Overconfidence in personnel selection: When and why unstructured interview information can hurt hiring decisions

Autism and the Case Against Job Interviews

Unemployment rates among autistic people are high even among those with low-support needs. While a variety of measures is needed to address this problem, this article defends one that has not been defended in detail and that has profound implications for contemporary hiring practices. Building on empirical research showing that job interviews are a major contributor to autistic unemployment, it argues that such interviews should be abolished in many cases for autistic and non-autistic people alike.

Behavioral Economics in Radiology Training: Overcoming Irrational Behavior

Behavioral economics studies how external influences subconsciously affect decision making. Everyone is subject to a range of cognitive biases, which can affect the radiology training environment and can impact resident selection, resident education, feedback, workflow, and report composition. Understanding the cognitive sources of error and patterns of deviation can help faculty and trainees better engage in an optimal learning environment. This review focuses on the role of cognitive biases as they impact multiple facets of radiology education and training environments.

Reducing discrimination against job seekers with and without employment gaps

Past research shows that decision-makers discriminate against applicants with career breaks. Career breaks are common due to caring responsibilities, especially for working mothers, thereby leaving job seekers with employment gaps on their résumés. In a preregistered audit field experiment in the United Kingdom (n = 9,022), we show that rewriting a résumé so that previously held jobs are listed with the number of years worked (instead of employment dates) increases callbacks from real employers compared to résumés without employment gaps by approximately 8%, and with employment gaps by 15%. A series of lab studies (an online pilot and two preregistered experiments; n = 2,650) shows that this effect holds for both female and male applicants-even when compared to applicants without employment gaps-as well as and for applicants with less and more total job experience. The effect is driven by making the applicant's job experience salient, not as a result of novelty or ease of reading.

Getting on the Same Page: The Impact of Interviewer Education and Structured Interviews on Interrater Agreement in Residency Interviews

INTRODUCTION

We explored the impact of implementing structured interviews and associated interviewer education on interrater agreement within a large academic residency program.

METHODS

Faculty and senior resident interviewers from a large academic residency program participated in a 3-hour structured interview course. Before and after the course, participants completed a 15-item assessment pertaining to the characteristics, logistics, and guidelines associated with structured interviews. Along with interviewer training, interview day logistics also changed from an unstructured format (no specific questions, one overall 1-9 rating scale) to a structured interview format, including incorporation of behavioral-based competency questions that would be asked of every applicant and behavioral anchored rating scales (1-10; 10 = highest). Interrater agreement was assessed via intraclass correlation coefficients (ICC1) for the 2 years before and 2 years after incorporation of the structured interview format.

RESULTS

A total of 45 faculty and resident interviewers participated in the course in 2018. Participant knowledge significantly increased from an average of 36% to 79% after the course (p < 0.01). Prior to the intervention, overall interrater agreement was "poor" to "fair," with an ICC1 of 0.51 in 2016 and 0.49 in 2017. After the structured interview intervention, overall agreement increased to the "good" level with an ICC1 of 0.71 in 2018 and 0.66 in 2019. The proportion of applicants who received interview scores with at least 2 ratings more than 2 points apart significantly decreased from 59% to 47% after the intervention (p < 0.01).

CONCLUSIONS

Incorporating an interviewer educational session and a structured interview format into residency selection can help increase agreement in ratings between interviewers. However, these data suggest that ongoing refresher trainings may be needed to maintain acceptable levels of interrater agreement.

When Employees Experience Low Levels of Job Autonomy, Fair Procedures Buffer Unfair Outcomes

Organizations play a key role in maintaining employee wellbeing. Some research suggests that one way to protect employee wellbeing is to treat them fairly (procedural justice), especially when fair job outcomes (distributive justice) cannot be ensured. Yet, previous studies have not consistently found this interaction effect between distributive and procedural justice. This study investigates job autonomy as a boundary condition to the Distributive Justice × Procedural Justice effect on wellbeing outcomes. To test our hypothesized three-way interaction between distributive justice, procedural justice, and job autonomy, we collected cross-sectional data among Dutch employees in two studies. We used validated self-report measures of our core constructs to test our hypothesis on two employee wellbeing indicators: job satisfaction and emotional exhaustion. Results show a significant three-way interaction effect on both job satisfaction and emotional exhaustion in Study 1 (N = 411), and a significant three-way interaction effect on emotional exhaustion in Study 2 (N = 1117). Simple slopes analyses of the significant three-way interactions showed that distributive justice and procedural justice interact to predict wellbeing outcomes among employees with low job autonomy. Among employees with high job autonomy, distributive justice and procedural justice do not interact to predict wellbeing. The results contribute to the employee wellbeing literature by showing that job autonomy is a boundary condition to the Distributive Justice × Procedural Justice effect on wellbeing outcomes. We discuss other implications of our findings for the workplace and the ramifications for employees with low and high job autonomy.

