NEMODE Professional Development Workshop. BAM 9-11 September, 2014
NEMODE Professional Development Workshop. BAM 9-11 September, 2014
The digital economy, big data and social media: implications for the practice of management research
Organised by
Dr Richard Adams
NEMODE Senior Research Fellow
University of Exeter
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As digital technologies, big data, social media and computational thinking become more pervasive, research practice across all disciplines is on the brink of a potentially transformative era: Management and Organizational Research (MOR) is not excluded. Data is becoming available in unprecedented volumes from sources and in forms previously unimaginable. Thedigital revolution and emergence of ‘Big Data’ and ‘Social Media’ poses important questions for the way in which MOR is conducted and its traditional model of practice(George et al., 2014). To extract the scientific value from these dataMOR scholars, it has been argued, will need to reconfigure their skills profiles to include computer science, develop novel analytic capabilities and research collaborations and even to ask different questions. But, just how are BigData, Social Media and computational thinkingimpacting the domain of MOR and how should scholars be responding? The implications, opportunities and challenges of Big Data, Social Media and computational thinking for MOR are wide but underexplored. The purpose of this PDW, organised by RCUK DE’s New Economic Models in the Digital Economy (NEMODE) programme, is to begin to explore the implications of Big Data, Social Media and computational thinking for MOR and develop some understanding of the profile and practice of the (new) type of MOR scholar capable of meeting its emerging opportunities and challenges.
George, G., Haas, M.R. & Pentland, A. (2014). Big Data and Management. Academy of Management Journal, 57, 2. 321-326.
Speakers
Valuing Datafication? Theorising the Social Life of Big Data
Dr Mark Coté: Lecturer in Digital Cultures, King’s College London where he leads development in the analysis of big social data via an AHRC-funded research project.
I will examine the challenges in understanding the ‘value’ of big data. While enterprises like Google and Facebook and security organisations like the GCHQ and NSA demonstrate the economic and surveillance value of big data, there remains myriad forms of untapped value in its underlying ‘social life’. Using the conceptual frames of the ‘computational turn’ and ‘datafication’ I will go beyond new forms of empirical analytics to theorise emerging social and organisational value inherent in the very materiality of big data.
Quantifying Economic Behaviour Using Big Data
Dr Tobias Preis: Associate Professor of Behavioural Science and Finance at Warwick Business School. His recent research has aimed to carry out large scale experiments on complex social and economic systems by exploiting the volumes of data being generated by our interactions with technology.
In this talk, we will outline some recent highlights of our research, addressing two questions. Firstly, can big data resources provide insights into crises in financial markets? By analysing Google query volumes for search terms related to finance and views of Wikipedia articles, we find patterns which may be interpreted as early warning signs of stock market moves. Secondly, can we provide insight into international differences in economic wellbeing by comparing patterns of interaction with the Internet? To answer this question, we introduce a future-orientation index to quantify the degree to which Internet users seek more information about years in the future than years in the past. We analyse Google logs and find a striking correlation between the country's GDP and the predisposition of its inhabitants to look forward. Our results illustrate the potential that combining extensive behavioural data sets offers for a better understanding of large scale human economic behaviour.
Preis, T., Moat, H. S., Stanley, H. E. & Bishop, S. R. Quantifying the Advantage of Looking Forward.Sci. Rep. 2, 350 (2012).
Preis, T., Moat, H. S. & Stanley, H. E. Quantifying trading behavior in financial markets using Google Trends.Sci. Rep. 3, 1684 (2013).
Moat, H. S., Curme, C., Avakian, A., Kenett, D. Y., Stanley, H. E. & Preis, T. Quantifying Wikipedia usage patterns before stock market moves. Sci. Rep. 3, 1801 (2013).
Digital Methods as Mainstream Methodology?
Dr Helene Snee: Lecturer in Sociology at Manchester Metropolitan University. Research interests include the methodological and ethical implications of new media research, such as how developments in internet communication open up new avenues of research and new ways of addressing sociological questions as well as engaging with broader issues, such as prompting critical reflection on ‘traditional’ research methods.
Social researchers have started to use internet-based, digitally inspired methods to help develop an understanding of how social life is played out online, but of vital concern are ways in which we can incorporate digital technologies into our methodological tool box to keep pace with these fundamental changes in social life. This presentation shares the insights from a research network – ‘Digital Methods as Mainstream Methodology’ - that aimed to foster debates on digital data as it augments, enhances and problematises our conventional methods of research. It briefly maps out current debates in digital methods; showcases a cross-disciplinary range of contemporary social science research projects that effectively and innovatively utilise digital methods; and identifies future roles for such methods within the mainstream of social research.
Customers as co-creators in scientific research
Dr. Albrecht Fritzsche: senior researcher at the institute of information systems, chair of innovation and value creation at the University of Erlangen-Nuremberg; doctoral degrees in philosophy and industrial economics. Research interests include service innovation and prototyping, evolutionary models of innovation, technology forecasting and ethics.
JOSEPHS is a new co-creation facility in the city centre of Nuremberg where companies can collaborate openly with customers during the innovation process. Giving an appropriate account of this kind of collaboration is quite a challenge for scientific research. Big data solutions are very useful for capturing the whole bandwidth of contributions made by the customers and their implications for the outcome of the process. At the same time, however, they contradict the principle of customer involvement in co-creation, if customers have no insight into the interpretation of their behaviour and the conclusions drawn out of it. This does not only pose a methodological problem, but also an ethical one, since customers are explicitly encouraged to bring in their individual preferences and personal experiences. Customer involvement can therefore not stop where scientific research begins. It has to be considered in the research design and the choice of technology for data collection and analysis.
The profile of the management (data) scientist: Potential scenarios and skills for B/SMD-based Management research
Dr Juan Mateos-Garcia: Economics Research Fellow, Creative and Digital Economy, NESTA.Juan is interested in how new production and distribution tools, social media and widespread data access are transforming innovation and learning in organisations, communities, industries and society.
Big and Social Media data opens up new scenarios and opportunities for management research (such as using internal communication data to map knowledge networks inside firms, or using web data to study firm capabilities and strategies). This presentation proposes a typology of such scenarios, describes the skills required to exploit them, and considers implications for the education and training of management researchers.