Visualization Research on the China’s Present Situation of Quantitative Research in Library and Information under the Big Data Background

2018 ◽  
Keyword(s):  
Big Data ◽  
Quantitative Research ◽  
Present Situation

Visual Artists’ Use of Digital Self-Marketing Strategies – A Quantitative Survey on the Present Situation in Germany

2019 ◽  
Vol 3 (2) ◽  
pp. 53-72
Author(s):  
Jessica Hodgkiss ◽  
Sarah Fassio ◽  
Adrianna Rosa
Keyword(s):  
Quantitative Research ◽  
Present Situation ◽  
Marketing Strategies ◽  
Visual Artists ◽  
Further Training ◽  
Quantitative Survey ◽  
Research Survey

Faced with increased competition on the market, visual artists today opt for digital self-marketing strategies to promote their work. In order to determine applicable measures for best results, the au-thors of this paper carried out a quantitative research survey among 158 artists working in Germany. Findings show that a large number of participants act as digital entrepreneurs, and over 50 per cent indicate a need for further training.

scholarly journals Big Data and Digital Aesthetic, Arts, and Cultural Education: Hot Spots of Current Quantitative Research

2019 ◽  
pp. 089443931988845 ◽  
Author(s):  
Alexander Christ ◽  
Marcus Penthin ◽  
Stephan Kröner
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Predictive Modeling ◽  
Hot Spots ◽  
Quantitative Research ◽  
Hot Spot ◽  
Future Research ◽  
Original Research ◽  
Cultural Education ◽  
Data Volume

Systematic reviews are the method of choice to synthesize research evidence. To identify main topics (so-called hot spots) relevant to large corpora of original publications in need of a synthesis, one must address the “three Vs” of big data (volume, velocity, and variety), especially in loosely defined or fragmented disciplines. For this purpose, text mining and predictive modeling are very helpful. Thus, we applied these methods to a compilation of documents related to digitalization in aesthetic, arts, and cultural education, as a prototypical, loosely defined, fragmented discipline, and particularly to quantitative research within it (QRD-ACE). By broadly querying the abstract and citation database Scopus with terms indicative of QRD-ACE, we identified a corpus of N = 55,553 publications for the years 2013–2017. As the result of an iterative approach of text mining, priority screening, and predictive modeling, we identified n = 8,304 potentially relevant publications of which n = 1,666 were included after priority screening. Analysis of the subject distribution of the included publications revealed video games as a first hot spot of QRD-ACE. Topic modeling resulted in aesthetics and cultural activities on social media as a second hot spot, related to 4 of k = 8 identified topics. This way, we were able to identify current hot spots of QRD-ACE by screening less than 15% of the corpus. We discuss implications for harnessing text mining, predictive modeling, and priority screening in future research syntheses and avenues for future original research on QRD-ACE.

Research challenges for the use of big data in policy-making

2020 ◽  
Vol 14 (4) ◽  
pp. 593-604
Author(s):  
Francesco Mureddu ◽  
Juliane Schmeling ◽  
Eleni Kanellou
Keyword(s):  
Big Data ◽  
Information And Communication Technologies ◽  
Policy Making ◽  
Quantitative Research ◽  
Future Research ◽  
The European Union ◽  
Public Governance ◽  
Content Type ◽  
Research Challenges ◽  
Data Informed

Purpose This paper aims to present pertinent research challenges in the field of (big) data-informed policy-making based on the research, undertaken within the course of the European Union-funded project Big Policy Canvas. Technological advancements, especially in the past decade, have revolutionised the way that both every day and complex activities are conducted. It is, thus, expected that a particularly important actor such as the public sector, should constitute a successful disruption paradigm through the adoption of novel approaches and state-of-the-art information and communication technologies. Design The research challenges stem from a need, trend and asset assessment based on qualitative and quantitative research, as well as from the identification of gaps and external framework factors that hinder the rapid and effective uptake of data-driven policy-making approaches. Findings The current paper presents a set of research challenges categorised in six main clusters, namely, public governance framework, privacy, transparency, trust, data acquisition, cleaning and representativeness, data clustering, integration and fusion, modelling and analysis with big data and data visualisation. Originality/value The paper provides a holistic overview of the interdisciplinary research challenges in the field of data-informed policy-making at a glance and shall serve as a foundation for the discussion of future research directions in a broader scientific community. It, furthermore, underlines the necessity to overcome isolated scientific views and treatments because of a high complex multi-layered environment.

