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Record W4231668779 · doi:10.1504/ijbex.2021.111914

Gender-based behavioural segmenting of the cellphone youth market

2020· article· en· W4231668779 on OpenAlexaff
Matti Haverila, Kai Haverila, Caitlin McLaughlin

Bibliographic record

VenueInternational Journal of Business Excellence · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsConcordia UniversityThompson Rivers University
Fundersnot available
KeywordsMarket segmentationCluster (spacecraft)MarketingPsychologyAdvertisingBusinessComputer science

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate the existence of behavioural segments (clusters) among youth regarding the cellphone communication market in Finland. Specific cellphone behaviours are used as a cluster variate. The cellphone behaviours used are: 'necessity of modern times', 'cost efficiency', 'safety/security', 'dependency', 'negatives' and 'functionality', per Aoki and Downes's (2003) research on cellphone use. Separate behavioural clusters were devised for both males and females on the basis of the behaviours. Four unique behavioural clusters among the male respondents ('middle of the road', 'feature freaks', 'all important', and 'minimalists') and three unique behavioural clusters among the female respondents ('dependents', 'advanced users', and 'cost matters') were detected. Each cluster had a unique group of benefits sought, with no overlapping clusters between genders. After these clusters were identified they were then profiled using age, gender and the behavioural variables as profilers. This research serves to help market researchers and cellphone producers alike better understand how and why cellphones are used in the youth marketplace.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.062
GPT teacher head0.296
Teacher spread0.234 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2020
Admission routes1
Has abstractyes

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