The biggest methodological advances of the project are expected in the areas of social network analysis with adaptation using methods for analysis of temporal networks, blockmodeling and the development of survey measurement tools.
RELEVANCE AND IMPACT: This project is an innovative interdisciplinary study. The results will make an important contribution to understanding the dynamics of the mentoring relationship as an integral part of SC. At the same time, they will highlight the strengths and weaknesses of the system and the local environment in which researchers operate with a view to providing initiatives and recommendations for achieving a stable and stimulating environment for new knowledge so as to advance the system of science. In addition to contributing to basic science, it also has an applied value: the project results could be directly used in the development of the Doctoral School of the University of Ljubljana and be evaluated in collaboration with Mlada Akademija society.
ORGANISATION AND FEASIBILITY: Data on collaboration networks will be obtained from existing databases; namely, greater emphasis will be on conducting a survey, the qualitative data collection, and building appropriate research models. Informal collaboration with the Doctoral School of the University of Ljubljana and the Mlada akademija society will provide a direct source for evaluating the results during the course of the project and dissemination of the results.
The phases of the project and their realization
The research will be organised as mutually related work packages with one or several tasks. Each work package can
be seen as a smaller research project with its specific results for use in the later work packages or to achieve the aim
of the research project.
The work packages are the following:
- Development of the theoretical model (WP 1),
- Collaboration network analysis (WP 2),
- Cognitive network analysis (WP 3),
- Qualitative in-depth interviews and data analysis (WP 4),
- Collecting and analysing survey data (WP 5),
- Integration of the results (WP 6),
- and Project management and dissemination (WP 7).
Apart from the formally assigned interdisciplinary team members, the team will be enriched by Professor Anuška
Ferligoj (02465). Informal agreements on collaboration have been made with the Mlada akademija society, the
Doctoral School of the University of Ljubljana, and the Social Networks Lab (ETH Zürich).
WP 1: Development of the theoretical model
Description: This work package aims to provide an extensive literature review and to develop an initial, holistic,
theoretical model, which will be analysed and improved during the research project.
Task 1. A literature review: To exceed the existing research findings, a detailed and critically oriented comparative
review of existing literature and research will be carried out.
Task 2. Supporting other work packages: The holders of this work package will collaborate with all project
members in all research project stages.
Specific goals:
- To perform a critical literature review focusing on mentorship relationship characteristics and differences
across scientific fields
- To develop the initial theoretical model
WP 2: Collaboration network analysis
Description: This work package targets the social layer of science: (i) to position a mentorship relationship within a
wider scientific disciplinary community (i.e., macro level) in time; and (ii) to study the mentor–mentee relationship
in its active period, and the career paths of each after the relationship has formally ended.
Task 1. Operationalisation: Other (besides successfully defended and published dissertations) relevant
bibliometric and personal indicators will be identified to measure a mentoring relationship.
Task 2. Data curation: The analysis will be based on data from the COBISS and SICRIS national bibliographic
services. Data on all Slovenian researchers included in the system spans from 1990 and covers all disciplines from
seven research fields. The availability of access to the personal bibliographies of Slovenian researchers through the
national COBISS and SICRIS systems allows a better examination of the position of a given mentoring relationship
in the scientific production system (based on different types of complete networks, e.g. co-authorship networks or
networks of cooperation between organisations). This work package will update the bibliographic data from the
COBISS and SICRIS national bibliometric databases, as used in our former projects and address questions that refer
to the social layer of the scientific system.
Task 3. Analysis: Several approaches will be used to address the aims of this package. The appropriate
blockmodeling approach (Žiberna, 2014) for temporal, linked and multilevel networks will be applied to position a
mentorship relationship within a scientific disciplinary community. Then, the approaches for analysing timestamped
network data (DyNam) (Hoffman et al, 2020) will be used to study the mentor and mentee’s career paths,
possibly considering their position in the scientific community.
Specific goals:
- To collect and prepare the data retrieved from the SICRIS and COBISS information systems
- To select and apply the most appropriate blockmodeling approach
- To estimate the DyNam model (possibly by considering the results of the blockmodeling approach)
- To retrieve the sample of mentees and mentors for qualitative interviews based on their position in a
scientific disciplinary community
WP 3: Cognitive network analysis
Description: This work package will operationalise and measure scientific knowledge production through the
cognitive layer of science by answering the research questions: “What is the contribution of a single researcher and
the mentoring relationship to the common knowledge space, and what is happening with the position of researchers
in this space over the course of their careers?”.
Task 1. Operationalisation: Researchers’ publications will be used to identify their scientific ideas, and these will
be transformed into knowledge spaces or ‘maps of science’ through citations and term co-occurrence.
Task 2. Data preparation: The project team will integrate the database prepared within WP 1 (i.e., the data from
the national bibliographic databases) with international sources (WoS). This will allow the analysis of citation
networks. Cognitive networks are networks derived from the set of two-mode relational datasets (researchers by
publications and publications by publication topics).
