One realm of AI, recommender systems have attracted significant research attention due to concerns about its devastating effects to society’s most vulnerable and marginalised communities. Both media press and academic literature provide compelling evidence that AI-based recommendations help to perpetuate and exacerbate racial and gender biases. Yet, there is limited knowledge about the extent to which individuals might question AI-based recommendations when perceived as biased. To address this gap in knowledge, we investigate the effects of espoused national cultural values on AI questionability, by examining how individuals might question AI-based recommendations due to perceived racial or gender bias. Data collected from 387 survey respondents in the United States indicate that individuals with espoused national cultural values associated to collectivism, masculinity and uncertainty avoidance are more likely to question biased AI-based recommendations. This study advances understanding of how cultural values affect AI questionability due to perceived bias and it contributes to current academic discourse about the need to hold AI accountable.

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This is an exploratory study to analyze the permeation of the term/word “analytics” in the operations management domain by reviewing 2175 papers published in the Production and Operations Management journal (POMJ) since its inception in the winter 1992 until the summer of 2022. We identify the words that commonly precede the term “analytics” in these papers. We also identify the most common words that appear in the research papers in which the term “analytics” has appeared. This analysis allows us to reflect on the contexts in which analytics studies have focused and the influence they might exert in future studies. Results show that the usage of the term “analytics” has increased from less than 2% of articles published in POMJ during the period (2007–2016) to about 1 out of every 10 articles published from 2020 to the summer of 2022.