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Social Sciences
Special Issues
Big Data and Employee Wellbeing
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Big Data and Employee Wellbeing

A special issue of Social Sciences (ISSN 2076-0760).

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 40863

Special Issue Editors

Dr. Carolyn Axtell

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Guest Editor
Institute of Work Psychology, Sheffield University Management School, Sheffield S10 2TN, UK
Interests: employee wellbeing; technology at work; work design; virtual work/telework
Prof. Dr. Bridgette Wessels

E-Mail Website
Guest Editor
School of Social & Political Sciences, Glasgow G12 8QQ, UK
Interests: the use of digital technology and data in social contexts including telehealth, e-policing, e-services, open data, social media data, and big data
Dr. Mark J Taylor

E-Mail Website
Guest Editor
Melbourne Law School, University of Melbourne, Parkville, VIC 3010, Australia
Interests: data protection; health privacy; reasonable expectations and public interest in secondary uses of data
Dr. Christine Sprigg

E-Mail Website
Guest Editor
Institute of Work Psychology, Sheffield University Management School, Sheffield S10 1FL, UK
Interests: work-related aggression (including violence, bullying, cyberbullying); psychological wellbeing; work design

Special Issue Information

Dear Colleagues,

Organisations are starting to use Big Data and data analytics to examine employee health and wellbeing (e.g., through the use of fitness tracking technology) with the aim of improving the health and wellbeing of their workforce. There are also vast amounts of digitally captured data gathered within organisations (e.g., network traffic, log-in/off data) that are currently under-utilized but could be used to indicate areas of highly intensive workload where wellbeing risks might occur. While there could be very positive effects of using data to proactively assess and intervene where health and wellbeing risks are highlighted, there are also several challenges—not only in terms of the technical difficulties of handling this data, but also concerns about the reliability/validity of the data, what decisions or actions will be taken as a result of the analysis, and issues relating to data protection, trust, and consent. The complexity of these issues means that a critical multi-disciplinary perspective is required to understand this area better. This Special Issue therefore seeks to draw together theoretical, methodological, and empirical papers from different disciplinary perspectives that examine some of the opportunities, challenges, and lessons learned from other contexts that inform our understanding of using Big Data/data analytics to examine wellbeing within organisations.

This Special Issue was stimulated by an inter-disciplinary ESRC-funded seminar series on the topic of Data and Employee Wellbeing (www.dew.group.shef.ac.uk).

Dr. Carolyn Axtell
Prof. Bridgette Wessels
Dr. Mark J Taylor
Dr. Christine Sprigg
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Social Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Big Data
  • Data Analytics
  • Employee Wellbeing
  • Assessment of Wellbeing
  • Wellbeing Intervention
  • Digital Sociology
  • Data Protection

Published Papers (4 papers)

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Editorial

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5 pages, 174 KiB  
Editorial
Big Data and Employee Wellbeing: Walking the Tightrope between Utopia and Dystopia
by Carolyn Axtell, Mark Taylor and Bridgette Wessels
Soc. Sci. 2019, 8(12), 321; https://doi.org/10.3390/socsci8120321 - 22 Nov 2019
Cited by 3 | Viewed by 4198
Abstract
This special issue was inspired by an Economic & Social Research Council funded seminar series that explored the possibilities for using Big Data and data analytics for assessing health and wellbeing risks within organisations. The aim of this special issue was to build [...] Read more.
This special issue was inspired by an Economic & Social Research Council funded seminar series that explored the possibilities for using Big Data and data analytics for assessing health and wellbeing risks within organisations. The aim of this special issue was to build on some of the themes developed in the seminar series and draw together and update some key insights from different disciplinary perspectives on the opportunities, challenges and lessons that could be applied in this area. This editorial, therefore, draws together the findings and themes from the submitted papers and interprets these in light of the findings from the seminar series. Full article
(This article belongs to the Special Issue Big Data and Employee Wellbeing)

