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Record W1968887010 · doi:10.1080/0965254x.2012.746998

Cell phone product-market segments using product features as a cluster variate: a multi-country study

2013· article· en· W1968887010 on OpenAlex
Matti Haverila, Michel Rod, Nicholas J. Ashill

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Strategic Marketing · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsPhoneMarket segmentationProduct (mathematics)MarketingResidenceBusinessExploratory researchChinaCluster (spacecraft)EconomicsDemographic economicsComputer scienceGeographySociology

Abstract

fetched live from OpenAlex

Acknowledging the importance of hybrid bases for segmenting international markets and drawing upon means–ends chain theory, this study investigates the existence of inter-market product-market segments among adolescents and young adult cell phone consumers across five country markets. On the basis of exploratory research aiming to identify a comprehensive list of cell phone features we examine the existence of inter-market segments using these feature preferences as the cluster variate. Data were gathered from 403 high school and 892 undergraduate students in Finland, UAE, China, Canada and New Zealand. The results of a two-step cluster analysis approach suggest the inter-market segments do exist in these five countries, but their existence varies to some degree from country to country. These clusters were then profiled with gender, country of residence and frequency of usage of certain cell phone functions as background variables. The paper concludes with a discussion of managerial implications and directions for future research.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.269
Teacher spread0.233 · 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