MétaCan
Menu
Back to cohort
Record W161467966

The Effects of Perceived Information Quality and Perceived System Quality on Trust and Adoption of Online Reputation Systems

2010· article· en· W161467966 on OpenAlexaff
Sherrie Komiak

Bibliographic record

VenueJournal of the Association for Information Systems · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsReputationReputation systemAffect (linguistics)Quality (philosophy)Information qualityPerceptionDatabase transactionInformation systemBusinessE-commerceKnowledge managementMarketingInternet privacyComputer sciencePsychologyWorld Wide WebEngineeringDatabase
DOInot available

Abstract

fetched live from OpenAlex

Online reputation systems are the means for reducing information asymmetry among the parties involved in an online transaction. When customers interact with a reputation system, they actually interact both with the system and with other customers who feed information into the system. Recognizing the dual nature of this interaction, this study examines the effects of perceived systems quality and perceived information quality on online customers’ intention to adopt the online reputation system. The research model proposes that perceptions will affect the intention to adopt through both psychological routes (i.e. trust in customers feeding the reputation system) and functional routes (perceived usefulness of the reputation system). An online survey was conducted. The results show that users’ perceived information quality and perceived system quality indeed significantly affect their intention to adopt the system, mainly through the functional route, but not significantly through the psychological route.

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)0.012
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.044
GPT teacher head0.360
Teacher spread0.317 · 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.

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

Citations16
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Association for Information SystemsSame topicTechnology Adoption and User BehaviourFrench-language works237,207