scholarly journals The Impact of Increasing Returns on Knowledge and Big Data: From Adam Smith and Allyn Young to the Age of Machine Learning and Digital Platforms

2019 ◽  
Author(s):  
Yao-Su Hu
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Adam Smith ◽  
Increasing Returns ◽  
Digital Platforms ◽  
The Impact

The impact of machine learning and big data on credit markets

2021 ◽  
Author(s):  
Peter Eccles ◽  
Paul Grout ◽  
Paolo Siciliani ◽  
Anna Zalewska
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Credit Markets ◽  
The Impact

scholarly journals Are environmental-related online reviews more helpful? A big data analytics approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marcello Mariani ◽  
Matteo Borghi
Keyword(s):  
Big Data ◽  
North America ◽  
Data Analytics ◽  
Negative Binomial ◽  
Big Data Analytics ◽  
Online Reviews ◽  
Environmental Discourse ◽  
Content Type ◽  
Digital Platforms ◽  
The Impact

Purpose Based on more than 2.7 million online reviews (ORs) collected with big data analytical techniques from Booking.com and TripAdvisor.com, this paper aims to explore if and to what extent environmental discourse embedded in ORs has an impact on electronic word-of-mouth (e-WOM) helpfulness across eight major destination cities in North America and Europe. Design/methodology/approach This study gathered, by means of Big Data techniques, 2.7 million ORs hosted on Booking.com and TripAdvisor, and covering hospitality services in eight different destinations cities in North America (New York City, Miami, Orlando and Las Vegas) and Europe (Barcelona, London, Paris and Rome) over the period 2017–2018. The ORs were analysed by means of ad hoc content analytic dictionaries to identify the presence and depth of the environmental discourse included in each OR. A negative binomial regression analysis was used to measure the impact of the presence/depth of online environmental discourse in ORs on e-WOM helpfulness. Findings The findings indicate that the environmental discourse presence and depth influence positively e-WOM helpfulness. More specifically those travelers who write explicitly about environmental topics in their ORs are more likely to produce ORs that are voted as helpful by other consumers. Research limitations/implications Implications highlight that both hotel managers and platform developers/managers should become increasingly aware of the importance that customer attach to environmental practices and initiatives and therefore engage more assiduously in environmental initiatives, if their objective is to improve online review helpfulness for other customers reading the focal reviews. Future studies might include more destinations and other operationalizations of environmental discourse. Originality/value This study constitutes the first attempt to capture how the presence and depth of hospitality services consumers’ environmental discourse influence e-WOM helpfulness on multiple digital platforms, by means of a big data analysis on a large sample of online reviews across multiple countries and destinations. As such it makes a relevant contribution to the area at the intersection between big data analytics, e-WOM and sustainable tourism research.

scholarly journals BIG DATA IN THE DEVELOPMENT OF THE PLATFORM

2021 ◽  
Vol 9 (6) ◽  
pp. 194-198
Author(s):  
N. K. Samoilov ◽  
O. S. Shkarlat ◽  
D. A. Stepanenko
Keyword(s):  
Big Data ◽  
Business Models ◽  
Negative Impact ◽  
Public Cloud ◽  
Digital Platforms ◽  
Doing Business ◽  
Private And Public ◽  
Effective Use ◽  
The Impact ◽  
Domestic Companies

The study examines cloud platforms, the use of Big Data in modern companies and the impact of big data on the development of digital platforms. We give concepts and comparative characteristics of the most common cloud platforms - private and public. The article defines the conditions for the effective use of platform types of doing business. We have been studied the experience of using Big Data in international and domestic companies. Based on the identified Big Data tasks, conclusions were drawn about the preferred use of private cloud platforms by large enterprises and the use of public cloud platforms by small and medium-sized enterprises. The positive and negative impact of the use of platform business models and big data analysis has been investigated. We made assumptions about the prospects for the development of private and public cloud platforms in Russia and the world.

scholarly journals The Digital Transformation and Disruption in Business Models of the Banks under the Impact of FinTech and BigTech

2020 ◽  
Vol 14 (1) ◽  
pp. 294-305
Author(s):  
Virginia Mărăcine ◽  
Oona Voican ◽  
Emil Scarlat
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Business Models ◽  
Business Processes ◽  
Digital Transformation ◽  
Operating Costs ◽  
Continuous Increase ◽  
The Impact

