AI-Based Digital Assistants in the Workplace: An Idiomatic Analysis
Bibliographic record
Abstract
Artificial Intelligence (AI)-based digital assistants are increasingly being adopted by organizations to support tasks. Nevertheless, our understanding of how organizational members perceive digital assistants still needs further investigation. Using figurative language analysis involving in-depth interviews, we explore the idiomatic expressions that organizational participants drew on in their accounts of digital assistants. Our analysis reveals the value of idioms for understanding themes regarding how digital assistants are perceived in a workplace context. These themes depict both the opportunities and challenges, with the former encompassing the ability to focus on value-added activities, productivity, and efficiency gains, as well as reducing job monotony, and the latter including themes such as uncontrollability and unexpectedness, tracking and privacy, transparency, and trust. The study illustrates the usefulness of idiomatic expressions as a fresh lens to understand how people express their thoughts, views, and feelings, as well as uncover issues associated with digital assistants that are not well understood.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".