Relevance in Information Systems Research

Information Systems as an academic discipline makes two contributions to society. The first, knowledge exploration, is the creation of new knowledge that is not -- and should not be -- relevant to today\u27s practitioner. The goal of knowledge exploration is to change the future, not improve the present. The second, knowledge exploitation, is the dissemination of knowledge to serve current practice (and to train future practitioners, our students). While I believe we have done a good job of knowledge exploration, I believe we need develop new vehicles to promote, nurture, and validate knowledge exploitation much like our academic cousins in Medicine, Engineering, and Computer Science

A high-speed digital camera system for the observation of rapid H-alpha fluctuations in solar flares

Researchers developed a prototype digital camera system for obtaining H-alpha images of solar flares with 0.1 s time resolution. They intend to operate this system in conjunction with SMM's Hard X Ray Burst Spectrometer, with x ray instruments which will be available on the Gamma Ray Observatory and eventually with the Gamma Ray Imaging Device (GRID), and with the High Resolution Gamma-Ray and Hard X Ray Spectrometer (HIREGS) which are being developed for the Max '91 program. The digital camera has recently proven to be successful as a one camera system operating in the blue wing of H-alpha during the first Max '91 campaign. Construction and procurement of a second and possibly a third camera for simultaneous observations at other wavelengths are underway as are analyses of the campaign data

Selecting Research Topics: Personal Experiences and Speculations For the Future

In the rapidly changing field of information systems, every researcher faces important choices about what research topics to explore and how to pursue that research. This paper addresses these questions by summarizing a panel discussion at the 2001 Decision Sciences Institute (DSI) annual meeting. The first part of this paper provides a framework explaining factors that can be used in selecting research topics. Then we explain how our own past choices of research topics reflect the factors in the framework. In the final section, we use the framework to speculate about promising research topics for the future

Closing Thoughts on “Information Systems Research: Thinking Outside the Basket and Beyond the Journal”

This essay provides a rejoinder by the original authors to the set of responses to Fitzgerald, B., Dennis, A. R., An, J., Tsutsui, S., & Muchala, R. C. (2019). Information systems research: Thinking outside the basket and beyond the journal. Communications of the Association for Information Systems, 45, 110-133

Information Search on the Web: Understanding the Impact of Response Time Delays with Information Foraging Theory

Web delays are a persistent and highly publicized problem. Long delays have been shown to reduce information search, but less is known about the impact of more modest “acceptable” delays — delays that do not substantially reduce user satisfaction. Prior research suggests that as the time and effort required to complete a task increases, decision-makers tend to reduce information search at the expense of decision quality. In this study, the effects of an acceptable time delay (seven seconds) on information search behavior were examined. Results showed that increased time and effort caused by acceptable delays provoked increased information search

‘Collective intelligence’ is not necessarily present in virtual groups

When we communicate online, we miss an important element of group intelligence: social sensitivity, write Jordan B. Barlow and Alan R. Denni

Conducting Experimental Research in Information Systems

This article presents a summary of key success factors for publishing research in top-tier IS journals; it is not intended to be an introduction to research, but to go beyond the rational model presented by most introductory works. The paper begins by discussing the processes by which research projects are identified and developed, specifically focusing on where project ideas are found and how projects are selected and refined. Next, we discuss the fundamental role that theory development, testing and refinement plays in research. This discussion is followed by an examination of several interrelated research design issues, including maximizing publication potential, and executing the study\u27s activities. Next, the importance of writing quality as well as the cultivation and refinement of a project\u27s message is discussed. Finally, a checklist is provided on how to be rejected which summarizes the central themes of this article

A Replication Manifesto

Replication is one of the main principles of the scientific method. The social sciences, and in particular the information systems discipline, has lagged behind the physical sciences which have more established traditions of independently replicating studies from other labs. In this essay, we outline the need for replication in the information systems discipline, identifying three possible approaches for executing such studies. There are numerous benefits to the discipline from embracing and valuing replication research. Replication will either improve confidence in our research findings or identify important boundary conditions. Replications also enhance various scientific processes and offer methodical and educational improvements. Collectively, these benefits will help the information systems discipline mature and prosper

Fitting Graphical DSS to Task Characteristics

Previous research has found the use of graphical Decision Support Systems (DSS) to be more effective than tabular DSS in some decision situations, but not in others [8, 9]. This paper presents the results of two laboratory experiments testing the hypothesis that the features provided by graphical DSS may best fit some tasks, while those of tabular DSS best fit other tasks. The first experiment, which examined decision outcomes, found that a tabular DSS better fit a less complex task, while a graphical DSS better fit a more complex task with high information load in which decision makers needed to understand relationships among data elements. The second experiment, which examined decision processes, found that decision makers using graphical DSS tended to use less information in making their decisions than those using tabular DSS