The impact of digital image-based features on users' emotions and online behaviours in the food industry

BRITISH FOOD JOURNAL(2022)

引用 5|浏览1
暂无评分
摘要
Purpose Online images can convey sensory-based elements affecting digital users' emotions and digital engagement. The purpose of this study is to investigate which image-based features are more effective in conveying and stimulating particular emotions and engagement towards organizations operating in the food industry. Design/methodology/approach An online experimental survey was implemented. Two image-based features, narrativity and dynamism were chosen. The stimuli comprise four images, one with high and one with low level of narrativity, and one with high and one with low dynamism, published by a food company on its official Instagram account. Food-identity, emotional appeals and digital visual engagement behaviours were measured. A total of 141 students between 19 and 25 years old of a European University completed the questionnaire. Data was analysed through SPSS software using t-test analysis. Findings Results show that both narrativity and dynamism impact digital users' emotions and it was found to impact digital visual engagement attitude. Food involvement was measured in terms of food identity impact the effects of specific image-based features on emotions and visual engagement. Research limitations/implications The study focuses on only two visual social semiotics features - narrativity and dynamism - and therefore, only partially captures the potentialities of images in digital communications. Practical implications This study provides professionals with empirical evidence and insights for effectively planning a visual social media strategy. Originality/value This paper contributes to the stream of research in social media communications by investigating the visual social semiotic features of images published online by a food company.
更多
查看译文
关键词
Food imagery, Visual communication, Instagram, Visual engagement
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要