Sponsoring SIG: SIGDSA
- Ashish Gupta, Auburn University, firstname.lastname@example.org (primary contact)
- Sagnika Sen, Pennsylvania State University, email@example.com
- Uzma Raja, The University of Alabama, firstname.lastname@example.org
Description of Track:
Recent technological innovations and novel applications that are being driven by data science & analytics are changing the way organizations and the society-at-large consumes data and information in an unprecedented way. For instance, big data approaches supported by social media computing and the Internet of Things (IoT) is revolutionizing the way individuals communicate and live as well as how organizations operate and develop strategies. It has led to the need for novel tools and techniques for advanced analytics to gain valuable insights for decision makers and organizations. The ability to manage big data and glean insightful knowledge is also leading towards process-centric transformations in organizations with respect to how they operate and maintain their competitive advantage. At a higher level, big data and analytics applications are able to drive positive impact on the society in the areas of food safety, energy and sustainability.
Organizations are allocating greater resources to enhance and develop new decision support applications driven by advanced analytics to garner insights and knowledge. As organizations transform into data and analytics centric enterprises (e.g. health insurance companies), more research is needed not only on the technical aspects of analytics such as application of new data science approaches like deep learning, computing infrastructure, but also on various other organizational issues in the analytics context. Examples include managerial, strategic, leadership, data governance issues; process innovation, inter-organizational issues, etc. Research contributions in this space can inform industry on handling various organizational and technical opportunities along with various challenges associated with building and executing big data driven organization.
This track seeks original research that promotes technical, theoretical, design science, pedagogical, and behavioral research as well as emerging applications in the innovative areas of analytics and big data.
Research areas in big data, analytics include but are not limited to: data analytics & visualization for varied data (or sources) such as sensors or IoT data, text, multimedia, clickstreams, user-generated content involving issues dealing with curation, management and infrastructure for (big) data; standards, semantics, privacy, security and legal issues in big data, analytics and KM; performance analysis, intelligence and scientific discovery in big data, analytics and KM; analytics applications in smart cities, sustainability, smart grids, detecting financial fraud, digital learning, healthcare, criminal justice, energy, environmental and scientific domains, and the like; business process management applications such as process discovery, conformance and mining using analytics and KM; cost-sensitive, value-oriented data analytics and utility-based data mining; data-driven decision analysis and optimization.
Minitrack 1: Big Data and Business Transformation
Ilias Pappas, email@example.com
Patrick Mikalef, firstname.lastname@example.org
Paul Pavlou, email@example.com
The minitrack aims to explore the business transformations big data entail, and how they are harnessed to enable innovative ways of conducting business and to support rapid decision making with external stakeholders such as business partners, customers, and public authorities. Yet, to understand how big data can be of value requires an examination of the interplay between various factors (e.g., social, technical, economical, environmental). In order to gain insight and solve such challenges, research methods and accompanying theoretical perspectives need to go beyond the traditional scope of Information Systems. Papers that address topics on how information sources, technological infrastructure, human skills and knowledge, organizational/team structures, and management practices coalesce to achieve desired ends, are of increased interest. Emphasis will be placed on interdisciplinary papers bridging organizational science, information systems strategic management, marketing, and computer science. The minitrack welcomes quantitative, qualitative, and mixed methods papers, as well as reviews, conceptual papers, and theory development papers.
Minitrack 2: Social Media Marketing
Yusan Lin, firstname.lastname@example.org
Tawei (David) Wang, email@example.com
Social media has profoundly changed how individuals communicate and interact with other individuals or companies. Such communication and interactions on social media provide a valuable source to support firms’ marketing activities. Though with the development of data analytics skills and techniques, it remains challenging for companies to leverage social media data to create marketing insights or to further improve service/product quality and performance. This minitrack welcomes submissions of original work addressing challenges in the context of social media marketing. We also encourage submissions of research in progress or studies that are more practically oriented. Relevant topics for this minitrack include, but are not limited to, the following in the context of social media marketing: Applications of data analytics, Branding strategies, Competition dynamics, Image processing or location based services, Information security and privacy issues, Marketing campaigns, Opinion leader and its impact on information dissemination, Purchasing behavior, Sentiment analysis, Sales performance, Strategies and tactics, Use of social media marketing for the fashion industry
Minitrack 3: Openness, Privacy and Compliance in Big Data: Ethical issues and Beyond
Ciara Heavin, firstname.lastname@example.org
Yvonne O’Connor, Y.OConnor@ucc.ie
Big data creates opportunities to generate new valuable insights for individuals, organizations and society in areas such as healthcare, education, finance and manufacturing. Indeed, we are only beginning to scratch the surface in terms of understanding the vast possibilities for big data. We continue to struggle, however, to negotiate the balance between exploring the possibilities for big data, big data analytics, and the need to protect the privacy of an individual’s data and the new regulatory landscape. The forthcoming European General Data Protection Regulation (GDPR) and other regulatory/compliance standards highlight the implications for the use and/or implementation of information systems across a variety of domains in diverse, geographical locations. As individuals, organizations, legislators and societies grapple with understanding the boundaries of big data, this mini-track is interested in research engaging with the ethical, integrity and related challenges of big data and big data analytics.
