Identifying Risks in IS Post-Merger Integration in Academic Organizations With the Help of an Early Warning System

Abstract

This master thesis focuses on identifying and assessing indicators that signal the risk of failure in post-merger integration of information systems in academic mergers and acquisitions with the help of an early warning system. Design Science Research is the applied research method. It is complemented by a case study of a real-world merger.The EWS is the main artifact of my approach. By applying semantic technologies – namely an ontology and an inferencing engine, for the automatic identification, validation and quantification of risk factors with key risk indicators and their metrics, a prototype of the EWS was developed.The starting point for my approach is the case study: to determine common risk factors that might occur during the post-merger phase of academic organizations, a risk catalogue was developed. All identified risk factors are determined by key risk indicators that are valued based on appropriate metrics. Additionally, scores for warning signals are calculated to indicate their ponderosity for the risk to fail. If a certain threshold is reached, an early warning signal is triggered and an early warning including the risk significance and risk priority of a risk factor, as well as metric scores is initiated.My approach was evaluated within the context of two real-world scenarios: One scenario evaluated the IS integration of an academic merger in retro-perspective, the other scenario evaluated an on-going international joint venture between two companies of the private sector – a bank and a Swiss services/software solutions provider.