Process Management and Blockchain Technology


Blockchain technology provides a distributed, immutable record of transactions that can be and are independently verified by actors on a peer-to-peer network. Blockchains can provide the infrastructure for distribute applications (DApps) in a wide variety of areas. The distributed nature of the blockchain, together with independent verification of transactions, and the ability to specify and execute smart contracts, makes blockchains useful in applications where actors have to cooperate but may not trust each other. This makes it ideally suited for applications of workflow management in these contexts as well. Blockchains can serve as the infrastructure that connects workflow participants and coordinates their shared processes.

Publications on this Topic

Process Management with Big Data and Deep Learning


Process mining is the analysis of event logs. Many information systems produce event logs that capture the actions of their users. Examples of event logs are page requests of web-servers and business-object method calls in ERP systems. Process discovery is that area of process mining that deals with the identification of the processes followed by system users, for example the process of ordering a product on an e-commerce web-site, or the process of scheduling a manufacturing order in an ERP system. Process discovery is important when the underlying systems are not process-aware. Another area of process mining is compliance assurance, determining whether the actually executed process conforms to a prescribed one.


Modern information systems, such as web-crawlers, web-servers, ERP systems, databases, etc., are increasingly distributed, with replicated instances deployed on multiple physical machines, for example as part of a load-balancing architecture or for geographic proximity to users. Given the distributed nature of event logs, it is natural to look for a distributed way to mine these for processes. The Map-Reduce approach is a scalable means of analyzing distributed data and performing distributed computations on such data. In this ongoing research project, I investigate how well-known process mining algorithms can be implemented using Map-Reduce. Such implementations take advantage of the natural fit between distributed event logs and distributed computations, to make the algorithms scalable to large data sets. Related to this is the use of modern Deep Learning frameworks for process prediction. Predicting the future behaviour of a process at runtime is important for the active and timely management of the process to reduce operational risk and the probability of process failure. The size of event data that is available for past and present process instances requires modern Deep Learning frameworks to process them. Recent advances in neural network architectures and GPU processing capabilities make the application of Deep Learning methos feasible.

Video

  • I have presented a very brief introduction and summary of my work on this topic at a Memorial University symposium on AI applications (October 2020)
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Publications on this Topic

  • Mehdiyev, N., Evermann, J., and Fettke, P.: A Novel Business Process Prediction Model Using a Deep Learning Method. BISE Business and Information Systems Engineering (accepted May 22nd, 2018).
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  • Mehdiyev, N., Fettke, P. and Evermann, J.: A Multi-Stage Deep Learning Approach for Business Process Event Prediction. 19th IEEE Conference on Business Informatics (CBI), Thessaloniki, Greece, July 24-27, 2017.
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  • Evermann, J., Rehse, J.-R., and Fettke, P.: XES Tensorow - Process Prediction using the Tensorflow Deep-Learning Framework. Forum of the Conference on Advanced Information Systems Engineering (CAiSE), Essen, Germany, June 12-16, 2017.
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  • Evermann, J., Rehse, J.-R., and Fettke, P.: Predicting Process Behaviour Using Deep Learning" Decision Support Systems, 100, 129-140.
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  • Evermann, J., Rehse, J.-R., and Fettke, P.: Process Discovery from Event Stream Data in the Cloud - A Scalable, Distributed Implementation of the Flexible Heuristics Miner on the Amazon Kinesis Cloud Infrastructure. CloudBPM Workshop on Business Process Monitoring and Performance Analysis in the Cloud at the 8th IEEE International Conference on Cloud Computing Technologies and Science (CloudCom 2016) .
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  • Evermann, J., Rehse, J.-R., and Fettke, P.: A Deep Learning Approach for Predicting Process Behaviour at Runtime. PRAISE International Workshop on Runtime Analysis of Process-Aware Information Systems at the 14th International Conference on Business Process Management (BPM). (accepted July 4, 2016).
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  • Evermann, J., Thaler, T. and Fettke, P.: Clustering Traces Using Sequence Alignment Business Process Intelligence Workshop at BPM 2015 (accepted June 26th, 2015).
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  • Evermann, J.: Scalable Process Discovery using Map-Reduce. IEEE Transactions on Services Computing, 9 (3), 469-481.
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  • Evermann, J. and Assadipour, G. (2014) Big Data meets Process Mining: Implementing the Alpha Algorithm with Map-Reduce. ACM Symposium on Applied Computing, March 24-28, Gyeongju, Korea.
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Data Integration


