Predicting smart wrist wearable adoption intention among South African youth

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Type of the article: Research Article

Abstract
Smart wrist wearables (SWWs) represent the leading segment in the multi-billion-dollar global fitness tracker industry. Despite the robust and thriving global market, South Africa remains far behind in adoption, penetration rate and market value. As such, this study aims to predict SWW adoption intention among South African youth by examining the influence of perceived usefulness, perceived ease of use, information availability, social image, brand name, perceived performance risk, perceived cost, and attitude. The study surveyed 312 South African Parkrun attendees aged 18-34 using a self-administered questionnaire. The youth market was specifically targeted as they represent the primary users of wearable technology and because of their high digital engagement. SPSS and AMOS v. 27 were used for model validation and hypothesis testing through confirmatory factor analysis and path analysis, respectively. The results show that device ease of use boosts perceived usefulness (β = 0.82, p < 0.01), consequently influencing attitudes (β = 0.78, p < 0.01) and adoption intentions. Information availability (β = 0.20, p < 0.01) significantly influences adoption intentions, where perceived performance risk (β = 0.22, p < 0.01) echoes this finding for attitude. Social image (β = –0.11, p < 0.05) and perceived cost (β = –0.47, p < 0.01) had a significant, adverse effect on respondents’ attitude towards SWWs. Notably, brand name (β = –0.01, p > 0.01) plays no notable role in attitude formation and ultimate adoption intentions. These findings offer actionable insights for SWW brands seeking to develop targeted, competitive strategies.

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    • Figure 1. Conceptual model
    • Figure 2. Prediction model for smart wrist wearable adoption among South African youth
    • Table 1. Sample’s demographic data
    • Table 2. PCA results
    • Table 3. Measurement model reliability and validity
    • Table 4. Measurement model estimates and model fit
    • Table 5. Summary statistics and one-sample t-test
    • Table 6. Relationships between latent factors and collinearity diagnostics
    • Table 7. Structural model paths per hypothesis
    • Table 8. Structural model fit
    • Conceptualization
      Chantel Muller
    • Data curation
      Chantel Muller
    • Formal Analysis
      Chantel Muller, Marko van Deventer
    • Project administration
      Chantel Muller, Marko van Deventer
    • Visualization
      Chantel Muller
    • Writing – original draft
      Chantel Muller, Marko van Deventer
    • Writing – review & editing
      Chantel Muller, Marko van Deventer
    • Methodology
      Marko van Deventer
    • Software
      Marko van Deventer