Understanding the Generation Z Behaviour Intention to Purchase on Social Media: A Unified Theory of Acceptance and Use of Technology (UTAUT) Approach
DOI:
https://doi.org/10.53748/jmis.v3i2.58Keywords:
Technology Acceptance, User Behavior, Hypothesis Testing, Application AdoptionAbstract
Purposes - The purpose of this study is to examine the significant impact of four factors—Performance Expectancy, Effort Expectancy, Social Influences, and Facilitating Conditions—on Behavioral Intention to use an application. Specifically, the study seeks to identify which of these factors significantly influence users' behavioral intentions and compare the findings to prior research in the field. Methodology - The study employs a quantitative approach, using a survey to gather data from application users. Hypothesis testing is conducted using structural equation modeling (SEM) to analyze the relationships between the constructs. The decision-making process is based on p-values and t-statistics, with hypotheses being accepted or rejected based on predefined thresholds: p < 0.05 and t-statistic > 1.96 for acceptance of the alternative hypothesis (Ha). Findings - The study reveals that Performance Expectancy and Facilitating Condition have significant positive impacts on Behavioral Intention. Specifically, Performance Expectancy shows a significant positive relationship with Behavioral Intention, with a path coefficient of 0.448 and a t-statistic of 2.736. Similarly, Facilitating Condition also significantly impacts Behavioral Intention, with a path coefficient of 0.337 and a t-statistic of 2.137. However, Effort Expectancy and Social Influences are found to have no significant impact on Behavioral Intention, as evidenced by their lower t-statistics and non-significant path coefficients. Novelty - The novelty of this research lies in the context-specific examination of the influence of Facilitating Condition and Performance Expectancy on Behavioral Intention, as well as the investigation into the non-significance of Effort Expectancy and Social Influences. This study provides updated insights that diverge from previous research, such as that of Oliveira et al. (2016) and Al-Okaily et al. (2020), which reported significant effects for Effort Expectancy and Social Influences. Research Implications - The findings suggest that developers and marketers should focus on enhancing Performance Expectancy and improving Facilitating Conditions to increase users' behavioral intentions to adopt and use the application. Efforts to improve the usability and social appeal of the application may not be as critical as previously thought, according to the results of this study. These insights can guide future application development and marketing strategies, as well as contribute to the ongoing academic discussion on technology acceptance models.
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