Data integration is an increasingly important activity for businesses that are faced with a dynamic environment of re-organization, mergers and acquisitions of business units and other business. Each organization provides their own data and information systems which manage that data. Being able to integrate often widely different systems and information is a requirement for making mergers, acquisitions and re-organizations successful at the operational level. Data integration also plays a crucial role in ensuring data flow between the organizations of supply chain, in enabling the seamless interaction of multiple organizations in industries such as healthcare, in collecting accurate information from and providing up to date information to citizens by government organizations, and for improved decision making and operational efficiencies within a business.


The main aim of my data integration research is to provide decision support to humans for the data integration process. Whereas prior work has focused on technical aspects, my work in this area emphasizes the human aspect of the integration process. It is based on the premise that any decision support tool will be accepted by its users only when it provides integration decisions that conform to their own reasoning about the information. Consequently, my work draws on cognitive sciences to identify the mechanisms by which humans make data integration decisions. The main outcomes of this research stream are a series of empirical studies that have identified the cognitive processes and cognitive biases that are at play when humans make data integration decisions.


This research stream is funded by a discovery grant from the Natural Sciences and Engineering Research Council (NSERC) and by the 2010 Terra-Nova Young Innovators award. Data integration also plays a significant part in the "Too big to be ignored" project on the global collection of data about small-scale fisheries, which is funded by a $2.5 million partnership grant from the Social Sciences and Humanities Research Council (SSHRC).


Publications on this Topic

  • Raad, E., and Evermann, J. (2015) The Role of Analogy in Ontology Alignment: A Study on LISA Cognitive Systems Research 33, pp. 1-16.
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  • Raad, E., and Evermann, J. (2014) Is Ontology Alignment like Analogy? Knowledge Integration with LISA. ACM Symposium on Applied Computing, March 24-28, Gyeongju, Korea.
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  • Nasir, M., Hoeber, O., and Evermann, J. (2013) Supporting Ontology Alignment Tasks with Edge Bundling. Proceedings of the i-KNOW International Conference on Knowlege Management and Knowledge Computing, Graz, Austria.
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  • Evermann, J. (2012) Applying Cognitive Principles of Similarity to Data Integration - The Case of SIAM. Proceedings of the 18th Americas Conference on Information Systems (AMCIS), Seattle, WA, August 2012.
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  • Lukyanenko, R. and Evermann, J. (2011)Using Cognitive Theories to Support Data Integration. Proceedings of the 17th Americas Conference on Information systems (AMCIS), Detroit, MI, August 2011.
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  • Nasir, M., Hoeber, O., and Evermann, J. (2011) Effective Visualization of Ontology Mappings using Edge Bundling. Poster at the Graphics Interface Conference, St. John's, NL, May 2011. (editorially reviewed)
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  • Evermann, J. (2010) Contextual Factors in Database Integration -- A Delphi Study. Proceedings of the 29th International Conference on Conceptual Modeling (ER 2010), Vancouver, BC, November 2010.
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  • Evermann, J. (2009) Theories of Meaning in Schema Matching. In: Erickson, J. (ed.) Database Technologies: Concepts, Methodologies, Tools, and Applications, IGI Global, Hershey, PA.
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  • Evermann, J. (2009) Theories of Meaning in Schema Matching: An Exploratory Study. Information Systems. Vol 34, 28-44, Jan 2009.
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  • Evermann, J. (2008) Theories of Meaning in Schema Matching: A Review. Journal of Database Management. Vol 19, No 3, 55-83. 2008
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  • Evermann, J. (2008) An Exploratory Study of Database Integration Processes. IEEE Transactions on Knowledge and Data Engineering. Vol 20, No 1, Jan 2008, 99-115.
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Research Methodology and Statistical Data Analysis


The validity of research results depends critically on the use of appropriate research methodology. Quantitative research methods and statistical data analysis are an important aspect of research in Information Systems. Specifically, recent years has seen a sharp increase in the use of structural equation models with latent variables as a statistical technique to represent and evaluate causal theories. These models represent entire theories, their constructs and measurement. However, best practices and guidelines for their application to Information Systems research are still evolving.


