Critical analysis of Big Data challenges and analytical methods
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In this article, the authors present a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions.About:
This article is published in Journal of Business Research.The article was published on 2017-01-01 and is currently open access. It has received 1267 citations till now. The article focuses on the topics: Analytics & Big data.read more
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Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices
TL;DR: In this article, the authors developed and tested a holistic model that depicts and examines the relationships among green innovation, its drivers, as well as factors that help overcome the technological challenges and influence the performance and competitive advantage of the firm.
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A survey towards an integration of big data analytics to big insights for value-creation
Mandeep Kaur Saggi,Sushma Jain +1 more
TL;DR: This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies and presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city.
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Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review
TL;DR: In this article, the authors investigated the role of Artificial Intelligence (AI) in the construction of sustainable business models (SBMs) and provided a quantitative overview of the academic literature that constitutes the field.
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Big data analytics as an operational excellence approach to enhance sustainable supply chain performance
TL;DR: In this paper, the authors used dynamic capability theory as a foundation for evaluating the role of BDA capability as an operational excellence approach in improving sustainable supply chain performance, and identified two pathways that managers can use to improve sustainable supply-chain outcomes in the mining industry based on big data analytics capabilities.
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Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities
TL;DR: In this article, the authors employed institutional theory and resource-based view theory to elucidate the way in which automotive firms configure tangible resources and workforce skills to drive technological enablement and improve sustainable manufacturing practices and furthermore develop circular economy capabilities.
References
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An analytical journey towards big data
TL;DR: A case study approach is employed to identify management challenges in creating a data-driven organisation that has aspirations to incorporate big data into decision-making at an international franchiser in the hospitality industry with a centralised CRM team and well-known brands.
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Evaluating cost taxonomies for information systems management
TL;DR: The authors position the need to identify, control and reduce IS-related costs within the information systems evaluation domain, through culminating and then synthesizing the literature into a frame of reference that supports the evaluation of information systems through a deeper understanding of IS-cost taxonomies.
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Massively parallel feature selection: an approach based on variance preservation
TL;DR: A novel large-scale feature selection algorithm that is based on variance analysis is presented, which selects features by evaluating their abilities to explain data variance and can be readily implemented in most distributed computing environments.
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iCARE: A framework for big data-based banking customer analytics
TL;DR: The Intelligent Customer Analytics for Recognition and Exploration (iCARE) framework is presented to analyze banking customer behaviors from banking big data, through analytical modeling methodologies and techniques designed for a key business scenario.
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Research Directions for Engineering Big Data Analytics Software
Carlos E. Otero,Adrian M. Peter +1 more
TL;DR: The details of big data software are explored, the main problems encountered when engineering big dataSoftware are discussed, and avenues for future research are proposed.