Can Stylized Products Generated by AI Better Attract User Attention? Using Eye-Tracking Technology for Research
Abstract
:1. Introduction
2. Literature Review
2.1. Research Attempts with AIGC
2.2. Research Gap and Eye-Tracking Technology
3. Methods
3.1. Stimuli
3.2. Participants
3.3. Apparatus
3.4. Eye-Tracking Measures
3.5. Procedure
4. Results
4.1. Analysis of Female Participants’ Attention to Stimuli Group 1
4.2. Analysis of Male Participants’ Attention to Stimuli Group 2
4.3. Analysis of Male and Female Participants’ Attention to Stimuli Group 3
5. Discussion
6. Limitations and Future Work
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Guo, D.; Chen, H.; Wu, R.; Wang, Y. AIGC challenges and opportunities related to public safety: A case study of ChatGPT. J. Saf. Sci. Resil. 2023, 4, 329–339. [Google Scholar] [CrossRef]
- Chen, Y.; Ma, H. AIGC’ s divine assistance in the field of art and design majors-an example of Stable Diffusion. Fash. China 2024, 24, 73–84. [Google Scholar] [CrossRef]
- Foo, L.G.; Rahmani, H.; Liu, J. Ai-generated content (aigc) for various data modalities: A survey. arXiv 2023, arXiv:2308.14177. [Google Scholar]
- Radford, A.; Kim, J.W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J. Learning transferable visual models from natural language supervision. In Proceedings of the International Conference on Machine Learning, Virtual, 18–24 July 2021; pp. 8748–8763. [Google Scholar]
- Xia, X.; Dong, G.; Li, F.; Zhu, L.; Ying, X. When CLIP meets cross-modal hashing retrieval: A new strong baseline. Inf. Fusion 2023, 100, 101968. [Google Scholar] [CrossRef]
- Cao, Y.; Li, S.; Liu, Y.; Yan, Z.; Dai, Y.; Yu, P.S.; Sun, L. A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt. arXiv 2023, arXiv:2303.04226. [Google Scholar]
- Grechka, A.; Couairon, G.; Cord, M. GradPaint: Gradient-guided inpainting with diffusion models. Comput. Vis. Image Underst. 2024, 240, 103928. [Google Scholar] [CrossRef]
- Liu, J.; Cheng, P.; Dai, J.; Liu, J. DiffuCom: A novel diffusion model for comment generation. Knowl. Based Syst. 2023, 281, 111069. [Google Scholar] [CrossRef]
- Bu, K.; Liu, Y.; Ju, X. Efficient Utilization of Pre-trained Models: A Review of Sentiment Analysis via Prompt Learning. Knowl. Based Syst. 2023, 111148. [Google Scholar] [CrossRef]
- Wu, J.; Gan, W.; Chen, Z.; Wan, S.; Lin, H. Ai-generated content (aigc): A survey. arXiv 2023, arXiv:2304.06632. [Google Scholar]
- Joshi, R.M.; Tao, S.; Aaron, P.; Quiroz, B. Cognitive component of componential model of reading applied to different orthographies. J. Learn. Disabil. 2012, 45, 480–486. [Google Scholar] [CrossRef]
- Lu, Z.; Song, X.; Jin, Y. State of arts and development of intelligent design methods under the AIGC trend. Packag. Eng. 2023, 44, 18–33+13. [Google Scholar] [CrossRef]
- Wang, B.; Niu, C. From ChatGPT to GovGPT: Generative Artificial Intelligence-driven Government Service Ecosystem Construction. E-Government 2023, 25–38. [Google Scholar] [CrossRef]
- Guo, P.; Mahjoubi, S.; Liu, K.; Meng, W.; Bao, Y. Self-updatable AI-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC). Case Stud. Constr. Mater. 2023, 19, e02625. [Google Scholar] [CrossRef]
- Han, C.; Kim, D.W.; Kim, S.; You, S.C.; Park, J.Y.; Bae, S.; Yoon, D. Evaluation of GPT-4 for 10-year cardiovascular risk prediction: Insights from the UK Biobank and KoGES data. Iscience 2024, 27, 109022. [Google Scholar] [CrossRef] [PubMed]
- Leng, G.; Zhang, G.; Xiong, Y.-J.; Chen, J. CODP-1200: An AIGC based benchmark for assisting in child language acquisition. Displays 2024, 82, 102627. [Google Scholar] [CrossRef]
- Xu, Y.; Zhi, C.; Guo, H.; Zhang, M.; Wu, H.; Sun, R.; Dong, Z.; Yu, L. ChatGPT for textile science and materials: A perspective. Mater. Today Commun. 2023, 37, 107101. [Google Scholar] [CrossRef]
- Wang, C.; Zhao, J.; Chan, T.-M. Artificial intelligence (AI)-assisted simulation-driven earthquake-resistant design framework: Taking a strong back system as an example. Eng. Struct. 2023, 297, 116892. [Google Scholar] [CrossRef]
- Zhang, Y.; Prebensen, N.K. Co-creating with ChatGPT for tourism marketing materials. Ann. Tour. Res. Empir. Insights 2024, 5, 100124. [Google Scholar] [CrossRef]
