HR Analytics

Module title	HR Analytics
Module code	BEMM395
Academic year	2025/6
Credits	15
Module staff	Mrs Fiona Smith (Convenor)

Duration: Term	1	2	3
Duration: Weeks		11

Number students taking module (anticipated)	100

Module description

This module investigates opportunities for HR functions to improve strategic decision making by transforming data into information and insights, bringing analytical rigour to HRM and ultimately enabling organisations to make more informed decisions and gain competitive advantage. The emergence of cloud-based data storage, data warehouses, AI and machine learning, alongside reduced data storage costs, means organisations have access to large pools of data, including employee specific data, plus technology capable of interpreting and analysing large data sets. Students will briefly consider historical HRM metrics, then consider implementation of HR analytics using a variety of contemporary HR analytics models and academic frameworks, gaining understanding of key data-related terminology and processes, using example data sets to develop understanding of the HR analytics process using statistical models, design thinking and root-cause analysis. Students will make connections between core HRM activities, HR analytics insights and strategic goals.

Drawing from a variety of disciplines including psychology, management, information technology, economics, law and education, the module calls upon examples from a wide range of business scenarios, including consideration of ethics, governance and Corporate Social Responsibility (CSR). This module will provide you with an opportunity to learn the theoretical potential of HR analytics, and appreciate the practical hurdles required to successfully implement theory to practice, based upon organisational and HRM context, using case study examples from a variety of organisational scenarios. This module reflects the increasingly inter-disciplinary nature of research and employment and is suitable for students from interdisciplinary pathways.

Module aims - intentions of the module

Understanding how HR analytics is a core element of organisational strategy is at the heart of the module, and this module aims to equip students with the foundation knowledge and skills to critically interpret, evaluate and select HR analytics methods and models to introduce HR analytics to the HRM function, By taking an interdisciplinary approach, the module will expose students to a variety of models, core principles and ways of working to enable students to demonstrate contextual awareness of both organisational and wider external factors. As a result, the module provides students with core HR analytics employability skills and knowledge.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

1. Identify and apply appropriate HR analytics theories, methods and tools to demonstrate a strong understanding of HR analytics
2. Explain and interpret contemporary issues regarding HR analytics and use of data including ethics, governance and change management,

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

3. Critically evaluate the impact of HR analytics on businesses and individuals
4. Discuss how to develop HR Analytics capability within organisations

ILO: Personal and key skills

On successfully completing the module you will be able to...

5. Develop HR analytics skills including ability to apply a variety of statistical models

Syllabus plan

Core themes considered in the module will include:

Introduction to HR analytics
HR analytics as a strategic enabler
Organisational context for successful HR analytics: AI, machine learning, data mining, psychometric testing
The language of HR analytics, models, frameworks, statistics, data & insights
Problem identification, predictive analytics, prescriptive analytics
The future of HR analytics

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching Activities	Guided independent study	Placement / study abroad
18	132	0

Details of learning activities and teaching methods

Category	Hours of study time	Description
Scheduled learning and teaching activities	10	10 x 1 hour whole cohort session
Scheduled learning and teaching activities	8	4 x 2 hour seminar session
Guided Independent Study	66	Core and supplementary reading
Guided Independent Study	66	Assignment preparation

Formative assessment

Form of assessment	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
Seminar exercises	Via 4 x 2 hour seminars	1-5	Group verbal/written feedback

Summative assessment (% of credit)

Coursework	Written exams	Practical exams
100	0	0

Details of summative assessment

Form of assessment	% of credit	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
Individual essay	30	1000 words	1-3	Written
Individual report	70	2500 words	1-5	Written

Details of re-assessment (where required by referral or deferral)

Original form of assessment	Form of re-assessment	ILOs re-assessed	Timescale for re-assessment
Individual essay	Individual essay (1000 words, 30%)	1-3	Referral/deferral period
Individual report	Individual report (2500 words, 70%)	1-5	Referral/deferral period

Re-assessment notes

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to submit a further assessment as necessary. If you are successful on referral, your overall module mark will be capped at 50%.

