Skip to main content

“That’s Not Damning with Faint Praise”: Understanding the Adoption of Artificial Intelligence for Digital Preservation Tasks

  • Conference paper
  • First Online:
  • 952 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13971))

Abstract

Memory organisations need to constantly address the adoption of digital technology to remain relevant in light of recent innovations that constitute the so-called fourth technological revolution. This study aims to expand the understanding of the current adoption of Artificial Intelligence for digital preservation tasks by investigating it through the lenses of the Diffusion of Innovations theory in relation to disruptive innovations. The analysis takes the form of an exploratory qualitative inquiry, performed on the transcripts of four focus groups presenting opinions on specific applications of Artificial Intelligence systems, mostly related to Computer Vision, expressed by professionals engaged in digital preservation. The study results indicate that there is strong interest in adopting these innovations. However, further research and the development of a dialogue among the involved communities of practice are necessary to determine the implications and potential outcomes of this technological advancement in the context of digital preservation.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Aizstrauta, D., Ginters, E., Eroles, M.A.P.: Applying theory of diffusion of innovations to evaluate technology acceptance and sustainability. Procedia Comput. Sci. 43, 69–77 (2015)

    Article  Google Scholar 

  2. Balkun, M.M., Deyrup, M.M.: Transformative Digital Humanities: Challenges and Opportunities. Routledge, Abingdon (2020)

    Book  Google Scholar 

  3. Borowiecki, K.J., Navarrete, T.: Digitization of heritage collections as indicator of innovation. Econ. Innov. New Technol. 26(3), 227–246 (2017). https://doi.org/10.1080/10438599.2016.1164488

    Article  Google Scholar 

  4. Ch’ng, E., Cai, S.: Methods for evaluating the adoption and use of digital technologies in glams. MethodsX 7, 100559 (2020)

    Article  Google Scholar 

  5. Christensen, C.M.: The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail. Harvard Business School Press, Boston (1997)

    Google Scholar 

  6. Cordell, R.: Machine Learning + Libraries. A Report on the State of the Art of the Field. Technical report, Library of Congress (2020)

    Google Scholar 

  7. Darby, A., Coleman, C.N., Engel, C., van Strien, D., Trizna, M., Painter, Z.W.: AI training resources for GLAM: a snapshot. Technical report. arXiv:2205.04738, arXiv (2022)

  8. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35(8), 982–1003 (1989)

    Article  Google Scholar 

  9. Dearing, J.W., Cox, J.G.: Diffusion of innovations theory, principles, and practice. Health Aff. 37(2), 183–190 (2018). https://doi.org/10.1377/hlthaff.2017.1104

    Article  Google Scholar 

  10. Farnsworth, J., Boon, B.: Analysing group dynamics within the focus group. Qual. Res. 10(5), 605–624 (2010)

    Article  Google Scholar 

  11. Fast, E., Horvitz, E.: Long-term trends in the public perception of artificial intelligence. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)

    Google Scholar 

  12. Ford, M.: Could artificial intelligence create an unemployment crisis? Commun. ACM 56(7), 37–39 (2013)

    Article  Google Scholar 

  13. Gefen, A., Saint-Raymond, L., Venturini, T.: AI for digital humanities and computational social sciences. In: Braunschweig, B., Ghallab, M. (eds.) Reflections on Artificial Intelligence for Humanity. LNCS (LNAI), vol. 12600, pp. 191–202. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69128-8_12

    Chapter  Google Scholar 

  14. Girasa, R.: Artificial Intelligence as a Disruptive Technology. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-35975-1

    Book  Google Scholar 

  15. Godin, B., Vinck, D.: Critical Studies of Innovation: Alternative Approaches to the Pro-innovation Bias. Edward Elgar Publishing, Cheltenham (2017)

    Book  Google Scholar 

  16. Haider, M., Kreps, G.L.: Forty years of diffusion of innovations: utility and value in public health. J. Health Commun. 9(S1), 3–11 (2004)

    Article  Google Scholar 

  17. Hwang, Y., Jeong, S.H.: Revisiting the knowledge gap hypothesis: a meta-analysis of thirty-five years of research. Journal. Mass Commun. Q. 86(3), 513–532 (2009)

    Article  Google Scholar 

  18. Jun, K.N., Weare, C.: Institutional motivations in the adoption of innovations: the case of e-government. J. Public Adm. Res. Theory 21(3), 495–519 (2011)

    Article  Google Scholar 

  19. Kee, K.F.: Adoption and diffusion. Int. Encycl. Organ. Commun. 1, 41–54 (2017)

    Google Scholar 

  20. Makowsky, M.J., Guirguis, L.M., Hughes, C.A., Sadowski, C.A., Yuksel, N.: Factors influencing pharmacists’ adoption of prescribing: qualitative application of the diffusion of innovations theory. Implement. Sci. 8(1), 1–11 (2013). https://doi.org/10.1186/1748-5908-8-109

    Article  Google Scholar 

  21. Markus, G., et al.: AI in relation to GLAMs Task Force. Report and recommendations. Technical report, Europeana Network Association (2021). https://pro.europeana.eu/project/ai-in-relation-to-glams

  22. McCarthy, J., Minsky, M.L., Rochester, N., Shannon, C.E.: A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI Mag. 27(4), 12–12 (2006)

    Google Scholar 

  23. Meyer, G.: Diffusion methodology: time to innovate? J. Health Commun. 9(S1), 59–69 (2004)

    Article  MathSciNet  Google Scholar 

  24. Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning. MIT Press, Cambridge (2018)

