Survey Dataset on Mobile Financial Service (MFS) Switching Behavior in Bangladesh

Description

This dataset contains survey responses collected from 311 mobile financial service (MFS) users in urban and peri-urban areas in and around Dhaka, Bangladesh. The dataset includes 50 dataset columns: 7 demographic and MFS usage-related variables and 43 reflective measurement indicators representing 11 latent constructs. The constructs include Ease of Use, Information Literacy, Information Quality, Satisfaction, Sunk Cost, Service Encounter Quality, Social Influence, System Quality, Switching Cost, Switching Intention, and Transition Cost. The data can support future research on FinTech adoption, financial inclusion, service switching behavior, and customer retention in emerging markets.

Steps to reproduce

This dataset was created through a structured survey administered to mobile financial service (MFS) users in urban and peri-urban areas in and around Dhaka, Bangladesh. Data were collected using online Google Forms and offline paper-based questionnaires. Respondents were eligible to participate if they used MFS at least occasionally. After data collection, incomplete responses and responses showing flat response patterns were removed. The final cleaned dataset contains 311 valid responses. The dataset can be reproduced using the survey questionnaire included in the dataset package. All measurement items were evaluated on a 7-point Likert scale, where 1 = Strongly Disagree and 7 = Strongly Agree. The dataset includes 7 demographic and MFS usage-related variables and 43 reflective measurement indicators representing 11 latent constructs. Before computing construct-level scores, EoU1, IL1, and IL2 should be reverse-coded using the formula: reversed score = 8 − raw score. The analysis results can be reproduced using SmartPLS 3 or later. Researchers may import the anonymized dataset into SmartPLS, assign the indicators to their respective reflective constructs, and conduct reliability analysis, convergent validity analysis, discriminant validity analysis, HTMT analysis, and VIF analysis. Descriptive statistics and tabulations can also be reproduced using Microsoft Excel or other statistical software.

Institutions

• Daffodil International University

  Dhaka Division, Dhaka
• Universiti Malaysia Perlis

  Perlis, Perlis

Categories

Licence