Supporting Pre-Existing Teams in Crisis with IT: A Preliminary Organizational-Team Collaboration Framework

A number of pre-existing teams are trained to operate in crisis. These teams can be found in aviation, navy, nuclear power, offshore oil, air traffic control facilities, and trauma centers. Understanding how to support pre-existing teams like these, with IT is essential. To date, most support for these teams is automation support such as an electronic checklist for an airplane flight crew responding to an engine fire rather than collaboration support such as linking paramedics in the field to doctors in emergency rooms. While automated support is rapidly developing, very little consideration has been given to enhancing the collaboration support for teams that face crisis. With advances in network capacity and sensors, IT has enabled pre-existing teams that face crisis the opportunity to obtain collaboration support from others in the organization. Collaboration with other human experts is necessary to aid problem discovery and to consider ramifications of responses. Here we suggest a preliminary set of IT system guiding principles to support collaboration for a particular, but common type of pre-existing team that faces crisis. These principles are based on two frameworks that have been developed to mitigate the effects of crisis. One is an organizational approach called the High Reliability Organization (HRO); the other, a team approach, was developed in the aviation community known as Crew Resource Management (CRM). Here we briefly explain each approach, highlight their principles, and then suggest principles of a Collaboration Crisis IT (CCIT) system to support the collaboration needs of teams that face crisis

Teaching Awareness of Ambiguity in Data

The widespread and growing use of analytics has highlighted the need for more data savvy students. One recently identified aspect of data literacy is the awareness of the ambiguity. This paper outlines a method and initial results for raising awareness of data ambiguity in a short, one lesson, active learning format suitable for business or computer science courses. The paper also includes a summary prior research on ambiguity, data literacy taxonomies, implications, and suggestions for further research

MI Systems Taxonomy

Systems theory is often used in MIS research and applications. It is frequently assumed that the underlying principles of system theory are shared by both the author and audience. However, as will be presented here, multiple variants of systems theory exist, with often conflicting basic tenets which can lead authors and audiences to misunderstanding. This paper offers a taxonomy of four systems theories. Brief suggestions for applications of each are made. The limitations of systems theories are presented

Big Data Critical Thinking Skills for Analysts—Learning to Ask the Right Questions

Big data analysis is in high demand; however, little work has identified important critical thinking skills for big data analysts. Here we suggest these critical thinking skills include understanding the limits of measurement and representation, the challenges of the uniqueness of information, and of underdeterminism, and the difficulty of selecting an appropriate software tool. In addition to explaining these topics and their impacts on analysis, we provide example questions for each and suggested overall learning outcomes as well as need for further research

A Preliminary Information Theory of Difference

We propose a difference theory of information. The difference theory of information defines information as adaptation, as any difference that makes a difference (Bateson 1973). The theory posits that information workers such as symbolic analysts, end users and systems analysts perceive differences and conceive information. This theory of information is applied to several IS scenarios to demonstrate its utility. One primary use from this theory is to enforce a difference between data and information

Informing Students about Information: Seven Semantic Exercises

Information is a term widely but carelessly used in our day-to-day language While used poorly as a common expression, a growing number of recent publications on information have identified the importance of the term for both IS research and practice. Most of these publications, seeking to anchor the term more specifically, attribute meaning to information to distinguish it from data. In this paper, we present several in-class exercises we have developed to help students understand the implications of this semantic distinction. While one can use these exercises to teach and explain any semantic theory of information, they were originally designed to reinforce a particular semantic theory of information—the difference theory of information (McKinney & Yoos, n.d.). We discuss the lessons we learned and the paper’s limitations and implications

Teaching Tip: Active Learning in the IS Classroom: A Student Crowdpolling Exercise for IS Courses

Active learning pedagogy has many documented benefits, and while several positive examples of its recent use in STEM classes have led to better performance, greater diversity, more equity, and improved retention of underrepresented student populations, more research in IS and IT classrooms is needed. Most active learning exercises are in a traditional in-person format; however, the COVID-19 pandemic has created a demand for more online classes. Here we present an easy-to-adopt, active learning crowdpolling exercise that can be used for all modalities, including online, hybrid, and face-to-face, moreover, can be used throughout the semester or for a portion of it. The exercise creates a small crowdpolling results database that can be used to enhance student data literacy and teach a variety of IS topics such as database, systems analysis and design, and data analytics. An extended example of how it is used in the Introduction to IS course is provided