Use of Big Data for Competitive Advantage of Company

Kubina, Milan; Varmus, Michal; Kubinová, Irena

doi:10.1016/s2212-5671(15)00955-7

Cited by 79 publications

(62 citation statements)

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“…This research take reference from Kubina et.al. findings about the benefits of big data as a company's tool for understanding consumer behavior [16]. So, this research implements the theory of Kubina et.al.…”

Section: Introductionmentioning

confidence: 81%

Comparison Analysis Of Social Influence Marketing For Mobile Payment Using Support Vector Machine

Prihono

Sari

2019

KINETIK

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There are many digital-based financial services today, one of them is mobile payment service. Users can deposit money and make online transaction with their smartphone through mobile application. Five mobile payment service providers with the most users in Indonesia, according to Dailysocial are GOPAY, OVO, LinkAja, DANA, and PayTren. This study uses sentiment analysis to classify user’s opinion into positive and negative classes. The classification method used is Support Vector Machine. This study utilizes three metrics, namely Net Sentiment, Share of Voice, and Social Influence Marketing Score. Those metrics are useful for knowing reputation, reach, and influence of brands in social media. The findings in this study indicate that GOPAY, OVO, DANA, and PayTren have a positive dominant sentiment, while LinkAja has a negative dominant sentiment. The brand with the biggest influence and reaches in the mobile payment industry is GOPAY. While the highest reputation brand is PayTren. The implication of this research is to encourage mobile payment providers to be able to monitor their brand conditions among their competitors by utilizing social network analysis method.

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Section: Introductionmentioning

confidence: 81%

Comparison Analysis Of Social Influence Marketing For Mobile Payment Using Support Vector Machine

Prihono

Sari

2019

KINETIK

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“…can establish a reasonable and sound sales plan (C6). Through the big data integration system, enterprises can obtain business-related data sources to understand customers' behavior and provide feedback, which will help improve products and services and get them closer to the target market in order to meet the needs of consumers and enable more effective customer relationship management (C7) (Kubina et al, 2015;Anshari et al, 2019).…”

Section: Marketing Managementmentioning

confidence: 99%

Constructing a Hierarchical Framework for Assessing the Application of Big Data Technology in Entrepreneurship Education

Lang

Liu

et al. 2020

Front. Psychol.

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The emergence of big data technology continues to innovate and change the world, bringing opportunities and challenges to all walks of life. Against the background of this era, traditional entrepreneurship education requires reform and innovation. This research attempts to explore the ways and practices of applying big data technology to entrepreneurship education so as to improve and perfect traditional entrepreneurship education and achieve its sustainable development. Based on classic theories, such as entrepreneurial theory, strategic management theory, and leadership theory, this paper develops a relatively systematic attribute system of entrepreneurship education under big data technology, comprehensively uses Fuzzy-DEMATEL and ISM methods to explore the relationship between different attributes and their importance, and finally constructs a hierarchical framework for the application of big data technology in entrepreneurship education. The results show that the attributes of entrepreneurship education under big data technology can be divided into four levels, each with different priorities and degrees of importance, and there are complex interactions and constraints among them. This study provides important guidance and suggestions for the development of entrepreneurship education and multiattribute decision-making management under the given resources, which is conducive to the sustainable development of entrepreneurs and new ventures.

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“…Big data is significant for national development, industrial upgrades, scientific research, interdisciplinary research, help in perceiving the present and predicting the future. The study identifies the challenges to big data as Data complexity, Computational complexity and System complexity and finally calls for advances in processes to handle the data better Kubina and et al opines that to performance in a competitive environment depends on the ability tom process relevant information at the appropriate time [7]. The study emphasized that vast quantities of information is present in various forms.…”

Section: Litrature Reviewmentioning

confidence: 99%

Big Data Science and EXASOL as Big Data Analytics tool

2019

IJITEE

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Big data and Data science are the two top trends of recent years. Both can be combined together as big data science. This leads to the demand for new system architectures which facilitates the development of processes which can handle huge data volumes without deterring the agility, flexibility and the interactive feel which suits the exploratory approach of a data scientist. Businesses today have found ways of using data as the principal factor for value generation. These data-driven businesses apply a variety of data tools as data analysis is one of the chief elements in this process. In order to raise data science to the new computational level that is required to meet the challenges of big data and interactive advanced analytics, EXASOL has introduced a new technological approach. This tool enables us more effective and easy data analysis.

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Use of Big Data for Competitive Advantage of Company

Cited by 79 publications

References 1 publication

Comparison Analysis Of Social Influence Marketing For Mobile Payment Using Support Vector Machine

Comparison Analysis Of Social Influence Marketing For Mobile Payment Using Support Vector Machine

Constructing a Hierarchical Framework for Assessing the Application of Big Data Technology in Entrepreneurship Education

Big Data Science and EXASOL as Big Data Analytics tool

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