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Record W4415469628 · doi:10.1177/00081256251376318

Boards of Directors and the Governance of Large IT Investments: They Don’t Know What They Don’t Know

2025· article· en· W4415469628 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

VenueCalifornia Management Review · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCorporate governanceSet (abstract data type)Key (lock)Mental modelFocus (optics)Ask price

Abstract

fetched live from OpenAlex

Information technology (IT) is central to most organizations’ success. As IT systems age, organizations replace them, yet many of those replacement projects fail to achieve expected outcomes. This article explores what boards of directors can do to prevent such failures, increasing the chances that these replatforming programs succeed. Boards are typically affected by seven blind spots, including misplaced optimism, an abundance of data but too little information, and too much focus on technology. This article describes all seven blind spots and provides four recommendations to overcome them: developing a shared mental model, ensuring organizational readiness, agreeing on a reporting scorecard, and focusing on benefits and pivoting when necessary. The article also includes a framework for developing a shared mental model as well as a set of key questions for board members to ask about replatforming programs.

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.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.867
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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