Quantifying Employee Voices: A Study of Perception on the AI-Powered Chatbot
Bibliographic record
Abstract
Abstract Purpose This study focuses on the employees’ attitudes towards Artificial Intelligence (AI)-based feedback-gathering mechanisms in a business setting to measure the attitudes and investigate the factors that may influence them. Design/Methodology Mixed method of research is used which consists of exploratory and analytical research. A structured questionnaire was used for data collection, and statistical tests were used for data analysis. Findings Key results include that males are more technologically literate than females, and no statistical differences were observed in the levels of trust in AI or the ease of navigation. Research Limitations/Implications The study is limited to the AI-powered Employee Chatbot. The findings indicate that the AI-powered system is effective in capturing feedback. Thus, the results contain recommendations for improving the effectiveness of the system. Practical Implications This study offers several suggestions which may help managers enhance the management of AI Systems in the organisations. According to Krejcie and Morgan’s, when the employees are trained on how to interact with the AI systems the user acceptance is enhancing the performance of the systems. Social Implications This study helps to design for and launch certain digital-skill development programmes to start society to have an equal chance to benefit from the value addition AI tools. Originality/Value This study fills the prevailing literature gap on the role of AI in the corporate Indian sector specifically concerning the employee’s view on the AI feedback system and contributes to the discussion on AI use in the Human Resource Management (HRM) context.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".