{'fraction_non_alphanumeric': 0.02019856213625471, 'fraction_numerical': 0.0, 'mean_word_length': 5.177589852008457, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Data-Driven Approach to High-Volume Recruitment: Application to Student Admission

Problem definition: Service providers often recruit a large number people over a short period of time, a practice known as high-volume recruitment. In this study, we describe a data-driven approach that can be used to streamline the recruitment process and aid decision making. The recruitment process consists of two stages: screening and interview. All candidates are evaluated in the screening stage, but only those with sufficiently high screening scores are short-listed for an interview. After the interview stage, offers are made based on the screening and interview scores. We define the error rate as the probability that a candidate who is rejected during either stage might have had a higher job performance than the median job performance of the candidates recruited had he or she been accepted. To ensure the error rate is no higher than a certain level, how many candidates should be short-listed, and, after the interview, how should candidates be ranked based on the two scores? Academic/practical relevance: High-volume recruitment is challenging because decisions have to be made for many people, under tight time constraints, and under uncertainty. Our approach does not require knowledge about the cost of evaluating candidates and the utility of selecting candidates; hence, it is easier to implement in practice. We apply the approach to the process of recruiting students for a postgraduate business program. Methodology: We use stochastic modeling and regression. Results: We provide a procedure for estimating the error rate as a function of the percentage of candidates short-listed for interviews. We show that the estimated error rate is asymptotically unbiased and converges to the true error rate in probability. We then run a linear regression analysis to estimate the relationship between job performance and the screening and interview scores. In a case study involving a postgraduate business program, the job performance measure we adopt is the grade point average in the program, observable only for the students enrolled in the program. We find that the interview score is statistically significant, but the screening score is not. Managerial implications: For the postgraduate program, our study demonstrates that the time-intensive interview process has substantial value. We should increase, rather than reduce, as suggested by the program administrators before our study, the weight assigned to the interview score and the time spent on the interview process. Knowing the relationship between the error rate and the percentage of candidates short-listed for interviews, the program administrators can determine the appropriate percentage for any given error rate deemed acceptable and improve the ranking of candidates. Our approach is general and can be applied to other high-volume recruiters.