Uzma Raja, The University of Alabama, email@example.com
Amit Deokar, University of Massachusetts Lowell, firstname.lastname@example.org
Ashish Gupta, Auburn University, email@example.com
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. It has led to the need for the creation of new and innovative tools and techniques for advanced analytics to gain valuable insights for decision makers and organizations. The ability to manage (big) data, information and knowledge to gain competitive advantage, and the importance of business analytics for this process has been well established.
Organizations are allocating greater time and 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, more research is needed not only on the technical aspects of analytics such as data science algorithms, 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, big data, and knowledge management.
Research areas in big data, analytics and knowledge management (KM) 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 decision analysis and optimization.
Mini-Track 1: Big Data and Business Transformation
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.
Ilias O. Pappas, Norwegian University of Science and Technology, firstname.lastname@example.org
Patrick Mikalef, Norwegian University of Science and Technology, email@example.com
Paul A. Pavlou, Temple University, firstname.lastname@example.org
Mini-Track 2: Big Data Driven Process Mining and Innovation
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 as a result 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.
Sagnika Sen, Pennsylvania State University, email@example.com
Arti Mann, University of Houston Clear Lake, firstname.lastname@example.org
Mini-Track 3: Business Analytics for Managing Organizational Performance
The goal of business analytics (BA) 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. BA 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 BA metrics are even more critical for measuring and enhancing organizational performance. Many technologies contribute to BA solutions, including databases, data warehouses, data marts, analytic processing, social analytics, and data mining, among others. BA 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 BA domains of organizational performance measurement and improvement.
Benjamin Shao, Arizona State University, email@example.com
Robert D. St. Louis, Arizona State University, firstname.lastname@example.org
Mini-Track 4: Business Intelligence & Analytics Cases
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 focus of this min-track is to investigate BI analytics applications in a case study approach. Topics including various elements of analytics such as descriptive/retrospective, predictive and prescriptive applications along with IT based reporting mechanisms that provide actionable information to stakeholders in an organization (e.g. dashboards, interactive reports, etc). Case studies incorporating streaming, real-time initiatives and approaches leveraging structured and unstructured data sources are welcomed. Case study submissions should go beyond a pure data and technology focus and extend the scope to include describing the information creation process (e.g. deciding data sources to be used to create most effective BI for decision support) and potentially mechanisms for monitoring effectiveness for BI users. Minitrack papers can be fast-tracked to the Journal of Organizational Computing and Electronic Commerce. Papers should be significantly enhanced and follow several other guidelines.
Jerry Fjermestad, New Jersey Institute of Technology, email@example.com
Stephan Kudyba, New Jersey Institute of Technology, Stephan.firstname.lastname@example.org
Kenneth Lawrence, New Jersey Institute of Technology, email@example.com
Mini-Track 5: Big Data for Supply Chain and Operations Management
Data Science, Predictive Analytics, and Big Data (DPB) describe a broad array of data-driven business practices that are reshaping the way firms complete in the marketplace. However, employment of these practices is proving tenuous, as firms find difficulty assimilating, analysing, and exploiting the high volume of data made available to them in order to support critical business decisions. Research is needed to build theory and inform practice regarding means through which firms adopt and use DPB for supply chain and operations management applications. In addition, evidence of performance and other desired outcomes is scarce, as is research into the mechanisms that support such outcomes. This minitrack covers topics across this domain, covering a wide range of topics related to DPB in supply chain and operations.
Benjamin T. Hazen, Air Force Institute of Technology, firstname.lastname@example.org
Bradley Boehmke, Air Force Institute of Technology, email@example.com
Mini-Track 6: Information Strategy and Data Privacy in Business Analytics
Business analytics is often employed by business to support their day-to-day decision-making and typically involves using human subject data such as customers, patients, and online and mobile users. There is an increasing concern about privacy, confidentiality, and security of the data by the individuals. On the other hand, organizations need to consider the impact of strategic behaviors of individual data providers on data utility and analytical model quality. Important issues in data analytics, such as strategic information disclosure, data quality, and data privacy, need to be addressed. This minitrack aims to promote cutting edge research in data strategy, information disclosure, privacy protection, and other challenges using data mining, text mining or a combination of other analytical approaches in the business analytics domain. Suggested topics: Data acquisition; Data quality in big data; Data privacy; Data security; Data sharing strategy; Data manipulation; Missing value imputation; Mobile and location data analytics; Online review data analytics; Strategic information disclosure.
