Understanding current research on the use and impact of big data analytics: a systematic literature review
Subjectssystematic literature review, Big Data, Adoption of Big Data Analytics, Analytics, Big Data Impact, Big Data Technologies
systematic literature review
adoption of Big Data analytics
Big Data impact
Big Data technologies
MetadataShow full item record
Other TitlesMCCSIS 2018 - Multi Conference on Computer Science and Information Systems; Proceedings of the International Conferences on Big Data Analytics, Data Mining and Computational Intelligence 2018, Theory and Practice in Modern Computing 2018 and Connected Sma
AbstractWith the increasing applications of Big Data Analytics, it is imperative for researchers to keep abreast with the rapid development and emerging research challenges in this field. Therefore, the research reported in this work in progress paper aims to update our knowledge and understanding of the state of the art research on the applications of Big Data Analytics by conducting a comprehensive and systematic review of the recent publications. The literature review is mainly focusing on the emerging new concepts and definitions, theories, research models, research methodologies, critical success factors, and impact on business performance. It is expected that the insights gained through this comprehensive review will contribute to our knowledge on the current status of Big Data Analytics research and associated emerging research challenges and opportunities. Due to the increased interests in Big Data Analytics, the critical analysis of emerging literature will identify the research gaps that provides valuable direction for future studies.
CitationDuan Y, Ramanathan R, Cao G, Khilji N (2018) 'Understanding current research on the use and impact of big data analytics: a systematic literature review', International Conferences Big Data Analytics, Data Mining and Computational Intelligence 2018; Theory and Practice in Modern Computing 2018; and Connected Smart Cities 2018 - Madrid, IADIS.
TypeConference papers, meetings and proceedings
Showing items related by title, author, creator and subject.
Adoption of business analytics and impact on performance: a qualitative study in retailRamanathan, Ramakrishnan; Philpott, Elly; Duan, Yanqing; Cao, Guangming; University of Bedfordshire (Taylor & Francis, 2017-07-11)This paper describes a qualitative study aimed at understanding issues faced by retail firms when they start a project of implementing Business Analytics (BA) and understanding the impact of BA implementation on business performance. Our study is informed by prior literature and the theoretical perspectives of the Technology-Organisation-Environment (TOE) framework but is not constrained by this theory. Using case studies of nine retailers in the UK, we have found support for the link between TOE elements and adoption. In addition, we have identified more interesting involvement of additional factors in ensuring how firms could maximise benefit derived from BA and traditional TOE factors that potentially could have additional impacts different from the ones. For example, there appears a link between adoption of BA and business performance (including performance in terms of environmental sustainability), and this link is moderated by the level of BA adoption, IT integration and trust.
A path model linking business analytics, data-driven culture, and competitive advantageCao, Guangming; Duan, Yanqing; University of Bedfordshire (European Conference on Information Systems (ECIS), 2014-06)Business analytics (BA) has become increasingly important for companies to gain valuable insights from big data and ultimately competitive advantage. However, little empirical evidence exists regarding the mechanisms through which BA impacts on competitive advantage. In light of this paucity, this paper aims to advance our understanding of the impact of BA on competitive advantage. First, this paper provides a BA classification. Second, drawing on contingency theory and the resource-based view, it develops a research model that specifies the paths from BA to competitive advantage. Third, it empirically tests the proposed model using structural equation modelling, offering valuable insights into how different types of BA impact on competitive advantage. Fourth, it systematically tests how resource valu, rarity and inimitability impact on competitive advantage. The findings demonstrate that BA, through the mediation of a data-driven culture, positively impacts on information processing capabilities, which in turn have a positive effect on competitive advantage. The findings also demonstrate that resource valu, rarity and inimitability partially but strongly mediate the impact of information processing capabilities on competitive advantage. Finally, the paper contributes to managers´ knowledge by demonstrating how different types of BA should be implemented to develop information processing capabilities and gain competitive advantage.
MyEvents: a personal visual analytics approach for mining key events and knowledge discovery in support of personal reminiscenceParvinzamir, Farzad; Zhao, Youbing; Deng, Zhikun; Dong, Feng (John Wiley & Sons Ltd., 2019-01-05)Reminiscence is an important aspect in our life. It preserves precious memories, allows us to form our own identities and encourages us to accept the past. Our work takes advantage of modern sensor technologies to support reminiscence, enabling self-monitoring of personal activities and individual movement in space and time on a daily basis. This paper presents MyEvents, a web-based personal visual analytics platform designed for non-computing experts, that allows for the collection of long-term location and movement data and the generation of event mementos. Our research is focused on two prominent goals in event reminiscence: 1) selection subjectivity and human involvement in the process of self knowledge discovery and memento creation; and 2) the enhancement of event familiarity by presenting target events and their related information for optimal memory recall and reminiscence. A novel multi-significance event ranking model is proposed to determine significant events in the personal history according to user preferences for event category, frequency and regularity. The evaluation results show that MyEvents effectively fulfils the reminiscence goals and tasks.