scholarly journals Big data in marketing literature

2019 ◽  
Vol 1 (2) ◽  
pp. 15-24
Author(s):  
Fatih Pinarbasi ◽  
Zehra Nur Canbolat
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Current Literature ◽  
Statistical Information ◽  
Business Decision ◽  
Marketing Literature ◽  
Data Production ◽  
Social Media Platforms ◽  
Bibliometric Approach

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.  

scholarly journals SOCIAL MEDIA’S OFFICIAL DESTINATION PROMO VIDEOS AND COUNTRY IMAGE INFLUENCING VISITING INTENTION TO RISKY DESTINATIONS

2021 ◽  
Vol 14 (8) ◽  
pp. 02`-16
Author(s):  
Dyuty Firoz
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Information Sources ◽  
Study Data ◽  
Country Image ◽  
Promotion Strategy ◽  
The Social ◽  
Study Results ◽  
Social Media Platforms ◽  
Travel Decision

Social media destination promo videos (DPVs), are among the most important information sources of travel decision-making for their interactive and sharing features, and outstanding destination promotion strategy. Country image is also another important factor for travel decision-making. This study’s purpose is to assess whether the social media DPVs like DMOs’ promo videos and country image have any impact on visiting intentions towards risky destinations. A quantitative method was used for this study. Data was collected by online questionnaires, and 609 valid responses were considered for the analysis of the study. The results showed that the country's image positively influences the attitude of young tourists towards the country and that attention towards the promo videos positively influences young tourists’ overall emotions, attitudes, social norms, interests, desires and behaviours toward visiting a risky destination. This study results would be beneficial for those who are interested in using social media DPVs as part of their destination-promotion strategy, and also can guide destination-marketers to monitor and create better destination promotional contents in social media platforms, to encourage tourism to the destinations, especially risky ones.

scholarly journals KLASIFIKASI TEKS SOSIAL MEDIA TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE (Studi Kasus Penusukan Wiranto)

2019 ◽  
Vol 2 (2) ◽  
pp. 43
Author(s):  
Lalu Mutawalli ◽  
Mohammad Taufan Asri Zaen ◽  
Wire Bagye
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Big Data ◽  
Mass Communication ◽  
Confusion Matrix ◽  
Classification Model ◽  
Support Vector ◽  
Large Set ◽  
Appearance Time ◽  
Data Production

In the era of technological disruption of mass communication, social media became a reference in absorbing public opinion. The digitalization of data is very rapidly produced by social media users because it is an attempt to represent the feelings of the audience. Data production in question is the user posts the status and comments on social media. Data production by the public in social media raises a very large set of data or can be referred to as big data. Big data is a collection of data sets in very large numbers, complex, has a relatively fast appearance time, so that makes it difficult to handle. Analysis of big data with data mining methods to get knowledge patterns in it. This study analyzes the sentiments of netizens on Twitter social media on Mr. Wiranto stabbing case. The results of the sentiment analysis showed 41% gave positive comments, 29% commented neutrally, and 29% commented negatively on events. Besides, modeling of the data is carried out using a support vector machine algorithm to create a system capable of classifying positive, neutral, and negative connotations. The classification model that has been made is then tested using the confusion matrix technique with each result is a precision value of 83%, a recall value of 80%, and finally, as much as 80% obtained in testing the accuracy.

Marketing and Social Media

2018 ◽  
pp. 90-97 ◽  
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.

The Role of Big Data and Business Analytics in Decision Making

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.

Business Decision Making Based on Social Media Analysis

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

Social Media Analytics

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.

Digital Behavioral Criminalistics to Elucidate the Cyber Pathway to Intended Violence

2021 ◽  
pp. 554-611
Author(s):  
Cameron H. Malin
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Digital Evidence ◽  
The Internet ◽  
Thought Processes ◽  
The Public ◽  
Online Technologies ◽  
Social Media Platforms ◽  
Predatory Violence ◽  
Social Messaging

With the vast advances in computer, mobile, and online technologies, visibility into an offender’s thought processes and decision-making trajectory has been markedly enhanced. Digital behavioral artifacts, or digital evidence “breadcrumbs” of an offender’s behaviors, are now often left in publicly accessible locations on the Internet—such as social media platforms and social messaging applications—and in locations not privy to the public—such as the offender’s devices. Importantly, early seminal literature introduced and described examining an offender’s actions as series of steps along a path of threat escalation, or “pathway.” The totality of these emerging digital behavioral artifacts allows investigators to piece together an offender’s behavioral mosaic at a much more intimate and granular level, warranting a revised pathway—the cyber pathway to intended violence (CPIV)—that captures the thoughts and actions of an offender leading up to an act of deliberative, predatory violence. This chapter introduces the emerging discipline of Digital Behavioral Criminalistics and how this process can meaningfully be used by threat assessors to elucidate an offender’s steps on the CPIV.

Creating “(Social) Network Art” with NodeXL

2017 ◽  
pp. 342-393
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Network ◽  
Network Analysis ◽  
Web 2.0 ◽  
Data Visualization ◽  
Network Graph ◽  
Social Media Platforms ◽  
Machine Art ◽  
Social Sharing

If human-created objects of art are historically contingent, then the emergence of (social) network art may be seen as a product of several trends: the broad self-expression and social sharing on Web 2.0; the application of network analysis and data visualization to understand big data, and an appreciation for online machine art. Social network art is a form of cyborg art: it melds data from both humans and machines; the sensibilities of humans and machines; and the pleasures and interests of people. This chapter will highlight some of the types of (social) network art that may be created with Network Overview, Discovery and Exploration for Excel (NodeXL Basic) and provide an overview of the process. The network graph artwork presented here were all built from datasets extracted from popular social media platforms (Twitter, Flickr, YouTube, Wikipedia, and others). This chapter proposes some early aesthetics for this type of electronic artwork.

Decision-Making with Big Data Using Open Source Business Intelligence Systems

2016 ◽  
pp. 120-147 ◽  
Author(s):  
Jorge Bernardino ◽  
Pedro Caldeira Neves
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Open Source ◽  
Business Intelligence ◽  
Business Performance ◽  
Specific Area ◽  
Enterprise Management ◽  
Business Decision ◽  
Business Intelligence Systems ◽  
Fundamental Shift

The importance of supporting decision making for improving business performance is a crucial, yet challenging task in enterprise management. 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 becoming 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 Intelligence (BI) is a collection of decision support technologies for enterprises aimed at enabling knowledge workers such as executives, managers, and analysts to make better and faster decisions. We review the concept of BI as an open innovation strategy and address the importance of BI in revolutionizing knowledge towards economics and business sustainability. Using Big Data with Open Source Business Intelligence Systems will generate the biggest opportunities to increase competitiveness and differentiation in organizations. In this chapter, we describe and analyze four popular open source BI systems - Jaspersoft, Jedox, Pentaho and Actuate/BIRT.

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