Abstract
PurposeThe data economy, emerging from the current hyper-technological landscape, is a global digital ecosystem where data is gathered, organized and exchanged to create economic value. This paper aims to shed light on the interplay of the different topics involved in the data economy, as found in the literature. The study research provides a comprehensive understanding of the opportunities, challenges and implications of the data economy for businesses, governments, individuals and society at large, while investigating its impact on business value creation, knowledge and digital business transformation.Design/methodology/approachThe authors conducted a literature review that generated a conceptual map of the data economy by analyzing a corpus of research papers through a combination of machine learning algorithms, text mining techniques and a qualitative research approach.FindingsThe study findings revealed eight topics that collectively represent the essential features of data economy in the current literature, namely (1) Data Security, (2) Technology Enablers, (3) Business Implications, (4) Social Implications, (5) Political Framework, (6) Legal Enablers, (7) Privacy Concerns and (8) Data Marketplace. The study resulting model may help researchers and practitioners to develop the concept of data economy in a structured way and provide a subset of specific areas that require further research exploration.Practical implicationsPractically, this paper offers managers and marketers valuable insights to comprehend how to manage the opportunities deriving from a constantly changing competitive arena whose value is today also generated by the data economy.Social implicationsSocially, the authors also reveal insights explaining how the data economy features may be exploited to build a better society.Originality/valueThis is the first paper exploring the data economy opportunity for business value creation from a critical perspective.
Subject
Management of Technology and Innovation
Reference143 articles.
1. Business data sharing through data marketplaces: a systematic literature review;Journal of Theoretical and Applied Electronic Commerce Research,2021
2. Topic modeling algorithms and applications: a survey;Information Systems,2023
3. Airoldi, E.M., Blei, D., Fienberg, S. and Xing, E. (2008), “Mixed membership stochastic blockmodels”, in Koller, D., Schuurmans, D., Bengio, Y. and Bottou, L. (Eds), Advances in Neural Information Processing Systems, Vol. 21, Curran Associates.
4. Big data analytics in E-commerce: a systematic review and agenda for future research;Electronic Markets,2016
Cited by
17 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献