Emote, Buy, Enjoy: Mapping the Intensity of Emotions for Virtual Skins with Plutchik's Wheel
Abstract
The abstract (approximately 150 to 250 words) should briefly describe the background, methods, results and conclusions of your research. It should be written in a single paragraph using single line spacing, Virtual skin significantly influences players' engagement and purchasing decisions in the constantly evolving mobile gaming landscape. This study explores the emotional intensity that virtual skin in PlayerUnknown's Battlegrounds (PUBG) Mobile evokes. It uses Plutchik's Wheel of Emotion to measure these responses and their influence on players' purchasing intentions and visual enjoyment. A quantitative survey was implemented to gather data from 204 participants and use statistical analysis to show the frequency of emotion intensity appearing towards the virtual skin elements. The Emotion Wheel visually categorises human emotions by intensity and similarity. Helping individuals describe their feelings improves self-awareness and emotional management. In addition, its vast emotional vocabulary makes communicating sentiments and comprehending others simpler. Close emotions are similar, whereas opposed emotions are more contrasted. In order to map the emotions of the participants in this study, only one polar opposite emotion—the intensity of Disgust to Trust—was examined. This research suggests that virtual skin can evoke diverse emotions, including Trust and Disgust, through elements such as colour, style, uniqueness, texture, and theme. These emotional responses substantially determine a player's acceptance or rejection of a virtual skin. Furthermore, a significant correlation exists between the intensity of these emotions, the level of visual enjoyment the user experiences, and their intent to purchase. The study underscores the importance of emotive design in virtual items, highlighting its potential to enhance user experience and loyalty by catering to aesthetic preferences and emotional connections. These insights offer developers valuable guidance for optimising virtual skin to increase players’ engagement and retention. Times New Roman, 11pt, italic, justify and A4 format. Do not include reference citations in the abstract.
Keywords: Emote, virtual skin, Plutchik's Wheel Emotion.
References
Angeli, D. De, Kelly, R. M., & O’Neill, E. (2020). Beyond Happy‐or‐Not: Using Emoji to Capture Visitors’ Emotional Experience. 167–191. https://onlinelibrary.wiley.com/doi/epdf/10.1111/cura.12352
Butkutė, D. (2023). Exploring Emotional Awareness through User Experience Design.
de Byl, P. (2015). A conceptual affective design framework for the use of emotions in computer game design. Cyberpsychology, 9(3). https://doi.org/10.5817/CP2015-3-4
Esparza, Luz Judith R, jesus salazar ibarra. (2022). Modelling dyads of emotions via sentiment analysis. 15(58), 185–208.
Gu, S., Wang, F., Patel, N. P., Bourgeois, J. A., & Huang, J. H. (2019). A model for basic emotions using observations of behavior in Drosophila. Frontiers in Psychology, 10(APR), 1–13. https://doi.org/10.3389/fpsyg.2019.00781
Hafiz Hassan, M., Hidayat Wardi, R., Mohd Razali, N., Amy Azura Hishamuddin, N., Fakhruddin Ahmad, M., Mat Nashir, R., & Nasir, M. (2023). “Help ASD’s with Fun Way”: Detective Looking Chart-Plutchik Emotion Games For Kids Through Vark Model To ASD Children. Digit360.Com.My, 2(1), 2976–2170. http://digit360.com.my/mjii/index.php/home/article/view/20
Karimova, H., & Millacci, T. S. (2017). The Emotion Wheel: What It Is and How to Use It . In PositivePsychology.com.
Kołakowska, A., Landowska, A., Szwoch, M., Szwoch, W., & Wrobel, M. R. (2015). Modeling emotions for affect-aware applications. January.
