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

A Study on the Influencing Factors of International Investments between China and Europe

China and Europe are two big economies in the world, and there is much investment between them. As we know China has been investing almost in all around the world, Chinese investments in Europe have begun in recent years, and have become a symbol of Europe-China relations. According to data by the Rhodium Group, the amount of Chinese FDI in the EU has increased approximately 37 billion Euros in the first half of 2016, comparing with only 1.6 billion Euros in 2010.A big part of Chinese direct investment is mainly concentrated in the major economies in southern Europe or we can call it the BIG 3, as the UK, France and Germany all together. DOI: 10.7176/EJBM/13-6-04 Publication date:March 31st 2021

Platforms, Power, and the Antitrust Challenge: A Modest Proposal to Narrow the U.S.-Europe Divide

Big platforms dominate the new economy landscape. Colloquially known as GAFA [Google, Amazon, Facebook, and Apple] or FAANG [Facebook, Amazon, Apple, Netflix, and Google], the high tech big data companies are charged with using the power of their platforms to squelch start-ups, appropriate rivals’ ideas, and take and commercialize the personal data of their users. Are the platforms violating the antitrust laws? Should they be broken up? Or are they the agents of progress in the new economy? On these points, the United States antitrust law and the European Union competition law may diverge. The Competition Directorate-General of the European Commission has brought proceedings against or is investigating Google, Amazon, Apple, and Facebook. Germany, under its own competition law, has condemned Facebook’s conduct. Meanwhile, in the United States, authorities are skeptical, but they have commenced investigations. This Article is a comparative analysis of U.S. and EU law regarding monopolization/abuse of dominance as background to understanding why EU law is aggressive and U.S. law may be meek in the treatment of the big tech platforms. First, it examines the factors that underlie the two perspectives. Second, it considers three cases or problems—Google/Comparative Shopping (EU), Facebook-Personal Data (Germany), and dominant platforms’ acquisitions of start-ups that are inchoate competitive threats, such as Facebook’s acquisitions of WhatsApp and Instagram. The Article considers what lessons the latest Supreme Court antitrust decision, Ohio v. American Express (AmEx), holds for the analysis of the big data antitrust issues. Third, it asks what U.S. antitrust law and enforcement should do. It concludes that U.S. antitrust law should reclaim its role as watchdog to stop abuses of economic power, and makes suggestions for U.S. antitrust law to meet the big-platform challenge in a modest but meaningful and practicable way. I. Introduction II. A Brief Comparison of U.S. and EU Law of Monopolization/Abuse of Dominance ... A. The United States ... B. Europe ... C. Presumptions and Divergences III. Implications for High Tech, Big Data IV. Three Examples of Alleged Platform Abuse ... A. Google/Comparative Shopping ... 1. EU Law ... 2. U.S. Law ... B. Facebook—Abuse of Data ... 1. German Law ... 2. U.S. Law ... C. Start-Ups: Nipping Competition in the Bud V. Proposals VI. Conclusio

Is it too early to learn lessons from the developed world on the potential of big data technology: Harnessing and nurturing intuition in organisational decision makers

YesAs big data (BD) and data analytics gain significance in Europe, the industry expects that executives will eventually move towards evidence based decision making, and consequently would build sustainable competitive advantages for their organisations. Therefore, the lessons learned from experiences of European executives can be key for human development and also economic development. However, it also seems that in some cases decision makers in Europe seem to not use business intelligence systems at all. Since, executives are intelligent human beings with credible and proven expertise, it seems to raise a question mark on effectiveness of business intelligence systems, and the potential it has in human and economic development. Furthermore, repeated evaluation of literature pointed out that ultimately executives in Europe make decisions by intuition, and this leads to the question whether big data would ever replace intuition. In this paper, the mind-sets of executives about application and limitations of big data have been explored, by taking into account the cognitive factors in decision making. By using this, it is evaluated whether BD technologies can use to accelerate intuition development of the executives, and consequently lead to faster and sustainable economic development in the developing world

