Study title

The data is from qualitative case study research into the implementation of the Rapid Recruitment project at Walmart, US, in 2020. One of the key elements of the rapid recruitment project was the use of a machine learning algorithm in the hiring system for hourly paid store-level associates (employees). The research involved semi-structured interviews with fourteen respondents with different roles and responsibilities in relation to the hiring process including: seven head office staff responsible for developing and implementing the system, five store-level managers and HR staff who used the system and two recently recruited employees. Interviews lasted 30 to 90 minutes and were conducted via video conferencing during the Covid-19 pandemic from September to December 2020. Interviews were supplemented with bi-weekly meetings with a business sponsor at the organisation and follow-up information gathered by email. Interviews were recorded and transcribed by the researchers. The interviews explored: recent changes to the hiring system, aims and objectives of the changes, the of motivations behind the changes, the development and implementation process, user adoption and perceptions of the new system and its effectiveness. The research found that the Rapid Recruitment project had largely been successful. Most users were using the new system as intended, the system had sped up the hiring process, enabled the organisation to hire greater numbers of staff during the increased demand due to the pandemic and the organisation reported that it had improved hiring outcomes (90-day turnover rates). However, not all users were confident in the new system or trusted the technology used, which in some cases meant that they were not using the system in the way intended, potentially undermining some of the objectives of the changes. Interview data could not be deposited to the archive because it was protected by a non-disclosure agreement (NDA) but research documents and metadata...