Determinants influencing online shopping behaviour among 18-30-year-olds in Gauteng, South Africa: an examination of pricing, quality, security, time and information availability
DOI:
https://doi.org/10.34117/bjdv10n8-008Keywords:
online shopping behaviour, technology acceptance model, perceived risk theory, consumer preferences and e-commerceAbstract
This study explores the determinants influencing online shopping behaviour among 18-30-year-olds in Gauteng, South Africa, focusing on key factors, such as pricing, quality, security, time, and information availability. Grounded in the Technology Acceptance Model (TAM) and Perceived Risk Theory, this research aims to provide a comprehensive understanding of how these factors impact consumer decisions and behaviours. A cross-sectional survey methodology was employed, targeting 1,043 respondents who have shopped online within the past six months in Gauteng. The findings reveal that all five factors significantly influence online shopping behaviour, with information availability and time efficiency showing the strongest correlations. The study also highlights that despite the high perceived convenience and satisfaction with online shopping, security concerns remain a barrier to adoption. The study suggests that online retailers should enhance their security measures, provide detailed product information, and streamline their online processes to improve customer experience and trust. The study developed a conceptual framework to identify the factors influencing online shopping behaviour among 18-30-year-olds in Gauteng. This model includes six constructs: pricing, quality, security, time, information availability, and online shopping behaviour. This framework can be applied in future research. Limitations of the study include its narrow geographic and demographic focus on the 18-30-year-old population in Gauteng, South Africa, which restrict the generalisability of findings to other age groups or regions. Additionally, the cross-sectional design provides a snapshot rather than longitudinal insights into how online shopping behaviours evolve over time.
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