Online Hiring Research Articles

Sourcing algorithms: Rethinking fairness in hiring in the era of algorithmic recruitment

AbstractSourcing algorithms are technologies used in online platforms to identify, screen, and inform potential applicants about job openings. The popularity of such technologies is rapidly increasing due to their pervasiveness in online advertising and beliefs that sourcing algorithms can decrease time to hire while improving the quality of new hires. What is little known, however, are their potential risks: sourcing algorithms could (intentionally or unintentionally) encode or exacerbate occupational demographic disparities, thereby hindering organizational diversity and/or decreasing the effectiveness of online hiring practices. Because sourcing algorithms identify and screen potential job applicants before they are made aware of employment opportunities, methods for evaluating discrimination in hiring which focus solely on job applicants (e.g., adverse impact ratio), may fail to detect the effects of discriminatory sourcing algorithms. Thus, we propose an expanded model of the employee hiring process to take into account the role of sourcing algorithms. Based on empirical approximations, we conducted a Monte Carlo simulation study to examine the magnitude and nature of sourcing algorithms' influence on hiring outcomes. Our findings suggest that sourcing algorithms could hinder the diversity of new hires while misleadingly suggesting positive diversity outcomes in personnel selection. We provide practical guidance for the use of sourcing algorithms and call for a systematic re‐examination of how to evaluate selection system fairness in the era of algorithmic recruitment.

COLLEGE STUDENTS COVID-19 PANDEMIC ONLINE LEARNING/ WILL THERE BE A TRANSITIONAL PERIOD OF IMPACT OF THEIR ENTRANCE TO AN EVOLVING ONSITE BUSINESS WORLD

ABSTRACT The COVID-19 pandemic has changed the way that all businesses go about conducting their business. In the educational forum, colleges and universities quickly made the switch to an online learning environment. Thereby, leaving students with the need to adjust to this Online environment. Negative effects of this were the lack of ability to build relationships with colleagues. While the positive effect was there was no need to travel (Bienkowska, Koszela, Salamacha, Tworek, 2021.) Many organizations face the same challenges which has caused the need for them to re-design jobs. Likely, these changes such as a move to the hybrid more will persist for a long time (Bienkowska, Koszela, Salamacha, Tworek, 2021.) More hybrid/remote work arrangements have proven that many employees can work from just about anywhere (Reidy, 2022). Over the past few years, there has been a move towards an Online Hiring process causing a faceless hiring process. College hiring is one area where this has had a strong influence (Gurgaon, 2022). The ability to do so has hastened the process, alleviating the need to conduct on and off campus recruitment (Gurgaon 2022). Though it is difficult to predict whether these current organizational changes will last. There is an importance to understand how college students that lived through the online educational world will be affected by the way that organizations choose to conduct their business operations. There is a need to understand that students who were overwhelmingly taught Online during the pandemic will be affected by a transitional period for the students. During the pre-pandemic time, college students were used to Onsite classes and Onsite jobs. The focus will be important to see that students would be comfortable moving into a job as Onsite if their prior pandemic education was predominantly Online. This will be a qualitative study that will measure student response across the globe. The intention is for the workforce to have a better understanding and prepare for future students' arrival. Keywords College Students, Workplace, COVID-19, Graduating, Onsite

Gender Differences in the Use of Recommendation Letters in the Job Search Process

Who is more likely to use social contacts to search for jobs, and why is this the case? Despite substantial evidence that social contacts improve individuals’ success in the process of job-seeking in various ways, previous studies show that individuals with more social capital are not more likely to use social contacts to search for jobs than those with less social capital. By using records of activities on an online hiring platform, this paper seeks to explore variations in job seekers’ decisions to use recommendation letters and the implications of these decisions. Specifically, I propose that female applicants, who may face bias, and hence feel disadvantaged in the labour market, utilise recommendation letters more than male applicants. I also posit that when recommendation letters are used, their effect is more substantial for female applicants than for male applicants in terms of positively affecting their job search outcomes. The results of this study suggest that female applicants use recommendation letters as a mitigation strategy for their labour market disadvantage while also providing additional information; thus, signalling the applicants’ qualities in the job search process.

Staff Time in the Community: An Enduring Critical Component of Individual Placement and Support in the Digital Age.

