机器学习在人力资源管理领域中的应用研究

本研究关注的是机器学习在人力资源管理领域的应用,通过目前已有研究的梳理,以了解在人力资源管理领域中人工智能/机器学习的应用程度和研究热点。同时,基于管理实践,本研究提出了人力资源管理者如何利用算法,以有价值的方式处理和解释数据,将其真正应用于人力资源管理的六大模块工作中,以降低管理成本,提供不断增长的优势和潜力。最后,在现有学术研究和管理实践的基础上,本研究列举了机器学习在人力资源管理领域应用可能面临的挑战,以及未来的研究方向,以期为后续的研究提供一定的参考。     

中国机构投资者的学习机制研究——理性贝叶斯还是简单强化式学习

理论文献通常假设机构投资者遵循理性贝叶斯法则更新其信念,在此学习模式下,盈亏经验本身并不能直接影响机构行为。但该假设的合理性尚未得到实证研究的充分支持。中国新股发行中的抽签分配制度为检验上述命题提供了比较理想的随机实验机会。本文基于新股抽签分配数据,系统检验了随机的盈亏经验对机构投资行为的影响,结果发现:(1)机构投资者显著受制于幼稚的强化式学习机制的影响,即通过随机抽签在前期获得新股分配的机构(处置组)相对未获配机构(控制组),其下期参与新股申购的概率显著提高,并且前期收益率能够有效地强化这种盈利经历与参与概率之间的正向关系。(2)盈亏经验能够显著改变专业机构的估值信念,即前期通过随机抽签获得高收益体验的机构相对未获配机构,在后续新股询价过程中给出了显著更高的报价上调水平。(3)盈亏经验对机构行为产生影响的一种可能渠道是借助机构投资管理人的强化式学习过程,基于基金经理个人特征变量的调节机制研究表明,丰富的长期从业经验、高学历的教育水平以及多位基金经理的相互竞争都能在一定程度上缓解盈亏经历对基金行为的影响。本文基于随机实验的设计为经验与行为之间的因果关系提供了可信证据,证实了即使是被奉为理性投资者代表的专业机构也会受制于简单强化式学习的显著影响。     

Gender differences in the first course in accounting: An achievement goal approach

We examine whether gendered patterns can be observed in first-year students' achievement goals in an introductory accounting course; a question largely overlooked by prior literature. This investigation is motivated by perceptions of accounting as a masculine field involving gender role stereotypes and business schools as competitive and performance-oriented environments. Our findings suggest that male students tend to adopt performance-approach goal, implying that they are more competitive than female students, and that their performance is thus driven by a desire to outperform others. Our findings further suggest that male students' expectations of learning accounting are higher than those of female students. The expectations explain the gender differences in the performance-approach goal. Finally, we find that this performance-approach goal mediates gender differences in course performance depending on the mode of assessment; male students received higher grades for exams but not for teamwork. Overall, our study highlights the importance of considering contextual aspects related to competitiveness, masculinity, and the mode of assessment on an accounting course when addressing students’ achievement goals and expectations of learning accounting. We thus contribute to the understanding of how learning environment, accounting pedagogy, and the broader field of professional accounting intersects with individual student attributes, creating differential learning outcomes.     

The era of Omni-learning: Frameworks and practices of the expanded human resource development

According to the ever-changing organizational environment, we also adopt an ever-expanding HRD in contents and scope. Focusing on the drivers of the recent HRD reforms, the growing demand for organizational agility and holistic capabilities of human resources is driving the need for change, and the pandemic crisis is pushing the revolutionary changes of HRD. Such trends of the expanded HRD can be characterized as a ‘march toward Omni-learning’. In specific, there are at least four noticeable and intertwined waves of HRD reforms toward Omni-learning: (1) embracing holistic capabilities such as benchmarking, modeling, forecasting, and backcasting (BMFB); (2) integrating working and learning by promoting on-the-job learning (OJL), on-the-life learning (OLL), and on-the-life training (OLT); (3) standardizing communication tools such as LMF (logic tree; multi-dimensional matrix/map; flowchart) and EEOSP (everything/everyone on the same page); and (4) diversifying communication space-time across diverse places (close; remote) and times (synchronized; a-synchronized). And all the HRD waves are commonly facilitated and promoted by technological breakthroughs of artificial intelligence (AI) and the metaverse. Beyond the current innovations of HRD, no one would be certain about the answer to the question “What’s next?”. But what is certain is that HRD will continue to be deepened and widened as long as human resources are needed to respond to the ever-changing organizational environment.     

Improved hybrid resampling and ensemble model for imbalance learning and credit evaluation

A clustering-based undersampling (CUS) and distance-based near-miss method are widely used in current imbalanced learning algorithms, but this method has certain drawbacks. In particular, the CUS does not consider the influence of the distance factor on the majority of instances, and the near-miss method omits the inter-class(es) within the majority of samples. To overcome these drawbacks, this study proposes an undersampling method combining distance measurement and majority class clustering. Resampling methods are used to develop an ensemble-based imbalanced-learning algorithm called the clustering and distance-based imbalance learning model (CDEILM). This algorithm combines distance-based undersampling, feature selection, and ensemble learning. In addition, a cluster size-based resampling (CSBR) method is proposed for preserving the original distribution of the majority class, and a hybrid imbalanced learning framework is constructed by fusing various types of resampling methods. The combination of CDEILM and CSBR can be considered as a specific case of this hybrid framework. The experimental results show that the CDEILM and CSBR methods can achieve better performance than the benchmark methods, and that the hybrid model provides the best results under most circumstances. Therefore, the proposed model can be used as an alternative imbalanced learning method under specific circumstances, e.g., for providing a solution to credit evaluation problems in financial applications.     

Speed of internationalization of new ventures and survival in export markets

Speed of internationalization may refer to how early a new venture goes abroad as well as how fast it expands its activities post-entry. The present paper incorporates both aspects and analyzes to what extent several dimensions of speed influence the ability of new ventures to survive in export markets. Based upon extant theories, two perspectives are deduced – a Learning perspective and a Resource perspective – leading to partly contrasting hypotheses. The hypotheses are tested based upon a unique data set consisting of all new ventures established in Norway a specific year that started to export goods in the following nine years. Among the findings are that survival rates increase when ventures go international immediately after inception and when they expand rapidly into new countries rather than focusing on expanding their export share in a limited number of markets, thus lending support to the Resource perspective.