The main contributions of my work in this research stream are the development of guidelines for the appropriate representation of Information Systems theories in structural equation models, the comparative evaluation of two statistical techniques to estimate and evaluate structural equation models, and the development of guidelines for building theories from quantitative studies.


Publications on this Topic

  • Rönkkö, M., Lee, N., Evermann, J., McIntosh, C., and Antonakis, J.: Markting or Methodology - Exposing the Fallacies of PLS with Simple Demonstrations European Journal of Marketing, in press (accepted Feb 28, 2022)
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  • Rönkkö, M., Lee, N., Evermann, J., McIntosh, C., and Antonakis, J.: Fractures in the Edifice of PLS - Rejoinder to Commentaries on Marketing or Methodology European Journal of Marketing, in press (accepted Feb 28, 2022)
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  • Evermann, J. and Rönkkö, M.: Recent Developments in PLS Communications of the AIS, in press (accepted March 23, 2021)
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  • Evermann, J. and Tate, M.: Assessing the predictive performance of structural equation model estimators. Journal of Business Research, 69 (10), 4565-4582.
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  • Lukyanenko, R., Evermann, J. and Parsons, J.: Guidelines for Establishing Instantiation Validity in IT Artifacts: A Survey of IS Research. Design Science Research in Information Systems and Technologies Conference (DESRIST), May 21-22, Dublin, Ireland.
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  • Lukyanenko, R., Parsons, J., and Evermann, J. (2014) Instantiation Validity in IS Design Research. Proceedings of the Design Science Research in Information Systems and Technologies Conference (DESRIST), May 22-23, Miami, FL.
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  • Evermann, J. and Tate, M.: Comparing Out-of-Sample Predictive Ability of PLS, Covariance, and Regression Models. Proceedings of the 35th International Conference on Information Systems (ICIS), Auckland, New Zealand, December 2014.
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  • Evermann, J. and Tate, M. (2014) Bayesian Structural Equation Models for Cumulative Theory Building in Information Systems - A Brief Tutorial using BUGS and R. Communications of the Association for Information Systems 34(1), article 76.
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  • Rönkkö, M. and Evermann, J. (2013) A Critical Examination of Common Beliefs about Partial Least Squares Path Modeling. Organizational Research Methods 16, 425-448.
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  • Evermann, J. and Tate, M. (2013) Is My Model Right? Model Quality and Model Misspecification in PLS - Recommendations for IS Research
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  • Evermann, J. and Tate, M. (2012) An ontology of structural equation models with application to computer self-efficacy. Proceedings of the 33rd International Conference on Information Systems (ICIS), Orlando, FL, December 2012.
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  • Evermann, J. and Tate, M. (2012)A note of caution on covariance-equivalent models in Information Systems. Proceedings of the 33rd International Conference on Information Systems (ICIS), Orlando, FL, December 2012.
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  • Evermann, J. and Tate, M. (2012) Comparing the Predictive Ability of PLS and Covariance Analysis. Proceedings of the 33rd International Conference on Information Systems (ICIS), Orlando, FL, December 2012.
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  • Evermann, J. and Tate, M. (2012) Bayesian Structural Equation Models for Cumulative Theory Building in Information Systems. Proceedings of the 18th Americas Conference on Information Systems (AMCIS), Seattle, WA, August 2012.
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  • Evermann, J. and Tate, M. (2011) Fitting Covariance Models for Theory Generation. Journal of the Association for Information Systems. 12(9), 632-661.
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  • Evermann, J. (2010) Multiple group analysis using the sem package in the R system. Structural Equation Modeling - A Multidisciplinary Journal. 17(4), 677-702.
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  • Tate, M. and Evermann, J. (2010) Obstacles to building effective theory about attitudes and behaviours towards technology. Workshop on Information Systems Foundations, Australia National University, Canberra, ACT, Australia, 30 Sept, 2010.
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  • Evermann, J. and Tate, M. (2010) Testing Models or Fitting Models? Identifying Model Misspecification in PLS. Proceedings of the 31st International Conference on Information Systems, ICIS, St. Louis, MS, December 2010.
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  • Evermann, J. and Tate, M. (2009) Constructs in the Mist: The Lost World of the IT Artifact. Proceedings of the 8th JAIS Theory Development Workshop at ICIS 2009, Phoenix, AZ
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  • Evermann, J. and Tate, M. (2009) Building Theory from Quantitative Studies, or, How to Fit SEM Models. Proceedings of the 30th International Conference on Information Systems (ICIS), Phoenix, AZ, December 2009.
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  • Evermann, J. and Tate, M. (2009) Constructs in the Mist: The Lost World of the IT Artifact. Proceedings of the 30th International Conference on Information Systems (ICIS), Phoenix, AZ, December 2009.
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  • Tate, M. and Evermann, J. (2009) Perceptive Users with Attitudes: Some Heuristics on Theorizing. Proceedings of the 30th International Conference on Information Systems (ICIS), Phoenix, AZ, December 2009.
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Domain Modelling


