انتخاب مدل بلوغ فرایند کسب و کار مناسب
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|17207||2013||23 صفحه PDF||سفارش دهید||16890 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Information & Management, Volume 50, Issue 7, November 2013, Pages 466–488
We have built and tested a decision tool which will help organisations properly select one business process maturity model (BPMM) over another. This prototype consists of a novel questionnaire with decision criteria for BPMM selection, linked to a unique data set of 69 BPMMs. Fourteen criteria (questions) were elicited from an international Delphi study, and weighed by the analytical hierarchy process. Case studies have shown (non-)profit and academic applications. Our purpose was to describe criteria that enable an informed BPMM choice (conform to decision-making theories, rather than ad hoc). Moreover, we propose a design process for building BPMM decision tools.
Business processes describe how organisations operate, and therefore impact on how organisations perform. Due to higher performance challenges and IT opportunities , business process maturity models (BPMMs) have increased in significance to help organisations obtain mature (or excellent) business processes . Since the 1970s, maturity models have been recognised as important improvement tools for organisations. Accordingly, dozens of BPMMs have been designed , like CMMI  or OMG-BPMM . They are evolutionary tools to systematically assess and improve capabilities (i.e. skills or competences) in order to reach business (process) excellence . For instance, a BPMM may assess how capable an organisation is in modelling its processes or in running them faultlessly. The huge number of BPMMs raises questions about their substantial differences. Some comparative studies have been made, albeit with a small number of BPMMs . To our knowledge, the BPMM literature is mainly restricted to a design perspective, by creating a theory to design BPMMs or by designing particular BPMMs, as in de Bruin and Rosemann . Mettler  presents design criteria for maturity models from both a developer's and user's perspective, although not specific to the BPMM context and without offering an overview of existing models. Röglinger et al.  propose design criteria for BPMMs, in particular. They present a limited BPMM overview to illustrate their criteria, but without practical advice on BPMM selection. Consequently, organisations and academics have no comprehensive overview of academic and industry-owned BPMMs and have an incomplete state of knowledge of how to select a BPMM that best fits their (organisational or research) needs. Therefore, the research question that this article hopes to address is: Which criteria help users (i.e. organisations or academics) choose a BPMM? This study is in line with recent research on information systems, which focuses more on users as consumers than on system development as such, e.g. . Our objective is to advance knowledge on criteria that enable a well-advised BPMM choice (in accordance with decision-making theories, rather than on an ad hoc basis). The identification of the most relevant criteria should result in a practical decision tool to make an informed BPMM choice out of a large BPMM sample. The criteria are identified by addressing key questions and trade-offs faced by many organisations, consultants, and scholars, and are then used to design a decision tool to recommend the most appropriate BPMM (out of the numerous available models), depending on individual needs. Our research distinguishes from existing studies by: (i) identifying a diverse set of BPMMs (in response to the lacking BPMM overview), (ii) identifying the most decisive selection criteria (in response to the lacking knowledge of BPMM selection), and (iii) designing a decision tool based on these criteria. We provide knowledge contributions by filling these important gaps, and by extending the literature with a thorough design process, resulting in users being able to make more informed decisions with the BPMM decision tool. The major managerial implication is that the tool helps each organisation choose a BPMM that will best suit its particular needs. The theoretical background to this problem statement is explained in Section 2, while Section 3 proposes our solution on which we elaborate in Section 4. We also explain how the decision tool was built (Section 5) and tested (Section 6). Section 7 discusses the tool as a solution to our research problem. We conclude by summarising the contributions and limitations (Section 8), and main findings (Section 9).
نتیجه گیری انگلیسی
Choosing a BPMM for what you want to achieve is critical (i.e. fit for purpose). Therefore, an online decision tool, called BPMM Smart-Selector, was built and tested. As illustrated, it serves organisations and academics wishing to choose a BPMM. The tool consists of a questionnaire with 14 decision criteria and trade-offs, linked to a decision table that guides users to the BPMM that best matches their needs. Particularly, it concerns six assessment criteria, five improvement criteria, and three non-design criteria. They were elicited after a content analysis of 69 BPMMs and an international Delphi study (or consensus-seeking decision-making). One of the criteria (i.e. presence of capabilities) corroborates the findings of Van Looy et al.  by confirming three clusters of business process capabilities addressed by BPMMs. Additionally, AHP (or multi-criteria decision-making) was used to weigh criteria. The final scores for selection and transparency allowed a thorough BPMM overview and an a priori quality check to decide whether a BPMM is included in the tool. We genuinely applied the IS design guidelines of Hevner et al. , the IS artefact types of March and Smith , and the IS design theory components of Walls et al. . The design requirements are supported by the empirical evidence that we collected. Future research could investigate whether our methodology allows theory building on other decision tools (e.g. for selecting other maturity models). Another avenue is to build a theory to explain why organisations opt for a specific capability cluster, based on the data collected by the tool.