Arman, Razi Alvaro (2026) Pengembangan Model E-S-QUAL Untuk Meningkatkan Continuous Intention Pengguna Chatbot Perbankan Menggunakan Partial Least Squares Structural Equation Modeling. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Latar Belakang: Adopsi chatbot berbasis Artificial Intelligence (AI) di industri perbankan Indonesia terus meningkat pesat. Hal ini didukung oleh data Market Research Future bahwa pertumbuhan bot layanan di Indonesia mencapai 17% setiap tahunnya. Perkembangan ini menciptakan tantangan bagi penyedia layanan untuk mempertahankan kepuasan dan mendorong penggunaan berkelanjutan. Permasalahan: Meskipun adopsi chatbot tinggi, 53% konsumen global menilai chatbot "tidak efektif" atau "cukup efektif." Gap terjadi antara kecanggihan teknis dan pengalaman layanan aktual pengguna, terutama dalam menangani kebutuhan kompleks dan memberikan interaksi yang natural serta personal. Hal ini menjadikannya landasan untuk perlu adanya pengujian untuk kualitas layanan chatbot di Indonesia, khususnya di perbankan, dan implikasi kognitifnya. Tujuan: Penelitian mengembangkan model integratif berbasis E-S-QUAL (Electronic Service Quality) dan Expectation Confirmation Model (ECM) yang diintegrasikan dengan variabel kognitif seperti Perceived Financial Benefits, Perceived Enjoyment, dan Need for Interaction with Service Employees sebagai anteseden persepsi kognitif, kepuasan, dan niat penggunaan berkelanjutan. Data dan Metode: Data dikumpulkan melalui survei online kepada 319 responden pengguna aktif chatbot perbankan Indonesia (usia 19 ke atas). Analisis dilakukan menggunakan Partial Least Squares Structural Equation Modeling (PLS-SEM) untuk validasi model dan pengujian hipotesis. Validasi meliputi uji reliabilitas, validitas konvergen, dan validitas diskriminan. Hasil: Dari 14 hipotesis yang diuji, delapan hipotesis diterima dan enam ditolak. Core AI Bot Service Quality terbukti berpengaruh positif dan signifikan terhadap Continuous Intention, Satisfaction, dan Perceived Financial Benefits. AI Bot Conversational Quality berpengaruh signifikan terhadap Perceived Enjoyment, Satisfaction, dan Continuous Intention secara langsung. Satisfaction menjadi prediktor paling dominan terhadap Continuous Intention (β = 0,727), sementara Perceived Enjoyment dan Perceived Financial Benefits tidak terbukti memengaruhi Satisfaction. Need for Interaction with Service Employee berpengaruh negatif terhadap Satisfaction, namun tidak memoderasi hubungan antar variabel kognitif. Model menjelaskan 72,8% varians Continuous Intention dan 31,0% varians Satisfaction. Nilai Tambah: Penelitian ini memperluas kerangka E-S-QUAL dan ECM melalui validasi empiris konstruk AI Bot Conversational Quality dalam konteks perbankan Indonesia, serta menghasilkan rekomendasi strategis berbasis indikator bagi penyedia layanan untuk memprioritaskan keandalan dan keamanan sistem chatbot dibandingkan investasi pada aspek hiburan atau insentif finansial dalam mendorong loyalitas penggunaan jangka panjang.
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Background: The adoption of Artificial Intelligence (AI)–based chatbots in the Indonesian banking industry has been growing rapidly. This trend is supported by Market Research Future data showing that the growth of service bots in Indonesia reaches around 17% per year. This development creates challenges for service providers to maintain customer satisfaction and to encourage continued use of chatbots. Problem Statement: Despite the high adoption rate, 53% of global consumers still rate chatbots as “ineffective” or only “somewhat effective.” There is a gap between technical sophistication and the actual service experience, especially in handling complex needs and providing natural, personalized interactions. This situation motivates the need to test chatbot service quality in Indonesia’s banking sector and to examine its cognitive implications for users. Purpose: This study plans to develop an integrative model based on E‑S‑QUAL and the Expectation Confirmation Model (ECM), which is combined with cognitive variables such as Perceived Financial Benefits, Perceived Enjoyment, and Need for Interaction with Service Employees as antecedents of cognitive perceptions, satisfaction, and continuous usage intention. Data and Methods: The research will collect data through an online survey of 319 active users of banking chatbots in Indonesia aged 19 years and above. The analysis will employ Partial Least Squares Structural Equation Modeling (PLS‑SEM) to validate the proposed model and to test the hypotheses, including reliability testing, convergent validity, and discriminant validity. Results: Of the 14 hypotheses tested, eight were supported and six were rejected. Core AI Bot Service Quality had a significant positive effect on Continuous Intention, Satisfaction, and Perceived Financial Benefits. AI Bot Conversational Quality significantly influenced Perceived Enjoyment, Satisfaction, and Continuous Intention directly. Satisfaction was the strongest predictor of Continuous Intention (β = 0.727), while Perceived Enjoyment and Perceived Financial Benefits did not significantly affect Satisfaction. Need for Interaction with Service Employee had a negative effect on Satisfaction but did not moderate the relationships among cognitive variables. The model explained 72.8% of the variance in Continuous Intention and 31.0% in Satisfaction. Value Added: This study extends the E-S-QUAL and ECM frameworks through empirical validation of the AI Bot Conversational Quality construct in the Indonesian banking context, and provides indicator-based strategic recommendations for service providers to prioritize chatbot reliability and security over investments in hedonic features or financial incentives to drive long-term usage loyalty.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Chatbot layanan pelanggan, E-S-QUAL, Expectation Confirmation Model, Continuous Intention, PLS-SEM, customer service chatbot, E‑S‑QUAL, Expectation Confirmation Model, continuous intention, PLS‑SEM |
| Subjects: | T Technology > T Technology (General) > T58.6 Management information systems T Technology > T Technology (General) > T59.7 Human-machine systems. |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information System > 57201-(S1) Undergraduate Thesis |
| Depositing User: | Razi Alvaro Arman |
| Date Deposited: | 28 Jul 2026 02:32 |
| Last Modified: | 28 Jul 2026 02:32 |
| URI: | http://repository.its.ac.id/id/eprint/138201 |
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