Priority Rules for Multi‐Task Due‐Date Scheduling under Varying Processing Costs

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135700/1/poms12606.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135700/2/poms12606_am.pd

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

The BG News February 13, 2001

The BGSU campus student newspaper February 13, 2001. Volume 86 - Issue 95https://scholarworks.bgsu.edu/bg-news/7760/thumbnail.jp

A study of technological literacy in writing programs.

This dissertation empirically studies computer-assisted writing administration as a site of agency for social change by inquiring how writing program administrators (WPAs) use their agency and power when developing and maintaining computer-mediated spaces (CM) or computer-assisted instruction (CAI). This study asks, What are the results when individual agency meets technological literacy in the academic workplace? Numerous articles have examined WPAs as agents of social change (R. Miller, C. Selfe, M. Pemberton); few have used empirical data as their grounds. To date, no articles have examined the WPA\u27s agency with technological literacy in computer-mediated environments. This study utilizes triangulated and multi-modal research methods including site observations as well as interviews and email surveys with WPAs, students, technicians, and non-departmental stakeholders. The study assumes that WPAs act as administrative agents who use their agency to move beyond the role of functionaries such as boss compositionists (Sledd in Harris, 2000) or academic bureaucrats (R. Miller, 1988). Special features of this dissertation include original primary data on WPA decision-making, education, compensation. Preliminary data show the following: WPAs report using their agency when managing technology use for departments and institutions; 70 percent of WPAs report that their technological literacy affords them power in their departments; 70 percent of WPAs state that their technological literacy has improved their relations with students; most WPAs receive little compensation for their technological skills other than salary; and search and screen committees increasingly require technology proficiency of their writing faculty. WPAs should take into consideration managerial trends: faculty who resist CAI professional development because they are in the retirement track arguments that position CAI as a Technology vs. X false dilemma ( We can support either labor and people or technology ); assumptions that link technology with democracy and unexamined grand social narratives; and the conflation of technological literacy with critical technological literacy This data suggests that job skills and intellectual contributions of WPAs who work with CAI are not fully recognized and compensated in departments and contribute to a rise in invisible labor. The dissertation includes the following chapters: (I) Introduction: Shoulders to Stand On and the Work Already Done in Computer-Mediated Writing; (II) The Discourse on Technological Literacy: A Bakhtinian Reading of the National Infrastructure Initiative; (III) What WPAs Say about Their Work as a Site of Agency; (IV) Results from Online Survey of WPAs and Technological Literacy; and (V) Conclusion and Supplementary Materials

Central Florida Future, Vol. 36 No. 41, February 9, 2004

Tuition increase: SGA candidates tackle rising cost of college; Teacher shortage forces scramble; Pricey mural unveiled at Rosen School campus; Teachers working longer hours to stretch budget; Ephedra faces possible ban in March.https://stars.library.ucf.edu/centralfloridafuture/2733/thumbnail.jp