HR Analytics
| Module title | HR Analytics |
|---|---|
| Module code | BEMM395 |
| Academic year | 2023/4 |
| 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 | 1,2,3,4 | Written feedback |
| Individual report | 70 | 2500 words | 1,2,3,4,5 | Written feedback |
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 |
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, 1000 words, 30% | Individual essay, 1000 words, 30% | 1,2,3,4 | Relevant re-assessment period |
| Individual report, 2500 words, 70% | Individual report, 2500 words, 70% | 1,2,3,4,5 | Relevant re-assessment period |
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., & Edwards, K. (2016). Predictive HR Analytics Mastering the HR Metric. London: KoganPage. Retrieved from https://encore.exeter.ac.uk/iii/encore/record/C__Rb3973922
- 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:
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., & Hagerty, A. (2021). Introducing HR Analytics with Machine Learning Empowering Practitioners, Psychologists, and Organizations. Champ: Springer International Publishing. Retrieved from https://encore.exeter.ac.uk/iii/encore/record/C__Rb4432440
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
| 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 | 10/05/2022 |
