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Big Data Research for the Knowledge Economy

Big Data Research for the Knowledge Economy

Big data has emerged as a new scientific paradigm that has made a tidal wave across various sectors in the knowledge economy ranging from national security to scientific discovery, from economic and business activities to public administration (Chen et al. 2012; McAfee and Brynjolfsson 2012; Chen and Zhang, 2014;). Big data has attracted enormous attention in recent years due to its huge operational and strategical potential. However, it is not straightforward for potential adopters to understand the concept and capture the value of big data. This may be caused by many different definitions of big data highlighting various aspects of the concept. Among these definitions, the notion of ‘V’ is often used by scholars and practitioners to help define big data (McAfee and Brynjolfsson 2012; Lycett 2013; Erevelles et al. 2015; Fosso Wamba et al. 2015). The big data definitions have evolved from the classic three ‘V’s definition: volume, variety and velocity, to a more recent two additional Vs: value and veracity.

The phenomenon big data is mainly driven by the new technological and methodological development. The extensive application of information communication technologies such as the Internet, mobile phones, sensor devices, and social media networks in the modern world is generating enormous volume of data in various forms. As the Internet has become many people’s preferable means to communicate, game, and shop, each log, search, and browse by millions of Internet users on websites e.g. Google, Wikipedia, Amazon, eBay, and YouTube generate vast amount of data. Millions of mobile phone users also produce core data (e.g. phone calls, Internet usage, and messaging) as well as ambient data as by-products of their daily activities. Meanwhile, more and more people use social media platforms such as Facebook and Twitter, to share and exchange information and give their reviews on products, services, and policies. Moreover, the wide implementation of product identification and sensory technologies e.g. Global Positioning System (GPS), radio frequency identification technology (RFID) and time temperature indicator (TTI), provides huge amount of structured, semi-structured and unstructured real time data across the supply chain of all industries (Lee and Park 2008; Sarac et al. 2010; Wang and Li 2012; Fosso Wamba et al. 2013)."

An Empirical Study of Wearable Technology Acceptance in Healthcare,Big Data promises value: Is hardware technology taken onboard?,Gaining Competitive Intelligence from Social Media Data: Evidence from Two Largest Retail Chains in the World,Guest Editorial,Improving the predictability of business failure of supply chain finance clients by using external big dataset,Modeling and quantifying uncertainty in the product design phase for effects of user preference changes,On the Model Design of Integrated Intelligent Big Data Analytics Systems,Safety or no safety in numbers? Governments, big data and public policy formulation,Section Header,Social Information Landscapes: Automated Mapping of Large Multimodal, Longitudinal Social Networks,Understanding community citizenship behavior in social networking sites: An extension of the social identification theory,Using Twitter Data to Predict the Performance of Bollywood Movies

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