My initial area of research and my dissertation work, was that of domain analysis and domain modelling. The analysis and description or modelling of a business domain is a prerequisite for the development of information systems that allow the business to exploit new technology for competitive advantage. Diagrams and other graphical methods for analyzing and describing a business are important tools in business analysis and modelling. My research in this area investigates the suitability of various graphical methods and provides guidance for their application to business modeling. My work has focused on examining the Unified Modeling Language (UML), a widely-used graphical language, originally developed to create diagrams of software systems.


The main contribution of my work is a set of guidelines or rules on how to best use UML descriptions of the business as precise and unambiguous as possible. Later studies have verified these rules using case studies and experimental methods and found them to clearly improve the understanding of a business that is conveyed by appropriately constructed diagrams.


Publications on this Topic

  • Bera, P. and Evermann, J. (2014) Guidelines for Using UML Association Classes and their Effect on Domain Understanding in Requirements Engineering. Requirements Engineering Journal, 19(1), 63-80.
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  • Dobing, B., Evermann, J. and Parsons, J. (2010) Understanding Use Case-Driven Development in the UML. Proceedings of the 31st International Conference on Information Systems, ICIS, St. Louis, MS, December 2010.
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  • Evermann, J. and Fang, J. (2010) Evaluating Ontologies: Towards a Cognitive Measure of Quality. Information Systems. 35(4), 391-403.
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  • Evermann, J. and Wand, Y. (2009) Ontology Based Object-Oriented Domain Modeling: Representing Behaviour. Journal of Database Management, Vol 20, No 1, pp 48-77, Jan 2009.
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  • Evermann, J. (2009) A UML and OWL Description of Bunge's Upper-Level Ontology Model. Software and Systems Modeling. Vol 8, No 2, 235-249, February 2009.
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  • Evermann, J. (2008) A Cognitive Semantics for the Association Construct. Requirements Engineering Journal, Vol 13, No 3, 167-186, 2008.
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  • Evermann, J. and Halimi, H. (2008) Associations and Mutual Properties - An Experimental Assessment. Proceedings of the Americas Conference on Information Systems AMCIS, Toronto, August 2008.
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  • Fang, J. and Evermann, J. (2007) Evaluating Ontologies - Towards a Cognitive Measure of Quality. Proceedings of the Workshop on Vocabularies, Ontologies, and Rules for the Enterprise (VORTE), Annapolis, MD, October 2007.
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  • Evermann, J. and Wand, Y. (2006) Ontological Modelling Rules for UML: An Empirical Assessment. Journal of Computer Information Systems, Vol 46, No 5, Fall 2006, 14-29.
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  • Evermann, J. (2006) Organizational Paradigms and Organizational Modelling. In: Proceedings of the Workshop on Business and IT Alignment (BUSITAL) at CAiSE 2006, Luxembourg, June 2006.
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  • Evermann, J. (2005) The Association Construct in Conceptual Modeling - An Analysis using the Bunge Ontological Model. In: Proceedings of the 17th International Conference on Advanced Information Systems Engineering, CAiSE 2005, Porto, Portugal, June 2005, 33-47.
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  • Evermann, J. (2005) Towards Natural Language Semantics for the Association Construct. In: Proceedings of the Forum for the 17th International Conference on Advanced Information Systems Engineering, CAiSE 2005, Porto, Portugal, June 2005, 77-82.
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  • Evermann, J. (2005) Towards a Cognitive Foundation for Knowledge Representation. Information Systems Journal. Vol 15, pp 147-178. 2005
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  • Evermann, J. and Wand, Y. (2005) Ontology-Based Object-Oriented Business Modelling: Fundmental Concepts. Requirements Engineering Journal, Vol 10, Number 2, 2005, 146-160.
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  • Evermann, J. and Wand, Y. (2005) Towards formalizing domain modeling semantics in language syntax. IEEE Transactions on Software Engineering, Vol 31, No 1, Jan 2005, pp 21-37.
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  • Evermann, J. (2005) Thinking Ontologically - Conceptual versus design models in UML. In: Rosemann, M. and Green, P. (eds.) Ontologies and Business Analysis. Idea Group Publishing, 2005, 82-104.
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  • Evermann, J. and Wand, Y. (2001) An ontological examination of object interaction in conceptual modeling. In Proceedings of the Workshop on Information Technologies and Systems WITS'01, New Orleans, December 15-16, 2001.
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  • Evermann, J. and Wand, Y. (2001) Towards ontologically based semantics for UML constructs. In H. Kunii, S. Jajodia, and A. Solvberg, editors, Proceedings of the 20th International Conference on Conceptual Modeling, Yokohama, Japan, Nov. 27-30, 2001, ER 2001.
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Cognitive Theories and Natural Language in Information Systems


