| Abstract [eng] |
This master's thesis investigates the success factors of artificial intelligence (AI) integration and their impact on technical customer support process efficiency in Lithuanian companies operating in information technology, telecommunications, and high-tech manufacturing sectors. The study addresses the critical question of how AI implementation intensity, organizational readiness, and task-technology fit interact to influence technical customer support effectiveness. The main objective is to quantitatively assess the influence of AI implementation intensity, organizational readiness, and task-technology fit on technical customer support process efficiency. The research includes conducting scientific literature analysis to identify technical customer support processes and AI application possibilities; developing an integrated theoretical model combining Technology-Organization-Environment (TOE) and Task-Technology Fit (TTF) theories; empirically testing the proposed model; and providing recommendations for successful AI integration in IT companies. The study employs a quantitative research approach using Partial Least Squares Structural Equation Modeling (PLS-SEM) methodology. Data was collected through a structured questionnaire survey from 252 respondents working in Lithuanian companies that utilize AI solutions in their customer service processes. The research instrument consisted of 40 main statements measuring four higher-order constructs through their lower-order components, using a six-point Likert scale. The empirical analysis revealed significant positive relationships between AI implementation intensity and organizational readiness (β = 0.690; p < 0.001), and between AI implementation intensity and customer service efficiency (β = 0.319; p < 0.001). Organizational readiness demonstrated an even stronger effect on service efficiency (β = 0.443; p < 0.001), highlighting its critical role. Unexpectedly, task-technology fit did not significantly moderate the relationship between AI implementation and service efficiency, challenging traditional TTF theory in the AI context. The study demonstrates that organizational factors are equally important, if not more critical, than technological solutions for achieving improved technical customer support efficiency. Documentation quality and employee technical agility emerged as the most significant organizational readiness components, while technical infrastructure contribution was statistically insignificant. This challenges traditional IT investment priorities and suggests that "soft" organizational aspects may be more crucial for successful AI implementation than hardware capabilities. The findings recommend prioritizing organizational preparation before technology acquisition, creating an experimental culture that encourages employee learning and adaptation, ensuring active management involvement in AI initiatives, and developing systematic approaches to knowledge management and documentation quality improvement. This research contributes to the understanding of AI implementation success factors by providing empirical evidence for the critical role of organizational readiness as a mediator in the AI-performance relationship. The study also raises important questions about the applicability of traditional task-technology fit theory in dynamic AI contexts, suggesting the need for new theoretical frameworks better suited to evaluating AI solutions. The results have implications for companies seeking to implement AI solutions in technical customer support, emphasizing the need for a holistic approach that balances technological capabilities with organizational transformation and employee development. |
