| Abstract [eng] |
The aim of this Master’s thesis is to determine the impact of artificial intelligence personalization on marketing and sales processes in e-commerce. The thesis seeks to reveal the concept and significance of AI personalization in e-commerce, identify the main personalization techniques applied in this field, determine the directions of its impact on marketing and sales processes, analyse the findings and research gaps of previous empirical studies, and empirically evaluate this phenomenon from the consumer perspective. The thesis consists of three main parts. The first part analyses the theoretical foundations of AI personalization, its significance in e-commerce, the main personalization techniques, and their impact on marketing and sales processes. The second part presents the research methodology: the research model is substantiated, hypotheses are formulated, the constructs under analysis are defined, and the sample, data collection instruments, and data analysis procedure are described. The third part provides the analysis of empirical research results and discusses them in a theoretical context. The theoretical part is based on a systematic and comparative analysis of scientific literature, including methods of analysis, synthesis, and systematization. The empirical part applies a quantitative research method – an online questionnaire survey. Research data were collected using the Google Forms and Apklausa.lt platforms. The final sample consisted of 170 respondents. Data analysis was carried out using IBM SPSS software by applying descriptive statistics, frequency analysis, Cronbach’s alpha, Pearson correlation analysis, linear regression analysis, and one-way analysis of variance (ANOVA). The findings revealed that one of the most important factors explaining the impact of AI personalization is perceived relevance. The study found that perceived relevance significantly increases consumer engagement and trust, while these factors, in turn, strengthen purchase intention. It was also found that purchase intention is significantly related to loyalty intentions. The results showed that transparency and explainability strengthen trust, advertising–website congruence positively affects purchase intention, and privacy concerns weaken this effect. Meanwhile, the role of consumer control and the perceived fairness of dynamic pricing proved to be more complex and less straightforward than expected from the theoretical perspective. In summary, the impact of AI personalization on e-commerce marketing and sales processes is multidimensional and depends on several interrelated conditions: relevance, engagement, trust, transparency, advertising–website congruence, and the perception of privacy risks. The results of the thesis may be useful for e-commerce companies seeking to develop more effective and consumer-acceptable personalization solutions. They may also serve as a basis for further academic research and have the potential to be developed into a scientific publication. |
