Algorithmic management and startup performance: a mixed-method analysis of job performance and technical skills
Shefali Sharma, Amit Mittal, Seema SeemaPurpose
The present research investigates how algorithm-driven managerial control influences workers' performance and, in turn, startup performance, while also assessing the moderating role of perceived technical skills.
Design/methodology/approach
A convergent parallel mixed-method design was employed. Study 1 involved semi-structured interviews with 25 platform-based workers, analysed through thematic analysis. Study 2 surveyed 528 workers from technology-driven startups and employed PLS-SEM to test the mediation, moderation and moderated-mediation effects.
Findings
The qualitative findings revealed persistent themes of surveillance pressure, performance anxiety, rating-driven stress and limited autonomy. Quantitative analyses confirmed that algorithmic management (AM) significantly reduces job performance (JP) which in turn enhances startup performance (SP) and mediates the AM and SP relationship. Furthermore, perceived technical skills (PTS) weaken the negative impact of algorithmic management (AM) on job performance (JP) and weaken the indirect relationship between AM and SP, supporting moderated mediation mechanism.
Originality/value
To the best of the authors’ knowledge, this is among the first mixed-method studies in the Indian platform economy to integrate algorithmic management (AM), job performance (JP), perceived technical skills (PTS) and startup performance (SP) in one framework. It extends Platformisation Theory by showing how algorithmic management simultaneously supports organisational efficiency and generates worker-level constraints.