Understanding Consumer Acceptance of AI in the Leisure Economy: A Structural Equation Modeling Approach
DOI:
https://doi.org/10.33050/atm.v8i3.2348Keywords:
Artificial Intelligence, Consumer Acceptance, Leisure Economy, Structural Equation Modeling, SmartPLSAbstract
This research examines the determinants of consumer acceptance of artificial intelligence (AI) in the leisure economy, using a structural equation model to analyze responses from 560 participants. The study focuses on several psychological factors: Perceived Ease of Use (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Perceived Value (PV), and Habit (HB), and their impact on Behavioral Intention (BI) to adopt AI technologies. Results indicate significant influence of six constructs (PE, FC, SI, PV, HM, HB) on BI, with the exception of one hypothesis. The research also assesses the role of Personal Innovativeness in enhancing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model's predictive accuracy. This study contributes to understanding AI adoption in leisure, offering valuable insights for AI application development and marketing strategies in this sector.
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The increasing integration of artificial intelligence (AI) in the leisure economy is reshaping consumer experiences through personalized and efficient services. Despite its potential, consumer acceptance of AI in this sector remains under- explored. This study aims to investigate the psychological factors affecting be- havioral intention (BI) to adopt AI technologies in leisure activities, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. Key constructs include Perceived Ease of Use (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Perceived Value (PV), and Habit (HB). Data were collected through a quanti- tative cross-sectional survey of 560 participantswho had interacted with AI in leisure contexts. Partial Least Squares Structural Equation Modeling (PLS- SEM) analysis revealed that six constructs (PE, SI, FC, PV, HM, and HB) sig- nificantly influenced BI. Personal Innovativeness was also found to enhance the model’s predictive accuracy, contributing to a deeper understanding of consumer readiness for AI adoption. This research provides critical insights into the fac- tors driving AI adoption in the leisure economy and emphasizes the importance of aligning AI applications with consumer motivations. The findings provide actionable implications for AI development and marketing strategies aimed at optimizing consumer engagement and acceptance in this evolving sector.
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Copyright (c) 2024 Susilawati, Dyah Juliastuti, Marviola Hardini (Author)

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