AbstractThe explosive development of artificial intelligence, machine learning and big data methods in the last 10 years has been felt in the financial-banking field which has subjected to profound changes aimed at determining an unprecedented increase in the efficiency and profitability of the businesses they carry out. The tendencies of applying the concepts coming from AI, together with the continuous increase of the volume, complexity and variety of the data that the banks collect, store and process have acquired the generic names of FinTech, respectively BigTech. Five main areas exist where Fintechs and Bigtechs can provide improvements in business models for the banks: introducing specialized platforms, covering neglected customer segments, improving customer selection, reduction of the operating costs of the banks, and optimization of the business processes of the banks. We will present some of these improvements, and then we will show how the business models of the banks dramatically transform under the influence of these changes.

scholarly journals THE IMPLEMENTATION OF BIG DATA ANALYSIS IN REGULATING ONLINE SHORT-TERM RENTAL BUSINESS: A CASE OF AIRBNB IN BEIJING

2019 ◽  
Vol IV-4/W9 ◽  
pp. 79-86
Author(s):  
J. Li ◽  
F. Biljecki
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Housing Prices ◽  
Big Data Analysis ◽  
Price Sensitivity ◽  
Short Term ◽  
New Business ◽  
The Impact ◽  
The Relationship

Abstract. With the fast expansion and controversial impacts of short-term rental platforms such as Airbnb, many cities have called for regulating this new business model. This research aims to establish an approach to understand the impact of Airbnb (and similar services) through big data analysis and provide insights potentially useful for its regulation. The paper reveals how Airbnb is influencing Beijing’s neighbourhood housing prices through machine learning and GIS. Machine learning models are developed to analyse the relationship between Airbnb activities in a neighbourhood and prevailing housing prices. The model of the best fit is then used to analyse the neighbourhood price sensitivity in view of increasing Airbnb activities. The results show that the sensitivity is variable: there are neighbourhoods that are likely to be more price sensitive to Airbnb activities, but also neighbourhoods that are likely to be price robust. Finally, the paper gives policy recommendations for regulating short-term rental businesses based on neighbourhood’s price sensitivity.

The impact of machine learning on processing big data in high-performance computing

2021 ◽  
pp. 344-353
Author(s):  
Rohit ◽  
B. Gupta ◽  
K. K. Gola
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
High Performance Computing ◽  
High Performance ◽  
The Impact ◽  
Performance Computing

Big Data Technology To Improve The Reliability Of Solutions When Choosing A Contractor

2019 ◽  
pp. 65-70
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Prognostic Models ◽  
Modeling And Analysis ◽  
Current Economic Situation ◽  
Contract Failure ◽  
Contract Implementation ◽  
The Impact

Considering current economic situation, the level of competition among different companies is great. In order to gain a higher position in the ratings, to attract more new customers, to predict the demand for products, and finally to protect themselves from wrong decisions, companies are increasingly turning to big data analytics. In the sphere of construction an opportunity to foresee the probability of contract implementation before its conclusion is always relevant. The higher the probability, the more attractive the contractor and lower the risks of the customer. Developing the topic of applicability of machine learning methods to the problem of determining the probability of successful completion of the contract, the authors are experimenting with a set of analyzed indicators assessing the impact of each of them on the decision on the possibility of contract failure. The article considers in detail the stages of data preparation for modeling, direct modeling and analysis of the results obtained. The authors tested the adequacy of the models on actual data and set the metrics by which it is possible to customize and improve the models for the needs of a particular organization. The prognostic models with a predictive power, based on machine learning algorithms, such as logistic regression, decision tree, random forest, developed by the authors, have the potential for practical use in construction organizations at the stage of contract conclusion.

scholarly journals Education, Big Data and Artificial Intelligence: Mixed methods in digital platforms

Comunicar ◽  
2020 ◽  
Vol 28 (65) ◽  
pp. 43-52
Author(s):  
Beatrice Bonami ◽  
Luiz Piazentini ◽  
André Dala-Possa
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Research Application ◽  
Digital Platforms ◽  
Academic Field ◽  
Qualitative And Quantitative ◽  
Methodological Considerations ◽  
Complex Scenario ◽  
The Impact ◽  
Educational Field

