Conceptualising methodological diversity among born-digital users
Insights from the garbage can model
Bibliographic Data
| ID | 20398317 |
|---|---|
| Authors | Adam Nix (0000-0003-1539-8130, University of Birmingham, corresponding author), Stephanie Decker (0000-0003-0547-9594, University of Birmingham), David A Kirsch (0000-0002-2143-8306, University of Maryland, College Park) |
| Year | 2025 |
| Volume | 40 |
| Issue | 6 |
| Pages | 4499-4511 |
| Publication date | 2025-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | AI & Society (JOURNAL) |
| Journal identifiers | ISSN: 0951-5666 • E-ISSN: 1435-5655 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s00146-025-02229-6 |
| OpenAlex | W4408191938 |
| Language | EN |
| References cited | 59 |
The benefits of AI technologies in archival preservation are well recognised, though questions remain about their integration into existing processes. AI also shows promise for enhancing user experience and discovery in accessing born-digital materials. However, a limited understanding of the diverse methodological needs surrounding born-digital access risks the creation of one-size-fits-all solutions that suit certain approaches and research questions better than others. This article reviews current efforts in born-digital access and applies the Garbage Can Model from organisation theory to conceptualise the challenge of developing AI-based tools for multiple user types, highlighting the iterative and often decentralised nature of multi-stakeholder decision-making. We address this challenge by creating four born-digital archival user types—the aggregator, the synthesiser, the fact finder, and the narrator—each with distinct motivations and research approaches. Finally, we identify some new opportunities for stakeholders to inform how AI-based tools can be developed to better meet the variety of methodological needs that exist in relation to born-digital archives
Art · Data science · Garbage · Geography · Performing arts · Programming language · Sociology · Visual arts · World Wide Web · Anthropology · Computer Science · Innovative Human-Technology Interaction · Persona Design and Applications · Service and Product Innovation
Risk and Ruin
Human resource management in the age of generative artificial intelligence
Conceptualizing Historical Organization Studies
Topic Modeling in Management Research
Research Strategies for Organizational History
The Double-edged Sword of Oppositional Category Positioning
Unlocking digital archives
Finding light in dark archives
Openness and privacy in born-digital archives
Consuming History
Clues, Myths, and the Historical Method
Understanding the application of handwritten text recognition technology in heritage contexts
Paradigms lost
Archival dignity, colonial records and community narratives
Archives, linked data and the digital humanities
Placing records continuum theory and practice
Sorting through the garbage can
Organization Theory in Business and Management History
Using digital sources
The Treatment of History in Organisation Studies
Rethinking history and memory in organization studies
The Transnational and the Text-Searchable
Along the Archival Grain
| Citation velocity | historical |
|---|---|
| Highly cited | No |