This study examined the relationship between individual placement and support (IPS) employment specialists' time spent in the community and employment outcomes in the current digital age, featuring increased technology use and online hiring practices. The authors examined the relationship between employment outcomes and IPS employment specialists' time spent in the community at 78 sites in 2018 and 84 sites in 2019. The amount of time staff spent in the community was significantly and positively associated with better employment outcomes. These data support the continued importance of employment specialists' spending time in the community with employers and IPS recipients to achieve optimal outcomes for recipients.

The Secretary Problem with Competing Employers on Random Edge Arrivals

The classic secretary problem concerns the problem of an employer facing a random sequence of candidates and making online hiring decisions to try to hire the best candidate. In this paper, we study a game-theoretic generalization of the secretary problem where a set of employers compete with each other to hire the best candidate. Different from previous secretary market models, our model assumes that the sequence of candidates arriving at each employer is uniformly random but independent from other sequences. We consider two versions of this secretary game where employers can have adaptive or non-adaptive strategies, and provide characterizations of the best response and Nash equilibrium of each game.

Impact of the COVID-19 Pandemic on the Employment and Educational Outcomes of Individuals in a First-Episode Psychosis Clinic.

A central objective of early psychosis therapy is to restore social functioning (e.g., through employment and education). Employment and educational outcomes during the COVID-19 pandemic were examined in a well-defined cohort of patients receiving care in an early psychosis clinic. Data were extracted from the electronic health records of 128 patients receiving care at McLean Hospital's first-episode psychosis (FEP) clinic between January 1 and September 21 in 2019 and 2020. Using a generalized linear model with a Gaussian distribution and robust standard errors, the authors compared the average changes in the weekly employment and education proportions before and after COVID-19 lockdowns with the same changes in 2019. Employment losses among patients with FEP were greater than among the general population and persisted through the end of follow-up. In 2020, average employment after a stay-at-home order was instituted was 33% lower than before the order compared with the change in employment during the same period in 2019. The effect was stronger among men and those who identified as non-White, were age <21 years, or did not have a college education. Although educational engagement recovered in the fall of 2020, it still remained below the 2019 levels. Employment disruptions were major and persistent among the FEP population, which might affect short- and long-term outcomes. Innovative approaches are needed to help patients transition to remote employment, file unemployment claims, and use online hiring platforms to ameliorate the indirect effects of the COVID-19 pandemic.

Can Monitoring Help Flatten the World? An Empirical Examination of Online Hiring

With the prevalence of online employment platforms, employers are increasingly hiring remote workers from those platforms and using platform-provided monitoring systems to keep track of workers’ activities. We propose that the effect of monitoring goes beyond the findings from the extant monitoring literature that has predominantly examined the productivity effects. Specifically, we hypothesize that monitoring systems would reduce employers’ bias against foreign workers (home bias), thus flattening the global labor markets. Employers tend to have home bias owing to the high transaction risks and coordination costs in dealing with foreign workers. As monitoring systems automate the collection of workers’ activities during the project process, they inform employers on the work process and project progress in real time and facilitate the coordination process with remote workers. Thus, employers may reduce their home bias because the risks of hiring foreign workers and the associated coordination costs have been substantially reduced. With a unique large-scale data set from a major global online employment platform, we leverage a difference-in-differences model and the exogenous event of the introduction of a platform-provided monitoring system for time-based projects to estimate the impact of monitoring systems on employers’ home bias. We find that employers significantly reduce their home bias after the introduction of the monitoring system. Further, the decrease in employers’ home bias is smaller for employers who had positive hiring experiences with foreign workers before the introduction of the monitoring system. In addition, the decrease in employers’ home bias is larger in high-routine projects than in low-routine projects, with the latter being more difficult to monitor. Our findings are robust to alternative empirical specifications and provide important managerial implications for improving the design of online employment platforms.

A Novel Approach to Predict Personality of a Person

The main objective of this paper is to suggest a novel approach to predict personality of a person in online hiring processes, with the goal of automating candidate pre-screening. In this initiative, we propose a series of procedures and strategies that will make the recruiting process easier, more reliable and more efficient. Our key goal is to restrict the rounds of interviews and background analysis of the applicants exclusively to those capable of being recognized by the organization in terms of their personal attributes and the criteria of the organization. The organization is now more interested in the applicant's personality. As a result, the system runs a personality prediction test to determine the candidate's personality traits. Finally, it reports the results of the candidates to the HR manager for further screening.

