Unlocking The Potential: Examining The Fintech Adoption In Retail Sectors
Notice bibliographique
Résumé
Purpose: The research aims to investigate the technology adoption among retail shops and the study concentrates on the factors which are contributing to the fintech adoption among them. Nowadays, the majority of retail shops have adopted the concept of fintech still shops are there who does not adopt financial technology. so here the study emphasizes the influence of the demographic profile, financial literacy, and trust on the fintech adoption and it will help to identify whether these variables have any role and if it is yes, how they can be used to enhance the adoption of financial technology among the entire retail shops. Methodology: The study followed a quantitative research design and used both primary and secondary data. the researcher collected primary data from 55 retail shop owners with the help of a questionnaire and the data were analyzed with the help of different statistical tools. The researcher used descriptive statistics, t-tests, correlation, and regression to conclude the study. Then the questionnaire contains different statements some of them are constructed by the researcher and some are taken from previous studies. Then the researcher utilized Cronbach’s alpha to ensure the reliability of the data. .Research gap: many studies were conducted on the fintech adoption by concentrating on the adoption among specific industries like banking or finance and there lack of a study concentrated on retail shops. The majority of the studies are done based on the variables in the TAM (Technology Adoption Model), here the researcher added financial literacy and trust to evaluate the fintech adoption among the retail shops. Findings: The study found that the demographic variables of the owners don’t show any significant difference in financial literacy, trust, and fintech adoption. It means regardless of the difference in age, gender, and region of residence the owners have adopted the financial technology in their shops. The perceived ease of use, perceived usefulness and trust have an impact on the fintech adoption among the retail shops and these variables are correlated with each other. Whatever the benefits and risks the retailer considers the need for financial technology to improve customer involvement and satisfaction. The retailer considers the ease of use and trust because the study found that the people with more ease of use and trust are ready to accept the adoption of fintech in their retail store. Implication: The study provides valuable insights into the factors that impacted the fintech adoption among retail shops and it is helpful for the government and policymakers to make decisions on the enhancement of the fintech adoption in our society to improve financial inclusion. the study suggested facilitating more training and development to enhance the ease of use and trust among the people and it will lead to the adoption of technology in the field of finance by the entire society.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».