Digital Futures Symposium

Call for papers We invite contributions to a Special Issue on Big Data, AI and Digital Futures: Challenges, changes and continuities, to be published by the AI & Society Journal of Culture, Knowledge and Communication (Springer) http://link.springer.com/journal/146. This special issue arises from the Big Data, AI and Robotics (BDAIR 18) Research Symposium at the De Montfort University (DMU). The main objective of this special issue is to encourage cross-disciplinary, interdisciplinary research and international research collaboration. ============================= SPECIAL ISSUE THEMES ============================= Aims: The aim of this special issue is to address the societal complex issues emerging from the recent advances in AI. As we look for answers to address new challenges, we look at the impact of AI and big data on our futures in the context of society. Content How might algorithms and big data shape our digital futures? In what ways can the semantic web impact our everyday life? Are there ways of envisioning a structure for managing data in a meaningful way, which may offer a transformational experience? We are witnessing a shift in political, social, cultural and technical relations which are increasingly driven by big data and algorithms. Our external environment is being codified leading to an increased level of surveillance both at personal and professional levels. This in itself is a challenge to privacy and data protection. We are already experiencing self-monitoring and tracking with the devices we wear that prompt us to engage in certain behaviours. Are we far from a day when technology will induce behavioural changes, not only at cognitive level but also at conative levels? What for claims that Big Data will make theory redundant? What ontological and epistemological issues arise in relation to these technologies? Our thoughts, emotions and actions are increasingly getting interpellated by algorithms and data. How does that then impact on the ‘Logos-Pathos-Ethos’ of our lives? Sophia bot froze on the question of corruption in Ukraine. On the other hand, we witnessed “the great British Brexit robbery” (Guardian, 2017) that proved whoever owns the data actually wins the campaign, election and the world. Cambridge Analytics Brexit has been one of the popular searches on the internet. At the same time, big data pose challenges as they generate noise and that means data often can be indecipherable, bewildering and recherché. Disruptions are common when we deal with data in any subject area. Therefore, it is cardinal to address the technological complexity, not only through academic research, scholarship and pedagogic practice but also industry engagement. On the other hand, big data and algorithms embed innovation and we encounter technologies in a transformational way, where conversations and dialogic interventions are rapid. Perhaps due to the contrasting ways in which we engage with big data and algorithms, the need for well-defined theoretical frameworks and methodological tools are increasingly in demand Siapera, 2018). Readership National and International We will invite experts both nationally and internationally to contribute to this special issue Goal Our goal is to offer an interdisciplinary coverage of the area explored, by bringing together perspectives from different domains such as computer science, design studies, business, cultural anthropology, arts and humanities and social sciences. In particular, we welcome contributions that explore the following themes: Themes Topics include, but are not limited to, the following: Media datafication and neoliberalism Data and business Social media and big data Big data, PR and Advertising Big data and politics Ethics, privacy and technology Data and sustainability Personalisation, Machine learning and AI Social bots and the management of sociality Quantified self and data cultures Data and education Researching media and culture using data methods Data visualisation, art and design Social responsibility and innovation Data and health Mobile and locative media Data and surveillance Using Big Data to test social theories Social data collection and novelt

Statistics in the Big Data era

It is estimated that about 90% of the currently available data have been produced over the last two years. Of these, only 0.5% is effectively analysed and used. However, this data can be a great wealth, the oil of 21st century, when analysed with the right approach. In this article, we illustrate some specificities of these data and the great interest that they can represent in many fields. Then we consider some challenges to statistical analysis that emerge from their analysis, suggesting some strategies

Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes

This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5 million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception

Marketing relations and communication infrastructure development in the banking sector based on big data mining

