Optimal Hiring Research Articles

Digital Transformation in Talent Acquisition: An Investigation into the Integration of Big Data Analytics and Management Information Systems for Optimal Hiring Strategies

The primary goal was to empirically investigate the relationships amongst Big Data Analytics (BDA), Management Information System (MIS), optimal hiring strategies, leadership support, and talent acquisition process. This research aimed to fill crucial gaps in the literature by examining how technology adoption and leadership support influence the formulation and effectiveness of optimal hiring strategies, finally impacting talent acquisition results in the Chinese business landscape. Quantitative research was employed, targeting experts and decision-makers in human resource departments across numerous industries in China. The research used a random sampling approach to acquire facts through an online questionnaire, examining responses from 168 participants. Regression and mediation analyses, facilitated by SPSS, were applied to discover the relationships and mechanisms among BDA, MIS, optimal hiring strategies, leadership support, and talent acquisition success. The results discovered a significant and positive relationship between BDA and MIS with the optimal hiring techniques. Moreover, optimal hiring techniques have proven an advantageous association with talent acquisition success. The research similarly showed the mediating role of optimal hiring strategies in translating the advantages of BDA and MIS into talent acquisition success. Leadership support emerged as a moderator, amplifying the relationship between optimum hiring techniques and talent acquisition success. This study contributes empirical proof to the underexplored intersection of leadership support, technology, and talent hiring strategies. The findings offer practical insights for companies aiming to navigate the complexities of recruitment, emphasizing the significance of strategic decision-making, technology integration, and management endorsement in achieving talent acquisition success.

Discrimination, Regulations and the Optimal Hiring Process

Discrimination, Regulations and the Optimal Hiring Process

Managing Volunteers and Paid Workers in a Nonprofit Operation

Some nonprofit organizations (NPOs) manage a complex workforce composed of a mix of volunteers, part-time workers, and full-time workers. We study the NPO’s finite-horizon staffing problem to determine the optimal initial staff planning decisions and per period optimal hiring and assignment decisions given a budget, capacity constraints, and an uncertain supply of volunteers and part-time workers. Our main goal is to solve this problem in a way that is effective and easy to implement while obtaining interesting managerial insights. To this end, we first demonstrate that the optimal staffing policies are computationally challenging to identify in general. However, we demonstrate that a prioritization assignment policy and a hire-up-to policy for part-time workers can be conveniently applied and are close to optimal. These policies are, in fact, optimal under staff scarcity and staff sufficiency. In our numerical analysis, we study the value and impact of the general optimal solution that considers flexibility and turnover of part-time workers versus the prioritization assignment policy and a constant hire-up-to policy that omit flexibility and turnover behaviors. We further suggest two easy-to-implement heuristics and theoretically analyze them and run a numerical performance study. We observe that both heuristics have low relative optimality gaps. Finally, we extend our analysis by studying how the optimal policy varies under three different practical considerations: a concave social value objective, nonzero volunteer costs, and dynamic volunteer behaviors. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: This work was supported by the Comunidad de Madrid [Grant EPUC3M12], the Ministerio de Ciencia e Innovación [Grant PID2021-127657NA-I00], and the Ramon y Cajal Fellowship [RYC2020-029303-I]. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4923 .

Bias in Context: Small Biases in Hiring Evaluations Have Big Consequences

It is widely acknowledged that subgroup bias can influence hiring evaluations. However, the notion that bias still threatens equitable hiring outcomes in modern employment contexts continues to be debated, even among organizational scholars. In this study, we sought to contextualize this debate by estimating the practical impact of bias on real-world hiring outcomes (a) across a wide range of hiring scenarios and (b) in the presence of diversity-oriented staffing practices. Toward this end, we conducted a targeted meta-analysis of recent hiring experiments that manipulated both candidate gender and qualifications to couch our investigation within ongoing debates surrounding the impact of small amounts of bias in otherwise meritocratic hiring contexts. Consistent with prior research, we found evidence of small gender bias effects ( d = −0.30) and large qualification effects ( d = 1.61) on hiring managers’ evaluations of candidate hireability. We then used these values to inform the starting parameters of a large-scale computer simulation designed to model conventional processes by which candidates are recruited, evaluated, and selected for open positions. Collectively, our simulation findings empirically substantiate assertions that even seemingly trivial amounts of subgroup bias can produce practically significant rates of hiring discrimination and productivity loss. Furthermore, we found contextual factors can alter but cannot obviate the consequences of biased evaluations, even within apparently optimal hiring scenarios (e.g., when extremely valid assessments are used). Finally, our results demonstrate residual amounts of subgroup bias can undermine the effectiveness of otherwise successful targeted recruitment efforts. Implications for future research and practice are discussed.

