13. PERSPECTIVES
formal definitions
datasets and procedures
informal definitions
including indirect datasets and procedures
for the Shadow Economy
granularity levels
• Country
• Region
• City
• Organisation
• Project
Work
PlaceCommunity
20. OVERVIEW
● Official data from traditional organisations:
public, associations, networks, businesses
● Surveys, interviews, ethnography
● Data mining of social media services and
digital platforms;
● Places related to actors’ education, work,
and their community hubs;
● Methods based on the social structure of
these systems (pyramiding, social network
analysis).
● Importance of lead users (bridge amateurs and
professionals).
22. ACKNOWLEDGEMENTS
This research has partially received funding from:
• the Creative Europe Programme of the European Union: Distributed
Design Market Platform - submission numbers
591699-CREA-1-2017-1-ES-CULT-PLAT and
604420-CREA-2-2018-1-ES-CULT-PLAT;
• The Horizon 2020 Programme of the European Union: DSISCALE -
grant agreement n° 780473
The content of this publication does not reflect the official opinion of
the European Union. Responsibility for the information and views expressed
in the publication lies entirely with the author(s).
24. PROCESS
1. Analysis of existing dataset of SIA
frameworks.
2. Filter the dataset for SIA frameworks for
social entrepreneurs.
3. Filter the dataset for SIA frameworks for
social entrepreneurs and quantitative
approaches.
4. Filter the dataset for SIA frameworks for
social entrepreneurs and quantitative
approaches and with multiple level of
analysis.
5. Development of reflections about the
potential SIA framework for digital
platforms.
25. 1. DATASET
69 SIA frameworks:
Sbeih, Janosch, Bastian Pelka, Marthe Zirngiebl, Jeremy Millard, Elisabeth
Unterfrauner, David Langley, Bart Devoldere, Christopher Graetz, and Mathias
Cuypers. 2018. ‘WP6 SIA Model Matrix Dataset’.
http://make-it.io/open-data-api/
Sbeih, Janosch, Bastian Pelka, Marthe Zirngiebl, Jeremy Millard, Elisabeth
Unterfrauner, David Langley, Bart Devoldere, Christopher Graetz, and Mathias
Cuypers. 2017. D6.2 Societal Impact Analysis and Sustainability Scenarios.
MAKE-IT. D6.2.
http://make-it.io/deliverables/
Based on:
Grieco, Cecilia, Laura Michelini, and Gennaro Iasevoli. 2015. ‘Measuring Value
Creation in Social Enterprises: A Cluster Analysis of Social Impact Assessment
Models’. Nonprofit and Voluntary Sector Quarterly 44(6):1173–93.
31. 5. REFLECTIONS
● a digital platform would enable the data
gathering of Maker initiatives in an easy
way, and it could also integrate relevant
data from other online platforms or
datasets;
● a digital platform would be easy to use for
stakeholders and especially Makers, and
would thus democratize access to SIA;
● these functionalities could also be
integrated to already existing digital
platforms for Maker initiatives (directly or
externally through APIs), contributing thus
to the ecosystem of Maker services.
37. MULTICOMPOSITE INDEX
OECD and JRC, eds. 2008. Handbook on Constructing Composite Indicators:
Methodology and User Guide. Paris: OECD.
http://www.oecd.org/els/soc/handbookonconstructingcompositeindicators
methodologyanduserguide.htm
● Systematically observing impact at scale by aggregating
single Maker initiatives to provide a bigger picture.
● Enabling single initiatives to assess themselves and
see their place in the overall Maker Movement.
●
● Can summarise complex, multi-dimensional realities with
a view to supporting decisionmakers.
● Are easier to interpret than a battery of many separate
indicators.
● Enable users to compare complex dimensions effectively.
● Facilitate communication with general public (i.e.
citizens, media, etc.) and promote accountability.
38. MULTICOMPOSITE INDEX: OUR PROCESS
1. State of the art: research and comparison of existing SIA framework
with real-life cases.
2. Theoretical framework: provides the basis for the selection and
combination of variables.
3. Data selection: based on the analytical soundness, measurability and
relevance.
4. Imputation of missing data: to provide a complete dataset.
5. Multivariate analysis: to study the overall structure of the dataset.
6. Normalization: to render the variables comparable.
7. Weighting and aggregation: following the underlying theoretical
framework.
8. Uncertainty and sensitivity analysis: to assess the robustness of the
composite indicator.
9. Back to the data: to reveal the main drivers for the overall results.
10. Links to other indicators: to correlate the composite indicator (or its
dimensions) with existing (simple or composite) indicators.
11. Visualization of the results: to enhance interpretability.
12. Implementation: development of the software functionalities for data
entry (user interface, database), analysis (algorithm, computing
services), visualization and discussion.
13. Validation: testing the resulting digital tools and platforms with
stakeholders and real-life cases.
39. OVERVIEW
● First exploratory analysis with the goal
of finding what are the potential SIA.
● Frameworks for makers, designers and
social entrepreneurs.
● Three main frameworks identified.
● Guidelines for implementing a
multicomposite index on digital platforms
elaborated.