Utilizing Artificial Intelligence (AI) in enhancing customer-supplier relationship: An exploratory study in the banking industry
Bibliographic record
Abstract
This study provides a comprehensive overview of the field of enhancing the customer-supplier relationship through big data technology and artificial intelligence (AI), reveals existing gaps and offers promising solutions for future research. SMART PLS-4 software was used to analyze the data collected, the results led to the existence of significant relationships between artificial intelligence and enhancing the relationship between the supplier and the customer (customer interaction, customer communication, customer participation, customer learning, customer experience, customer feedback). The study contributes to developing a conceptual model through the application of artificial intelligence in managing customer relationships with suppliers in the banking industry. The study contributes to developing a conceptual model through the application of artificial intelligence in managing customer relationships with suppliers in the banking industry setting. Which supports increasing knowledge in this field and helps managers develop appropriate strategies. This research is the first of its kind to organize and discuss the literature related to using artificial intelligence within the customer-supplier relationship management setting, which provides great importance to the process of using and developing artificial intelligence technology and understanding recent trends in how to develop customer–supplier relationships within the technology era.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".