Challenges of information system use by knowledge workers: The email productivity paradox
Bibliographic record
Abstract
Abstract With the growing importance of social media, cloud computing and mobile device interactions, the digital work environment is being perpetually transformed while forcing users to adapt to the emerging technologies. In this volatile environment, research leading to innovative approaches to better support the information practices of knowledge workers is acquiring a critical importance. Aiming to improve the technological and organizational policy‐making decisions for the implementation of new information systems, this paper examines the current challenges of 34 knowldege workers using information systems to perform their daily tasks. A qualitative research approch entailing the use of semi‐directed interviews and diary journals is followed to this end. The outcomes of this research are both theoretical and practical. This research provides a critical insight into the key challenges facing knowledge workers in the digital work environment. From a purely practical perspective, this study helps uncover the needs and expectations of knowledge workers as they use email and other technologies to achieve their work tasks. In light of this study, the technological and organizational policy‐making decisions for the implementation of new information systems must ensure that email as a tool drives true productivity and avoids the productivity paradox.
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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 | 0.021 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".