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Human Resource Management Dissertation Topics
Info: 1480 words(1 pages) Human Resource Management Dissertation Topics
Published: 24th November 2025 in Human Resource Management Dissertation Topics
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Dissertation Topic 1:
Bridging the Knowledge–Practice Gap in Personnel Selection: HR Practitioners’ Understanding, Misconceptions, and Adoption of Evidence-Based Tools
Background context
HR professionals are aware of the gap between the HR selection methods used for recruiting and those validated for this purpose, and thus, this is the source of mistakes during the recruitment process (Maertens et al., 2025; Vandenabeele, 2024). Still, unstructured interviews, which have the least predictive validity and the most bias, remain as the preferred method by the majority of companies, overlooking the implementation of data-driven recruitment practices that not only have predictive power but also return on investment.
Position gap: why, despite decades of evidence, HR still prefers “intuitive” tools (belief in expertise, habit, face validity, organisational norms).
PhD-Level Verification
This is a strong doctoral topic as it addresses one of the most persistent and well-documented gaps in HRM: the divergence between validated selection research and actual HR practice. Literature supports that practitioners continue to use low-validity tools (e.g., unstructured interviews) despite decades of evidence favouring structured methods and psychometric assessments.
Research Questions
- How accurately do HR practitioners understand the validity and ROI of evidence-based selection tools (e.g., structured interviews, work samples, cognitive tests)?
- What misconceptions sustain continued reliance on low-validity methods, especially unstructured interviews?
- Which organisational factors (culture, leadership, incentives, legal concerns) hinder the adoption of validated tools?
- How do practitioners’ tool preferences change after targeted education on validity and ROI?
PhD-level Contributions:
- Design of practitioner-focused evidence-based selection training modules.
- Guidance for HR policy makers on phasing out unstructured interviews as default tools.
- Practical recommendations for integrating structured assessments into existing ATS / e‑recruitment platforms.
This topic is PhD-ready with a solid theoretical base.
Suggested Reading
- Maertens, L., Daniëls, E., Hondeghem, A., & Vandenabeele, W. (2025). Towards a new conceptualisation of evidence-based human resource management. Journal of Organisational Effectiveness: People and Performance, 12(3), 559-584.
- Vandenabeele, W. Towards a new conceptualisation of evidence-based human resource management.
Dissertation Topic 2:
Contextual Barriers to Evidence-Based HRM: Organisational Culture, Structure, and Professional Role Identity
Background Context
Research literature provides clear evidence of the benefits of evidence-based HRM practice; however, the majority of HR practitioners still express concern about the context, e.g. organizational culture and HRM, as the main reason for their focus on HR implementation challenges (Agarwal et al., 2024). Also, the very limited theoretical usage in HRM research makes the practical application more complicated (Sulistiawan et al., 2025).
Research Questions
- Which organisational, cultural, and institutional factors drive HR professionals’ reluctance to adopt evidence-based HRM?
- In what ways do HR role identity and perceived expectations from line managers and senior leaders affect the adoption of evidence-based recommendations?
- What theoretical frameworks (for example, institutional theory, practice theory, role identity theory) can be deemed the most appropriate in explaining these contextual barriers?
PhD-Level Contributions
- Advances a contextualised model of EB-HRM adoption.
- Design principles for context-aligned HR training that integrates organisational realities and political constraints.
- Models of academic–practitioner collaboration (e.g., co-sponsored PhDs, embedded researchers) tailored to specific organisational contexts.
- Contributes to HRM theory by integrating culture–structure–identity interactions.
- Bridges academic-practitioner collaboration approaches.
This topic is methodologically rich and highly publishable.
Suggested Reading
- Agarwal, A., Kapoor, K., & Walia, S. (2024). Modelling the barriers to blockchain implementation in human resource function. International Journal of Quality & Reliability Management, 41(8), 2075-2094.
- Sulistiawan, J., Herachwati, N., & Khansa, E. J. R. (2025). Barriers in adopting green human resource management under uncertainty: the case of the Indonesian banking industry. Journal of Work-Applied Management, 17(2), 200-219.
Dissertation Topic 3:
Power, Status, and Professional Identity in Resistance to Evidence-Based HRM
Background Context
HR practitioners reject standardised and evidence-based practices mainly due to power dynamics, status concerns, and fear of technology taking over their professional roles (Paauwe & Van,2025). Barends and Rousseau (2018) argue that in many cases, the recommendations made by empirical research are completely overshadowed by the politics of the organisation, thus suggesting that the research–practice gap is partially underpinned by power motives and status preservation in HR (Maertens et al., 2025).
PhD-Level Verification
This is one of the most innovative and underexplored areas in HRM scholarship.
