Call for Papers
The Association for Information Systems‘ Special Interest Group on Decision Support and Analytics (SIGDSA) is seeking submissions related to teaching decision support and data analytics concepts, tools, and technologies.
As organizations strive to foster innovation through the digitization of processes supporting their product and service offerings, the digitization efforts, as well as the ability to harness insights and value from the tremendous data being generated, present new challenges and opportunities. The explosive growth in big data, fueled in part, by the digital ubiquity of the Internet of Things (IoT), social media, and technology-facilitated human networks accentuates these opportunities and challenges. Educators need to dynamically respond to industry needs, developing/updating curricula and teaching practices to prepare the next-generation thinkers and workforce by empowering them with skills and concepts related to decision support and analytics, thus helping organizations progress on their digitization and transformation journey.
Aligned with the Symposium theme of Analytics for Digital Frontiers, we seek submissions focused on teaching business intelligence, big data analytics, cognitive analytics, social media analytics, machine learning, IoT analytics, data science, visualization, or other emerging analytic technologies. Submissions should address the acquisition, application, and development of the knowledge and skills required to use business analytics and data science in industry or topics related to the creation and maintenance of environments that support this education. We also seek prototype demonstrations or design research idea papers showcasing/discussing innovative analytics methodologies and applications.
Submission Categories: Teaching and Prototype Track
Contributions can be of varied types, including: teaching cases, tutorials, curriculum innovations, pedagogical studies, or infrastructure for teaching analytics, addressing topics including but not limited to:
- Business Analytics (BA) curriculum development with assessment methods
- Bodies of Knowledge (BOK) for Business Intelligence, Analytics, Machine Learning, Data Science
- Developing an interdisciplinary focus that cuts across traditional functional areas
- Differentiating business analytics curricula for Undergraduate, MBA, Master of Science, and Executive Education, and tailoring curricula to generalists and specialists
- Integration of IT and analytics skills
- New BA programs, courses, and centers
- Pedagogical innovations in business analytics
- Innovative pedagogical approaches to teaching ethics in business analytics design, development, and implementation
- Innovations in the delivery of analytics courses (online, hybrid, student interaction, etc.)
- Challenges and solutions related to the construction and maintenance of classrooms and labs in support of business analytics education
- Organizational case studies (with teaching notes) that provide rich stories of individual organizations’ management and analytics initiatives and address a variety of issues in management sponsorship, strategic priorities, adoption and implementation of analytics, technical challenges, collaboration, communication, and actionable results in business analytics:
- Different stakeholders involved in the decision-making process and their role in the implementation success
- Role of strategic planning in the adoption of analytics and implementation success
- Organizational challenges and barriers
- Metrics to measure the effectiveness of these technologies to enhance business value
- Success factors and reasons for failure
- Ethical and/or global issues
- Tutorial exercises (with teaching notes) and simulations that provide a business scenario, data sets, and step-by-step introduction to the latest business analytics tools, covering topics including but not limited to:
- Integration of data management and analytics
- Integration of analytics and visualization tools
- Analytics certification and digital badges programs
- Open source vs. proprietary platforms
- The use of platform and/or software to enhance pedagogy for data mining, machine learning, text mining, network analytics, cognitive analytics using big data and/or novel applications
- Data sets that can be used by other faculty members to augment their pedagogy
- Hands-on software experiences and software tutorials
Submissions for the teaching sub-track must not exceed ten (10) single-spaced pages and must conform to the paper submission template. The page limit covers everything in the submission, including title, text, tables, figures, appendices, and references. Submissions that exceed this limit will be automatically rejected. Based on the type of submission (e.g., teaching case, tutorial, etc.), the correct submission type should be mentioned in the submission.
Submissions can be of one of the types listed below
Prototype Demonstrations Proposals
These are showcasing innovative methodological developments or applications of artificial intelligence, machine learning, deep learning, reinforcement learning, text mining, or other data analytics/decision support techniques are encouraged. Prototypes should highlight aspects such as novelty, architecture, functioning, and ongoing/future work. This high-visibility category is different from the research paper presentation category and provides an opportunity to display a working artifact to an audience in an interactive manner. Prototype demonstrations will involve a poster display (printed or electronic) and a live demonstration of a prototype to the attendees.
The proposal submission must be five to seven (5-7) single-spaced pages and must conform to the paper submission template. The page limit covers everything in the submission, including title, text, tables, figures, appendices, and references. In addition to the Title, Abstract, and Keywords, a prototype proposal should contain only one or two sections describing the prototype or core research idea to be showcased, highlighting the key features and novelty. The submission type should be mentioned in the submission as Prototype Demonstration.
Design Research Idea Paper
Idea papers are supposed to identify innovative future lines of design science-oriented research related to artificial intelligence, machine learning, deep learning, reinforcement learning, text mining, or other data analytics/decision support topics. The idea papers should include a convincing motivation, a brief overview of the relevant state of the art, the core idea, possible contributions to research and practice, and an outline of a possible course of the research project.
The idea paper must be two to three (2-3) single-spaced pages extended abstract and must conform to the paper submission template. The page limit covers everything in the submission, including title, text, tables, figures, and references. The submission type should be mentioned in the submission as Design Research Idea Paper.
Submission Template, Submission System, and Awards and Publication Opportunities
Teaching and Prototype Track Co-Chairs
For questions regarding the submissions to the teaching and prototype track, please contact:
We recommend including ‘Pre-ICIS SIGDSA 2022 Teaching & Prototype Track Question’ as your email subject line.