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Artificial intelligence (AI) and machine learning (ML) have seen widespread adoption by organizations seeking to identify and hire high-quality job applicants. Yet the volume, variety, and velocity of professional involvement among I-O psychologists remains relatively limited when it comes to developing and evaluating AI/ML applications for talent assessment and selection. Furthermore, there is a paucity of empirical research that investigates the reliability, validity, and fairness of AI/ML tools in organizational contexts. To stimulate future involvement and research, we share our review and perspective on the current state of AI/ML in talent assessment as well as its benefits and potential pitfalls; and in addressing the issue of fairness, we present experimental evidence regarding the potential for AI/ML to evoke adverse reactions from job applicants during selection procedures. We close by emphasizing increased collaboration among I-O psychologists, computer scientists, legal scholars, and members of other professional disciplines in developing, implementing, and evaluating AI/ML applications in organizational contexts.

As a multifaceted construct reflecting one's self-esteem, generalized self-efficacy, locus of control, and emotional stability, core self-evaluations (CSE) has become popular to measure in applied psychology research, especially given its conceptual importance and empirical usefulness for understanding the dispositional effects on employee attitudes and behaviors. Yet, less attention has been paid to the internal properties of its measurement, relative to its criterion-related validity evidence. Thus, we believe that it is useful and timely to report on meta-analytic evidence regarding the psychometric reliability and associated study characteristics of Core Self-Evaluations Scale (CSES; Judge et al., 2003) to inform their nature, use, and future development. Results demonstrated support for acceptable levels of coefficient alpha across measures ([mu][subscript alpha] = 0.84, [tau] = 0.05). We discuss several implications for measuring CSE in a multidimensional and generalizable manner.

Operational validities are important to personnel selection research because they estimate how well a predictor in practical use correlates with a criterion construct, if the criterion measure were purged of measurement error variance. Because range restriction on a predictor or predictor composite creates incidental range restriction on the criterion, existing methodologies offer limited information and guidance for estimating operational validities. Although these effects of range restriction and criterion unreliability could be corrected with existing equations in a sequential fashion, proper use of sequential correction equations is not always as straightforward as it appears. This research reviews the existing equations for correcting validities, outlines the appropriate method for correcting validity coefficients via sequential equations, and proposes a new equation that performs a combined correction for the effects of incidental range restriction and criterion unreliability.

University science, technology, engineering, and math (STEM) summer bridge programs provide incoming STEM university students additional course work and preparation before they begin their studies. These programs are designed to reduce attrition and increase the diversity of students pursuing STEM majors and STEM career paths. A meta-analysis of 16 STEM summer bridge programs was conducted. Results showed that program participation had a medium-sized effect on first-year overall grade point average (d = 0.34) and first-year university retention (Odds Ratio [OR] = 1.747). Although this meta-analytic research reflects a limited amount of available quantitative academic data on summer STEM bridge programs, this study nonetheless provides important quantitative inroads into much-needed research on programs' objective effectiveness. These results articulate the importance of thoughtful experimental design and how further research might guide STEM bridge program development to increase the success and retention of matriculating STEM students.

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]

Multiple regression (MR) analyses are commonly employed in social science fields. It is also common for interpretation of results to typically reflect overreliance on beta weights, often resulting in very limited interpretations of variable importance. It appears that few researchers employ other methods to obtain a fuller understanding of what and how independent variables contribute to a regression equation. Thus, this paper presents a guidebook of variable importance measures that inform MR results, linking measures to a theoretical framework that demonstrates the complementary roles they play when interpreting regression findings. We also provide a data-driven example of how to publish MR results that demonstrates how to present a more complete picture of the contributions variables make to a regression equation. We end with several recommendations for practice regarding how to integrate multiple variable importance measures into MR analyses. (Contains 6 tables.)

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]

Second language (L2) research relies heavily and increasingly on ANOVA (analysis of variance)-based results as a means to advance theory and practice. This fact alone should merit some reflection on the utility and value of ANOVA. It is possible that we could use this procedure more appropriately and, as argued here, other analyses such as multiple regression may prove to be more illuminating in certain research contexts. We begin this article with an overview of problems associated with ANOVA; some of them are inherent to the procedure, and others are tied to the way it is applied in L2 research. We then present three rationales for when researchers might turn to multiple regression in place of ANOVA. Output from ANOVA and multiple regression analyses based on published and mock-up studies are used to illustrate major points.

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]