Research Questions
- How do perceived power and status shape HR professionals’ support for or resistance to evidence-based HR practices?
- How does professional identity (e.g., “experienced judge of people” vs. “evidence-based analyst”) influence reactions to standardised HR tools?
- What change management and governance mechanisms can reduce political resistance and safeguard identity while increasing evidence use?
Potential Implications
- Politically-aware change management in HRM framework.
- Rules and directions for HR role redesign that effectively integrate professional judgement with evidence-based tools rather than treating them as rival forces.
This subject matter is new, theoretically rich, and particularly appropriate for a dissertation that aims to make a strong theoretical impact.
Suggested Reading
- Paauwe, J., & Van De Voorde, K. (2025). Bridging the research-practice gap in modern human resource management. Human Resource Management Review, 35(2), 101076.
- Maertens, L., Daniëls, E., Hondeghem, A., & Vandenabeele, W. (2025). Towards a new conceptualisation of evidence-based human resource management. Journal of Organisational Effectiveness: People and Performance, 12(3), 559-584.
Dissertation Topic 4:
Noise, Bias, and Variability in HR Decision-Making: Predictors of Non-Evidence-Based Choices and Mechanisms for Reducing Judgment Variability
Background Context
Collaborative consumption (e.g., fashion sharing, renting, and swapping) can be a game-changer for the traditional fashion business model. Nevertheless, this segment of business appears to be restrained by socioeconomic barriers like price and social stigma. Research in organisational behaviour demonstrates that the variability of HR decisions—differences in judgment due to human bias—can affect the result of hiring. This research will investigate socio-economic barriers and whether marketing techniques can be used to encourage broader adoption (Tang et al., 2025; Liu, 2025).
PhD-Level Verification
This topic is timely, especially because “noise” (Kahneman, Sibony & Sunstein, 2021) has been extended into HR judgment studies. Yet, empirical application to HRM remains limited.
Research Questions
- How does judgmental noise in HR selection and promotion decisions affect decision quality, fairness, and organisational outcomes?
- Which organisational and process conditions (e.g., low structure, vague criteria, time pressure, panel composition) increase noise in HR decisions?
- To what extent do structured tools (e.g., scoring rubrics, structured interviews, algorithmic aids) reduce noise and improve consistency?
PhD-Level Contributions:
- Evidence-based design of low-noise HR processes (structured interviews, standardised rating scales, calibration meetings).
- Practical recommendations for HR analytics dashboards to monitor variability across raters and units.
This topic is highly publishable and methodologically robust.
Suggested Reading
- Tang, X., Chen, H., Lin, D., & Li, K. (2024). Harnessing LLMs for multi-dimensional writing assessment: Reliability and alignment with human judgments. Heliyon, 10(14).
- Liu, Y., Zhou, H., Guo, Z., Shareghi, E., Vulić, I., Korhonen, A., & Collier, N. (2024). Aligning with human judgement: The role of pairwise preference in large language model evaluators. arXiv preprint arXiv:2403.16950.
Dissertation Topic 5:
AI as a Bridge or Barrier to Evidence-Based HRM: Benefits, Risks, and Stakeholder Trust
Background Context
The usage of AI-based HR tools is on the rise, as they are now considered to be very effective in selection accuracy and bias reduction. Still, there are issues regarding the transparency, fairness, and trust of the applicants (Benabou et al.,2024). Although AI in HRM may serve as a link between the research and practice, resistance still exists in both the applicant and HR staff groups (Basnet et al., 2024).
PhD-Level Verification
This is a frontier area of research and there is already extensive literature on this topic (AI ethics, algorithmic fairness, HR analytics adoption).
Research Questions
- What is the effect of incorporating AI-based selection tools on the compatibility of HR practices with the principles of evidence-based (validity, reliability, fairness)?
- Which factors affect the trust and acceptance of AI tools by both HR professionals and job applicants (for example, transparency, explainability, perceived control)?
- Under what conditions does AI narrow the research–practice gap versus introduce new forms of opacity, bias, and resistance?
PhD-Level Contributions:
- Provides frameworks for algorithmic trust in HRM.
- Clarifies whether AI amplifies or reduces the longstanding HR research–practice divide.
- Bridges HR analytics, behavioural science, and technology adoption models.
This topic is highly aligned with current academic trends, making it suitable for a future-proof PhD project.
Suggested Reading
- Benabou, A., Touhami, F., & Demraoui, L. (2024, May). Artificial intelligence and the future of human resource management. In 2024 International Conference on Intelligent Systems and Computer Vision (ISCV) (pp. 1-8). IEEE.
- Basnet, S. (2024). Artificial Intelligence and machine learning in human resource management: Prospects and future trends. International Journal of Research Publication and Reviews, 5(1), 281-287.
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