University of Essex

BE781-7-AU-CO:
Digital Human Resource Management

The details

Year: 2024/25

Department: Essex Business School

Campus: Colchester Campus

Term: Autumn

Level: Postgraduate: Level 7

Status: Current

Start date: Thursday 03 October 2024

End date: Friday 13 December 2024

Credits: 10

Last updated: 10 July 2023

Requisites for this module

Pre-requisites: (none)

Co-requisites: (none)

Pre and / or co-requisites: (none)

Prohibited modules: (none)

Key module (requisite for): (none)

Key module for

MSC N60012 Human Resource Management,
MSC N60024 Human Resource Management

Module description

This module is designed to critically explore the meaning and nature of digital HRM in both organisations and the HR profession today.

The module will examine the development and evolution of emerging digital HRM technologies. In doing so students will unpack the complexities associated with definitional terms and key digital HRM tools, including the growth of AI applications. Through the use of real-life global case studies students will explore practical ways and potential challenges associated with implementing digital HRM strategies and tools.

Module aims

The aims of this module are:

To encourage students to think critically about the role and meaning attached to digital Human Resource Management (HRM) in organisations and the HR profession today.

To examine the origins and evolution of digital HRM and apply key techniques and principles of people analytics.

To evaluate the effectiveness of emerging digital HRM tools in a variety of organizational settings and the potential return investment.

To appreciate the complexities associated with data driven decision-making and the importance of managing stakeholder involvement.

To analyse the ethical, regulatory and risks associated with the effective adoption and implementation of digital HRM.

Module learning outcomes

By the end of this module, students will be expected to be able to:

Critically analyse how the use of technology can support the delivery of people practices, and improve the employee experience.

Understand how to use data relating to products, services and customers to provide insight into people solutions and practices .

Assess the quality and relevance of digital evidence available, by identifying sources of bias and using evidence-based questioning models.

Understand how organisational strategy translates to your work and partner with internal/external customers to understand their current and future needs and contract effectively.

Develop and present a business case for digital HR solutions and demonstrate return on investment.

Skills for Your Professional Life (Transferable Skills)

This module will also contribute to the development of the following employability skills:

Critical Thinking.

Commercial Awareness.

Written and verbal Communication.

Oral Communication.

Research Skills.

Module information

In accordance with the EBS tradition for sustainable and ethical business practices a key focus of the module is to explore the ethical and regulatory issues associated with the application of data science techniques and technologies in the management of people. These issues will be foregrounded when examining how data collected through digital tools is used and applied for decision making in organisations today. In doing so students are encouraged to think critically about dominant discourses that are proclaiming the benefits of digital HRM solutions for organisational and employee productivity today.

Learning and teaching methods

This module will be delivered via:

One workshop per week.

The module will be delivered over the course of five weeks. Weekly sessions are delivered in a workshop format, which combines interactive lecture input of key concepts, group case study analysis, and class debates. There will also be a number of practitioner specialists contributing to the workshops on the module

