Institutt for markedsføring

Behavior Research Methods 58(5) s. 1-21 Doi: https://link.springer.com/article/10.3758/s13428-026-02953-x#additional-informationhttps://doi.org/10.3758/s13428-026-02953-x

Remote photoplethysmography (rPPG) is a technique for the monitoring of cardiac signals using simple consumer-grade cameras, such as webcams. Its cost-effectiveness and accessibility make it a promising tool for widespread application, particularly in remote and resource-limited research settings. However, it remains unclear whether rPPG can accurately capture the moment-to-moment fluctuations in heart rate (HR) required for robust HR variability (HRV) estimation. In this study, we collected a novel dataset of 77 participants who completed three behavioral tasks under controlled laboratory conditions, designed to mirror the recording setup typically encountered in home-based online experiments. We validated four rPPG pipeline configurations against the electrocardiogram (ECG) ground truth, using a comprehensive set of measures capturing both signal quality and cardiac variability. Moreover, using data from an emotion anticipation task, we assessed whether, in a typical experimental setting, analyses based on rPPG data yielded statistical results comparable to those obtained from ECG. Our results indicate that rPPG is a promising tool for detecting HR and HRV at the group level, particularly in high-powered studies. At the individual level, rPPG reliably captures average HR, but the accuracy of individual-level HRV estimates remains limited. The findings underscore rPPG’s potential as a low-cost, noninvasive research tool, while highlighting the importance of managing its limitations such as sensitivity to excessive movement and poor illumination through thoughtful study planning and execution.

Oxford University Press Doi: https://global.oup.com/academic/product/consumer-consciousness-9780198909057?lang=en&cc=es#

How do people experience products, brands, and digital environments through their senses and awareness? Drawing on philosophy, psychology, marketing, and neuroscience, Consumer Consciousness examines how our engagement with everyday consumption and digital experiences can be enhanced or disrupted by technology. Conscious awareness through touch, sight, sound, and even virtual experiences shape our perception, decision-making, and well-being. Central to this argument is the concept up the conscioumer: an ethically aware, reflective, and engaged individual, whose market choices align with personal, societal, and environmental values. Olivia Petit and Carlos Velasco highlight the role of unconscious mechanisms by tracing the historical and philosophical evolution of the consumer, advocating for a renewed focus on conscious experience as a tool for empowerment. Multisensory marketing, sensory ecosystems, and immersive technologies are shown not only to influence perception, but also to expand the possibilities of reflective and meaningful engagement. Emerging technologies such as Artificial intelligence, IoT, XR, and robotics, are reshaping consumer consciousness, creating hybrid human-technology assemblages which challenge traditional notions of agency. The ethical stakes of these developments are high: Petit and Velasco make the case for responsible design, meta-cognitive capacities, and societal frameworks that foster well-being and sustainability. More than a theoretical exploration, Consumer Consciousness offers a practical and philosophical guide to understanding and cultivating conscious consumption.

United Nations Interregional Crime and Justice Research Institute

Leveraging artificial intelligence (AI) for crowd monitoring can help law enforcement officers and related authorities identify threats to public safety more efficiently, support human operators in making quicker, more informed decisions, improve response times and reduce workload. Yet, AI-enhanced surveillance raises critical ethical, societal, and human rights considerations. The AI4citizens: Responsible AI for Citizen Safety in Future Smart Cities project, funded by the Research Council of Norway, brings together a multidisciplinary team to explore how an AI system for crowd monitoring and anomaly detection can be designed and deployed responsibly by anonymizing CCTV footage. The AI system under investigation is PACA: AI-ENHANCED PUBLIC SAFETY MONITORING, developed in the Centre of AI Research (CAIR), University of Agder, Norway. This AI system is examined exclusively for scientific and research purposes and is not intended for commercial use or real-world deployment. Its dashboard can be accessed at https://ai4citizens.uia.no/app_dashboard. To reduce privacy impact, PACA anonymizes closed-circuit television (CCTV) footage by masking individuals. It then detects anomalies – incidents that may pose a threat to public safety – and generates alerts for human operators in law enforcement monitoring centres. Operators are then meant to assess the alerts and decide whether to dispatch a patrol. This process aims to replace manual video searches by law enforcement officers, thereby shortening response time to incidents, reducing human fatigue, and reducing errors in incident monitoring. The United Nations Interregional Crime and Justice Institute (UNICRI) Centre for AI and Robotics has contributed to this multidisciplinary effort by examining the responsible AI considerations underpinning this approach, through the associated project AI4Citizens: Legal, Ethical, and Societal Considerations of Implementing AI Systems for Privacy-Preserving Crowd Monitoring to Improve Public Safety, in collaboration with BI Norwegian Business School. This publication presents the resulting use case description and analysis conducted by UNICRI. The analysis presented herein is based on the information available to UNICRI as of June 2025 and reflects discussions held within the project on practical constraints of developing sociotechnical solutions for public safety through AI-enhanced crowd monitoring. It builds upon the foundational Toolkit for Responsible AI Innovation in Law Enforcement (AI Toolkit), developed by UNICRI and INTERPOL, with funding from the European Union. It includes suggestions presented to the technical team to align the research with responsible AI innovation principles. It does not, however, provide an exhaustive analysis of all human rights and ethical implications associated with this and similar AI systems, nor does it offer generalizable recommendations for all crowd monitoring systems. This work is published in the interest of transparency and knowledge sharing and aims to contribute to ongoing discussions surrounding the responsible design and deployment of AI systems.

