In the era of social media, online reviews have become a crucial factor influencing the exposure of tourist destinations and the decision-making of potential tourists, exerting a profound impact on the sustainable development of these destinations. However, the influence of review valence on visit intention, especially the role of affective commitment and reputation (ability vs. responsibility), remains unclear. Drawing on emotion as a social information theory, this paper aims to elucidate the direct impact of different review valences on tourists’ visit intentions, as well as mediating mechanisms and boundary conditions. Three experiments indicate that positive (vs. negative) reviews can activate stronger affective commitment and visit intention, with affective commitment also playing a mediating role. Additionally, destination reputation significantly moderates the after-effects of review valences. More specifically, a responsibility reputation (compared with an ability reputation) weakens the effect of negative valence on affective commitment and visit intention. This study provides valuable theoretical insights into how emotional elements in online reviews influence the emotions and attitudes of potential tourists. Particularly for tourism managers, review valence and responsibility reputation hold practical significance in destination marketing.
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Unlike common nouns, person names refer to unique entities and generally have a referring function. We used event-related potentials to investigate the time course of identifying the emotional meaning of nouns and names. The emotional valence of names and nouns were manipulated separately. The results show early N1 effects in response to emotional valence only for nouns. This might reflect automatic attention directed towards emotional stimuli. The absence of such an effect for names supports the notion that the emotional meaning carried by names is accessed after word recognition and person identification. In addition, both names with negative valence and emotional nouns elicited late positive effects, which have been associated with evaluation of emotional significance. This positive effect started earlier for nouns than for names, but with similar durations. Our results suggest that distinct neural systems are involved in the retrieval of names' and nouns' emotional meaning.
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We examined the neural correlates of facial attractiveness by presenting pictures of male or female faces (neutral expression) with low/intermediate/high attractiveness to 48 male or female participants while recording their electroencephalogram (EEG). Subjective attractiveness ratings were used to determine the 10% highest, 10% middlemost, and 10% lowest rated faces for each individual participant to allow for high contrast comparisons. These were then split into preferred and dispreferred gender categories. ERP components P1, N1, P2, N2, early posterior negativity (EPN), P300 and late positive potential (LPP) (up until 3000 ms post-stimulus), and the face specific N170 were analysed. A salience effect (attractive/unattractive > intermediate) in an early LPP interval (450–850 ms) and a long-lasting valence related effect (attractive > unattractive) in a late LPP interval (1000–3000 ms) were elicited by the preferred gender faces but not by the dispreferred gender faces. Multi-variate pattern analysis (MVPA)-classifications on whole-brain single-trial EEG patterns further confirmed these salience and valence effects. It is concluded that, facial attractiveness elicits neural responses that are indicative of valenced experiences, but only if these faces are considered relevant. These experiences take time to develop and last well beyond the interval that is commonly explored.
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Deze aanvraag onderzoekt hoe de beta-versie van de Dynamische Arousal-Valence Tool - ontwikkeld binnen SIA-RAAK’s Network is the Message – naar de markt gebracht kan worden. De DAVTool meet sentiment in social media posts. Sentimentanalyse levert cruciale informatie voor social media managers, online marketeers en content strategen. Zij móeten weten wat het sentiment over hun product, bedrijf of dienst is om adequaat te kunnen interveniëren. De huidige manier van meten levert niet voldoende inzicht. De nieuwe DAVTool vult de meting aan en meet sentiment op een innovatieve manier en geeft als gevolg meer en betere informatie.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
Electronic Sports (esports) is a form of digital entertainment, referred to as "an organised and competitive approach to playing computer games". Its popularity is growing rapidly as a result of an increased prevalence of online gaming, accessibility to technology and access to elite competition.Esports teams are always looking to improve their performance, but with fast-paced interaction, it can be difficult to establish where and how performance can be improved. While qualitative methods are commonly employed and effective, their widespread use provides little differentiation among competitors and struggles with pinpointing specific issues during fast interactions. This is where recent developments in both wearable sensor technology and machine learning can offer a solution. They enable a deep dive into player reactions and strategies, offering insights that surpass traditional qualitative coaching techniquesBy combining insights from gameplay data, team communication data, physiological measurements, and visual tracking, this project aims to develop comprehensive tools that coaches and players can use to gain insight into the performance of individual players and teams, thereby aiming to improve competitive outcomes. Societal IssueAt a societal level, the project aims to revolutionize esports coaching and performance analysis, providing teams with a multi-faceted view of their gameplay. The success of this project could lead to widespread adoption of similar technologies in other competitive fields. At a scientific level, the project could be the starting point for establishing and maintaining further collaboration within the Dutch esports research domain. It will enhance the contribution from Dutch universities to esports research and foster discussions on optimizing coaching and performance analytics. In addition, the study into capturing and analysing gameplay and player data can help deepen our understanding into the intricacies and complexities of teamwork and team performance in high-paced situations/environments. Collaborating partnersTilburg University, Breda Guardians.
