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FI-IMPACT Best Practices Reporting Tools

A set of tools were developed to measure the effectiveness of different practices put in place by phase three accelerators. Here, we would like to answer questions such as “Does investment business development support help the creation of high performance projects?”

FI-IMPACT identified a set of potential Accelerator Properties that may influence the performance of funded initiatives measured through several Performance Indicators. Accelerator properties are either accelerator practices or some other properties defining a specific accelerator. Examples of accelerator practices include proposal phase support, selection approach, business support and organisation of workshops. Examples of other accelerator properties include the number of partners in the accelerator consortium, number of countries covered, % of professional accelerators on partnership and degree of connectivity with other projects in the FI-PPP Phases 1-3.

To measure effectiveness, we use performance indicators, such as the FI-IMPACT KPI scores, indicators provided by Mattermark and FIWARE usage scores.

In FI-IMPACT, several R modules have been developed to produce automatically statistical reports about Accelerator Benchmarking and Best Practices based on FI-IMPACT databases. The databases are imported and merged into an R data frame. The tool then produces correlation heatmaps, practice scores with accompanying mean difference t-test results and FI PPP phase projects connectivity information. Most of the results are produced in final-form CSV files. The tool also outputs several sets of diagrams (boxplots for all categorical indicators, CDF diagrams for practice scores and score histograms). All the diagrams are produced in two versions - with all sub-grantees and per-accelerator.

Project information: FI-IMPACT

Developers: AILAB of the Josef Stefan Institute

Documentation: see project Wiki

More information: see project report

Acknowledgments

The FI-IMPACT Assessment and Benchmarking Webapp is developed by AILab at Jozef Stefan Institute.

The authors would like to acknowledge funding from the European Union Seventh Framework Programme under the Grant Agreement 632840 (FI-IMPACT).

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