-------- Weitergeleitete Nachricht -------- Betreff: [AISWorld] ECIS 2016 Call for Papers - Big Data, Business Analytics and Decision Support Datum: Tue, 10 Nov 2015 22:38:12 +1300 Von: Nazim Taskin nazimtaskin@gmail.com An: aisworld@lists.aisnet.org

Call for Papers

24th European Conference on Information Systems (ECIS 2016), 12-15 June, 2016, Istanbul/Turkey

Track: Big Data, Business Analytics and Decision Support

Submission Deadline: November 27th, 2015

Track Description

With the emergence of Big Data and Analytics in recent years, business decision-makers and government policy-makers are increasingly turning to Big Data and Analytics for insight to solve complex problems. Needless to say, in this day and age, the success of businesses relies heavily on the accuracy and the timeliness of the decisions made by their managers. Often called evidence based management, which is driven by Big Data and Analytics, has much to offer to businesses and government agencies to succeed in their challenging endeavours. In addition to its promises, Big Data also raises new challenges for both practitioners and academics. Big Data is not just “big”. The sheer volume of the data is only one of many challenges that are often associated with Big Data, the others include variety, velocity, veracity, variability, and value proposition, among others (Jagadish et al., 2014; McAfee & Brynjolfsson, 2012; O'Leary, 2013; Sathi, 2012). While advances in hardware and software are helping, there still are many more challenges to characterize and to tackle in order to realistically leverage the promises of Big Data and Analytics to solve complex real-world problems. Big Data by itself, regardless of the size, type, or accumulation speed, is worthless unless business users do something with it that delivers value to their organizations. That’s where “big” analytics comes into the picture. Although organizations have long run reports and dashboards against their transactional data repositories (under the names of Business Intelligence and Data Warehousing), most have not utilized unstructured (or less structured) data repositories for in-depth and on-demand explorations. This is partly because analysis tools either did not have the capabilities or the data repositories did not have the richness that the power users needed. But this phenomenon is about to change (and had already changed for some) in a dramatic fashion; thanks to the new Big Data and Analytics paradigms. This track invites paper submissions that address acquisition, storage, analysis, inference of Big Data and its implication/incorporation into business process, behaviours, and decision-making practices. We are particularly interested in those philosophical approaches, research methodologies, and managerial/practical implications/applications that provide novel insight into Big Data and Analytics as they relate to managerial decision making. This research track encourages submission of new and novel theoretical, applied, pedagogical, behavioural, design science, data analysis methods/methodologies, as well as best practice that focus on data mining, text mining, statistical analysis, econometrics, Web mining, social analytics and sentiment analysis.

Topics of interest include, but are not limited to: • Big data and emerging research philosophies and methodologies • Big data and new individual and group decision-making techniques and opportunities • The impact of big data on the traditionally prominent methodological approaches to IS studies • The classic philosophical/methodological approaches and the emerging field of big data • How the methodologies, analytical approaches and techniques may vary depending on the business decision-making levels; i.e. strategic, tactical, and operational • Social networks and big data • Modelling big data • How business strategy-formulating and government policy-making can benefit from big data • Big data velocity and decision-making • Big data and its impact on DSSs, Expert systems (new forms such as recommender systems), etc. • Implications of big data in making strategic, tactical, and operational decisions • Real time analysis • Implications for decision-making at the individual and organizational levels • Querying and interpreting unstructured data; such as text analytics and sentiment analysis • Integral real time analysis of structured and unstructured data • Integral visualization of structured and unstructured data • Use of big data and business models • Developing, using, managing decision support systems

Track Link: http://ecis2016.eu/files/downloads/Tracks/T02.pdf Submission Link: http://precisionconference.com/~ecis

Track Chairs Nazim Taskin, School of Management, Massey University, New Zealand, n.taskin@massey.ac.nz Dursun Delen, Spears School of Business, Oklahoma State University, USA, dursun.delen@okstate.edu Ali Intezari, School of Management, Massey University, New Zealand, a.intezari@massey.ac.nz _______________________________________________ AISWorld mailing list AISWorld@lists.aisnet.org