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

The details
2024/25
Essex Business School
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Thursday 03 October 2024
Friday 13 December 2024
10
10 July 2023

 

Requisites for this module
(none)
(none)
(none)
(none)

 

(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:



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

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

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

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

  5. 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:



  1. Critical Thinking.

  2. Commercial Awareness.

  3. Written and verbal Communication.

  4. Oral Communication.

  5. 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.
  • 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.
  • Tambe, P., Cappelli, P. and Yakubovich, V. (2019) ‘Artificial Intelligence in Human Resources Management: Challenges and a Path Forward’, California Management Review, 61(4), pp. 15–42. Available at: https://doi.org/10.1177/0008125619867910.
  • 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.
  • 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.
  • Lepinkäinen, N. and Malik, H.M. (2022) ‘Discourses on AI and Regulation of Automated Decision-Making’, Global Perspectives, 3(1).
  • 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.
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
Dr Elaine Yerby, email: e.yerby@essex.ac.uk.
Dr Elaine Yerby
ebshrm@essex.ac.uk

 

Availability
No
No
Yes

External examiner

Dr Sheena Vachhani
University of Bristol
Reader (Associate Professor) in Work and Organization Studies
Dr Ruth Reaney
University of Glasgow
Lecturer in HRM
Resources
Available via Moodle
Of 15 hours, 15 (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
Essex Business School

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