- Han, G.; Zhang, K. AIGC Marketing: Human-machine symbiotic marketing model promotes digital marketing to leapfrog to digital intelligence. Enterp. Econ. 2024, 43, 111–124. [Google Scholar] [CrossRef]
- Li, Y. With AIGC on the rise, it’s time for brand marketing to change its game again. PR Mag. 2023, 47–48. [Google Scholar] [CrossRef]
- Song, Y.; Qian, X.; Peng, L.; Ye, Z.; Qin, J. Cultural and creative design of AIGC Chinese aesthetic. Packag. Eng. 2023, 44, 1–8+33. [Google Scholar] [CrossRef]
- Wang, Y.; Gong, X.; Zhu, H.; Li, G. Research on creative design of ceramics under AIGC technology. Ceram. Sci. Art 2023, 57, 84–87. [Google Scholar] [CrossRef]
- Chen, Y.; Fang, Q.; Xiong, Z.; Yi, X.; Wang, Q. Opportunities and challenges of the application of ChatGPT and MJ in the field of home design. Furnit. Inter. Des. 2023, 30, 51–55. [Google Scholar] [CrossRef]
- Wu, F.; Hsiao, S.-W.; Lu, P. An AIGC-empowered methodology to product color matching design. Displays 2024, 81, 102623. [Google Scholar] [CrossRef]
- Miao, L.; Yang, F.X. Text-to-image AI tools and tourism experiences. Ann. Tour. Res. 2023, 102, 103642. [Google Scholar] [CrossRef]
- Zhang, B.; Romainoor, N.H. Research on artificial intelligence in new year prints: The application of the generated pop art style images on cultural and creative products. Appl. Sci. 2023, 13, 1082. [Google Scholar] [CrossRef]
- Liu, X. Application of AIGC technology in dynamic graphic design. Shanghai Packag. 2023, 30–32. [Google Scholar] [CrossRef]
- Chai, J.; Ding, H. AIGC and craftwork design. Shanghai Arts Crafts 2023, 75–77. [Google Scholar]
- Chung, C.-Y.; Huang, S.-H. Interactively transforming Chinese ink paintings into realistic images using a border enhance generative adversarial network. Multimed. Tools Appl. 2023, 82, 11663–11696. [Google Scholar] [CrossRef]
- Wang, L. An exploration of the application of AIGC drawing tools in UI interface design—Taking Midjourney as an example. Comput. Knowl. Technol. 2023, 19, 108–111. [Google Scholar] [CrossRef]
- Zhang, J.; Wang, Y.; Yuan, Z. AIGC empowered traditional culture inheritance design method and practice—Taking the design of digital exhibition center of Yongle Gong in Shanghai province as an example. Design 2023, 36, 30–33. [Google Scholar] [CrossRef]
- Tao, W.; Gao, S.; Yuan, Y. Boundary crossing: An experimental study of individual perceptions toward AIGC. Front. Psychol. 2023, 14, 1185880. [Google Scholar] [CrossRef] [PubMed]
- Kuhar, M.; Merčun, T. Exploring user experience in digital libraries through questionnaire and eye-tracking data. Libr. Inf. Sci. Res. 2022, 44, 101175. [Google Scholar] [CrossRef]
- Ariely, D.; Berns, G.S. Neuromarketing: The hope and hype of neuroimaging in business. Nat. Rev. Neurosci. 2010, 11, 284–292. [Google Scholar] [CrossRef]
- Guo, F.; Ding, Y.; Liu, W.; Liu, C.; Zhang, X. Can eye-tracking data be measured to assess product design?: Visual attention mechanism should be considered. Int. J. Ind. Ergon. 2016, 53, 229–235. [Google Scholar] [CrossRef]
- Ho, C.-H.; Lu, Y.-N. Can pupil size be measured to assess design products? Int. J. Ind. Ergon. 2014, 44, 436–441. [Google Scholar] [CrossRef]
- Ho, H.-F. The effects of controlling visual attention to handbags for women in online shops: Evidence from eye movements. Comput. Hum. Behav. 2014, 30, 146–152. [Google Scholar] [CrossRef]
- Hansen, D.W.; Ji, Q. In the eye of the beholder: A survey of models for eyes and gaze. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 32, 478–500. [Google Scholar] [CrossRef]
- Majaranta, P.; Bulling, A. Eye tracking and eye-based human–computer interaction. In Advances in Physiological Computing; Springer: Berlin/Heidelberg, Germany, 2014; pp. 39–65. [Google Scholar]
- Ilhan, A.E.; Togay, A. Pursuit of methodology for data input related to taste in design: Using eye tracking technology. Displays 2023, 76, 102335. [Google Scholar] [CrossRef]
- Almourad, M.B.; Bataineh, E.; Hussain, M.; Wattar, Z. Usability Assessment of a University Academic Portal using Eye Tracking Technology. Procedia Comput. Sci. 2023, 220, 323–330. [Google Scholar] [CrossRef]
