-------- Forwarded Message -------- Subject: [AISWorld] Call for Papers: Information Technology & People - Special Issue on, "Perspectives on the values of Big Data sharing" Date: Wed, 27 Feb 2019 12:49:11 +0100 From: Gianluigi Viscusi gianluigi.viscusi@epfl.ch To: aisworld@lists.aisnet.org

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Call for Papers

Information Technology & People - Special Issue

"Perspectives on the values of Big Data sharing"

Call for Papers link: http://www.emeraldgrouppublishing.com/products/journals/call_for_papers.htm?...

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Special issue editors:

Christopher Tucci, EPFL CDM MTEI CSI ODY 1 04 (Odyssea) - Station 5 CH-1015 Lausanne - Switzerland,

email: christopher.tucci@epfl.ch

Gianluigi Viscusi, EPFL CDM MTEI CSI ODY 1 04 (Odyssea) - Station 5 CH-1015 Lausanne - Switzerland,

email: gianluigi.viscusi@epfl.ch

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Timeline for the special issue:

Deadline for the submission of papers:  April 15th 2019 Reviews returned: June 15th 2019 Revised papers submitted: September 15th 2019 Final papers due: October 15th 2019 Special issue published: December 15th 2019

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Submission instructions

Please submit your manuscript via our review website: http://mc.manuscriptcentral.com/itp

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Big Data has been first subject to industry hype (Davenport, Barth and Bean, 2012) with a consequent growing interest by academics (Buhl, Röglinger, Moser and Heidemann, 2013; Goes, 2014; Batini, Rula, Scannapieco and Viscusi, 2015; Abbasi, Sarker and Chiang, 2016; Rai, 2016; Günther, Rezazade Mehrizi, Huysman and Feldberg, 2017). The current common understanding of big data can be summarized by the following definition that appeared in 2013 in the first issue of Big Data, one of the first journals on the topic published by Mary Ann Liebert, Inc: "Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the structures of your database architectures. To gain value from this data, you must choose an alternative way to process it (Dumbill, 2013)." Furthermore, the big data hype and phenomenon followed and overlapped with the public sector interest in open government data (Bertot et al., 2014), symbolically enforced at global level by the memoranda and directives signed by Barack Obama in the early years of his first mandate (Obama, 2009; Chignard, 2013). This overlapping raised the question of the different values (economic, public, and social value) that Big Data may have, and the challenges related to having access and sharing them, such as data quality and privacy (Batini et al., 2015; Jain, Gyanchandani and Khare, 2016; Menon and Sarkar, 2016). This Special Issue aims to provide an outlook on these issues, especially considering the connection, on one hand, between Big Data, public safety, security, and quality of life; on the other hand, on the different paths of business models innovation enforced by Big Data such as social innovation (Misuraca, Pasi and Viscusi, 2018) and crowd-driven innovation (Afuah and Tucci, 2012; Afuah, Tucci and Viscusi, 2018).

Inspired by the rise of Big Data platforms and infrastructure that handle both structured and unstructured data from a multitude of domains and data sources (ranging from environmental and weather data to wearables, passenger vehicle sensors, financial and insurance institutions data streams, and social web data), the Special Issue will explore the benefits, advantages as well as the challenges, limitation and threats (at the data security and privacy levels) that emerge from the Big Data value chain (Miller and Mork, 2013; Curry, 2016),  delivering “intelligence” to support operations that surround various aspects of human living. Special attention will be dedicated but not limited to the following areas:

-    Digital governance and social innovation from Big Data -    Innovative meshed data services and ecosystems -    Intellectual property policies for Big Data -    New sustainable business models for Big Data sharing -    Open innovation, crowdsourcing, and Big Data -    Public safety early warning systems -    Public threat identification, pattern recognition, and risk mitigation techniques -    Big Data and open science challenges -    Ethical aspects of Big Data

It is worth noting that the Special Issue will investigate the topic of security from a social rather than technical perspective, with a specific focus on social value impacts of Big Data-driven innovation in terms of capabilities and “functionings” enabled by emergent Big Data ecosystems (Sen, 1992; Nussbaum, 2011). Taking these issues into account, Big Data and open linked data are a key resource for enabling capabilities, support decision-making on these issues, and develop appropriate policies and services, e.g., the examples provided by Viscusi et al. (2014). Furthermore, Big Data-related phenomena of the quantified self as individuals self-tracking of any kind of biological, physical, behavioral, or environmental information (Swan, 2013) has been recently associated with subjects other than human beings, e.g., to cars and vehicles in general, which are actually able to capture sensory data about themselves and about their environment, thus becoming quantified vehicles (Stocker, Kaiser and Fellmann, 2017). Accordingly, the emergence of different quantified subjects raise questions on the role of Big Data for public safety and security as well as the need for understanding the consequent infrastructural challenges and designing new platforms and services.

