Integrating trust and satisfaction into the UTAUT model to predict Chatbot adoption – A comparison between Gen-Z and Millennials

• The study uniquely examines chatbot adoption among Gen Z and Millennials, two tech-savvy generations with distinct online behaviors. • It incorporates trust and satisfaction into the unified theory of acceptance and use of technology (UTAUT), enhancing its applicability to chatbot adoption. • Performance expectancy, social influence, trust, and satisfaction are identified as critical drivers of chatbot adoption. • Trust emerges as a pivotal factor influencing both user satisfaction and adoption, with reduced perceived risks boosting trust levels. • The originality lies in addressing the gap, as existing research has not thoroughly explored chatbot adoption among Gen Z and Millennials. This paper examines the key determinants of behavioral intention, user satisfaction, and chatbot adoption among urban, college-educated student populations within Generation Z and Millennials in India. While Millennials grew up with the Internet, Gen Z was born into the era dominated by social media and smartphones, making them inherently tech-savvy and drawn to chatbots for information access. This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) by integrating technological elements with trust and satisfaction to propose a conceptual model. Using a mixed-method approach, data were collected through a cross-sectional online survey of 487 chatbot users from urban educational institutions in India. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test 11 hypothesized direct relationships. The results suggest that users' willingness to adopt chatbots is significantly influenced by performance expectancy, social influence, trust, and satisfaction. Regarding user satisfaction, both facilitating conditions and trust played substantial roles. Additionally, this study found meaningful associations between facilitating conditions, satisfaction, intention, and adoption. Multi-group analyses revealed notable differences in chatbot adoption factors between Gen Z and Millennials within the study's sampled population. Given the importance of trust in chatbot adoption, the paper highlights that reducing perceived risks can strengthen trust, enhance user satisfaction, and drive chatbot intention and adoption. The above findings offer context-specific insights for chatbot providers in devising strategies to improve user trust, satisfaction, and adoption within similar demographics.

Authors

Institutions

Publication Details

Journal
International Journal of Information Management Data Insights
Published
2025-03-11
DOI
https://doi.org/10.1016/j.jjimei.2025.100332
Citations
12
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
29.77
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integrating trust and satisfaction into the UTAUT model to predict Chatbot adoption – A comparison between Gen-Z and Millennials

Himanshu Joshi
12 citations
International Journal of Information Management Data Insights
AI in Service Interactions
29.77
article

Integrating trust and satisfaction into the UTAUT model to predict Chatbot adoption – A comparison between Gen-Z and Millennials

Himanshu Joshi
article en
12 citations

Abstract

• The study uniquely examines chatbot adoption among Gen Z and Millennials, two tech-savvy generations with distinct online behaviors. • It incorporates trust and satisfaction into the unified theory of acceptance and use of technology (UTAUT), enhancing its applicability to chatbot adoption. • Performance expectancy, social influence, trust, and satisfaction are identified as critical drivers of chatbot adoption. • Trust emerges as a pivotal factor influencing both user satisfaction and adoption, with reduced perceived risks boosting trust levels. • The originality lies in addressing the gap, as existing research has not thoroughly explored chatbot adoption among Gen Z and Millennials. This paper examines the key determinants of behavioral intention, user satisfaction, and chatbot adoption among urban, college-educated student populations within Generation Z and Millennials in India. While Millennials grew up with the Internet, Gen Z was born into the era dominated by social media and smartphones, making them inherently tech-savvy and drawn to chatbots for information access. This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) by integrating technological elements with trust and satisfaction to propose a conceptual model. Using a mixed-method approach, data were collected through a cross-sectional online survey of 487 chatbot users from urban educational institutions in India. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test 11 hypothesized direct relationships. The results suggest that users' willingness to adopt chatbots is significantly influenced by performance expectancy, social influence, trust, and satisfaction. Regarding user satisfaction, both facilitating conditions and trust played substantial roles. Additionally, this study found meaningful associations between facilitating conditions, satisfaction, intention, and adoption. Multi-group analyses revealed notable differences in chatbot adoption factors between Gen Z and Millennials within the study's sampled population. Given the importance of trust in chatbot adoption, the paper highlights that reducing perceived risks can strengthen trust, enhance user satisfaction, and drive chatbot intention and adoption. The above findings offer context-specific insights for chatbot providers in devising strategies to improve user trust, satisfaction, and adoption within similar demographics.

International Journal of Information Management Data InsightsVol. 5(1)
International Management Institute (IN)
Gender equality
Openalex Percentile: Top 0%
AI in Service Interactions
29.77
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.