Computational thinking and self-leadership as predictor of innovative work behavior among employees in green product firms : An explanatory sequential mixed method
Аннотация
This study employed an explanatory sequential mixed-method design to investigate the factors influencing innovative work behavior in green product firms in Pakistan. Guided by social cognitive theory, data from 278 employees were analyzed using structural equation modeling in AMOS, followed by qualitative interviews to further explain and contextualize the quantitative findings. The findings showed that computational thinking (β = 0.62, p < 0.001) and self‑leadership (β = 0.56, p < 0.001) have a significant positive association with creative self-efficacy. Additionally, creative self-efficacy has a significant direct positive influence on innovative work behavior (β = 0.76, p < 0.001). The mediation analysis confirmed that creative self-efficacy significantly mediated the relationship between computational thinking and innovative work behavior (indirect β = 0.29) and between self‑leadership and innovative work behavior (indirect β = 0.26). Notably, knowledge sharing significantly moderated the relationship between creative self-efficacy and innovative work behavior (β = 0.32, p < 0.001) strengthening the effect of creative self-belief on innovative. Eighteen (n = 18) interviews were conducted to gain insight into how these mechanisms worked. During the thematic analysis, results revealed that knowledge sharing weakens negative effect of hierarchical constraints, enabling employees to act on their creative self-efficacy. Computational thinking is associated with a language of credibility for innovative ideas, while self‑leadership is associated with a necessary internal motivation against bureaucratic fatigue. These findings are relevant for green product firms operating in high power distance, resource-constrained contexts such as Pakistan.