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Record W2166841722 · doi:10.17705/1jais.00025

Mimetic Isomorphism and Technology Evaluation: Does Imitation Transcend Judgment?

2002· article· en· W2166841722 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

VenueJournal of the Association for Information Systems · 2002
Typearticle
Languageen
FieldDecision Sciences
TopicInnovation Diffusion and Forecasting
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsImitationCompetitor analysisProduct (mathematics)Isomorphism (crystallography)Sample (material)PsychologyTest (biology)MarketingProcess (computing)Knowledge managementBusinessComputer scienceSocial psychologyMathematics

Abstract

fetched live from OpenAlex

Although contemporary technology adoption theories incorporate societal norms or peer references, it is unclear to what extent these factors influence choices. In this research, we apply institutional theory and the concept of mimetic isomorphism as peer influences to the technology evaluation process to determine the degree to which managers conform when selecting between competing information technologies. More specifically, we test if peer influence is sufficient to overcome a product evaluation where the choice is believed to be inferior. An experiment is conducted using the World Wide Web and a national sample of 348 senior information technology and business decision makers. Significant effects are found where inferior technologies are selected if respondents are informed that competitors have selected them. Further research is warranted to investigate the presence and extent of these effects but overall implications are that product evaluations may be more ornamental than substantive.

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)0.009
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.082
GPT teacher head0.320
Teacher spread0.238 · 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