Artificial intelligence is reshaping knowledge work and creating new challenges for employee experience in mission-oriented higher education. This study aimed to identify and prioritize factors affecting knowledge worker experience in the AI era and develop a managerial roadmap for a technology university. Using a mixed-methods approach, data were collected from 31 knowledge workers and experts through a researcher-developed questionnaire based on ISO 34000. ISM and MICMAC analyzed structural relationships, driving power, and dependence, while ANP determined factor importance. Results identified nine interrelated dimensions. Intelligent leadership and organizational and cultural experience emerged as fundamental drivers, while intention to remain was the final dependent outcome. ANP ranked intelligent leadership, organizational and cultural experience, and digital maturity as the most important factors. Based on these findings, a five-phase managerial roadmap was developed, covering digital foundation, managerial and cultural transformation, work experience optimization, innovation and scientific excellence, and knowledge capital retention.
Introduction
Artificial intelligence (AI) is transforming knowledge work, creating both opportunities and challenges for employee experience, particularly in mission-oriented higher education. As technology universities adopt AI, technological progress often outpaces organizational and human adaptation. However, integrated frameworks that capture the interdependencies among these factors and translate them into actionable strategies remain limited. This study therefore aimed to identify, analyze, and prioritize the factors affecting knowledge worker experience in the AI era and develop a context-sensitive managerial roadmap for a mission-oriented technology university. By moving beyond isolated technological investments toward an integrated organizational and cultural approach, the study provides a strategic foundation for sustaining knowledge capital, enhancing digital maturity, and fostering scientific excellence.
Methodology
This applied study used a survey-based, cross-sectional, mixed-methods design with a descriptive-analytical approach in a mission-oriented technology university. Initial factors were identified through a literature review, analysis of international and domestic employee experience models, and the ISO 34000 Human Resource Management Standard, with emphasis on knowledge-intensive work and AI. The indicators were then integrated and adapted to the study context. Data were collected from 31 knowledge workers and experts using a researcher-developed 37-item questionnaire covering nine dimensions: digital work experience, organizational and cultural experience, intelligent leadership, digital maturity, engagement and work enthusiasm, job and organizational integration, digital organizational citizenship behavior, innovation and knowledge performance, and intention to remain. Content validity was established through literature, ISO 34000 alignment, and expert review, while Cronbach’s alpha of 0.83 confirmed satisfactory reliability. Purposive sampling was used given the expert-oriented analytical methods. Descriptive statistics assessed the dimensions, while ISM identified relationships and hierarchical levels, MICMAC classified factors by driving power and dependence, and ANP determined their relative importance considering interdependencies and feedback. Finally, the integrated findings were used to develop a five-phase managerial roadmap.
Results and Discussion
Descriptive findings showed an overall mean of 2.98 across the nine dimensions, indicating a moderate to relatively weak condition of knowledge worker experience. Innovation and knowledge performance had the highest mean (3.28), followed by digital work experience (3.18), while job and organizational integration (2.65), digital maturity (2.82), and organizational and cultural experience (2.84) had the lowest scores. This suggested that technological and task-related benefits had advanced faster than organizational, cultural, and relational aspects. ISM positioned organizational and cultural experience and intelligent leadership as foundational factors, while engagement and integration occupied intermediate levels and digital citizenship, innovation and knowledge performance, and intention to remain appeared at higher levels. MICMAC confirmed F1–F4 as independent, F5–F6 as linkage, and F7–F9 as dependent factors, highlighting intelligent leadership and organizational and cultural experience as major system drivers. ANP ranked intelligent leadership first (0.185), organizational and cultural experience second (0.172), digital maturity third (0.145), and digital work experience fourth (0.120). The consistency of ISM, MICMAC, and ANP indicated that improving knowledge worker experience should begin with organizational and managerial foundations rather than isolated technological investments. Accordingly, a five-phase managerial roadmap was developed: Phase 1 (0–12 months), digital foundations and maturity; Phase 2 (12–24 months), managerial and cultural transformation; Phase 3 (24–36 months), work experience optimization; Phase 4 (36–48 months), innovation, knowledge performance, and scientific excellence; and Phase 5 (48–60 months), knowledge capital retention. This sequence provided a practical pathway from foundational capabilities to sustainable retention.
Conclusion
This study developed an integrated, context-sensitive model of knowledge worker experience in the AI era within a mission-oriented technology university. Findings showed that employee experience is not simply a result of technological adoption but emerged from the interaction of organizational, cultural, managerial, technological, and behavioral factors. Intelligent leadership and organizational and cultural experience emerged as fundamental drivers, while intention to remain was the final dependent outcome. ANP reinforced these findings by assigning the highest weights to intelligent leadership, organizational and cultural experience, and digital maturity. Thus, sustainable AI-driven transformation required organizational and cultural foundations, capable leadership, digital maturity, and employee participation, rather than technology acquisition alone. Based on these findings, a five-phase roadmap was developed covering digital foundation, managerial and cultural transformation, work experience optimization, innovation and scientific excellence, and knowledge capital retention. The roadmap provided managers with a structured basis for prioritizing interventions and resources. Theoretically, the study conceptualized knowledge worker experience as an interconnected system; methodologically, it integrated ISM, MICMAC, and ANP to assess structural relationships, driving power, dependence, and relative importance. Future research may test the model using larger samples, structural equation modeling, longitudinal and comparative designs, and objective indicators of innovation, knowledge performance, and retention.