MétaCan
Menu
Back to cohort
Record W4239769773 · doi:10.24124/2006/bpgub1309

Strategic information systems planning and information technology roadmapping: case study of a small primary forest products manufacturer in northern British Columbia

2006· dissertation· en· W4239769773 on OpenAlexaffabout
Ryan William Schroeder

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicInformation Technology Governance and Strategy
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsContext (archaeology)Strategic planningProcess (computing)Plan (archaeology)Process managementBusinessBusiness planInformation systemInformation technologyKnowledge managementComputer scienceEngineeringMarketingGeography

Abstract

fetched live from OpenAlex

The purpose of this paper was to analyze the strategic information technology requirements (IT) of Gateway Forest Products (GFP) and to provide a plan outlining what, when, and how various ITs should be implemented.Literature related to the use of IT in the forest products industry (FPI) was reviewed to provide the background knowledge required to support a strategic information systems planning (SISP) process for the benefit of a small forest products company based in Northern British Columbia.GFP is selected as the target company of this case study.ITs were identified that could benefit GFP.A methodology for systematically identifying IT needs was necessary to select potential IT implementation projects.Various strategic information system planning frameworks are reviewed and the Fast-Start technology roadmapping process was selected for soliciting and developing high level information requirements of GFP.The Fast-Start technology roadmapping process was beneficial in defining the environmental context, business drivers, strategies, and capabilities related to GFP's goals.IT needs were logically deduced from an understanding of what capabilities were required to support business goals, combined with an understanding of which ITs were available, considered to be best practice, and predicted by industry experts to have the most impact on forest products operations into the future.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.006
Open science0.0000.000
Research integrity0.0010.000
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 head0.192
Teacher spread0.183 · 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 teacher head, not a consensus.

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
Published2006
Admission routes2
Has abstractyes

Explore more

Same topicInformation Technology Governance and StrategyFrench-language works237,207