Bibliographic record
Abstract
Purpose To the extent that customer relationships with service providers provide value to service firms suggests that these relationships can be viewed as social capital. This paper seeks to use social capital as a theoretical framework to examine the effect of these relationships on customer loyalty. Design/methodology/approach Data were collected using an online survey of 342 adult consumers of services. Findings Results of structural equation modeling analyses indicate that social capital variables explain unique variance in customer responses. The effect of each of the three forms of social capital – structural, cognitive, and relational – are contingent on whether the service is personal (e.g. hairstylist, medical services) or non‐personal (e.g. mechanic, banker). Research limitations/implications This research suggests that customer relationships can be viewed as social capital and that the form and content of such relationships are important in terms of influencing customer loyalty. Practical implications Managers can build “social capital” by focusing on its three forms – structural, cognitive, and relational social capital. The paper provides prescriptions for such relationship building activities. Such social capital translates into firm value/profits through customer loyalty. Originality/value This study uses a theoretical framework from research in social capital to help explain the value of customer relationships with individual service providers to the firm. The idea of social capital is compelling to service managers since it implies that investments in relationship building tactics have real results for firm profitability.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".