Understanding artificial intelligence experience: A customer perspective
Bibliographic record
Abstract
The engagement between customers and brands is being transformed by artificial intelligence (AI). However, there has been little study into AI-powered customer experiences; hence, this research aims to examine how the incorporation of AI in purchasing might result in a better AI-powered customer experience. This research will develop a conceptual model based on the service quality model and trust-commitment theory. Further to this, an online questionnaire was distributed to individuals who had utilised an AI-powered service provided by a particular brand, and consequently, a total of 354 responses were analysed using Structural Equation Modelling (SEM). The results that were deduced from the responses demonstrated that relationship commitment has begun to substantially impact AI-powered customer experiences. In addition to this, the results also revealed that perceived sacrifice and trust both play an important role in mediating the impacts of perceived convenience, personalisation, and AI-powered service quality. This finding contributes to the previous literature by highlighting the mediating impacts of perceived sacrifice and trust, as well as the significant influence of relationship commitment on AI-powered customer experience. Furthermore, the research poses significant implications for merchants who use AI in services provided to their customers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".