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Retailer-Non-Profit Organization (NPO) Partnerships

2014· book-chapter· en· W2491800725 on OpenAlexaff
Janice Rudkowski

Bibliographic record

VenueAdvances in marketing, customer relationship management, and e-services book series · 2014
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBusinessGlobeCorporate social responsibilitySustainabilityMarketingSocial responsibilityProfit (economics)Perspective (graphical)Public relationsPolitical scienceEconomics

Abstract

fetched live from OpenAlex

This chapter focuses on strategic retailer-Non-Profit Organization (NPO) partnerships, based in North America and Europe, from a management perspective. It explores how and why these partnerships have had an impact on the retailer-consumer relationship, how they have shaped and influenced socially conscious shoppers, and how they have affected consumer trust as well as retail business practices and strategies, within the last decade. Retailer-NPO partnerships have emerged as a viable business strategy to support Corporate Social Responsibility (CSR) initiatives now commonplace among most large retail organizations. Consumers have become empowered, with the help of new social media technologies, to efficiently communicate, influence, and persuade other consumers around the globe. Therefore, consumers increasingly expect retailers to have an ethical and social responsibility to their people, products, operations, and communities. CSR practices have become integral to retailer sustainability and managing complex retailer-consumer relationships. This chapter reviews relevant theoretical frameworks, discusses the latest research findings from literature sources, and examines the industry practices (case studies) of several retailer-NPO partnerships across North America and Europe.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.919
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.230
Teacher spread0.213 · 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
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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