Karina Febriantika, Dwi Yulianti, Zikhaila Diva Priamita

ANALYSIS OF USER ACCEPTANCE OF SEABANK SERVICES USING THE TECHNOLOGY ACCEPTANCE MODEL (TAM) APPROACH

Introduction

Analysis of user acceptance of seabank services using the technology acceptance model (tam) approach. Analisis penerimaan layanan digital SeaBank menggunakan extended TAM. Temukan bagaimana keamanan, kemudahan penggunaan, dan pengaruh sosial membentuk niat perilaku pengguna di perbankan digital Indonesia.

Abstract

Abstrak: Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi penerimaan pengguna terhadap layanan digital SeaBank menggunakan pendekatan Technology Acceptance Model (TAM) yang diperluas. Model mencakup enam konstruk utama: Perceived Usefulness, Perceived Ease of Use, Perceived Security, Social Influence, Brand Image, dan User Behavior, yang dianalisis terhadap Behavioral Intention. Metodologi yang digunakan adalah pendekatan kuantitatif dengan pengumpulan data melalui survei terhadap 385 individu yang merupakan pengguna SeaBank.. Teknik analisis data menggunakan Structural Equation Modeling (SEM) melalui software WARPPLS. Hasil penelitian menunjukkan bahwa seluruh konstruk berpengaruh signifikan terhadap perilaku dan niat pengguna. Perceived Security dan Perceived Ease of Use menjadi faktor paling dominan terhadap perilaku pengguna, sementara User Behavior memiliki pengaruh paling kuat terhadap Behavioral Intention. Temuan ini menunjukkan pentingnya aspek keamanan, pengalaman pengguna, dan pengaruh sosial dalam membentuk niat penggunaan layanan digital. Penelitian ini berkontribusi pada pengembangan teori TAM dan UTAUT dalam konteks perbankan digital di Indonesia, serta memberikan masukan praktis bagi pengembangan strategi layanan SeaBank di masa depan.   Kata Kunci— Digital Banking, Behavioral Intention, Perceived Ease of Use, Perceived Security, Technology Acceptance Model   Abstract: This study aims to examine the key factors influencing users’ acceptance of SeaBank’s digital services by employing an extended framework of the Technology Acceptance Model (TAM). The model incorporates six core constructs: Perceived Usefulness, Perceived Ease of Use, Perceived Security, Social Influence, Brand Image, and User Behavior, each evaluated in relation to Behavioral Intention. A quantitative method was adopted, utilizing survey data collected from 385 SeaBank users. Data analysis was conducted using Structural Equation Modeling (SEM) via WARPPLS software. The findings reveal that all constructs significantly influence both user behavior and behavioral intention. Among these, Perceived Security and Perceived Ease of Use emerged as the most dominant factors impacting user behavior, while User Behavior demonstrated the strongest influence on Behavioral Intention. These results emphasize the importance of digital security, user experience, and social influence in shaping behavioral intentions toward digital banking services. The study contributes to the advancement of TAM and UTAUT theories within the Indonesian digital banking landscape and provides practical recommendations for future SeaBank service strategies.   Keywords— Digital Banking, Behavioral Intention, Perceived Ease of Use, Perceived Security, Technology Acceptance Model

Review

This study provides a timely and relevant analysis of user acceptance of SeaBank's digital services, employing an extended Technology Acceptance Model (TAM) framework. By incorporating constructs such as Perceived Security, Social Influence, Brand Image, and User Behavior alongside the traditional Perceived Usefulness and Perceived Ease of Use, the research offers a comprehensive perspective on factors driving Behavioral Intention. The quantitative methodology, utilizing a substantial sample size of 385 SeaBank users and Structural Equation Modeling (SEM) with WARPPLS, lends robustness to its findings. The core aim of understanding user acceptance in the evolving Indonesian digital banking landscape is well-articulated and addressed. The paper's strengths lie in its empirical rigor and the specificity of its findings. The identification of Perceived Security and Perceived Ease of Use as the most dominant factors influencing user behavior, and User Behavior itself as the strongest predictor of Behavioral Intention, offers valuable insights for both academic understanding and practical application. This differentiation helps to refine the understanding of technology acceptance models in a specific industry context. Furthermore, the study's contribution to extending TAM and UTAUT theories within the Indonesian digital banking sector is noteworthy, providing a regional context that is often underrepresented in global research. The practical recommendations for SeaBank's future service strategies are a direct benefit of this applied research. While the study offers significant contributions, a few areas could enhance its impact. The abstract mentions "User Behavior" as a construct influencing Behavioral Intention, which is somewhat tautological as behavioral intention often precedes behavior. Clarifying the conceptualization of "User Behavior" as a distinct antecedent or a measure of prior interaction would strengthen this aspect of the model. Additionally, while the abstract notes the contribution to TAM and UTAUT theories, further elaboration on *how* the extended constructs specifically advance these theories beyond merely adding new variables would be beneficial. Future research could explore mediating or moderating effects among the constructs, or incorporate qualitative methods to gain deeper insights into user perceptions and experiences, particularly regarding the nuances of security and social influence.

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