| Title |
Assessing the Effectiveness of Digital Advertising for Green Products: A Facial Expression Evaluation Approach |
| ID_Doc |
62332 |
| Authors |
Wang, CY; Lin, FS |
| Title |
Assessing the Effectiveness of Digital Advertising for Green Products: A Facial Expression Evaluation Approach |
| Year |
2022 |
| Published |
|
| DOI |
10.1007/978-3-031-05544-7_17 |
| Abstract |
Effectiveness of advertisement can be measured in terms of a person's emotional response to the advertisement media. Besides traditional survey method, the emotional feedback of the consumers is valuable to understand the purchasing intention. Artificial intelligence and machine learning have provided researchers and practitioners of marketing and advertisement with new tools, such as facial expression recognition analysis, to explore the context of advertisement effectiveness. In this study, a facial expression recognition survey was carried out for analyzing the advertising impact and consumer feedback on types of advertisement. Participants were separated into two groups, where the first group was asked question related to one randomly chosen digital poster advertisements of green products and the second group was surveyed for an advertisement without sustainable properties. The facial expressions of participants were recorded and later classified into three states as positive, neutral, and negative using a machine learning model. The results reveal people show relatively higher positive emotion for green products and their purchasing intentions are driven by the willingness to save environment and perceived value. Whereas, for normal products, purchasing intentions are mainly driven by the brand image. This study also shows that sustainable cues in product advertisement leads to positive consumer feedback. |
| Author Keywords |
Advertisement; Emotional feedback; Facial expression; Green product |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) |
| EID |
WOS:000911437300017 |
| WoS Category |
Computer Science, Software Engineering; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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