People Analytics: an organizational psychology perspective on data-oriented leadership

Reindl, Cornelia

doi:10.1007/s11612-016-0325-7

Cited by 6 publications

(3 citation statements)

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“…With the rise of people analytics, human resource (HR) management today relies heavily on algorithm-based decision support systems to assist HR managers in the assessment of the value of individual employees as part of their organizations’ human capital asset ( Reindl, 2016 ; Leicht-Deobald et al, 2019 ). People analytics is the creation of unique workforce insights by integrating originally disparate data sources from both inside and outside the organization to create HR insights with strategic value that lead to a competitive advantage.…”

Section: Introductionmentioning

confidence: 99%

“…While metric-driven analytical approaches to HR management date back to the beginning of the 20 th century ( Huselid, 1995 ; Leicht-Deobald et al, 2019 ), quantifying individuals’ human capital as a specific asset to the firm has become much more sophisticated because of recent technological advances in algorithm-based machine learning and big data ( Reindl, 2016 ). In a 2017 survey of 10,400 firms across 140 countries, Deloitte identified people analytics as one of the main global trends in human capital, with 71% of companies surveyed rating algorithm-based people analytics as a high priority in their organizations ( Fineman et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

“…Since the aforementioned workforce insights—generated by scoring employees based on computer-aided procedures—may inform HR practices not only descriptively but may also be used to predict future performance, applying people analytics raises substantial questions regarding legal and ethical matters ( Leicht-Deobald et al, 2019 ), particularly regarding the quality of algorithmic predictions and their effect on HR managers’ choice architectures ( Reindl, 2016 ). The idea of enhancing the quality of strategic HR management by using IT-based support systems that facilitate personnel decisions is deeply rooted in the paradigm of evidence-based management ( Sharma and Sharma, 2017 ; Leicht-Deobald et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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Blame the Machine? Insights From an Experiment on Algorithm Aversion and Blame Avoidance in Computer-Aided Human Resource Management

Maasland

Weißmüller

2022

Front. Psychol.

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Algorithms have become increasingly relevant in supporting human resource (HR) management, but their application may entail psychological biases and unintended side effects on employee behavior. This study examines the effect of the type of HR decision (i.e., promoting or dismissing staff) on the likelihood of delegating these HR decisions to an algorithm-based decision support system. Based on prior research on algorithm aversion and blame avoidance, we conducted a quantitative online experiment using a 2×2 randomly controlled design with a sample of N = 288 highly educated young professionals and graduate students in Germany. This study partly replicates and substantially extends the methods and theoretical insights from a 2015 study by Dietvorst and colleagues. While we find that respondents exhibit a tendency of delegating presumably unpleasant HR tasks (i.e., dismissals) to the algorithm—rather than delegating promotions—this effect is highly conditional upon the opportunity to pretest the algorithm, as well as individuals’ level of trust in machine-based and human forecast. Respondents’ aversion to algorithms dominates blame avoidance by delegation. This study is the first to provide empirical evidence that the type of HR decision affects algorithm aversion only to a limited extent. Instead, it reveals the counterintuitive effect of algorithm pretesting and the relevance of confidence in forecast models in the context of algorithm-aided HRM, providing theoretical and practical insights.

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Section: Introductionmentioning

confidence: 99%