Researching network-like phenomena – the importance of considering three different observation logics
Year: 2025
Type: article
Abstract: Purpose The purpose of this paper is to discuss when an observed process requires investigation as a network-like phenomenon. The research question is as follows: How does the way of observing a network-like phenomenon impact on what aspects it is possible to shed light on, and conceptualise? Design/methodology/approach The research design is based on the basic characteristics of network-like phenomenon identified in Industrial Network research; non-linearity, connectivity and self-organising. Three types of observation logics are discussed, especially what aspects each can bring forward. A published study is used to illustrate the discussion empirically. Finally, the authors discuss the relation between chosen observation logic, drawing of borders and possibility to conceptualise. Findings The findings stress that each research project concerning a network-like phenomenon should rest on a conscious choice of observation logic; an awareness that network-like phenomena always take place on a network level; and finally, a consciousness that each observation logic can provide clear images of certain – but not all – forces involved. This implies that researching network-like phenomena in general should allow a plurality of ways to observe. Research limitations/implications See above. Practical implications The paper sheds light over the importance of recognising network-like phenomenon; especially how self-organising can be identified and acted on, in processes of significance for businesses, organisations and society at large. Social implications See above. Originality/value The paper draws attention to the fact that behind the commonly used case study research design radically different observations logics might be hiding, with different ability to catch the content and consequences of interactivity, non-linearity and self-organising.
Authors: Hakan Hakansson, Alexandra Waluszewski
Institutions: BI Norwegian Business School, Uppsala University
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7118358050