-------- Weitergeleitete Nachricht -------- Betreff: [AISWorld] Call for Papers for Journal of Business Research - Special Issue on Big Data and Analytics in Technology and Organizational Resource Management Datum: Sat, 5 Sep 2015 11:50:02 +0000 Von: Muhammad Kamal Muhammad.Kamal@brunel.ac.uk An: 'aisworld@lists.aisnet.org' aisworld@lists.aisnet.org

Journal of Business Research (3* on ABS List): Special Issue on Big Data and Analytics in Technology and Organizational Resource Management

Deadline: 15 January 2016

Guest Editors: Zahir Irani (Professor and Dean of College, Brunel University London, UK) and Peter E.D Love (John Curtin Distinguished Professor, Curtin University, Australia)

Big data analytics (BDA) is the "collection of data and technology that accesses, integrates, and reports all available data by filtering, correlating, and reporting insights not attainable with past data technologies" (APICS 2012). BDA is an evolving phenomenon generating considerable response from both practitioners and academics. Some researchers have marked Big Data as the "next big thing in innovation" (Gobble 2013), "the fourth paradigm of science" (Strawn, 2012), and also the "next frontier for innovation, competition, and productivity" (Manyika et al., 2011). Big Data though is still emerging but is now certainly considered as a reality. Nevertheless, what does it really mean for organization from the technological and organizational resource management perspective? Big Data is a revolution that has capability to transform the way in which organizations visualize and function (Mayer-Sch?nberger and Cukier, 2013). In the era of Big Data, organizations (large, SMEs, etc.) are f aced with inexplicable amount of information that can provide them with invaluable insights about the What rather than the Why, but with the use of the right technology and organizational resources competitive advantage can be gained. According to International Data Corporation (IDC) - 2011, Big Data technologies describe a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high velocity capture, discovery, and/or analysis. The 7Vs (Volume, Variety, Velocity, Variability, Veracity, Visualization and Value) of data penetrating organizations continues to reach extraordinary levels. This remarkable evolution signifies that managements must not only recognize the significance of Big Data so as to decode the information that essentially matters to them, but also comprehend the potentials of Big Data Analytics to both the public and private sectors.

As Eric Schmidt (Executive Chairman, Google) states "From the dawn of civilization until 2003, humankind generated 5 Exabytes of data. Now we produce 5 Exabytes every 2 days ... and the pace is accelerating." This quote is followed by Robert J. Moore (RJMetrics Co-Founder and CEO) who states that "23 Exabytes of information was recorded and replicated in 2002. We now record and transfer that much information every 7 days". Thus, with the ever rising repository of Big Data, it becomes extremely important for organizations to make sense of this data and information in a timely and effective way - and this is where analytics comes to play. A number of research studies have highlighted that organizations that use business analytics to automate their decision-making have generated more productive results such as improved performance, in-depth understanding of consumer behaviour, better planning and forecasting, experiencing higher returns on equity (or value if in the public sector), and enabling organizations to operate more efficiently and staying ahead of the competition (e.g. Russom, 2011; Sagiroglu and Sinanc, 2013). While effective management and utilization of data have always been a concern for organizations, it is the scale and scope of transformation which Big Data and Business Analytics is bringing that has forced practitioners and academics to conduct empirical studies. The richness and enormity of Big Data is creating unprecedented research opportunities in a number of fields, as also evident from its strong application in the areas of manufacturing sector, financial services, insurance, retailing and healthcare sectors (Goodwin 2013). In the context of Technology and Organizational Resource Management, Big Data and Business Analytics has the potential to facilitate more sophisticated data-driven decision-making for real-time business processes and new ways to organize and manage data. We see this topic as being topical to both public and private secto r organizations, and being explored quantitatively and through mixed methods.

Special Issue Objective: The proposed special issue seeks to present novel solutions to challenging technological and organizational resource management issues. This special issue will share related practical experiences to benefit readers, and will provide clear evidence that Big Data Analytics is playing an ever-increasing important and critical role in technological and organizational resource management (strategically, tactically and operationally). Therefore, we would seek to invite scholars and practitioners to look at the ways and means to co-create and capture business value from Big Data e.g. in terms of new business opportunities, improved performance, and competitive advantage. The results will in turn reveal the implications of Big Data on technological and organizational resource management practices and strategies.

Recommended Topics: The topics to be discussed in this special issue include but are not limited to the following:

* Big Data analytics-enabled business process innovation.

* Big Data to identify and manage Organizational Resource Management.

* Public Sector Big Data Challenges.

* Big data evidence driven decision making.

* Evaluating the impact of Big Data Analytics on the decision-making processes in information technological resource management.

* Evaluating facilitators and inhibitors of Big Data Analytics adoption.

* In-depth and longitudinal case studies on Big Data initiatives for enhancing technological and organizational resource management.

* Organizational challenges related to Big Data Analytics.

* Big Data approaches applied to improve cognitive performance and reduce the dark side of technology.

* Application of Big Data Analytics for global development.

* Security and privacy issues in Big Data Analytics.

* Social Media, Cloud Computing and Big Data Analytics.

Manuscripts should apply the general author guidelines of the Journal of Business Research (http://www.elsevier.com/journals/journal-of-business-research/0148-2963/guid... authors#20100http://www.elsevier.com/journals/journal-of-business-research/0148-2963/guide-for%20authors#20100). Manuscripts should not have been previously published or be under consideration by other journals and must explicitly state what is unique and valuable about the paper within the context of the Special Issue theme. All submissions should be sent to the guest editors at: JBR@Brunel.ac.uk<mailto:JBR@Brunel.ac.ukmailto:JBR@Brunel.ac.uk%3cmailto:JBR@Brunel.ac.uk> before the 15th January 2016, when the initial screening will commence.

References

* APICS (2012). APICS 2012 Big Data Insights and Innovations Executive Summary.

* IDC (2011). Big Data Analytics: Future Architectures, Skills and Roadmaps for the CIO.

* Gobble, M. M. (2013). Big Data: The Next Big Thing in Innovation. Research Technology Management, 56(1), 64-66.

* Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. Report by McKinsey Global Institute.

* Mayer-Sch?nberger, V. and Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.

* Russom, P. (2011). Big data analytics. TDWI Best Practices Report, Fourth Quarter.

* Sagiroglu, S. and Sinanc, D. (2013). Big data: A review. IEEE International Conference on Collaboration Technologies and Systems, San Diego, CA, pp. 42-47.

* Strawn, G. O. (2012). Scientific Research: How Many Paradigms? EDUCAUSE Review, 47(3), 26-34.

Dr Muhammad Mustafa Kamal (PhD, MSc DCS, OCP 'DBA', MCS, BBA) Lecturer in Operations and Supply Chain Management Senior Editor - Information Systems Management (ISM) Journal Assistant Editor - Journal of Enterprise Information Management (JEIM) Assistant Editor - Transforming Government: People, Process and Policy (TGPPP) T: +44 (0) 1895 267728 | F: +44 (0) 1895 269775 | Twitter: @muhammadmkamal

Brunel University London College of Business, Arts and Social Sciences Brunel Business School

Eastern Gateway Building, Brunel University London, Uxbridge, UB8 3PH, United Kingdom T: +44 (0) 1895 274000 | F: +44 (0) 1895 232806 www.brunel.ac.ukhttp://www.brunel.ac.uk/ Connect with the university on LinkedInhttp://www.linkedin.com/groups/Official-Brunel-University-London-Alumni-60617?trk=myg_ugrp_ovr, Twitterhttp://www.twitter.com/bruneluni, Facebookhttp://www.facebook.com/bruneluniversity

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