Moderation effect of client special treatment benefits on the relationship between logistics inte-gration and logistics performance in the logistics services providers’ context
Bibliographic record
Abstract
In the face of global competition and the coronavirus disease-19 (COVID-19) pandemic, the logistics service providers (LSPs) are facing severe challenges to attain their logistics performance indicators. To continue in such a market place, LSPs need to maintain a dedicated integration relationship with their clients by enhancing client special treatment benefits. The aim of this study is to apply the relational view (RV) theory and the relationship marketing (RM) perspective to examine the moderation effect of special treatment benefits on the link between logistics integration and LSPs’ logistics performance (i.e., cost leadership and customer services innovation). Data was collected from 214 Malaysian LSPs, and analysed using partial least squares-structural equation modelling (PLS-SEM). Although the results show that logistics integration has a strong impact on both performances, further analysis shows that a high level of logistics integration has an association with high levels of special treatment benefits (moderating effect), in turn, maintaining performance at a high level. The exploring of the moderation effect of special treatment benefits contributes to the RV theory by incorporating the RM to reflect the moderation effect. Additionally, the study contributes empirically to the field of strategy and RM within the LSPs’ industry. Finally, the findings enable LSPs to better allocate resources to ensure more effective value-based strategies that emphasise on client special treatment benefits to develop financial confidence and maintain long-term dedicated relationships, so as to achieve the target outcomes.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".