    MATH  Google Scholar 

  25. Monarch, R.M.: Human-in-the-loop machine learning: active learning and annotation for human-centered AI. Simon Schuster (2021)

    Google Scholar 

  26. Navarrete, T.: Digital heritage tourism: innovations in museums. World Leisure J. 61(3), 200–214 (2019). https://doi.org/10.1080/16078055.2019.1639920

    Article  Google Scholar 

  27. Artificial Intelligence: Oxford English dictionary (2021). https://www.oed.com/view/Entry/271625?

  28. Padilla, T.: Responsible operations: data science, machine learning, and AI in libraries (Dublin, Oh: OCLC Research, 2019) (2019)

    Google Scholar 

  29. Peirce, C.S.: Collected Papers of Charles Sanders Peirce, vol. 5. Harvard University Press, Cambridge (1974)

    Google Scholar 

  30. Rasmussen, C.H., Hjorland, B.: Libraries, archives and museums (LAM): conceptual issues with focus on their convergence (2021). https://www.isko.org/cyclo/lam

  31. Rogers, E.M.: Diffusion of Innovations. The Free Press of Glencoe (1962)

    Google Scholar 

  32. Rogers, E.M.: Diffusion of Innovations, 5th edn. Free Press, New York (2003)

    Google Scholar 

  33. Ryan, B., Gross, N.C., et al.: Acceptance and diffusion of hybrid corn seed in two Iowa communities, vol. 372. Agricultural Experiment Station, Iowa State College of Agriculture and Mechanic Arts (1950)

    Google Scholar 

  34. Salahshour Rad, M., Nilashi, M., Mohamed Dahlan, H.: Information technology adoption: a review of the literature and classification. Univ. Access Inf. Soc. 17(2), 361–390 (2018). https://doi.org/10.1007/s10209-017-0534-z

  35. Saldaña, J.: The Coding Manual for Qualitative Researchers. SAGE Publications Ltd., Thousand Oaks (2021)

    Google Scholar 

  36. Si, S., Chen, H.: A literature review of disruptive innovation: what it is, how it works and where it goes. J. Eng. Tech. Manag. 56, 101568 (2020). https://doi.org/10.1016/j.jengtecman.2020.101568

    Article  Google Scholar 

  37. Strien, D.V., Bell, M., McGregor, N.R., Trizna, M.: An introduction to AI for GLAM. In: Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, pp. 20–24. PMLR (2022). https://proceedings.mlr.press/v170/strien22a.html. ISSN 2640-3498

  38. Tavory, I., Timmermans, S.: Abductive Analysis: Theorizing Qualitative Research. University of Chicago Press, Chicago (2014)

    Google Scholar 

  39. Timmermans, S., Tavory, I.: Theory construction in qualitative research: from grounded theory to abductive analysis. Sociol Theory 30(3), 167–186 (2012). https://doi.org/10.1177/0735275112457914

    Article  Google Scholar 

  40. Vila-Henninger, L., et al.: Abductive coding: theory building and qualitative (re) analysis. Sociol. Methods Res. 00491241211067508 (2022). https://doi.org/10.1177/00491241211067508

  41. Wood, B.A., Evans, D.: Librarians’ perceptions of artificial intelligence and its potential impact on the profession. Comput. Libr. 38(1) (2018)

    Google Scholar 

  42. Yoon, J., Andrews, J.E., Ward, H.L.: Perceptions on adopting artificial intelligence and related technologies in libraries: public and academic librarians in north America. Library Hi Tech (2021)

    Google Scholar 

  43. Zhang, H., Xu, X., Xiao, J.: Diffusion of e-government: a literature review and directions for future directions. Gov. Inf. Q. 31(4), 631–636 (2014)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was conducted with the financial support of the Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (d-real) under Grant No. 18/CRT/6224.

The authors would also like to thank Stephen Howell of Microsoft Ireland for his support with using Microsoft Azure to request tags and descriptions for the phase one focus group prompts. In addition, the authors would like to thank the following students who assisted with data collection in the study: Rachael Agnew, MacKenzie Barry, Nancy Bruseker, Sinead Carey, Emma, Carroll, Lauren Caravati, Na Chen, Caroline Crowther, Aoife Cummins Georghiou, Marc Dagohoy, Desree Efamaui, Haichuan Feng, Laura Finucane, Nathan Fitzmaurice, Conor Greene, Yazhou He, Yuhan Jiang, Joang, Zhou, Grainne Kavanagh, Kate Keane, Mark Keleghan, Miao Li, Danyang Liu, Xijia Liu, Siqi Liu, Hannah Lynch, Conor Murphy, Niamh Elizabeth Murphy, Rebecca Murphy, Kyanna Murray, Kayse Nation, Blaithin NiChathain, Roisin O’Brien, Niall O’Flynn, Abigail Raebig, Bernadette Ryan, Emma Rothwell, John Francis Sharpe, Lin Shuhua, Zhongqian Wang, Robin Wharton, Zhillin Wei, India Wood, Bingye Wu, Deyan Zhang, Zhongwen Zheng and Zheyuan Zhang.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Giulia Osti .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Osti, G., Cushing, A. (2023). “That’s Not Damning with Faint Praise”: Understanding the Adoption of Artificial Intelligence for Digital Preservation Tasks. In: Sserwanga, I., et al. Information for a Better World: Normality, Virtuality, Physicality, Inclusivity. iConference 2023. Lecture Notes in Computer Science, vol 13971. Springer, Cham. https://doi.org/10.1007/978-3-031-28035-1_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-28035-1_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28034-4

  • Online ISBN: 978-3-031-28035-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics