About the award winning thesis

Business process modelling has gained widespread acceptance, and is increasingly employed to reshape organisations and industries. However, there has been little attention on ‘how to’ conduct process modelling effectively, or on the evaluation of process modelling initiatives and outcomes. This study, employing a combination of systematic qualitative case studies and an international survey of process modellers, has developed the first rigorously validated model for measuring process modelling success.

Business process modelling has gained widespread acceptance, particularly in large IT-enabled business projects. It is applied as a process design and management technique across all project lifecycle phases. While there has been much research on process modelling, there has been little attention on 'how to' conduct process modelling effectively, or on the evaluation of process modelling initiatives and outcomes. This study addresses this gap by deriving a process modelling success model that contains both the success factors (independent variables) and success dimensions (dependent variables) of process modelling.
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The study employs a multi-method approach, blending both qualitative and quantitative research methods. The research design commenced with a comprehensive literature review, which includes the first annotated bibliography in process modelling research. A multiple case study approach was used to build the conceptual process modelling success model which resulted in a model with eleven (11) success factors (namely Modeller Expertise, Team Structure, Project Management, User Competence, User Participation, Management Support, Leadership, Communication, Modelling Tool, Modelling Language and Modelling Methodology), two (2) moderating variables (namely Process Complexity and Project Importance) and five (5) process modelling success dimensions (namely Modeller Satisfaction, Model Quality, User Satisfaction, Model Use and Modelling Impact). This conceptual model was then operationalised and tested across a global sample, with an online survey instrument.

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290 valid responses were received. The constructs were analysed seeking a parsimonious, valid and reliable model. The statistical analysis of this phase assisted in deriving the final process modelling success model. The dependent variables of this model consisted of three (3) contextual success factors (namely Top Management Support, Project Management and Resource Availability), two (2) Modelling specific success factors (namely Modelling Aids and Modeller Expertise), and two (2) moderating variables (namely Importance and Process Complexity). The dependent variable; Process Modelling Success (PMS) was derived with three (3) success measurement dimensions (namely Model Quality, Process Impacts and Process Efficiency). All resulting success factors proved to have a significant role in predicting process modelling success. Interaction effects with the moderating variables (Importance and Process Complexity) proved to exist with Top Management Support (TMS) and Resource Availability (RA). A close analysis to their interaction relationship illustrated that Importance (IMP) moderated the relationship between Top Management Support (TMS) and Process Modelling Success (PMS) in a linear manner and that Process Complexity (PC) moderated the relationship between Resource Availability (RA) and Process Modelling Success (PMS), also in a linear manner.

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This is the first reported study with empirical evidence on process modelling success. The progressive outcomes of this study have been readily accepted by the practitioner and academic community, with a number of published international-refereed-conference papers [including best paper award at the Pacific Asian Conference on Information Systems (PACIS 2004)], journal publications, and industry presentations made upon invitation.

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