Who Benefits from Online Gig Economy Platforms?

This paper estimates the magnitude and distribution of surplus from the knowledge worker gig economy using data from an online labor market. Labor demand elasticities determine workers’ wages, and buyers’ past market experience shapes both their job posting frequency and hiring rates. We find that workers on the supply side capture around 40% of the surplus from filled jobs. Under counterfactual policies that resemble traditional employment regulation, buyers post fewer online jobs and fill posted jobs less often, reducing expected surplus for all market participants. We find negligible substitution on the demand side between online and offline jobs by assessing how changes in local offline minimum wages affect online hiring. The results suggest that neither online or offline knowledge workers will benefit from applying traditional employment regulation to the online gig economy. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Keeping Up with the Trends: Analyzing the Dynamics of Online Learning and Hiring Platforms in the Software Programming Domain

The Fourth Industrial Revolution has considerably sped up the pace of skill changes in many professional domains, with scores of new skills emerging and many old skills moving towards obsolescence. For these domains, identifying the new necessary skills in a timely manner is a difficult task, where existing methods are inadequate. Understanding the process, by which these new skills and technologies appear in and diffuse through a professional domain, could give training providers more time to identify these new skills and react. For this purpose, in the present work, we look at the dynamics between online learning platforms and online hiring platforms in the software programming profession, a rapidly evolving domain. To do so, we fuse four data sources together: Stack Overflow, an online community questions and answers (Q&A) platform; Google Trends, which provides online search trends from Google; Udemy, a platform offering skill-based Massively Open Online Courses (MOOCs) where anyone can create courses; and Stack Overflow Jobs, a job ad platform. We place these platforms along two axes: i) how much expertise it takes, on average, to create content on them, and ii) whether, in general, the decision to create content on them is made by individuals or by groups. Our results show that the topics under study have a systematic tendency to appear earlier on platforms where content creation requires (on average) less expertise and is done more individually, rather than by groups: Stack Overflow is found to be more agile than Udemy, which is itself more agile than Stack Overflow Jobs (Google Trends did not prove usable due to extreme data sparsity). However, our results also show that this tendency is not present for all new skills, and that the software programming profession as a whole is remarkably agile: there are usually only a few months between the first Stack Overflow appearance of a new topic, and its first appearance on Udemy or Stack Overflow Jobs. In addition, we find that Udemy’s agility has dramatically increased over time. Our novel methodology is able to provide valuable insights into the dynamics between online education and job ad platforms, enabling training program creators to look at said dynamics for various topics and to understand the pace of change. This allows them to maintain better awareness of the trends and to prioritize their attention, both on the right topics and on the right platforms.

“This Gig Is Not for Women”: Gender Stereotyping in Online Hiring

This study examines gender segregation in the context of the so-called gig economy. In particular, it explores the role that stereotypes about male and female occupations play in sorting men and women into different jobs in an online freelance marketplace. The findings suggest that gender stereotypes are particularly salient in online hiring because employers typically contract for short-term, relatively low-value jobs based on limited information about job applicants. These conditions trigger the use of cognitive shortcuts about intrinsic gender characteristics linked to different skills and occupations. The results corroborate that female candidates are less likely to be hired for male-typed jobs (e.g., software development) but more likely to be hired for female-typed jobs (e.g., writing and translation) than equally qualified male candidates. Further, the study investigates three mechanisms predicted to attenuate the female penalty in male-typed jobs. The penalty is found to be self-reinforcing, as it perpetuates gender imbalances in worker activity across job categories that strengthen the sex typing of occupations.

‘This Gig Is Not for Women’: Gender Stereotyping in Online Hiring

This study examines gender segregation in the context of the so-called gig economy. In particular, it explores the role that stereotypes about male and female occupations play in sorting men and women into different jobs in an online freelance marketplace. The findings suggest that gender stereotypes are particularly salient in online hiring because employers typically contract for short-term, relatively low-value jobs based on limited information about job applicants. These conditions trigger the use of cognitive shortcuts about intrinsic gender characteristics linked to different skills and occupations. The results corroborate that female candidates are less likely to be hired for male-typed jobs (e.g., software development) but more likely to be hired for female-typed jobs (e.g., writing and translation) than equally qualified male candidates. Further, the study investigates three mechanisms predicted to attenuate the female penalty in male-typed jobs. The penalty is found to be self-reinforcing, as it perpetuates gender imbalances in worker activity across job categories that strengthen the sex-typing of occupations.