Exploring the impact of leader humility on different types of voice: the role of employee other-oriented motivations

Drawing on social learning theory and taking a motivational perspective, this study mainly investigates how leader humility can promote employees' other-oriented motivations, and uncovers the other-serving motivational mechanism through which leader humility can impact their employees' different types of voice behavior. By collecting data from 152 leader–subordinate dyads through an online survey, the results revealed that leader humility was positively related to both employees' prosocial motivation and organizational concern motivation. Meanwhile, these two motivations play mediating roles in explaining how leader humility can positively affect employees' supportive voice and challenging voice. It is noteworthy that leader humility, which features highlighting the value and strength of others, is more likely to trigger employees' prosocial motivation and thus influence their voice behavior. This research extends our understanding of leader humility, employee motivation, and workplace voice. Practical implications and limitations of the results are also discussed.

Joint Representation Learning with Relation-Enhanced Topic Models for Intelligent Job Interview Assessment

The job interview is considered as one of the most essential tasks in talent recruitment, which forms a bridge between candidates and employers in fitting the right person for the right job. While substantial efforts have been made on improving the job interview process, it is inevitable to have biased or inconsistent interview assessment due to the subjective nature of the traditional interview process. To this end, in this article, we propose three novel approaches to intelligent job interview by learning the large-scale real-world interview data. Specifically, we first develop a preliminary model, named Joint Learning Model on Interview Assessment (JLMIA), to mine the relationship among job description, candidate resume, and interview assessment. Then, we further design an enhanced model, named Neural-JLMIA, to improve the representative capability by applying neural variance inference. Last, we propose to refine JLMIA with Refined-JLMIA (R-JLMIA) by modeling individual characteristics for each collection, i.e., disentangling the core competences from resume and capturing the evolution of the semantic topics over different interview rounds. As a result, our approaches can effectively learn the representative perspectives of different job interview processes from the successful job interview records in history. In addition, we exploit our approaches for two real-world applications, i.e., person-job fit and skill recommendation for interview assessment. Extensive experiments conducted on real-world data clearly validate the effectiveness of our models, which can lead to substantially less bias in job interviews and provide an interpretable understanding of job interview assessment.

Impact of Biases in Selection and Evaluation on the Composition of the Radiology Physician Workforce

Lack of diversity in Radiology is a public health problem and may be self perpetuating as diverse candidates view the field as hostile to their entry and advancement, and consequently do not apply into the field. Solutions require understanding the obstacles, which range from enrollment in medical school to achieving leadership positions in Radiology. An understanding of the effect of demographic data on diversity in Radiology, disparate effects of Step examinations, medical school grades and induction into academic honor societies, and existing faculty disparities will allow us to better recruit, train, and retain a diverse group of physicians in our field. The downstream effect of a diverse workforce is improvement in health outcomes and disparities in medical care for our communities.

Should you follow your gut? The impact of expertise on intuitive hiring decisions for complex jobs

As jobs become increasingly complex, organizations are challenged with finding effective ways to select and hire successful employees. The high level of uncertainty generally associated with hiring decisions is greater for complex jobs where it is difficult to identify the predictors of good job performance. Intuition research has found expert intuition to be effective in highly uncertain decision environments. However, most employment selection research dismisses the use of intuition and argues that even expert interviewers should not rely on their intuition. To bridge the two research streams, this paper addresses the research question: for complex jobs, can the intuition of expert decision-makers enhance the effectiveness of hiring decisions? The hypotheses were tested via an experimental study design using expert and nonexpert interviewer samples. The results demonstrate that, when recruiting for complex jobs, interviewer expertise does increase the quality of intuitive hiring decisions.

Innovative Infrastructure Fund to Ensure the Financial Sustainability of PPP Projects: The Case of Chile

Recently, the Chilean government created the Chilean Infrastructure Fund (CIF) that will allow taking advantage of the value of existing infrastructure assets. The fund will enable the government to finance new infrastructure projects with the resources obtained from new concessionaires’ payments to the government for the value of retendering existing public-private partnership (PPP) projects. The purpose of this paper is to evaluate the contribution that this new public institution will bring to the financial sustainability of infrastructure in Chile, the opportunities and challenges for its implementation, and its economic value. The research has a qualitative and quantitative approach. Based on the understanding of the new concept of asset recycling, the application of economic foundations, and financial methodologies such as discounted cash flows and real options, the research assessed the institutional and economic value of the new Chilean Infrastructure Fund. The main findings are that the new Chilean Infrastructure Fund will create public value and public wealth since it will foster good governance, streamline the concession system, accelerate the implementation of sustainable PPP projects, and improve planning schemes among others. Other countries could follow this experience, promoting infrastructure funds to improve governance and accelerate the implementation of sustainable PPP projects.

