MétaCan
Menu
Back to cohort

Social Media Optimisation for Retailers

2024· book-chapter· en· W4401021767 on OpenAlexaff
Ayse Begum Ersoy

Bibliographic record

VenueAdvances in business information systems and analytics book series · 2024
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsCape Breton University
Fundersnot available
KeywordsSocial mediaBusinessAdvertisingComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Retail business has been growing fast particularly during the last decade. High penetration rates of mobile communication devices such as smart phones and high usage of social media make the retailers especially in food across the world with Fee WIFI access, very attractive off-line and on-line social venues. The growth of the internet is continuous and offers many e-commerce opportunities for retail businesses to penetrate, grow and achieve loyalty also supported by globalisation. This chapter aims to identify how retail businesses can optimize social media usage in order to increase their customer base, reach a higher level of customer satisfaction and hence increase the rate of customer loyalty in the long run. The literature review focuses on social media engagement by small businesses and retailers.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.009
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 head0.028
GPT teacher head0.255
Teacher spread0.226 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in business information systems and analytics book seriesSame topicConsumer Retail Behavior StudiesFrench-language works237,207