Will AI-enabled conversational agents acting as digital employees enhance employee job identity?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Artificial Intelligence (AI)-enabled conversational agents (CAs) increasingly transform online customer service by acting as frontline workers. Understanding employees' attitudes toward these digital colleagues is crucial, as CAs blur the boundaries between human and machine roles. However, existing research often views CAs merely as tools rather than digital employees, neglecting their impact on employees' psychological drivers, such as job identity. This study introduces the perception of CAs as digital employees and develops a Job Identity Enhancement model to examine how human employees' job identity is influenced by their experience working with intelligent CAs. Empirical validation through a survey of frontline service workers reveals that the employees' perceptions of CA autonomy and learning capabilities enhance their job variety and job control, ultimately boosting their job identity and organizational commitment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.033 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it