INFORMATION OVERLOAD IN USING CONTENT MANAGEMENT SYSTEMS: CAUSES AND CONSEQUENCES
Bibliographic record
Abstract
In order to effectively manage organizational information assets, more and more organizations are beginning to implement content management systems (CMS) to consolidate multiple information sources into a single, centralized repository. While organizations implement CMS in attempts to help employees easily identify information sources and quickly locate relevant organizational documents, prior findings have suggested that many CMS projects fail to reach the expected adoption rates. The present paper proposes that advanced information technologies such as CMS may pose a problem of information overload. Drawing upon the information processing theory, this paper develops a research model examining users’ experiences and interactions with CMS in relation to information seeking and retrieval in organizations. In particular, the model centres on exploring some potential causes of individuals’ perceived information overload when using a CMS, and clarifying the effects of information overload on actual performance outcomes and on users’ system evaluations. The present paper responds to calls for more empirical research aimed at understanding the challenges and issues surrounding user adoption of CMS. Further, the proposed research model provides a conceptual basis to inform future research initiatives for advancing CMS designs and improving the implementation processes of CMS in organizations.
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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 | 0.015 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".