The influence of performance expectancy, hedonic motivation, and effort expectancy on mobile augmented reality habits

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The growing use of mobile augmented reality in gaming, e-commerce, education, and social media has influenced user interactions with digital material. The purpose of this study is to assess the technological acceptability and learning transfer theories in India among Gen Z and Millennial smartwatch purchasers using mobile augmented reality. In the months of July to September of 2024, 389 individuals were selected at random from a shopping center in Chennai, India. All of the participants got their hands on a free augmented reality shopping app for their smartwatches. They filled out a Google Form survey after a quick overview of the features of mobile augmented reality. Participants are already actively engaged in purchasing and may be more inclined to employ an Augmented Reality (AR) shopping application in a shopping center. The study results found that mobile augmented reality habits significantly impact Behavioral Intention, Performance Expectancy, Hedonic Motivation, and Effort Expectancy (β = 0.589, p = 0.001-0.871, p = 0.003-0.024). Behavioral intention between Generation Z and Millennials is not moderated by mobile augmented reality habits (β = 0.254, p = 0.227), but Performance Expectancy, Hedonic Motivation, and Effort Expectancy demonstrate significant moderating effects (β = 0.168, p = 0.012, p = 0.029, p = 0.000, β = 0.261, p = 0.005). This model states that Mobile Augmented Reality habits influence Millennials’ performance expectancy, hedonic motivation, and effort expectancy. Age negatively moderates Habit using Mobile Augmented Reality on Performance Expectancy and Behavioral Intention, indicating Gen Z values quality more.

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    • Figure 1. Conceptual framework
    • Figure 2. Cumulative result (Gen Z vs Millennials)
    • Figure 3. Gen Z output
    • Figure 4. Millennials output
    • Table 1. Demographic profile and characteristics
    • Table 2. Reliability statistics
    • Table 3. Hypotheses testing (Gen Z vs Millennials)
    • Table 4. Hypotheses testing (Gen Z)
    • Table 5. Hypotheses testing (Millennials)
    • Conceptualization
      Prabhavathy R, S. Senthilkumar
    • Formal Analysis
      Prabhavathy R, Paul Arun Kumar J, K. Subathra, Ajith L
    • Investigation
      Prabhavathy R, Paul Arun Kumar J, K. Subathra
    • Methodology
      Prabhavathy R, S. Senthilkumar
    • Writing – original draft
      Prabhavathy R
    • Supervision
      S. Senthilkumar, K. Subathra
    • Writing – review & editing
      S. Senthilkumar
    • Data curation
      Paul Arun Kumar J, Ajith L
    • Project administration
      Paul Arun Kumar J, K. Subathra, Wang Yuyang, Rohan Thomas Jinu
    • Validation
      Ajith L, Wang Yuyang, Rohan Thomas Jinu
    • Visualization
      Ajith L, Wang Yuyang, Rohan Thomas Jinu
    • Resources
      Wang Yuyang, Rohan Thomas Jinu