Faculty Publications 2018-2019

The production of scholarly research continues to be one of the primary missions of the ILR School. During a typical academic year, ILR faculty members published or had accepted for publication over 25 books, edited volumes, and monographs, 170 articles and chapters in edited volumes, numerous book reviews. In addition, a large number of manuscripts were submitted for publication, presented at professional association meetings, or circulated in working paper form. Our faculty\u27s research continues to find its way into the very best industrial relations, social science and statistics journal

Rational bidding using reinforcement learning: an application in automated resource allocation

The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized

Business analytics in sport talent acquisition: methods, experiences, and open research opportunities

Recruitment of young talented players is a critical activity for most professional teams in different sports such as football, soccer, basketball, baseball, cycling, etc. In the past, the selection of the most promising players was done just by relying on the experts' opinions but without systematic data support. Nowadays, the existence of large amounts of data and powerful analytical tools have raised the interest in making informed decisions based on data analysis and data-driven methods. Hence, most professional clubs are integrating data scientists to support managers with data-intensive methods and techniques that can identify the best candidates and predict their future evolution. This paper reviews existing work on the use of data analytics, artificial intelligence, and machine learning methods in talent acquisition. A numerical case study, based on real-life data, is also included to illustrate some of the potential applications of business analytics in sport talent acquisition. In addition, research trends, challenges, and open lines are also identified and discussed

딥러닝과 직원 의견으로 파악한 조직의 무형내부자산

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2021. 2. 조성준.Intangible resources are non-physical firm resources that are critical to a firms success. Among them, we refer to those that directly impact employee experience at work as intangible internal resources (IIR). We attempted to create a comprehensive list of IIR by applying a deep learning model to a large-scale company review dataset. We collected over 1.4 million company reviews written for S&P 500 firms from Glassdoor, one of the largest anonymous company rating and review website. Since Glassdoor reviews represent the collective employee voice, we hypothesized that prominent topics from the collective voice would represent different types of IIR. By applying a deep learning model to the review data, we discovered 24 resource types, among which 15 types such as Atmosphere at Work, Coworkers, and Technological Resources aligned with frameworks from the past literature. We then implemented a keyword extraction model to identify each firms unique characteristics regarding different IIR types. We believe firms could utilize our findings to better understand and manage their strategic resources.무형자산이란 조직이 보유한 자산 중 형태가 없는 자산을 뜻하며, 최근 들어 유형자산처럼 기업의 성과에 기여하는 동력 중 하나로 주목받고 있다. 그런데 정작 무엇이 무형자산인지, 무형자산의 종류에는 무엇이 있는지에 대한 연구는 활발하게 진행되어오지 않은 실정이다. 특히 직원의 관점에서 바라본 무형자산, 즉 무형내부자산에 대한 연구 역시 이론에 기반한 프레임워크 이상으로 이루어지지 않았다. 본 연구는 대량의 회사 리뷰 데이터에 딥러닝을 접목시켜 무형내부자산의 종류를 포괄적으로 파악하고자 했다. 이를 위해 세계 최대 회사 평점 및 리뷰 사이트인 글래스도어에서 S&P 500 회사에 대해 게재된 140만 개 이상의 리뷰 데이터를 수집했다. 방대한 양의 직원의 목소리에서 자주 등장하는 주제가 무형내부자산의 종류와 일치할 것이라고 가정한 것이다. 해당 데이터에 어텐션 기반의 뉴럴 네트워크 모델을 적용하여 24개의 주제를 추출하였고, 이 중 직장 분위기, 동료, 기술적인 자원 등 15개의 주제가 기존 문헌에서 언급되어온 무형자산 종류와 일치했음을 확인했다. 이후 키워드 추출 방법을 적용해 회사별로 보유한 각 무형내부자산의 특징을 파악했다. 본 연구가 제시한 방법론을 통해 회사들이 전략적인 자산을 보다 잘 이해하고 활용할 수 있을 것으로 사료된다.Abstract i Contents ii List of Tables iv List of Figures v Chapter 1 Introduction 1 Chapter 2 Literature Review 7 2.1 Intangible Resources 7 2.2 Glassdoor 11 2.3 Unsupervised Aspect Extraction Methods 13 2.4 Unsupervised Keyword Extraction Methods 16 Chapter 3 Glassdoor Data 18 3.1 Data Collection 18 3.2 Descriptive Statistics 20 3.3 Text Preprocessing 22 Chapter 4 Unsupervised Methods for IIR and Firm Characteristic Analysis 24 4.1 ABAE Method for IIR Discovery 24 4.2 TF-IDF Method for Firm Characteristic Discovery 28 Chapter 5 Experimental Results 30 5.1 15 IIR Types from ABAE 30 5.2 Unique Firm Characteristics from TF-IDF 39 5.3 Managerial Implications 45 5.4 Evaluation of ABAE 46 Chapter 6 Conclusion 49 Bibliography 51 Appendix 58 국문초록 75 감사의 글 76Maste

Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms

Supervised and unsupervised data mining approaches in loan default prediction

Given the paramount importance of data mining in organizations and the possible contribution of a data-driven customer classification recommender systems for loan-extending financial institutions, the study applied supervised and supervised data mining approaches to derive the best classifier of loan default. A total of 900 instances with determined attributes and class labels were used for the training and cross-validation processes while prediction used 100 new instances without class labels. In the training phase, J48 with confidence factor of 50% attained the highest classification accuracy (76.85%), k-nearest neighbors (k-NN) 3 the highest (78.38%) in IBk variants, naïve Bayes has a classification accuracy of 76.65%, and logistic has 77.31% classification accuracy. k-NN 3 and logistic have the highest classification accuracy, F-measures, and kappa statistics. Implementation of these algorithms to the test set yielded 48 non-defaulters and 52 defaulters for k -NN 3 while 44 non-defaulters and 56 defaulters under logistic. Implications were discussed in the paper

Supplier Selection and Relationship Management: An Application of Machine Learning Techniques

Managing supply chains is an extremely challenging task due to globalization, short product life cycle, and recent advancements in information technology. These changes result in the increasing importance of managing the relationship with suppliers. However, the supplier selection literature mainly focuses on selecting suppliers based on previous performance, environmental and social criteria and ignores supplier relationship management. Moreover, although the explosion of data and the capabilities of machine learning techniques in handling dynamic and fast changing environment show promising results in customer relationship management, especially in customer lifetime value, this area has been untouched in the upstream side of supply chains. This research is an attempt to address this gap by proposing a framework to predict supplier future value, by incorporating the contract history data, relationship value, and supply network properties. The proposed model is empirically tested for suppliers of public works and government services Canada. Methodology wise, this thesis demonstrates the application of machine learning techniques for supplier selection and developing effective strategies for managing relationships. Practically, the proposed framework equips supply chain managers with a proactive and forward-looking approach for managing supplier relationship