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Branched situational judgment tests (BSJTs) are an increasingly common employee selection method, yet there is no theory and very little empirical work explaining the designs and impacts of branching. To encourage additional research on BSJTs, and to provide practitioners with a common language to describe their current and future practices, we sought to develop a theory of BSTJs.

Given the absence of theory on branching, we utilized a ground theory qualitative research design, conducting interviews with 25 BSJT practitioner subject matter experts.

Our final theory consists of three components: (1) a taxonomy of BSJT branching features (contingency, parallelism, convergence, and looping) and options within those features (which vary), (2) a causal theoretical model describing impacts of branching in general on applicant reactions via proximal effects on face validity, and (3) a causal theoretical model describing impacts on applicant reactions among branching designs via proximal effects on consistency of administration and opportunity to perform.

Our work provides the first theoretical foundation on which future confirmatory research in the BSJT domain can be built. It also gives both researchers and practitioners a common language for describing branching features and their options. Finally, it reveals BSJTs as the results of a complex set of interrelated design features, discouraging the oversimplified contrasting of “branching” vs “not branching.”

The purpose of this paper is to examine the interactive effects of gender and age on evaluations of job applicants. Given the double jeopardy hypothesis, the authors might anticipate that older women would be denigrated most in hiring evaluations. However, given expectations of normative gender behavior, the authors might anticipate that older men would be penalized most for not already having stable employment. This study aims to examine which hypothesis best describes selection biases based on age and gender.

Stimuli depicting male and female job applicants at the various ages were developed. The stimuli were standardized by collecting facial photos of older White men and women at ages 20, 40, and 60, and morphing these faces onto standardized bodies using Adobe Photoshop. Participants viewed six stimuli, one from each age by gender combination, and made evaluations across job relevant dimensions.

Results showed an interaction between age and gender, such that older male applicants were evaluated more negatively than older female and younger male applicants. These findings support for the violation of gender normative behavior hypothesis.

This study has implications for organizational leaders who can use this information to provide training for selection officers concerning biases against older workers and how to avoid them.

Original, novel stimuli are used in an experimental design to examine the effects of age in employment in a standardized manner which controls for extraneous variables such as attractiveness across age.

Turnover in high-tech companies has long been a concern for managers and executives. Recent meta-analyses from the general turnover literature consistently show that job satisfaction is a major attitudinal antecedent to turnover intention and turnover behavior. Additionally, the available research on information technology (IT) employees focuses primarily on turnover intentions and not on a risky decision-making perspective and actual turnover (turnover behavior). The paper aim is to focus on that.

This study uses hierarchical ordinary least squares, process (Preacher and Hayes, 2004) and logistic regression.

The main predictor of actual turnover is risky decision-making, whereas job satisfaction is the main predictor of turnover intention.

The joint effects of risk and job satisfaction on turnover intention and behavior have not been studied in the IT domain. Hence, this study extends our understanding of turnover in general and particularly among IT employees by studying the combined effect of risk and job satisfaction on turnover intentions and turnover behavior. The study’s theoretical and practical implications are likewise discussed.

Employees facing challenges in their careers – i.e. female, migrant, elderly and lower-educated employees – might expect job searches to have a low likelihood of success and might therefore more often stay in unsatisfactory positions. The goal of this study is to discover inequalities in job mobility for these employees.

We rely on a large sample of Dutch public sector employees (N = 30,709) and study whether employees with challenges in their careers are hampered in translating job dissatisfaction into job searches. Additionally, we assess whether this is due to their perceptions of labor market alternatives.

Findings show that non-Western migrant, elderly and lower-educated employees are less likely to act on job dissatisfaction than their advantaged counterparts, whereas women are more likely than men to do so. Additionally, we find that although they perceive labor market opportunities as limited, this does not affect their propensity to search for different jobs.

This paper is novel in discovering inequalities in job mobility by analyzing whether employees facing challenges in their careers are less likely to act on job dissatisfaction and therefore more likely to remain in unsatisfactory positions.

The purpose of this paper is to offer a critical analysis of talent acquisition software and its potential for fostering equity in the hiring process for underrepresented IT professionals. The under-representation of women, African-American and Latinx professionals in the IT workforce is a longstanding issue that contributes to and is impacted by algorithmic bias.

Sources of algorithmic bias in talent acquisition software are presented. Feminist design thinking is presented as a theoretical lens for mitigating algorithmic bias.

Data are just one tool for recruiters to use; human expertise is still necessary. Even well-intentioned algorithms are not neutral and should be audited for morally and legally unacceptable decisions. Feminist design thinking provides a theoretical framework for considering equity in the hiring decisions made by talent acquisition systems and their users.

This research implies that algorithms may serve to codify deep-seated biases, making IT work environments just as homogeneous as they are currently. If bias exists in talent acquisition software, the potential for propagating inequity and harm is far more significant and widespread due to the homogeneity of the specialists creating artificial intelligence (AI) systems.

This work uses equity as a central concept for considering algorithmic bias in talent acquisition. Feminist design thinking provides a framework for fostering a richer understanding of what fairness means and evaluating how AI software might impact marginalized populations.