What Drives Firms' Hiring Decisions? An Asset Pricing Perspective

In a neoclassical dynamic model of the firm with labor market frictions, optimal hiring is a forward-looking decision that depends on both discount rates and expected cash flows. Empirically, we show that: a) the aggregate hiring rate of publicly traded firms in the U.S. economy negatively predicts stock market excess returns and long-term cash flows both in-sample and out-of-sample, and positively predicts short-term cash flows; and b) through a variance decomposition, the time series variation in the aggregate hiring rate is mainly driven by changes in discount rates and short-term expected cash flows, each contributing roughly to 50% of the variation, with no contribution from variation in long-term expected cash flows. Through a structural estimation of the model, we show that labor adjustment costs and, to a lesser extent, time-variation in the price of aggregate productivity risk, are essential for the model to replicate the empirical patterns.

Endogenous Learning, Persistent Employer Biases, and Discrimination

I present a statistical discrimination model of the labor market in which employers are initially uncertain about the productivity of worker groups and endogenously learn about it through their hiring. Previous hiring experiences with groups shape subsequent incentives of employers to hire from these groups again and learn more about their productivity, leading to differential learning across employers and biased beliefs about the productivity of groups. Given a market-clearing wage, optimal hiring follows a cutoff rule in posterior beliefs: employers with sufficiently negative experiences with workers from a group stop hiring from the group, preserving negative biases and leading to a negatively-skewed distribution of beliefs about their productivity. When employers have noisier initial information on the productivity of one worker group, discrimination against the group can arise and persist without productivity differentials or prior employer biases, with market competition, and with or without worker signaling or investment decisions. The model generates steady state predictions analogous to the Becker (1957) taste-based model, in a statistical framework with beliefs replacing preferences, explaining apparent prejudice as the result of incorrect statistical discrimination. The model also generates additional predictions with policy implications that contrast with traditional models of discrimination.

Entrepreneurial Hiring Strategies in Standalone and Portfolio Ventures

Acquiring talent is a central decision for new ventures. Yet recruiting decisions are made under demand uncertainty, making the optimal hiring strategy unclear. We argue that such strategy depends on the type of entrepreneurship pursued: whether entrepreneurship entails founding a standalone venture or a portfolio of ventures. We predict that standalone ventures will prioritize size flexibility by minimizing the number of initial hires. By contrast, portfolio ventures will make greater investments because they can redeploy unused labor within the portfolio. Thus, portfolio entrepreneurs will prioritize resource flexibility by hiring non-specialized workers, who are easier to redeploy. Finally, we expect differences between hiring strategies to diminish with labor market flexibility. We test our predictions on the population of Finnish entrepreneurs between 2007 and 2017.

Whom to hire and how to coach them: a longitudinal analysis of newly hired salesperson performance

Salesperson hiring decisions are critical for firms, and managers typically accept one of two viewpoints regarding optimal hiring strategies. The first asserts that prior sales experience allows new salespeople to perform immediately upon hire and represents a valuable hiring heuristic. The second believes lack of prior experience allows managers to mold new salespeople to the hiring firm’s needs. Further complicating matters, formal sales education programs are gaining in popularity and may represent an alternative hiring heuristic for sales managers. Using unique multisource data (from both B2B and B2C firms), the authors explore the effects of these hiring heuristics in driving salespeople’s longitudinal performance trajectories, along with the moderating role of post-hire manager coaching behaviors. Results of the longitudinal growth models show the distinct and opposing effects of each hiring heuristic and coaching strategy. The authors also identify critical areas of future research and managerial practice.