Purpose: The article aims to study the methodological tools for applying the technologies of intellectual analysis of big data in the modern digital space, the further implementation of which can become the basis for the marketing relations concept implementation in the banking sector of the Russian Federation‘ economy. Structure/Methodology/Approach: For the marketing relations development in the banking sector in the digital economy, it seems necessary: firstly, to identify the opportunities and advantages of the big data mining in banking marketing; secondly, to identify the sources and methods of processing big data; thirdly, to study the examples of the big data mining successful use by Russian banks and to formulate the recommendations on the big data technologies implementation in the digital marketing banking strategy. Findings: The authors‘ analysis showed that big data technologies processing of open online and offline sources of information significantly increases the data amount available for intelligent analysis, as a result of which the interaction between the bank and the target client reaches a new level of partnership. Practical Implications: Conclusions and generalizations of the study can be applied in the practice of managing financial institutions. The results of the study can be used by bank management to form a digital marketing strategy for long-term communication. Originality/Value: The main contribution of this study is that the authors have identified the main directions of using big data in relationship marketing to generate additional profit, as well as the possibility of intellectual analysis of the client base, aimed at expanding the market share and retaining customers in the banking sector of the economy.peer-reviewe

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

Tackling food marketing to children in a digital world: trans-disciplinary perspectives. Children’s rights, evidence of impact, methodological challenges, regulatory options and policy implications for the WHO European Region

There is unequivocal evidence that childhood obesity is influenced by marketing of foods and non-alcoholic beverages high in saturated fat, salt and/or free sugars (HFSS), and a core recommendation of the WHO Commission on Ending Childhood Obesity is to reduce children’s exposure to all such marketing. As a result, WHO has called on Member States to introduce restrictions on marketing of HFSS foods to children, covering all media, including digital, and to close any regulatory loopholes. This publication provides up-to-date information on the marketing of foods and non-alcoholic beverages to children and the changes that have occurred in recent years, focusing in particular on the major shift to digital marketing. It examines trends in media use among children, marketing methods in the new digital media landscape and children’s engagement with such marketing. It also considers the impact on children and their ability to counter marketing as well as the implications for children’s rights and digital privacy. Finally the report discusses the policy implications and some of the recent policy action by WHO European Member States

A review of ten years of implementation and research in aligning learning design with learning analytics at the Open University UK

There is an increased recognition that learning design drives both student learning experience and quality enhancements of teaching and learning. The Open University UK (OU) has been one of few institutions that have explicitly and systematically captured the designs for learning at a large scale. By applying advanced analytical techniques on large and fine-grained datasets, the OU has been unpacking the complexity of instructional practices, as well as providing conceptual and empirical evidence of how learning design influences student behaviour, satisfaction, and performance. This study discusses the implementation of learning design at the OU in the last ten years, and critically reviews empirical evidence from eight recent large-scale studies that have linked learning design with learning analytics. Four future research themes are identified to support future adoptions of learning design approaches

Assessing collaborative learning: big data, analytics and university futures

Traditionally, assessment in higher education has focused on the performance of individual students. This focus has been a practical as well as an epistemic one: methods of assessment are constrained by the technology of the day, and in the past they required the completion by individuals under controlled conditions, of set-piece academic exercises. Recent advances in learning analytics, drawing upon vast sets of digitally-stored student activity data, open new practical and epistemic possibilities for assessment and carry the potential to transform higher education. It is becoming practicable to assess the individual and collective performance of team members working on complex projects that closely simulate the professional contexts that graduates will encounter. In addition to academic knowledge this authentic assessment can include a diverse range of personal qualities and dispositions that are key to the computer-supported cooperative working of professionals in the knowledge economy. This paper explores the implications of such opportunities for the purpose and practices of assessment in higher education, as universities adapt their institutional missions to address 21st Century needs. The paper concludes with a strong recommendation for university leaders to deploy analytics to support and evaluate the collaborative learning of students working in realistic contexts

Data and Predictive Analytics Use for Logistics and Supply Chain Management

Purpose The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations. Design/methodology/approach The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC. Findings Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations. Practical implications This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place. Originality/value The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area