The identity conflict process: Appraisal theory as an integrative framework for understanding identity conflict at work.

Identity conflict-the experience of perceiving incompatibilities between aspects of one's identity content that call into question the individual's ability to meet the identity standard of at least one of these identities-can significantly impact individuals' work experiences. As individuals navigate experiences of identity conflict at work, managers and organizations also grapple with how to support employees' multiple identities while mitigating the primarily negative outcomes of identity conflict. However, the scholarship on work-relevant identity conflict faces several challenges, including disciplinary fragmentation, conceptual imprecision, and diverse but deficient theoretical perspectives, which together have limited our ability to accumulate knowledge about this experience and to develop useful management tools. To overcome these, we conducted a thorough review of the cross-disciplinary literature, allowing us to offer a refined integrative definition of identity conflict and a reconceptualization of identity conflict as the result of an appraisal process. As we delineate what we know about the appraisal process of identity conflict, we provide a detailed theoretical explanation of its antecedents, outcomes, and responses and shed light on the mechanisms that drive the process. This approach not only enhances theoretical depth and guides new research directions but also equips managers to address and reduce identity conflict experienced by their employees. This research contributes to the literature by offering clarity and coherence to the identity conflict domain, providing theoretical and practical guidance, and outlining promising directions for future inquiry. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Using natural language processing to increase prediction and reduce subgroup differences in personnel selection decisions.

The purpose of this research is to demonstrate how using natural language processing (NLP) on narrative application data can improve prediction and reduce racial subgroup differences in scores used for selection decisions compared to mental ability test scores and numeric application data. We posit there is uncaptured and job-related constructs that can be gleaned from applicant text data using NLP. We test our hypotheses in an operational context across four samples (total N = 1,828) to predict selection into Officer Training School in the U.S. Air Force. Boards of three senior officers make selection decisions using a highly structured rating process based on mental ability tests, numeric application information (e.g., number of past jobs, college grades), and narrative application information (e.g., past job duties, achievements, interests, statements of objectives). Results showed that NLP scores of the narrative application generally (a) predict Board scores when combined with test scores and numeric application information at a level of correlation equivalent to the correlation between human raters (.60), (b) add incremental prediction of Board scores beyond mental ability tests and numeric application information, and (c) reduce subgroup differences between racial minorities and nonracial minorities in Board scores compared to mental ability tests and numeric application information. Moreover, NLP scores predict (a) job (training) performance, (b) job (training) performance beyond mental ability tests and numeric application information, and (c) even job (training) performance beyond Board scores. Scoring of narrative application data using NLP shows promise in addressing the validity-adverse impact dilemma in selection. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Multiple jobholding motivations and experiences: A typology and latent profile analysis.

Researchers have traditionally suggested that multiple jobholders (MJHers; individuals who work more than one job) are economically deprived and piece together employment to make ends meet. More recently, scholars have demonstrated that MJHers are also motivated for nonpecuniary benefits. In the current research, we employ a mixed-methods, three-study research design on 1,487 MJHers to develop a comprehensive typology of multiple jobholding (MJH) motivations, advance our understanding of how MJH motivations co-occur through the generation of latent MJH motivational profiles, and test how MJH experiences differ by profile. In Study 1 (N = 801), we content analyze qualitative survey responses and uncover eight motivational categories. In Study 2 (N = 260), we find evidence of four MJH motivational profiles based on motivations found in Study 1: Identity Builders, Value Optimizers, Pragmatic Enjoyment Seekers, and Instrumentalists. In Study 3a (N = 426), we empirically replicate the four-profile solution and conceptually replicate three profiles-instead of Instrumentalists, we find evidence of Precarious Workers. In Study 3b, we develop and test hypotheses as to how MJH experiences pooled across the primary and secondary job differ by profile. Findings suggest there are optimal MJH motivational patterns and that some MJHers (Identity Builders, Value Optimizers) are more likely to experience enrichment than other MJHers (Precarious Workers). Theoretically, we integrate the careers and MJH motivation literature with the enrichment and depletion model of multiple role engagement within one domain (work). Finally, we discuss practical implications for MJHers, managers, and organizations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Videoconference fatigue? Exploring changes in fatigue after videoconference meetings during COVID-19.

