MétaCan
Menu
Back to cohort
Record W4379987667 · doi:10.54691/bcpbm.v46i.5076

Financial and Marketing Performance of P&G and Unilever

2023· article· en· W4379987667 on OpenAlexaff
Danlin Yang

Bibliographic record

VenueBCP Business & Management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSWOT analysisCorporate social responsibilityBusinessSustainabilityMarketingGreen marketingMarketing strategySustainable developmentFinancial servicesSocial responsibilityAccountingFinancePublic relationsPolitical science

Abstract

fetched live from OpenAlex

As the idea of environmental protection and CSR (Corporate Social Responsibility) is gaining more attention, sustainability is now one of the determinants in assessing whether a company is ethical and has further development potential. Therefore, sustainability analysis is becoming critical for a business nowadays. This research aims to shed light on the financial and marketing performances of two leading companies in the personal care industry – P&G and Unilever, who put much effort into conducting environmentally friendly production and establishing sustainable development plans. This research uses financial statements to analyze the companies’ financial status and comes up with a marketing strategy analysis of the two companies. It also uses SWOT analysis as a tool to reveal their performances generally. Through analysis of their financial and marketing performance, this research concludes that P&G and Unilever are in good financial condition and doing well with their sustainable plans. This paper also provides general and separate suggestions for these two companies in the end.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.024
GPT teacher head0.235
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBCP Business & ManagementSame topicCorporate Social Responsibility ReportingFrench-language works237,207