Descriptions or models of businesses and other organizations are special cases of explicit representations of human knowledge about a domain. To ensure accurate and unambiguous descriptions of a domain, the language (graphical, diagrams, or otherwise) to describe it must be able to express the concepts that humans use to think and reason about the domain. My work in this area has drawn on cognitive sciences and linguistics to identify these core concepts and to evaluate existing knowledge representation languages. Based on this evaluation, I have provided guidelines for developing improved knowledge representation languages.


The main contributions are a formal description of a comprehensive set of cognitive concepts for describing businesses and other organizations, a novel way of assessing the quality of knowledge representation languages and guidelines for the use of the Unified Modeling Languages which have been empirically shown to increase the understanding of a business that can be conveyed. Dr. Natasha Noy (Stanford University, 2nd most cited author in this field) commented on my work by saying "Thank you for the very intriguing and stimulating piece of work! It has definitely contributed quite a new perspective to ontology evaluation - the field that has been searching for new fresh ideas for some time."


Publications on this Topic

  • Raad, E., and Evermann, J. (2015) The Role of Analogy in Ontology Alignment: A Study on LISA Cognitive Systems Research 33, pp. 1-16.
  • BibTeX
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  • Preprint (PDF)
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  • Full Paper (External Link)
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  • Raad, E., and Evermann, J. (2014) Is Ontology Alignment like Analogy? Knowledge Integration with LISA. ACM Symposium on Applied Computing, March 24-28, Gyeongju, Korea.
  • BibTeX
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  • Full Paper (External Link)
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  • Evermann, J. (2012) Applying Cognitive Principles of Similarity to Data Integration - The Case of SIAM. Proceedings of the 18th Americas Conference on Information Systems (AMCIS), Seattle, WA, August 2012.
  • BibTeX
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  • Full Paper (PDF)
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  • Full Paper (External Link)
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  • Lukyanenko, R. and Evermann, J. (2011) Using Cognitive Theories to Support Data Integration. Proceedings of the 17th Americas Conference on Information systems (AMCIS), Detroit, MI, August 2011.
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  • Full Paper (PDF)
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  • Evermann, J. and Fang, J. (2010) Evaluating Ontologies: Towards a Cognitive Measure of Quality. Information Systems. 35(4), 391-403.
  • BibTeX
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  • Full Paper (External Link)
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  • Evermann, J. (2008) A Cognitive Semantics for the Association Construct. Requirements Engineering Journal, Vol 13, No 3, 167-186, 2008.
  • BibTeX
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  • Full Paper (External Link)
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  • Fang, J. and Evermann, J. (2007) Evaluating Ontologies - Towards a Cognitive Measure of Quality. Proceedings of the Workshop on Vocabularies, Ontologies, and Rules for the Enterprise (VORTE), Annapolis, MD, October 2007.
  • BibTeX
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  • Full Paper (External Link)
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  • Evermann, J. (2005) Towards Natural Language Semantics for the Association Construct. In: Proceedings of the Forum for the 17th International Conference on Advanced Information Systems Engineering, CAiSE 2005, Porto, Portugal, June 2005, 77-82.
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  • Evermann, J. Towards a Cognitive Foundation for Knowledge Representation. Information Systems Journal. Vol 15, pp 147-178. 2005
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Service Quality


Businesses increasingly rely on the Internet as a channel to not just communicate with customers and sell products, but to also deliver services. While service quality is well understood in the physical word with its face-to-face service encounters, its dimensions, determinants, and consequences in the online channel are still unknown. The main result of my work in this research area has been the development and validation of a new survey instrument to measure perceptions of online service quality as well as a novel conceptualization of online service quality in terms of information system characteristics, drawing especially on human-computer interaction research.


