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The purpose of this study was to investigate the impact of artificial intelligence (AI) adoption on knowledge workers' innovative work behaviors (IWB), as well as the mediating role of stress appraisal and the moderating role of individual learning abilities.

This study analyzed the questionnaire results of 313 knowledge workers, and data analysis was conducted by using SPSS 25.0, SPSS 25.0 macro-PROCESS and AMOS 28.0.

This study found that AI adoption has a double-edged sword effect on knowledge workers' IWB. Specifically, AI adoption can promote IWB by enhancing knowledge workers' challenging stress appraisal, while inhibiting IWB by fostering their hindering stress appraisal. Moreover, individual learning ability significantly moderated the relationship between AI adoption and stress appraisal, which further influenced IWB.

This study integrates the conflicting findings of previous studies and proposes a comprehensive theoretical model based on the theory of cognitive appraisal of stress. This study enriches the research on AI in the field of knowledge management, especially extending the understanding of the relationship between AI adoption and knowledge workers’ IWB by unraveling the psychological mechanisms and behavior outcomes of users' technology usage. Additionally, we provide new insights and suggestions for organizations to seek the cooperation and support of employees in introducing new technologies or driving intelligent transformation.

The purpose of this study is to identify and analyze the characteristics and visual patterns of successful knowledge workers using quantitative methods, particularly eye-tracking technology. By conducting a systematic review and matching identified factors with theoretical literature, the research aims to uncover key attributes that contribute to the effectiveness of knowledge workers. These insights are intended to improve employee selection processes, ensuring the right candidates are chosen based on their cognitive, behavioral and visual traits.

A mixed-methods approach is employed in this study, consisting of three phases: (1) a systematic literature review identifies key characteristics of successful knowledge workers, (2) these factors are aligned with theoretical frameworks and expert insights to assess their applicability and (3) empirical data is collected through questionnaires and eye-tracking assessments involving ten high-performing site design employees and ten students from Shahid Beheshti University. SPSS software and Tobii Pro Lab tools are used for data analysis to establish correlations between eye movement patterns and attributes of effective knowledge workers.

The findings reveal that students whose eye movement patterns resemble those of high-performing knowledge workers also share similar cognitive and behavioral characteristics. Identified key attributes include enhanced problem-solving skills, adaptability and effective communication. The study further highlights the potential of eye-tracking technology as a valuable tool in employee selection, offering insights into visual behaviors that correlate with high performance in knowledge work. These findings provide a deeper understanding of the critical traits that optimize organizational performance.

This study presents a novel approach by integrating eye-tracking technology into the knowledge worker selection process. It provides empirical evidence of the visual and cognitive patterns associated with high performance, thereby enhancing the theoretical understanding of knowledge worker selection. The study contributes valuable insights for organizations aiming to refine their hiring practices, emphasizing the importance of both cognitive skills and visual behaviors in candidate assessment. This research lays the groundwork for future studies exploring the intersection of technology and human resource management to optimize workforce effectiveness.