Optimal team composition for tool‐based problem solving

AbstractIn this paper, we construct a framework for modeling teams of agents who apply techniques or procedures (tools) to solve problems. In our framework, tools differ in their likelihood of solving the problem at hand; agents, who may be of different types, vary in their skill at using tools. We establish baseline hiring rules when a manager can dictate tool choice and then derive results for strategic tool choice by team members. We highlight three main findings: First, that cognitively diverse teams are more likely to solve problems in both settings. Second, that teams consisting of types that master diverse tools have an indirect strategic advantage because tool diversity facilitates coordination. Third, that strategic tool choice creates counterintuitive optimal hiring practices. For example, optimal teams may exclude the highest ability types and can include dominated types. In addition, optimal groups need not increase setwise. Our framework extends to cover teamwork on decomposable problems, to cases where individuals apply multiple tools, and to teams facing a flow or set of problems.

A reinforcement learning methodology for a human resource planning problem considering knowledge-based promotion

This paper addresses a combined problem of human resource planning (HRP) and production-inventory control for a high-tech industry, wherein the human resource plays a critical role. The main characteristics of this resource are the levels of “knowledge” and the learning process. The learning occurs during the production process in which a worker can promote to the upper knowledge level. Workers in upper levels have more productivity in the production. The objective is to maximize the expected profit by deciding on the optimal numbers of workers in various knowledge levels to fulfill both production and training requirement. As taking an action affects next periods’ decisions, the main problem is to find the optimal hiring policy of non-skilled workers in long-time horizon. Thus, we develop a reinforcement learning (RL) model to obtain the optimal decision for hiring workers under the demand uncertainty. The proposed interval-based policy of our RL model, in which for each state there are multiple choices, makes it more flexible. We also embed some managerial issues such as layoff and overtime-working hours into the model. To evaluate the proposed methodology, stochastic dynamic programming (SDP) and a conservative method implemented in a real case study are used. We study all these methods in terms of four criteria: average obtained profit, average obtained cost, the number of new-hired workers, and the standard deviation of hiring policies. The numerical results confirm that our developed method end up with satisfactory results compared to two other approaches.

Optimal Hiring and Retention Policies for Heterogeneous Workers Who Learn

We study the hiring and retention of heterogeneous workers who learn over time. We show that the problem can be analyzed as an infinite-armed bandit with switching costs, and we apply results from Bergemann and Välimäki [Bergemann D, Välimäki J (2001) Stationary multi-choice bandit problems. J. Econom. Dynam. Control 25(10):1585–1594] to characterize the optimal hiring and retention policy. For problems with Gaussian data, we develop approximations that allow the efficient implementation of the optimal policy and the evaluation of its performance. Our numerical examples demonstrate that the value of active monitoring and screening of employees can be substantial.This paper was accepted by Yossi Aviv, operations management.

Place Based Policies with Unemployment

Many countries have policies aimed at creating jobs in depressed areas with high unemployment rates. In standard spatial equilibrium models with perfectly competitive labor and land markets, local job creation efforts are distortionary. We develop a stylized model of frictional local labor markets with the goal of studying the efficiency of unemployment differences across areas and the potential for place based policies to correct local market failures. Our model builds on the heavily studied Diamond - Mortensen - Pissarides framework, adapted to a local labor market setting with a competitive housing market. The result is a simple search analogue of the classic Roback (1982) model that provides a tractable environment for studying the effects of local job creation efforts. In the model, workers are perfectly mobile and the productivity of worker-firm matches may vary across metropolitan areas. In equilibrium, higher local productivity results in higher nominal wages, higher housing costs, and lower unemployment rates. Although workers can move freely to arbitrage away differences in expected utility across metropolitan areas, equilibrium unemployment rates are not equalized across space. We find that if hiring costs are excessive, firms may post too few vacancies. This problem may be offset via local hiring subsidies of the sort found in many place based policies. The optimal hiring subsidy is city specific in the sense that it depends upon the local productivity level.

Worker Matching and Firm Value

This paper studies the hiring and firing decisions of firms and their effects on firm value. This is done in an environment where the productivity of workers depends on how well they match with their co-workers and the firm acts as a coordinating device. Match quality derives from a production technology whereby workers are randomly located on the Salop circle, and depends negatively on the distance between the workers. It is shown that a worker's contribution in a given firm changes over time in a nontrivial way as co-workers are replaced with new workers. The paper derives optimal hiring and replacement policies, including an optimal stopping rule, and characterizes the resulting equilibrium in terms of employment, firm output and the distribution of firm values. The paper stresses the role of horizontal differences in worker productivity, as opposed to vertical, assortative matching issues. Simulations of the model reveal a rich pattern of worker turnover dynamics and their connections to the resulting firm value and age distributions.

