Newport, Jessica (2025) HR and Candidate Perspectives on AI-driven Recruitment in the UK Hospitality Sector: Benefits, Challenges, and Future Directions. Masters thesis, University of Wales Trinity Saint David.
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Abstract
This dissertation investigates the perspectives held by HR and hospitality professionals on the use of AI-driven recruitment in the UK hospitality industry. Through the analysis of both empirical research and existing available data, it has been possible to understand further the benefits, challenges, and potential future directions. This research was determined to be necessary due to the increasingly widespread use of AI in recruitment. The need for understanding of its usage in the UK due to the absence of specific legislation as of 2024 makes it a timely and relevant dissertation topic. It has been determined that AI as a concept is only going to continue to evolve, and so it is important to appreciate fair use to mitigate risk. The available data for the UK hospitality industry is limited, and so it was identified that obtaining perspectives from those directly impacted by its use would enhance clarity and enable accurate organisational decision-making during strategic planning. This dissertation was structured with a positivist approach in order to gain organic perspectives that were not influenced by any personal thoughts or opinions of the researcher. The primary research consisted of a 20 question, multiple choice questionnaire that was sent to UK wide HR and hospitality professionals, and Swansea based hospitality academics. The researcher utilised an existing contact list that consisted of trusted professionals that they currently work alongside. The compiled questionnaire was created using Microsoft Forms and was entirely anonymous. For these reasons, it can be concluded that the research was both valid and reliable in nature. In-built statistical analysis tools, supported by further analysis in Microsoft Excel allowed for the researcher to test hypotheses and understand objectives. These online platforms also enabled the researcher to successfully meet time and financial constraints associated with data collection. The findings have been presented in the form of graphs, charts and cross-tabulation tables, along with written analysis and synthesis. It was of great importance to analyse current available literature in order to understand how the gathered perspectives from the primary research could complement and contribute to the existing data. The researcher placed value on understanding the concept and history of AI in general, the current usage of AI in hospitality, AI-driven recruitment in general, and more specifically in the hospitality sector. The literature review discovered that the current data is rather limited and is particularly brief in regard to AI in recruitment in UK hospitality specifically. This further cemented the need for perspectives from those directly impacted by its use, and thus highlighted the value that the primary research would bring to both academia and industry. Prior to the release of the survey the researcher identified specific hypotheses that would be tested. The first prediction was that candidates would show more resistance than HR professionals due to fears of fair use. The second prediction was that age would influence perspectives, with it expected that the older age brackets would show heightened resistance due to the likelihood that they have experienced less sophisticated technology overall compared to the younger population. The final prediction was that the country of origin of respondents would influence answers with patterns of similar responses expected from the data collected. The primary research determined that the first two hypotheses were unproven, whilst the third remained underdetermined, and in need of further research. The survey was sent to potential participants UK wide currently working in a range of hospitality positions. These included HR representatives, management hospitality employees, non-management hospitality employees and hospitality academics. This was a deliberate choice to enable clear perspectives from a range of HR professionals and candidates. The potential respondents were encouraged to share the survey link with any contacts they felt would be relevant to increase the overall sample size. This decision was taken to encourage a larger response rate due to the lack of existing data currently available. The aim was for 80-100 participants in total, with the result of 80 questionnaire responses gained overall. A larger number of responses would have been preferrable; however, trends and conclusions have been identified, and a concrete basis for future research and industry recommendations has been made. The findings of this dissertation outlined an overriding demand for transparency of use, and clear concerns regarding ethics and potential bias of automated algorithms. There was also a clear trend of respondents suggesting that the loss of human interaction is in direct conflict with the core values of the hospitality industry. However, the expectation that the older population would be more resistant to the concept was unfounded, as was the assumption that candidates would be more resistant to AI in recruitment due to fears of misuse. Instead, the primary research showed that it is HR professionals that exercise the most caution. It has been noted that this could be due to preoccupations regarding job security. The primary research has reinforced the findings of the literature review and identified recommendations for future study and industry. It has been acknowledged that when the responses were separated into subcategories the number of responses therein were rather small, which presented difficulty when attempting to determine hypotheses outcomes. For example, only 14 respondents selected rest of the world as their country of origin, which presented challenges when attempting to identify patterns of answering based on where respondents were born. The researcher has therefore made recommendations that include a larger number of respondents, and a mixed methods approach that was not possible for this dissertation due to time constraints. The reason for this is that the optional open questions within the quantitative questionnaire provided insightful information that deepen perspective. A focus group or interviews would enhance this further. For industry, the primary research and literature review synthesis has enabled the recommendation to communicate use of AI to potential candidates. This will assist with mitigating fears which was an overriding concern that was evident throughout the primary research responses. Overall, this dissertation has discovered that there is an increased trust in AI-driven recruitment, and far less resistance than had been anticipated. This is reassuring as it is a concept that will only continue to become more widespread as the evolution continues. The need for clear communication, regular testing for accuracy, and a clear understanding of potential ethical pitfalls is crucial for successful implementation.
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | H Social Sciences > HD Industries. Land use. Labor T Technology > T Technology (General) |
| Divisions: | Theses and Dissertations > Masters Dissertations |
| Depositing User: | Victoria Hankinson |
| Date Deposited: | 07 Mar 2025 15:16 |
| Last Modified: | 07 Mar 2025 15:16 |
| URI: | https://repository.uwtsd.ac.uk/id/eprint/3631 |
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