Social Media Marketing of Football Clubs: A Study with Portuguese Football Clubs—SL Benfica, FC Porto, and Sporting CP

Pereira, J. D.; Rodrigues, Ricardo Gouveia; Duarte, Paulo; Gouveia, Ana Isabel

doi:10.1007/978-981-16-9272-7_45

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…Regarding the basic review situation, several works have been published to date in the context of AI and sport-specific fields of its application [21,[32][33][34][35]. In addition to AI topics, numerous general works can be found in the context of sports using bibliometric methods (e.g., football [36][37][38]; sports tourism [39,40]; sports sustainability [41]; sports management [42]; sports nutrition [43]; sports science in general [44]). However, looking at different literature analysis methods applied in the context of sports-related AI research shows that only a few works have been published to date using bibliometric techniques.…”

Section: Introductionmentioning

confidence: 99%

Conceptual Structure and Current Trends in Artificial Intelligence, Machine Learning, and Deep Learning Research in Sports: A Bibliometric Review

Dindorf

Bartaguiz

Gassmann

et al. 2022

IJERPH

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Artificial intelligence and its subcategories of machine learning and deep learning are gaining increasing importance and attention in the context of sports research. This has also meant that the number of corresponding publications has become complex and unmanageably large in human terms. In the current state of the research field, there is a lack of bibliometric analysis, which would prove useful for obtaining insights into the large amounts of available literature. Therefore, the present work aims to identify important research issues, elucidate the conceptual structure of the research field, and unpack the evolutionary trends and the direction of hot topics regarding key themes in the research field of artificial intelligence in sports. Using the Scopus database, 1215 documents (reviews and articles) were selected. Bibliometric analysis was performed using VOSviewer and bibliometrix R package. The main findings are as follows: (a) the literature and research interest concerning AI and its subcategories is growing exponentially; (b) the top 20 most cited works comprise 32.52% of the total citations; (c) the top 10 journals are responsible for 28.64% of all published documents; (d) strong collaborative relationships are present, along with small, isolated collaboration networks of individual institutions; (e) the three most productive countries are China, the USA, and Germany; (f) different research themes can be characterized using author keywords with current trend topics, e.g., in the fields of biomechanics, injury prevention or prediction, new algorithms, and learning approaches. AI research activities in the fields of sports pedagogy, sports sociology, and sports economics seem to have played a subordinate role thus far. Overall, the findings of this study expand knowledge on the research situation as well as the development of research topics regarding the use of artificial intelligence in sports, and may guide researchers to identify currently relevant topics and gaps in the research.

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Section: Introductionmentioning

confidence: 99%

Conceptual Structure and Current Trends in Artificial Intelligence, Machine Learning, and Deep Learning Research in Sports: A Bibliometric Review

Dindorf

Bartaguiz

Gassmann

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Conceptual structure and current trends in Artificial Intelligence, Machine Learning, and Deep Learning research in sports: A bibliometric review

Dindorf

Bartaguiz

Gassmann

et al. 2022

Preprint

View full text Add to dashboard Cite

Artificial intelligence and its subcategories of machine learning and deep learning are gaining increasing importance and attention in the context of sports research. This has also meant that the number of corresponding publications has become complex and unmanageably large in human terms. In the current state of the research field, there is a lack of bibliometric analysis, which would prove useful for obtaining insights into the large amounts of available literature. Therefore, the present work aims to identify important research issues, elucidate the conceptual structure of the research field, and unpack the evolutionary trends and the direction of hot topics regarding key themes in the research field of artificial intelligence in sports. Using the Scopus database, 1215 documents (reviews and articles) were selected. Bibliometric analysis was performed using VOSviewer and bibliometrix R package. The main findings are as follows: (a) the literature and research interest concerning AI and its subcategories is growing exponentially; (b) the top 20 most cited works comprise 32.52% of the total citations; (c) the top 10 journals are responsible for 28.64% of all published documents; (d) strong collaborative relationships are present, as along with small, isolated collaboration networks of individual institutions; (e) the three most productive countries are China, the USA, and Germany; (f) different research themes can be characterized using author keywords with current trend topics, e.g., in the fields of biomechanics, injury prevention or prediction, new algorithms, and learning approaches. AI research activities in the fields of sports pedagogy, sports sociology, and sports economics seem to have played a subordinate role thus far. Overall, the findings of this study expand knowledge on the research situation as well as the development of research topics regarding the use of artificial intelligence in sports, and may guide researchers to identify currently relevant topics and gaps in the research.

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Social media adoption as a marketing communication tool by non-professional sports clubs: a multiple case study approach

Marthinus,

Duffett,

Knott

2024

IJSMS

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PurposeSocial media has revolutionized marketing communication (MC). Rugby is South Africa’s most professionalized sport, leading the industry in its business management structure as well as its high-performance achievements. However, below the professional level, local rugby clubs face a large disparity compared to their professional counterparts, often relying on volunteers or part-time employees to manage the organizations. Therefore, the purpose of this study is to investigate how non-professional rugby clubs use social media as a MC tool.Design/methodology/approachThe study was exploratory, and a cross-sectional sample of twelve organizations was selected and employed a multiple case study approach. Club managers responsible for MC or social media participated in semi-structured interviews. The qualitative data analysis software, ATLAS.ti, facilitated the researchers’ use of an inductive approach to develop codes and themes for further analysis.FindingsThe findings revealed a high level of adoption of social media by the multiple cases (i.e. rugby sports club respondents), with only slight variations in the usage of specific social media applications (viz., Facebook, Instagram, Twitter, and WhatsApp). The sports clubs perceived that employing social media added substantial value to their MC. There were six emergent themes related to the organization’s perceived benefits from adopting social media MC, namely: brand awareness, relationship-building, player recruitment, attracting sponsors, storytelling, and information sharing.Originality/valueThe study makes a novel contribution in terms of how rugby clubs use social media as an MC tool. The paper advances scant knowledge and awareness of the relationship between South Africa’s sports marketing and social media. The conclusions will aid non-professional sports organizations in enhancing the effectiveness of their social media marketing by ensuring that their objectives and target audiences are well-defined.

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Social Media Marketing of Football Clubs: A Study with Portuguese Football Clubs—SL Benfica, FC Porto, and Sporting CP

Cited by 4 publications

References 32 publications

Conceptual Structure and Current Trends in Artificial Intelligence, Machine Learning, and Deep Learning Research in Sports: A Bibliometric Review

Conceptual Structure and Current Trends in Artificial Intelligence, Machine Learning, and Deep Learning Research in Sports: A Bibliometric Review

Conceptual structure and current trends in Artificial Intelligence, Machine Learning, and Deep Learning research in sports: A bibliometric review

Social media adoption as a marketing communication tool by non-professional sports clubs: a multiple case study approach

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