Publications on this Topic

  • Tate, M. and Evermann, J. The End of ServQual in Online Services Research: Where to from here? e-Service Journal 7(1), 60-85.
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  • Tate, M. and Evermann, J. (2009) Descendents of ServQual in Online Services Research: The End of the Line? Proceedings of the 15th Americas Conference on Information Systems AMCIS, San Francisco, CA, Aug 2009.
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  • Tate, M., Evermann, J., Hope, B. (2008) Old Theory and New Service Quality: An Exploratory Study of the Nature and Nomological Net of Online Service Quality and Continuing Use using Information Systems Theory. Proceedings of the Australasian Conference on Information Systems (ACIS), Christchurch, New Zealand, December 3-5, 2008.
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  • Tate, M., Evermann, J., Hope, B., Barnes, S. (2008) Stakeholder expectations of service quality in a university web portal. In: Oliver, D.; Romm-Livermore, C. and Sudweeks, F. (eds.) Self-service in the Internet Age: Expectations and Experiences. Springer-Verlag, Berlin, 2008.
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  • Tate, M., Hope, B., Evermann, J., and Barnes, S. (2007) Perceived Online Service Quality: Latent Dimensions and Ontological Implications. Proceedings of the Pacific Asia Conference on Information Systems (PACIS), Auckland, NZ, July 2007.
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  • Tate, M., Evermann, J., Hope, B. and Barnes, S. (2007) Perceived Service Quality in a University Web Portal: Revising the E-Qual Instrument. In Proceedings of the Hawaii International Conference on System Sciences HICSS-40, January 3-6, 2007, Hawaii.
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Other Research


In addition to the above research streams, I have worked on a number of other topics. Aspect-oriented information system development allows information systems developers to extend existing systems with additional functionality. While computer science researchers have introduced the concept in programming languages over the previous 15 years, the ideas are also applicable to the description of information systems for business domains, i.e. in modelling. The main results of my work in this area are extensions to the Unified Modeling Language to support aspect-oriented information system development.


The Information Systems discipline is not a purely technical discipline and can benefit from thoughts and ideas in philosophy, sociology, and related areas. For example, the principles of semiotics can be applied to measure and improve understanding of system requirements that are collected in information system development. Ideas in sociology, especially different views or images of organizations, present alternatives to the dominant technical perspective on information systems and have important implications for business and software modelling. The scientific method of inquiry is concerned with the understanding, explanation, and description of a domain of interest, analogous to the way in which business analysts might try to understand and describe an organizational or business domain. These parallels have implications on how business analysis should be conducted.


