| A Critical Examination of Common Beliefs About Partial Least Squares Path Modeling M Rönkkö, J Evermann Organizational Research Methods 16 (3), 425-448, 2013 | 434 | 2013 |
| Predicting process behaviour using deep learning J Evermann, JR Rehse, P Fettke Decision Support Systems, 2017 | 298 | 2017 |
| Assessing the predictive performance of structural equation model estimators J Evermann, M Tate Journal of Business Research 69 (10), 4565-4582, 2016 | 195 | 2016 |
| Ontology based object-oriented domain modelling: fundamental concepts J Evermann, Y Wand Requirements Engineering 10 (2), 146-160, 2005 | 178 | 2005 |
| Towards ontologically based semantics for UML constructs J Evermann, Y Wand International Conference on Conceptual Modeling, 354-367, 2001 | 147 | 2001 |
| Toward formalizing domain modeling semantics in language syntax J Evermann, Y Wand IEEE Transactions on Software Engineering 31 (1), 21-37, 2005 | 127 | 2005 |
| A deep learning approach for predicting process behaviour at runtime J Evermann, JR Rehse, P Fettke International Conference on Business Process Management, 327-338, 2016 | 112 | 2016 |
| A meta-level specification and profile for AspectJ in UML J Evermann Proceedings of the 10th international workshop on Aspect-oriented modeling …, 2007 | 91 | 2007 |
| Towards a cognitive foundation for knowledge representation J Evermann Information Systems Journal 15 (2), 147-178, 2005 | 76 | 2005 |
| Evaluating ontologies: Towards a cognitive measure of quality J Fang, J Evermann 2007 Eleventh International IEEE EDOC Conference Workshop, 109-116, 2007 | 74* | 2007 |
| Evaluating ontologies: Towards a cognitive measure of quality J Evermann, J Fang Information Systems 35 (4), 391-403, 2010 | 67 | 2010 |
| An integrated framework for theories of individual attitudes toward technology M Tate, J Evermann, G Gable Information & Management 52 (6), 710-727, 2015 | 64 | 2015 |
| Introduction to the special issue: the literature review in information systems M Tate, E Furtmueller, J Evermann, W Bandara Communications of the Association for Information Systems 37 (1), 103-111, 2015 | 63 | 2015 |
| A novel business process prediction model using a deep learning method N Mehdiyev, J Evermann, P Fettke Business & information systems engineering 62 (2), 143-157, 2020 | 59 | 2020 |
| Ontological modeling rules for UML: An empirical assessment J Evermann, Y Wand Journal of Computer Information Systems 46 (5), 14-29, 2006 | 59 | 2006 |
| A multi-stage deep learning approach for business process event prediction N Mehdiyev, J Evermann, P Fettke 2017 IEEE 19th Conference on Business Informatics (CBI) 1, 119-128, 2017 | 55 | 2017 |
| A UML and OWL description of Bunge’s upper-level ontology model J Evermann Software & Systems Modeling 8 (2), 235-249, 2009 | 52 | 2009 |
| Instantiation validity in IS design research R Lukyanenko, J Evermann, J Parsons International Conference on Design Science Research in Information Systems …, 2014 | 51 | 2014 |
| Scalable Process Discovery using Map-Reduce J Evermann IEEE Transactions on Services Computing, 1-1, 0 | 47 | |
| Ontology based object-oriented domain modeling: representing behavior J Evermann, Y Wand Journal of Database Management (JDM) 20 (1), 48-77, 2009 | 44 | 2009 |
