Assessing business value of Big Data Analytics in European firms

Côrte-Real, Nadine; Oliveira, Tiago; Ruivo, Pedro

doi:10.1016/j.jbusres.2016.08.011

Cited by 390 publications

(330 citation statements)

References 64 publications

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“…Few of the researchers analyze how organizational agility influences competitive advantage (Côrte-Real et al, 2017;Mikalef & Pateli, 2017). Other analyses focus on individual factors and their influence on organizational agility.…”

Section: The Concept Of Organization Agilitymentioning

confidence: 99%

Organizational Agility Conceptual Model

Žitkienė¹,

Deksnys²

2018

Montenegrin Journal of Economics

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Section: The Concept Of Organization Agilitymentioning

confidence: 99%

Organizational Agility Conceptual Model

Žitkienė¹,

Deksnys²

2018

Montenegrin Journal of Economics

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“…Barreto () and Teece () regard dynamic capability viewing as the ability to sense and shape opportunities and threats, seize market opportunities and maintain competitiveness. In big data research, dynamic capabilities that are triggered by BDA capabilities have been identified as intermediate variables that contribute to firm performance (Côrte‐Real, Oliveira and Ruivo, ; Wamba et al ., ).…”

Section: Theoretical Background and Research Modelmentioning

confidence: 97%

“…As an extension of RBT, the KBV views knowledge as a value, rare, inimitable and non‐substitutable (VRIN) resource, and argues that knowledge absorption plays a critical role in acquiring new knowledge (Grant, ). Côrte‐Real, Oliveira and Ruivo () have adopted this theory to develop BDA‐enabled knowledge assets, namely exogenous knowledge management, endogenous knowledge management and knowledge‐sharing with partners. They provide evidence from a survey of 500 European firms to suggest that these BDA‐enabled knowledge assets create firms’ organizational agility, thereby strengthening their competitive advantage.…”

Section: Theoretical Background and Research Modelmentioning

confidence: 99%

Leveraging Big Data Analytics to Improve Quality of Care in Healthcare Organizations: A Configurational Perspective

Wang

Kung

Gupta

et al. 2019

British J of Management

117

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Big data analytics (BDA) is beneficial for organizations, yet implementing BDA to leverage profitability is fundamental challenge confronting practitioners. Although prior research has explored the impact that BDA has on business growth, there is a lack of research that explains the full complexity of BDA implementations. Examination of how and under what conditions BDA achieves organizational performance from a holistic perspective is absent from the existing literature. Extending the theoretical perspective from the traditional views (e.g. resource‐based theory) to configuration theory, the authors have developed a conceptual model of BDA success that aims to investigate how BDA capabilities interact with complementary organizational resources and organizational capabilities in multiple configuration solutions leading to higher quality of care in healthcare organizations. To test this model, the authors use fuzzy‐set qualitative comparative analysis to analyse multi‐source data acquired from a survey and databases maintained by the Centres for Medicare & Medicaid Services. The findings suggest that BDA, when given alone, is not sufficient in achieving the outcome, but is a synergy effect in which BDA capabilities and analytical personnel's skills together with organizational resources and capabilities as supportive role can improve average excess readmission rates and patient satisfaction in healthcare organizations.

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“…People at all levels must recognize the importance of embracing data and using not only an analytic, but also a socially responsible approach to decision-making. Effective training programmes 315 and actions of the top management of organisations 316 can help to leverage the way users extract and manage data. Nevertheless, it depends a lot on the big data strategy whether data is used in a responsible way, whether disruptive insights are likely, whether a well-founded analytics methodology is used, and whether the cost-benefit ratio and other risks are foreseeable or not.…”

Section: Data Culturementioning

confidence: 99%

Lists of Ethical, Legal, Societal and Economic Issues of Big Data Technologies

et al. 2017

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Data-driven innovation is deeply transforming society and the economy. Although there are potentially enormous economic and social benefits this innovation also brings new challenges for individual and collective privacy, security, as well as democracy and participation. The main objective of the CSA e-SIDES is to complement the research on privacy-preserving big data technologies, by analyzing, mapping and clearly identifying the main societal and ethical challenges emerging from the adoption of big data technologies, conforming to the principles of responsible research and innovation; setting up and organizing a sustainable dialogue between industry, research and social actors, as well as networking with the main Research and Innovation Actions and Large Scale Pilots and other framework program projects interested in these issues. It will investigate stakeholders' concerns, and collect their input, framing these results in a clear conceptual framework showing the potential trade-offs between conflicting needs and providing a basis to validate privacy-preserving technologies. It will prepare and widely disseminate community shared conclusions and recommendations highlighting the best way to ultimately build confidence of citizens and businesses towards big data and the data economy. Second, the legal perspective focuses on the lists of human rights derived from the European Convention on Human Rights (ECHR) and the EU Charter of Fundamental Rights (the EU Charter), which together constitute the main legal framework for the EU in the field of human rights. The rights of particular relevance in the context of big data technologies are the rights to private and family life, personal data protection, freedom of expression and information, freedom of assembly and association, nondiscrimination, fair trial and consumer protection. By analysing the normative scope of each of these human rights, looking at both legislation and case law of the European courts and the way in which big data technologies challenge different aspects of each human right at stake, the legal part distils the list of the most relevant issues at the nexus of big data technologies and human rights in the EU. The legal issues identified are: lack of transparency, vagueness of the concept of harm, accountability, proportionality, establishing a regulatory framework and the role of private actors in applying fundamental rights.Third, the societal perspective makes use of the extensive literature on Societal Impact Assessments (SIA). The analysis of literature was combined with a review of research project propositions and complemented by discussions at two workshops. Societal impact is very generally understood as changes to one or more of a number of elements of social life: people's way of life, their culture, their community, their political systems, their environment, their health and well-being, their personal and property and their fears and aspirations. The societal issues are mapped by examining different actors and distinctions between thes...

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Assessing business value of Big Data Analytics in European firms

Cited by 390 publications

References 64 publications

Organizational Agility Conceptual Model

Organizational Agility Conceptual Model

Leveraging Big Data Analytics to Improve Quality of Care in Healthcare Organizations: A Configurational Perspective

Lists of Ethical, Legal, Societal and Economic Issues of Big Data Technologies

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