Nothing Special   »   [go: up one dir, main page]

skip to main content
research-article

Influence of emojis on user engagement in brand-related user generated content

Published: 01 November 2022 Publication History

Abstract

Emojis are increasingly adopted in various platforms, such as text messages, social media, or blogs, as part of other digital communications. Recently, emojis have been used in brand-related user-generated content (UGC) as a strategic communication tool to promote positive outcomes. In spite of their increasing importance, little is known about the impact of incorporating emojis in brand-related UGC on consumer reactions. In particular, few research studies have investigated the joint effect of emojis with texts, which is the most important context where emojis function, and the contextual conditions that affect the influence of emojis on consumer reactions. To fill this research gap, we study brand-related UGC, focusing on the effect of emojis and the contextual conditions (e.g., texts). Using large-scale brand-related social media posts from Instagram, we find that, overall, the presence of emojis is positively associated with consumer engagement. Economically, the presence of emojis increases the average number of likes by 72% and the average number of comments by 70% in comparison to posts without any emojis. The interaction effects between emojis and texts reveal that emotional emojis have a positive and significant relationship with consumer engagement only when the texts in brand-related UGC are being skewed toward positive sentiments. However, informational emojis are negatively related with consumer engagement in the similar context. Additionally, the investigations regarding the contextual conditions show that using more emotional emojis has a positive influence on consumer engagement in commercial posts but a negative influence in general posts.

Highlights

The study examines the effect of emojis and the contextual conditions in UGC.
Having emojis is likely to increase consumer engagement (CE) in brand-related UGC.
A greater use of emojis may not be beneficial in CE in brand-related UGC.
Emotional emojis increase CE in the posts where texts are being positively skewed.
Emotional emojis positively impact on CE in commercial posts, not in general posts.

