Retaining customers through relationship quality: a services business marketing case
Bibliographic record
Abstract
Purpose This paper seeks to explore customer‐perceived relationship quality in a B2B setting, and to propose a conceptual model for this construct. Design/methodology/approach An instrumental single case study design is adopted, and Eisenhardt's case study method for theory development is used to collect and analyse data from 55 different customer companies. Findings The research identifies a list of 208 components that are important to customers' relationship quality perceptions. These are grouped into seven parsimonious dimensions, which are assembled into a conceptual model. The IMP Group's relationship substance framework, composed of actor bonds, resource ties and activity links is built upon and expanded by adding four new dimensions: competitive position, external association, relationship impact, and situational factors. Together, these dimensions successfully encapsulate the items that customers within this study identify as important when evaluating the quality of their relationships with their service provider. Research limitations/implications Though the case study design used potentially limits the generalisability of findings, it is believed that the proposed model does have a wider resonance in terms of helping both academics and practitioners to understand relationship quality. Practical implications Because customer relationships (and the benefits derived from them) are difficult to duplicate, these may be a source of competitive advantage for firms. Managing these relationships, as well as their quality, emerges as a point of competitive distinction. Originality/value To one's knowledge, there is no published paper that provides a conceptual model of relationship quality using the customer's perspective in a B2B setting. It is believed that the research makes a significant contribution in terms of filling this gap in the knowledge.
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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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".