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Record W4401551370 · doi:10.1051/itmconf/20246601005

Leveraging Service Science for Strategic Marketing: A Case Study of a Canadian Mattress Company

2024· article· en· W4401551370 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueITM Web of Conferences · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsUniversité du Québec en OutaouaisUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsBusinessMarketingService (business)

Abstract

fetched live from OpenAlex

This study utilizes a service science perspective to elaborate on the strategic marketing plan of a Canadian mattress company. Founded in 2014, the company has transformed the mattress industry’s traditional product-centric paradigm by adopting a service-dominant logic and employing service systems thinking to fuel its competitive advantage. The research investigates how the company has incorporated concepts from service science, including value co-creation, customer involvement, and tailored solutions, into its marketing approach. The article highlights the importance of service science in developing strategic marketing approaches that work for creative, tech-driven companies by examining the company’s target market identification, marketing mix, and competitive positioning. The results offer valuable insights into the strategic use of service science in the contemporary, digitally-enabled marketplace for both scholars and practitioners.

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)0.001
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 score0.620
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.117
GPT teacher head0.297
Teacher spread0.179 · 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