Minitrack 4: Business Intelligence and Analytics Case Studies
Jerry Fjermestad, email@example.com
Stephan kudyba, firstname.lastname@example.org
Ken Lawrence, email@example.com
The availability of data is driving organizations to store, organize, and analyze information to make better decisions. What types of decisions are being made and with what tools? Organizations need proper information in the right form at the right time. Business intelligence and analytics are the tools that organizations can use. How are they justified, used and implemented? \ The theme of this mini-track highlights the need for conceptualization and empirical study of the implications of the roll of business intelligence and analytics within organizational structures. For this mini-track, we call for high quality research studies from academia, industry, governments, and non-profits, especially collaborations among these groups, to address the benefits, justifications, implementation and use of BI analytics applications in a case study approach.
Minitrack 5: Data Analytics for Managing Organizational Performance
Benjamin Shao, Benjamin.Shao@asu.edu
Robert St. Louis, firstname.lastname@example.org
The goal of data analytics (DA) is to summarize massive amounts of disparate corporate and customer data into succinct information that can help management better understand their business processes, make informed decisions, and measure and improve organizational performance. DA can provide managers with the ability to integrate enterprise-wide data into metrics that link specific objectives to the performance of different business units. In today’s hypercompetitive environment, accurate real-time DA metrics are even more critical for measuring and enhancing organizational performance. Many technologies contribute to DA solutions, including databases, data warehouses, data marts, analytic processing, social analytics, and data mining, among others. DA needs to acquire data from multiple platforms and provide ubiquitous access. This requirement to leverage so-called “big data”presents numerous managerial challenges. This mini-track aims to promote innovative research in the DA domains of organizational performance measurement and improvement.
Minitrack 6: Digital Disruption: Implications for the Geospatial Realm (SIGGIS)
Daniel Farkas, email@example.com
Brian Hilton, Brian.Hilton@cgu.edu
James Pick, James_Pick@redlands.edu
Hindupur Ramakrishna, Hindupur_Ramakrishna@redlands.edu
This mini-track provides a research forum on the varied aspects of GIS for organizational intelligence, location-based analytics, and geospatial data management. Aligned with the AMCIS 2018 theme, “Digital Disruption”, manuscript submissions related to “The Geospatial Realm and Digital Disruption” are encouraged. Digital disruption through various technologies such as “The Cloud”, Internet of Things, Artificial Intelligence, Augmented and Virtual Reality along with trends such as the massification of maps, demand for real-time information, and a booming geospatial start-up community, are impacting the Geospatial Realm. As such, papers are solicited across topics including big spatial data, spatial decision making, spatial knowledge management, cloud-based GIS, spatial crowdsourcing, management decision-making using GIS, spatial workforce development, managerial concerns, regulation, privacy, security, ethical aspects concerning spatial data and related technologies, mobile location-based applications, location-based theory, mobile-based GIS, software development incorporating place, societal issues of big spatial data, and emerging areas of GIS and geospatial analytics.
Minitrack 7: Social Media and Network Analytics
Amit Deokar, firstname.lastname@example.org
Haya Ajjan, email@example.com
Uday Kulkarni, firstname.lastname@example.org
The novel IT capabilities of social media platforms are affecting the underlying theories of social network analysis which were primarily developed for offline social networks. Online social networks differ from traditional offline social networks in both structure and content (Kane, et al, 2014). For example, online social structures (e.g., Facebook ‘friends’, Twitter ‘followers’, LinkedIn ‘connections’) offer novel structural formalisms. And online content (e.g., online reviews, eWOM via tweets, likes, thumbs) that differs in nature, frequency, reach, propagation speed, etc., by orders of magnitude, can be continuously captured at the finest level of granularity desired. Unstructured data in the form of text and emoticons communicated over social media provides a lean yet in many ways unique and emotionally rich means of communication that has the potential to influence message receivers (consumers, colleagues, stakeholders, etc.). Such unstructured data in social media presents research challenges that goes beyond sentiment analysis, and includes nuanced aspects such as uncertainty, specificity, and so forth. Further, the indulgence and adoption of social media has shown to affect human behavior and decision-making in unique ways that has led to many open topics of research. This mini-track is inviting papers focused on how analytical techniques can be used to understand social influence and impacts, data models for social media, social network structure and information diffusion, and social network analysis.
