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Record W2976832345 · doi:10.5430/rwe.v10n3p45

The Essentials and Challenges of Online Business Among Bumiputera SME Entrepreneurs in Malaysia

2019· article· en· W2976832345 on OpenAlex
Hardy Loh Rahim, Mohd Ali Bahari Abdul Kadir, Che Asniza Osman, Hardi Emrie Rosly, Adlan Ahmad Bakri

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueResearch in World Economy · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
FundersUniversiti Teknologi MARA
KeywordsRevenueBusinessCoachingOnline businessMarketingThe InternetPopulationE-commerceEntrepreneurshipFinanceEconomicsManagement

Abstract

fetched live from OpenAlex

The e-commerce trend has increasingly grown in Malaysia. The revenue for e-commerce platforms in 2019 is USD3.7 billion with more than 20 million users. There were various calls from the ministries for Malaysian to take up e-commerce as it is progressively contributing to the country’s GDP. Bumiputera entrepreneurs have been demanded to play active roles in e-commerce, however their participation and performance are in an alarming state. Therefore, this paper aims to identify the challenges faced, examine the awareness of the existing business support and identify support and assistance which can be implemented to address the issues faced by Bumiputera SME online entrepreneurs. The research was done using survey format. Questionnaire was distributed via online survey. The population is the online entrepreneurs from Malaysian Internet Entrepreneurs Association (PUIM). 493 respondents participated in this study. Findings show that the main challenges were the lack of access to financial assistance and lack of knowledge to conduct market study. While the training needed are Facebook Ads and Business Coaching. Research also shows that the higher the level of education, the lesser the constraints experienced and the greater the level of confidence in business success. Finally, several improvements and suggestions have been proposed.

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.

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)0.000
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 score0.027
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.192
GPT teacher head0.420
Teacher spread0.228 · 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