Organizational Issues of Business Intelligence, Business Analytics and Big Data Minitrack

Permanent URI for this collection

https://hdl.handle.net/10125/42593

The provision of the right data with appropriate quality according to the needs of decision makers or automated processes is crucial for successful operations of companies and government agencies. Management Information Systems, Decision Support Systems, Executive Information Systems, interactive online analysis (OLAP), data mining, dashboards and recently predictive analytics are examples for the historic advancement of business intelligence/business analytics (BI/BA) concepts for the front-end, while databases, data warehousing and increasingly ‘Big data’ are examples for the development of the underlying technical infrastructure concepts. The smart combination of task-oriented front-end innovations and technology-driven infrastructure innovations allows for enhanced decision speed, more efficient extracting, cleaning, and aggregating data from source systems, maintaining and analyzing larger data sets, and demand-oriented access to data.

From an information systems perspective, business intelligence, business analytics, and recently, big data analytics constitute a dynamic, fascinating and highly relevant field of research and practice. Examples of open research challenges include managerial considerations (BI/BA/Big data - related strategy, organization and governance, value creation, data quality management, etc.), process-centric business intelligence, Big data ethics and many others. As organizations continue to learn how to leverage ‘Big data’ (including social media data, mobile data, web data and network data) new innovative applications of big data analytics are expected to emerge, and with them new research challenges, yet to be discovered.

This minitrack will accept papers with a managerial, an economic, a methodological or a technical perspective on the above topics. The main emphasis is placed on the business and organizational aspects of Business Intelligence, Business Analytics and Big Data rather than technology. Contributions from the fields of theory building, design research (methods and models), action research as well as analyses of existing or innovative applications are welcome.

Minitrack Co-Chairs:

Olivera Marjanovic (Primary Contact)
University of Sydney Business School
Email: olivera.marjanovic@sydney.edu.au

Barbara Dinter
Chemnitz University of Technology, Germany
Email: barbara.dinter@wirtschaft.tu-chemnitz.de

Thilini Ariyachandra
Xavier University
Email: ariyachandrat@xavier.edu

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Now showing 1 - 5 of 5

Understanding Intention to Explore Business Intelligence Systems: The Role of Fit and Engagement
(2017-01-04) Yang, Junyi; Pinsonneault, Alain; Hsieh, J.J. Po-An
This paper explores how user engagement affects users’ intention to explore business intelligence system (BIS) and how user engagement is promoted by the cognitive fit between BIS interface and tasks and the regulatory compatibility between BIS interface and personal characteristics, such as style of information processing. Results from the lab experiment suggest that the cognitive fit and the regulatory compatibility could both influence users’ engagement experience, which in turn affected users’ intention to explore BIS. This study may contribute to the extant information systems (IS) literature by uncovering the impacts of engagement experience on intention to explore and responding to the call for investigation of the BIS context where rich visualizations of the systems influence users’ engagement experience.
Enablers and Mechanisms: Practices for Achieving Synergy with Business Analytics
(2017-01-04) Asadi Someh, Ida; Wixom, Barbara; Davern, Michael; Shanks, Graeme
Business Analytics (BA) systems use advanced statistical and computational techniques to analyze organizational data and enable informed and insightful decision-making. BA systems interact with other organizational systems and if their relationship is synergistic, together they create higher-order BA-enabled organizational systems, which have the potential to create value and gain competitive advantage. In this paper, we focus on the enablers and mechanisms of synergy between BA and other organizational systems and identify a set of organizational practices that underlie the emergence of BA-enabled organizational systems. We use a case study involving a large IT firm to identify the organizational practices associated with synergistic relationships that lead to the emergence of higher-order BA-enabled organizational systems.
Capturing Value from Data: Revenue Models for Data-Driven Services
(2017-01-04) Schüritz, Ronny; Seebacher, Stefan; Dorner, Rebecca
Undisputedly, the amount of data is growing exponentially and huge opportunities exist to exploit them. New service business models are being built around value propositions based on data and analytics. Suitable revenue models need to reap the benefits of these value propositions. However, the question of how to best turn a value proposition into revenue for data-driven services is not systematically addressed in literature. \ \ We provide an overview of possible revenue models for data-driven services. Based on a sample of 100 start-ups, we apply qualitative analysis to identify different revenue models for newly established data-driven services such as subscription, gain sharing and multi-sided revenue models. \ \ This paper will contribute to the fundamental understanding of how companies can capture value from data-driven services. It should give guidance on the design and selection of appropriate revenue models and, thus, inspire new forms of revenue generation from the use of data. \
25+ Years of Business Intelligence and Analytics Minitrack at HICSS: A Text Mining Analysis
(2017-01-04) Marjanovic, Olivera; Dinter, Barbara
This research project is inspired by the occasion of the 50th anniversary of the Hawaii International Conferences on Systems Sciences (HICSS). As the current co-chairs of the longest-running minitrack on Business Intelligence (BI), Business Analytics (BA) and Big Data (as it is currently known) at HICSS, we report on its 27-year history of relevant and interesting research. Our insights into the key research themes and their progress over time were obtained through a semantic text mining of all research publications included in this minitrack since 1990. We also illustrate a practical method of using a sophisticated text-mining tool (Leximancer) so that it could be replicated by other researchers interested in content analysis methods in other research fields.
Introduction to Organizational Issues of Business Intelligence, Business Analytics and Big Data Minitrack
(2017-01-04) Marjanovic, Olivera; Dinter, Barbara; Ariyachandra, Thilini

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