Bibliographic record
Abstract
Purpose The purpose of this paper is to focus on building and testing a model of buyer‐seller relationships from a dialectical perspective. It aims to provide both academics and managers with a better understanding of the relationships among the key relational variables in business settings. The model distinguishes four levels of social complexity (individual, interaction, relationship, and intergroup level) and includes the key relational variable for each level. Design/methodology/approach Data were collected from 151 French professional buyers. Exploratory and confirmatory factor analysis was used to test the validity of the measures. The hypotheses were tested using structural equation modeling. The empirical test includes linear, non‐linear, moderating, and mediating effects testing. Research limitations/implications The limitations of the study relate to the sample of respondents and the measurement scales. More precisely, the sample is based on a unique company's customer data file and a single informant source. Results confirm the hypothesized model and its four‐level structure. Practical implications This study demonstrates that buyer relational orientation as well as seller expertise influence the course of business relationships. Although the necessity to train salespeople is obvious, the importance of training buyers is not as well documented. This study shows that they both need to be trained to manage business relationships appropriately. Originality/value This research examines the relationships among the key relational variables within a framework of four successive levels of explanation. It provides an alternative approach to studying business relationships.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".