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Record W4245727347 · doi:10.32920/14651643.v1

Defining obstacles & opportunities for innovation in the Canadian retail sector

2021· preprint· en· W4245727347 on OpenAlexaffabout
Sean Sedlezky

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMindsetBusinessMarketingVendorPerspective (graphical)Product (mathematics)Qualitative researchProduct innovationOpen innovationMeaning (existential)Process (computing)Sociology

Abstract

fetched live from OpenAlex

This thesis examines how retailers approach innovation and strategic decision making. While there is vast prior research about innovation across different sectors, until recently, the perspective of retailers was under-represented in the literature. Where it does exist, the focus has typically been on product manufacturing or process improvements through technology or partnering with their vendor community, rather than an exploration of how retailers think about and develop innovation strategy internally. In some cases, prior research has identified what retail executives say about innovation. However, their words appear at times to be inconsistent with their actions, perhaps due to the wide spectrum of how each interprets the meaning of innovation. Thus, interviews were conducted with 14 experts who offer considerable experience and a broad perspective on the Canadian retail industry as consultants and partners. The purpose of this inductive, qualitative study was to identify what potential industry-specific and internal obstacles to innovation retailers may wish to consider in the future. Results have led to seven emergent themes classified as “legacy issues”, “daily business”, “risk avoidance”, leadership”, “sharing culture”, “unseen technology” and “customer focus”. The barriers or obstacles noted within each theme suggest several potential opportunities for retailers to encourage a more innovative mindset within their organization.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.236
GPT teacher head0.297
Teacher spread0.061 · 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 designObservational
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

Citations0
Published2021
Admission routes2
Has abstractyes

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