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Record W1982798724 · doi:10.4018/jeco.2006100101

Interorganizational Relationships in the Context of SMEs' B2B E-Commerce

2006· article· en· W1982798724 on OpenAlexaboutno aff
Assion Lawson‐Body, Timothy P. O’Keefe

Bibliographic record

VenueJournal of Electronic Commerce in Organizations · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessLoyalty business modelThe InternetLoyaltyContext (archaeology)Relation (database)E-commerceMarketingCustomer relationship managementComputer scienceWorld Wide WebService (business)DatabaseService quality

Abstract

fetched live from OpenAlex

Electronic commerce on the Internet has been discussed in literature as an interorganizational information system that can provide a strategic advantage via customer loyalty to small and medium-sized enterprises (SMEs). However, that is not always true, because many SMEs have difficulties achieving the benefits, as suggested by media and early research. This study is an empirical examination of the impact of the Internet’s Web tools on the interorganizational relationships (IOR) between SMEs and their loyal customers. Data collected from 386 SMEs in North America (United States and Canada) and processed with Partial Least Square (PLS) show that the use of Web tools (i.e., the level of Web content and the level of security on the Internet) has a positive impact on the relation between cooperation and interdependence, and customer loyalty. However, the impact of the Internet’s Web tools on the relation between trust and customer loyalty is different, because the use of nonsecure Web tools reduces the impact of trust on customer loyalty, and surprisingly, the use of secure Web tools doesn’t increase or decrease the impact of trust on customer loyalty. The implications of the results of the study are discussed.Request access from your librarian to read this article's full text.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.245
Teacher spread0.227 · 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 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

Citations49
Published2006
Admission routes1
Has abstractyes

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