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Proactive personality, innovative work behavior, and employee performance: evidence from the insurance sector in
Ahmet Çukurovali1, Ayşem Iyikal Çelebi1, Serdal Işiktaş1
1Department of Business Administration, Faculty of Social Sciences and Humanities, Cyprus Health and Social Science University, Northern Cyprus, Güzelyurt, Türkiye.
Introduction:
Proactive personality is increasingly recognized as a key individual difference that may drive innovative work behavior and enhance employee performance in dynamic service sectors. However, empirical evidence examining the interrelationships among proactive personality, innovative work behavior, task performance, and contextual performance remains limited, particularly in the insurance industry of small economies such as Northern Cyprus. This study aims to investigate these relationships among insurance employees.
Methods:
A cross-sectional survey design was employed. Data were collected in 2023-2024 from 313 insurance employees in Northern Cyprus using validated scales measuring proactive personality, innovative work behavior, task performance, and contextual performance. Participants completed self-report questionnaires, and the data were analyzed using SPSS 30.0 with correlation and regression analyses.
Results:
The results revealed that proactive personality was positively and significantly associated with innovative work behavior, task performance, and contextual performance. Innovative work behavior was also positively related to both dimensions of employee performance (task and contextual). These findings indicate that proactive dispositions and innovation-oriented behaviors co-occur with stronger performance outcomes in this service setting.
Discussion:
Overall, the findings underscore the managerial importance of fostering proactive dispositions to promote discretionary innovation and enhance both in-role and extra-role performance in insurance organizations. Given the cross-sectional and self-report nature of the data, the relationships should be interpreted as associative rather than causal. Future research should employ longitudinal designs and multi-source data to establish causality and generalizability.
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