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Record W4200329507 · doi:10.1108/ijlm-03-2021-0157

Supply chain integration for middle-market firms: a qualitative investigation

2021· article· en· W4200329507 on OpenAlexaff
Matthew A. Schwieterman, Manus Rungtusanatham, Thomas J. Goldsby, W.C. Benton, Martha C. Cooper, Esen Andiç-Mortan

Bibliographic record

VenueThe International Journal of Logistics Management · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsYork University
Fundersnot available
KeywordsBusinessSupply chainMarketingContext (archaeology)Middle managementIndustrial organizationSupply chain managementRevenueModerationOperationalizationContextualizationFinance

Abstract

fetched live from OpenAlex

Purpose This research seeks to identify the motivations, means and outcomes of supply chain integration (SCI) among firms in the middle market (i.e. those with annual revenues between US$10m and US$1bn). These firms often interface with larger, more powerful firms in the supply chain – both suppliers and customers. Understanding how these firms are challenged and benefit from integrative mechanisms in supply chain relations can lead to better outcomes more often. Design/methodology/approach The research utilizes an online focus group methodology featuring 39 participants. The participants were able to interact in written form with a professional moderator, as well as each other, over the course of three days. Findings The research presents evidence that firms in the middle market adopt SCI as a response to pressure from customers and suppliers. These firms also view technology as a primary means of achieving integration. Despite their disadvantageous size position relative to larger customers and suppliers, firms in the middle market achieved positive outcomes from integration. Research limitations/implications Because of the specific context of middle-market firms, this research may lack generalizability. However, providing contextualization regarding firm size contributes specificity to the large number of studies detailing the challenges and benefits of SCI. Practical implications Managers of firms in the middle market should find value in this study as it explicates the possible benefits their firms may realize through integration with customers and suppliers. Moreover, this research outlines several of the possible means through which integration can be achieved. Further, managers in smaller and larger firms can better understand the motives and needs of middle-market companies with which they interact. Originality/value Despite voluminous literature on SCI, this paper provides context-specific findings by isolating the implications of SCI to firms in the middle market.

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)0.015
metaresearch head score (Gemma)0.016
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.016
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0110.007
Scholarly communication0.0060.006
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.087
GPT teacher head0.315
Teacher spread0.228 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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