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Publications

Publications by Jorge Esparteiro Garcia

2024

Using Principal Component Analysis to Support Content Marketing Strategies

Authors
Matos, B; Garcia, JE; Correia, F;

Publication
INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2022, ICNAAM-2022

Abstract
After the pandemic we experienced, companies have felt the need to reinvent themselves and adapt to the present moment. The Internet and social networks have developed and increased their activity substantially. Users spend more time on social networks, shop more online, and feel more than ever a need for information and to view content. The main objective of this research is to define and implement a content marketing strategy for the social networks, through a quarterly content plan in the marketing services company Naive. In the first part of the research, presented in this paper, the work consisted of designing and implementing a questionnaire, obtaining a sample of 200 respondents to assess their perceptions and habits regarding social networks and the content offered on social networks, to study the results. The results obtained and analysis done will be used to develop a content strategy for Naive, which include studying the specific objectives for the company's different social networks, the actions to be developed and the content to be implemented.

2024

Sustainable Development Goal 9 in a Cluster Perspective: a Case Study for Alto Minho Region

Authors
Abreu, M; Rodrigues, HS; Silva, A; Garcia, JE;

Publication
INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2022, ICNAAM-2022

Abstract
The United Nations has set Sustainable Development Goals (SDGs) to build a more sustainable future. The SDG analyzes progress to understand major implementation challlenges, define disparities across nations or regions, and propose priorities for action. It has 17 objectives and more than 200 indicators. Cluster analysis was used to categorize the 10 municipalities. It was carried out using IBM SPSS software, which calculated the Euclidean distance and put the investigated regions into clusters with the traits they shared the most in common.

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