Bibliography

Sen, S. (2020c) Digital HR strategy: achieving sustainable transformation in the digital age. London: KoganPage.
Edwards, M.R. and Edwards, K. (2019c) Predictive HR analytics: mastering the HR metric. Second edition. London: KoganPage. Available at: https://ebookcentral.proquest.com/lib/universityofessex-ebooks/detail.action?docID=5718930.
Mondal, S.R., Di Virgilio, F. and Das, S. (eds) (2022) HR analytics and digital HR practices: digitalization post COVID-19. Basingstoke: Palgrave Macmillan.
Diez, F., Bussin, M. and Lee, V. (2020b) Fundamentals of HR analytics: a manual on becoming HR analytical. Bingley: Emerald Publishing. Available at: https://ebookcentral.proquest.com/lib/universityofessex-ebooks/detail.action?docID=5967831.
Tanya Bondarouk and Jeroen Meijerink (2024) Research Handbook on Human Resource Management and Disruptive Technologies. Elgar Publishing Limited, Edward. Available at: https://app.kortext.com/Shibboleth.sso/Login?entityID=https://idp0.essex.ac.uk/shibboleth&target=https://app.kortext.com/borrow/2578835.
Strohmeier, S. (2020) ‘Digital human resource management: A conceptual clarification’, German Journal of Human Resource Management: Zeitschrift für Personalforschung, 34(3), pp. 345–365. Available at: https://doi.org/10.1177/2397002220921131.
Myllymäki, D. (2021) ‘Beyond the “e-” in e-HRM: integrating a sociomaterial perspective’, The International Journal of Human Resource Management, 32(12), pp. 2563–2591. Available at: https://doi.org/10.1080/09585192.2021.1913624.
Thite, M. (2022) ‘Digital human resource development: where are we? Where should we go and how do we go there?’, Human Resource Development International, 25(1), pp. 87–103. Available at: https://doi.org/10.1080/13678868.2020.1842982.
Tursunbayeva, A. et al. (2022) ‘The ethics of people analytics: risks, opportunities and recommendations’, Personnel Review, 51(3), pp. 900–921. Available at: https://doi.org/10.1108/PR-12-2019-0680.
Vrontis, D. et al. (2022) ‘Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review’, The International Journal of Human Resource Management, 33(6), pp. 1237–1266. Available at: https://doi.org/10.1080/09585192.2020.1871398.
Malik, A., Budhwar, P., Patel, C., et al. (2022) ‘May the bots be with you! Delivering HR cost-effectiveness and individualised employee experiences in an MNE’, The International Journal of Human Resource Management, 33(6), pp. 1148–1178. Available at: https://doi.org/10.1080/09585192.2020.1859582.
Malik, A., Budhwar, P., Mohan, H., et al. (2022) ‘Employee experience –the missing link for engaging employees: Insights from an ’s -based ecosystem’, Human Resource Management [Preprint]. Available at: https://doi.org/10.1002/hrm.22133.
‘Predictive HR analytics and talent management: a conceptual framework.’ (2021) Journal of Management Analytics [Preprint]. Available at: https://search-ebscohost-com.uniessexlib.idm.oclc.org/login.aspx?direct=true&db=bsu&AN=150284104&site=ehost-live&authtype=sso&custid=s9814295.
Pan, Y. et al. (2022) ‘The adoption of artificial intelligence in employee recruitment: The influence of contextual factors’, The International Journal of Human Resource Management, 33(6), pp. 1125–1147. Available at: https://doi.org/10.1080/09585192.2021.1879206.
Retkowsky, J., Hafermalz, E. and Huysman, M. (2024) ‘Managing a ChatGPT-empowered workforce: Understanding its affordances and side effects’, Business Horizons, 67(5), pp. 511–523. Available at: https://doi.org/10.1016/j.bushor.2024.04.009.
Budhwar, P. et al. (2023) ‘Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT’, Human Resource Management Journal, 33(3), pp. 606–659. Available at: https://doi.org/10.1111/1748-8583.12524.
Humlum, A. and Vestergaard, E. (no date) ‘The Adoption of ChatGPT’. Available at: https://www.iza.org/publications/dp/16992/the-adoption-of-chatgpt.
Marabelli, M. and Lirio, P. (2024) ‘AI and the metaverse in the workplace: DEI opportunities and challenges’, Personnel Review [Preprint]. Available at: https://doi.org/10.1108/PR-04-2023-0300.
Dutta, D., Srivastava, Y. and Singh, E. (2023) ‘Metaverse in the tourism sector for talent management: a technology in practice lens’, Information Technology & Tourism, 25(3), pp. 331–365. Available at: https://doi.org/10.1007/s40558-023-00258-9.
