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Record W2054141672 · doi:10.1108/17557501111157751

Building customer confidence in the automobile age: Canadian Tire 1928‐1939

2011· article· en· W2054141672 on OpenAlexaboutno aff
Dale Miller

Bibliographic record

VenueJournal of Historical Research in Marketing · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsnot available
Fundersnot available
KeywordsMarketingProduct (mathematics)Order (exchange)Quality (philosophy)OriginalityAutomotive industryValue (mathematics)BusinessConsumer confidence indexEngineeringComputer scienceSociologyQualitative research

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to examine how one Canadian retailer developed customer confidence in the interwar years when the automobile was in its infancy. The emphasis is on products and product information in the mail‐order catalogue. Design/methodology/approach The research design strategy draws on a longitudinal case study research using primary archival data collection and analysis. Findings In the 1930s, the firm used multiple approaches to respond to opportunities and challenges and to reassure customers through product assortment, guarantees, branding, quality assurance and support services. Generating an extensive mail‐order business occurred in tandem with the opening of stores, and together these approaches created rapid growth. In the early years, the emphasis was on maintenance, repairs and some augmentation through accessories. From the mid‐to late 1930s, with easing economic conditions, the focus shifts from automobile functionality to include roles for leisure and sport products, and the injunction to engage with the Canadian countryside. Originality/value The paper uses original historical research to contribute a new way of understanding how retailers developed customer confidence. The study contributes to knowledge about Canadian retailing in the interwar years, and the means for building customer confidence using a range of marketing techniques. For researchers, the study demonstrates a further example of the efficacy of using archival materials to explore marketing questions.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.498

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0200.007
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.247
GPT teacher head0.353
Teacher spread0.107 · 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 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

Citations3
Published2011
Admission routes1
Has abstractyes

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