Designing strategic information systems in complexity science concepts

Abstract

Aligning strategic information systems (SISs) with business objectives poses a big challenge, alignment is a complex and a dynamic process, especially with shifting business strategies. Advancement in the digital age has altered the dynamism of SISs, deployment of these ubiquitous technologies has increased competitive capabilities in organizations, as well as turned SISs management into chaotic affair. SISs are turbulent, non-linear, open, dancing systems: these features have introduced uncertainty and unpredictability, resulting into problematic alignment of SISs. Complexity science is thought to offer a solution in this alignment chaos; it is concerned with dynamism of the system.

The research in this thesis focused on SISs design, designing SISs in conformity to complexity science concepts. It analyses complexity science use in SISs design. Complexity science constitutes complex adaptive and evolving systems (CAS & CES), both referred to as complex systems hereafter. To achieve SISs alignment with business objectives, organizations would not only need to design alignment applications, but also aligned network structures that enable strategic knowledge sharing, for competitive advantages. The diversity and the non-linear topology of SISs, does not allow organizations to isolate themselves from the outside world anymore; in the digital era competitive advantages arise from shared information. This thesis proposed the use of Knowledge Assets Value Dynamics Map (KAVDM) to determine the priority and order for sharing knowledge assets and for value conversion in organizations, so as to implement the proposed design.

Aligned network topology introduces the network paradigm, which deals with inter-organizational network strategies. The paradigm focuses on organizational systems connectivity for competitive advantages. Complexity science was explored for SISs networked interactions, for inter-organizations alliance benefits; demonstrating the use of dynamic network alignment that adapts and evolves as the organizations knowledge needs rises. The resultant SISs network was redundant, self-regulating, and self-learning. This thesis used simple Generative Network Automata (GNA) to demonstrate that the distributed network topology can adapt and evolve simultaneously as single computational framework, conforming to the complexity concepts.

This thesis employed design science research methodology combined with analytical research. It explored existing knowledge on complexity science, investigated and analyzed it possibilities for improving SISs design. This thesis also suggested how SISs should be designed in conformity to complexity science concepts. The thesis’ contribution was a conceptual model, a suggestion to the information systems research community. A literature review was applied while studying existing knowledge in SISs, open data, network paradigm and complexity science. Most publications came from MIS Quarterly (MISQ), Information Systems Research (ISR), IEEE Xplore, and Journal of Strategic Information Systems (JSIS).