Are There Gender Differences in Professional Self-Promotion? An Empirical Case Study of LinkedIn Profiles Among Recent MBA Graduates

Women are more modest than men in expressing accomplishments, referred to as the “feminine modesty effect”. Given the importance of highlighting accomplishments and skills for professional advancement, our research revisits the classical question of equal opportunity with a modern dataset to examine how women leverage LinkedIn, a professional social networking site. We first apply propensity score matching methods to identify a subset of similarly qualified female and male U.S. users who recently graduated/will be graduating (2011-2017) from a top-ranked MBA program as indicated on their LinkedIn profile. We then analyze gender differences in online self-promotion choices, an often overlooked aspect of understanding the role of gender in the professional hiring pipeline. Among matched subsets of female and male users, we find that females are less likely relative to males to utilize data fields that require writing in free-form such as the Summary and Job Description fields. However, we find for most universities that females and males are equally likely to include more structured data fields such as Honors and Skills, and for some universities females are more likely to include at least one Skill. This work begins to quantify gender biases in user-provided data and introduces important considerations for how self-presentation choices affect professional opportunity in online hiring platforms.

Landing the First Job: The Value of Intermediaries in Online Hiring

Online markets for remote labour services allow workers and firms to contract with each other directly. Despite this, intermediaries—called outsourcing agencies—have emerged in these markets. This article shows that agencies signal to employers that inexperienced workers are high quality. Workers affiliated with an agency have substantially higher job-finding probabilities and wages at the beginning of their careers compared to similar workers without an agency affiliation. This advantage declines after high-quality non-affiliated workers receive good public feedback scores. The results indicate that intermediaries have arisen endogenously to permit a more efficient allocation of workers to jobs.

Conformity under uncertainty: Reliance on gender stereotypes in online hiring decisions

We apply Bentley et al.'s theoretical framework to better understand gender discrimination in online labor markets. Although such settings are designed to encourage employer behavior in the northwest corner of Homo economicus, actual online hiring decisions tend to drift southeast into a "confirmation bias plus weak feedback loops" pattern of discrimination based on inaccurate social stereotypes.

Landing the First Job: The Value of Intermediaries in Online Hiring

Online markets for remote labor services allow workers and firms to contract with each other directly. Despite this, intermediaries -- called outsourcing agencies -- have emerged in these markets. This paper shows that agencies signal to employers that inexperienced workers are high quality. Workers affiliated with an agency have substantially higher job-finding probabilities and wages at the beginning of their careers compared to similar workers without an agency affiliation. This advantage declines after high-quality non-affiliated workers receive good public feedback scores. The results indicate that intermediaries have arisen endogenously to permit a more efficient allocation of workers to jobs.

Virtual Teaching And Strategies: Transitioning From Teaching Traditional Classes To Online Classes

As more technology has become available in many parts of the globe, a new type of student population has emerged. The traditional student image of higher learning has been somewhat limited in many countries, but given the impact of the Internet, this traditional “student body” has changed. Rather than being limited to regional demographics, the student body for educational institutions offering online courses has changed drastically. In fact, these online courses have started yet another chapter in the history of education, known as virtual learning communities. While online instructors may start out teaching students from local areas, this particular opportunity has been changing over the past decade. In fact, many online instructors have noted an increase in a more diversified student population in their classes. Further, they have realized the need to update their teaching skills, practices and strategies in order to accommodate the changing needs of the learners in the classroom, as well as updating their own teaching portfolio. This paper will provide a brief overview of current recruitment and hiring methods used in the traditional hiring versus online hiring of educators. Also, this paper will address the growing concerns of current traditional teachers as they approach the decision to transition over to online learning and how to obtain proper online instruction. Finally, this paper will overview how one online university has approached online teacher training for both experienced instructors, as well as new teaching recruits as they prepare to transition from traditional classrooms over to virtual classes.