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Record W4390581595 · doi:10.3390/jrfm17010018

How Does Age Moderate the Determinants of Crowdfunding Adoption by SMEs’s: Evidences from Morocco?

2024· article· en· W4390581595 on OpenAlexvenueno aff
Soukaina Laaouina, Sara El Aoufi, Mimoun Benali

Bibliographic record

VenueJournal of risk and financial management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
Fundersnot available
KeywordsExpectancy theoryUnified theory of acceptance and use of technologyStructural equation modelingBusinessAffect (linguistics)MarketingEmpirical evidenceSocial influenceRisk perceptionPsychologySocial psychologyPerception

Abstract

fetched live from OpenAlex

In recent years, crowdfunding has emerged as a new fundraising technique for start-up ventures; however, Moroccan small and medium-sized businesses are still wary of this novel source of funding. This is confirmed by the low adoption rate of this financial innovation as well as the limited number of crowdfunding platforms in Morocco. This study aims to identify the impact of performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and perceived risk (PR) on SMEs’s intention to use crowdfunding platforms using a research model based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Empirical data were collected from 241 respondents through a survey, and structural equation modelling was used to analyze the findings. The results show that performance expectancy (PE), effort expectancy (EE), and facilitating conditions (FE) affect SMEs’s intentions to use crowdfunding. However, social influences (SI) and perceived risk (PR) were not found to be significant determinants. Regarding the moderating effect of age, our study has highlighted that this variable has moderated the relationship between the three independents variables: performance expectancy, facilitating conditions and perceived risk. Finally, this paper offers recommendations for how to increase SMEs’s intention to use crowdfunding platforms.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.012
GPT teacher head0.222
Teacher spread0.210 · 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 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

Citations10
Published2024
Admission routes1
Has abstractyes

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