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Record W2178545248 · doi:10.5539/res.v7n12p146

Technology Acceptance Model, Organizational Commitment and Turnover Intention: A Conceptual Framework

2015· article· en· W2178545248 on OpenAlexvenueno aff
Alireza Parvari, Roya Anvari, Nur Naha Abu Mansor, Masoomeh Jafarpoor, Maliheh Parvari

Bibliographic record

VenueReview of European Studies · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsOrganizational commitmentContinuanceTurnover intentionTechnology acceptance modelJob satisfactionInformation technologyConceptual modelKnowledge managementPsychologyConceptual frameworkWork (physics)NormativeInformation systemOutcome (game theory)Formative assessmentBusinessUsabilitySocial psychologyComputer scienceSociologyPolitical scienceEngineeringEconomics

Abstract

fetched live from OpenAlex

<p>Information system is being implemented to improve job performance and facilitate employees’ work. However, implementing Information system has some negative impacts on employees and organization if employees do not accept it. Several previous studies investigated some consequences of information technology such as turnover intention and job satisfaction. This study provides a conceptual framework that shows other independent consequences of information system implementation. The model presented the impact of attitude towards using information technology on three components of organizational commitment (formative, normative and continuance). It also addresses the impact of attitude towards using information technology on turnover intention directly and via organizational commitment. Based on technology acceptance and its consequences, we develop a number of testable propositions that can guide to further research on work related outcome due to technology acceptance. Finally, we provide some recommendations for future research.</p>

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)0.001
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.770
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.223
GPT teacher head0.422
Teacher spread0.199 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations7
Published2015
Admission routes1
Has abstractyes

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