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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".