Mondal, A., & Gokhale, S. S. (2020). Mining Emotions on Plutchik’s Wheel. 2020 7th International Conference on Social Network Analysis, Management and Security, SNAMS 2020, April. https://doi.org/10.1109/SNAMS52053.2020.9336534
Niedenthal, P. M., & Brauer, M. (2012). Social functionality of human emotion. Annual Review of Psychology, 63, 259–285. https://doi.org/10.1146/annurev.psych.121208.131605
Plutchik, R. (2001). The nature of emotions. American Scientist, 89(4), 344–350. http://www.jstor.org/stable/27857503
Semeraro, A., Vilella, S., & Ruffo, G. (2021). PyPlutchik: Visualising and comparing emotion-annotated corpora. PLoS ONE, 16(9 September), 1–24. https://doi.org/10.1371/journal.pone.0256503
Su, C.-H., & Cheng, C.-H. (2016). Developing and Evaluating Creativity Gamification Rehabilitation System: The Application of PCA-ANFIS Based Emotions Model. Eurasia Journal of Mathematics, Science and Technology Education, 12(2), 203–231. https://doi.org/10.12973/eurasia.2016.1523a
Suttles, J., & Ide, N. (2013). Distant supervision for emotion classification with discrete binary values. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7817 LNCS(PART 2), 121–136. https://doi.org/10.1007/978-3-642-37256-8_11
Vojković, J. (2020). Film Costume as a Visual Narrative Element ; Defining the Abstract Emotions of the Film Viewer via Plutchik ’ s Wheel of Emotions. 3(2), 92–100. https://doi.org/10.31881/TLR.2019.34
Angeli, D. De, Kelly, R. M., & O’Neill, E. (2020). Beyond Happy‐or‐Not: Using Emoji to Capture Visitors’ Emotional Experience. 167–191. https://onlinelibrary.wiley.com/doi/epdf/10.1111/cura.12352
Butkutė, D. (2023). Exploring Emotional Awareness through User Experience Design.
de Byl, P. (2015). A conceptual affective design framework for the use of emotions in computer game design. Cyberpsychology, 9(3). https://doi.org/10.5817/CP2015-3-4
Esparza, Luz Judith R, jesus salazar ibarra. (2022). Modelling dyads of emotions via sentiment analysis. 15(58), 185–208.
Gu, S., Wang, F., Patel, N. P., Bourgeois, J. A., & Huang, J. H. (2019). A model for basic emotions using observations of behavior in Drosophila. Frontiers in Psychology, 10(APR), 1–13. https://doi.org/10.3389/fpsyg.2019.00781
Hafiz Hassan, M., Hidayat Wardi, R., Mohd Razali, N., Amy Azura Hishamuddin, N., Fakhruddin Ahmad, M., Mat Nashir, R., & Nasir, M. (2023). “Help ASD’s with Fun Way”: Detective Looking Chart-Plutchik Emotion Games For Kids Through Vark Model To ASD Children. Digit360.Com.My, 2(1), 2976–2170. http://digit360.com.my/mjii/index.php/home/article/view/20
Karimova, H., & Millacci, T. S. (2017). The Emotion Wheel: What It Is and How to Use It . In PositivePsychology.com.
Kołakowska, A., Landowska, A., Szwoch, M., Szwoch, W., & Wrobel, M. R. (2015). Modeling emotions for affect-aware applications. January.
Mondal, A., & Gokhale, S. S. (2020). Mining Emotions on Plutchik’s Wheel. 2020 7th International Conference on Social Network Analysis, Management and Security, SNAMS 2020, April. https://doi.org/10.1109/SNAMS52053.2020.9336534
Niedenthal, P. M., & Brauer, M. (2012). Social functionality of human emotion. Annual Review of Psychology, 63, 259–285. https://doi.org/10.1146/annurev.psych.121208.131605
Plutchik, R. (2001). The nature of emotions. American Scientist, 89(4), 344–350. http://www.jstor.org/stable/27857503
Semeraro, A., Vilella, S., & Ruffo, G. (2021). PyPlutchik: Visualising and comparing emotion-annotated corpora. PLoS ONE, 16(9 September), 1–24. https://doi.org/10.1371/journal.pone.0256503
Su, C.-H., & Cheng, C.-H. (2016). Developing and Evaluating Creativity Gamification Rehabilitation System: The Application of PCA-ANFIS Based Emotions Model. Eurasia Journal of Mathematics, Science and Technology Education, 12(2), 203–231. https://doi.org/10.12973/eurasia.2016.1523a
Suttles, J., & Ide, N. (2013). Distant supervision for emotion classification with discrete binary values. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7817 LNCS(PART 2), 121–136. https://doi.org/10.1007/978-3-642-37256-8_11
Vojković, J. (2020). Film Costume as a Visual Narrative Element ; Defining the Abstract Emotions of the Film Viewer via Plutchik ’ s Wheel of Emotions. 3(2), 92–100. https://doi.org/10.31881/TLR.2019.34
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