Publications on this Topic

  • Jewer, J., and Evermann, J.: Enhancing Learning Outcomes through Experiential Learning: Using Open-Source Systems to Teach Enterprise Systems and Business Process Managament Journal of Information Systems Education (JISE) (accepted January 29th, 2016).
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  • Jewer, J. and Evermann, J.: Experiential Learning with an Open-Source Enterprise System. Proceedings of the 20th Americas Conference on Information Systems (AMCIS), Aug 7-10, Savannah, GA.
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  • Evermann, J.: Developing a Realistic Workflow Management Environment for Teaching: An Interface from YAWL to OpenERP. Proceedings of the YAWL User Group Symposium, St. Augustin, Germany, June 7th.
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  • Evermann, J., Furey, M. and Hussey, T.: Using YAWL in a Business Undergraduate Course on Process Management: An Experience Report. Proceedings of the YAWL User Group Symposium, St. Augustin, Germany, June 7th.
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  • Evermann, J., Fiech, A., and Alam, F.E. (2011) A Platform-Independent UML Profile for Aspect-Oriented Development. Proceedings of the Fourth International C* Conference on Computer Science and Software Engineering (C3S2E), Montreal, QC, May 2011.
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  • Alam, F., Evermann, J. and Fiech, A. (2009) Modeling for Dynamic Aspect-Oriented Development. Proceedings of the C3S2E-9 Conference, Montreal, May 2009.
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  • Evermann, J. (2009) A UML and OWL Description of Bunge's Upper-Level Ontology Model. Software and Systems Modeling. Vol 8, No 2, 235-249, February 2009.
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  • Evermann, J. and Mistri, R. (2008) System Analysis as Scientific Inquiry. Proceedings of the Americas Conference on Information Systems AMCIS, Toronto, August 2008.
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  • Kim, H.M., Sengupta, A. and Evermann, J. (2007) MOQ: Web-Services Ontologies for QoS and General Quality Evaluations. International Journal of Metadata, Semantics, and Ontologies. Vol 2, No 5, 195-200. 2007.
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  • Evermann, J. (2007) A Meta-Level Specification and Profile for AspectJ in UML. Journal of Object Technology. Vol 6, No 7, Aug 2007.
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  • Evermann, J., Ferreira, J., and Haggard, G. (2007) Improving Mutual Understanding of Development Artifacts: A Semiotics Based Approach. Proceedings of the Americas Conference on Information Systems (AMCIS), Keystone, CO, Aug 2007.
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  • Evermann, J. (2007) A Meta-Level Specification and Profile for AspectJ in UML. Proceedings of the Workshop on Aspect Oriented Modeling (AOM) at AOSD 2007.
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  • Evermann, J. (2006) Organizational Paradigms and Organizational Modelling. In: Proceedings of the Workshop on Business and IT Alignment (BUSITAL) at CAiSE 2006, Luxembourg, June 2006.
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  • Kim, H., Sengupta, A. and Evermann, J. (2005) MOQ: Web Services Ontologies for QoS and General Quality Evaluations. In Proceedings of the 13th European Conference on Information Systems, ECIS 2005, Regensburg, Germany, May 2005.
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  • Evermann, J. (2004) The Science of IS Development - Implications for Applications. In Proceedings of the 3rd Symposium on Research in System Analysis and Design (AIS SIGSAND), St. John's, Canada, June 12-14, 2004.
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Multi-Disciplinarity and Inter-Discplinary Research


My research is strongly interdisciplinary in nature, going beyond the traditional core of the IS discipline, and references the following disciplines.


My work on online service quality draws heavily on theories in the marketing field. Conceptualization and measurement of service quality as an application of expectation disconfirmation theory originated with face-to-face interactions in the marketing research discipline and has later been adapted and applied to online channels by information systems researchers.


My research on business analysis and business modelling draws on research in cognition, psychology, and the psychology of language. What constitutes the perceived reality of a business depends in part on existing cognitive concepts which are reflected in our language grammars. Cognitive principles on memory organization and recall affect how best to represent our knowledge of a business domain. Cognitive processes and principles of similarity judgements can help identify appropriate methods to support data integration processes.


Ideas from philosophy have contributed to my work in many areas. Philosophy of science can contribute to the way business analysts investigate and describe businesses. Semiotics, the philosophy of signs and their meaning developed by Peirce, can inform how representations and models of a business domain are used to convey the knowledge of a business analyst to other stakeholders. Theories of meaning, include those by Frege, Russell, Wittgenstein, Quine, or Kripke, are important in providing a basis for how humans assign meaning to data elements during the data integration process .


Methodological Expertise and Diversity


Design science research creates artifacts such as models or software applications to demonstrate the feasibility of new ideas or principles. Created artifacts include software prototypes, formal computer-readable descriptions of domains, and computer-readable extensions to modelling languages. I have used case studies to support findings from design research, for example to study information systems development projects to investigate the use of design science artifacts and their impact on the development process. Bringing together qualitative as well as quantitative methodologies, I have employed the process tracing methodology to study the cognitive processes of data integrators. I have extended the traditional qualitative analysis of such studies with quantitative methods, such as hidden Markov models (from operations research) and sequence analysis techniques (from bio-informatics). I have extensively used laboratory experiments for my empirical studies, also with complex designs, such as fractional and blocked experimental designs in my study on data integration. For my work on online service quality I have employed cross-sectional surveys, combined with structural equation statistical models for data analysis. As part of my ongoing work on data integration, I have completed a study using the Delphi methodology, a tool appropriate for inductive theory building from panels of experts. The study sheds additional insight on the data integration process from a practitioner perspective.