Emotion-based insights into pro-environmental video campaigns: A study on waste sorting behavior in Ukraine

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This study aims to examine how different types of pro-environmental video content (featuring humans versus AI-generated characters) influence household waste sorting attitudes and behaviors among Ukrainian residents. The research was conducted in two stages using a mixed-method approach. In the first stage, 102 individuals aged 18–45 watched two videos on waste sorting and completed an online questionnaire. Cluster and variance analyses were performed using Statistica software. In the second stage, 35 participants underwent a laboratory-based emotion analysis using iMotions software, heart rate monitors, and galvanic skin response sensors at the Behavioral Lab of Sumy State University (Ukraine) from May to July 2024. The results revealed that videos featuring real people were more effective in generating interest (average rating: 3.5 vs. 3.2) and emotional engagement, particularly joy and contempt, which were the most frequently expressed emotions. Cluster analysis identified four distinct respondent groups. Cluster 1 (39.2%) – primarily young women – responded positively to human-led videos but showed limited behavioral change. Cluster 2 (19.6%) – women aged 26–35 – reacted positively to both videos and were most willing to adopt waste sorting behavior. Cluster 3 (23.5%) – primarily men – showed moderate engagement and sorted waste occasionally. Cluster 4 (17.6%) – highly educated women – exhibited the least positive responses and were least likely to change their behavior. The emotion analysis confirmed that videos featuring real people elicited stronger emotional responses across all categories, whereas AI-generated videos prompted higher levels of anger but generally weaker engagement.

Acknowledgments
The authors would like to acknowledge the Behavioral Laboratory at Sumy State University for providing the essential facilities and resources that enabled the successful completion of this research. Additionally, sincere appreciation is extended to all study participants for their valuable time, commitment, and contributions, which significantly enriched our understanding of consumer behavior related to waste management and the perception of advertising content.
The research is supported by the International Visegrad Fund: Visegrad Fellowship Program. Project № 62410031, “Marketing research consumer behaviour in the waste management system”, and by the budget of the Ministry of Education and Science of Ukraine (research topic 0123U100112 “Post-war recovery of the energy industry of Ukraine: Optimization of waste management taking into account the health of the population, environmental, investment, tax determinants”.

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    • Figure 1. Real-time graph: Frame-by-frame analysis of emotional responses
    • Figure 2. Average emotion time for all respondents (contempt and joy)
    • Figure 3. Average percent of emotion time for all respondents (distress and anger)
    • Table 1. Generalized survey results about the respondents’ attitude to the proposed videos about waste management approaches
    • Table 2. Analysis of variance for 12 parameters
    • Table 3. Average percent of each emotion time for two videos for all respondents
    • Table B1. Emotion analysis table for all respondents (video 1)
    • Table C1. Emotion analysis table for all respondents (video 2)
    • Conceptualization
      Serhiy Lyeonov, Anna Rosokhata, Liliia Khomenko, Anzhela Kuznyetsova
    • Formal Analysis
      Serhiy Lyeonov, Anna Rosokhata, Svitlana Bilan, Iuliia Myroshnychenko, Nataliia Letunovska
    • Methodology
      Serhiy Lyeonov, Svitlana Bilan, Liliia Khomenko, Anzhela Kuznyetsova, Iuliia Myroshnychenko, Nataliia Letunovska
    • Project administration
      Serhiy Lyeonov
    • Supervision
      Serhiy Lyeonov
    • Writing – review & editing
      Serhiy Lyeonov, Svitlana Bilan, Iuliia Myroshnychenko
    • Data curation
      Anna Rosokhata, Nataliia Letunovska
    • Funding acquisition
      Anna Rosokhata
    • Resources
      Anna Rosokhata, Svitlana Bilan, Liliia Khomenko, Anzhela Kuznyetsova, Iuliia Myroshnychenko, Nataliia Letunovska
    • Writing – original draft
      Anna Rosokhata, Liliia Khomenko, Nataliia Letunovska
    • Investigation
      Svitlana Bilan, Liliia Khomenko, Anzhela Kuznyetsova, Nataliia Letunovska
    • Validation
      Svitlana Bilan, Liliia Khomenko, Iuliia Myroshnychenko, Nataliia Letunovska
    • Software
      Liliia Khomenko, Anzhela Kuznyetsova, Nataliia Letunovska
    • Visualization
      Liliia Khomenko, Anzhela Kuznyetsova, Iuliia Myroshnychenko, Nataliia Letunovska