Big data-assisted social media analytics for business model for business decision making system competitive analysis

The concept of big data is one of the important issues in business decision making in recent years. The expansion of social media platforms, the increase in data production devices and the evaluation and interpretation of the data produced by developing technology become crucial. Previous studies in the big data area have addressed the issue in limited contexts, and there are few studies in the field of marketing with a bibliometric approach. This study, which aims to examine how big data concept is evaluated in marketing literature, examines the publications on big data in indexed marketing journals using bibliometric methodology. This study starts with descriptive statistical information and then includes the top published journals, authors and corresponding author’s countries statistics. This study also includes most influential studies for big data concept in marketing literature, employs spectroscopy for detecting historical roots of studies and finally plots growth progress of keywords for predicting, future themes. This study contributes to current literature by providing a summarizing and instructive content for researchers interested in big data in marketing.

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IoT Based Decision Making System to Improve Veracity of Big Data

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.1.16799 ◽

2018 ◽

Vol 7 (3.1) ◽

pp. 63 ◽

Cited By ~ 1

Author(s):

R Revathy ◽

R Aroul Canessane

Keyword(s):

Decision Making ◽

Big Data ◽

Internet Of Things ◽

Data Streams ◽

Data Cleaning ◽

Data Handling ◽

Secure Data ◽

Decision Making System ◽

Data Investigation ◽

Impact Data

Data are vital to help decision making. On the off chance that data have low veracity, choices are not liable to be sound. Internet of Things (IoT) quality rates big data with error, irregularity, deficiency, trickery, and model guess. Improving data veracity is critical to address these difficulties. In this article, we condense the key qualities and difficulties of IoT, which impact data handling and decision making. We audit the scene of estimating and upgrading data veracity and mining indeterminate data streams. Also, we propose five suggestions for future advancement of veracious big IoT data investigation that are identified with the heterogeneous and appropriated nature of IoT data, self-governing basic leadership, setting mindful and area streamlined philosophies, data cleaning and handling procedures for IoT edge gadgets, and protection safeguarding, customized, and secure data administration.

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Review of social media analytics process and Big Data pipeline

Social Network Analysis and Mining ◽

10.1007/s13278-018-0507-0 ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 4

Author(s):

Hiba Sebei ◽

Mohamed Ali Hadj Taieb ◽

Mohamed Ben Aouicha

Keyword(s):

Social Media ◽

Big Data ◽

Social Media Analytics ◽

Data Pipeline

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Big Data and Analytics

Big Data Analytics for Sustainable Computing - Advances in Data Mining and Database Management ◽

10.4018/978-1-5225-9750-6.ch003 ◽

2020 ◽

pp. 47-65

Author(s):

Sheik Abdullah A. ◽

Priyadharshini P.

Keyword(s):

Social Media ◽

Big Data ◽

Predictive Analytics ◽

Data Warehousing ◽

Big Data Analytics ◽

Structured Data ◽

Social Media Analytics ◽

Data Set ◽

Data Volume ◽

Tools And Techniques

The term Big Data corresponds to a large dataset which is available in different forms of occurrence. In recent years, most of the organizations generate vast amounts of data in different forms which makes the context of volume, variety, velocity, and veracity. Big Data on the volume aspect is based on data set maintenance. The data volume goes to processing usual a database but cannot be handled by a traditional database. Big Data is stored among structured, unstructured, and semi-structured data. Big Data is used for programming, data warehousing, computational frameworks, quantitative aptitude and statistics, and business knowledge. Upon considering the analytics in the Big Data sector, predictive analytics and social media analytics are widely used for determining the pattern or trend which is about to happen. This chapter mainly deals with the tools and techniques that corresponds to big data analytics of various applications.

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Marketing and Social Media

Social Media Marketing ◽

10.4018/978-1-5225-5637-4.ch005 ◽

2018 ◽

pp. 90-97 ◽

Cited By ~ 2

Author(s):

Reshu Goyal ◽

Praveen Dhyani ◽

Om Prakash Rishi

Keyword(s):

Decision Making ◽

Social Media ◽

Web 2.0 ◽

The Internet ◽

Internet Applications ◽

Business Decision ◽

World Today ◽

The World

Time has changed and so does the world. Today everything has become as a matter of one click. With this effort we are trying to explore the new opportunities features and capabilities of the new compeers of Internet applicability known as Social Media or Web 2.0. The effort has been put in to use the internet, social media or web 2.0 as the tool for marketing issues or the strategic business decision making. The main aim is to seek social media, web 2.0 internet applications as the tool for marketing.