Digital technology has provided users with new connections that have reset our understanding of social architectures. As a reaction to Artificial Intelligence (AI) and Big Data, the educational field has rearranged its structure to consider human and non-human stakeholders and their actions on digital platforms. In light of this increasingly complex scenario, this proposal aims to present definitions and discussions about AI and Big Data from the academic field or published by international organizations. The study of AI and Big Data goes beyond the search for mere computational power and instead focuses upon less difficult (yet perhaps more complex) areas of the study social impacts in Education. This research suggests an analysis of education through 21st century skills and the impact of AI development in the age of platforms, undergoing three methodological considerations: research, application and evaluation. To accomplish the research, we relied upon systematic reviews, bibliographic research and quality analyses conducted within case studies to compose a position paper that sheds light on how AI and Big Data work and on what level they can be applied in the field of education. Our goal is to offer a triangular analysis under a multimodal approach to better understand the interface between education and new technological prospects, taking into consideration qualitative and quantitative procedures. La tecnología digital ha traído características de conexión que restablecen nuestra comprensión de arquitecturas sociales. Sobre la Inteligencia Artificial (IA) y Big Data, el campo educativo reorganiza su estructura para considerar a los actores humanos y no humanos y sus acciones en plataformas digitales. En este escenario cada vez más complejo, esta propuesta tiene como objetivo presentar definiciones y debates sobre IA y Big Data de naturaleza académica o publicados por organizaciones internacionales. El estudio de IA y Big Data puede ir más allá de la búsqueda de poder computacional / lógico y entrar en áreas menos difíciles (y quizás más complejas) del campo científico para responder a sus impactos sociales en la educación. Esta investigación sugiere un análisis de la educación a través de las habilidades del siglo XXI y los impactos del desarrollo de IA en la era de las plataformas, pasando por tres ejes de grupos metodológicos: investigación, aplicación y evaluación. Para llevar a cabo la investigación, confiamos en revisiones sistemáticas, investigaciones bibliográficas y análisis de calidad de estudios de casos para componer un documento de posición que arroje luz sobre cómo funcionan la IA y el Big Data y en qué nivel se pueden aplicar en el campo de la educación. Nuestro objetivo es ofrecer un análisis triangular bajo un enfoque multimodal para comprender mejor la interfaz entre la educación y las nuevas perspectivas tecnológicas.

scholarly journals Data Science Methods for Psychology

Psychology ◽  
2020 ◽  
Author(s):  
Jeffrey Stanton
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Data Collection ◽  
Data Science ◽  
Large Data ◽  
Large Data Sets ◽  
Predictive Analysis ◽  
Data Sets ◽  
The Impact

The term “data science” refers to an emerging field of research and practice that focuses on obtaining, processing, visualizing, analyzing, preserving, and re-using large collections of information. A related term, “big data,” has been used to refer to one of the important challenges faced by data scientists in many applied environments: the need to analyze large data sources, in certain cases using high-speed, real-time data analysis techniques. Data science encompasses much more than big data, however, as a result of many advancements in cognate fields such as computer science and statistics. Data science has also benefited from the widespread availability of inexpensive computing hardware—a development that has enabled “cloud-based” services for the storage and analysis of large data sets. The techniques and tools of data science have broad applicability in the sciences. Within the field of psychology, data science offers new opportunities for data collection and data analysis that have begun to streamline and augment efforts to investigate the brain and behavior. The tools of data science also enable new areas of research, such as computational neuroscience. As an example of the impact of data science, psychologists frequently use predictive analysis as an investigative tool to probe the relationships between a set of independent variables and one or more dependent variables. While predictive analysis has traditionally been accomplished with techniques such as multiple regression, recent developments in the area of machine learning have put new predictive tools in the hands of psychologists. These machine learning tools relax distributional assumptions and facilitate exploration of non-linear relationships among variables. These tools also enable the analysis of large data sets by opening options for parallel processing. In this article, a range of relevant areas from data science is reviewed for applicability to key research problems in psychology including large-scale data collection, exploratory data analysis, confirmatory data analysis, and visualization. This bibliography covers data mining, machine learning, deep learning, natural language processing, Bayesian data analysis, visualization, crowdsourcing, web scraping, open source software, application programming interfaces, and research resources such as journals and textbooks.

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