Big data: Dimensions, evolution, impacts, and challenges

By In Lee. 2017

454 citations recorded by the source corpus.

Subfields: Information Systems, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/f957ec0ae0a4bc19a3958a2bcff9223f97f58567

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. John S. Edwards
2. Yanqing Duan
3. Nripendra P. Rana
4. Yogesh Kumar Dwivedi
5. Michael D. Williams
6. Kuttimani Tamilmani
7. Rony Medaglia
8. Vishnupriya Raghavan

In Management Information Systems

1. Yogesh Kumar Dwivedi
2. Nripendra P. Rana
3. Banita Lal
4. Michael D. Williams
5. Rony Medaglia
6. Elvira R. Ismagilova
7. Marijn Janssen

In Management Science and Operations Research

1. Nripendra P. Rana
2. Yogesh Kumar Dwivedi
3. Kuttimani Tamilmani
4. Arpan Kumar Kar
5. Rohita Dwivedi
6. Michael D. Williams
7. Hatice Kizgin
8. John S. Edwards

Citations in the corpus

• Digital transformation: Five recommendations for the digitally conscious firm (2020, unknown): “…is prone to being associated with paradigm-shifting technologies such as artifi-cial intelligence, machine learning, or big data, all of which require significant up-front investments in time, money, and staff before any significant benefits can be realized (Lee, 2017; McAfee & Brynjolfsson, 2017).”
• Big Data technologies: A survey (2017, unknown): “Lee (2017) illustrates the application of data analytics using merchant review data.”
• Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2019, background): “Data quality : Data quality can be thought of as the fi tness of data to obtain actionable insights using appropriate analytical tools (Lee, 2017).”
• Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment (2019, unknown): “Such efforts have been documented for identifying customer and non-customer needs (I. Lee, 2017), locating operational inefficiencies (Seddon & Currie, 2017), monitoring competitor actions (Guo, Sharma, Yin, Lu, & Rong, 2017), and developing predictive models for future events (H. Chen et al.,…”