Research

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19 pages, 294 KiB  
Article
Insight or Intrusion? Correlating Routinely Collected Employee Data with Health Risk
by Mark J. Taylor and Megan Prictor
Soc. Sci. 2019, 8(10), 291; https://doi.org/10.3390/socsci8100291 - 16 Oct 2019
Cited by 3 | Viewed by 5129
Abstract
The volume, variety and velocity of data available to companies about their employees is already significant and likely to increase. Employers hold data about employees that could be used to explore the relationship between workplace practice in their organisation and risks to employee [...] Read more.
The volume, variety and velocity of data available to companies about their employees is already significant and likely to increase. Employers hold data about employees that could be used to explore the relationship between workplace practice in their organisation and risks to employee health. However, there is significant uncertainty about whether employers subject to English law are permitted to use this data for this purpose, and even whether they may be under a legal obligation to do so. In this article, the question of whether employers are legally permitted or legally obliged to use employee data to identify associations between workplace practice and risk to employee health is answered through an analysis of two spheres of English Law: data protection law, and health and safety law. The authors establish a hypothetical case study concerning a company that wishes to use employee data in this way, to illuminate a set of detailed legal issues. In particular, the question of whether a reasonable and prudent employer is under an obligation under health and safety law to use the data and analytic tools at his or her disposal to assess risk and inform his or her actions is considered. Also addressed is the question of whether such processing would satisfy the data protection law principles of “lawful, fair, and transparent” processing and that of “purpose limitation”. A complex picture emerges. The analysis reveals that data protection legislation may not support a trend towards the re-use of employee data to enhance workplace health and safety; nor is there currently a clear mandate that responsible employers use data in this way. The line between useful insight into workplace practices and intrusion into employees’ privacy remains blurred. Full article
(This article belongs to the Special Issue Big Data and Employee Wellbeing)
19 pages, 622 KiB  
Article
Big Data and Human Resources Management: The Rise of Talent Analytics
by Manuela Nocker and Vania Sena
Soc. Sci. 2019, 8(10), 273; https://doi.org/10.3390/socsci8100273 - 29 Sep 2019
Cited by 49 | Viewed by 23809
Abstract
The purpose of this paper is to discuss the opportunities talent analytics offers HR practitioners. As the availability of methodologies for the analysis of large volumes of data has substantially improved over the last ten years, talent analytics has started to be used [...] Read more.
The purpose of this paper is to discuss the opportunities talent analytics offers HR practitioners. As the availability of methodologies for the analysis of large volumes of data has substantially improved over the last ten years, talent analytics has started to be used by organizations to manage their workforce. This paper discusses the benefits and costs associated with the use of talent analytics within an organization as well as to highlight the differences between talent analytics and other sub-fields of business analytics. It will discuss a number of case studies on how talent analytics can improve organizational decision-making. From the case studies, we will identify key channels through which the adoption of talent analytics can improve the performance of the HR function and eventually of the whole organization. While discussing the opportunities that talent analytics offer organizations, this paper highlights the costs (in terms of data governance and ethics) that the widespread use of talent analytics can generate. Finally, it highlights the importance of trust in supporting the successful implementation of talent analytics projects. Full article
(This article belongs to the Special Issue Big Data and Employee Wellbeing)
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Review

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19 pages, 286 KiB  
Review
Integrating Social Scientific Perspectives on the Quantified Employee Self
by Thomas Calvard
Soc. Sci. 2019, 8(9), 262; https://doi.org/10.3390/socsci8090262 - 15 Sep 2019
Cited by 9 | Viewed by 7045
Abstract
A key technological trend in big data science is that of the quantified self, whereby individuals can self-track their health and well-being using various sources of information. The aim of this article was to integrate multidimensional views on the positive and negative implications [...] Read more.
A key technological trend in big data science is that of the quantified self, whereby individuals can self-track their health and well-being using various sources of information. The aim of this article was to integrate multidimensional views on the positive and negative implications of the quantified self for employees and workplaces. Relevant human and social scientific literature on the quantified (employee) self and self-tracking were drawn upon and organized into three main influential perspectives. Specifically, the article identified (1) psychological perspectives on quantified attitudes and behaviors, (2) sociological perspectives on sociomaterial user construction, and (3) critical theoretical perspectives on digital power and control. This article suggests that the three perspectives are complementary and can be usefully integrated into an embodied sensemaking perspective. Embodied sensemaking views the employee as a self-conscious user of big data seeking to make sense of their embeddedness in wider digital and organizational environments. This article concludes with implications for protecting employee agency in tension with employers’ big data strategies for governing and managing the performance of quantified digital employee selves. Full article
(This article belongs to the Special Issue Big Data and Employee Wellbeing)
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