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Record W2621323561

Organizational Support for Innovative Use of IT: A Slack Resources Perspective

2017· article· en· W2621323561 on OpenAlexaff
Mayur Joshi, Yasser Rahrovani

Bibliographic record

VenueJournal of the Association for Information Systems · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsMcGill UniversityWestern University
Fundersnot available
KeywordsPerspective (graphical)Knowledge managementComputer scienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

With the increasing pervasiveness of Information Systems (IS), research on innovative use of IT is gaining momentum. Research shows that intrinsic motivation is a key predictor of innovative use of IT; it is, however, inconclusive as some scholars have not found a significant relationship between users’ intrinsic motivations and their use of utilitarian IS. Organizational support has been found to be a facilitating factor for the relationship between intrinsic motivation and innovative use of IT, a deviating behaviour associated with risk. The complex, emergent, and iterative nature of innovating with IT, however, warrants specific support beyond verbal encouragement for users. This article applies the theory of slack resources to conceptualize the environmental support required for the innovative use of IT. The article also explores the impact of managerial framing by which availability of environmental IS slack resources is communicated to employees on innovation with IT.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.007
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.112
GPT teacher head0.380
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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