MétaCan
Menu
Back to cohort
Record W2579272996

Internet Support Companies: The Impact of Marketing Orientation

2006· article· en· W2579272996 on OpenAlexvenueno aff
José Antonio Varela González, Larry Chiagouris

Bibliographic record

VenueThe Journal of Internet Banking and Commerce · 2006
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsMarket orientationThe InternetOrientation (vector space)Context (archaeology)MarketingService-orientationVariety (cybernetics)BusinessWork (physics)Service (business)Computer scienceWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

Several studies have expanded the work by Jaworski and Kohli (1993) by using their market orientation framework model and applying it to a variety of industries. The results of recent studies have uncovered strong relationships between the concepts of market orientation and organizational performance. The large body of research on the topic of market orientation has not been applied to companies critical to commercial transactions over the Internet. This study explored implementation of market orientation in the context of web hosting and Internet service providers to understand impact of market orientation on these kinds of companies. The study results indicate that market orientation is positively related to performance. Implications for theory construction and managerial practice relevant to companies engaged in ecommerce 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 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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.068
GPT teacher head0.367
Teacher spread0.299 · 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

Citations5
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Internet Banking and CommerceSame topicTechnology Adoption and User BehaviourFrench-language works237,207