References

[1]
W. Ai, X. Lu, X. Liu, N. Wang, G. Huang, Q. Mei, Untangling emoji popularity through semantic embeddings, in: Proceedings of the international AAAI conference on web and social media, Vol. 11, 2017, April, No. 1.
[2]
H. Alboqami, W. Al-Karaghouli, Y. Baeshen, I. Erkan, C. Evans, A. Ghoneim, Electronic word of mouth in social media: The common characteristics of retweeted and favorited marketer-generated content posted on twitter, Journal of Internet Marketing and Advertising 9 (4) (2015) 338–358.
[3]
S. Bakhshi, D.A. Shamma, E. Gilbert, Faces engage us: Photos with faces attract more likes and comments on Instagram, in: Proceedings of the SIGCHI conference on human Factors in computing systems , 2014, April, pp. 965–974.
[4]
G. Baltas, Determinants of internet advertising effectiveness: An empirical study, International Journal of Market Research 45 (4) (2003) 1–9.
[5]
F. Barbieri, G. Kruszewski, F. Ronzano, H. Saggion, How cosmopolitan are emojis? Exploring emojis usage and meaning over different languages with distributional semantics, in: Proceedings of the 24th ACM international Conference on multimedia , 2016, October, pp. 531–535.
[6]
I. Boutet, M. LeBlanc, J.A. Chamberland, C.A. Colin, Emoji influence emotional communication, social attributions, and information processing, Computers in Human Behavior 119 (2021).
[7]
W.B. Cannon, Organization for physiological homeostasis, Physiological Reviews 9 (3) (1929) 399–431.
[8]
A. Castillo, J. Benitez, J. Llorens, X. Luo, Social media-driven customer engagement and movie performance: Theory and empirical evidence, Decision Support Systems 145 (2021, June) 113516.
[9]
Z. Chen, X. Lu, W. Ai, H. Li, Q. Mei, X. Liu, Through a gender lens: Learning usage patterns of emojis from large-scale android users, in: Proceedings of the 2018 world wide web conference , 2018, April, pp. 763–772.
[10]
Q. Chen, C. Min, W. Zhang, G. Wang, X. Ma, R. Evans, Unpacking the black box: How to promote citizen engagement through government social media during the COVID-19 crisis, Computers in Human Behavior 110 (2020).
[11]
H. Cho, N. Schwarz, If I don't understand it, it must be new: Processing fluency and perceived product innovativeness, ACR North Amer. Advances 33 (1) (2006) 319–320.
[12]
M.A. Coyle, C.L. Carmichael, Perceived responsiveness in text messaging: The role of emoji use, Computers in Human Behavior 99 (2019) 181–189.
[13]
H. Cramer, P. de Juan, J. Tetreault, Sender-intended functions of emojis in US messaging, in: Proceedings of the 18th international Conference on human-computer Interaction with mobile Devices and services , 2016, September, pp. 504–509.
[14]
G. Das, H.J. Wiener, I. Kareklas, To emoji or not to emoji? Examining the influence of emoji on consumer reactions to advertising, Journal of Business Research 96 (2019) 147–156.
[15]
L. De Vries, S. Gensler, P.S. Leeflang, Popularity of brand posts on brand fan pages: An investigation of the effects of social media marketing, Journal of Interactive Marketing 26 (2) (2012) 83–91.
[16]
L. De Vries, A.M. Peluso, S. Romani, P.S. Leeflang, A. Marcati, Explaining consumer brand-related activities on social media: An investigation of the different roles of self-expression and socializing motivations, Computers in Human Behavior 75 (2017) 272–282.
[17]
C. Ding, H.K. Cheng, Y. Duan, Y. Jin, The power of the “like” button: The impact of social media on box office, Decision Support Systems 94 (2017, February) 77–84.
[18]
K.Y. Goh, C.S. Heng, Z. Lin, Social media brand community and consumer behavior: Quantifying the relative impact of user-and marketer-generated content, Information Systems Research 24 (1) (2013) 88–107.
[19]
G.J. Gorn, M.E. Goldberg, K. Basu, Mood, awareness, and product evaluation, Journal of Consumer Psychology 2 (3) (1993) 237–256.
[20]
S. Haberstroh, College counselors' use of informal language online: Student perceptions of expertness, trustworthiness, and attractiveness, Cyberpsychology, Behavior and Social Networking 13 (4) (2010) 455–459.
[21]
J. Hartmann, M. Heitmann, C. Schamp, O. Netzer, The power of brand selfies in consumer-generated brand imagery, Columbia Business School Research Paper Forthcoming, 2021.
[22]
G.S.U. Hewage, Y. Liu, Z. Wang, H. Mao, Consumer responses toward symmetric versus asymmetric facial expression emojis, Marketing Letters (2020) 1–12.
[23]
J.H. Hill, The impact of emojis and emoticons on online consumer reviews, perceived company response quality, brand relationship, and purchase intent, 2016.
[24]
T. Hu, H. Guo, H. Sun, T.V. Nguyen, J. Luo, Spice up your chat: The intentions and sentiment effects of using emojis, in: Proceedings of the international AAAI conference on web and social media, Vol. 11, 2017, May, No. 1.
[25]
L.L. Jones, L.H. Wurm, G.A. Norville, K.L. Mullins, Sex differences in emoji use, familiarity, and valence, Computers in Human Behavior 108 (2020).
[26]
L.K. Kaye, S. Rodriguez-Cuadrado, S.A. Malone, H.J. Wall, E. Gaunt, A.L. Mulvey, C. Graham, How emotional are emoji? Exploring the effect of emotional valence on the processing of emoji stimuli, Computers in Human Behavior 116 (2021).
[27]
E. Ko, D. Bowman, Content engineering of images: The effect of sentiment and complexity on consumer engagement with brand-themed user-generated content, 2018, Working Paper.
[28]
D. Lee, K. Hosanagar, H.S. Nair, Advertising content and consumer engagement on social media: Evidence from Facebook, Management Science 64 (11) (2018) 5105–5131.
[29]
X. Li, K.W. Chan, S. Kim, Service with emoticons: How customers interpret employee use of emoticons in online service encounters, Journal of Consumer Research 45 (5) (2019) 973–987.
[30]
M. Lovett, R. Peres, R. Shachar, A data set of brands and their characteristics, Marketing Science 33 (4) (2014) 609–617.
[31]
X. Lu, W. Ai, X. Liu, Q. Li, N. Wang, G. Huang, Q. Mei, Learning from the ubiquitous language: An empirical analysis of emoji usage of smartphone users, in: Proceedings of the 2016 ACM international joint Conference on Pervasive and ubiquitous computing , 2016, September, pp. 770–780.
[32]
A.M. Molina, M.M. Rojas-de Gracia, P. Alarcón-Urbistondo, M. Romero-Charneco, Exploring the opportunities of the emojis in brand communication: The case of the beer industry, Inter. J Business ComInfor Sys Res 2329488419832964 (2019).
[33]
D.G. Muntinga, M. Moorman, E.G. Smit, Introducing COBRAs: Exploring motivations for brand-related social media use, Inter J Adv 30 (1) (2011) 13–46.
[34]
A. Nikolinakou, K.W. King, Viral video ads: Emotional triggers and social media virality, Psy Marketing 35 (10) (2018) 715–726.
[35]
M.A. Riordan, The communicative role of non-face emojis: Affect and disambiguation, Computers in Human Behavior 76 (2017) 75–86.
[36]
N. Schwarz, G.L. Clore, Mood, misattribution, and judgments of well-being: Informative and directive functions of affective states, J Personality Soc Psy 45 (3) (1983) 513–523.
[37]
D. Shin, S. He, G.M. Lee, A.B. Whinston, S. Cetintas, K.C. Lee, Enhancing social media analysis with visual data analytics: A deep learning approach, MIS Quarterly (2020) 1459–1492.
[38]
Statista, Most popular social networks worldwide as of July 2021, ranked by number of active users, 2021, statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/ .
[39]
S. Stieglitz, L. Dang-Xuan, Emotions and information diffusion in social media—sentiment of microblogs and sharing behavior, Journal of Management Information Systems 29 (4) (2013) 217–248.
[40]
G.J. Tellis, D.J. MacInnis, S. Tirunillai, Y. Zhang, What drives virality (sharing) of online digital content? The critical role of information, emotion, and brand prominence, Journal of Marketing 83 (4) (2019) 1–20.
[41]
M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, A. Kappas, Sentiment strength detection in short informal text, Journal of the American Society for Information Science and Technology 61 (12) (2010) 2544–2558.
[42]
P. Thollander, N. Kumar, Examining the "global" language of emojis: Designing for cultural representation, in: Proceedings of the 2019 CHI Conference on human Factors in computing systems , 2019, May, pp. 1–14.
[43]
O. Toubia, A.T. Stephen, Intrinsic vs. image-related utility in social media: Why do people contribute content to twitter?, Marketing Science 32 (3) (2013) 368–392.
[44]
Q. Ye, R. Law, B. Gu, W. Chen, The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings, Computers in Human Behavior 27 (2) (2011) 634–639.
[45]
X. Zeng, L. Wei, Social ties and user content generation: Evidence from Flickr, Information Systems Research 24 (1) (2013) 71–87.
[46]
S. Zhang, K. Lee, P. Singh, K. Srinivasan, What makes a good image? Airbnb demand analytics leveraging interpretable image features, Management Science (2021),.
[47]
M. Zhang, L. Luo, Can user generated content predict restaurant survival: Deep learning of yelp photos and reviews, Social Science Research Network (SSRN), 2018.

