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Journal of Management Information Systems

Volume 36 Number 1 2019 pp. 11-13

Special Section: Engineering Artifacts and Processes of Information Systems

Giboney, Justin S, Briggs, Robert, and Nunamaker, Jr., Jay

JUSTIN SCOTT GIBONEY ([email protected]) is an Assistant Professor of Information Technology at Brigham Young University. He received his Ph.D. in Management Information Systems from the University of Arizona. His research focuses on behavioral information security, deception detection, and knowledge-based systems. He has been an investigator on six NSF-funded grants on deception and forensics-related technologies. Dr. Giboney has published 29 papers related to information security, deception detection, and decision support in such journals as MIS Quarterly, Computers & Security, Decision Support Systems, and Computers in Human Behavior, and others.

ROBERT O. BRIGGS ([email protected]; corresponding author) is a Professor of Information Systems at San Diego State University. He earned his Ph.D. in Information Systems from University of Arizona. Dr. Briggs researches the cognitive foundations of collaboration and uses his findings to design and deploy new collaboration systems and new collaborative work practices. He is co-founder of Collaboration Engineering as a scholarly discipline, co-inventor of the thinkLets design pattern language, and the inventor of Computer Assisted Collaboration technology and Process Support Applications. Dr. Briggs has published more than 250 peer-reviewed manuscripts related to economic, social, political, cognitive, emotional, and technological aspects of collaboration. He lectures around the world on the logic of scientific inquiry.

JAY F. NUNAMAKER JR ([email protected]) is Regents and Soldwedel Professor of MIS, Computer Science and Communication and Director of the Center for the Management of Information and the National Center for Border Security and Immigration at the University of Arizona. He received his Ph.D. in operations research and systems engineering from Case Institute of Technology. Dr. Nunamaker has held a professional engineer’s license since 1965. His specialization is in the fields of system analysis and design, collaboration technology, and deception detection. The commercial product Group Systems ThinkTank, based on his research, is often referred to as the gold standard for structured collaboration systems. He was inducted into the Design Science Hall of Fame and received the LEO Award for Lifetime Achievement from the Association for Information Systems. Dr. Nunamaker was featured in the July 1997 issue of Forbes Magazine on technology as one of eight key innovators in information technology. He founded the MIS Department at the University of Arizona in 1974 and served as department head for 18 years.


Information Systems (IS) is, and by definition must be, in part, an engineering research discipline. The unique purpose that defines IS as a discipline, and distinguishes us from our reference disciplines is:

To understand and improve the ways people create value with information

If we seek-to-improve, then it is inescapable that some part of our research community must design and test solutions, and so it does. IS researchers design hardware, software, and procedures for using the technology in ways that create value; they design data models, infrastructure, organizational structures, and operational standards. We are, in part, a design-driven engineering research discipline.

Engineering research is different than engineering practice. Engineering practice creates a specific solution to a specific problem for specific people in a specific context, and the solution need not be novel, only useful. Engineering research, by contrast uses scientific knowledge and techniques to develop novel, generalizable solutions to major real-world issues, important classes of unsolved problems in the field. A generalizable solution must be adaptable to the many contexts in which a problem appears. The integrated circuit (IC) chip in one’s phone, for instance, is a product of engineering: a specific solution for a specific problem. The IC chip as a concept is a generalizable solution, one can adapt it to the many contexts where high speed digital logic could add value. Engineering research uses scientific methods to validate a generalizable solution, and then, builds a design theory, which is a body of knowledge that practitioners can use to create their own instances of the generalizable solution. Action Research, Design Science Research, and Action Design Science Research are instantiations of Engineering Research.

The focus of this special issue is Engineering IS Artifacts and Practices. There are at least three objects-of-research at the heart of every engineering discipline: design artifacts, (e.g., diagrams, plans); designed artifacts (In our case, the information systems we create); and design processes (e.g., Joint Application Design; Rapid Prototyping). Each of the papers in this issue concerns one of more of these objects.

The paper, “Nuanced Responses to Enterprise Architecture Management: Loyalty, Voice, and Exit,” by Lena Hylving and Bendik Bygstad concerns the engineering of IS governance processes at the organizational level. The paper investigates why a substantial portion of Enterprise Architecture Management (EAM) initiatives fail to produce organizational change. EAM governance is centralized by necessity, but practical responses to a centralize plan emerge from social processes in departments and projects, where local optimization of utility may precipitate resistance to EAM initiatives. The authors investigate three field cases in a large government agency and discover how stakeholders at the project level respond to EAM initiatives and how to characterize observed differences. The study found responses ranging from active cooperation to disinterested disregard. The more successful implementations of EAM used innovation processes that fostered opportunities for mutual learning among EAM and project personnel. The paper recommends that “EAM should drop its obsession with compliance, and partner with stakeholders to learn from them instead of trying to control them.” The authors conclude that, for EAM to succeed, its leaders should embrace modern agile approaches to management based on iterative learning and frequent feedback.

The work of Hao Hua Sun Yin, Klaus Langenheldt, Mikkel Harlev, Raghava Rao Mukkamala, and Ravi Vatrapu tests the utility of a designed object. The authors use a machine learning approach to deduce the identities of cryptocurrency accounts associated with criminal activity in their paper, “Regulating Cryptocurrencies: A Supervised Machine Learning Approach to De-Anonymizing the Bitcoin Blockchain.” This paper presents a novel approach for de-anonymizing the Bitcoin Blockchain by using Supervised Machine Learning to predict the type of yet-unidentified entities. Results revealed that the approach could indeed predict the type of a yet-unidentified entity. Authors achieved an average cross-validation accuracy of 80.42% in the test set. The authors conclude that the assumed level of anonymity of the Bitcoin Blockchain may not as high as commonly believed.

In their paper, “Collaboration Engineering Research and Practice: Contributions, Insights, and Future Directions,” Gert-Jan de Vreede and Robert O. Briggs examine the current state of an emerging design discipline, presenting an exhaustive review and analysis of the Collaboration Engineering literature from its first scholarly paper in 2001 through the peer-reviewed articles of 2017. Collaboration Engineering (CE) is an approach to designing technology-supported collaborative work practices for high-value tasks and transferring them to practitioners to execute without support from collaboration experts. The paper summarizes the contributions of 331 papers from authors from 21 countries on all permanently settled continents except Australia. The paper organizes the scholarly contributions of CE under four themes: Foundations, Approach, Tools, and Professionalization, with a number of related topics in each theme. It then synthesizes key insights from the extant body of CE research. It identifies significant areas of inquiry that have not yet been explored and looks ahead to CE research opportunities that emerge as society, organizations, technologies, and the nature of collaboration evolve. The paper may be useful as a step toward compiling a validated canon of CE concepts into a design theory of Collaboration Engineering, as well as for shaping a research agenda for the coming decade. It may also offer a gateway for scholars with new interests in CE to gain familiarity with the literature.

The studies in this issue build on some of the best work reported at the Hawaii International Conference on Information Systems. We commend them for your reading.

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