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Innovation Platforms

Multi-Sided Platforms (MSPs) and Sharing Strategies in the Digital Economy - Advances in E-Business Research ◽

10.4018/978-1-5225-5457-8.ch003 ◽

2018 ◽

pp. 72-95

Keyword(s):

Decision Making ◽

Big Data ◽

Business Model ◽

Value Chain ◽

Analytical Techniques ◽

Human Interaction ◽

Business Relationship ◽

Potential Relationship ◽

Relationship Of ◽

Cross Fertilization

Data and analytics is the heart of a digital business platform. Today, big data (BD) becomes useful when it enriches decision making that is enhanced by application of analytical techniques and some element of human interaction. With the merging of data and information vs. knowledge and intelligence, this chapter investigates an opportunity for cross-fertilization between BD and the field of digital business with related disciplines. Primary BD and analytics platform is a set of business capabilities. This chapter aims to investigate the potential relationship of BD and analytics platform and digital business platform. In doing so, it develops a BD value chain framework, BD business model pattern (BDBMP) with related levels of BD maturity improvement. This framework could be used to find answers on the basic BD and digital business relationship questions.

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The Role of Big Data and Business Analytics in Decision Making

Human-Computer Interaction and Technology Integration in Modern Society - Advances in Human and Social Aspects of Technology ◽

10.4018/978-1-7998-5849-2.ch010 ◽

2021 ◽

pp. 226-257

Author(s):

Pedro Caldeira Neves ◽

Jorge Rodrigues Bernardino

Keyword(s):

Decision Making ◽

Big Data ◽

Open Source ◽

Specific Area ◽

Business Analytics ◽

Business Decision ◽

Business Sustainability ◽

Open Source Systems ◽

Fundamental Shift

The amount of data in our world has been exploding, and big data represents a fundamental shift in business decision-making. Analyzing such so-called big data is today a keystone of competition and the success of organizations depends on fast and well-founded decisions taken by relevant people in their specific area of responsibility. Business analytics (BA) represents a merger between data strategy and a collection of decision support technologies and mechanisms for enterprises aimed at enabling knowledge workers such as executives, managers, and analysts to make better and faster decisions. The authors review the concept of BA as an open innovation strategy and address the importance of BA in revolutionizing knowledge towards economics and business sustainability. Using big data with open source business analytics systems generates the greatest opportunities to increase competitiveness and differentiation in organizations. In this chapter, the authors describe and analyze business intelligence and analytics (BI&A) and four popular open source systems – BIRT, Jaspersoft, Pentaho, and SpagoBI.

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Business Decision Making Based on Social Media Analysis

Advances in Intelligent Systems and Computing - Trends and Applications in Information Systems and Technologies ◽

10.1007/978-3-030-72651-5_20 ◽

2021 ◽

pp. 203-213

Author(s):

C. Santiago Morales ◽

M. Mario Morales ◽

S. Glenda Toala ◽

B. Alicia Andrade ◽

U. Giovanny Moncayo

Keyword(s):

Decision Making ◽

Social Media ◽

Media Analysis ◽

Social Media Analysis ◽

Business Decision

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Social Media Analytics

10.4018/978-1-7998-8528-3.ch021 ◽

2022 ◽

pp. 385-410

Author(s):

Časlav Kalinić ◽

Miroslav D. Vujičić

Keyword(s):

Social Media ◽

Big Data ◽

Cultural Tourism ◽

Social Media Analytics ◽

Online Platforms ◽

New Information ◽

People Participation ◽

Management And Development ◽

Social Media Platforms ◽

Tourism Destinations

The rise of social media allowed greater people participation online. Platforms such as Facebook, Twitter, Instagram, or TikTok enable visitors to share their thoughts, opinions, photos, locations. All those interactions create a vast amount of data. Social media analytics, as a way of application of big data, can provide excellent insights and create new information for stakeholders involved in the management and development of cultural tourism destinations. This chapter advocates for the employment of the big data concept through social media analytics that can contribute to the management of visitors in cultural tourism destinations. In this chapter, the authors highlight the principles of big data and review the most influential social media platforms – Facebook, Twitter, Instagram, and TikTok. On that basis, they disclose opportunities for the management and marketing of cultural tourism destinations.

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