The dominant factor of social tags for users’ decision behavior on e‐commerce websites: Color or text

Colored Tags (abbr.Tag) as a unique type of social tags is used on e‐commerce websites (e.g., Taobao) to summarize the high‐frequency keywords extracted from users' online reviews about products they bought before. Tag is represented inked red or green according to users' personal experiences and judgments about purchased items: red for positive comments, green for negative ones. The valence of users' emotion induced by red or green is controversial. This study firstly discovers that colored tags inked in red incite users' positive emotion (evaluations) and colored tags inked in green incite negative emotion (evaluations) using an ERP experiment, which is manifested in ERP components (e.g., N170, N2c, and LPC). There are two main features of Tag: the text of Tag (abbr. Text) and the color of Tag (abbr.Color). Our study then proves that Color (red or green) is the dominant factor in users' decision behavior compared with Text under the high cognitive load condition, while users' decision behavior is influenced by Text (positive tags or negative tags) predominately rather than by Color under the low cognitive load condition with the help of Eye tracking instrument. Those findings can help to design colored tags for recommendation systems on e‐commerce websites and other online platforms.

[1]  C Alain,et al.  Event-related neural activity associated with the Stroop task. , 1999, Brain research. Cognitive brain research.

[2]  Alexander C. Loui,et al.  Tag Cloud++ - Scalable Tag Clouds for Arbitrary Layouts , 2012, 2012 IEEE International Symposium on Multimedia.

[3]  Sascha Steinmann,et al.  Does Color Matter? An Experimental Study on Icon Design for Mobile Gaming Apps: An Abstract , 2017 .

[4]  Ludovic Le Bigot,et al.  Colour and emotion: children also associate red with negative valence. , 2016, Developmental science.

[5]  P. Kay Basic Color Terms: Their Universality and Evolution , 1969 .

[6]  Montserrat Zurrón,et al.  Semantic Conflict Processing in the Color-Word Stroop and the Emotional Stroop Event-Related Potential (ERP) Correlates , 2013 .

[7]  Shuk Ying Ho,et al.  Web Personalization as a Persuasion Strategy: An Elaboration Likelihood Model Perspective , 2005, Inf. Syst. Res..

[8]  Marie-Jeanne Lesot,et al.  Using Association Rules to Discover Color-Emotion Relationships Based on Social Tagging , 2010, KES.

[9]  Bamshad Mobasher,et al.  The Role of Emotions in Context-aware Recommendation , 2013, Decisions@RecSys.

[10]  Kun Chang Lee,et al.  Exploring the effect of the human brand on consumers' decision quality in online shopping: an eye-tracking approach , 2013, Online Inf. Rev..

[11]  A. Elliot,et al.  Romantic red: red enhances men's attraction to women. , 2008, Journal of personality and social psychology.

[12]  Ralf Steinmetz,et al.  Multigranularity reuse of learning resources , 2011, TOMCCAP.

[13]  Yi-Hsuan Yang,et al.  Exploiting online music tags for music emotion classification , 2011, TOMCCAP.

[14]  Qi Wang,et al.  How do social-based cues influence consumers’ online purchase decisions? An event-related potential study , 2016, Electron. Commer. Res..

[15]  Hyoung-Joo Kim,et al.  Item recommendation using tag emotion in social cataloging services , 2017, Expert Syst. Appl..

[16]  Rocco Palumbo,et al.  When green is positive and red is negative: Aging and the influence of color on emotional memories. , 2016, Psychology and aging.

[17]  Jacek Gwizdka,et al.  Temporal dynamics of eye‐tracking and EEG during reading and relevance decisions , 2017, J. Assoc. Inf. Sci. Technol..

[18]  Jannis Kallinikos,et al.  Computing the everyday: Social media as data platforms , 2017, Inf. Soc..

[19]  Robert J. K. Jacob,et al.  Eye tracking in human-computer interaction and usability research : Ready to deliver the promises , 2002 .