In summary, the Special Issue aims to provide a multidisciplinary understanding of the impact of Big Data on personal safety, personal security, and well-being. In addition, the Special Issue aims to presents solutions and case studies.

The Special Issue dissemination and organization will be supported by the AEGIS EC H2020 Innovation Action, aiming at creating an interlinked “Public Safety and Personal Security” Data Value Chain, and at delivering a novel platform for Big Data curation, integration, analysis and intelligence sharing.

References

* Abbasi, A., S. Sarker and R. H. L. Chiang. (2016). “Big data research in information systems: Toward an inclusive research agenda.” Journal of the Association for Information Systems, 17(2), 3. * Afuah, A. and C. L. Tucci. (2012). “Crowdsourcing as a solution to distant search.” Academy of Management Review, 37(3), 355–375. * Afuah, A., C. L. Tucci and G. Viscusi. (2018). Creating and Capturing Value Through Crowdsourcing. Oxford University Press. * Batini, C., A. Rula, M. Scannapieco and G. Viscusi. (2015). “From data quality to big data quality.” Journal of Database Management, 26(1), 60–82. * Bertot, J. C., U. Gorham, P. T. Jaeger, L. C. Sarin and H. Choi. (2014). “Big data, open government and e-government: Issues, policies and recommendations.” Information Polity, 19, 5–16. * Buhl, H. U., M. Röglinger, F. Moser and J. Heidemann. (2013). “Big Data - A Fashionable Topic with(out) Sustainable Relevance for Research and Practice?” Business & Information Systems Engineering, 5(2), 65–69. * Chignard, S. (2013). “A brief history of Open Data.” Retrieved from http://parisinnovationreview.com/2013/03/29/brief-history-open-data/ * Curry, E. (2016). “The Big Data Value Chain: Definitions, Concepts, and Theoretical Approaches BT  - New Horizons for a Data-Driven Economy: A Roadmap for Usage and Exploitation of Big Data in Europe.” In: J. M. Cavanillas, E. Curry, & W. Wahlster (Eds.), (pp. 29–37). Cham: Springer International Publishing. * Davenport, T. H., P. Barth and R. Bean. (2012). “How “Big Data” Is Different.” MIT Sloan Management Review, 54(1), 43–46. * Dumbill, E. (2013). “Making Sense of Big Data (Editorial).” Big Data, 1(1), 1–2. * Goes, P. (2014). “Editor’s Comments: Big Data and IS Research.” Management Information Systems Quarterly, 38(3), iii–viii. * Günther, W. A., M. H. Rezazade Mehrizi, M. Huysman and F. Feldberg. (2017). “Debating big data: A literature review on realizing value from big data.” The Journal of Strategic Information Systems, 26(3), 191–209. * Jain, P., M. Gyanchandani and N. Khare. (2016). “Big data privacy: a technological perspective and review.” Journal of Big Data, 3(1), 25. * Menon, S. and S. Sarkar. (2016). “Privacy and Big Data: Scalable Approaches to Sanitize Large Transactional Databases for Sharing.” MIS Quarterly, 40(4), 963–981. * Miller, H. G. and P. Mork. (2013). “From Data to Decisions: A Value Chain for Big Data.” IT Professional, 15(1), 57–59. * Misuraca, G., G. Pasi and G. Viscusi. (2018). “Understanding the Social Implications of the Digital Transformation: Insights from Four Case Studies on the Role of Social Innovation to Foster Resilience of Society. BT - Electronic Participation - 10th IFIP WG 8.5 International Conference, ePart 2018, K.” * Nussbaum, M. C. (2011). Creating Capabilities - The Human Development Approach. Cambridge (MA): The Belknap Press of Harvard University Press. * Obama, B. (2009). “Transparency and open government. Memorandum for the heads of executive departments and agencies.” Retrieved from https://www.whitehouse.gov/open/documents/open-government-directive * Rai, A. (2016). “Synergies between big data and theory.” MIS Q., 40(2), iii–ix. * Sen, A. (1992). Inequality Re-examined. Oxford: Clarendon Press. * Stocker, A., C. Kaiser and M. Fellmann. (2017). “Quantified Vehicles.” Business & Information Systems Engineering, 59(2), 125–130. * Swan, M. (2013). “The quantified self: Fundamental disruption in big data science and biological discovery.” Big Data, 1(2), 85–99. * Viscusi, G., M. Castelli and C. Batini. (2014). “Assessing social value in open data initiatives: a framework.” Future Internet, 6(3), 498–517.

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