Recruitment and selection analytics on harnessing performance for sustainable development of retail small to medium enterprise in Mashonaland West Province Zimbabwe

The study aimed to examine the eff ects of recruitment and selection analytics on the performance of retail SMEs in Mashonaland West Province. SMEs are globally recognised as pivotal drivers of economic growth due to their significant contribution to Gross Domestic Product (GDP), and are therefore of considerable value (World Bank, 2022). Notably, there exists a paucity of literature on the relationship between recruitment and selection analytics and the performance of the retail SME sector in Zimbabwe, a gap this study sought to address. The research also explored how data-driven decision-making off ers valuable insights that enable retail SMEs to make informed hiring decisions. Through the analysis of recruitment data, SMEs can identify trends, patterns, and correlations that guide the decision-making process. A purposive sample of 22 retail SMEs was selected, and the study employed a purely qualitative approach. Interview guides were utilised to collect data from SME owners and managers, and thematic analysis was applied to interpret the findings. The study established that recruitment and selection analytics enhance hiring efficiency by enabling SMEs to utilise data-driven insights to identify the most eff ective recruitment channels, redine job descriptions, and target the most suitable candidates. Furthermore, the research found that analytics help reduce high employee turnover, a factor detrimental to retail SME performance, by identifying turnover patterns and their contributing factors. Data related to employee satisfaction, engagement, and performance can be analysed to identify areas for improvement, thereby fostering a more stable and productive workforce (Bersin, 2019). The study recommends that the government should actively support SMEs by providing essential resources and technological infrastructure necessary to facilitate access to high-quality data.