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This study aims to investigate the configurational effects of five rules – artificial intelligence (AI)-based hiring decision transparency, consistency, voice, explainability and human involvement – on applicants' procedural justice perception (APJP) and applicants' interactional justice perception (AIJP). In addition, this study examines whether the identified configurations could further enhance applicants' organisational commitment (OC).

Drawing on the justice model of applicants' reactions, the authors conducted a longitudinal survey of 254 newly recruited employees from 36 Chinese companies that utilise AI in their hiring. The authors employed fuzzy-set qualitative comparative analysis (fsQCA) to determine which configurations could improve APJP and AIJP, and the authors used propensity score matching (PSM) to analyse the effects of these configurations on OC.

The fsQCA generates three patterns involving five configurations that could improve APJP and AIJP. For pattern 1, when AI-based recruitment with high interpersonal rule (AI human involvement) aims for applicants' justice perception (AJP) through the combination of high informational rule (AI explainability) and high procedural rule (AI voice), there must be high levels of AI consistency and AI voice to complement AI explainability, and only this pattern of configurations can further enhance OC. In pattern 2, for the combination of high informational rule (AI explainability) and low procedural rule (absent AI voice), AI recruitment with high interpersonal rule (AI human involvement) should focus on AI transparency and AI explainability rather than the implementation of AI voice. In pattern 3, a mere combination of procedural rules could sufficiently improve AIJP.

This study, which involved real applicants, is one of the few empirical studies to explore the mechanisms behind the impact of AI hiring decisions on AJP and OC, and the findings may inform researchers and managers on how to best utilise AI to make hiring decisions.

The purpose of this study is to empirically explore the relationship between a new venture top management team's (NVTMT’s) shared leadership and strategic performance in opportunity recognition and entrepreneurial bricolage by drawing on the upper echelons theory.

Data were collected from 344 new manufacturing ventures located in Eastern China. The hypotheses were tested using structural equation modelling (SEM) through the AMOS 23.0 software package. The confluence of the contextual factors of the new venture is examined by a fuzzy-set qualitative comparative analysis (fsQCA).

The results indicate that NVTMT shared leadership has an indirect and positive effect on strategic performance through opportunity recognition, especially in a highly uncertain environment, while the mediating effect of entrepreneurial bricolage is not significant. Furthermore, although the SEM results show that the impact of NVTMT shared leadership on entrepreneurial bricolage is negative, the fsQCA shows that NVTMT shared leadership can significantly and positively affect entrepreneurial bricolage in an environment with high uncertainty, ultimately enhancing strategic performance.

This study contributes to the shared leadership literature by proposing a model on how shared leadership shapes the strategic performance of new ventures via opportunity recognition and entrepreneurial bricolage. The findings not only enrich relevant research on the upper echelons theory, but also help in understanding the patterns of contextual conditions that facilitate the value-adding properties of NVTMT shared leadership.