Intelligent Agent Appropriation in the Tracking Phase of an Environmental Scanning Process: A Case Study of a French Trade Union

Introduction: The rapid growth of the Internet has modified the boundaries of information acquisition (tracking) in environmental scanning. Despite the numerous advantages of this new medium, information overload is an enormous problem for Internet scanners. In order to help them, intelligent agents (i.e., autonomous, automated software agents that are theoretically able to make inferences) have been developed. In this context, this paper examines how information acquisition activities based on intelligent agents could be structured in the long term in an environmental scanning process. Method: A longitudinal case study (action research) was conducted. As part of this qualitative approach, the data acquisition strategy employs three different methods (i.e., semi-structured interviews, observations and examinations of internal and external documents), which allows the collected information to be triangulated. Analysis: The data analysis strategy employed two different methods. Semi-structured interviews were recorded, transcribed and systematically coded. A thematic dictionary has been used for analysing the content (open and axial codings). Observations and internal or external documents were summarized. We then extracted the relevant data. These data were triangulated with those of semi-structured interviews. Results: Based on the information provided from our case study, it appears that our initial model must be revised. Three elements seem to be missing from the model: "information system infrastructure", "external entities with an organizational influence" and "environmental scanning service providers". Moreover, we highlight new relationships between components of our initial model. Conclusions: This action-research emphasizes the importance of the components, external entities with an organizational influence, information systems infrastructure and environmental scanning service providers for understanding the structuring mechanism of the tracking phases based on intelligent agents. (Contains 2 figures and 5 tables.)