In 2016, Duke reconfigured its clinical research job descriptions and workforce to be competency-based, modeled around the Joint Taskforce for Clinical Trial Competency framework. To ensure consistency in job classification amongst new hires in the clinical research workforce, Duke subsequently implemented a Title Picker tool. The tool compares the research unit’s description of job responsibility needs against those standardized job descriptions used to map incumbents in 2016. Duke worked with human resources and evaluated the impact on their process as well as on the broader community of staff who hire clinical research professionals. Implementation of the tool has enabled Duke to create consistent job classifications for its workforce and better understand who composes the clinical research professional workforce. This tool has provided valuable workforce metrics, such as attrition, hiring, etc., and strengthened our collaboration with Human Resources.

We explore whether ageist stereotypes in job ads are detectable using machine-learning methods measuring the linguistic similarity of job-ad language to ageist stereotypes identified by industrial psychologists. We then conduct an experiment to evaluate whether this language is perceived as biased against older workers searching for jobs. We find that job-ad language classified by the machine-learning algorithm as closely related to ageist stereotypes is perceived by experimental subjects as biased against older job seekers. These methods could potentially help enforce anti-discrimination laws by using job ads to predict or identify employers more likely to be engaging in age discrimination.

Cybervetting is the widespread practice of employers culling information from social media and/or other internet sources to screen and select job candidates. Research evaluating online screening is still in its infancy; that which exists often assumes that it offers value and utility to employers as long as they can avoid discrimination claims. Given the increasing prevalence of cybervetting, it is extremely important to probe its challenges and limitations. We seek to initiate a discussion about the negative consequences of online screening and how they can be overcome. We draw on previous literature and our own data to assess the implications of cybervetting for three key stakeholders: job candidates, hiring agents, and organizations. We also discuss future actions these stakeholders can take to manage and ameliorate harmful outcomes of cybervetting. We argue that it is the responsibility of the organizations engaged in cybervetting to identify specific goals, develop formal policies and practices, and continuously evaluate outcomes so that negative societal consequences are minimized. Should they fail to do so, professional and industry associations as well as government can and should hold them accountable.

This article examines why employers struggle to include disability as part of their active diversity approach. Drawing on cross-national interview data from Norway and the USA, we point to the common finding of employers – who are the target of regulatory disability employment policies – typically falling into the passive employer category of employer engagement, with positive attitudes but negative hiring behaviour. As a partial explanation, we demonstrate the difficulty of identifying and demographically monitoring disabled people among job seekers and employees. We argue that these problems are linked to unique aspects of disability as a diversity category, and tie these to the significance of disability heterogeneity, lack of disclosure and the difficulty of acquiring information related to health. We conclude that organisations need to go beyond mere legislative compliance and be more proactive towards disability as a distinct diversity category.

This article examines the complaint that arbitrary algorithmic decisions wrong those whom they affect. It makes three contributions. First, it provides an analysis of what arbitrariness means in this context. Second, it argues that arbitrariness is not of moral concern except when special circumstances apply. However, when the same algorithm or different algorithms based on the same data are used in multiple contexts, a person may be arbitrarily excluded from a broad range of opportunities. The third contribution is to explain why this systemic exclusion is of moral concern and to offer a solution to address it.

Older job applicants are vulnerable to stereotype-related bias in the recruitment process. In the current study, we examined how managers’ job interview invitation decisions regarding older job applicants are influenced by applicants’ human capital-related characteristics, general economic conditions and managers’ perceptions of changes in organisational job demands. Data were collected in two waves of a vignette experiment, three years apart, among a sample of 211 Dutch managers from various organisations. Multi-level analysis showed that managers were more likely to invite older job applicants who had matching qualifications, were employed at the time of application and came with recommendations. In addition, managers’ propensity to invite older job applicants was higher in better economic conditions. The effects of recommendations were moderated by the general economic conditions and changes in organisational job demands, such that a recommendation from another employer was especially influential in bad economic conditions, while a recommendation from an internal employee was especially influential when job demands had increased. The results emphasise the importance of considering the organisational and economic context in understanding the recruitment of older workers. The findings also suggest that older workers, employers and policy makers should invest in older workers’ human capital to protect their employability.

Ever more agricultural economics departments are offering appointments for nine rather than twelve months but little if any analysis of the impact of this change has been done. Our research shows that converting to nine-month contracts is an effective way to raise salaries without an initial outlay of new funds and thus meets the retention criterion. Lower ranks do not suffer significantly lower salaries (without supplements) and professors earn more. Because the nine-month alternative costs more, justification for converting all twelve-month faculty members must rest on other factors, such as enhanced grants or comparability.

Although employees react negatively when employers hire individuals with whom the employers have personal ties, the practice is prevalent worldwide. One factor contributing to the discrepancy between reactions to the practice may be differences in cultural beliefs and institutions regarding perceptions about hiring decisions. To examine cross-national differences in perceptions about hiring personal ties, we conducted a consensus analysis on the perceived fairness, profitability, and overall evaluation of hiring decisions in China and the United States. We find cross-national differences in consensus levels as to whether people believe it is fair or unfair to hire moderately qualified candidates with employer ties (kinships or close friends with the employer) and whether people positively or negatively evaluate the hiring of unqualified candidates with stakeholder ties (ties to business associates or government officials). We also find contrasting areas of consensus about whether hiring unqualified candidates with stakeholder ties is profitable. Implications for research on cultural comparisons of perceptions of hiring practices and guanxi are discussed.