Maes, A., & Poels, G. (2007). Evaluating quality of conceptual modelling scripts based on user perceptions. DATA & KNOWLEDGE ENGINEERING, 63(3), 701-724.
In the paper we present a novel approach for evaluating the quality of conceptual data models from the perspective of model users, who are mostly not experts in Information Technology. The proposed quality evaluation model was developed based on theories of information system success and technology adoption, which are themselves partly grounded on theories from the field of social psychology like the Theory of Planned Behavior and the Theory of Reasoned Action. The model was extensively validated using structural equation modeling techniques. Our methodological contribution is widely used in empirical research on conceptual models (currently 127 citations in Google Scholar and 20 citations in Web of Science).
Cumps, B., Martens, D., De Backer, M., Haesen, R., Viaene, S., Dedene, G., Baesens, B, & Snoeck, M. (2009). Inferring comprehensible business/ICT alignment rules. INFORMATION & MANAGEMENT, 46(2), 116-124.
The application of the AntMiner+ rule induction technique on the business/ICT alignment topic was discussed in this paper. The data set contained survey data from 641 organizations in 7 European countries. By using a rule induction technique (AntMiner+) we were able to distill practical guidelines for managers in obtaining better alignment of ICT investments. The novelty of this study was awarded by a publication in the highly ranked journal Information and Management.
Poels, G., Maes, A., Gailly, F., & Paemeleire, R. (2011). The pragmatic quality of Resources-Events- Agents diagrams: an experimental evaluation. INFORMATION SYSTEMS JOURNAL, 21(1), 63-89.
In our on-going quest for methods to improve the quality of conceptual models, we came across the Resource-Event-Agent (REA) ontology, which is a normative theory from accounting providing concepts and axioms for describing business transaction cycles (e.g., order-to-cash, acquire- to-pay). Together with, at that time PhD students, now professors, Frederik Gailly (UGent) and Wim Laurier (FUSL), Prof. Poels explored and further developed the REA ontology as a conceptual basis for designing enterprise information systems (e.g., ERP systems). We published many papers on this topic, although this one was the first that presented an explanatory theory for the benefits of structuring conceptual data models using axiomatic patterns of REA concepts. The theoretical basis for our experimentally corroborated hypotheses was a collection of cognitive theories that explain pattern recognition phenomena and the resulting reduction in cognitive effort for understanding information representations. Although the paper is recent, it currently lists 34 citations in Google Scholar and 6 citations in Web of Science.
Nelson, H. J., Poels, G., Genero, M., & Piattini, M. (2012). A conceptual modeling quality framework. SOFTWARE QUALITY JOURNAL, 20(1), 201-228.
This paper synthesizes the main output of a ten-year long collaboration with professors Jim Nelson, Marcela Genero and Mario Piattini on developing a sound theoretical foundation for quality assurance in conceptual modeling. The quality framework presented in the paper results from a unique effort in combining ontological theory with semiotics to arrive at a multi-dimensional descriptive theory of quality of conceptual models. The framework was well received by researchers, counting today already 102 citations in Google Scholar and 7 citations in Web of Science.
Van Looy, A., De Backer, M., & Poels, G. (2014). A conceptual framework and classification of capability areas for business process maturity. ENTERPRISE INFORMATION SYSTEMS, 8(2), 188-224.
In this paper we develop and validate a conceptual framework with business process capabilities arranged in different maturity types. The proposed framework was developed based on critical success factors found in the business process literature, and validated by means of a content analysis of 69 sampled process maturity models. Afterwards, the maturity types were identified based on a cluster analysis of the sampled process maturity models, and validated based on discriminant analysis. This paper will be the cornerstone for our future research on maturity models and to build a BPM performance theory within our research group. This contribution is published in a top peer- reviewed journal (which had an impact factor of 9.256 in 2012, and a 1/132 ranking). The paper currently has 44 Google Scholar citations.