THE IMPACT OF PERCEIVED MARKET ORIENTATION ON SELLER-BUYER RELATIONSHIPS
Bibliographic record
Abstract
Several models focus on the nature of relationships between firms in business markets (e.g., Morgan and Hunt, 1994; Anderson and Narus, 1990; Anderson and Weitz, 1989; Dwyer, Schurr, and Oh, 1987). In a number of studies, the dyad—the unique physical and psychological relationship between two firms—is the key unit of analysis. Though scholars studying Relationship Marketing (RM) focus on relationships between dyadic partners, and seek to explain why relationships develop and the ingredients that are necessary to maintain them, Market Orientation (MO) scholars largely ignore the ‘perceptual’ factor that is important in building and maintaining relationships. In this paper we propose an alternative approach to viewing MO and ways it impacts relationships. We link our MO construct to key relationship elements: trust, relational norms, and commitment. We also introduce the notion of connectivity (CN)—a construct that follows directly from commitment. Our paper develops the CN construct by identifying its antecedents and structural components borrowed from transactions cost theory and the relationship marketing literature. As we describe in the paper, our model explains the linkage between MO (from the seller’s perspective), trust (TR), relational norms (RN), commitment (CO) and connectivity (CN).
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".