Towards the Development of a Typology of Big Data Analytics in Innovation Ecosystems
2019
- 263Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage263
- Downloads173
- Abstract Views90
Article Description
The digital transformation of society and economy leads to fundamental changes in the planning and execution of innovation processes in organizations. Possibilities and application scenarios of digital, data-driven innovation are frequently discussed in academia and in industry, but a comprehensive and systematic examination of the various types and roles of big data analytics technologies in innovation ecosystems is currently missing. Organizations need support in identifying and implementing the new opportunities of big data analytics to generate value through innovation. Starting from the theoretical perspective of service-dominant logic, we present a three-dimensional conceptual framework with associated characteristics that can be used to derive different archetypes of big data analytics in innovation ecosystems. The findings will be further developed as part of a qualitative case study in the field of electromobility in Germany.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know