Organizational Readiness to Adopt Artificial Intelligence in the Library and Information Sector of Pakistan
Bibliographic record
Abstract
Objective – This study investigates the readiness for artificial intelligence (AI) adoption in library and information centres of Pakistani universities. The projected outcomes of this study are expected to contribute to the development of best practices for effectively motivating university administrators and preparing librarians for adopting AI in library and information centres. Methods – A theoretical framework combining the technology-organization-environment (TOE) framework and the Technology Readiness Index (TRI) guided this qualitative study. Interviews were conducted with 27 senior representatives, including library managers and registrars, from 27 universities across four provinces and the capital city, Islamabad. A systematic approach was employed to analyze the data. Results – The findings indicate that the concept of AI adoption in Pakistani university libraries is new. The library and information sector of Pakistan is slow in adopting AI, which could have implications for its future competitiveness, despite the push for AI adoption by university librarians and administrators. The readiness for AI adoption in this sector is influenced by factors such as organizational technological practices, financial resources, university size, and data management and protection concerns. Conclusion – Library managers and researchers can implement the TOE framework and TRI scale to facilitate AI adoption in a manner that is relevant to library and information settings in Pakistan as well as other parts of the world. Our research indicates that most adoptions are still in their nascent phases, and numerous library managers feel uneasy due to either uncertainties about the precise benefits AI can bring to their libraries or a lack of knowledge and skills for its effective implementation. To manage the networks of internal and external stakeholders essential for successful AI adoption, universities should consider appointing individuals with a specialized knowledge of AI within their libraries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".