Conflicts in start-ups: coping with stress and conflicts to promote well-being and entrepreneurial success

Objective

Start-ups are Europe’s 2020 Priorities for strengthening economic growth. However, about 50% of new European businesses fail during their first five years, conflicts between founders and effects of work stress being two of the top reasons for failure. However, conflicts and stress in start-ups, as well as factors for their entrepreneurial success are under-studied.
The aim of STARFLICT is to elaborate a typology of conflicts in start-ups, develop an assessment tool for conflicts in start-ups, identify the early indicators of start-ups’ entrepreneurial success in terms of individual- and group-level coping, and offer practical guidelines for start-ups on how to effectively deal with conflicts and stress using individual and group-level coping strategies. This will allow to take preventive actions to avoid destructive spirals of conflicts and stress that lead to start-up’s failure, ensuring start-up’s entrepreneurial success and their founders’ WB, while increasing start-ups’ success rate.

The objectives of STARFLICT will be achieved using new and powerful techniques for data collection (IT-supported diary, actigraphy) and cutting-edge data analysis methods (Latent Growth Curve Modeling, Multilevel Structural Equation Modeling) that allow for obtaining fine-grained high quality (hard) data and modeling dynamics.

STARFLICT’s objectives are relevant to the work program because they are innovative, interdisciplinary, address a timely issue of start-ups’ entrepreneurial success, and considerate the gender issues. STARFLICT’s plan of work includes bi-directional transfer between myself and KU Leuven. KU Leuven will provide me with resources, specific training in advanced methodology, and with an excellent stimulating scientific environment. STARFLICT will clearly enhance my future career prospects and allow me undertake higher risk research in the future that, in turn, will have an impact on the European science-based knowledge.

Fields of science (EuroSciVoc)

CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.

• natural sciences computer and information sciences data science

Keywords

Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)

• Conflict
• occupational stress
• coping
• well-being
• start-ups
• Latent Growth Curve Modeling
• Multilevel analysis
• longitudinal study
• diary

Coordinator