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Record W4403482728 · doi:10.1111/jscm.12331

Process Research Methods for Studying Supply Chains and Their Management

2024· article· en· W4403482728 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

VenueJournal of Supply Chain Management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsProcess (computing)Supply chain managementBusinessSupply chainProcess managementComputer scienceMarketing

Abstract

fetched live from OpenAlex

ABSTRACT Processes are fundamental to supply chains and their management. Yet, traditional research approaches to supply chain management (SCM) reflect only a limited understanding of process, offering accounts that overlook the constitutive role of dynamically interrelated processes and how their interplay over time shapes the trajectories of supply chains. This article argues that a process‐philosophical perspective is better suited as a starting point for identifying, analyzing, and interpreting the fluid and interwoven processes of supply chains and their co‐evolving environments. Drawing on examples from SCM research, the article offers insights into the nature of process‐thinking and process‐theoretical assumptions, including the analytical choices and challenges entailed in process research. Besides providing methodological guidance, the article highlights how process research methods equip SCM scholars with a powerful lens for studying transformational issues in this field, including sustainability, resilience, and the use of digitalization and technology.

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)0.014
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0000.001
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 head0.096
GPT teacher head0.408
Teacher spread0.312 · 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