Indicative learning resources - Basic reading

No single textbook is used throughout this module. Example data sets and case studies are drawn from the following two books, both are available online via the library, and further guided reading will be provided in advance of lecture via the ELE pages. Indicative recommended reading will include:

For case studies:

Edwards, M. R., & Edwards, K. (2019). Predictive HR analytics : mastering the HR metric (Second edition.). KoganPage. https://librarysearch.exeter.ac.uk/permalink/44UOEX_INST/17rj75m/alma9911484740805136.
Khan, N., & Millner, D. (2023). Introduction to people analytics [ebook]?: a practical guide to data-driven HR (2nd edition). Kogan Page, Limited. https://librarysearch.exeter.ac.uk/permalink/44UOEX_INST/1rdi7h6/alma991016927929507446
Provost, F., & Fawcett, T. (2013). Data Science for business. Beijing: O'Reilly. Retrieved from https://encore.exeter.ac.uk/iii/encore/record/C__Rb3510932

Indicative learning resources - Web based and electronic resources

Online learning:

References include:

Di Prima, C., Kotaskova, A., Yildiz, H., & Ferraris, A. (2024). How to survive social crises? An HR analytics data-driven approach to improve social sustainable operations’ effectiveness. Management Decision, 62(7), 2064–2084. https://doi.org/10.1108/MD-06-2023-0973 https://librarysearch.exeter.ac.uk/permalink/44UOEX_INST/9u3b61/cdi_hal_primary_oai_HAL_hal_04783998v1
Edwards, M. R., Charlwood, A., Guenole, N., & Marler, J. (2024). HR analytics: An emerging field finding its place in the world alongside simmering ethical challenges. Human Resource Management Journal, 34(2), 326–336. https://doi.org/10.1111/1748-8583.12435 https://librarysearch.exeter.ac.uk/permalink/44UOEX_INST/9u3b61/cdi_proquest_journals_3035373749
Gaur, B. (2020). HR4.0: An Analytics Framework to redefine Employee Engagement in the Fourth Industrial Revolution. 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-6). Kharagpur: IEEE. Retrieved from https://uoelibrary.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edseee&AN=edseee.9225456&site=eds-live&scope=site
Rasmussen, T., & Ulrich, D. (2015). Learning from practice: how HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236-242. Retrieved from https://uoelibrary.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edselp&AN=S0090261615000443&site=eds-live&scope=site
Rosett, C. M., Hagerty, A., SpringerLink, & SpringerLink. (2021). Introducing HR Analytics with Machine Learning Empowering Practitioners, Psychologists, and Organizations (1st ed. 2021.). Springer International Publishing. https://doi.org/10.1007/978-3-030-67626-1 https://librarysearch.exeter.ac.uk/permalink/44UOEX_INST/1rdi7h6/alma991008713709707446
Sharma, G. (2021). A literature review on application of Artificial Intelligence in Human Resource Management and its practices in current organizational scenario. 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (pp. 594-600). Palladam: IEEE. Retrieved from https://uoelibrary.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edseee&AN=edseee.9640954&site=eds-live&scope=site
Shukla, V. K., Kulkarni, P., Gaur, D., N, P., G., L. J. P., & Omrane, A. (Eds.). (2024). Industry 4. 0 and people analytics : A technical perspective of hrm. Apple Academic Press, Incorporated. https://librarysearch.exeter.ac.uk/permalink/44UOEX_INST/1rdi7h6/alma991017219317907446
Wang, L., Zhou, Y., Sanders, K., Marler, J. H., & Zou, Y. (2024). Determinants of effective HR analytics Implementation: An In-Depth review and a dynamic framework for future research. Journal of Business Research, 170, 114312-. https://doi.org/10.1016/j.jbusres.2023.114312 https://librarysearch.exeter.ac.uk/permalink/44UOEX_INST/9u3b61/cdi_crossref_primary_10_1016_j_jbusres_2023_114312

Key words search

Human Resource Management, HR Analytics, People Analytics

Credit value	15
Module ECTS	7.5
Module pre-requisites	This module is restricted to HRM Masters students only in the first year of delivery. To be reviewed after the first year.
Module co-requisites	None.
NQF level (module)	7
Available as distance learning?	No
Origin date	09/05/2022
Last revision date	18/03/2025