Einflussfaktoren auf Schweizer Finanzorganisationen – Empirische Entwicklung eines Einflussfaktorenmodells

This thesis examines which influencing factors and manifestations affect Swiss financial organisations and to what extent. Particular attention was paid to the weighting and prioritisation of the individual factors. The aim was to develop an influencing factor model that offers the CFO in particular the opportunity to view relevant influencing factors with corresponding effects and mutual interactions, taking into account selection options at a glance. A holistic concept of influencing factors/manifestations in relation to Swiss or international financial organisations could be defined as a gap in the research. To close the research gaps, three different research methods were implemented, which were quantitatively and qualitatively performed, analysed and interpreted in the sense of mixed method research. The first research method dealt with a quantitative analysis of more than 6000 Swiss finance director/CFO positions over a period of 8 years. The second research method aimed at a primarily quantitative survey of Swiss experts using online questionnaires (501 questionnaires returned). The third research method – twelve guided expert interviews – pursued the goal of critically reviewing, questioning and improving on the results of the previous research methods. The results of this method were subjected to a qualitative content analysis based on the Mayring concept. As a result, it can be stated that the selected research methods were suitable for answering the research questions. As representative principal results of this thesis, it was possible to evaluate 20 influencing factors including their manifestations with prioritisation and weighting according to the Likert scale. The results are summarised in an influencing factor model. Due to the inclusion of 10 socio‐demographic characteristics (including industry, employees, professional experience or ownership structure), the model theoretically allows over 7.9 million possible selections. The model was checked in practice, and the experts improved upon it with optimisation inputs. Among other things, it can be used as an orientation aid, benchmark analysis, or as an aid in optimising or realigning the financial organisation.