| Abstract [eng] |
The aim of the master's thesis is to evaluate the impact of artificial intelligence (AI) application on environmental sustainability, mediated by green product and process innovations. The objectives are: 1. To summarize the definitions, structural elements, and models of environmental sustainability, artificial intelligence, and green innovation through a literature review. 2. To evaluate the direct impact of artificial intelligence on green product and process innovations and on environmental sustainability by analyzing the literature and empirical data. 3. To evaluate the direct impact of green product and process innovations on environmental sustainability through literature analysis and empirical data. 4. To assess the impact of artificial intelligence on environmental sustainability, mediated by green product and process innovations, based on literature review and empirical findings. The study consists of an introduction, three chapters, conclusions and recommendations, and a list of references and sources. In the literature review, environmental sustainability, artificial intelligence (including the tools used and areas of application), and green innovation are analyzed. Based on the literature review, a conceptual research model was set, in which artificial intelligence was divided into artificial intelligence tools and application areas. Likewise, green innovation was divided into green product innovation and green process innovation. A quantitative research methodology was selected for the study. A questionnaire was developed based on scientific literature analysis, with constructs adapted from existing questionnaires aligned for empirical research, combined into a single survey instrument. The target population for the study consisted of employees working in biotechnology organizations in Lithuania. Data were processed using the IBM SPSS Statistics software. Factor analysis was conducted to test construct validity, and Cronbach’s alpha was calculated to determine reliability. Descriptive statistical analysis was used to assess relationships between variables. Appropriate quantitative methods were applied to ensure statistically representative results. In total, 213 respondents completed the survey. Based on the empirical research findings, it can be concluded that AI tools and application areas do not have a statistically significant impact on environmental sustainability (p > 0.05) in biotechnology workplace in Lithuania. Also, AI tools do not have a statistically significant impact (p > 0.05) on green innovations. However, AI application areas do have a statistically significant impact on green innovations (p < 0.05), although the model is very weak (R² = 0.037). Based on the results of factor analysis and Cronbach’s alpha, it can be concluded that green product and process innovation can be treated as a single variable – green innovation. Based on the statistical analysis, it can be concluded that green innovations are statistically significant for environmental sustainability (p < 0.05). Furthermore, based on the empirical results in Lithuanian biotechnology organizations, green innovation does not act as a mediator between AI tools and environmental sustainability, but it does act as a full mediator between AI application areas and environmental sustainability. |
