Artificial Intelligence-Driven Recruitment as Business Capability: Evidence on Hiring Accuracy from Firms Operating in Dar es Salaam

Aims: This study examined whether AI-based recruitment systems serve only administrative efficiency or also function as a strategic Business Capability through improved recruitment accuracy, employee performance, and employee retention. Study Design: The study was conducted using an ex-post facto design of the quantitative research type. Place and Duration of Study: The study was conducted among technology and financial service firms in Dar es Salaam, Tanzania. Methodology: The research was conducted using archival data collected from 400 middle-level employees of technology and financial service firms. The data were collected from two sets of 200 employees each who were recruited using AI-based recruitment systems and those recruited using traditional manual screening methods. The accuracy of the recruitment was compared using standardized performance appraisal ratings. Employee retention was also compared based on the outcomes of the 18-month retention period. Additionally, the AI-generated match scores were compared using the 0-100 scale to evaluate the association between the match scores and the subsequent employee performance outcomes for the various job roles. Results: The research findings indicated that the performance ratings of the employees recruited using AI-based recruitment systems were significantly higher compared to those recruited using the traditional recruitment method (p < 0.01). Additionally, the employees recruited using AI-based recruitment systems also showed higher retention outcomes compared to those recruited using the traditional method (p < 0.05). The AI-generated match scores were also strongly associated with the subsequent employee performance outcomes (r = .68). The predictive validity of the match score was higher for technical roles (r = .75) compared to creative roles (r = .42). Conclusion: The findings suggest that AI-based recruitment systems may not only be used for administrative efficiency purposes but could potentially be used as strategic tools to improve accuracy in recruitment processes, quality of the workforce, and stability in emerging markets. This paper offers empirical findings on the efficacy of AI-based recruitment systems in terms of accuracy, employee performance, and retention outcomes. This paper contributes to the literature by offering quantitative findings from the emerging market context, where such evidence is scarce. By incorporating the constructs of Task-Technology Fit (TTF) and Resource-Based View (RBV), this paper extends the literature on AI-based recruitment as an operational and Business Capability.