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Record W1560502875 · doi:10.4018/jgim.2015070104

Exploring Information Technology and Supply Chain Governance

2015· article· en· W1560502875 on OpenAlexaff
Pietro Cunha Dolci, Antônio Carlos Gastaud Maçada, Gerald Grant

Bibliographic record

VenueJournal of Global Information Management · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicERP Systems Implementation and Impact
Canadian institutionsCarleton University
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsBusinessTransactional leadershipCorporate governanceSupply chainGeneral partnershipProcurementInterviewSupply chain managementTransaction costIndustrial organizationMarketingDatabase transactionProcess managementEconomicsFinanceManagementComputer science

Abstract

fetched live from OpenAlex

The aim of this study is to investigate how information technology (IT) investment supports and relates to SCG and its conceptions (transactional and relational. The authors conducted exploratory case studies in two large Brazilian companies and two major suppliers, interviewing top supply chain executives. In the first case, they identified a more relational type of governance that was mainly based on the company's relationship with its suppliers which was driven by the desire to achieve a greater market share. IT investments focused on sales and operations planning projects where all systems were being integrated. In the second case, they identified transactional governance as the predominant form. This reflects the presence of a great number of suppliers, low partnership and low supply on time delivery rate. IT investments are being made to achieve more relational governance through expenditure on e-procurement and greater integration with their suppliers using ERP.

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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.005
Scholarly communication0.0050.006
Open science0.0000.002
Research integrity0.0010.001
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 head0.048
GPT teacher head0.264
Teacher spread0.216 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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