Task 3. Measuring knowledge production: The bibliometric method for mapping the state-of-the-art and
identifying research gaps and trends (de Oliveria et al. 2019) will be adjusted and applied to obtain the variable
values for each mentor and mentee. Within this task, a form of similarity measure will be proposed. This will
measure the similarity between the research topics (based on bibliographic data) and define researchers’ knowledge
space. Kronegger has already presented the concept of directed similarity on the individual level, evaluating the
mentoring relationship in terms of the “scientific reference frame” of researchers (Kronegger, 2017).
Specific goals:
- To gather the citation data (from WoS) based on national bibliographic data
- To extract the content from the available bibliographic units
- To establish the knowledge space on a disciplinary level by generating a network of terms used in
bibliographic units’ descriptions
- To establish the personal profiles of researchers according to the terms they used in descriptions of
bibliographic units
- To analyse the intersection of mentors and mentees’ profiles within the previously established knowledge
space and searching for patterns of changes over time
- To obtain the intersection between the knowledge space as a whole and individual reference frames over
time as a measure of individual knowledge production
WP 4: Qualitative in-depth interviews and data analysis
Description: This WP is intended to explore and compare personal views, perceptions and experiences related to the
mentorship relationship. Accordingly, the qualitative study will be focused on providing insights from both
perspectives entailed in this relationship, i.e. mentor and mentee. The qualitative study’s primary goal is to explore
different mentoring relationship stages as experienced by both mentor and mentee, motives to become a mentor and
a mentee, conformity between the mentor’s and mentee’s personal characteristics and the main characteristics of a
satisfactory mentorship relationship for both parties. This WP will also explore views and perceptions of the external
formal environment and system influences and various aspects of teamwork and collaboration within the primary
research group and with other researchers. The study will also explore the expected results or outcomes of the
mentorship relationship and further possibilities for mentees’ career paths. An important task of this WP is to
contextualise the findings obtained in WP 2 (Collaboration network analysis) and WP 3 (Cognitive network
analysis) and to provide insights to support the development of the measurement instruments for the questionnaire in
WP 5.
Task 1. Sampling and recruitment process: Based on the sample administered by the bibliographic data obtained
in the WP2, a heterogeneous purposive sampling procedure will be used to provide a diverse range of participants
(mentors and mentees) according to their demographic characteristics (gender, age, educational background) and
professional characteristics (mentors: mentorship experiences, academic or professional (industry) status; mentees:
PhD student status, research stage). Both recruited mentors and mentees will be representatives of various
disciplines (social science, humanities, natural and technical sciences) at the University of Ljubljana. The interview
participants’ recruitment will be assisted by collaboration with the Mlada akademija society as a leading institution
for liaising with young researchers and the Doctoral School of the University of Ljubljana.
Task 2. Data collection procedure: All in-depth interviews with participants will be conducted face-to-face and
audio-recorded with the participants’ permission. The researcher conducting the interviews will ensure that
participants clearly understand the study’s nature and their rights. All participants will be asked to sign a written
informed consent. The data collected with the in-depth interviews will be anonymised. In all stages of the data
collection, ethical guidelines according to GDPR and ethical research standards of the University of Ljubljana will
be followed. Altogether, we will conduct 10–20 interviews with mentors and 10–20 with mentees.
Task 3. Data analysis: All of the in-depth interviews will be transcribed verbatim and accompanied by
corresponding field notes imported into the qualitative data analysis software. The participants’ personal
characteristics will be anonymised to guarantee confidentiality and pseudonyms will also be employed. The data
will be analysed via inductive thematic analysis.
Task 4. Implementation of the qualitative research findings: The findings of the qualitative data analysis will be
used to advance and upgrade the current state of the art by providing insights into various aspects and factors of the
mentorship relationship embedded on different (micro, mezzo, macro) levels. The qualitative findings of the indepth
interviews will also give the basis for the operationalisation and development of the measurement instruments
included in WP5.
Specific goals:
- To design a set of interview questions in relation to the literature review
- To gather a sample from the bibliographic data obtained in WP 2
- To prepare the sampling and recruitment process protocol for the in-depth interviews
- To collect the data via in-depth interviews conducted with mentors and mentees
WP 5: Survey data collection
Description: The goal of this work package is to collect the survey data regarding the concepts on the individual
level proposed in the theoretical model (e.g., mentorship style, mentorship satisfaction, the perception of benefits
and disadvantages of mentorship, conflict-coping approaches).
Task 1. Operationalisation of the constructs and preparation of the measurement instruments: The
operationalisation of the constructs is to be based on the extensive literature review. These constructs will be used in
the development and adjustment of the existing measurement instruments which will be used in a web survey.
Task 2. Evaluation of the survey: The proposed measurement instruments will be tested using standard procedures
that include cognitive interviews, expert evaluation and different approaches for the reliability and validity
assessment.
