How does digital payment transform society as a cashless society? An empirical study in the developing economy
Bibliographic record
Abstract
Purpose After analyzing these uncountable benefits of digital or cashless payment, many European countries like Sweden, Finland and Canada has been trying to convert their payment system into cashless. Following these developed countries, the Bangladesh Government has taken a decision to transfer society as a cashless society by using information technologies for adopting the fourth industrial revolution over the world. Digital payment system is among the various options available for transforming a cashless society. First, this empirical study presents demographic information and digital payment characteristics on the basis of income levels. This study identifies influential factors of adopting digital payment systems. Finally, this study aims to justify how digital payments transform the Bangladeshi economy into a cashless society in developing countries. Design/methodology/approach The study was administered to a sample of 1,000 Bangladeshi customers who had engaged in online banking transactions for the purpose of acquiring items and services through both social media platforms in Google Form format and face-to-face interactions in hard copy format. Among these, 647 questions were deemed usable and were used for data analysis, where the response rate was 68%. The SmartPLS is used to create and validate the structural equation modeling model presented for the research, as well as to evaluate the hypothesized correlations between the different constructs. Findings This cross-sectional study conducted the extended technology acceptance model (TAM) with perceived security (PS) and personal innovation (PI) variables to identify the influencing adoption factors of digital payment systems. This study finds that perceived ease of use, PI and perceived usefulness have a favorable impact on individuals’ attitudes toward adopting digital payment methods (DPMs). The study also indicated that PS did not influence negatively the adoption of digital payment system. Besides this, the adoption of digital payment will help to transform society into a cashless society in the future. Research limitations/implications Increasingly prevalent across the nation. Several variables are required to facilitate the transition toward a cashless society. This study exclusively focuses on DPMs. Additionally, the data has been obtained exclusively from a single urban area. The adoption of DPMs has become increasingly prevalent across the nation. Practical implications This study would help policymakers, marketers and bankers understand which factors affect digital payment infrastructure expansion. So, they can produce digital payment apps that are compatible with different devices, have fast transactions, are user-friendly, easy to use and highly secure to maintain good attitudes toward digital payment systems. Social implications Few studies have examined how DPMs affect cashless societies in developing countries like Bangladesh. According to researchers, to the best of the authors’ knowledge, this is the first study to explore how digital payments affect cashless society in Bangladesh and raise awareness about it. Originality/value The study extended the TAM model to PS and PI. This paper is also unique in the conceptual arguments and the subject theme of the research area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".