Causal framework through retroduction and retrodiction

While the discussion in the IS research community has moved from describing critical realism as simply a compromise philosophy between positivists and interpretivists to its acceptance in its own right, it is still lacking in a choice of methods or processes for the IS researcher to utilise. This paper presents a proposed method that can be used by IS researchers following the critical realist paradigm. The suitability of a critical realist approach to IS research is discussed, and the importance of the combined ontological and epistemological elements described. The relevance of the search for causal mechanisms is shown and the benefits of the processes of retroduction and retrodiction in this search. A ‘causal framework’ is proposed as an artefact in the IS critical researcher’s “toolkit” and an example provided to show how it can be used. A three step process is described which uses causal frameworks the guide the analysis

OFFERING ACCOUNTS OF COMPLEX IS-PHENOMENA: TOWARDS A COMBINATION OF MECHANISTIC PREDICTIONS AND GENERATIVE EXPLANATIONS

Information Systems (IS) phenomena have become increasingly volatile, complex and fast changing. Capturing their essence is an increasingly daunting task. Data science have emerged in awe to predict future outcomes. Decision-making thus becomes faster while data become bigger. Yet, in the wake of this promising path, many of these predictions lack accuracy due to the unpredictability of complex phenomena. That is why researchers promote the importance of thick qualitative data analysis as a way of seeking explanations of the generativity underlying complex phenomena. This approach is (in comparison) slow, but can answer why events occurred. Thus, we argue that sound accounts of complex IS-phenomena must come from a combinatory approach of fast predictions with slower accounts. Predictions apply laws theorized as causal mechanisms. When these outcomes do not arise, we suggest applying explanatory accounts that apply a different form of causality - generative mechanisms. Generative mechanisms can explain unpredictable outcomes, but can only be inferred through longitudinal qualitative studies. This paper opens up a research agenda for combinatory approaches of fast mechanistic predictions from big data and slower generative explanations from thick data. This combination will help capturing the essence of complex socio-technical phenomena in our capricious digitalized world

Collaborative group reasoning in ward rounds: A critical realist case study

The thesis explored the group reasoning occurring between practitioners during hospital ward rounds. A model of the reasoning was constructed, focused on information gathering, sense-making and decision making. The model explained the role of group reasoning and generated suggestions for evaluating ward rounds, improving medical education and redesigning rounds

CAUSAL FRAMEWORK THROUGH RETRODUCTION AND RETRODICTION

a compromise philosophy between positivists and interpretivists to its acceptance in its own right, it is still lacking in a choice of methods or processes for the IS researcher to utilise. This paper presents a proposed method that can be used by IS researchers following the critical realist paradigm. The suitability of a critical realist approach to IS research is discussed, and the importance of the combined ontological and epistemological elements described. The relevance of the search for causal mechanisms is shown and the benefits of the processes of retroduction and retrodiction in this search. A ‘causal framework’ is proposed as an artefact in the IS critical researcher’s “toolkit” and an example provided to show how it can be used. A three step process is described which uses causal frameworks the guide the analysi