AI-Duction for Organizational Theory Building: (How) can we Overcome Ontological Neglect with AI?

Abstract

With rapid advancements in (AI), we consider it presents an emerging issue for organizational theory building. AI is increasingly being used in organizational research, but recently it has been highlighted that there is ontological neglect. While it is acknowledged that AI can advance human reasoning in organizational research, the risk is that it reduces the task of research to essentially prediction only, thereby neglecting the branch of organizational research concerned with understanding and interpretation of social phenomena. This neglect comes at a cost in explaining social phenomena in organizations. Thus, our paper progresses in three sections. First, we provide an overview of three principle forms of AI (assistive, interpretative, and augmentative). We explain them in relation to common modes of human intelligence in research (i.e., supervised, unsupervised and reinforced learning), their role in research and their explorative quality. We also show examples relating to qualitative organizational scholarship, highlighting the contribution and limits of AI-supported research. Next, we present a model that shows the potential for AI to support research and theory building we call AIduction. Through this model, we propose that AI can help theory building via continuous hermeneutic cycling. Finally, we show how AI can support theory building that pays attention to the ontological appreciation - closely linked to the hermeneutical cycle - that can explain relationships between constructs in more enriching ways.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 1 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.