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Currently, in the organizational research community, artificial intelligence (AI), machine learning (ML), and big data techniques are being vigorously explored as a set of modern‐day approaches contributing to a multidisciplinary science of people at work. This paper discusses more specifically how these sophisticated technologies, methods, and data might together advance the science of people at work through various routes, including improving theory and knowledge, construct measurements, and predicting real‐world outcomes. Inspired by the four articles in the current special issue highlighting several of these aspects in essential ways, we also share other possibilities for future organizational research. In addition, we indicate many key practical, ethical, and institutional challenges with research involving AI/ML and big data (i.e., data accessibility, methodological skill gaps, data transparency, privacy, reproducibility, generalizability, and interpretability). Taken together, the opportunities and challenges that lie ahead in the areas of AI and ML promise to reshape organizational research and practice in many exciting and impactful ways. [ABSTRACT FROM AUTHOR]

The article focuses on the critical examination of job performance ratings and the variance components that contribute to these ratings. It highlights the limitations of conventional performance ratings and emphasizes the need for improved definitions and measurements of performance to enhance the accuracy and interpretability of rating statistics.

Investigating individual differences in human-product interactions can improve the development of usable products for a diverse range of users. This study explores personality and cognitive ability as predictors of usability. A sample of 972 respondents took the Mini-IPIP measure of Big Five personality, a matrix reasoning test for cognitive ability, and the System Usability Scale (SUS) as an assessment of usability across seven commonly used products (Microsoft Word, Google, Amazon, computer, Microsoft Excel, TV remote, microwave). We ran a hierarchical regression analysis, predicting SUS score from demographic variables (gender, age), then individual differences (personality, cognitive ability). The personality traits of emotional stability and openness were statistically and practically significant predictors of SUS across every product, and gender and conscientiousness were significant predictors across many products. Results suggest that the subjective assessment of usability is not impacted by cognitive ability, and that personality traits are small-to-moderate, but consistent, predictors of SUS score. [ABSTRACT FROM AUTHOR]

To reduce the burden of Alzheimer's disease, the use of assistive technologies for patients and their informal caregivers is considered essential. However, these technologies are made as "one size fits all" instead of being tailored to accommodate people with varying degrees of cognitive impairment and those with diverse races/ethnicities. Thus, the aim of this survey was to determine whether the types of assistance needed most, and the technology used by those with cognitive impairment differed by race (White/non-Hispanics, Black or African Americans, and Hispanic/Latinos or Puerto Ricans) and severity of dementia (mild, moderate, severe). One hundred and eighty informal caregivers of people with different levels of severity of cognitive impairment and several different races/ethnicities filled out an online survey regarding assistance needed and technologies used. The results show that racial minorities considered the needs for assistance with Basic Activities of Daily Living as more important compared to White/non-Hispanics with mild dementia. Furthermore, Hispanic/Latinos or Puerto Ricans and White/non-Hispanics with severe dementia were shown to use technology that is designed to help with Instrumental Activities of Daily Living more than those with moderate dementia. Lastly, during COVID-19, devices to assist with walking, preparing meals and personal hygiene have been used significantly more by White/non-Hispanics with severe dementia compared to Hispanic/Latinos or Puerto Ricans. The results point to the need to design for those with severe dementia, regardless of race, and should focus on addressing needs related to both Instrumental and Basic Activities of Daily Living. Developers of assistive technology should consider designing technology that can accommodate all severity levels of cognitive impairment. More research is needed to determine the usability of assistive technology that is designed for those with cognitive impairments. [ABSTRACT FROM AUTHOR]

This article explores the use of AI-based assessments in employment and the legal and ethical considerations surrounding them. It emphasizes that these assessments must comply with anti-discrimination laws and guidelines. State and local governments have also enacted legislation to regulate their use. Professional groups have provided recommendations and guidance on the use of AI-based assessments. The article summarizes discussions from a conference on topics such as bias in machine learning algorithms, transparency in data collection, and concerns about privacy. It highlights the importance of collaboration between psychologists and other professions to ensure scientific integrity and protect human dignity in the workplace. [Extracted from the article]

Using game‐based assessments (GBAs) to assess and select job applicants presents the dual challenges of measuring intended job‐relevant constructs while analyzing GBA data that contain more predictors than observations. Exploring those challenges, we analyzed two GBAs that were designed to measure conscientiousness facets (i.e., achievement striving, self‐discipline, and cautiousness). Scores on traditional measures of personality and cognitive ability were modeled using either a restricted set of GBA predictors using cross‐validated ordinary least squares (OLS) regression or by the fuller set (p = 248) using random forests regression. Overall, the prediction of personality was near‐zero; but the latter approach explained 14%–30% of the variance in predicting cognitive ability. Our findings warn of GBAs potentially measuring unintended constructs rather than their intended constructs. Practitioner points: Game‐based assessment (GBA) vendors often claim to measure individual differences, but related GBA research is often lacking.The current study developed two GBAs intended to measure conscientiousness and its facets.Results demonstrated that our GBAs were measuring cognitive ability instead of conscientiousness, despite deliberate design attempts to avoid the former.Results suggested the usefulness of machine learning to understand GBA data and the constructs underlying them. [ABSTRACT FROM AUTHOR]