Task 3. Data collecting procedure: The data will be collected by a survey that will include both mentors and
mentees. The sample is to be based on the data obtained within WP 2. The sample will cover disciplinary mentoring
networks from research disciplines, selected following the previous bibliographic analysis.
Specific goals:
- To review and select appropriate indicators for measuring the selected studied phenomena (these will be
based on the general research questions and the interview results – see WP 4)
- To design and evaluate the questionnaire
- To collect the data
- To analyse the data
WP 6: Multilevel data analysis
Description: This work package aims to integrate the quantitative data (and qualitative data) from the different
work packages into a single data frame for use in the empirical evaluation of the proposed theoretical model.
Task 1. Integration of the databases: The knowledge-production data (obtained within WP 3) will be merged with
the survey data (collected within WP 5), with the data on scientific collaboration and the position within the
scientific disciplinary community (retrieved within WP 2), and with other relevant data on the disciplinary level
obtained from SICRIS and COBISS.
Task 2. Multilevel structural modelling: The theoretical model and different data types will allow the use of a
multilevel structural modelling approach. This approach is necessary since it can be expected that the variables’
values on the individual level are not independent of those on higher levels.
Task 3. Evaluation of the theoretical model: Suggestions for improving the theoretical model will be provided
according to the results for the empirical model.
Specific goals:
- To test the theoretical model on the empirical data by using a multilevel structural modelling approach
- To suggest optimisations of the theoretical model
WP 7: Project management and dissemination
Task 1. Website: The website will be set up at the start of the project (month 3). It will contain relevant information
on the project and its results for the national and international public.
Task 2. Dissemination of the scientific results: The results will be disseminated through a series of scientific
papers and presentations at international conferences.
Specific goals:
- To present and evaluate the results of the study at national and international scientific conferences.
- To publish the study results in international scientific journals
- To create a website
Research Organisation
http://www.sicris.si/public/jqm/prj.aspx?lang=eng&opt=2&subopt=403&hits=1&id=18756&search_term=J5-3101
Researchers
http://www.sicris.si/public/jqm/prj.aspx?lang=eng&opt=2&subopt=402&hits=1&id=18756&search_term=J5-3101
Citations for bibliographic records
http://www.sicris.si/public/jqm/prj.aspx?lang=eng&opt=2&subopt=400&hits=1&id=18756&search_term=J5-3101
Results / Key findings
### Key Findings of the project
1. **Mentorship and Knowledge Production**
- Mentorship plays a central role in fostering scientific collaboration and knowledge production during doctoral studies. Various mentor-mentee dynamics influence the success and experience of doctoral students.
- Distinct styles of mentorship (e.g., managerial, parental, collegial) impact mentees differently, particularly in terms of their academic confidence, work habits, and long-term collaboration outcomes.
2. **Patterns of Collaboration**
- Three primary collaboration patterns were identified:
- **Study-Limited:** Focused collaboration within the doctoral period, common in natural and technical sciences.
- **Already Established:** Existing collaborative networks before the doctoral journey.
- **Born and Raised:** Initial isolation evolving into strong integration and high productivity.
- Mentees participating in the "Young Researchers Programme" tend to follow a structured, well-supported path, often leading to higher success rates in publishing and professional integration.
3. **Systemic Factors**
- The Slovenian academic system's reliance on the Young Researchers Programme has significantly contributed to structured mentorship and integration of doctoral students into research ecosystems. However, challenges such as limited interdisciplinary collaboration and financial instability persist in some areas.
- Negative systemic aspects, including bureaucratic hurdles and constrained funding, were noted as barriers to seamless mentor-mentee collaboration.
4. **Interdisciplinary Collaboration**
- Disciplines such as social sciences and medical sciences exhibit higher levels of interdisciplinary collaboration compared to natural sciences and humanities. The trend towards interdisciplinary approaches, though slowly increasing, still faces skepticism among academic staff.
5. **Impact of Co-Mentorship**
- Co-mentorship patterns revealed that while some pairs of mentors engage in long-term collaboration, although a significant portion of mentor pairs show minimal collaboration beyond the doctoral period. Effective co-mentorship seems to depend on shared disciplines and aligned research interests.
6. **The Role of Cultural and Social Backgrounds**
- The qualitative findings highlight the importance of mentees' socio-cultural environments in shaping their academic trajectories. Supportive family backgrounds and exposure to intellectual resources were key enablers of academic success.
### Ongoing Work
The survey data analysis is currently underway and is expected to provide further insights into mentees' experiences and perspectives on mentorship and knowledge production. This additional layer of quantitative data will complement the existing qualitative and bibliometric analyses.
Key words
foctoral studies, knowledge production, mentorship, scientific collaboration, supervision
Sustainable Development Goals
SDG3 | Good health and well-being
SDG4 | Quality education
SDG5 | Gender equality
SDG8 | Decent work and economic growth
SDG9 | Industry, innovation and infrastructure
SDG10 | Reduce inequalities
SDG17 | Partnerships for the goals
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