Trust and Security in Digital Payments Drive Small Business Growth in Bandung
Description
This research focuses on the following constructs: 1. Security in Digital Payments 2. Trust in Digital Payments 3. FinTech Service Adoption 4. Business Performance The purpose of this research is to investigate how the perceived security and trust in digital payments influence the adoption of FinTech services among Micro, Small, and Medium Enterprises (MSMEs) in Bandung, Indonesia. Using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, this study examines how these perceptions ultimately impact MSME business performance through improved operations, customer engagement, and learning processes. The theoretical framework incorporates the Unified Theory of Acceptance and Use of Technology (UTAUT), Resource-Based View (RBV), and Balanced Scorecard (BSC) perspectives. Data were collected through structured questionnaires from 400 MSME respondents in Bandung, with validation conducted through SmartPLS 4.
Files
Steps to reproduce
1. Survey Distribution: A structured questionnaire was developed based on validated indicators from prior research (Pavlou, Kim, Venkatesh, and Kaplan & Norton). The survey included items measuring perceived security, trust, FinTech adoption, and business performance using a 5-point Likert scale. 2. Data Collection: The survey was distributed online and offline to Micro, Small, and Medium Enterprises (MSMEs) in Bandung, Indonesia. A total of 399 valid responses were collected after data cleaning. 3. Variable Construction: The following constructs were used: - Security (technical protection, transaction procedures, policy statements) - Trust (reputation, satisfaction, frequency) - FinTech Service Adoption (UTAUT-based indicators) - Business Performance (financial, customer, internal, and growth) 4. Data Analysis: The dataset was analyzed using SmartPLS 4 software. Structural Equation Modeling (PLS-SEM) was used to assess both the measurement and structural models. Path coefficients, R², Q², and bootstrapping values were evaluated. 5. Reproducibility: Researchers may reproduce the analysis by importing the dataset into SmartPLS 4 and using the original path model. All indicator names and constructs are labeled consistently.
Institutions
- Bina Nusantara University