Innovative Knowledge-Based Methods for Business Success: Analysing User Generated Content

Dear Colleagues,

The global development of the Internet, which has enabled the analysis of large amounts of data and the services linked to their use, has led companies to modify their business strategies in search of new ways to increase marketing productivity and profitability. Many strategies are based on business intelligence and marketing intelligence that make it possible to extract profitable knowledge and insights from large amounts of data generated by company customers in digital environments.

Additionally, the use of social networks and the Internet have become habits for consumers to the point that there are millions of devices connected to the Internet that are constantly generating new data. Furthermore, as the use of these technologies has become habitual for users, it has also become commonplace for users to share information about individual experiences and opinions, as well as content related to the interests of users and companies via social networks, known as user-generated content (UGC).

In looking at these types of data sources, several studies have analyzed the influence of the application of data mining to marketing strategies and knowledge discovery. UGC is defined as the content generated by users in social networks and digital platforms. Such content includes comments, opinions, expressions, and interactions between users and brands, or any other type of content shared publicly on the Internet that seeks to generate engagement between different profiles. The study of this type of content is important in the context of new business models and marketing strategies, as it can enable managers to generate meaningful insights that may in turn help to refine strategic responses or become the basis for further research.

Therefore, the purpose of this Special Issue is to analyze how these new data analysis techniques can influence the development of marketing strategies and decision-making processes in companies. The objective of this Special Issue, consequently, is to analyze how the application of automatic and semiautomatic data analysis techniques applied to marketing affects the business environment and decision-making.

For this Special Issue, we invite paper contributions related to any of the topics outlined above and which clearly relate to knowledge management and data mining for marketing using research approaches such as data mining, social network analysis, UGC analysis, sentiment analysis, big data, machine learning approaches, support vector machines, neuromarketing, case studies or reviews of literature on this topic as well as another quantitative, qualitative or mixed/multimethod perspectives.

Important References

Saura, J.R.; Bennett, D.R. A Three-Stage method for Data Text Mining: Using UGC in Business Intelligence Analysis. Symmetry 2019, 11, 519. doi:10.3390/sym11040519.

Reyes-Menendez, A.; Saura, J.R.; Alvarez-Alonso, C. Understanding #WorldEnvironmentDay User Opinions in Twitter: A Topic-Based Sentiment Analysis Approach. Int. J. Environ. Res. Public Health 2018, 15, 2537. doi:10.3390/ijerph15112537.

Daugherty, T.; Eastin, M.S.; Bright, L. Exploring consumer motivations for creating user generated content. J. Interact. Advert. 2008, 8, 16–25. doi:10.1080/15252019.2008.10722139.

Saura, J.R.; Rodriguez Herráez, B.; Reyes-Menendez, A. Comparing a traditional approach for financial Brand Communication Analysis with a Big Data Analytics technique. IEEE Access 2019, doi:10.1109/ACCESS.2019.2905301.

Goh, K.Y.; Heng, C.S.; Lin, Z. Social media brand community and consumer behavior: Quantifying the relative impact of user-and marketer-generated content. Inf. Syst. Res. 2013 24, 88–107. doi:10.1287/isre.1120.0469.

Prof. Dr. José Ramón Saura
Prof. Dr. Ana Reyes-Menendez
Guest Editors

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