Trust beyond Technology Algorithms: A Theoretical Exploration of Consumer Trust and Behavior in Technological Consumption and AI Projects

In an era dominated by artificial intelligence (AI), establishing customer confidence is crucial for the integration and acceptance of AI technologies. This interdisciplinary study examines factors influencing customer trust in AI systems through a mixed-methods approach, blending quantitative analysis with qualitative insights to create a comprehensive conceptual framework. Quantitatively, the study analyzes responses from 1248 participants using structural equation modeling (SEM), exploring interactions between technological factors like perceived usefulness and transparency, psychological factors including perceived risk and domain expertise, and organizational factors such as leadership support and ethical accountability. The results confirm the model, showing significant impacts of these factors on consumer trust and AI adoption attitudes. Qualitatively, the study includes 35 semi-structured interviews and five case studies, providing deeper insight into the dynamics shaping trust. Key themes identified include the necessity of explainability, domain competence, corporate culture, and stakeholder engagement in fostering trust. The qualitative findings complement the quantitative data, highlighting the complex interplay between technology capabilities, human perceptions, and organizational practices in establishing trust in AI. By integrating these findings, the study proposes a novel conceptual model that elucidates how various elements collectively influence consumer trust in AI. This model not only advances theoretical understanding but also offers practical implications for businesses and policymakers. The research contributes to the discourse on trust creation and decision-making in technology, emphasizing the need for interdisciplinary efforts to address societal challenges associated with technological advancements. It lays the groundwork for future research, including longitudinal, cross-cultural, and industry-specific studies, to further explore consumer trust in AI.

Direct Marketing and Consumer Trust in Organic Food Products： Vilnius （Lithuania） Case

The market of organic food products has a continuous upward trend though the growth rate and sale channels in individual countries are rather different. This research aims to identify the key factors that influence consumers＇ choice to buy organic food products in Lithuania focusing on the consumer trust in the quality and the most acceptable marketing channels. In order to get the necessary information, the residents of Vilnius （the capital of Lithuania）, as having the greatest potential for the organic food market in the country, were interviewed. The analysis of collected data was done using the methods of mathematical statistics and comparison of relative frequencies. The results of this research prove the increasing consumers＇ interest in organic food products. The majority of consumers consider these products to be healthier, better-tasting and fresher. Only about a third of the purchasers （usually buying frequently） can see differences between organic and non-organic products, therefore, there is a problem to distinguish organic food from conventional products and, consequently, remains a possibility to cheat the consumer. An additional quality control and direct sales from known and familiar farmers might increase trust in the quality of organic food products. So far, supermarkets are the most important place to buy organic foods, however, the greater consumer confidence in the quality of organic products bought at farmers＇ markets and the acceptance of the majority of purchasers to buy organic food products directly from the farmers suppose the need to develop a direct marketing system.