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Hiring teachers is among principals' most critical work but what remains uncertain is the relationship between a principal's tenure in a school and the rate at which they hire teachers who will stay. Teacher retention and principal experience are key predictors of school stability. This study therefore investigates the influence of principal tenure on the retention rates of teachers they hire over time.

The authors followed 11,717 Texas principals from 1999 to 2017, and tracked the teachers they hired in each year of their tenure in a school to see if principals became more effective at hiring teachers who stay over time. The authors use regression with fixed effects and find that the longer a principal stayed in a school, the more effective they were at hiring teachers who stay to both three- and five-year benchmarks.

Principals hire significantly more teachers who persist after they have led their first school for five or more years; however, the average principal in Texas leaves a school after four years thus never realizing those gains. The authors' second main finding indicates that principals who enter an unstable school (less than 69% retention in the two years prior to the principal's arrival) and stay at least five consecutive years, can counteract prior instability.

This study provides initial evidence that principals establish a great deal of building-specific situational expertise that is not easily portable or applicable in a subsequent school placement.

This paper analyses the effect of a particular Active Labour Market Policy, the hiring incentives, on firms hiring policies. The effects of a programme on firms' behaviour have in fact rarely been evaluated.

The analysis is based on micro-data drawn from Rilevazione su Imprese Lavoro (RIL), conducted by Inapp in 2010, 2015 and 2018 on a representative sample of limited liability and partnership firms. The authors apply a policy evaluation framework to investigate the impact of the use of incentives in the short run. The authors infer the counterfactual policy scenario thanks to a survey question that asks about firms' behaviour in the absence of the incentives. The authors also control for firms' unobserved heterogeneity, including firm's fixed effects, and endogeneity issues, estimating a differences-in-differences model that exploit the longitudinal component of the RIL survey.

The authors find that the use of at least one incentive scheme in 2017 is associated with an increase in the share of newly hired of about 0.07 percentage point in the short run. Moreover, hiring incentives have a small positive relationship with the share of both young workers and temporary contracts. Finally, these results are robust to endogeneity issues.

The analysis provides an updated evaluation of the effectiveness of hiring incentives in Italy by focusing on firms' behaviour rather than on the evolution of individual employment. It identifies the impact by using a rich set of econometric methods as well as counterfactual analysis.

A rental service is a service in which customers arrive to request the hire of a rental unit. Customers arriving when all units are out on hire are turned away and considered lost to the service. Customers, successful in obtaining the hire of a unit, pay a hire fee per unit per day. A graphical tool is presented as a decision aid in determining the total number of units to be made available for hire. The graphical tool minimises the total daily relevant costs and provides an easy means of visually examining the sensitivity of the “optimal” number of units to changes in estimates of the associated demand, hire fee and cost parameters. A short account of the application of the graphical tool by a small car hire business is presented.

In the context of new workplace environment, this study aims to study and generate insights about artificial intelligence (AI) adoption in hiring process of firms. It is very relevant when AI is dramatically reshaping hiring function in the changing scenario.

The objectives are achieved with the help of three studies involving Delphi method to explore the criteria for AI adoption decision. Followed by two multi criteria decision-making techniques, i.e. analytic hierarchy process to identify weights of the criteria and fuzzy technique for order preference by similarity to ideal solution to assess the extent of AI adoption in hiring.

The findings reveal that information security and return on investment are considered two very important criteria by human resources managers while contemplating the adoption of AI in hiring process. It was found that AI adoption will be suitable at the sourcing and initial screening stages of hiring. And the suitability of the hiring stage where AI can be applied has been found to have changed from before and after the onset of COVID-19 pandemic situation. The findings and its discussion assist and enhance better decisions about AI adoption in hiring processes of firms amid changing scenario – external and internal to a firm.

Findings also highlight research implications for future research studies in this emerging area.

Results act as a starting point for other human resources managers, who are still pondering over the idea of adopting AI in hiring in future.

This paper through a systematic approach contributes by identifying important evaluation criteria influencing AI adoption in firms and extent of its application in the stages of hiring. It makes a substantial contribution to the under-developed yet emerging paradigm of AI based hiring in practice and research.

There is a dearth of human resource management (HRM) literature examining the generalizability of research employing undergraduate student participants. The purpose of this study is to conduct an experiment to compare the job applicant evaluations and hiring decisions of undergraduate student participants with those of working adults with hiring experience.

This study employed a between-person 2 × 2 × 4 experimental design: participant group (undergraduate students or working adults with hiring experience) × job gender-type (male typed or female typed) × job applicant (heterosexual female, lesbian female, heterosexual male or gay male). Participants read descriptions of a job and a job applicant and then evaluated the applicant.

The results supported a moderated mediation model where participant group moderated the interaction of applicant gender and job gender-type in predicting perceptions of competence, which in turn predicted perceptions of person-job fit, likeability and respect-worthiness, which then predicted hiring decisions. Undergraduate student participants, but not working adults with hiring experience, evaluated female applicants applying for a male-typed job in a manner consistent with gender stereotypes and were less likely to hire the female applicant than the male applicant.

To inform HRM practice, research must reflect real-world decision-making. The literature on the roles of gender stereotypes and bias in hiring, and other important HRM decisions, relies heavily on undergraduate student participants. Findings of this study suggest a need to further examine whether those studies can be generalized to working adults actually making those decisions.

Recent labor market research has called into question whether social capital effects are causal, or are spuriously due to the influence of social homophily. This essay adopts the demand-side perspective of organizations to examine the causal status of social capital. In contrast with supply-side approaches, we argue that homophily is a key mechanism by which organizations derive social capital. We develop an approach to bolster inferences about the causal status of social capital, and illustrate these ideas using data from a retail bank.