As the fourth industrial revolution unfolds and the use of digital humans becomes more commonplace, understanding digital humans' potential to replace real human interaction or enhance it, particularly in storytelling marketing contexts, is becoming evermore important. To promote interaction and increase the entertainment value of technology-enhanced storytelling marketing, brands have begun to explore the use of augmented digital humans as storytelling agents. In this article, we examine the effectiveness of leveraging advanced technologies and delivering messages via digital humans in storytelling advertisements. In Study 1, we investigate the effectiveness of narrative transportation on behavioral responses after exposure to an interactive augmented reality mobile advertisement with a digital human storyteller. In Study 2, we compare how consumers respond to augmented digital human versus real human storytelling advertisements after conducting an exploratory neurophysiological electroencephalography study. The findings show that both types of agents promote narrative transportation when the story fits the product well. Moreover, a digital human perceived as more human-like elicits stronger positive consumer responses, suggesting an effective new approach to storytelling marketing.
Explainable Artificial Intelligence (XAI) aims to provide insights into the inner workings and the outputs of AI systems. Recently, there’s been growing recognition that explainability is inherently human-centric, tied to how people perceive explanations. Despite this, there is no consensus in the research community on whether user evaluation is crucial in XAI, and if so, what exactly needs to be evaluated and how. This systematic literature review addresses this gap by providing a detailed overview of the current state of affairs in human-centered XAI evaluation. We reviewed 73 papers across various domains where XAI was evaluated with users. These studies assessed what makes an explanation “good” from a user’s perspective, i.e., what makes an explanation meaningful to a user of an AI system. We identified 30 components of meaningful explanations that were evaluated in the reviewed papers and categorized them into a taxonomy of human-centered XAI evaluation, based on: (a) the contextualized quality of the explanation, (b) the contribution of the explanation to human-AI interaction, and (c) the contribution of the explanation to human- AI performance. Our analysis also revealed a lack of standardization in the methodologies applied in XAI user studies, with only 19 of the 73 papers applying an evaluation framework used by at least one other study in the sample. These inconsistencies hinder cross-study comparisons and broader insights. Our findings contribute to understanding what makes explanations meaningful to users and how to measure this, guiding the XAI community toward a more unified approach in human-centered explainability.
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The enhancement of GPS technology enables the use of GPS devices not only as navigation and orientation tools, but also as instruments used to capture travelled routes: as sensors that measure activity on a city scale or the regional scale. TU Delft developed a process and database architecture for collecting data on pedestrian movement in three European city centres, Norwich, Rouen and Koblenz, and in another experiment for collecting activity data of 13 families in Almere (The Netherlands) for one week. The question posed in this paper is: what is the value of GPS as ‘sensor technology’ measuring activities of people? The conclusion is that GPS offers a widely useable instrument to collect invaluable spatial-temporal data on different scales and in different settings adding new layers of knowledge to urban studies, but the use of GPS-technology and deployment of GPS-devices still offers significant challenges for future research.
Intelligent technology in automotive has a disrupting impact on the way modern automobiles are being developed. New technology not only has brought complexity to already existing information in the car (digitization of driver instruments) but also brings new external information to the driver on how to optimize the driving style amongst others from the perspective of communicating with infrastructures (Vehicle to Infrastructure communication (V2I)). The amount of information that a driver has to process in modern vehicles is increasing rapidly due to the introduction of multiple displays and new external information sources. An information overload lies awaiting, yet current Human Machine Interface (HMI) designs and the corresponding legal frameworks lag behind. Currently, many initiatives (Pratijkproef Amsterdam, Concorda) are being developed with respect to V2I, amongst others with Rijkswaterstaat, North Holland and Brabant. In these initiatives, SME’s, like V-Tron, focus on the development of specific V2I hardware. Yet in the field of HMI’s these SME’s need universities (HAN University of Applied Science, Rhine Waal University of Applied Science) and industrial designers (Yellow Chess) to help them with design guidelines and concept HMI’s. We propose to develop first guidelines on possible new human-machine interfaces. Additionally, we will show the advantages of HMI’s that go further than current legal requirements. Therefore, this research will focus on design guidelines averting the information overload. We show two HMI’s that combine regular driver information with V2I information of a Green Light Optimized Speed Advise (GLOSA) use case. The HMI’s will be evaluated on a high level (focus groups and a small simulator study). The KIEM results in two publications. In a plenary meeting with experts, the guidelines and the limitations of current legal requirements will be discussed. The KIEM will lead to a new consortium to extend the research.
Aanleiding: Automatisering kan leiden tot beter gebruik van materialen en afval reduceren. Dit brengt verbeteringen met zich mee voor 'people, planet and profit' (PPP) - mensen, het milieu en de winst. Een specifieke vorm van automatisering, de ontwikkeling van zelfrijdende auto's en vrachtauto's, gaat snel. Maar om zelfrijdende voertuigen beschikbaar te maken is er nog veel onderzoek en ontwikkeling nodig op verschillende gebieden. Er zijn nog veel vragen te beantwoorden op het gebied van onder andere truckontwerp, betrouwbare software, aansprakelijkheid, trajectplanning en logistiek. Doelstelling Het doel van het Intralog-project is om voor de maatschappij en de private sector een significante bijdrage te leveren aan de mogelijkheden van zelfrijdende voertuigen in de commerciële transportsector. Het Intralog-project onderzoekt de toegevoegde waarde voor PPP van 'automated guided trucks' (AGT's) aan logistieke operaties bij distributiecentra en interterminal/intermodal traffic hubs. Dit gebeurt in twee stappen: 1) het identificeren van het potentieel met betrekking tot de vraag vanuit de logistieke omgeving; 2. het ontwerpen, realiseren, testen en valideren van mogelijke strategieën voor het implementeren van AGT's in een logistiek scenario. Beoogde resultaten Het concrete resultaat van het project bestaat uit onderzoekstools en hardware- en softwaremodellen voor Intralog. Deze bieden een goede mogelijkheid om de opgedane kennis te verspreiden. De projectdeelnemers zullen bijdragen aan workshops, tentoonstellingen en in Nederland georganiseerde symposia. De onderzoeksresultaten verspreiden ze op conferenties en door middel van publicaties in technische vakbladen. De uiteindelijke Intralog-resultaten worden gepresenteerd op een afsluitend congres. De resultaten zullen worden samengevat in een boekje.
