The impact of ChatGPT factors on consumers' decision-making at commercial banks in Jordan
Bibliographic record
Abstract
In today's digitally enabled world, banks use ChatGPT to handle customer inquiries, expedite service encounters, and provide intelligent, human-like responses; thus, ChatGPT has grown in popularity within the banking sector. The purpose of this study is to investigate the key factors of ChatGPT that influence consumer banking decision-making at Jordanian commercial banks. To accomplish the study's goals, the researcher employed a descriptive-analytical approach. The study sample consists of 445 customers with valid accounts at commercial banks in Amman city. The total number of valid and completed questionnaires was 419 and included in the final analysis. The reported results demonstrate a significant impact of ChatGPT factors that include credibility, informativeness, interaction, content suitability, perceived trustworthiness, and personalization on consumers' banking decision-making. The results can help banks develop a digital plan to enhance consumer awareness and boost benefits, which allows them to overcome challenges when utilizing AI for transactions. ChatGPT banking services are still in their early phases in Jordan. Few empirical studies have examined actual user behavior, and this study may give valuable insights to scholars and practitioners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".