Abstract:
This research emphasises the enhancement of artificial intelligence (AI) technologies, particularly algorithmic technologies, by aligning them with essential human principles and evaluating their effects on perceptions of autonomy in consumer decision-making. The study underscores the importance of managing algorithmic technologies in a manner that honours consumer autonomy. Data was collected from 14 diverse South African online shoppers to investigate purchasing behaviours, focusing on factors such as gender, age, and educational qualifications. Prioritising depth over breadth, the research employed purposive and non-probability sampling techniques to secure rich qualitative insights while safeguarding participant confidentiality and anonymity.
Key themes emerged regarding how AI-targeted marketing shapes consumer decision-making, highlighting the significance of consumer autonomy and transparency in navigating AI recommendations. The results highlighted the pressing necessity for responsible AI practices and improved consumer engagement to enhance the shopping experience and strengthen brand connections. Participants expressed varied perspectives on the influence of algorithms on their decision-making, with some voicing concerns about autonomy while others valued tailored recommendations. This discourse underscored the ethical considerations of algorithmic dependence, stressing that businesses honour consumer autonomy while utilising AI technology for personalised marketing.
The study identified various critical factors affecting consumer behaviour in AI technologies, such as the balance between consumer autonomy and convenience, the need for personalised yet subtle suggestions, and the repercussions of impulsiveness and decision paralysis. Moreover, understanding scepticism towards the accuracy of algorithms and the risks of online manipulation was crucial for addressing consumer needs and shaping effective marketing and policy strategies.
This study encountered several limitations, including inherent researcher bias and restrictions related to sampling, data collection techniques, and participant demographics. The rapid evolution of AI technologies and a narrow theoretical focus may have limited the general applicability and relevance of the findings.
