Survey Dataset on Digital Transformation and Payment System Innovation in Indonesian Commercial Banking
Description
This repository contains an executive-level survey dataset on digital transformation and payment system innovation in Indonesian commercial banking. The dataset was developed using a census-based survey covering 105 Indonesian commercial banks in the defined study population. Each bank is represented by one director or senior executive key informant involved in digital transformation, governance, technology, payment systems, or strategic decision-making. The dataset includes 105 anonymized bank-level observations and 75 Likert-scale indicators organized into five multidimensional construct groups: Digital Dynamic Capabilities (DDC), Institutional Pressure (IP), Digital Transformation Strategy (DTS), Payment System Innovation (PSI), and Sustained Competitive Advantage (SCA). Each construct consists of three dimensions with five indicators per dimension. The indicators were measured using a six-point Likert scale, ranging from 1 = strongly disagree to 6 = strongly agree. The dataset also includes respondent profile variables and bank-level control variables, including KBMI classification and ownership type. The dataset package includes an analysis-ready anonymized dataset, codebook, metadata, README documentation, construct structure table, and dataset architecture figure. The data are provided in XLSX and CSV formats to support reuse in SmartPLS, SPSS, Stata, R, Python, and other statistical software. The dataset is useful for researchers studying digital banking transformation, payment system innovation, digital dynamic capabilities, institutional pressure, fintech ecosystem development, open banking, payment interoperability, QRIS, BI-FAST, and sustained competitive advantage in emerging-market banking. It may support empirical analysis using PLS-SEM, mediation analysis, moderation analysis, multi-group analysis, and comparative digital banking research. All direct identifiers were removed before publication. Bank names were replaced with anonymized identifiers ranging from Bank_001 to Bank_105. The dataset does not contain respondent names, email addresses, timestamps, phone numbers, customer-level data, transaction-level banking records, confidential supervisory data, or confidential institutional operational information. Bank-level characteristics are retained only in categorized form.
Files
Institutions
- Binus UniversityJakarta, Jakarta