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Title: A framework for social BPM based on social tagging
Author: Rangiha, M. E.
ISNI:       0000 0004 5915 2631
Awarding Body: City University London
Current Institution: City, University of London
Date of Award: 2016
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Traditional Business Process Management (BPM) has a number of limitations. The first one is the typical separation between process design and execution, which often causes discrepancies between the processes as they are designed and the way in which they are actually executed. Additionally, because of this separation, valuable first-hand knowledge generated during process execution may remain unused during process design and also prevented to be shared within the organisation. Social BPM, which predicates to integrate social software into the BPM lifecycle, has emerged as an answer to such limitations. Although there have been a number of approaches to Social BPM, they have not been able to address all the issues of traditional BPM. This thesis proposes a novel Social BPM framework in which social tagging is used to capture process knowledge emerging during the enactment and design of the processes. Process knowledge concerns both the type of activities chosen to fulfil a certain goal (i.e. what needs doing), and the skills and experience of users in executing specific tasks (i.e. skills which are needed to do it). Such knowledge is exploited by recommendation tools to support the design and enactment of future process instances. This framework overcomes the limitations of traditional BPM systems as it removes the barrier between the design and execution of the processes and also enables all users to be part of the different phases of the BPM lifecycle. We first provide an analysis of the literature to position our research area, and then we provide an overview of our framework discussing its specification and introducing a static conceptual model of its main entities. This framework is then elaborated further with a more dynamic model of the behaviour and, in particular, of the role and task recommendations, which are supported by social tagging. These mechanisms are then applied in a running example. Finally the framework is evaluated through the implementation of a prototype and its application in a case study. The thesis ends with a discussion about the different evaluation approaches of the proposed framework, limitations of our framework and future research.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: HD28 Management. Industrial Management ; QA76 Computer software