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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.

The aim of this paper is to better understand European consumers' behaviour in relation to Short Food Supply Chains (SFSCs), so as to provide insights to support their development as part of a sustainable food system. Specifically, it aims to analyse consumer purchase patterns, motivations and perceived barriers and to identify patterns of behaviour amongst different consumer groups.

An online consumer survey was conducted in 12 European countries (n = 2,419). Quantitative data analysis, including principal component analysis (PCA) and cluster analysis, was undertaken using SPSS.

Four consumer clusters are named according to their behavioural stage in terms of SFSC engagement: Unaware Unengaged, Aware Unengaged, Motivationally Engaged and Executively Engaged. Unaware Unengaged and Aware Unengaged are in the non-engagement phase of behaviour. Motivationally Engaged are motivationally activated to engage in the behaviour but fail to do so consistently. Executively Engaged is the fully engaged cluster, being motivated to act and purchasing local food on a frequent basis. The results show an interesting interplay between motivations and barriers, i.e. higher scores for motivations and lower scores for barriers do not necessarily translate into higher purchase frequency.

The research gleans insights into the contextual factors that may inhibit SFSC purchases in different consumer segments. It offers practical implications for policymakers and others seeking to develop SFSCs as part of a sustainable food system.

The purpose of this study is to investigate the impact of the construction and operation of underground gas storage (UGS) facilities, under the prism of the recent rise in energy prices. The focus is on developing energy markets interconnected with gas producers through pipelines and has access to liquefied natural gas (LNG) facilities in parallel.

Through a focal market in Europe, the authors estimate the economic value for both stakeholders and consumers by introducing a methodology, appropriately adjusted to the specificities of the domestic energy market. The Transmission System Operator, the Energy Market Regulator, the Energy Exchange and Eurostat are the main data sources for our calculations and conclusions.

The authors investigate the perspectives of UGS facilities, identifying financial challenges considering specific energy market conditions which are barriers to new storage facilities. Nevertheless, the energy price rocketing coupled with the security of gas supply issues, which arose in autumn 2021 and were continuing in 2022 due to the Russia–Ukraine crisis, highlight that gas storage remains, at least for the midterm, at the core of European priorities.

The paper emphasizes on developing markets toward green transition, proposing tangible policy recommendations regarding gas storage. A new methodological approach is proposed, appropriate to quantify the economic value of UGSs in such markets. Last, a mix of energy policy options is suggested which include regulatory reforms, support schemes and new energy infrastructures that could make the gas storage investments economically viable.

There is extensive literature on the determinants of job tenure insecurity. However, very little is known about the individual drivers of labour market insecurity. Additionally, while a piece of literature shows that volunteering improves workers' income, no study considers volunteering as an activity which could help workers to feel more confident about their perception of labour market insecurity if they lost or resigned their jobs. Therefore, purpose of this paper is to study whether workers who volunteer are less likely to perceive labour market insecurity.

The paper employs data from the sixth European working conditions survey which provides a great deal of information on working conditions. For the empirical investigation, probit model as well as robustness analysis have been implemented.

Results show that employees who do voluntary activities have a greater likelihood of declaring perceived labour market insecurity, which is nearly 3 percentage points lower, than employees who do not volunteer. Findings suggest that governments need to improve the relationship between for-profit and non-profit sectors to encourage volunteering.

This is the first study which considers volunteering as an activity which could help workers to feel more confident about their perception of “labour market insecurity”. Most of the studies on “labour market insecurity” do not focus on the workers individual characteristics but mainly on the labour markets institutional characteristics and welfare regimes differences.

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.