Julie Zhang, University of Massachusetts Lowell, Juheng_Zhang@uml.edu
Jialun Qin, University of Massachusetts Lowell, Jialun_Qin@uml.edu
Xiaobai (Bob) Li, University of Massachusetts Lowell, Xiaobai_Li@uml.edu
Mini-Track 7: Locational Big Data and Analytics: Implications for the Sharing Economy (SIGGIS)
This mini-track provides a research forum on varied aspects of GIS for business intelligence, location-based analytics, knowledge management, and spatial data management. Aligned with the AMCIS 2017 theme, “A Tradition of Innovation”, research contributions related to “Location and the Sharing Economy” are encouraged, e.g. firms such as Uber and Airbnb depend on enterprise-wide spatial data and related analytic software. Topics include the locational aspects of the shared economy, spatial big data, spatial decision making, spatial knowledge management, cloud-based GIS, spatial crowdsourcing, management decision-making using GIS, volunteered geographic information (VGI), spatial workforce development, managerial concerns, regulation, privacy, security, ethical aspects concerning the sharing economy, mobile location-based applications, location-based theory, mobile-based GIS, software development incorporating place, societal issues of spatial big data, and emerging areas of GIS and location analytics. This mini-track encourages manuscript submissions on theory, methodology, applications, behavioral studies, case studies, and emerging areas with encouragement for the sharing economy.
James B. Pick, University of Redlands, James_Pick@redlands.edu
Daniel Farkas, Pace University, firstname.lastname@example.org
Brian Hilton, Claremont Graduate University, Brian.Hilton@cgu.edu
Avjit Sarkar, University of Redlands, Avijit_Sarkar@redlands.edu
Hindupur Ramakrishna, University of Redlands, Hindupur_Ramakrishna@redlands.edu
Namchul Shin, Pace University, email@example.com
Mini-Track 8: Social and Ethical Issues in Big Data
Proliferation of social media, online networks and web 2.0-3.0 technologies are enabling individuals and companies to engage with digital technologies at an unprecedented scale. An individual’s digital footprint is further informed by their communication preferences, purchase behaviour, financial transactions, geospatial location tracking, medical records, and even heredity. This accumulation of big data can be used by governments and various business enterprises to peer into human behaviour at a finer granularity than never before. While, big data can be used to predict the spread of diseases, potential genetic anomalies, and provide other useful insights, it comes at a price. Society is increasingly questioning the ethical risks associated with unfettered analysis of mass surveillance, profiling, and the creation of data mosaics without users’ volition. This mini-track focuses on research that addresses social and ethical issues associated with big data technologies and their uses.
Thilini Ariyachandra, Xavier University, firstname.lastname@example.org
Babita Gupta, California State University Monterey Bay, email@example.com
Gloria Phillips-Wren, Loyola University Maryland, firstname.lastname@example.org
Mini-Track 9: Sports Analytics
Sports are a huge portion of the global economy with global annual revenues expected to be approximately $145 Billion for year 2015. United States alone accounts for over 40% of this revenue. Sports Management is a complex operation involving marketing of game, fan engagement, ticket sale, game operations, player performance, team ranking, sports medicine, injury risk prevention, concussions, athletic training and sports rehab, talent scouting, etc. to more recently integration of wearable technologies. This complexity coupled with the existence of different types of data sets and the integration of mobile technologies provides for a fertile playground to apply analytics to reap immediate benefits. This mini track invites original and high quality submissions from all aspects of sports that apply analytics, including injuries and concussions during sports practice and game play. For a detailed call, please visit https://sites.google.com/site/sportsmoneyball/. Selected papers from this minitrack will be invited for inclusion in a Springer book on Big Data in Sports.
Ashish Gupta, Auburn University, email@example.com
Gary B. Wilkerson, University of Tennessee at Chattanooga, Gary-Wilkerson@utc.edu
David Paradice, Auburn University, firstname.lastname@example.org
Ramesh Sharda, Oklahoma State University, Ramesh.email@example.com