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Record W2094891697 · doi:10.2753/mis0742-1222230102

Understanding Business Process Change Failure: An Actor-Network Perspective

2006· article· en· W2094891697 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Management Information Systems · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsPricewaterhouseCoopers (Canada)
Fundersnot available
KeywordsProblematizationSociotechnical systemActor–network theoryProcess (computing)Computer scienceBusiness processPerspective (graphical)BetrayalAbstractionProcess managementKnowledge managementSociologyEpistemologyBusinessArtificial intelligenceWork in processMarketingPsychology

Abstract

fetched live from OpenAlex

Abstract In this paper, we use concepts from actor-network theory (ANT) to interpret the sequence of events that led to business process change (BPC) failure at a telecommunications company in the United States. Through our intensive examination of the BPC initiative, we find that a number of issues suggested by ANT, such as errors in problematization, parallel translation, betrayal, and irreversible inscription of interests, contributed significantly to the failure. We provide nine abstraction statements capturing the essence of our findings in a concrete form. The larger implication of our study is that, for sociotechnical phenomena such as BPC with significant political components, an ANT-informed understanding can enable practitioners to better anticipate and cope with emergent complexities. Keywords: actor-network theorybusiness process changecase studyinformation systems implementationinformation systems politicsinterpretive researchorganizational changepowerreengineeringsocial construction of technology

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualmedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.011
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 head0.091
GPT teacher head0.332
Teacher spread0.242 · 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