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Record W4390890071 · doi:10.53555/sfs.v10i2.1986

A Study on Effectiveness of Social Media Advertising and their Purchase Intentions

2023· article· en· W4390890071 on OpenAlexvenueno aff
D. Adinarayana, Dr T. Yogesh Babu, B. Kishore Babu, Dr Ravindra babu Veguri, Dr Gadagamma Balakrishna, Daniel Pilli

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasingAdvertisingSocial mediaStructural equation modelingBrand equityBrand imageContext (archaeology)PsychologyMarketingBusinessPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Background: The research obtains the evolving circumstances of the effectiveness of social media advertising in Vijayawada in 2022 among several undergraduate students. With a concentration on the applied study, the examination seeks to assess the nuances of social media advertisements and their respective effect on purchasing intentions in the context of the dynamic digital ecosystem. Method: Implementing a descriptive survey method, the research surveyed all chosen undergraduate students with a particular 11-item questionnaire. Therefore, data analysis applied Structural Equation Modelling and SPSS with AMOS for an advanced exploration of purchase intentions, brand equity and brand image. Results and Discussions: The research model revealed a good fit, asserting its suitability. Therefore, accepted hypotheses emphasized the potential role and applicability of brand image in fostering brand equity and their respective strategic impact on the purchase intentions of consumers. Conclusion: The outcomes facilitate actionable perspectives for marketers, highlighting the significance of cultivating positive images of brands and strategically controlling brand equity in the strategies or policies of social media advertising.

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)0.004
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 score0.007
Threshold uncertainty score0.168

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.142
GPT teacher head0.295
Teacher spread0.152 · 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
Published2023
Admission routes1
Has abstractyes

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