Work

Acceptance model of adolescent health promotion application (Konco SREGEP) through modifications of technology acceptance model and integrated behavior model

Year: 2026

Type: article

Abstract: The “Konco SREGEP” application is a digital adolescent health promotion medium developed according to adolescents’ needs, characteristics, and capacities. However, its acceptance had not been evaluated. This study aimed to evaluate acceptance of the “Konco SREGEP” application in Malang Regency using modified Technology Acceptance Model (TAM) and Integrated Behavior Model (IBM) constructs. This quantitative study used a single-group pre-test–post-test pre-experimental design with one month of assistance after application installation. Participants were first-grade junior high school students from Population Alert Schools who had access to a cellphone and the internet, had never received information about the application, and whose schools agreed to participate. A total of 94 students were recruited, with 92 included in analysis. Data were collected using questionnaires measuring knowledge and skills, curiosity, computer self-efficacy, perceived control, salience of behavior, environmental constraints, habits, perceived usefulness, perceived ease of use, attitude toward using, behavioral intention to use, and actual system use. Data were analyzed using descriptive analysis and partial least squares structural equation modeling (PLS-SEM) at pre-test and post-test stages. Differences between pre-test and post-test measurements were observed for knowledge and skills, perceived control, salience of behavior, and actual system use. The post-test model showed stronger explanatory power for behavioral intention to use (R 2 = 0.868) and actual system use (R 2 = 0.398). Perceived usefulness was a key determinant of acceptance in the post-test model. Perceived usefulness should be prioritized to increase acceptance and use of the “Konco SREGEP” application, while continued assistance may support engagement.

Source: Discover Artificial Intelligence

DOI: 10.1007/s44163-026-02590-5