Prediction of Convergent and Divergent Determinants of Organisational Development

Abstract

Different scholars study organisational development through prismatic lenses of various determinants. Despite extensive analysis, it was found that there is little evidence to date on the measurement, analysis and prediction of organizational development using digital tools. The knowledge gap revealed the potential to define convergent and divergent determinants of organisational development. The study in the context of predicting convergent and divergent determinants of organisational development is divided into two parts – the definition of determinants for the surrogate model and the construction of the prediction model. In this publication, the first part is presented. Considering the different approaches to measuring organizational success, the determinants of processes and company competences emerge. Although organisational development represents one of the focal points, its determinants tend to be recorded and analyzed only over the medium or long term, precluding a short-term conditional parameter adjustment. This publication explores the convergent and divergent determinants of organisational development by conducting a quantitative and qualitative publication analysis and network analysis. The conceptualized organisational development model specifies the described determinants by extending them with further parameters, which can be applied for prediction using algorithms based on artificial intelligence. Based on the publication results, network analysis, and structural equation modelling, 13 determinants and 42 parameters were identified. These show a high degree of interconnectedness, highlighting the approach of divergent and convergent determinants in the overall construct of organisational development. These determinants and parameters form the framework for surrogate models and can serve as input or forecast data for different algorithms. Furthermore, a conceptual model for predicting organisational development, formulated based on defined parameters using machine learning, is presented. The second part of the study will be presented separately, a framework based on artificial intelligence was created for analyzing the current state of organisational development and predicting the next development scenarios based on the findings.