Adoption Drivers of Intelligent Virtual Assistants in Banking: Rethinking the Artificial Intelligence Banker

The adoption of Intelligent Virtual Assistants (IVAs) in the banking sector presents new opportunities to enhance customer service efficiency, reduce operational costs, and modernize service delivery channels. However, the factors driving IVA adoption and usage, particularly in specific regional contexts such as Portugal, remain underexplored. This study examined the determinants of IVA adoption intention and actual usage in the Portuguese banking sector, drawing on the Technology Acceptance Model (TAM) as its theoretical foundation. Data were collected through an online questionnaire distributed to 154 banking customers after they interacted with a commercial bank’s IVA. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that perceived usefulness significantly influences the intention to adopt, which in turn significantly impacts actual usage. In contrast, other variables—including trust, ease of use, anthropomorphism, awareness, service quality, and gendered voice—did not show a significant effect. These results suggest that Portuguese users adopt IVAs based primarily on functional utility, highlighting the importance of outcome-oriented design and communication strategies. This study contributes to the understanding of technology adoption in mature digital markets and offers practical guidance for banks seeking to enhance the perceived value of their virtual assistants.

Authors

Institutions

Publication Details

Journal
Computers
Published
2025-05-27
DOI
https://doi.org/10.3390/computers14060209
Citations
3
Primary Topic
FinTech, Crowdfunding, Digital Finance
Type
article
Field-Weighted Citation Impact
6.85
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Adoption Drivers of Intelligent Virtual Assistants in Banking: Rethinking the Artificial Intelligence Banker

Joaquim A. Casaca, Rui Manuel Gens de Moura Ramos, Rui Patrício
3 citations
Computers
FinTech, Crowdfunding, Digital Finance
6.85
article

Adoption Drivers of Intelligent Virtual Assistants in Banking: Rethinking the Artificial Intelligence Banker

Joaquim A. Casaca, Rui Manuel Gens de Moura Ramos, Rui Patrício
article en
3 citations

Abstract

The adoption of Intelligent Virtual Assistants (IVAs) in the banking sector presents new opportunities to enhance customer service efficiency, reduce operational costs, and modernize service delivery channels. However, the factors driving IVA adoption and usage, particularly in specific regional contexts such as Portugal, remain underexplored. This study examined the determinants of IVA adoption intention and actual usage in the Portuguese banking sector, drawing on the Technology Acceptance Model (TAM) as its theoretical foundation. Data were collected through an online questionnaire distributed to 154 banking customers after they interacted with a commercial bank’s IVA. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that perceived usefulness significantly influences the intention to adopt, which in turn significantly impacts actual usage. In contrast, other variables—including trust, ease of use, anthropomorphism, awareness, service quality, and gendered voice—did not show a significant effect. These results suggest that Portuguese users adopt IVAs based primarily on functional utility, highlighting the importance of outcome-oriented design and communication strategies. This study contributes to the understanding of technology adoption in mature digital markets and offers practical guidance for banks seeking to enhance the perceived value of their virtual assistants.

ComputersVol. 14(6)
Universidade Europeia (PT), University of Aveiro (PT)
Openalex Percentile: Top 4%
FinTech, Crowdfunding, Digital Finance
6.85
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.