Unlocking The Potential: Examining The Fintech Adoption In Retail Sectors
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".