- Zhou, X.; Cen, Q.; Qiu, H. Effects of urban waterfront park landscape elements on visual behavior and public preference: Evidence from eye-tracking experiments. Urban For. Urban Green. 2023, 82, 127889. [Google Scholar] [CrossRef]
- Liu, W.; Cao, Y.; Proctor, R.W. How do app icon color and border shape influence visual search efficiency and user experience? Evidence from an eye-tracking study. Int. J. Ind. Ergon. 2021, 84, 103160. [Google Scholar] [CrossRef]
- Qu, Q.-X.; Guo, F. Can eye movements be effectively measured to assess product design?: Gender differences should be considered. Int. J. Ind. Ergon. 2019, 72, 281–289. [Google Scholar] [CrossRef]
- Liao, C.-N.; Chang, K.-E.; Huang, Y.-C.; Sung, Y.-T. Electronic storybook design, kindergartners’ visual attention, and print awareness: An eye-tracking investigation. Comput. Educ. 2020, 144, 103703. [Google Scholar] [CrossRef]
- Liao, W.; Lu, X.; Fei, Y.; Gu, Y.; Huang, Y. Generative AI design for building structures. Autom. Constr. 2024, 157, 105187. [Google Scholar] [CrossRef]
Category | Clarity | Detailing | Realism | Attractiveness | Overall Satisfaction | Total |
---|---|---|---|---|---|---|
Electric toothbrush | 3.49 | 3.57 | 3.68 | 3.52 | 3.6 | 17.86 |
Projector | 3.48 | 3.65 | 3.37 | 3.6 | 3.56 | 17.66 |
Bluetooth earphone | 3.71 | 3.65 | 3.83 | 3.78 | 3.75 | 18.72 |
Wireless mouse | 3.68 | 3.56 | 3.68 | 3.57 | 3.51 | 18 |
Watch | 3.4 | 3.52 | 3.52 | 3.52 | 3.51 | 17.47 |
Independent Variable | Acronym | Segmentation | Meaning |
---|---|---|---|
GPT4.0 generated wireless Bluetooth in-ear earphones | AF | A1, A2, A3 | GPT4.0 generated Bluetooth earphones for females. |
AM | A4, A5, A6 | GPT4.0 generated Bluetooth earphones for males. | |
The top three selling wireless Bluetooth in-ear earphones searched from the Taobao shopping platform | TF | T1, T2, T3 | The top three selling Bluetooth earphones for females on the Taobao shopping platform. |
TM | T4, T5, T6 | The top three selling Bluetooth earphones for males on the Taobao shopping platform. | |
Gender | G | GF | Female |
GM | Male |
Implicit Variable | Acronym | Meaning |
---|---|---|
Fixation counts | FC | The number of times the gaze is fixated on the area of interest. |
Fixation duration | FD | How long the gaze is fixated on the area of interest. |
Total fixation counts | TFC | The total number of times the gaze passes over the area of interest. |
Total fixation duration | TFD | The total time the gaze passes over the area of interest. |
Category | Category Segmentation | FC | FD(s) | TFC | TFD(s) | F | p | η |
---|---|---|---|---|---|---|---|---|
AF | A1 | 68 | 100.697 | 131 | 170.363 | 3.942 | 0.020 | 0.164 |
A2 | 33 | 39.064 | ||||||
A3 | 30 | 30.601 | ||||||
TF | T1 | 46 | 50.674 | 128 | 123.627 | |||
T2 | 36 | 25.683 | ||||||
T3 | 46 | 47.270 |
Category | Category Segmentation | FC | FD(s) | TFC | TFD(s) | F | p | η |
---|---|---|---|---|---|---|---|---|
AM | A4 | 49 | 29.472 | 122 | 161.882 | 8.824 | <0.001 | 0.235 |
A5 | 37 | 60.681 | ||||||
A6 | 36 | 71.729 | ||||||
TM | T4 | 32 | 20.267 | 127 | 96.938 | |||
T5 | 55 | 53.771 | ||||||
T6 | 40 | 22.9 |
Category | Category Segmentation | FC | FD(s) | F | p | η |
---|---|---|---|---|---|---|
G | AM | 260 | 243.4 | 4.866 | 0.008 | 0.129 |
AF | 255 | 247.433 | ||||
GM | AM | 130 | 122.684 | 1.312 | 0.271 | 0.092 |
AF | 133 | 123.633 | ||||
GF | AM | 130 | 120.716 | 4.666 | 0.010 | 0.182 |
AF | 122 | 123.801 |
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Tang, Y.; Chen, C. Can Stylized Products Generated by AI Better Attract User Attention? Using Eye-Tracking Technology for Research. Appl. Sci. 2024, 14, 7729. https://doi.org/10.3390/app14177729
Tang Y, Chen C. Can Stylized Products Generated by AI Better Attract User Attention? Using Eye-Tracking Technology for Research. Applied Sciences. 2024; 14(17):7729. https://doi.org/10.3390/app14177729
Chicago/Turabian StyleTang, Yunjing, and Chen Chen. 2024. "Can Stylized Products Generated by AI Better Attract User Attention? Using Eye-Tracking Technology for Research" Applied Sciences 14, no. 17: 7729. https://doi.org/10.3390/app14177729