Browsing by Author "Stebbings, H."

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    Business process resource networks: a multi-theoretical study of continuous organisational transformation
    (Cranfield University, 2016-04) Stebbings, H.; Denyer, David; Pilbeam, Colin; Braganza, Ashley
    Drawing on multiple theoretical lenses, this research studies continuous transformation, or ‘morphing’, of a business process resource network (BPRN). The aim is to further our understanding of continuous organisational change at the lowest levels of analysis within an organisation: that is, at the resource level, and that resource’s relationships to other resources as they exist within a BPRN. Data was gathered from a single, in depth case study. Analysis was achieved by means of mapping BPRN evolution using ‘temporal bracketing’, ‘visual’ and ‘narrative’ approaches (Langley, 1999). The analysis revealed two mechanisms that appear to govern microstate morphing: bond strength and stakeholder expectation. In addition, four factors emerged as important: environmental turbulence, timing and timeliness of changes, concurrency of changes, and enduring business logic. An emergent model of microstate morphing which acknowledges the importance of socio-materiality in actor network morphogenesis (ANM) is presented. This study shows how effective relationships and configuration of resources within the BPRN can be achieved to facilitate timely, purposeful morphing. Five propositions are offered from the emergent ANM model. Specifically, these relate to the conditional operating parameters and the identified generative mechanisms for continuous organisational transformation within the BPRN. Implications for practice are significant. A heuristic discussion guide containing a series of questions framed around the ANM model to highlight the challenges of microstate morphing for practitioners is proposed. Two routes for future research are suggested: replication studies, and quantifying BPRN change in relation to an organisation’s environment using a ii survey instrument and inferential statistical analysis based on the ANM model features and propositions.