Big data in human resources

Big Data can make companies more productive. In Switzerland, as in other countries, firms have increasingly introduced new technologies to monitor their employees’ performance. However, unreasonable monitoring can destroy trust in the employer. The project aimed at analysing how companies can avoid this.

  • Portrait / project description (Completed research project)

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    Our work was divided into four phases:

    1. We empirically grounded a modular framework to conceptually grasp big data in HR.
    2. We carried out a large-scale survey of Swiss companies to find out how Big Data is currently being used in the workplace.
    3. Five detailed case studies identified best practices about big data in HR.
    4. We used the data to construct a model covering a wide range of workplace/private life scenarios, which were experimentally tested and developed further in dialogue with the industry.

    We shared the resulting data and findings with the national and international scientific community as well as with the five participating companies in Switzerland.

  • Background

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    People analytics tools should make businesses more productive, transparent and flexible, and promote fairness. They are in increasing demand in all areas of the employee life cycle. However, their benefits can come with various, often incisive pitfalls, such as trust losses. Furthermore, oftentimes people analytics tools do not fulfil the necessary legal requirements from a data protection law, employment law and discrimination law perspective.

  • Aim

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    We answered the following questions:

    1. What does a sound conceptualisation of Big Data in HR look like?
    2. Which Big Data-based practices are Swiss companies currently using in HR?
    3. To what extent do these foster or damage trust in the employer?
    4. What scope for improvement is there from the HR, ethical and legal perspectives?

    We entered into a dialogue with the industry and carried out empirical investigations using various empirical research methods in Switzerland.

  • Relevance/application

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    Many aspects of our project enable a more nuanced understanding of what Big Data means for HR management in various organisational layers. Before our project, no robust data was available on how Swiss companies use Big Data tools in HR management. Furthermore, interactions with trust, ethical and legal considerations have been largely ignored. Our interdisciplinary research strengthens Switzerland as a centre of scientific achievement and has practical applications.

  • Results

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    Trust/HR Management: Our modular framework offers an initial possibility to systematically disentangle the complexity that comes with the implementation and use of Big Data. Besides, the framework can be used by managers to assess the impact of technology inside workplaces, or even as a tool to train digital literacy in a leadership context. Additionally, our results are forward-looking in that they outline trust challenges that emerge from ongoing leadership automation and point to critical management strategies to mitigate such trust-harming effects.

    Business Ethics: Development and implementation of big data-based HR tools are accompanied by significant normative challenges, which we have systematically elaborated from the perspective of personal integrity. In our publications, we have opened up new perspectives on the responsible use of technology and have explored concepts that have received little attention to date, such as monolatry or moral imagination. We argue for the organisational development of critical data literacy, moral awareness, participatory design and (beyond the implementing organisation) new private regulatory regimes.

    Law: Employers face data protection, employment law, and discrimination law challenges. Our publications have analysed these legal challenges and proposed solutions to issues that have not been addressed in Swiss law before. We have argued for professionalisation and democratisation of data protection law to achieve better law enforcement in practice. We have also shown that discrimination law does not adequately prevent or compensate algorithmic discrimination nor will it be capable to do so even with a revision of the law. We have analysed the employment law issues arising under Swiss law.

  • Original title

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    Big Data or Big Brother? – Big Data HR Control Practices and Employee Trust