Developing Interpersonal Influence in Retail Purchasing Networks: An Exploratory Analysis of Tie Quantity, Tie Strength, and Tie Type
Bibliographic record
Abstract
This study examines the impact of relationship characteristics existing between buyers and other members of a retail purchasing network. The number of ties and the strength of the ties buyers maintain with others in the network are examined to identify how they impact buyers’ interpersonal influence over others. Buyers’ tie quantity and tie strength are examined for different constituencies within the purchasing network to assess the differential impact of these relational factors on buyers’ interpersonal influence. The study finds that buyers’ tie quantity is a significant predictor of influence in both buyer and seller sub-networks, whereas buyers’ tie strength is only a significant factor in the seller sub-network. Further, independently examining the sub-networks that form organically around buyer and seller roles in the overall purchasing network leads to differential outcomes when compared to evaluating the entire network in which buyer and seller sub-networks are not differentiated. Collectively, the findings from this study reveal important conceptual, methodological, and substantive implications for marketing researchers and practitioners interested in purchasing network contexts.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".