Business Intelligence and Knowledge Management (SIGDSS)


Track Chairs:

Ozgur Turetken, Ryerson University,  This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Babita Gupta, California State University Monterey Bay, This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Lakshmi Iyer, The University of North Carolina at Greensboro,  This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Track Description:

The Business Intelligence and Knowledge Management (BI/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 organizational performance.

In addition to the plethora of applications that are already available for data mining and business intelligence, newer technologies such as web 2.0 and social networking environments bring in novel managerial and technical challenges related to analyzing large volumes of data. Knowledge management also encompasses knowledge that exists in unstructured forms and that intersects with technology, processes and people. The result is not only technical, but also business and management challenges that include economic, strategic, and behavioral issues. This research track aims to promote forward- thinking research in theoretical, design science, and behavioral aspects of BI/KM/DSS/Analytics.

Minitracks:

Business Intelligence for Organizational Performance Management

Benjamin Shao, Arizona State University, This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Robert D. St. Louis, Arizona State University, This e-mail 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 struggling economy, 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, and data mining, among others. BI needs to acquire data from multiple platforms and provide ubiquitous access. This requirement presents numerous managerial challenges. This mini-track aims to promote innovative research in the BI domains of organizational performance measurement and improvement.

Business Intelligence Success

Aleš Popovič, University of Ljubljana, This e-mail address is being protected from spambots. You need JavaScript enabled to view it

 

Evaluating the effectiveness of business intelligence systems (BIS) is vital to our understanding of the value and efficacy of management actions and investments. Yet, while IS success has been well-researched, our understanding of significant elements of the success of BIS, how are they interrelated, and how they affect BIS use, is limited.

While previous research about understanding BIS success focused especially on end-user BIS satisfaction formation, the research on BIS post-adoption environment is rather incomplete. Exploring the success factors affecting BIS continuance intention and continuance behavior fills this gap by adding pieces to the puzzle of BIS success. Moreover, rather than just achieving “success”, organizations are nowadays striving towards true business value of such systems, which calls for research to link upstream (i.e. antecedents of BIS end-user satisfaction) and downstream (i.e. BIS continued use) success activities and their embeddedness with existing management practices.

This mini-track aims to promote contributions dealing with a managerial, a methodological or a technical perspective on BIS post-implementation success. Submissions based on theoretical research, design research, action research, or behavioral research, are encouraged. We welcome both full research papers and research in progress.

Decision Support Systems for Risk and Crisis Management

Tina Comes, Karlsruhe Institute of Technology, This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Jose J Gonzalez, University of Agder, This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Ole-Christoffer Granmo, University of Agder, This e-mail address is being protected from spambots. You need JavaScript enabled to view it

 

One of the major challenges in risk and crisis management is handling complexity and uncertainty. This is most prominent when addressing problems that impact complex socio-economic or technical systems. In these situations, the consequences of a decision are difficult to assess, for instance as series of action-steps involving different organisations and actors may be required or as heterogeneous sources of information need to be integrated. To support decision-makers and to achieve organisational structures that turn planning and mitigation into high reliability organisation-type procedures, transparent and well-structured systems need to be developed that are continuously used. This track explores how resilient ICT systems can be built and exploited to facilitate information collection, sharing, processing and evaluation. The methods and tools presented in this minitrack should aim at providing coherent, understandable and reliable information facilitating communication and coordination and account for uncertainties, even when time is short and the pressure is high.

Implementation of Business Intelligence Systems

Anand Jeyaraj, Wright State University, This e-mail address is being protected from spambots. You need JavaScript enabled to view it

 

Business intelligence (BI) has gained considerable momentum within organizations. The confluence of data warehouses and business intelligence tools capable of data mining, data visualization, and data analytics enables organizations to more efficiently and effectively evaluate and understand their operations (e.g., Chaudhuri et al. 2011; Popovi? et al. 2012). However, the processes by which organizations identify, evaluate, design, implement, and realize business intelligence systems have not received much research attention (e.g., Jourdan et al. 2008; O’Leary 2011; Isik et al. 2011). This mini-track invites theoretical and empirical articles on various aspects related to the implementation of business intelligence systems in different types of organizations.

Knowledge Management Value, Success and Performance Measurements

Murray Eugene Jennex, San Diego State University, This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Stefan Smolnik, EBS Business School, This e-mail 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.

Predictive Analytics: Definition, Implementation, and Usage

Carsten Felden, TU Bergakademie Freiberg, This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Claudia Koschtial, TU Bergakademie Freiberg, This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Matthias Goeken, Frankfurt School of Finance & Management, This e-mail 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.

Spatial Business Intelligence, Decision Making, and Management

James B. Pick, University of Redlands, This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Avijit Sarkar, University of Redlands, This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Hindupur Ramakrishna, University of Redlands, This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Daniel Farkas, Pace University - New York, This e-mail 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, decision-making, knowledge management, and management.  This area is becoming an essential aspect for governments and is growing rapidly this decade in business.  The minitrack encourages manuscript submission on conceptual theory, methodology, applications, and cases in GIS.  Other areas of interest include rapidly growing mobile location-based applications, cloud-based GIS, spatial crowdsourcing, spatial data management and big data, spatial workforce, and ethical issues.  The intent is to advance knowledge from a relatively nascent level in light of the recent geospatial revolution and encourage exchange of findings, methodologies, and ideas between scholars and practitioners in an area ripe for rapid growth in business and information schools. The mini-track over the past two years has attracted increasing interest and participation.  It is part of the SIGDSS track and is sponsored by SIGGIS.