scholarly journals Innovation policies of SME's in the South Moravian and Moravian Silesian regions: results of the research

2009 ◽  
Vol 57 (6) ◽  
pp. 89-98
Author(s):  
Tomáš Heralecký ◽  
Tomáš Meluzín
Keyword(s):  
Quantitative Research ◽  
New Technologies ◽  
Present Situation ◽  
Market Potential ◽  
Foreign Languages ◽  
The South ◽  
Weak Points ◽  
Production Organization ◽  
Innovation Policies

The aim of the paper was to identify the present situation in innovation policies of small and medium-sized enterprises in the South Moravian and Moravian Silesian regions. In order to achieve the specified objective, quantitative research was carried out in the small and medium-sized enterprises by way of questionnaires. The achieved results imply that the enterprises under investigation apply competitive strategy focusing on top quality of goods on offer. The research manifested that the companies focused markedly on innovations in supplying products on offer with additional functions or features. The research results show that the companies do not conduct changes in production organization frequently, not even following their earlier innovative activities. The results of the research into the innovative activity “change in product design” imply that this activity is not applied frequently in comparison with the above-mentioned activities. Based on the interviewed companies' weak points in human resources, the elementary drawbacks include lack of management's command of foreign languages as well as production staff's expert skills. The results of the research imply that the interviewed companies perceive the sphere of products (improved product quality, extension of a product range), the sphere of new technologies and the sphere of an increase in market potential as the most significant. The questionnaire inquiry shows that innovative and development activities are most frequently financed from companies' own funds, subsidies/grants, bank credits and leases. Mortgages and venture capital are only made used of occasionally.

scholarly journals Influence of AI and Machine Learning in Insurance Sector

2022 ◽  
Author(s):  
Nitin Prajapati
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Processing ◽  
Quantitative Research ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Insurance Companies ◽  
Big Data Processing ◽  
Insurance Sector ◽  
Sector Analysis

The Aim of this research is to identify influence, usage, and the benefits of AI (Artificial Intelligence) and ML (Machine learning) using big data analytics in Insurance sector. Insurance sector is the most volatile industry since multiple natural influences like Brexit, pandemic, covid 19, Climate changes, Volcano interruptions. This research paper will be used to explore potential scope and use cases for AI, ML and Big data processing in Insurance sector for Automate claim processing, fraud prevention, predictive analytics, and trend analysis towards possible cause for business losses or benefits. Empirical quantitative research method is used to verify the model with the sample of UK insurance sector analysis. This research will conclude some practical insights for Insurance companies using AI, ML, Big data processing and Cloud computing for the better client satisfaction, predictive analysis, and trending.

scholarly journals Impact of Network Public Opinion Disposal Model on Public Value Factors under the Background of Big Data

CONVERTER ◽  
2021 ◽  
pp. 559-565
Author(s):  
Peng Bo, Xu Xiao-Long
Keyword(s):  
Big Data ◽  
Public Opinion ◽  
Quantitative Research ◽  
Prediction Models ◽  
Control Model ◽  
Government Control ◽  
Mechanism Model ◽  
The Government ◽  
And Control ◽  
Network Public Opinion

It is the key for the government to control the degree of information alienation to study the mechanism and control model of network public opinion information alienation for big data. This provides a theoretical basis for the government to deal with and manage the network public opinion. This paper uses qualitative analysis of the information alienation mechanism of network public opinion under the big data environment, and expands the evolution mechanism model of network public opinion to the information alienation control model. On this basis, the classification of government control information alienation is studied by numerical simulation. This paper takes the actual forum, blog, website with news comment function as the research object, and proposes a prediction platform construction scheme based on Java, which integrates a variety of prediction models. This provides useful exploration and ideas for quantitative research on the complex social phenomenon of network public opinion.

