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Record W2199597033

Network Catalysts to Small Businesses: A Strategy for Fragmented Industries

2003· article· en· W2199597033 on OpenAlexfundno aff
Suzanne Loker, Luke Stark, Judy Sasser-Watkins

Bibliographic record

VenueJournals @ Middle Tennessee State University (Middle Tennessee State University) · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsBusinessClothingCorporationMarketingIndustrial organizationTextile industryProfit (economics)CommerceEconomicsFinance
DOInot available

Abstract

fetched live from OpenAlex

The catalyst roles played by the New York City's Garment Industry Development Corporation (GIDC) and San Francisco's Garment 2000, two non-profit organizations providing services to ihe apparel industry, were analyzed using a network perspective.The networks of apparel businesses in the two cities were advanced by GIDC and Garment 2000 through their siructures and strategies, including building trust, providing easy entry and exit mechanisms to network members, offering dynamic programming, and establishing partnerships with other organizations serving the apparel industry.The loosely structured business networks in the apparel industry and other fragmented industries benefit from catalyst organizations that can increase communication across the memberships, identify and address needs of member businesses, and seek fundmg to supporr programs.Implications for businesses that want to initiate catalyst organizations to serve small business networks in other industries are discussed. Journal afSmall Business Strater0Val /4, No.I Spring/Summer 2003 Some industries are more fragmented than others, that is, have many small busmesses with many supply chain levels resulting in less well defined supply chain relationships.These fragmented industries, such as apparel and service, are less likely to have stable, formal, structured business networks.Indeed, by defimtion, the communication and inl'ormation sharing is fragmented.These industries may be helped by a catalyst organization that works on behalf of the fragmented business network membership.Networks can result from interpersonal relationships founded outside or within a business context that build trust among the parties, often based on repeated interactions.The literature has highlighted the role of geographic proximity, shared resources, and shared needs as positive influences to business network development.The general goal of a regional business network, or industrial district, is for businesses to benefit from clustenng in location by sharing resources such as labor, information, and business services as well as vendors and customers.Businesses that share input-output relationships, such as apparel cutting contractors, sewing contractors, and manufacturers, locate together to allow easy and frequent personal interactions that enhance the workforce and workplace opportunities for all businesses and promote the industry as a whole.Networks can be informal without a clear accounting of members and activities or more formalized with membership directories, regular meetings, or by-laws.Sometimes, established organizations that businesses have in common, such as trade associations, unions, or chambers of commerce, have a part in the initiation, development,or evolution of business networks, even to the extent of institutionalizing the network in the form of an independent, catalyst organization.The purpose of this paper is to analyze and compare the catalyst roles of two organizations from a network perspective: New York City's Garment Industry Development Corporation and San Francisco's GARMENT 2000.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.010
Science and technology studies0.0020.000
Scholarly communication0.0010.005
Open science0.0010.000
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 head0.067
GPT teacher head0.213
Teacher spread0.146 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations1
Published2003
Admission routes1
Has abstractyes

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