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Record W2901689168 · doi:10.5539/ijms.v10n4p13

Bottom of the Pyramid Marketing

2018· article· en· W2901689168 on OpenAlexvenueno aff
Melissa Martirano

Bibliographic record

VenueInternational Journal of Marketing Studies · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsnot available
Fundersnot available
KeywordsMarketingPeer pressurePovertyBottom of the pyramidThe InternetPopulationBusinessConsumption (sociology)AdvertisingPublic relationsSociologyPsychologyEconomicsPolitical scienceEconomic growthSocial psychologySocial science

Abstract

fetched live from OpenAlex

Throughout the developing world there have been numerous studies of the impact and ethics of marketing to consumers in the lowest socio-economic demographic, known as the Bottom of the Pyramid. These consumers make less than $2 USD per day in many countries, yet will buy expensive items marketing by the media/the Internet, discussed on social media, and to keep up with peers (peer pressure). When such items are of benefit to the purchaser or their region, the result may be positive (computers for schooling, etc.) Yet spending on luxury items can cause such consumers to go deeply into debt or forego necessities, calling into question the ethics of targeting this group. In the United States, the poverty level is higher than in many countries, and access to media/social media/the Internet is ubiquitous. Americans are also susceptible to peer pressure, according to studies. Bottom of the Pyramid research, however, is lacking on American respondents. This proposal would fill that gap, considering such marketing and consumption from a behavioral and perceptive viewpoint. Hypothetical recommendations drawn from survey questions based on research questions developed through theoretical frameworks and scholarly literature review will suggest practical courses for American industry to sell to this population without ethical question. This work may also spur more in-depth analysis involving clearly defined demographic groups for deeper analysis and understanding. The research follows the qualitative method and is to be analyzed thematically using Likert format numbers.

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)0.005
metaresearch head score (Gemma)0.004
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 score0.113
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
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 science0.0000.000
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 head0.022
GPT teacher head0.280
Teacher spread0.258 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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