Diffusion of Open Data and Crowdsourcing among Heritage Institutions This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 License. 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

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?

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

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.

Adoption Rates According to Institution Type

Adoption Rates – Country Comparison

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

Attitudes: Conditions for Releasing Content

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.

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

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

Thank you for your attention! Contact Details: Beat Estermann E-mail: beat.estermann@bfh.ch Phone: +41 31 848 34 38 Project Portal: http://survey.openglam.ch