Cited By

View all
  • (2024)Processing facial emojis as social informationComputers in Human Behavior10.1016/j.chb.2023.108106153:COnline publication date: 12-Apr-2024
  • (2023)A Clone-based Analysis of the Content-Agnostic Factors Driving News Article Popularity on TwitterProceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3625007.3627520(17-24)Online publication date: 6-Nov-2023

Index Terms

  1. Influence of emojis on user engagement in brand-related user generated content
        Index terms have been assigned to the content through auto-classification.

        Recommendations

        Comments

        Please enable JavaScript to view thecomments powered by Disqus.

        Information & Contributors

        Information

        Published In

        cover image Computers in Human Behavior
        Computers in Human Behavior  Volume 136, Issue C
        Nov 2022
        287 pages

        Publisher

        Elsevier Science Publishers B. V.

        Netherlands

        Publication History

        Published: 01 November 2022

        Author Tags

        1. Emojis
        2. UGC
        3. Consumer engagement
        4. Contextual conditions
        5. Social media

        Qualifiers

        • Research-article

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)0
        • Downloads (Last 6 weeks)0
        Reflects downloads up to 19 Feb 2025

        Other Metrics

        Citations

        Cited By

        View all
        • (2024)Processing facial emojis as social informationComputers in Human Behavior10.1016/j.chb.2023.108106153:COnline publication date: 12-Apr-2024
        • (2023)A Clone-based Analysis of the Content-Agnostic Factors Driving News Article Popularity on TwitterProceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3625007.3627520(17-24)Online publication date: 6-Nov-2023

        View Options

        View options

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media