Business process modelling has gained widespread acceptance, particularly inlarge IT-enabled business projects. It is applied as a process design andmanagement technique across all project lifecycle phases. While there has beenmuch research on process modelling, there has been little attention on 'how to'conduct process modelling effectively, or on the evaluation of process modellinginitiatives and outcomes. This study addresses this gap by deriving a processmodelling success model that contains both the success factors (independentvariables) and success dimensions (dependent variables) of process modelling.The study employs a multi-method approach, blending both qualitative andquantitative research methods. The research design commenced with acomprehensive literature review, which includes the first annotated bibliographyin process modelling research. A multiple case study approach was used to buildthe conceptual process modelling success model which resulted in a model witheleven (11) success factors (namely Modeller Expertise, Team Structure, ProjectManagement, User Competence, User Participation, Management Support,Leadership, Communication, Modelling Tool, Modelling Language and ModellingMethodology), two (2) moderating variables (namely Process Complexity andProject Importance) and five (5) process modelling success dimensions (namelyModeller Satisfaction, Model Quality, User Satisfaction, Model Use and ModellingImpact). This conceptual model was then operationalised and tested across aglobal sample, with an online survey instrument.290 valid responses were received. The constructs were analysed seeking aparsimonious, valid and reliable model. The statistical analysis of this phaseassisted in deriving the final process modelling success model. The dependentvariables of this model consisted of three (3) contextual success factors (namelyTop Management Support, Project Management and Resource Availability), two(2) Modelling specific success factors (namely Modelling Aids and ModellerExpertise), and two (2) moderating variables (namely Importance and ProcessComplexity). The dependent variable; Process Modelling Success (PMS) wasderived with three (3) success measurement dimensions (namely Model Quality,Process Impacts and Process Efficiency). All resulting success factors proved tohave a significant role in predicting process modelling success. Interactioneffects 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) andProcess Modelling Success (PMS) in a linear manner and that Process Complexity(PC) moderated the relationship between Resource Availability (RA) and ProcessModelling Success (PMS), also in a linear manner.This is the first reported study with empirical evidence on process modellingsuccess. The progressive outcomes of this study have been readily accepted bythe practitioner and academic community, with 16 published internationalrefereed-conference papers [including best paper award at the Pacific AsianConference on Information Systems (PACIS 2004)], 2 journal publications, andover 5 major industry presentations made upon invitation.
