3,762 research outputs found

    λ”₯λŸ¬λ‹κ³Ό 직원 의견으둜 νŒŒμ•…ν•œ 쑰직의 λ¬΄ν˜•λ‚΄λΆ€μžμ‚°

    Get PDF
    ν•™μœ„λ…Όλ¬Έ (석사) -- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : κ³΅κ³ΌλŒ€ν•™ 산업곡학과, 2021. 2. μ‘°μ„±μ€€.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.λ¬΄ν˜•μžμ‚°μ΄λž€ 쑰직이 λ³΄μœ ν•œ μžμ‚° 쀑 ν˜•νƒœκ°€ μ—†λŠ” μžμ‚°μ„ λœ»ν•˜λ©°, 졜근 λ“€μ–΄ μœ ν˜•μžμ‚°μ²˜λŸΌ κΈ°μ—…μ˜ 성과에 κΈ°μ—¬ν•˜λŠ” 동λ ₯ 쀑 ν•˜λ‚˜λ‘œ μ£Όλͺ©λ°›κ³  μžˆλ‹€. 그런데 μ •μž‘ 무엇이 λ¬΄ν˜•μžμ‚°μΈμ§€, λ¬΄ν˜•μžμ‚°μ˜ μ’…λ₯˜μ—λŠ” 무엇이 μžˆλŠ”μ§€μ— λŒ€ν•œ μ—°κ΅¬λŠ” ν™œλ°œν•˜κ²Œ μ§„ν–‰λ˜μ–΄μ˜€μ§€ μ•Šμ€ 싀정이닀. 특히 μ§μ›μ˜ κ΄€μ μ—μ„œ 바라본 λ¬΄ν˜•μžμ‚°, 즉 λ¬΄ν˜•λ‚΄λΆ€μžμ‚°μ— λŒ€ν•œ 연ꡬ μ—­μ‹œ 이둠에 κΈ°λ°˜ν•œ ν”„λ ˆμž„μ›Œν¬ μ΄μƒμœΌλ‘œ 이루어지지 μ•Šμ•˜λ‹€. λ³Έ μ—°κ΅¬λŠ” λŒ€λŸ‰μ˜ νšŒμ‚¬ 리뷰 데이터에 λ”₯λŸ¬λ‹μ„ μ ‘λͺ©μ‹œμΌœ λ¬΄ν˜•λ‚΄λΆ€μžμ‚°μ˜ μ’…λ₯˜λ₯Ό ν¬κ΄„μ μœΌλ‘œ νŒŒμ•…ν•˜κ³ μž ν–ˆλ‹€. 이λ₯Ό μœ„ν•΄ 세계 μ΅œλŒ€ νšŒμ‚¬ 평점 및 리뷰 μ‚¬μ΄νŠΈμΈ κΈ€λž˜μŠ€λ„μ–΄μ—μ„œ S&P 500 νšŒμ‚¬μ— λŒ€ν•΄ 게재된 140만 개 μ΄μƒμ˜ 리뷰 데이터λ₯Ό μˆ˜μ§‘ν–ˆλ‹€. λ°©λŒ€ν•œ μ–‘μ˜ μ§μ›μ˜ λͺ©μ†Œλ¦¬μ—μ„œ 자주 λ“±μž₯ν•˜λŠ” μ£Όμ œκ°€ λ¬΄ν˜•λ‚΄λΆ€μžμ‚°μ˜ μ’…λ₯˜μ™€ μΌμΉ˜ν•  것이라고 κ°€μ •ν•œ 것이닀. ν•΄λ‹Ή 데이터에 μ–΄ν…μ…˜ 기반의 λ‰΄λŸ΄ λ„€νŠΈμ›Œν¬ λͺ¨λΈμ„ μ μš©ν•˜μ—¬ 24개의 주제λ₯Ό μΆ”μΆœν•˜μ˜€κ³ , 이 쀑 직μž₯ λΆ„μœ„κΈ°, λ™λ£Œ, 기술적인 μžμ› λ“± 15개의 μ£Όμ œκ°€ κΈ°μ‘΄ λ¬Έν—Œμ—μ„œ μ–ΈκΈ‰λ˜μ–΄μ˜¨ λ¬΄ν˜•μžμ‚° μ’…λ₯˜μ™€ μΌμΉ˜ν–ˆμŒμ„ ν™•μΈν–ˆλ‹€. 이후 ν‚€μ›Œλ“œ μΆ”μΆœ 방법을 μ μš©ν•΄ νšŒμ‚¬λ³„λ‘œ λ³΄μœ ν•œ 각 λ¬΄ν˜•λ‚΄λΆ€μžμ‚°μ˜ νŠΉμ§•μ„ νŒŒμ•…ν–ˆλ‹€. λ³Έ 연ꡬ가 μ œμ‹œν•œ 방법둠을 톡해 νšŒμ‚¬λ“€μ΄ μ „λž΅μ μΈ μžμ‚°μ„ 보닀 잘 μ΄ν•΄ν•˜κ³  ν™œμš©ν•  수 μžˆμ„ κ²ƒμœΌλ‘œ μ‚¬λ£Œλœλ‹€.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 ꡭ문초둝 75 κ°μ‚¬μ˜ κΈ€ 76Maste

    Intelligent Management and Efficient Operation of Big Data

    Get PDF
    This chapter details how Big Data can be used and implemented in networking and computing infrastructures. Specifically, it addresses three main aspects: the timely extraction of relevant knowledge from heterogeneous, and very often unstructured large data sources, the enhancement on the performance of processing and networking (cloud) infrastructures that are the most important foundational pillars of Big Data applications or services, and novel ways to efficiently manage network infrastructures with high-level composed policies for supporting the transmission of large amounts of data with distinct requisites (video vs. non-video). A case study involving an intelligent management solution to route data traffic with diverse requirements in a wide area Internet Exchange Point is presented, discussed in the context of Big Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201

    Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

    Full text link
    Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

    Management Responses to Online Reviews: Big Data From Social Media Platforms

    Get PDF
    User-generated content from virtual communities helps businesses develop and sustain competitive advantages, which leads to asking how firms can strategically manage that content. This research, which consists of two studies, discusses management response strategies for hotel firms to gain a competitive advantage and improve customer relationship management by leveraging big data, social media analytics, and deep learning techniques. Since negative reviews' harmful effects are greater than positive comments' contribution, firms must strategise their responses to intervene in and minimise those damages. Although current literature includes a sheer amount of research that presents effective response strategies to negative reviews, they mostly overlook an extensive classification of response strategies. The first study consists of two phases and focuses on comprehensive response strategies to only negative reviews. The first phase is explorative and presents a correlation analysis between response strategies and overall ratings of hotels. It also reveals the differences in those strategies based on hotel class, average customer rating, and region. The second phase investigates effective response strategies for increasing the subsequent ratings of returning customers using logistic regression analysis. It presents that responses involving statements of admittance of mistake(s), specific action, and direct contact requests help increase following ratings of previously dissatisfied returning customers. In addition, personalising the response for better customer relationship management is particularly difficult due to the significant variability of textual reviews with various topics. The second study examines the impact of personalised management responses to positive and negative reviews on rating growth, integrating a novel method of multi-topic matching approach with a panel data analysis. It demonstrates that (a) personalised responses improve future ratings of hotels; (b) the effect of personalised responses is stronger for luxury hotels in increasing future ratings. Lastly, practical insights are provided
    • …
    corecore