1. Diffusion of Open Data and Crowdsourcing among Heritage Institutions
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Museum Georges Labit in Toulouse. Photo by VIGNERON. CC BY-SA 1.0 (Wikimedia Commons).
Diffusion of Open Data and Crowdsourcing among Heritage Institutions
Beat Estermann, 29 August 2015 – EGPA, Toulouse
Bern University of Applied Sciences | E-Government Institute || Open Knowledge | OpenGLAM Working Group

2. Share of institutions (%)
Research Questions
Early Adopters
13.5%
Share of institutions (%)
Innovators
2.5%
Early Majority
34%
Late Majority
Laggards
16%
Where do heritage institutions stand with regard to…
…Open Data?
…Linked Data / Semantic Web?
…Digitization
…Open Content?
…Engaging Audiences on the Internet
…Collaborative Content Creation
Innovation Diffusion Model,
Everett Rogers, 1962
Awareness
Evaluation
Adoption
Trial
Interest
What are the perceived risks and opportunities? (drivers vs. hindering factors)
What are the expected benefits?
What are the differences between different types of heritage institutions?
A further goal of the “OpenGLAM Benchmark Survey” are international comparisons: In what ways does the situation in the different countries vary?

3. Methodological Challenges
Varying structure of the population / sample in the different countries
Different sampling approach in some countries (e.g. Poland)
Differences in the structure of the heritage sector in the different countries
Self-selection bias in some countries regarding attitudes which are at the center of the investigation, for example:
PL: « digitization is not important »  44% drop outs
« digitization is important »  10% drop outs
NL: « opportunities of exchanging data do not prevail over risks »  70% drop outs
« opportunities of exchanging data do prevail over risks »  16% drop outs
but no such effects for CH and FI (which also have higher response rates)… CH FI NL PL
N institutions contacted
1532
356
1393
669
N inhabitants (in mio., as of 2013)
8.1
5.4
16.8
38.5
Surface (in 1000 sq km) 41
338 42
313
Density of heritage institutions (institutions per mio. inhabitants)
189.1
65.9
82.9
17.4
Density of heritage institutions (institutions per 1000 sq km)
37.4
1.1
33.2
2.1

4. Diffusion of Innovative Practices among Heritage Institutions
Share of institutions (%)
Innovators
2.5%
Early Adopters
13.5%
Early Majority
34%
Late Majority
34%
Laggards
16%
Finland, Poland, Switzerland, The Netherlands, all institution types combined, N = 584.
Cases with «stagnation» / «discontinuance» have been ignored.

5. Adoption Rates According to Institution Type

6. Adoption Rates – Country Comparison

7. Ordinal Logistic Regression – Synthesis of Results
Independent Variable(s)
Open Data
Linked Data
Digitization
Open Content
Social Media Use
Collab. Cont. Creation
Institution Type
+++
Country ++
Typical Objects +
(+)
Main Users
Geographical Reach
Size (paid FTE)
Size (revenues)
Pct of Volunters in Workforce
Revenue Sources
Form of Organization
Number of Metadata Types
Skills / Skills Acquisition
Attitudes reg. Open Content

8. Attitudes: Conditions for Releasing Content

9. Expected Dynamics of Adoption of Open Data, Open Content and Crowdsourcing
Three Estimators:
Perceived importance and desirability (opportunities vs. risks)
Presence (absence) of important prerequisites (or « show-stoppers »)
Indications regarding institutions’ future practice
Main Findings:
70% of institutions will have adopted open data as a practice in ca. 10 years from now. (limit to diffusion: ca. 30% don’t have metadata for their objects)
70% of institutions will have adopted open content as a practice in ca. 15 years from now. (limits to diffusion: advancement of digitization; copyright)
Ca. 20% of institutions are using crowdsourcing today; the data suggest that the adoption rate will be lower than for open data and open content.

10. Driving and Hindering Factors of Open Content and Crowdsourcing
Driving Factors1
Hindering Factors1
Open Content
Improving the visibility of the institution and its holdings
Making content more easily available for existing users and attract new users
Facilitating networking among institutions
Improving interactions with users
Doing a better job at fulfilling the institution’s core mission
Extra time effort and expenses (digitization, documentation, rights clearance)
Feeling of loss of control
Wish to prevent commercial use of content by third parties without due compensation
Technical issues and insufficient staff skills
Crowdsourcing
Intention to get access to external expertise and to have certain tasks carried out by volunteers
Quest for an improved relationship with users/visitors (trust, loyalty, public ownership and responsibility)
Extensive preparation and follow-up
Difficulties to estimate the time scope; low planning security; continuity of data maintenance is not guaranteed
1 Factors which are of relevance for more than 50% of responding institutions

11. Conclusions and Outlook
Earlier findings from Swiss pilot survey largely confirmed
Gathered further insights into the dynamics of the diffusion of open data and crowdsourcing among heritage institutions in Europe
If the data is to be used for benchmarking purposes, the self-selection bias present in some country samples needs to be adjusted for.
Paths for further enquiry:
Analyse context factors in the various countries which may influence adoption rates
Investigate the links and mutual influences between the various Internet-related practices
Investigate the change of perceptions as the institutions implement open data policies or crowdsourcing approaches

12. Thank you for your attention!
Contact Details:
Beat Estermann Phone:
Project Portal: