Creating business value from big data and business analytics : organizational, managerial and human resource implications

This paper reports on a research project, funded by the EPSRC’s NEMODE (New Economic Models in the Digital Economy, Network+) programme, explores how organizations create value from their increasingly Big Data and the challenges they face in doing so. Three case studies are reported of large organizations with a formal business analytics group and data volumes that can be considered to be ‘big’. The case organizations are MobCo, a mobile telecoms operator, MediaCo, a television broadcaster, and CityTrans, a provider of transport services to a major city. Analysis of the cases is structured around a framework in which data and value creation are mediated by the organization’s business analytics capability. This capability is then studied through a sociotechnical lens of organization/management, process, people, and technology. From the cases twenty key findings are identified. In the area of data and value creation these are: 1. Ensure data quality, 2. Build trust and permissions platforms, 3. Provide adequate anonymization, 4. Share value with data originators, 5. Create value through data partnerships, 6. Create public as well as private value, 7. Monitor and plan for changes in legislation and regulation. In organization and management: 8. Build a corporate analytics strategy, 9. Plan for organizational and cultural change, 10. Build deep domain knowledge, 11. Structure the analytics team carefully, 12. Partner with academic institutions, 13. Create an ethics approval process, 14. Make analytics projects agile, 15. Explore and exploit in analytics projects. In technology: 16. Use visualization as story-telling, 17. Be agnostic about technology while the landscape is uncertain (i.e., maintain a focus on value). In people and tools: 18. Data scientist personal attributes (curious, problem focused), 19. Data scientist as ‘bricoleur’, 20. Data scientist acquisition and retention through challenging work. With regards to what organizations should do if they want to create value from their data the paper further proposes: a model of the analytics eco-system that places the business analytics function in a broad organizational context; and a process model for analytics implementation together with a six-stage maturity model

P-Values: Misunderstood and Misused

P-values are widely used in both the social and natural sciences to quantify the statistical significance of observed results. The recent surge of big data research has made the p-value an even more popular tool to test the significance of a study. However, substantial literature has been produced critiquing how p-values are used and understood. In this paper we review this recent critical literature, much of which is routed in the life sciences, and consider its implications for social scientific research. We provide a coherent picture of what the main criticisms are, and draw together and disambiguate common themes. In particular, we explain how the False Discovery Rate is calculated, and how this differs from a p-value. We also make explicit the Bayesian nature of many recent criticisms, a dimension that is often underplayed or ignored. We conclude by identifying practical steps to help remediate some of the concerns identified. We recommend that (i) far lower significance levels are used, such as 0.01 or 0.001, and (ii) p-values are interpreted contextually, and situated within both the findings of the individual study and the broader field of inquiry (through, for example, meta-analyses)

Investment appraisal and evaluation: preserving tacit knowledge and competitive advantage

This research asks if intuitive investment appraisal and evaluation are appropriate under conditions of rapid change, uncertain outcomes, limited information, and when competitive advantage derives from tacit knowledge. Measures and rational approaches to appraisal and evaluation require distal knowledge made explicit in documents and techniques. Converting valuable tacit knowledge, residing in individuals and organisational context, into coded distal knowledge, which is more easily replicated, risks jeopardising the uniqueness of competencies and capabilities that underpin competitive advantage. The research investigates e-learning projects in higher education and finds little evidence of formal rational investment appraisal and evaluation in IS projects characterised by uncertainty and a lack of clear information

Sendero: An Extended, Agent-Based Implementation of Kauffman's NKCS Model

The idea of agents exploring a fitness landscape in which they seek to move from 'fitness valleys' to higher 'fitness peaks' has been presented by Kauffman in the NK and NKCS models. The NK model addresses single species while the NKCS extension illustrates coevolving species on coupled fitness landscapes. We describe an agent-based simulation (Sendero), built in Repast, of the NK and NKCS models. The results from Sendero are validated against Kauffman's findings for the NK and NKCS models. We also describe extensions to the basic model, including population dynamics and communication networks for NK, and directed graphs and variable change rates for NKCS. The Sendero software is available as open source under the BSD licence and is thus available for download and extension by the research community.Coevolution, Agent-Based Modelling, NK, NKCS, Fitness Landscape

Introduction to special issue: The sociomateriality of information systems: current status, future directions

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Detecting East Asian Prejudice on Social Media

The outbreak of COVID-19 has transformed societies across the world as governments tackle the health, economic and social costs of the pandemic. It has also raised concerns about the spread of hateful language and prejudice online, especially hostility directed against East Asia. In this paper we report on the creation of a classifier that detects and categorizes social media posts from Twitter into four classes: Hostility against East Asia, Criticism of East Asia, Meta-discussions of East Asian prejudice and a neutral class. The classifier achieves an F1 score of 0.83 across all four classes. We provide our final model (coded in Python), as well as a new 20,000 tweet training dataset used to make the classifier, two analyses of hashtags associated with East Asian prejudice and the annotation codebook. The classifier can be implemented by other researchers, assisting with both online content moderation processes and further research into the dynamics, prevalence and impact of East Asian prejudice online during this global pandemic.Comment: 12 page

What\u27s So Different about Developing Web Based Information Systems?

This paper considers the suitability of traditional IS development methods to Web-based information systems. A two year e-commerce development project is used to explore Web-based IS development using action research. To distinguish the project from consultancy a framework of ideas – Multiview - is declared and tested in the research process. Multiview was defined in 1985 and has been since refined to become an influential approach to information systems development. It has soft and hard aspects and, as a contingency approach, is not prescriptive but adapted to the particular situation in the organization and the application. The differences and similarities of traditional IS development projects and Web-based projects are reported and found to be more about concrete differences of methodology content than abstract concepts. The project also provided an opportunity to reflect more generally about the role of methodology in IS development

Tweeting Islamophobia: Islamophobic hate speech amongst followers of UK political parties on Twitter

The great promise of social media platforms such as Twitter is to connect people separated across time and space. This has had far-ranging consequences for politics by changing discursive, participative and organisational practices. However, despite much early techno-optimism about platforms like Twitter, concerns are growing that they enable harmful, hateful and divisive behaviours. In this thesis, I focus on one of the most concerning and harmful behaviours on Twitter and in politics more broadly: Islamophobic hate speech. The socio-political consequences of hate speech are deeply concerning, and include causing harm to targeted victims, spreading divisiveness, and normalizing dangerous and extremist ideas. The aim of this thesis is to enhance our understanding of the nature and dynamics of Islamophobic hate speech amongst followers of UK political parties on Twitter. I study four parties from across the political spectrum: the BNP, UKIP, the Conservatives and Labour. I make three main contributions. First, I define Islamophobia in terms of negativity and generality, thus making a robust, theoretically-informed contribution to the study of a deeply contested concept. This argument informs the second contribution, which is methodological: I create a multi-class supervised machine learning classifier for Islamophobic hate speech. This distinguishes between weak and strong varieties and can be applied robustly and at scale. My third contribution is theoretical. Drawing together my substantive findings, I argue that Islamophobic tweeting amongst followers of UK parties can be characterised as a wind system which contains Islamophobic hurricanes. This analogy captures the complex, heterogeneous dynamics underpinning Islamophobia on Twitter, and highlights its devastating effects. I also show that Islamist terrorist attacks drive Islamophobia, and that this affects followers of all four parties studied here. I use this finding to extend the theory of cumulative extremism beyond extremist groups to include individuals with mainstream affiliations. These contributions feed into ongoing academic, policymaking and activist discussions about Islamophobic hate speech in both social media and UK politics