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Record W2124028228 · doi:10.1509/jmkg.71.4.121

Friendship versus Business in Marketing Relationships

2007· article· en· W2124028228 on OpenAlexaff
Kent Grayson

Bibliographic record

VenueJournal of Marketing · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsFriendshipSocial psychologySurvey data collectionPsychologyBusinessMarketingPublic relationsPolitical science

Abstract

fetched live from OpenAlex

Although combining friendship and business in the same relationship can be beneficial, it can also create conflict. A source of this conflict is incompatible relational expectations. True friends are expected to be unmotivated by benefits that can be used beyond the relationship (e.g., money, status), whereas business partners are, by definition, at least partly motivated by these more “instrumental” concerns. Using a role theory framework and data collected from a survey of 685 direct-selling agents, this article reports evidence that a conflict between friendship and instrumentality can undermine some of the business outcomes that friendship might otherwise foster. It also suggests that this conflict is more severe for friendships that become business relationships than for business relationships that become friendships. Study conclusions do not suggest that friendship is entirely “bad” for business and, instead, propose that friendship's influence can be both positive and negative.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0030.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.050
GPT teacher head0.273
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations334
Published2007
Admission routes1
Has abstractyes

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