The purpose of this study is to understand the role of social media content on users’ engagement behavior in the context of online food delivery services (OFDS). Social media has become a tool for marketing campaigns in various industries but one of the interests is for OFDS because of its own challenge. The COVID-19 pandemic has engendered a pronounced proliferation in the utilization of) OFDS as a consequence of the digitalization of culinary establishments and the pervasive digital marketing campaigns promoting this service category. The marketing strategy for OFDS presents unique challenges due to the heavy reliance on visual communication through imagery, promotional incentives such as discounts and complimentary delivery, as well as bundled offerings (comprising various food items and beverages at discounted rates) to explore how to create effective visual and textual content on social media to drive user engagement. Prior studies have examined the effectiveness of social media textual content such as messages’ popularity or persuasiveness. Studies have also been conducted to understand how to optimize best practices to increase customer engagement on a social media platform. However, most of these studies only focus on textual context of social media. On a social media platform, the image has become popular to convey messages and attract users. Little has been done to understand how image content can be used effectively together with textual content to affect user engagement. In this study, we develop a research model that investigates how textual content and image content can be used to increase user engagement on social media of online food delivery services. We identify three dimensions of textual content, i.e., text sentiment, text topic, and entertainment embeddedness, and three dimensions of image content, i.e., image colorfulness, consummatory image, and entertainment embeddedness. We argue that each of these dimensions will positively affect user engagement. In addition, we propose that the compatibility of textual and image entertainment embeddedness will also help increase user engagement. We collected data from Twitter posts from Grubhub which is one of the top OFDS in the US. The data set includes 3200 tweets original tweets (not retweeted tweets) posted on Grubhub Twitter account from June 2021 to April 2022. The tweets include only the ones posted by Grubhub and each thread of posts counted as one post. Preliminary data analysis was conducted on the sentiments of the text message. In the future, we will conduct text analysis on the other dimensions of the text content. We plan to analyze images using Google Cloud Vision AI which trains machine-learning models that classify and derive insights from images. This study will contribute to the understanding of social media strategies for OFDS, including image context that has not been investigated before. It will also help understand how a textual message and image can complement each other to enhance their positive impact on user engagement.

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