Assessing the Impact of Relationship Length in the SMEs and Bank Association
Bibliographic record
Abstract
Despite the importance of satisfaction, loyalty and relationship length in the literature, there is very little evidence of studies within the Nigerian Business to Business (B2B) Relationship terrain. This paper seeks to investigate the effect of relationship length on SMEs association with their banks in Nigeria. Measurement Items were adapted from various scale sets presented in existing studies were combined to investigate the B2B relationship context. Data were collected from 221 SMEs via a self-administered questionnaire completed either by the SME owner or senior manager with responsibility for relationship with their bank, providing 199 usable records. Principal Component Exploratory Analysis (PCA) was used to determine the underlying data structure, with subsequent deployment of Cronbach’s alpha as a post-hoc assessment of the internal reliability of the retained factors. Subsequently, regression analysis was employed to determine the impact of satisfaction on loyalty in a short and long term relationship contexts. The analysis presented suggests that the SMEs’ had evidence of been satisfied with their bank, however, the regression analysis for both short and long term relationship length were both significant in impacting their loyalty towards their bank. The originality of this paper lies in the investigation of a B2B relationship involving SMEs and banks within a relationship context that hitherto was unknown and the validation of relevant relationship building blocks.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".