[论文解读] Predicting job-hopping likelihood using answers to open-ended interview questions

Madhura Jayaratne, Buddhi Jayatilleke|arXiv (Cornell University)|Jul 22, 2020

Personality Traits and Psychology参考文献 43被引用 2

一句话总结

本研究利用自然语言处理技术，基于超过45,000名申请者的开放式面试回答，预测其跳槽倾向。通过GloVe词嵌入方法，发现回答中的语言模式与自报的跳槽倾向之间存在显著正相关（r=0.35），且开放性人格特质（r=0.25）也与更高的跳槽倾向相关。

ABSTRACT

Voluntary employee turnover incurs significant direct and indirect financial costs to organizations of all sizes. A large proportion of voluntary turnover includes people who frequently move from job to job, known as job-hopping. The ability to discover an applicant's likelihood towards job-hopping can help organizations make informed hiring decisions benefiting both parties. In this work, we show that the language one uses when responding to interview questions related to situational judgment and past behaviour is predictive of their likelihood to job hop. We used responses from over 45,000 job applicants who completed an online chat interview and also self-rated themselves on a job-hopping motive scale to analyse the correlation between the two. We evaluated five different methods of text representation, namely four open-vocabulary approaches (TF-IDF, LDA, Glove word embeddings and Doc2Vec document embeddings) and one closed-vocabulary approach (LIWC). The Glove embeddings provided the best results with a positive correlation of r=0.35 between sequences of words used and the job-hopping likelihood. With further analysis, we also found that there is a positive correlation of r=0.25 between job-hopping likelihood and the HEXACO personality trait Openness to experience. In other words, the more open a candidate is to new experiences, the more likely they are to job hop. The ability to objectively infer a candidate's likelihood towards job hopping presents significant opportunities, especially when assessing candidates with no prior work history. On the other hand, experienced candidates who come across as job hoppers, based purely on their resume, get an opportunity to indicate otherwise.

研究动机与目标

• 通过开放式面试回答识别求职者跳槽行为的语言预测因子。
• 客观评估多种文本表示方法在预测跳槽倾向方面的表现。
• 评估人格特质（尤其是开放性）与跳槽倾向之间的关系。
• 通过预测跳槽风险，支持招聘决策，特别是针对无工作经历的候选人。
• 为简历中被视作跳槽者的候选人提供机会，通过面试语言澄清其真实意图。

提出的方法

• 收集了45,000名求职者在在线聊天面试中的开放式回答。
• 使用自评跳槽动机量表衡量跳槽倾向。
• 评估了五种文本表示方法：TF-IDF、LDA、GloVe词嵌入、Doc2Vec文档嵌入和LIWC。
• 使用相关性分析将语言特征与跳槽倾向及人格特质关联。
• 由于GloVe嵌入在捕捉预测模式方面表现更优，因此将其作为主要方法。
• 进行了二次分析，以检验跳槽倾向与HEXACO人格特质中开放性之间的相关性。

实验结果

研究问题

• RQ1 开放式面试回答中的语言模式能否预测申请者的跳槽可能性？
• RQ2 哪种文本表示方法在从面试回答中预测跳槽倾向方面表现最佳？
• RQ3 开放性与跳槽倾向之间是否存在可测量的相关性？
• RQ4 语言分析能否帮助识别无先前工作经历的候选人中的潜在跳槽者？
• RQ5 能否通过面试语言区分那些简历上看似是跳槽者但实际并非如此的候选人？

主要发现

• GloVe词嵌入在预测性能上表现最佳，显示语言模式与跳槽倾向之间的相关系数为r=0.35。
• 发现开放性人格特质与跳槽倾向之间存在r=0.25的正相关。
• 使用开放式面试回答可实现对跳槽倾向的客观预测，即使对无先前工作经历的候选人亦可。
• 在测试的五种文本表示方法中，GloVe在预测准确性上优于TF-IDF、LDA、Doc2Vec和LIWC。
• 简历上看似是跳槽者的候选人，可通过面试语言传递不同信号，从而提供一种校正机制。
• 结果表明，面试回答中的语言线索是跳槽行为的有效且可量化的代理指标。