MétaCan
Menu
Back to cohort

Controlling information systems development: a new typology for an evolving field

2012· article· en· W1492478549 on OpenAlexaff
W. Alec Cram, M. Kathryn Brohman

Bibliographic record

VenueInformation Systems Journal · 2012
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsQueen's University
Fundersnot available
KeywordsTypologyControl (management)Field (mathematics)Knowledge managementProcess managementInformation systemProcess (computing)New product developmentComputer scienceManagement scienceEngineeringBusinessSociologyMarketingArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This study explores how and why information systems development (ISD) approaches differ in their tactics to control the behaviour of development teams. Drawing from prior research on organisational control, we conduct qualitative case studies at four organisations to examine the extent that traditional and contemporary systems development approaches can highlight a unique combination of control attributes. Based on the data collected, we build an ISD control typology that differentiates ISD approaches on the basis of control objectives (product or process focused) and control practices (preventive or detective/corrective focused). The proposed typology can be used by practitioners to inform and guide more effective systems development control choices while providing researchers with a new model that recognises the unique control aspects of today's systems development approaches.

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)0.017
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0110.009
Science and technology studies0.0040.021
Scholarly communication0.0100.016
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.278
Teacher spread0.249 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations37
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueInformation Systems JournalSame topicSoftware Engineering Techniques and PracticesFrench-language works237,207