The influence of psychological contracts on exporter–distributor relationships and export venture performance: the conditional role of institutional distance
Bibliographic record
Abstract
Purpose This study explores the influence of informal “psychological contracts” (PCs), (as opposed to formal contractual relationships) on exporter–distributor relationships. Design/methodology/approach Data were obtained from a sample of 127 exporting small and medium-sized enterprises (SMEs) in New Zealand. The authors employed partial least squares structural equation modeling (PLS-SEM) for analyzing the measurement and structural models. Findings Psychological contract fulfillment (PCF) enhances affective commitment and calculative commitment. Moreover, affective and calculative commitments mediate the relationship between PCF and export venture performance (EVP). The authors also find that institutional distance (ID) weakens the relationship between PCF and both affective and calculative commitment. Additionally, ID moderates the strength of the mediating mechanism for affective commitment; thus, the authors present a moderated-mediation model. Originality/value To date, international relationship marketing (IRM) literature has focused on PC breach, and business-to-business (B2B) marketing literature has focused on the effects of PCs on affective/relational commitment. This study offers novel insights by demonstrating the positive indirect effect of PCF on EVP via the mediating variables – affective and calculative commitment. The authors' findings also present a conditioning role of ID on the micro-level relationships of PCs.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".