Entrofy your cohort: A transparent method for diverse cohort selection

Selecting a cohort from a set of candidates is a common task within and beyond academia. Admitting students, awarding grants, and choosing speakers for a conference are situations where human biases may affect the selection of any particular candidate, and, thereby the composition of the final cohort. In this paper, we propose a new algorithm, entrofy, designed to be part of a human-in-the-loop decision making strategy aimed at making cohort selection as just, transparent, and accountable as possible. We suggest embedding entrofy in a two-step selection procedure. During a merit review, the committee selects all applicants, submissions, or other entities that meet their merit-based criteria. This often yields a cohort larger than the admissible number. In the second stage, the target cohort can be chosen from this meritorious pool via a new algorithm and software tool called entrofy. entrofy optimizes differences across an assignable set of categories selected by the human committee. Criteria could include academic discipline, home country, experience with certain technologies, or other quantifiable characteristics. The entrofy algorithm then yields the approximation of pre-defined target proportions for each category by solving the tie-breaking problem with provable performance guarantees. We show how entrofy selects cohorts according to pre-determined characteristics in simulated sets of applications and demonstrate its use in a case study of Astro Hack Week. This two stage candidate and cohort selection process allows human judgment and debate to guide the assessment of candidates’ merit in step 1. Then the human committee defines relevant diversity criteria which will be used as computational parameters in entrofy. Once the parameters are defined, the set of candidates who meet the minimum threshold for merit are passed through the entrofy cohort selection procedure in step 2 which yields a cohort of a composition as close as possible to the computational parameters defined by the committee. This process has the benefit of separating the meritorious assessment of candidates from certain elements of their diversity and from some considerations around cohort composition. It also increases the transparency and auditability of the process, which enables, but does not guarantee, fairness. Splitting merit and diversity considerations into their own assessment stages makes it easier to explain why a given candidate was selected or rejected, though it does not eliminate the possibility of objectionable bias.

Does expertise and thinking mode matter for accuracy in judgments of job applicants' cognitive ability?

The present research examined the role of thinking mode for accuracy in recruiters and laypeople's judgments of applicants' cognitive ability. In Study 1, students who relied on their intuition were somewhat less accurate. In Study 2, an experimental manipulation of thinking mode (intuitive vs analytical) revealed no apparent differences in accuracy. Moreover, there were no differences in accuracy or agreement between recruiters and laypeople. Examination of the use of specific resume content suggested that intuitive thinking corresponds to basing one's judgments more on the way that applicants present themselves in their personal letter and less on diagnostic biographical information such as SAT scores. The findings point to the possibility that professional recruiters may not possess intuitive expertise in this context.

A Tutorial on Mechanical Decision-Making for Personnel and Educational Selection

In decision-making, it is important not only to use the correct information but also to combine information in an optimal way. There are robust research findings that a mechanical combination of information for personnel and educational selection matches or outperforms a holistic combination of information. However, practitioners and policy makers seldom use mechanical combination for decision-making. One of the important conditions for scientific results to be used in practice and to be part of policy-making is that results are easily accessible. To increase the accessibility of mechanical judgment prediction procedures, we (1) explain in detail how mechanical combination procedures work, (2) provide examples to illustrate these procedures, and (3) discuss some limitations of mechanical decision-making.

Executivos Com Maior Sofisticação Financeira São Mais Confiantes e Otimistas?

Resumo A competitividade no mercado de trabalho de altos executivos, e busca de qualificação dos Chief Executive Officer (CEO) por parte das empresas pode levar esse profissional a superestimar suas capacidades e/ou subestimarem os riscos nos processos de tomada de decisão, aumentando a propensão de que ele apresente os vieses cognitivos excesso de confiança e otimismo (ECO). Nesse sentido, o presente trabalho teve por objetivo identificar a relação entre a Sofisticação Financeira dos CEO's e os vieses cognitivos Excesso de Confiança e Otimismo. A amostra compreendeu 179 empresas listadas na BM&FBovespa durante os períodos de 2011 a 2015, totalizando 810 observações. A análise de componentes principais foi utilizada para a formação de três índices de sofisticação financeira: O Índice de Sofisticação Financeira Acadêmica (ISFA), o Índice de Sofisticação Financeira Profissional (ISFP), e o Índice de Sofisticação Financeira (ISF), composto pelos dois primeiros. Na análise de dados foram utilizadas regressões lineares multivariadas. Os resultados indicaram que apesar de o ISF não estar relacionado com os vieses ECO, o ISFA apresentou-se positiva e significativamente relacionado ao ECO, indicando que os CEO's que possuem formação internacional, formação financeira e experiência no ramo são propensos a apresentar os desvios cognitivos ECO.