3,762 research outputs found
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곡νκ³Ό, 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.무νμμ°μ΄λ μ‘°μ§μ΄ 보μ ν μμ° μ€ ννκ° μλ μμ°μ λ»νλ©°, μ΅κ·Ό λ€μ΄ μ νμμ°μ²λΌ κΈ°μ
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λ₯μ μΌμΉνμμ νμΈνλ€. μ΄ν ν€μλ μΆμΆ λ°©λ²μ μ μ©ν΄ νμ¬λ³λ‘ 보μ ν κ° λ¬΄νλ΄λΆμμ°μ νΉμ§μ νμ
νλ€. λ³Έ μ°κ΅¬κ° μ μν λ°©λ²λ‘ μ ν΅ν΄ νμ¬λ€μ΄ μ λ΅μ μΈ μμ°μ λ³΄λ€ μ μ΄ν΄νκ³ νμ©ν μ μμ κ²μΌλ‘ μ¬λ£λλ€.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
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κ°μ¬μ κΈ 76Maste
Intelligent Management and Efficient Operation of Big Data
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
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
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
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