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Record W2077534367 · doi:10.1177/1094670504268421

Managing Relational Exchanges

2004· article· en· W2077534367 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Service Research · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsAthabasca University
Fundersnot available
KeywordsOpportunismDistrustRelationship marketingComponent (thermodynamics)BusinessMarketingRelational modelRelational capitalControl (management)Computer scienceRelational databaseKnowledge managementMarketing managementEconomicsPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

The authors propose an analytic model that deals with both behavioral considerations between exchange partners and the determination of relational marketing efforts over time. On the basis of the behavioral marketing literature, they consider three main factors that drive the levels of relational commitments between two exchange partners: the trust/distrust component, the opportunism component, and the relational marketing effort of the seller. Incorporating these factors in a well-known model used in appliedmathematics for “love dynamics,” the authors claim that the issue of managing relational exchanges is an optimal control problem. Their analysis shows that the seller’s optimal policy for determining relational marketing effort over time is either time-invariant or time-variant, depending on whether or not the exchange partners are conservative and the structural and contextual environment of the relationship remains unchanged over time.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.152
GPT teacher head0.364
Teacher spread0.212 · 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