A Search Theory of Sectoral Reallocation

This paper contributes a theoretical framework to analyze how the labor market responds differently to aggregate and sectoral shocks in the presence of search frictions and imperfect mobility. In contrast to the previous literature, which views sectoral shocks as exogenous shocks on matching efficiency, the aggregate impacts of a sectoral shock are the endogenous outcome of the optimal hiring and moving decisions of firms and workers. The model predicts that the economy features the following distinct responses to a structural change: 1) matching efficiency drops and recovers slowly; 2) total vacancies first decrease and then overshoot their long-term equilibrium value; 3) the aggregate wage jumps while labor productivity drops during the transition; and 4) counter-intuitively, the impact on the overall unemployment is limited.

Labor and the Market Value of the Firm

What role does labor play in firms' market value? We use a production-based asset pricing model with factor adjustment costs and forward-looking agents to explore this question. We posit that the hiring of labor is akin to investment in capital and that the two interact, with the interaction being a crucial determinant of the dynamic behavior of market value. Using aggregate US corporate sector data, we estimate firms' optimal hiring and investment decisions and the consequences for firms' value. (JEL E22, E24, G31, G32, M51)

Labor and the Market Value of the Firm

What role does labor play in firms' market value? We explore this question using a production-based asset pricing model with frictions in the adjustment of both capital and labor. We posit that hiring of labor is akin to investment in capital and that the two interact, with the interaction being a crucial determinant of market value behavior. We use aggregate U.S. corporate sector data to estimate firms' optimal hiring and investment decisions and the consequences for firms' value. We then decompose this value, thereby quantifying the link between firms' market value and gross hiring flows, employment, gross investment and physical capital. We find that a conventional specification - quadratic adjustment costs for capital and no hiring costs - performs poorly. Rather hiring and investment flows, unlike employment and capital stocks, are volatile and both are essential to account for market volatility. A key result is that firms' value embodies the value of hiring and investment over and above the capital stock.

Optimal Tax Inspection Strategy

A game-theoretical model is constructed describing the interaction of tax inspectors with taxpayers. The models allows for tax evasion and bribing of inspectors. We find the primary and secondary audit probabilities that maximize the net tax revenue for various inspector hiring strategies. The comparative revenue statics is analyzed and the optimal hiring strategy is determined as a function of the model parameters.

Managing Learning and Turnover in Employee Staffing

We study the employee staffing problem in a service organization that uses employee service capacity to meet random, nonstationary service requirements. The employees experience learning and turnover on the job, and we develop a Markov Decision Process (MDP) model which explicitly represents the stochastic nature of these effects. Theoretical results show that the optimal hiring policy is of a state-dependent “hire-up-to” type, similar to an inventory “order-up-to” policy. For two important special cases, a myopic policy is optimal. We also test a linear programming (LP) based heuristic, which uses average learning and turnover behavior, in stationary environments. In most cases, the LP-based policy performs quite well, within 1% of optimality. When flexible capacity—in the form of overtime or outsourcing—is expensive or not available, however, explicit modeling of stochastic learning and turnover effects may improve performance significantly.

Hiring as Investment Behavior

This paper explores the determinants of hiring at the macroeconomic level. It treats the hiring decision as an investment decision, similar to the one taken for physical capital or for financial assets. At its core is a present value relation which defines the worker's “asset value” for the firm and determines optimal hiring. The paper validates this relation using volatility tests and infers the unobserved asset values by estimating it. Hiring and asset values are found to be weakly correlated with the business cycle and much more volatile. The paper also demonstrates the links between models employed and issues examined in finance and the labor market.

Involuntary unemployment and worker self-selection

Consider a firm that lacks information about the reservation wage or ability of any specific worker, but is aware that workers with higher reservation wages tend to be more able, on average. We show that under a simple condition—that the efficiency wage exceeds the lowest reservation wage—the optimal hiring policy for the firm involves (1) no wage discrimination, and (2) rationing of jobs, when demand for its product is low. The result also extends to the case where hours of work are variable, provided each worker is most productive when working a strictly positive number of hours per week.