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Record W2029056652 · doi:10.1300/j033v12n02_03

The Relative Effects of Relationship Quality and Exchange Satisfaction on Customer Loyalty

2005· article· en· W2029056652 on OpenAlexaff
Seigyoung Auh, Chuan‐Fong Shih

Bibliographic record

VenueJournal of Business-to-Business Marketing · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsBrock University
Fundersnot available
KeywordsLoyaltyBusinessLoyalty business modelMarketingQuality (philosophy)Customer satisfactionService quality

Abstract

fetched live from OpenAlex

ABSTRACT This paper addresses two key questions on how supplier firms can better manage industrial buyers to create higher loyalty. The first involves whether to focus on relationship quality or exchange satisfaction. This dual route model towards enhancing loyalty in essence is concerned with comparing the relative strengths of the effect from relationship quality to loyalty and from exchange satisfaction to loyalty. The second point of interest pertains to whether the effects from relationship quality and exchange satisfaction to loyalty are moderated by environmental conditions such as switching barriers and viable alternatives. Using data on business customers' ratings of a major information technology provider, we find that the effect of exchange satisfaction on loyalty is significantly greater than the effect of relationship quality on loyalty. We also find that the impact of exchange satisfaction on loyalty is less sensitive and more stable across different levels of switching barriers and viable alternatives. Conversely, the effect of relationship quality on loyalty is more pronounced to such moderating variables in that the effect of relationship quality on loyalty was greater when switching barriers were low and many viable alternatives existed. Implications for marketing theory and practice are discussed. KEYWORDS: Relationship qualityexchange satisfactionrelative effects

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 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)0.006
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.284
Teacher spread0.254 · 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 teacher head, not a consensus.

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

Citations31
Published2005
Admission routes1
Has abstractyes

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