Defining a new paradigm for data protection in the world of Big Data analytics

All the ongoing proposals for a reform of data protection regulations, both in the U.S. and Europe, are still focused on the purpose limitation principle and the “notice and choice” model. This approach is inadequate in the present Big Data context. The paper suggests a revision of the existing model and proposes the provision of a subset of rules for Big Data processing, which is based on the opt-out model and on a deeper level of control by data protection authorities

Working strategically with Big Data in the tourism sector: a qualitative study of twelve European destination management organisations

This article presents the results of 12 semi-structured in-depth interviews with data experts from destination management organisations across Europe. The analysis revealed three overarching themes concerning the use of Big Data in the tourism sector: (1) size matters when it comes to  utilising the information from Big Data sources – bigger is not perhaps better, but larger companies are more capable of harvesting the full  potentials of the information; (2) companies lack the required competencies to work with Big Data strategically; and (3) one of the proposed  solutions from the respondents was surprisingly a desire to share their data with the competitors thereby gaining a competitive leverage.  Concluding on the above we suggest further areas for potential research: clarification of relevant competencies when working with Big Data,  furthering collaboration between tourism companies to maximise the potential of sharing, and research into the effect on COVID-19 on Big Data  and strategy

Seeking togetherness: moving toward a comparative evaluation framework in an interdisciplinary DIY networking project

There is renewed interest in community networks as a mechanism for local neighbourhoods to find their voice and maintain local ownership of knowledge. In a post-Snowden, big data, age of austerity there is both widespread questioning of what happens to public generated data shared over ‘free’ services such as Facebook, and also a renewed focus on self-provisioning where there are gaps in digital service provision. In this paper we introduce an EU funded collaborative project (‘MAZI’) that is exploring how Do-It-Yourself approaches to building community networks might foster social cohesion, knowledge sharing and sustainable living through four pilots across Europe. A key challenge is to develop a shared evaluation approach that will allow us to make sense of what we are learning across highly diverse local situations and disciplinary approaches. In this paper we describe our initial approaches and the challenges we face

Organizational culture as a primary driver for utilizing big data analytics in organizations

Master's thesis Information Systems IS501 - University of Agder 2019Context:During this last decade we have witnessed a wave of digital disruption, where big data has had a central part. This has gotten many organizations to pay attention and investing in analytic tools for big data. Big data analytics can provide organizations with more knowledge from more data sources that can have a big impact on how organizations act. Many of the organizations that have purchased big data analytics have failed to derive benefits from it and this is demonstratedin the literature. Organizational culture is mentioned as being an important part of achieving success when adopting big data.Purpose: The purpose of this thesis is to investigate the effect that organizational culture has on big data adoption, more specifically the organizations big data analytic capabilities. To measure this, we looked at the organization’s performance.Methods: We decided to use a quantitative approach to answer the research question. In order to define the different constructs of this research, we conducted a systematic literature review where we based the study on.We conducted a survey that we distributed to organizations within Europe that were using big data. The items of the survey were carefully developed by looking at previousmeasurements of these constructs and evaluating them with our supervisor, Ilias Pappas. We managed to get 104 respondents where they were all using big data in their work. We then developed a model with three different hypotheses and analysed the responseby using partial least square path modelling (PLS-SEM). This was done by using the tool, SmartPLS.Results: Our analysis validated our three hypotheses. The first one that focused on the positive effect organizational culture have on big data analytic. Second, organizational cultures positive effect on big data analytic capabilities. Final, hypothesis showed that organizational culture had a positive effect on intangible resources.Conclusion: We can conclude the research by confirming that organizationalculture has a huge effect on big data analytic capabilities and organizations need to look at organizational factors as well as the technical when they are investing in big data solutions. Keywords: Big data, big data analytics, big data analytic capabilities, organizational culture, firm performanc