In response to the Coronavirus disease 2019 (COVID-19) global health pandemic, many employees transitioned to remote work, which included remote meetings. With this sudden shift, workers and the media began discussing videoconference fatigue, a potentially new phenomenon of feeling tired and exhausted attributed to a videoconference. In the present study, we examine the nature of videoconference fatigue, when this phenomenon occurs, and what videoconference characteristics are associated with fatigue using a mixed-methods approach. Thematic analysis of qualitative responses indicates that videoconference fatigue exists, often in near temporal proximity to the videoconference, and is affected by various videoconference characteristics. Quantitative data were collected each hour during five workdays from 55 employees who were working remotely because of the COVID-19 pandemic. Latent growth modeling results suggest that videoconferences at different times of the day are related to deviations in employee fatigue beyond what is expected based on typical fatigue trajectories. Results from multilevel modeling of 279 videoconference meetings indicate that turning off the microphone and having higher feelings of group belongingness are related to lower postvideoconference fatigue. Additional analyses suggest that higher levels of group belongingness are the most consistent protective factor against videoconference fatigue. Such findings have immediate practical implications for workers and organizations as they continue to navigate the still relatively new terrain of remote work. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Using practice employment tests to improve recruitment and personnel selection outcomes for organizations and job seekers.

This study introduces the use of practice employment tests during recruitment as a tool with the potential to improve outcomes for both an organization and its (potential) applicants during personnel selection. Synthesizing research on recruitment, selection, job search, adverse impact, signaling theory, and human capital theory, we propose that practice tests reduce information asymmetry regarding the nature of an organization's assessment procedures, thereby acting as short-term human capital investment opportunities. Using a large sample of potential applicants and applicants who later decided to apply for jobs within a professional occupation in a large organization, we demonstrate that (a) those who took the practice tests scored higher on the actual tests; (b) score gains between practice tests and actual tests were greater for Blacks and Hispanics when compared to Whites; (c) the practice test exhibited a self-selection effect, encouraging those with higher scores to apply; and (d) score gains between practice tests and actual tests were similar to scores observed for those retesting on the actual tests. These findings suggest practice tests may be capable of simultaneously enhancing organizational outcomes (e.g., increased quality of applicants, reduced cost of testing unqualified applicants, and reduced adverse impact) and applicant outcomes (e.g., increased human capital, increased chances of eventual employment, and reduced disappointment and wasted effort from unsuccessful application). (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Initial investigation into computer scoring of candidate essays for personnel selection.

[Correction Notice: An Erratum for this article was reported in Vol 101(7) of Journal of Applied Psychology (see record 2016-32115-001). In the article the affiliations for Emily D. Campion and Matthew H. Reider were originally incorrect. All versions of this article have been corrected.] Emerging advancements including the exponentially growing availability of computer-collected data and increasingly sophisticated statistical software have led to a "Big Data Movement" wherein organizations have begun attempting to use large-scale data analysis to improve their effectiveness. Yet, little is known regarding how organizations can leverage these advancements to develop more effective personnel selection procedures, especially when the data are unstructured (text-based). Drawing on literature on natural language processing, we critically examine the possibility of leveraging advances in text mining and predictive modeling computer software programs as a surrogate for human raters in a selection context. We explain how to "train" a computer program to emulate a human rater when scoring accomplishment records. We then examine the reliability of the computer's scores, provide preliminary evidence of their construct validity, demonstrate that this practice does not produce scores that disadvantage minority groups, illustrate the positive financial impact of adopting this practice in an organization (N ∼ 46,000 candidates), and discuss implementation issues. Finally, we discuss the potential implications of using computer scoring to address the adverse impact-validity dilemma. We suggest that it may provide a cost-effective means of using predictors that have comparable validity but have previously been too expensive for large-scale screening. (PsycINFO Database Record