Factors fostering the success of IoT services in academic libraries: a study built to enhance the library performance
Bibliographic record
Abstract
Purpose In the modern era, libraries confront significant service challenges. Some challenges are linked to information resource management which includes direct availability of information for immediate decision making. The Internet of Things (IoT) is a recent technological shift that library personnel should be aware of because it has the potential to enhance information resource management. The purpose of the research is to highlight the willingness to adopt IoT technology in libraries. Design/methodology/approach This study uses a quantitative research design in which a survey of public sector universities in Nanjing, China, is conducted to investigate the determinants of IoT adoption intention in libraries. A total of 389 responses were captured from experienced library personnel. The literature on technology adoption is then used to formulate quantitative theories. For data analysis, partial least squares structural equation modeling using SmartPLS. Findings The research highlights the various success factors which support the IoT service adoption process. It is concluded that IoT augmented services in academic libraries must be supported through robust management practices and effective utilization of technological resources. Many libraries have made substantial modifications to their structure in terms of technology and design to satisfy the demands of patrons. Originality/value This is an empirical paper that looks at IoT adoption intention in libraries using a quantitative approach through surveying library personnel. The library personnel can aid in the understanding of the motivations behind technology adoption in libraries, particularly of IoT services that may bring about advances in the libraries' capability to provide information access services.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".