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The Nature of E-Loyalty in B2C E-Commerce

2002· book-chapter· en· W2506799604 on OpenAlexaff
Daniel Tomiuk, Alain Pinsonneault

Bibliographic record

VenueAdvances in global information management (AGIM) book series · 2002
Typebook-chapter
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsMcGill University
Fundersnot available
KeywordsLoyaltyLoyalty business modelValue (mathematics)Affect (linguistics)Social exchange theoryMarketingBusinessInterpersonal communicationPsychologySocial psychologyComputer scienceService (business)

Abstract

fetched live from OpenAlex

The present chapter discusses the nature of e-loyalty in B2C e-commerce. Based on previous theoretical work on loyalty in traditional commercial settings, we argue that highly affective forms of loyalty are unlikely to develop in online environments. Rather, we suggest that the e-commerce environment promotes self-sufficiency and mitigate the need for customer/employee interaction which represents a primary source of affect. Research on relationships in both social psychology and marketing suggests that this loss may not affect all customers equally. We distinguish between customers who value establishing interpersonal relationships with company employees (communally oriented) from customers who see customer/employee interactions as utilitarian and who derive little social benefits from such encounters (exchange oriented). The chapter suggests that online environments may be seen as useful by communally-oriented customers but relationally unsatisfying. Conversely, there exists a good “fit” between what exchange oriented customers value in their relationships with companies and what online environments offer. Consequently, online companies should not be surprised to find that e-loyal customers may be predominantly exchange oriented. Finally, we argue that because e-loyalty lacks a strong affective foundation, it may be less enduring than “traditional” customer loyalty. Implications of our analysis and areas for future research are discussed.

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)0.001
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.933
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.006
Open science0.0010.000
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 head0.006
GPT teacher head0.264
Teacher spread0.258 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2002
Admission routes1
Has abstractyes

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