United Nations Interregional Crime and Justice Research Institute

Public trust is the bedrock for legitimate, effective law enforcement. Contemporary law enforcement, grounded in the principle of policing by consent and characterized by approaches such as community-oriented policing, relies on public approval and cooperation to prevent and respond to crime.1 Trust drives such cooperation, yet it is remarkably hard to gain and sustain. To encourage it, transparency is essential, especially for law enforcement agencies seeking to introduce artificial intelligence (AI) to their practices. Although AI systems already support various law enforcement tasks, reflecting agencies’ efforts to enhance their efficiency and strengthen resource allocation, the pairing of AI with law enforcement remains uniquely challenging. Issues such as algorithmic bias and expanded surveillance capabilities pose inherent human rights risks and raise concern among the public. In such a setting, transparency – meaning the extent to which agencies disclose relevant information about the decision-making processes, procedures, performance and outcomes related to their AI innovation endeavours – can be a crucial enabler of appropriate governance, scrutiny and accountability. The need for openness is especially critical today, considering evidence from a global survey (with around 34,000 respondents from 28 countries) that 70% of people worry that leaders and institutions intentionally mislead the public and 60% report moderate to high grievances towards institutions. Beyond such general distrust, public perceptions of AI are even more concerning. Most people indicate an unwillingness to trust AI systems, particularly in advanced economies, and trust levels appear to be declining over time (e.g., by 17% between 2022 and 2025).8 Yet AI perceptions also tend to depend on the context, and people’s understanding of benefit–risk trade-offs seems limited, resulting in varied, nuanced attitudes towards AI use across different public sector settings. The same underlying technology might be rejected in some cases but accepted in others, depending on the public service for which it is deployed When it comes to law enforcement agencies’ uses of AI, the way they engage with and communicate about the systems can powerfully shape public attitudes. A comprehensive survey of public perceptions of AI uses by law enforcement, conducted by the United Nations Interregional Crime and Justice Research Institute (UNICRI) and the International Criminal Police Organization (INTERPOL) revealed a correlation between trust in authorities and AI acceptance. In addition, acceptance increased if safeguards were in place, and human oversight and strong legal frameworks emerged as essential to public confidence. Yet the survey participants consistently cited a lack of information about how their local law enforcement agencies were using AI.11 This gap may stem, at least in part, from a lack of guidance regarding how to devise transparency requirements. Which information should law enforcement agencies convey to the public to assure them of the trustworthiness of AI systems and the entities that design, deploy or operate them? Answering this question and bridging the public perception gap represent a governance imperative. Any innovation might be rejected if it is not presented with openness, respect for human rights and responsible practices. For example, controversial uses of AI can undermine its promise to enhance crime prevention and investigation efforts, erode the public’s confidence in law enforcement agencies and ultimately compromise the pursuit of justice. Successful endeavours require public cooperation. Therefore, deploying opaque or harmful AI systems risks fostering misunderstanding and damaging the very relationships required by effective policing. In an attempt to avoid such failures and facilitate success, this report offers comprehensive guidance to help law enforcement decision makers and related actors build and maintain public confidence, through transparency, during their responsible implementation of AI systems to enhance public safety.

Services, Experiences, and Sustainability s. 39-52 Doi: https://doi.org/10.4324/9781003477839

Sustainable service experiences can be designed. By strategically integrating sensory cues, marketers can create compelling and cohesive sustainable service experiences that differentiate one company from another and influence customers’ expectations, perceptions, judgements, and behaviour. After defining and clarifying how multisensory marketing, sustainability and service experiences are understood, this chapter offers a research-based presentation of how sensory cues, such as images, sounds, smells and texture, can be utilised to design service experiences perceived as environmentally friendly. The chapter ends with an example of how sensory cues can come together to create an environmentally friendly perceived multisensory restaurant experience. All services are, by definition, sensory experiences, and the better businesses become at developing multisensory experiences, the more attractive the service offers become for customers.