Consumers' Drivers of Generative Pre-Trained Transformer (GPT) Conversational Bot Adoption
Bibliographic record
Abstract
This chapter examines the relationship between society and artificial intelligence (AI), emphasizing the factors driving consumer adoption of AI conversational bots. The authors examine how societal norms, past experiences, and trust in technology influence the acceptance and usage of generative-pre trained transformer (GPT) bots. They provide a theoretical framework, integrating key concepts from social influence and technology acceptance theory, to understand the complex dynamics of GPT bot adoption. Conducting a survey, they analyze data from 412 participants in North America to test various hypotheses. The findings broadly support the proposed model, highlighting the significant roles of social norms, word of mouth, and trust in shaping consumer behaviour towards AI conversational bots. However, an intriguing exception is found in the lack of a direct relationship between behavioural intention and actual technology usage, pointing to the need for further investigation into the factors that bridge the gap between the intention to use and the actual use of AI technologies in everyday contexts.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".