Group Consensus in Business Process Modeling: A Measure and Its Application

Group Consensus in Business Process Modeling: A Measure and Its Application

Peter Rittgen (University of Borås, Borås, Sweden)
Copyright: © 2013 |Pages: 15
DOI: 10.4018/ijec.2013100102
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Abstract

Consensus is an important measure for the success of any business process modeling effort. Although intensively studied in the general literature on group processes, consensus has hardly been considered in business process modeling and never seriously measured. The author defines consensus as the level of agreement of group members’ views on the process and introduce business process similarity as a proxy. The author validates the measure by comparing it to an existing self-reported measure of consensus. The author then uses this measure to inform and guide the process of modeling.
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Introduction

The literature on business process modeling is vast and the importance of measuring the success of process modeling projects and sessions has been widely recognized (Dennis, Carte, & Kelly, 2003; Lu & Sadiq, 2007; Luo & Tung, 1999; Recker, Rosemann, Indulska, & Green, 2009; Rosemann, 2006; Sedera, Gable, Rosemann, & Smyth, 2004). But prevalent success measures for individual modeling sessions primarily involve some form of model quality measure (Dean, Orwig, Lee, & Vogel, 1994; Mendling & Recker, 2007; Moody, Sindre, Brasethvik, & Sølvberg, 2003; Sánchez-González, García, Mendling, Ruiz, & Piattini, 2010). While it is undisputed that the quality of a business process model is relevant to modeling success it is not the only and perhaps not even the most important success factor.

The reason for this is twofold: the process model itself is a social construction, and its purpose is again to support some social process, e.g. a change project or system development project. In other words: the model documents the results of one social process (modeling) and serves as a point of departure for another one.

If the model were to be processed by a computer its quality would be of prime importance to ensure correct interpretation by the machine. But the results that are documented in the model are primarily the mutual knowledge that has been developed in the modeling session, the conflicts that had to be solved on the way, and the consensus that has been achieved among the group members as a result.

It is precisely this consensus that is a prerequisite for people’s commitment to the ensuing change project, for example. Often a poor model with high consensus goes further than a good model with little consensus. Hence consensus is a major result that needs to be achieved in business process modeling sessions much like in many other forms of group work.

But while there is considerable research on consensus in other areas (DeStephen & Hirokawa, 1988; Priem, 1990; Yoo & Alavi, 2001) the topic received little attention in business process modeling with researchers barely mentioning the issue (Clegg, 2007; Decker et al., 2005; Kumarapeli, De Lusignan, Ellis, & Jones, 2007; Rittgen, 2010b) and, to the best of our knowledge, not researching it in a systematic way, let alone measuring consensus.

The purpose of this paper is to develop such a measure. To do so we first define the concept of consensus in the next section, “Group consensus in process modeling”. For this purpose we rely on cognitive theories of modeling.

Based on the cognitive concept of a view and the model as its externalization we can interpret consensus as “view agreement” and hence as “model similarity”. The section “Business process model similarity” therefore introduces a measure for the latter.

To evaluate the new measure we resort to validity by comparison to an existing measure of the same concept. The section “Other group consensus measures” introduces established measures for group consensus and argues for the choice of the most suitable one. Validation of the new measure was done in field experiments. The set-up of these experiments is described in the section “Comparing model similarity and consensus in field experiments”.

The section “Data analysis and discussion” reports on the analysis of the data that we collected in the experiments and discusses the respective results and implications. We then proceed by extending the dyadic measure to a group measure in the section “From individual consensus to group consensus”.

The next section, “Applying the measure in collaborative modeling”, discusses the potential uses of this measure in the area of collaborative modeling where external representations of views are abundant as intermediate results.

The paper concludes with a summary of the findings and an outlook on future work.

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