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Research Report: Richness Versus Parsimony in Modeling Technology Adoption Decisions—Understanding Merchant Adoption of a Smart Card-Based Payment System

2001· article· en· 629 citations· W2085666545 on OpenAlex· 10.1287/isre.12.2.208.9697

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.

Full frame distilled prediction

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.

Candidate categories
Metaresearch, Bibliometrics
Consensus categories
none
Domain
Candidate signal: noneConsensus signal: none
Study design
Candidate signal: Simulation or modelingConsensus signal: Simulation or modeling
Genre
Candidate signal: EmpiricalConsensus signal: Empirical
Teacher disagreement score
0.308
Threshold uncertainty score
0.998
Validation status
machine_predicted_unvalidated · codex-gemma-dda1882f352a

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0440.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0130.011
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

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.

Opus teacher head0.498
GPT teacher head0.483
Teacher spread
0.015 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

The Technology Acceptance Model (TAM) has received considerable research attention in the IS field over the past decade, placing an emphasis on the roles played by perceived ease-of-use and perceived usefulness in influencing technology adoption decisions. Meanwhile, alternative sets of antecedents to adoption have received less attention. In this paper, sets of antecedent constructs drawn from both TAM and the Perceived Characteristics of Innovating (PCI) inventory are tested and subsequently compared with one another. The comparison is done in the context of a large-scale market trial of a smart card-based electronic payment system being evaluated by a group of retailers and merchants. The PCI set of antecedents explains substantially more variance than does TAM, while also providing managers with more detailed information regarding the antecedents driving technology innovation adoption.

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.

The record

Venue
Information Systems Research
Topic
Technology Adoption and User Behaviour
Field
Decision Sciences
Canadian institutions
Western University
Funders
not available
Keywords
Antecedent (behavioral psychology)Technology acceptance modelPayment cardContext (archaeology)Variance (accounting)MarketingSmart cardPaymentBusinessUsabilityScale (ratio)Set (abstract data type)Knowledge managementComputer sciencePsychologyAccountingComputer security
Has abstract in OpenAlex
yes