Genuine small talk, rapport, and negotiation outcomes in B2B relationship
Bibliographic record
Abstract
Purpose Small talk is often regarded as important in business interactions, yet the effect of genuine engagement on B2B communication remains underexplored. Hence, the purpose of this study is to explore the concept of genuine small talk, contextualize its key dimensions and examine how it contributes to building rapport and mediates negotiation outcomes in B2B relationships. Design/methodology/approach This study uses a qualitative abductive research approach for this exploratory investigation as it allows for an in-depth examination of the complex relational dynamics inherent in B2B communication. Data were collected through semistructured interviews with 35 industry professionals from diverse sectors, ensuring a diverse understanding of the phenomenon across different B2B contexts. Findings The study identifies eight core dimensions of genuine small talk in B2B interactions: empathy, curiosity, adaptability, active listening, a nonjudgmental disposition, respect for boundaries, positivity and humility. These dimensions collectively contribute to the development of rapport. The findings also highlight that rapport, fostered through genuine small talk, plays a mediating role in achieving favorable negotiation outcomes. Originality/value This study adds to the B2B marketing literature by advancing the understanding of genuine small talk and its strategic importance in building rapport and improving negotiation outcomes.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".