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Record W3021315665 · doi:10.14707/ajbr.200079

Social Media Effectiveness Indicators of Microenterprise Strategic Planning

2020· article· en· W3021315665 on OpenAlexaff
Alex Cheing, Efendi Haslim Hong, Thiam Yong Kuek, Boon Hui Bobby Chai, Tat‐Huei Cham

Bibliographic record

VenueAsian Journal of Business Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsBriercrest College and Seminary
Fundersnot available
KeywordsBusinessSocial mediaProcess managementComputer scienceKnowledge managementWorld Wide Web

Abstract

fetched live from OpenAlex

The use of social media in the support of marketing mix has been gaining prominence in contemporary strategic planning. The indicators on how microenterprises can effectively benchmark their social media usage is ambiguous. This paper aims to identify possible indicators of social media effectiveness towards microenterprises strategic planning. A conceptual framework is developed based on marketing mix elements of promotion and place in identifying the possible indicators of social media effectiveness. Owner/managers of food and beverage microenterprises in the Malaysian state of Sarawak were approached to understand their Facebook promotional strategy, features in generating interest to their outlet and general outcome in supporting their marketing mix elements. Through thematic analysis, the findings indicated sales increment, customer retention and viral marketing are vital for social media effectiveness. The context of the paper is limited to microenterprises in developing market through a subjective-based strategic planning evaluation framework. Selective marketing mix elements are applied through the perspective of Food and Beverage industry in the development of the social media effectiveness indicators. The three themes from the findings: sales increment, customer retention and viral marketing could serve as the foundation for microenterprise strategic planning.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.112
GPT teacher head0.405
Teacher spread0.293 · 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 designQualitative
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

Citations17
Published2020
Admission routes1
Has abstractyes

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