Rodgers, W. et al. (2022) ‘An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes’, Human Resource Management Review [Preprint]. Available at: https://doi.org/10.1016/j.hrmr.2022.100925.
Wiblen, S. and Marler, J.H. (2021) ‘Digitalised talent management and automated talent decisions: the implications for HR professionals’, The International Journal of Human Resource Management, 32(12), pp. 2592–2621. Available at: https://doi.org/10.1080/09585192.2021.1886149.
Ellmer, M. and Reichel, A. (2021) ‘Staying close to business: the role of epistemic alignment in rendering HR analytics outputs relevant to decision-makers’, The International Journal of Human Resource Management, 32(12), pp. 2622–2642. Available at: https://doi.org/10.1080/09585192.2021.1886148.
Budhwar, P. et al. (2022) ‘Artificial intelligence – challenges and opportunities for international HRM: a review and research agenda’, The International Journal of Human Resource Management, 33(6), pp. 1065–1097. Available at: https://doi.org/10.1080/09585192.2022.2035161.
Suseno, Y. et al. (2022) ‘Beliefs, anxiety and change readiness for artificial intelligence adoption among human resource managers: the moderating role of high-performance work systems’, The International Journal of Human Resource Management, 33(6), pp. 1209–1236. Available at: https://doi.org/10.1080/09585192.2021.1931408.
Jaiswal, A., Arun, C.J. and Varma, A. (2022) ‘Rebooting employees: upskilling for artificial intelligence in multinational corporations’, The International Journal of Human Resource Management, 33(6), pp. 1179–1208. Available at: https://doi.org/10.1080/09585192.2021.1891114.
Pan, Y. and Froese, F.J. (2022) ‘An interdisciplinary review of AI and HRM: Challenges and future directions’, Human Resource Management Review [Preprint]. Available at: https://doi.org/10.1016/j.hrmr.2022.100924.
Thite, M. and Iyer, R. (2024) ‘Addressing the gap in information security: an HR-centric and AI-driven framework for mitigating insider threats’, Personnel Review [Preprint]. Available at: https://doi.org/10.1108/PR-04-2023-0358.
Bareis, J. and Katzenbach, C. (2022) ‘Talking AI into Being: The Narratives and Imaginaries of National AI Strategies and Their Performative Politics’, Science, Technology, & Human Values, 47(5), pp. 855–881. Available at: https://doi.org/10.1177/01622439211030007.
Vanessa Chen (2018) The gendered politices of AI representation and discourses. Available at: https://www.academia.edu/36749804/The_Gendered_Politics_of_Artificial_Intelligence_Representations_and_Discourses.
Bester, M.S. and Stander, E. (2021) ‘Strengthening the Psychological Contract Through Talent-Enabled Assessment Journeys: An Employee-Experience Guide’, in M. Coetzee and A. Deas (eds) Redefining the Psychological Contract in the Digital Era. Cham: Springer International Publishing, pp. 179–201. Available at: https://doi.org/10.1007/978-3-030-63864-1_10.

The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam	Description	Deadline	Coursework weighting
Coursework	2,500 word Case Study Analysis		100%

Exam format definitions

Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework	Exam
100%	0%

Reassessment

Coursework	Exam
100%	0%

Module supervisor and teaching staff

Supervisor: Dr Elaine Yerby, email: e.yerby@essex.ac.uk.

Teaching staff: Dr Elaine Yerby

Contact details: ebshrm@essex.ac.uk

Availability

Available to incoming Essex Abroad / Exchange students: No

Available to Outside Option: No

Available to Audit: Yes

External examiner

Name: Dr Sheena Vachhani

Institution: University of Bristol

Academic role: Reader (Associate Professor) in Work and Organization Studies

Name: Dr Ruth Reaney

Institution: University of Glasgow

Academic role: Lecturer in HRM

Resources

Teaching materials: Available via Moodle

Lecture recording: Of 3 hours, 3 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.

Further information

Department website: Essex Business School

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The full Procedures, Rules and Regulations of the University governing how it operates are set out in the Charter, Statutes and Ordinances and in the University Regulations, Policy and Procedures.

Module Directory

BE781-7-AU-CO:Digital Human Resource Management