Lower than expected but still willing to use: User acceptance toward current intelligent conversational agents
Bibliographic record
Abstract
Intelligent conversational agents (ICAs) are revolutionizing how humans interact with information systems. Designed to provide human-like service, ICAs are generally evaluated by users in comparison to their human counterparts, often resulting in less-than-expected user experiences. Our research investigates user acceptance of ICAs in this suboptimal condition of commercial customer service. Drawing from the dual perspectives of expectancy confirmation theory and task technology fit theory, we theorize and test an integrated research model on the collective impact of user expectancy confirmation regarding ICA capabilities and their assessment of service-ICA fit on user acceptance. Results from a field survey of 350 users of five ICAs deployed by major Canadian telecom service providers reveal the significant influence of both user expectancy confirmation with ICA capabilities and their assessment of ICA fit-to-service, with the latter playing a more prominent role in shaping user acceptance. Even though ICA performance may not always meet user expectations, users are still willing to engage with ICA services when they perceive the ICA as a fitting solution for their specific service complexity and availability requirements.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".