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Technological advances have led to the development of automated methods for personnel assessment that are purported to augment or outperform human judgment. However, empirical research providing validity evidence for such techniques in the selection context remains scarce. In addressing this void, this study focuses on language-based personality assessments using an off-the-shelf, commercially available product (i.e., IBM Watson Personality Insights) in the context of video-based interviews. The scores derived from the language-based assessment were compared to self and observer ratings of personality to examine convergent and discriminant relationships. The language-based assessment scores showed low convergence with self-ratings for openness, and with self- and observer ratings for agreeableness. No validity evidence was found for extraversion and conscientiousness. For neuroticism, the patterns of correlations were in the opposite of what was theoretically expected, which raised a significant concern. We suggest more validation work is needed to further improve emerging assessment techniques and to understand when and how such approaches can appropriately be applied in personnel assessment and selection.

Research suggests that understanding workers' attitudes towards artificial intelligence (AI) application is a prerequisite to successfully integrating AI into an organization. However, few studies have clarified the meaning of attitudes towards AI application at work (AAAW) as a multifaceted construct that can be assessed with psychometric validity. To address this issue, we developed and validated a scale to capture individuals' AAAW using three independent samples (total N = 2841). The resulting 25‐item scale covers an overall construct of AAAW as well as six dimensions that are subsumed under the construct (i.e., perceived humanlikeness, perceived adaptability, perceived quality of AI, AI use anxiety, job insecurity and personal utility). Our findings suggest that the AAAW scale has good psychometric properties and can be used to predict important recruiting outcomes. The scale offers opportunities to better understand and measure workers' attitudes towards AI application at work in a comprehensive and integrative manner. [ABSTRACT FROM AUTHOR]

Background: As globalization continues to impact the engineering profession, many programs aim to prepare current and future engineers to work across national and cultural boundaries. Yet there remains a lack of quality tools for assessing global competency among engineers and other technical professionals, including their behavioral tendencies in global work situations. Purpose: We introduce development of a situational judgment test (SJT) covering three dimensions of global engineering competency (GEC) in Chinese national/cultural context. The main aim of this article is to describe how the SJT was developed through a systematic multistep process. Secondarily, we explore relationships between SJT performance and other theoretically relevant variables. Methods: After generating a large initial pool of SJT scenarios and behavioral response items, we used ratings from subject matter experts (SMEs) to select six SJT scenarios and create scoring keys for 26 response items. To further explore the instrument's validity, we deployed the SJT items, other relevant measures, and a demographic survey to a sample of practicing engineers (n = 400). Results: SME ratings provide strong evidence for the content relevance of the GEC-SJT tool. Survey results also suggest positive relationships between SJT performance and Chinese cultural knowledge, age, and years of work experience. However, more validity and reliability evidence is needed before recommending wider use of the instrument. Conclusions: Our findings suggest the SJT format as a promising behavior-based approach to measuring global competency and other professional attributes in engineering. We also discuss directions for future research and training efforts related to assessing and developing global competency.

Organisations are increasingly relying on people analytics to aid human resources decision‐making. One application involves using machine learning to automatically infer applicant characteristics from employment interview responses. However, management research has provided scant validity evidence to guide organisations' decisions about whether and how best to implement these algorithmic approaches. To address this gap, we use closed vocabulary text mining on mock video interviews to train and test machine learning algorithms for predicting interviewee's self‐reported (automatic personality recognition) and interviewer‐rated personality traits (automatic personality perception). We use 10‐fold cross‐validation to test the algorithms' accuracy for predicting Big Five personality traits across both rating sources. The cross‐validated accuracy for predicting self‐reports was lower than large‐scale investigations using language in social media posts as predictors. The cross‐validated accuracy for predicting interviewer ratings of personality was more than double that found for predicting self‐reports. We discuss implications for future research and practice. [ABSTRACT FROM AUTHOR]

