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ABSTRACT Organizations inevitably face various forms of disruptive events (e.g., external crises), and sustaining long‐term prosperity requires them to stay resilient when encountering unexpected adversity. Prior crisis management research predominantly relied on qualitative case studies to examine efforts after a crisis had occurred, treating the crisis as a “given” rather than a variable. The exceptionality of crisis situations and the ad hoc nature of crisis countermeasures largely limit current knowledge about how organizations may manage employees to remain in a preparative stance for disruptive events. Integrating the inclusion literature, crisis management research, and event system theory, we propose inclusion management practices as a viable pathway for organizations to develop resilience resources and capabilities prior to a crisis, allowing them to exhibit greater robustness and agility when a crisis arises. Such robustness and agility, in turn, enhance organizational performance thereafter. We further pinpoint the strength of a crisis event as an important contingency shaping the effects of pre‐crisis inclusion management practices on organizations’ resilient responses and thereby performance. We tested our hypotheses in the context of the COVID‐19 pandemic crisis using longitudinal manager‐report survey data (N= 884 workplaces). We found that workplaces that implemented more inclusion management practices before COVID‐19 were more robust and agile in response to the pandemic crisis. Agility (but not robustness), in turn, was positively related to organizational performance. In addition, the effect of inclusion management practices on agility was stronger for workplaces with greater COVID‐19 event strength. 

Artificial intelligence (AI) has impacted human life at many levels, entailing economic and societal changes. AI algorithms are increasingly used by organizations to generate predictions that feed into decisions (e.g., who is eligible for insurance coverage, approved for bank loans, selected for job interviews). Since the data used for developing the algorithms can contain bias such as gender or racial prejudice, AI predictions can become discriminatory. For-profit and not-for-profit organizations face the hurdles of developing, applying, and maintaining governance of AI, making sure that goal optimization responds to ethical and fairness values. 

Abstract The prevailing view of intracellular RNA trafficking in eukaryotic cells is that RNAs transcribed in the nucleus either stay in the nucleus or cross the nuclear envelope, entering the cytoplasm for function. However, emerging evidence illustrates that numerous functional RNAs move in the reverse direction, from the cytoplasm to the nucleus. The mechanism underlying RNA nuclear import has not been well elucidated. Viroids are single-stranded circular noncoding RNAs that infect plants. Using Nicotiana benthamiana, tomato (Solanum lycopersicum), and nuclear-replicating viroids as a model, we showed that cellular IMPORTIN ALPHA-4 (IMPa-4) is likely involved in viroid RNA nuclear import, empirically supporting the involvement of Importin-based cellular pathway in RNA nuclear import. We also confirmed the involvement of a cellular protein (viroid RNA-binding protein 1 [VIRP1]) that binds both IMPa-4 and viroids. Moreover, a conserved C-loop in nuclear-replicating viroids serves as a key signal for nuclear import. Disrupting C-loop impairs VIRP1 binding, viroid nuclear accumulation, and infectivity. Further, C-loop exists in a subviral satellite noncoding RNA that relies on VIRP1 for nuclear import. These results advance our understanding of subviral RNA infection and the regulation of RNA nuclear import. 

The science of organizations increases its credibility when it embraces research with an explicit focus on robustness and reliability. This special issue of curated commentaries recommends and illustrates how to incorporate robust and reliable research practices in organizational research. Together, these commentaries help researchers make contributions to improving every step of the research trajectory—theory development, methodology, and the process of quality control through peer review. 

Abstract Researchers have investigated whether machine learning (ML) may be able to resolve one of the most fundamental concerns in personnel selection, which is by helping reduce the subgroup differences (and resulting adverse impact) by race and gender in selection procedure scores. This article presents three such investigations. The findings show that the growing practice of making statistical adjustments to (nonlinear) ML algorithms to reduce subgroup differences must create predictive bias (differential prediction) as a mathematical certainty. This may reduce validity and inadvertently penalize high‐scoring racial minorities. Similarly, one approach that adjusts the ML input data only slightly reduces the subgroup differences but at the cost of slightly reduced model accuracy. Other emerging tactics involve weighting predictors to balance or find a compromise between the competing goals of reducing subgroup differences while maintaining validity, but they have been limited to two outcomes. The third investigation extends this to three outcomes (e.g., validity, subgroup differences, and cost) and presents an online tool. Collectively, the studies in this article illustrate that ML is unlikely to be able to resolve the issue of adverse impact, but it may assist in finding incremental improvements.