Minitrack 8: Unintended Consequences of Artificial Intelligence, Machine Learning and (Big) Data Analytics
Oyku Isik, email@example.com
Ales Popovic, firstname.lastname@example.org
Anna Sidorova, Anna.Sidorova@unt.edu
This century’s gold rush is the rush towards artificial intelligence by means of (big) data analytics, and machine learning; not only did it fuel the growth of the Internet Giants as we know them, but it also enabled countless start-ups to monetize data and machine learning capabilities. From retail and banking to transportation and healthcare, artificial intelligence, machine learning and big data analytics have reinvented nearly every industry, with its power to transform infinite quantities of information into an actionable recommendation or instantaneous action. But that power can go wrong – in fact, it already has – and the consequences may not hit us until years or even generations from now. Lost in the glamour of endless possibilities, companies investing into artificial intelligence, machine learning and data analytics (a.k.a. autonomous analytics) capabilities pay little attention to how their work impacts the rest of the society; especially in terms of biases, ethics, and morality. For instance, even in the wonderfully useful case of predictive policing (using mathematical, predictive and analytical techniques in law enforcement to identify potential criminal activity), a dark side has recently emerged: data-driven racial profiling. Analytics have no ethical principles; when not well-designed or well-managed, it can harm psychologically, financially or even physically, the subject. Thus, IS discipline needs to pay more attention to the role that organizations play in the design, development, and dissemination of artificial intelligence and data analytics artifacts within the firm, towards their customers, and the rest of the society. This mini track encourages submissions examining unintended negative consequences of artificial intelligence, machine learning and analytics, especially within firm-customer relationships, and exploring how (mis-)management of AI, machine learning and (big) data analytics impacts these relationships. We especially encourage conceptual and empirical research building on organization science, ethics, marketing, human resources and management theories.
Minitrack 9: Big Data Driven Process Mining and Innovation
Arti Mann, email@example.com
Faruk Arslan, firstname.lastname@example.org
One of the main aspects of business analytics is process innovation driven by the use of data generated from the day-to-day business operations of an organization. Process innovation involves workflow re-design and resource re-configuration for higher efficiency, better quality and effectiveness; improving decision-making processes for better information flow and decision-enablement. Process mining, a relatively new research discipline, may play a significant role in enabling such innovations. The objective of Process Mining is to discover, monitor and improve actual business processes by extracting knowledge from voluminous event logs generated because of the execution of those processes. The aim of this mini-track is to promote theoretical and empirical research addressing the aforementioned aspects of process innovation. Example topics may include, but are not limited to – design of data driven decision-making processes, case studies and empirical evaluation of data-driven process innovation, process mining approaches and algorithms.
Minitrack 10: The Analytics of Things
Michael Goul, email@example.com
Jason Nichols, firstname.lastname@example.org
A convergence of the Internet of Things, Big Data, artificial intelligence and analytics will occur as Moore’s Law impacts computational capabilities at the edge. The phrase ‘Analytics of Things’ (AoT) has come to characterize this convergence. Companies are already undergoing strategic shifts to prepare for AoT-based competition. Product designers are rethinking traditional lifecycles as smart products will have dormant properties that can be ‘turned on’ long after a sale. Predictive analytics at the edge will deliver personalized services in new environments including smart cities and stadiums. Machine learning at the edge will mean it is possible that unpredictable emergent system behavior will occur. There are strategic, tactical, computational, ethical and governance implications to AoT. However, there is a short window of opportunity for the IS research community to help prepare for this future. This minitrack addresses a broad spectrum of IT research that can help to advance thought leadership in AoT.
Minitrack 11: Healthcare Data Analytics
Raj Sharman, email@example.com
Pamella Howell, firstname.lastname@example.org
Mohamed Abdelhamid, Mohamed.Abdelhamid@csulb.edu
Victoria Kisekka, email@example.com
The extensive monitoring of healthcare has led to an explosion of data that is available for processing using a variety of predictive analytics methods. At one end of analytics, there are tools that help with visualization of data to enable a quick understanding of the data and at the other end are more rigorous advanced econometric tools. Research in the area also includes development of information technology tools, contributions on the methodological front, impacts and information assurance issues relating to Healthcare Analytics. This mini-track solicits all papers relevant to the Information Systems community that relates to the Healthcare Analytics. The extensive monitoring of healthcare has led to an explosion of data that is available for processing using a variety of predictive analytics methods. At one end of analytics, there are tools that help with visualization of data to enable a quick understanding of the data and at the other end are more rigorous advanced econometric tools. Research in the area also includes development of information technology tools, contributions on the methodological front, impacts and information assurance issues relating to Healthcare Analytics. This mini-track solicits all papers relevant to the Information Systems community that relates to the Healthcare Analytics. Suggested topics include, but are not limited to, the following: Payer Analytics, Provider Analytics, Patient Centered Analytics, Supply Chain Analytics for Pharma, Analytics for Life Sciences, Analytics relating to Information Assurance in the Healthcare Area, Analytics relating Healthcare blogs, Analytics with Healthcare Twitter Data, Analytics with data from Healthcare Social Networks, Analytics relating to Healthcare Quality, Analytics relating to Healthcare Information Quality, Analytics to improve care delivery, Analytics stemming from remote monitoring of patients and telemedicine, Adoption and Use of Healthcare Analytics , Predictive Analytics relating to delivery of care, Predictive analytics relating to Personalized Medicine and Prescriptive Analytics, Predictive analytics relating to clinical interventions, Analytics for Clinical Pathways.