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Record W3211738256 · doi:10.1111/poms.13627

Customer Bargaining Power, Strategic Fit, and Supplier Performance

2021· article· en· W3211738256 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

VenueProduction and Operations Management · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBargaining powerBusinessIndustrial organizationMarketingCustomer orientationSupplier relationship managementPower (physics)MicroeconomicsSupply chainEconomicsSupply chain management

Abstract

fetched live from OpenAlex

Prior studies report mixed evidence on the impact of customers' bargaining power on supplier performance. We shed light on this mixed evidence by considering the moderating role of strategic fit. A strategically aligned supplier can provide long‐term benefits to the customer. As a result, powerful customers may trade off the short‐term benefits obtained through supplier concessions with the long‐term benefits derived from strategic fit. Thus, strategic fit can mitigate the negative impact of customers' bargaining power on supplier performance. We use data on supplier–customer dyads identified using financial disclosures of firms' major customers to examine this research question. We find that a strategic fit between suppliers and their customers on three distinct dimensions (innovation, customer orientation, and efficiency) attenuates the negative association between customers' relative bargaining power and supplier performance. These findings are robust to several sensitivity tests.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.247
Teacher spread0.215 · 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