Summary: The rising use of artificially intelligent (AI) technologies, including generative AI tools, in organizations is undeniable. As these systems become increasingly integrated into organizational practices and processes, understanding their impact on workers' experiences and job designs is critical. However, the ongoing discourse surrounding AI use in the workplace remains divided. Proponents of the technology extol its benefits for enhancing efficiency and productivity, while others voice concerns about the potential harm to human workers. To provide greater clarity on this pressing issue, this article presents a systematic review of empirical research that sheds light on the implications of AI use at work. Organized under five inductively generated themes within a multilevel framework, we uncover individual, group, and organizational factors that shape the interplay between humans and AI. Specifically, the themes are: (1) human–AI collaboration; (2) perceptions of algorithmic and human capabilities; (3) worker attitudes towards AI; (4) AI as a control mechanism in algorithmic management of platform‐based work; and (5) labor market implications of AI use. Our review offers insights into these themes and identifies five pathways for future research. Finally, we provide practical recommendations for organizational leaders seeking to implement AI technologies while prioritizing their employees' well‐being. [ABSTRACT FROM AUTHOR]

This study attempts to clarify conceptual and operational inconsistencies in the literature around "Ghiselli's hobo syndrome." I propose that defining characteristics of hobo syndrome should include both the exhibition of frequent job movement behavior and positive attitudes about such behavior. This definition effectively differentiates the construct from other similar phenomena associated with frequent job movement (e.g., job/career mobility, protean careers). Using latent class cluster analysis of a diverse sample of 944 U.S. workers, it was empirically validated that a small number of individuals resembling the proposed characteristics of hobos did emerge as a distinct group (N = 42), providing person-centered evidence for the construct validity of hobo syndrome. The dispositional roots of hobo syndrome and work-related outcomes were also explored. It was found that individuals with high levels of openness to experience were likely to exhibit hobo syndrome, whereas impulsivity was not related to such tendency. Also, workers who were more likely to be categorized as hobos tended to report less positive views about their current jobs. Based on these findings, implications for research and practice are discussed. (Contains 4 tables and 1 figure.)

Admissions committee members often speak of "it" as an important consideration during the admissions process; however, empirical evidence documenting the efficacy of "it" as a basis for admission is scarce. The purpose of this research was to test the validity of student-institution it as a significant predictor of college success--specifically, first-year GPA, cumulative GPA, and college graduation. Using data from two large national samples, the congruence between a student's preferred college characteristics and the characteristics of his or her attending institution was calculated across six dimensions (i.e., year, sector, campus, distance, gender, and size). No dimensions of it were of practical significance. Implications are discussed. (Contains 5 tables.)

In this rejoinder, we discuss several areas of agreement as well as some noteworthy divergence in perspectives that are worth exploring further. We also note a few areas where immediate clarifications may be necessary. Next, we discuss practical solutions and challenges for improving the validity and fairness of graduate admissions. We conclude with a call for intellectual humility and openness in further advancing the field's discussions on this critical topic as well as for authenticity and persistence in effecting real changes to the system. [ABSTRACT FROM AUTHOR]

As many schools and departments are considering the removal of the Graduate Record Examination (GRE) from their graduate-school admission processes to enhance equity and diversity in higher education, controversies arise. From a psychometric perspective, we see a critical need for clarifying the meanings of measurement "bias" and "fairness" to create common ground for constructive discussions within the field of psychology, higher education, and beyond. We critically evaluate six major sources of information that are widely used to help inform graduate-school admissions decisions: grade point average, personal statements, resumes/curriculum vitae, letters of recommendation, interviews, and GRE. We review empirical research evidence available to date on the validity, bias, and fairness issues associated with each of these admission measures and identify potential issues that have been overlooked in the literature. We conclude by suggesting several directions for practical steps to improve the current admissions decisions and highlighting areas in which future research would be beneficial. [ABSTRACT FROM AUTHOR]