Pembelajaran Online dan Hasil Menulis Karangan Argumentasi di Era Big Data

2020 ◽  
Vol 3 (1) ◽  
pp. 32
Author(s):  
Susi Alawiyah
Keyword(s):  
Big Data ◽  
Online Learning ◽  
Statistical Method ◽  
Quantitative Research ◽  
Significance Level ◽  
Negative Effect ◽  
Study Population ◽  
11Th Grade ◽  
Parameter Coefficient

Abstract:  This study aims to determine the effect of the application of online learning on the results of writing arguments at SMK Negeri 5 Tangerang district in the era of big data. This research is a descriptive quantitative research in which the process of extracting information is manifested in the form of numbers as a tool to find information about what is known. The study population was all 11th grade students of SMK N 5 Tangerang Regency. The sample of this research is the 11th grade students of TPM 1 and 11th grade of TPM 2. The descriptive statistical method is used to analyze the application of online learning and its effect on the results of writing arguments. The research was conducted in March and April 2020, data were obtained from questionnaires and assessments of the results of writing student argument essays. The results of this study indicate that the magnitude of the parameter coefficient (-0.065) means that there is a negative effect on the application of online learning and the t statistical value is 0.632 with a significance level of 5% = 1.84, so the t statistical value is smaller than t table. The conclusion is that the application of online learning has a negative effect on the results of writing arguments in the era of big data.Abstrak: Penelitian ini bertujuan untuk mengetahui pengaruh penerapan pembelajaran online terhadap hasil menulis karangan argumentasi di SMK Negeri 5 kabupaten Tangerang di era big data. Penelitian ini adalah penelitian kuantitatif deskriptif dimana proses penggalian informasi diwujudkan dalam bentuk angka-angka sebagai alat untuk menemukan keterangan mengenai apa yang diketahui. Populasi penelitian adalah seluruh siswa kelas 11 SMK N 5 Kabupaten Tangerang. Sampel penelitian ini adalah siswa kelas 11 TPM 1 dan kelas 11 TPM 2. Metode statistic deskriftip digunakan untuk menganalisis penerapan pembelajaran online dan pengaruhnya terhadap hasil menulis karangan argumentasi. Penelitian dilaksanakan pada bulan Maret dan April 2020, data diperoleh dari kuesioner dan penilaian hasil menulis karangan argumentasi siswa.  Hasil penelitian ini menyatakan bahwa besarnya koefisien parameter (-0.065) yang berarti terdapat pengaruh yang negatif penerapan pembelajaran online dan nilai t statistik sebesar 0.632 dengan taraf signifikansi 5% = 1.84 maka nilai t statistik lebih kecil dari t tabel. Kesimpulannya bahwa penerapan pembelajaran online berpengaruh negatif terhadap hasil menulis karangan argumentasi di era big data.

Predicting Longevity: An Analysis of Potential Alternatives to Life Expectancy Reports

2018 ◽  
Keyword(s):  
Big Data ◽  
Life Expectancy ◽  
Psychological Factors ◽  
Quantitative Research ◽  
Insurance Industry ◽  
Analysis Model ◽  
Physiological Factors ◽  
Considerable Potential ◽  
Qualitative And Quantitative ◽  
Resilience Factors

Retirees, pension funds, and the insurance industry have all been negatively affected by the wrongful estimation of longevity. The inaccuracies in current life expectancy (LE) reports primarily result from misinterpretations of the influence of resilience factors on longevity. This study examines different and more accurate measurement metrics to minimize the risks related to biased LE calculations. By using both qualitative and quantitative research approaches, this research develops a new conceptual model: a two-factor-LE-analysis model with a telomere test as a medical basis (physiological factors) and a big data approach to filter the psychological factors to longevity. The authors suggest that the new model, together with the insights of the existing LE-projection methodologies, has considerable potential to improve LE predictions.

The Transformation of Auditing From Traditional to Continuous Auditing in the Era of Big Data

2019 ◽  
pp. 137-152
Author(s):  
Adem Çabuk ◽  
Alp Aytaç
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Present Situation ◽  
Decision Makers ◽  
Reliable Information ◽  
Continuous Auditing ◽  
Digital Devices ◽  
Volume Velocity ◽  
Future Direction ◽  
Shed Light

Massive usage of internet and digital devices make it easier accessing the desired information. In the past, auditing was a periodic, reactive approach, but this must change. Today, volume, velocity, variety, veracity, and value of the information, which are the main criteria of big data, are crucial. Decision makers demand timely, true, and reliable information. This need has affected every sector including auditing. For this reason, the continuous auditing system comes to debate in the big data era. The main aim of this chapter is to shed light on how traditional auditing transformed into the continuous auditing and where big data stands in this transformation. It is concluded that even though many obstacles arise, continuous auditing systems and harvesting big data benefits are crucial to gain a competitive advantage. Also, using big data analytics and continuous auditing system together, management and shareholders gain detailed information about the company's present situation and future direction.

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