The palette that stands out: Color compositions of online curated visual UGC that attracts higher consumer interaction

Examining accuracy-prompt efficacy in combination with using colored borders to differentiate news and social content online

“…Color coding and border systems have also been incorporated in online spaces: for example, different types of labels and borders have been used to distinguish ads and paid content from other posts on newsfeeds and in search engine results (Johnson et al, 2018); green circles around Instagram stories denote stories from close friends relative to standard reddish-orange circles (Johnson, 2022); and Reddit posts and comments that have received awards are highlighted with a colored background and orange border (Reddit, 2021). Prior research also suggests color differences may have meaningful impacts on outcomes such as click-rates (Jalali & Papatla, 2016) and sharing (Can et al, 2013). Altogether, we aim to further existing research and application of color-concept borders by examining the effects of news borders and whether they may affect the efficacy of interventions relying on recognizing the relevance of accuracy.…”

Visual Elicitation of Brand Perception

“…A stream of research has developed ways to use this UGC for deriving valuable insights on product and brand perceptions. Researchers have used text data such as reviews (Lee and Bradlow 2011), blogs (Gelper, Peres, and Eliashberg 2018), microblogs (Culotta and Cutler 2016), social tags (Nam, Joshi, and Kannan 2017;Nam and Kannan 2014), and discussion forums (Netzer et al 2012), as well as visual social media content (Jalali and Papatla 2016;Liu, Dzyabura, and Mizik 2020;Pavlov and Mizik 2019), for this purpose. Some of the aforementioned studies exploited the richness of the data to apply unsupervised algorithms to derive relevant associations.…”

“…Following the large body of literature demonstrating the power of visuals in extracting deep metaphors and depicting consumers' attitudes, moods, and associations (Jalali and Papatla 2016;Liu, Dzyabura, and Mizik 2020;Pavlov and Mizik 2019;Reavey 2011;Zaltman and Coulter 1995;Zhang et al 2017), our method uses data of visual images. However, extant visual methods either were qualitative (Zaltman and Coulter 1995) or used predesigned sets of attributes (Jalali and Papatla 2016;Liu, Dzyabura, and Mizik 2020;Pavlov and Mizik 2019;Zhang et al 2017). Our elicitation is also among the first (see also Klostermann et al 2018) to provide unsupervised extraction of associations from visual data.…”

High-Energy Ad Content: A Large-Scale Investigation of TV Commercials

“…For instance,Puccinelli, Wilcox, and Grewal (2015) find that energetic advertising will lead to less watching when the context of the program is one of low arousal Gorn, Pham, and Sin (2001). did not consider tuning rates but find that increased arousal may not always increase ad evaluation.5 This research includes studies on text (e.g.,Liu, Singh, and Srinivasan 2016;Netzer et al 2012), images (e.g.,Jalali and Papatla 2016;Liu, Dzyabura, and Mizik 2020;Xiao and Ding 2014), voice (e.g.,Marinova, Singh, and Singh 2018;Xiao, Kim, andDing 2013), and videos (e.g., Li, Shi, andWang 2019;Lu, Xiao, and Ding 2016), among others. 6 Documentation from iSpot.tv describes that the population of tracked televisions is adjusted to ensure that the number of monitored televisions in each designated market area (DMA) and zip code of the country reflects the proportion of all TVs in the country present in that DMA and zip code.…”

“… 5 This research includes studies on text (e.g., Liu, Singh, and Srinivasan 2016; Netzer et al 2012), images (e.g., Jalali and Papatla 2016; Liu, Dzyabura, and Mizik 2020; Xiao and Ding 2014), voice (e.g., Marinova, Singh, and Singh 2018; Xiao, Kim, and Ding 2013), and videos (e.g., Li, Shi, and Wang 2019; Lu, Xiao, and Ding 2016), among others.…”