Journal of Management Information Systems

Volume 33 Number 4 2016 pp. 938-941

Special Issue: Designing Tools to Answer Great Information Systems Research Questions

Giboney, Justin Scott, Briggs, Robert O, and Nunamaker, Jay F

JUSTIN SCOTT GIBONEY ([email protected]) is an assistant professor in Information Security and Digital Forensics at the University at Albany. He received his Ph.D. in management information systems from the University of Arizona. His research focuses on behavioral information security, deception detection, expert systems, and meta-analytic processes. He emphasizes design science research and system building to solve real-world problems with technology. He has published in International Journal of Human–Computer Studies, Communications of the AIS and Decision Support Systems, as well as in cybersecurity, management, and psychology journals.

ROBERT O. BRIGGS ([email protected]; corresponding author) is a professor of information systems at San Diego State University. He earned his doctorate in management information systems at the University of Arizona. He researches the cognitive foundations of collaboration and uses his findings to design new collaborative work practices and technologies. He is a cofounder of the collaboration engineering field and coinventor of the ThinkLets design pattern language for collaborative work processes. He has designed collaboration systems and collaborative workspaces for industry, academia, government, and the military. He has published more than 200 scholarly works on collaboration systems and technology, addressing issues of team productivity, technology-supported learning, creativity, satisfaction, and technology transition.

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. He has held a professional engineer’s license since 1965. He was inducted into the Design Science Hall of Fame and received the LEO Award for Lifetime Achievement from the Association for Information Systems. He was featured in the July 1997 issue of Forbes Magazine on technology as one of eight key innovators in information technology. He specializes in the fields of system analysis and design, collaboration technology, and deception detection. The commercial product GroupSystems ThinkTank, based on his research, is often referred to as the gold standard for structured collaboration systems. He founded the MIS Department at the University of Arizona in 1974 and served as department head for 18 years.

The designing of artifacts is at the core of the information systems discipline [4]. In this way, the discipline can tackle great research questions. A great research question has five elements: it is a substantial benefit to someone, it affects a broad and diverse population, it has no obvious path forward, all its easy solutions have failed, and it is targeted to a certain goal (see Figure 1) [2]. First, a great research question must produce a tangible and noticeable change for people when answered. Without a change in society, the research to answer the question offers little value to those that fund the research. Second, a great research question must affect as many people as possible. Such questions may make great societal changes even if the individual effect is small. Third, a great research question will not have an obvious answer. The contribution of an obvious answer is much smaller than one that has no such answer. Fourth, a great research question will be one that people have tried and failed to answer with simple solutions. This element of a research question allows the research to succeed where others have failed and to provide a desired answer. Lastly, a great research question is targeted to a specific goal with a specific intended effect. This allows the value of the research to be quantified. 10.1080/07421222.2016.1267512-F0001Figure 1.

Elements of a Great Research Problem

This special issue contains five studies that contribute to some of the great research questions by employing design elements [1, 3] in their research via building tangible systems to answer their research questions. Specifically, these studies contribute to answering the questions:

How can people use technology to overcome organizational-interpersonal issues?

How can people use technology to better understand online social exchanges?

The first article, “Understanding Information Systems Integration Deficiencies in Mergers and Acquisitions: A Configurational Perspective,” by Stefan Henningsson and William J. Kettinger, explains the sources of six deficiencies resulting from information systems (IS) integration when two companies merge: business inefficiencies, business disruption, unrealized potential, staff reaction, delay, and overspending. The article makes use of 37 published case studies to demonstrate nine configurations that cause these deficiencies: information-technology (IT)-based capability destruction, cultural imposition, growing pains, untapping potential, asymmetric standardization, emulsion, paralysis, death march, and rebounder. Each of these configurations may be caused by one of four integration strategies: IS absorption, IS coexistence, IS best of breed, and IS renewal. Understanding the causal mechanisms of IS integration deficiencies will help practitioners take necessary precautions to avert problematic mergers.

The second study, “Trust Development in Globally Distributed Collaboration: A Case of U.S. and Chinese Mixed Teams,” by Xusen Cheng, Shixuan Fu, and Douglas Druckenmiller, discusses trust development in online collaboration. Using a design science approach, this article studies teams of U.S. and Chinese students working together in an online collaborative environment. The study shows that process-guided collaborative teams report decreased risk, increased benefits, and increased trust compared to non–process-guided collaborative teams. Specifically, the article indicates that the use of timed thinkLet modules weakens the influence of time zone differences and other communication barriers. Managers and facilitators of globally distributive teams can use collaboration processes to help organize more efficient meetings and reach more satisfying outcomes.

The third article, “A Friend Like Me: Modeling Network Formation in a Location-Based Social Network,” by Gene Moo Lee, Liangfei Qiu, and Andrew B. Whinston, combines physical, social, and technical elements to study the network formation of location-based social networks. As people are increasingly using social media to report their location data, the authors study the homophily effect in friendship creation based on that use. This work builds four user similarity measures using unstructured biographical text, geographic data, activity check-ins, and short messages (e.g., tweets). By combining the four measures, the study develops an algorithm to calculate user proximity. The contributions of this article can be used by managers and data analysts to discover similarities of customers and potential customers to create better services and fulfill their needs.

In the fourth article, “Targeted Twitter Sentiment Analysis for Brands Using Supervised Feature Engineering and the Dynamic Architecture for Artificial Neural Networks,” Manoochehr Ghiassi, David Zimbra, and Sean Lee propose, build, and test a novel method of extracting sentiments of short messages (e.g., tweets). It is difficult for current sentiment analysis methods to determine the sentiment of short messages, and this study contributes an algorithm to decrease the dimensionality of tweet features and problem complexity. The algorithm increases the number of sentiment classes from three to five, to increase the accuracy of sentiment analysis. The study tests the algorithm using tweets about a popular beverage company and a popular government official. This algorithm consistently outperforms alternative algorithms.

The last article in the Special Issue, “Identifying and Profiling Key Sellers in the Cyber Carding Community: AZSecure Text Mining System,” by Weifeng Li, Hsinchun Chen, and Jay F. Nunamaker Jr., develops a system to identify and profile key sellers in online underground marketplaces. The system uses sentiment analysis, customer reviews, and advertisements to analyze eight international underground economy forums. By scraping underground forums, the study identifies key words and extract features to review sentiment and build seller characteristic models. The article proposes an algorithm for each step and for identifying top sellers. This work can be especially useful to law enforcement agencies in combating cybercrime. Using this tool, law enforcement agencies can identify tops sellers to inhibit their criminal activity.

These articles were developed in a refereed process from the initial versions presented at the Hawaii International Conference on System Sciences in Koloa from 2016 and previous years. Each of these works develops a system that increases our understanding of how people can use technology to improve their interactions and pursuits. Specifically, these studies allow managers, analysts, and law enforcement to identify attributes of people engaging in online interactions and to develop processes to enrich these interactions.


1.Hevner, A.R.; March, S.T.; Park, J.; and Ram, S. Design science in information systems research. MIS Quarterly, 28, 1 (2004), 75–105.

2.Nunamaker Jr., J.F.; Twyman, N.W.; Giboney, J.S.; and Briggs, R.O. Creating high-value real-world impact through systematic programs of research. MIS Quarterly, Forthcoming.

3.Nunamaker Jr., J.F., and Briggs, R.O. Toward a broader vision for information systems. ACM Transactions on Management Information Systems, 2, 4 (2011), 1–20.

4.Zwass, V. Editorial Introduction. Journal of Management Information Systems, 32, 3 (2015), 1–2.