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Social media usage has greatly increased during the pandemic. The use of social media has been linked to mental health consequences. Especially TikTok is under scrutiny because of its captivating UX Design as well as controversial privacy policies. This study shows how Mobile Affective Computing Research can reliably and objectively measure the implicit-emotional UX. The results suggest that TikTok triggers positive emotions during onboarding and strengthens the emotional bond with increasing usage - mainly due to the personalization algorithm. In particular, the study further reveals that there are no clear relationships when “only self-reported data” is used to understand app usage behavior. However, emotion data based on facial coding shows that TikTok users get emotionally aroused. Initially, a positive and relaxed mood triggers further usage time, while - counter-intuitively - later sessions result in an increasingly negative and aroused mood. However, people are largely unaware of these relationships when questioned. Thus, implicit measures, such as facial coding, may help identify valid social media usage reactions. These results yield implications that call for political regulation considering aspects of social media apps and user behavior.
New technologies require aesthetic and hedonic user experience (UX) design to remain competitive. For guidance, a conceptual digital empathy framework explains and recommends individual UX research methods. The digital empathy framework is supported by two case studies of triangulated UX research methods – facial coding, eye tracking, and surveys – that provide valuable complementary insights.
This study highlights the importance of pre-testing social media ads and explores the challenges of objectively assessing ad performance. It outlines how a multi-method approach combining facial coding and eye-tracking can help identify successful ads before launch. The findings emphasize the growing importance of user experience
research (via digital empathy) for social media advertising and performance marketingrketing.
Traditionally, ideating new product innovations is primarily the responsibility of marketers, engineers, and designers. However, a rapidly growing interest lies in leveraging generative artificial intelligence (AI) to brainstorm new product and service ideas. This study conducts a comparative analysis of ideas generated by human professionals and an AI system. The results of a blind expert evaluation show that AI-generated ideas score significantly higher in novelty and customer benefit, while their feasibility scores are similar to those of human ideas. Overall, AI-generated ideas comprise the majority of the top-performing ideas, while human-generated ideas scored lower than expected. The executive's emotional and cognitive reactions were measured during the evaluation to check for potential biases and showed no differences between the idea groups. These findings suggest that, under certain circumstances, companies can benefit from integrating generative AI into their traditional idea-generation processes.
This research provides new insights into consumer reactions and acceptance of true cost price labels. It focuses on how design elements and cognitive and emotional responses impact perceived price fairness and purchase intent towards food prices that include externalities. These insights are crucial for policymakers, marketers, and businesses promoting sustainable consumption practices to communicate true cost prices effectively. The true cost accounting (TCA) method integrates external costs, such as environmental impacts, into the market price of food products. The four dimensions included in this study are greenhouse gas emissions, nitrogen derivatives, energy consumption, and biodiversity loss created by three food product categories. Findings show that TCA information on true cost price labels increases the perceived price fairness. Building on behavioral pricing literature, the study also compares label design variants of TCA information per externality in absolute (currency) versus relative (percentages) markup prices, finding that relative true cost price labels are easier for consumers to understand and lead to higher purchase intent. Using innovative UX research methods like facial coding and eye tracking, this research investigates the emotional drivers behind these choices, revealing that surprise and confusion mediate the relationship between true cost price labels and purchase intent. The counter-intuitive finding suggests that even subtle emotional cues can steer consumer intent.
Produkt- und Dienstleistungsinnovationen müssen neben rein funktionalen Kundenwünschen vermehrt ästhetische und hedonistische Anforderungen erfüllen. Dies erfordert eine neue und ganzheitliche Sicht auf das Thema User Experience (UX) und setzt die Kenntnis geeigneter Research-Methoden voraus. Ziel dieser Methoden sollte es sein, die Interaktion zwischen Usern und Technik auf kognitiver und emotionaler Ebene zügig und valide erfassen zu können. Der vorliegende Beitrag bietet eine Orientierungshilfe in Form einer UX-Toolbox, in der verschiedene UX-Research-Methoden systematisch erläutert und kontextabhängig ihr Nutzen und ihr Kombinationspotenzial aufzeigt werden. Zwei Use Cases aus dem Kontext der Preiswahrnehmung und der Werbewirkung in sozialen Medien verdeutlichen, dass die UX-Methoden Facial Coding, Eye-Tracking und Befragungen in der Marketingpraxis kombiniert eingesetzt werden sollten, um sowohl kognitive als auch emotionale Bedürfnisse der Nutzenden explizit und implizit aufzudecken.
Embracing Digital Empathy
(2026)
Für die Marken- und Ideenentwicklung im Produkt- und Dienstleistungsbereich sind in der Regel Marketing- und Markenmanager sowie Designer verantwortlich. Gleichzeitig wächst das Interesse am Einsatz generativer Künstlicher Intelligenz (KI) zur Ideenfindung im Markenmanagement. Diese Studie analysiert, wie KI-generierte Ideen im Vergleich zu von Menschen generierten Ideen abschneiden. Die Ergebnisse einer blinden Expertenbeurteilung zeigen, dass KI-generierte Ideen in Bezug auf Neuheit und Kundennutzen signifikant besser bewertet werden, während sie in Bezug auf die Umsetzbarkeit ähnlich abschneiden wie von Menschen generierte Ideen. Diese Ergebnisse legen nahe, dass Markenmanager in bestimmten Kontexten von der Integration generativer KI in ihren traditionellen Ideenfindungsprozess profitieren können.
