Association for Information Systems Conference, August 7 - 9, 2014

Details

Track Chairs:

Babita Gupta, California State University Monterey Bay, This email address is being protected from spambots. You need JavaScript enabled to view it.
Thilini Ariyachandra, Xavier University, This email address is being protected from spambots. You need JavaScript enabled to view it.
Amit Deokar, Dakota State University, This email address is being protected from spambots. You need JavaScript enabled to view it.

Track Description:

The Business Intelligence, Analytics, & Knowledge Management (BIA&KM) track aims to attract novel research on technologies, applications, and processes for gathering, storing, accessing, analyzing, and presenting data, information, and knowledge for informed managerial decisions and enhanced organizational performance. Techniques, tools, and best practices in Business Intelligence (BI), decision support/analytics, and Knowledge Management (KM) can help support organizational business processes and systems for collecting and utilizing information related to sustainability indicators, analytics, dashboards, scorecards, and trend analyses.

As organizations embrace technologies such as social media, and new data sources (e.g., sensors, RFID), they also face new challenges related to analyzing and leveraging data characterized by large volume, velocity, and variety – typically referred to as ‘Big Data’. Research focused on managerial as well as technical challenges in this area is needed. Also, Knowledge Management can play a key role in dealing with varied forms of knowledge, structured and unstructured, at the intersection of technology, processes and people. Knowledge Management strategies and solutions need to be further investigated and brought to bear on new organizational challenges, including economic, strategic, and behavioral issues.

This research track aims to promote forward-thinking research in theoretical, design science, and behavioral aspects of BI/Big Data/Analytics/DSS/KM. Also, applied research in these areas related to creating and managing sustainable organizations is welcome.

Mini-Tracks:

BI and Analytics in Intelligence and Security Informatics

Wingyan Chung, Stetson University, This email address is being protected from spambots. You need JavaScript enabled to view it.
Shahram Amiri, Stetson University, This email address is being protected from spambots. You need JavaScript enabled to view it." target="_blank">This email address is being protected from spambots. You need JavaScript enabled to view it.
Joseph Woodside, Stetson University, This email address is being protected from spambots. You need JavaScript enabled to view it.

Intelligence and Security Informatics (ISI) is a cross--‐disciplinary field defined as the development of advanced information technologies, systems, algorithms, and databases for international, national and homeland security related applications, through an integrated technological, organizational, and policy--‐based approach. Recent developments of big data and business analytics in business and public organizations have aroused attention to the various issues on ISI. For example, the rapid rise of social media has impacted the Middle--‐Eastern regions' political stability, supported social movements in the U.S. immigration reform and border security, and triggered stock market fluctuation in a recent hoax about White House explosions. New techniques and applications of ISI are desperately needed to overcome challenges in growing volumes and varieties of social media and organizational data. Meanwhile, industries and governments demand high--‐caliber human talents to support their ISI initiatives. New pedagogies, teaching materials, and curriculums at the university level are needed. In this mini--‐track, we solicit high--‐quality, original research papers that address a variety of ISI issues. Topics may include (but are not limited to) the following:

• New predictive analytics and knowledge management approaches for ISI
• Big data solutions and applications for security surveillance
• Social media intelligence and analytics for ISI
• Privacy protection methods and applications
• Infrastructure protection and security surveillance using data and text mining
• Audit analytics and detection of financial and accounting fraud
• Social network analysis (radicalization, recruitment, conducting operations), visualization, and simulation
• Information sharing and intelligence--‐related knowledge discovery
• Terrorism informatics, analytical methodologies and software tools
• Enterprise risk management and information systems security
• Disaster and crisis management
• ISI education and workforce developments
• Innovative pedagogical approaches and curriculums in ISI

Business Intelligence Systems Implementation & Success

Ales Popovic, University of Ljubljana, This email address is being protected from spambots. You need JavaScript enabled to view it.
Anand Jeyaraj, Wright State University, This email address is being protected from spambots. You need JavaScript enabled to view it.

The IS literature has long emphasized the positive impact of information provided by business intelligence systems (BIS) on decision-making, particularly when organizations operate in highly competitive environments. BIS reflect quality information in well-designed data stores, coupled with business-friendly software tools that provide knowledge workers timely access, effective analysis and intuitive presentation of the right information, enabling them to take the right actions or make the right decisions. Evaluating the effectiveness of BIS is vital to our understanding of the value and efficacy of management actions and investments. Yet, while IS implementation and success have been well-researched, our understanding of 1) the processes by which organizations identify, evaluate, design, implement, and realize BIS, as well as of 2) the elements of the success of BIS, how are they interrelated, and how they affect BIS use, is limited.

This mini-track aims to contributions dealing with BIS implementation and success in different types of organizations. Both theoretical and empirical research are encouraged.

