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Record W2990545410

IT Resource Orchestration, Strategic Moves, and Business Environment

2019· article· en· W2990545410 on OpenAlexaff
Inmyung Choi, Alain Pinsonneault, Kunsoo Han

Bibliographic record

VenueJournal of the Association for Information Systems · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsMcGill University
Fundersnot available
KeywordsOrchestrationComputer scienceProcess managementBusinessResource (disambiguation)Knowledge management
DOInot available

Abstract

fetched live from OpenAlex

This study examines the influence of IT resources on the strategic behaviors of firms. Based on institutional theory, we postulate that firms can orchestrate their IT resources in the way of differentiating their strategic behaviors without losing institutional legitimacy. Specifically, we suggest two ways of IT resource orchestration: integrative IT orchestration and compensatory IT orchestration. Then, using a sample of 1,843 firm-year observations from the United States, we find that integrative IT orchestration leads to strategic distinctiveness, and compensatory IT orchestration results in strategic dynamism. Further, we investigate the moderation effect of business environments on the relationship between IT resource orchestration and the strategic moves of firms. Overall, our research implies that orchestrated IT resources can be used to generate and support the strategic moves and competitive advantage of firms.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.232
Teacher spread0.197 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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