Staying close to business: the role of epistemic alignment in rendering HR analytics outputs relevant to decision-makers

Abstract

HR Analytics (HRA) are said to create value when providing analytical outputs that are relevant to decision-makers' immediate business issues. While extant research on HRA attributes success (or lack thereof) in providing business relevant outputs to the presence or absence of particular skills and resources, we know little about how practitioners actually mobilize these skills and resources in daily practice. Drawing on observational and interview data from a case study of an HRA team, we identify boundary spanning, customizing dashboards, and speaking a language of numbers as three epistemic practices in which team members combine and mobilize a particular set of skills and resources that allows them to accomplish epistemic alignment, i.e. aligning to decision-makers' perception of business reality when creating analytical outputs. Epistemic alignment enables the team members to produce complex analytical outputs while at the same time staying close to the decision-makers' immediate business problems. At the same time, team members are capable of accounting for conditions in the broader organizational context, such as compliance issues, dependencies, political tensions, and a prevailing data-driven decision culture. Our findings contribute to knowledge on how organizations can build effective HRA and how advanced forms of digitalization transform the work of HRM in contemporary organizations.

Exploring the phenomenon of HR analytics: a study of challenges, risks and impacts in 40 large companies

“…Implementation follows the decision to adopt and in order to understand how successful implementation is mobilized, Ellmer and Reichel (2021) conceptualized HRA as an epistemic practice that relies on a set of technologies and tools, which enable HRA practitioners to generate insightful analytical outputs. Their in-depth, qualitative study of a German technology multinational applies the “HRA-as-practice” approach developed by Bjorkman et al.…”

Human resources analytics: A legitimacy process

“…While the concept of human resources (HR) Analytics has existed for over 20 years (Angrave et al., 2016; Cappelli, 2017; Davenport et al., 2010; Huselid, 2018; Marler & Fisher, 2013), scholarship shows it has been particularly slow in delivering on its promises (Ellmer & Reichel, 2021; Minbaeva, 2018). A review of current literature reveals that because HR Analytics in practice is a relatively recent HRM innovation (Marler & Boudreau, 2017), the added value it can bring to the heart of HR, from its very strategic position and contribution through to its operational deliverables across the organisation is, for the most part, still unclear (Angrave et al., 2016; Huselid, 2018; Marler & Boudreau, 2017).…”

“…This state of performance‐limbo is exacerbated when, despite a growing body of HR Analytics literature, the gap persists. This is likely due to challenges defining the ‘epistemic object’ of HR Analytics, whose key performance indicators (KPIs) are more complex than in other business domains such as finance, operations or marketing (Ellmer & Reichel, 2021; Greasley & Thomas, 2020). Secondly, HR Analytics is a HRM innovation that involves multiple stakeholders, a challenge that is intensified by the fact that many of these are non‐HRM experts (Boudreau & Cascio, 2017; Minbaeva, 2018).…”

(How) do advanced data and analyses enable HR analytics success? A neo-configurational analysis

“…Our study focused “decision quality” since it is habitually understood as core outcome in literature (e.g. Dulebohn and Johnson, 2013; Ellmer and Reichel, 2021; Levenson, 2011). Yet, as systematic improvements of HR decisions on their part are expected to induce contributions to organizational strategy and performance (e.g.…”

“…KPMG, 2019; PwC, 2017) and scholarly (e.g. Minbaeva, 2018; Ellmer and Reichel, 2021) studies deal empirically with some facets of HRA success. According to existing narrative reviews, there are no studies that consider data, analyses and their interaction as conditions of HRA success.…”