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Making Sense of Information Technology Investment on Type of Supply Chain Governance

2017· book-chapter· en· W2948939637 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

VenueAdvances in business strategy and competitive advantage book series · 2017
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicERP Systems Implementation and Impact
Canadian institutionsCarleton University
Fundersnot available
KeywordsSupply chainTransactional leadershipProcurementBusinessCorporate governanceGeneral partnershipIndustrial organizationInvestment (military)Supply chain managementMarketingEconomicsFinanceManagement

Abstract

fetched live from OpenAlex

This study makes sense of how information technology (IT) investment supports and relates to SCG and its conceptions (transactional and relational). The authors conducted a qualitative research using exploratory case studies in two large Brazilian companies and two major suppliers. Top supply chain executives of these companies were interviewed. We found differences in how these companies invest in IT to govern their supply chain. In the first case, we 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. Here IT investments were used to improve sales and enable operations planning projects where all systems were being integrated. In the second case, we 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. Thus, investments on e-procurement and ERP are being made to achieve more relational governance through integration with their suppliers.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.945
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.005
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 head0.024
GPT teacher head0.288
Teacher spread0.264 · 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