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This study aims to identify the determining factors of perceived altruism and attitude toward an inclusive sponsorship activation, as well as the impact of these variables on the attitude toward the sponsor.

Online survey data were obtained from 1,228 respondents from France, the UK and South Africa. The data were analyzed using partial least squares structural equation modeling (PLS-SEM).

The results show that the cause-brand fit has a strong positive impact on the perceived altruism toward the motivations underlying inclusive activation, while skepticism toward advertising has a very weak negative impact. In return, perceived altruism positively influences the attitude toward inclusive activation and sponsor attitude. Furthermore, this attitude toward inclusive activation is positively influenced by involvement in women’s soccer and France men’s national football team identification. The attitude toward inclusive activation also positively influences the attitude toward sponsor attitude. However, contrary to what had been advanced, identification with the France women’s national football team and the nationality of the respondents (French, British or South African) had no impact on the attitude toward inclusive activation, while the perceived importance of the cause had very weak impact on attitudes toward inclusive activation.

This study highlights the potential benefits of investing in inclusive sponsorship activations, particularly with respect to their positive impact on consumer attitude toward sponsor attitude. It also highlights the importance of establishing, in advance, a strong association between the brand image and the cause supported, so that the motivations underlying the inclusive activations are perceived as more altruistic.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.