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Record W2016731289 · doi:10.1109/hicss.2013.553

Too Much or Not Enough: Information Systems Integration in Post-merger Context -- A Sociomaterial Practice Perspective

2013· article· en· W2016731289 on OpenAlexaff
Dragos Vieru, Marie-Claude Trudel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInformation Technology Governance and Strategy
Canadian institutionsHEC MontréalUniversité du Québec
Fundersnot available
KeywordsPerspective (graphical)DilemmaContext (archaeology)NegotiationAutonomyKnowledge managementResistance (ecology)DialecticInformation systemProcess managementPhase (matter)BusinessInformation technologyBoundary (topology)Computer scienceSociologyEngineeringPolitical scienceEpistemology

Abstract

fetched live from OpenAlex

During the post-merger integration phase (PMI), new information systems (IS) that span the boundaries of the previously independent organizations need to be implemented to enable a specific level of integration. Although the literature emphasizes the important role played by ISs in support of the amalgamated organizations, there is a lack of studies on the issue of boundary management at the information technology (IT) level in a PMI context. We draw on a sociomaterial practice perspective to analyze two IS implementation projects in a healthcare organization resulting from a merger of previously independent hospitals. The results suggest there is a dilemma of post-merger IT integration versus autonomy, which is reflected by the unpredictability of the implementation's outcomes for the ISs designed to enable planned practices. The model also suggests that post-merger practices reflect the outcomes of dialectic processes of resistance to, and negotiation of, the IS configuration during its implementation.

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)0.020
metaresearch head score (Gemma)0.019
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0130.070
Scholarly communication0.0210.013
Open science0.0020.010
Research integrity0.0070.005
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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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
Published2013
Admission routes1
Has abstractyes

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