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Record W3024824470 · doi:10.3390/jrfm13050096

Entrepreneurial Finance: Insights from English Language Training Market in Vietnam

2020· article· en· W3024824470 on OpenAlexvenueno aff
Thanh-Hang Pham, Manh‐Toan Ho, Thu‐Trang Vuong, Manh-Cuong Nguyen, Quan‐Hoang Vuong

Bibliographic record

VenueJournal of risk and financial management · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsnot available
Fundersnot available
KeywordsEntrepreneurshipContext (archaeology)Profit (economics)Profit marginEmerging marketsBusinessPovertyMarketingEconomicsFinanceEconomic growth

Abstract

fetched live from OpenAlex

Entrepreneurship plays an indispensable role in the economic development and poverty reduction of emerging economies like Vietnam. The rapid development of technologies during the Fourth Industrial Revolution (Industry 4.0) has a significant impact on business in every field, especially in the innovation-focused area of entrepreneurship. However, the topic of entrepreneurial activities with technology applications in Vietnam is under-researched. In addition, the body of literature regarding entrepreneurial finance tends to focus on advanced economies, while mostly neglecting the contextual differences in developing nations. Therefore, this research contributes to these topics by investigating the main characteristics of a high potential market for entrepreneurs in Vietnam, which is the English language training market (ELTM). It also aims at indicating the impacts of technology on the entrepreneurial firms within this market, with an emphasis on financing sources. To answer the research questions, this study employs a qualitative analysis and conducts 12 in-depth, semi-structured interviews with entrepreneurs and researchers in the field. The key findings in our study highlight the main contributing factors to the growth of the market, both universally and context-specific for a developing nation like Vietnam. It also lists the leaders in each market segment and the industry’s potential profit margin. The results also show that most entrepreneurs in the ELTM utilized private sources of finance rather than external ones, such as bank loans. It again confirms the idea from previous works that even with the rapid development of the economic and technological landscape, entrepreneurial activities in general barely benefit from additional sources of funding. However, it also points out the distinct characteristics of the ELTM that may influence these financing issues; for example, English training services usually collect revenues from customers before delivering their classes. This is of advantage for entrepreneurs in this area and helps significantly reduce the financial barriers. These findings, which are among the first attempts to contribute to a better understanding of entrepreneurial opportunities in the Industry 4.0 in Vietnam, provide valuable insights for policymakers and entrepreneurs, as well as investors.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.189
Teacher spread0.180 · 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 designOther design
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

Citations19
Published2020
Admission routes1
Has abstractyes

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