Spatial Business Intelligence, Analytics, and Sustainable Management

James Pick, University of Redlands, This email address is being protected from spambots. You need JavaScript enabled to view it.
Daniel Farkas, Pace University, This email address is being protected from spambots. You need JavaScript enabled to view it." target="_blank">This email address is being protected from spambots. You need JavaScript enabled to view it.
Avijit Sarkar, University of Redlands,This email address is being protected from spambots. You need JavaScript enabled to view it.
Hindupur Ramakrishna, University of Redlands, This email address is being protected from spambots. You need JavaScript enabled to view it.
Namchul Shin, University of Redlands, This email address is being protected from spambots. You need JavaScript enabled to view it.

The mini-track on Geographic Information Systems (GIS) seeks to provide a forum for research on varied aspects of GIS for business intelligence, analytics, knowledge management, and management. This area is becoming an essential aspect for governments and is growing rapidly this decade in business. The mini-track encourages manuscript submission on conceptual theory, methodology, applications, and cases in GIS. Current areas of interest include rapidly growing mobile location-based applications, cloud-based GIS, spatial crowdsourcing, spatial big data, spatial workforce, privacy and security aspects, ethical issues, and sustainability.

In concert with the AMCIS 2014 theme, GIS fosters sustainable solutions through reducing environmental impacts by analyzing spatial proximities, configurations, and hot spots. GIS is a primary approach to achieving ecological solutions for smart cities and societies. The mini-track over the past three years has attracted increasing interest and participation. It is part of the SIGDSS track and is sponsored by SIGGIS.

Knowledge Management Value, Success and Performance Measurements

Murray Jennex, San Diego State University, This email address is being protected from spambots. You need JavaScript enabled to view it.
Stefan Smolnik , University of Hagen, This email address is being protected from spambots. You need JavaScript enabled to view it.This email address is being protected from spambots. You need JavaScript enabled to view it.

Research into knowledge management (KM), organizational memories, and organizational learning has been affected by investigations such as implementation aspects, system developments, or knowledge flows during a number of years. Therefore, a high maturity level of KM research has been achieved. However, organizational KM initiatives are more and more faced with budget cuts and justification demands due to intense competition in today’s business environments. The influences of the rapid pace of globalization and of the ongoing liberalization of national and international markets lead to the emergence of increased pressure on existing companies. Project managers of KM initiatives like Chief Knowledge Officers need to justify their budgets and thus are in need of qualitative and quantitative evidence of the initiatives’ success. In addition, ROI calculations and traditional accounting approaches do not tell an adequate story when proposing knowledge-based initiatives. This minitrack explores research into strategies, methodologies, and stories that relate to measure this success. In addition, this minitrack will be used to explore the bodies of performance measurements that define the current state of research in measuring KM, organizational memory, and organizational learning success. Eventually, another purpose of this minitrack is to present research on how to value knowledge-based initiatives.

Business Intelligence, Analytics in Healthcare

Rahul Bhaskar, California State University - Fullerton, This email address is being protected from spambots. You need JavaScript enabled to view it.
Au Vo, California State University - Fullerton
Vahideh Abedi, California State University - Fullerton

Universally, the healthcare industry has been slow in adopting the information technology. As the healthcare reforms has started to be implemented in the United States, healthcare plans are looking for solutions to keep the industry profitable while insuring customer- centric healthcare. Information technology is bringing a fundamental change to the way the health care industry works. Overall, there is a shift to use information technology by health insurance providers, healthcare providers, government, and hospitals. Meaningful data analytics will lead to knowledge base business intelligence.

Business Intelligence for Organizational Performance Management

Benjamin Shao, Arizona State University, This email address is being protected from spambots. You need JavaScript enabled to view it." target="_blank">This email address is being protected from spambots. You need JavaScript enabled to view it.
Robert D. St. Louis, Arizona State University, This email address is being protected from spambots. You need JavaScript enabled to view it.

The goal of business intelligence (BI) 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. BI 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 BI metrics are even more critical for measuring and enhancing organizational performance. Many technologies contribute to BI solutions, including databases, data warehouses, data marts, analytic processing, social analytics, and data mining, among others. BI 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 BI domains of organizational performance measurement and improvement.

Predictive Analytics: Definition, Implementation, and Usage

Claudia Koschtial, TU Freiberg, Claudia.Koschtial(at)bwl.tu-freiberg.de
Carsten Felden, TU Freiberg, This email address is being protected from spambots. You need JavaScript enabled to view it.

The term Predictive Analytics is based in the field of Business Intelligence. Business Intelligence is concerned with the support of decision-making. Decisions are by nature future oriented (past and presence cannot be influenced anymore). In any case of uncertainty about the future, forecasts are necessary to provide a decision support. Predictive Analytics is a form of data analysis to gather information and apply methods to predict future developments. Therefore, Predictive Analytics can be regarded as a conceptual part of Business Intelligence. The aim of the mini-track is to address aspects of academic background and practical ones as well. With a critical perspective all aspects of the described field need to be regarded to support an academic perception of the research field.