Antecedents and consequences of buyer‐seller relationship quality in the financial services industry
Bibliographic record
Abstract
Purpose The purpose of this study is to develop a model that investigates the antecedents and the consequences of buyer‐seller relationship quality in the financial services. Design/methodology/approach Data were collected from a survey of more than 400 dyads (414 financial advisors and 772 clients in Canada) and were analyzed using structural equation modeling (SEM). Findings The results notably show that, for both financial advisors and clients, customer orientation has an impact on buyer‐seller relationship quality, whereas buyer‐seller similarity does not. The link between relationship quality and both consequences (purchase intention and word‐of‐mouth) is significant for the two samples. Research limitations/implications Limitations and research directions refer to the measure of word‐of‐mouth construct, which is only weakly reliable, and the need to consider a multilevel approach. Practical implications The study can be helpful for financial advisors to build effective strategies for enhancing their relationships with clients. Originality/value The study is one of the few to consider both perceptions (financial advisors and clients) in order to analyze buyer‐seller relationship quality in the financial services sector.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".