A framework to capture and reuse process knowledge in business process design and execution using social tagging. Issue 4 (4th July 2016) - Rangiha, Mohammad Ehson; Comuzzi, Marco; Karakostas, Bill
Abstract : Purpose: – The purpose of this paper is to present a framework for social business process management (BPM) in which social tagging is used to capture process knowledge emerging during the design and enactment of the processes. Process knowledge concerns both the type of activities chosen to fulfil a certain goal and the skills and experience of users in executing specific tasks. This knowledge is exploited by recommendation tools to support the design and enactment of current and future process instances. Design/methodology/approach: – The literature about traditional BPM is analysed to highlight the limitations of traditional BPM regarding management of ad hoc and semi-structured processes. Having identified this gap, an innovative BPM framework based on social tagging is proposed to address these limitations. This model is exemplified in a real case scenario and evaluated through the implementation of a prototype and a case study in real world non-profit organisation. Findings: – An overview of the social BPM framework is presented, introducing the concepts of role and task recommendation, which are supported by social tagging. The prototype shows the buildability of the social BPM framework as an extension of a Wiki platform. The case study demonstrates that the social BPM framework improves user collaborativeness in designing and executing process instances. Research limitations/implications: – The applicability of the framework is targeted to ad hoc and possibly semi-structured business processes and it does not extend to highly procedural and codified processes. A single case study limits the generalisability of the evaluation results. Originality/value: – The social BPM framework is the first to introduce task and role recommendation supported by social tagging to overcome the limitations of traditional BPM models.