Modern Talent Management Strategies for Improving Employee Engagement and Performance in the Gig Economy
Keywords:
Gig Economy, Employee Engagement, Task Performance, Digital Feedback, Autonomy, Upskilling Programs, AI-Driven Tools.Abstract
The research problem in this study was the effectiveness of contemporary and technology-based talent management practices in boosting employee engagement and task performance in the gig economy. As digital platforms like Uber, Upwork, and Fiverr gained popularity, the existing engagement models built around more stable, office-based employment did not fit gig workers, who lacked regular managerial support. The study centered on four strategies: algorithmic feedback, worker autonomy, digital upskilling programs, and digital community-building. The data were gathered using a quantitative cross-sectional survey design, in which workers across different gig platforms were asked about the effects of these strategies on engagement (via the Utrecht Work Engagement Scale) and task performance (via relevant KPIs). The results indicated that frequent and useful algorithmic feedback was effective at enhancing engagement, whereas the greater autonomy in work tasks was positively related to work performance. The engagement in digital upskilling programs improved performance indicators such as efficiency and customer satisfaction, and engagement via digital communities increased participation by reducing isolation and promoting peer support. In summary, the findings indicated that technology-based solutions, such as feedback mechanisms, autonomy, upskilling, and digital community-building, offered useful opportunities to address the challenges of a decentralized workforce. The research provided practical guidance for gig platforms on adopting AI-based tools, increasing workers' autonomy, and providing convenient access to learning and community. It was likely to guide further research on the long-term effects of such measures across various categories of the gig economy.
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