---
res:
  bibo_abstract:
  - <jats:sec><jats:title content-type="abstract-subheading">Purpose</jats:title><jats:p>Enabled
    by increased (“big”) data stocks and advanced (“machine learning”) analyses, the
    concept of human resource analytics (HRA) is expected to systematically improve
    decisions in human resource management (HRM). Since so far empirical evidence
    on this is, however, lacking, the authors' study examines which combinations of
    data and analyses are employed and which combinations deliver on the promise of
    improved decision quality.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title><jats:p>Theoretically,
    the paper employs a neo-configurational approach for founding and conceptualizing
    HRA. Methodically, based on a sample of German organizations, two varieties (crisp
    set and multi-value) of qualitative comparative analysis (QCA) are employed to
    identify combinations of data and analyses sufficient and necessary for HRA success.</jats:p></jats:sec><jats:sec><jats:title
    content-type="abstract-subheading">Findings</jats:title><jats:p>The authors' study
    identifies existing configurations of data and analyses in HRM and uncovers which
    of these configurations cause improved decision quality. By evidencing that and
    which combinations of data and analyses conjuncturally cause decision quality,
    the authors' study provides a first confirmation of HRA success.</jats:p></jats:sec><jats:sec><jats:title
    content-type="abstract-subheading">Research limitations/implications</jats:title><jats:p>Major
    limitations refer to the cross-sectional and national sample and the usage of
    subjective measures. Major implications are the suitability of neo-configurational
    approaches for future research on HRA, while deeper conceptualizing and researching
    both the characteristics and outcomes of HRA constitutes a core future task.</jats:p></jats:sec><jats:sec><jats:title
    content-type="abstract-subheading">Originality/value</jats:title><jats:p>The authors'
    paper employs an innovative theoretical-methodical approach to explain and analyze
    conditions that conjuncturally cause decision quality therewith offering much
    needed empirical evidence on HRA success.</jats:p></jats:sec>@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Stefan
      foaf_name: Strohmeier, Stefan
      foaf_surname: Strohmeier
  - foaf_Person:
      foaf_givenName: Julian
      foaf_name: Collet, Julian
      foaf_surname: Collet
  - foaf_Person:
      foaf_givenName: Rüdiger
      foaf_name: Kabst, Rüdiger
      foaf_surname: Kabst
  bibo_doi: 10.1108/bjm-05-2021-0188
  bibo_issue: '3'
  bibo_volume: 17
  dct_date: 2022^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/1746-5265
  dct_language: eng
  dct_publisher: Emerald@
  dct_subject:
  - Management of Technology and Innovation
  - Marketing
  - Organizational Behavior and Human Resource Management
  - Strategy and Management
  - Business and International Management
  dct_title: (How) do advanced data and analyses enable HR analytics success? A neo-configurational
    analysis@
...