| Keywords [eng] |
dirbtinio intelekto branda, organizacijos veiklos rezultatai, skaitmeninė transformacija, blokų grandinės branda, AI maturity, Organizational performance, Digital transformation, Blockchain maturity. |
| Abstract [eng] |
The thesis examined the smooth inter-relationships between Digital Transformation, Artificial Intelligence (AI) Maturity, Blockchain Maturity, and Organizational Performance with the objective of learning how the emerging digital technologies can work together to create value in organizations. The study created and tested an overall conceptual model, based on digital transformation theory, technology maturity perspectives, and views of the firm as a capability based on its responsiveness to emerging digital technologies, in response to growing organizational investment in digital technologies and the lack of integrated empirical evidence on how these interact. The study gave solid empirical information on both the direct and indirect processes by which digital technologies have an impact on organizational performance using a Partial Least Squares Structural Equation Modelling (PLS-SEM) approach. The thesis began by pinpointing a critical research gap in the available literature. Although previous research has thoroughly investigated digital transformation, AI, and blockchain as a single phenomenon, little attention has been given to their relationships and to their maturity-related impact on organizational performance. To fill these gaps, the research proposed a model that conceptualized AI and blockchain maturity as distinct yet complementary technological capabilities operating within broader digital transformation projects. It premise that digital transformation automatically entails enhanced AI capabilities has seldom been empirically validated. A quantitative research design was employed, and a structured survey instrument was used to collect primary data. The establishment of reliability and convergent and discriminant validities was based on numerous criteria, such as factor loadings, Cronbach's alpha, composite reliability, Average Variance Extracted (AVE), HTMT ratios, and Fornell-Larcker. The validated scales helped measure the items, thereby providing content validity. Smart-PLS was used to analyze the data, following a strict two-step process that included evaluating the measurement and structural models. The evaluation of the structural model included path coefficients, t-values, and p-values, mediation analysis, R2 values, and multi-group analysis, providing a thorough assessment of the hypotheses presented. The study contributes to the development of theory and practice by integrating several emerging technologies into a single explanatory framework, providing a strong basis for future research on the constantly evolving field of digital transformation and organizational performance. It is concluded that the thesis presents a holistic, empirically supported framework for the effects of digital transformation, AI maturity, and blockchain maturity on organizational performance. |
