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Record W2075211046 · doi:10.1108/10662240010349408

A buyer behaviour framework for the development and design of software agents in e‐commerce

2000· article· en· W2075211046 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

VenueInternet Research · 2000
Typearticle
Languageen
FieldDecision Sciences
TopicAuction Theory and Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer sciencePurchasingSoftwareSoftware developmentFocus (optics)USableField (mathematics)Work (physics)Software agentProduct (mathematics)Software engineeringWorld Wide WebKnowledge managementMarketingBusinessEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Software agents are computer programs that run in the background and perform tasks autonomously as delegated by the user. Although there has been much research on this topic recently, usable software agents are at an early stage of development, and are only now starting to appear in real applications. Typical of these early stages, there has been a technology focus, rather than a product focus, in much of the development work to date. A fruitful application area for software agents is in the area of e‐commerce, where potential buyers can easily be overwhelmed by the flood of information that is available, thus potentially making less than optimal purchasing decisions. This paper blends models from marketing and learnings from the field of decision support systems to build a framework for the design of software agents to support in e‐commerce buying applications.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.496
GPT teacher head0.540
Teacher spread0.044 · 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