[20]  Thorsten Joachims,et al.  Accurately interpreting clickthrough data as implicit feedback , 2005, SIGIR '05.

[21]  Jie Fang,et al.  Incorporating Sentiment Analysis for Improved Tag-Based Recommendation , 2011, 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing.

[22]  Meng Wang,et al.  Tag Tagging: Towards More Descriptive Keywords of Image Content , 2011, IEEE Transactions on Multimedia.

[23]  Shu-Fei Yang,et al.  An eye-tracking study of the Elaboration Likelihood Model in online shopping , 2015, Electron. Commer. Res. Appl..

[24]  Jonathan R. Folstein,et al.  Influence of cognitive control and mismatch on the N2 component of the ERP: a review. , 2007, Psychophysiology.

[25]  Han Li,et al.  Optimized Cost per Click in Taobao Display Advertising , 2017, KDD.

[26]  Hao Liu,et al.  Search product and experience product online reviews: An eye-tracking study on consumers' review search behavior , 2016, Comput. Hum. Behav..

[27]  Franziska Marquart,et al.  Communication and persuasion : central and peripheral routes to attitude change , 1988 .

[28]  Qiuzhen Wang,et al.  An eye-tracking study of website complexity from cognitive load perspective , 2014, Decis. Support Syst..

[29]  Warih Maharani,et al.  Discovering Users' Perceptions on Rating Visualizations , 2016 .

[30]  Simon Hanslmayr,et al.  The Electrophysiological Dynamics of Interference during the Stroop Task , 2008, Journal of Cognitive Neuroscience.

[31]  Siu-Ming Yiu,et al.  New Word Detection and Tagging on Chinese Twitter Stream , 2015, DaWaK.

[32]  D. Kliger,et al.  Red light, green light: Color priming in financial decisions , 2012 .

[33]  David Carmel,et al.  Social media recommendation based on people and tags , 2010, SIGIR.

[34]  B. Kopp,et al.  N200 in the flanker task as a neurobehavioral tool for investigating executive control. , 1996, Psychophysiology.

[35]  J. Ridley Studies of Interference in Serial Verbal Reactions , 2001 .

[36]  Maria Sicilia,et al.  The effects of the amount of information on cognitive responses in online purchasing tasks , 2010, Electron. Commer. Res. Appl..

[37]  Amar Cheema,et al.  The Effect of Red Background Color on Willingness-to-Pay: The Moderating Role of Selling Mechanism , 2013 .

[38]  Tamás D. Gedeon,et al.  Eye‐tracking analysis of user behavior and performance in web search on large and small screens , 2015, J. Assoc. Inf. Sci. Technol..

[39]  R. H. Phaf,et al.  The automaticity of emotional Stroop: a meta-analysis. , 2007, Journal of behavior therapy and experimental psychiatry.

[40]  Jes A. Koepfler,et al.  An experimental study of social tagging behavior and image content , 2011, J. Assoc. Inf. Sci. Technol..

[41]  A. Pellicer‐Sánchez INCIDENTAL L2 VOCABULARY ACQUISITION FROM AND WHILE READING , 2015, Studies in Second Language Acquisition.

[42]  Daofang Chang,et al.  Online Shopping Recommendation Based on Customer Comment Analysis and Missing Value Complement , 2016 .

[43]  Nicholas Wymbs,et al.  Neural correlates of conflict processing , 2005, Experimental Brain Research.

[44]  Abdulmotaleb El-Saddik,et al.  Collaborative user modeling with user-generated tags for social recommender systems , 2011, Expert Syst. Appl..

[45]  Chen Xu,et al.  Social tagging in the scholarly world , 2013, J. Assoc. Inf. Sci. Technol..

[46]  Kathrin Knautz,et al.  Collective indexing of emotions in videos , 2011, J. Documentation.

[47]  Nils Magne Larsen,et al.  Consumer attention to price in social commerce: Eye tracking patterns in retail clothing☆ , 2016 .

[48]  Rong Jin,et al.  Deep Learning at Alibaba , 2017, IJCAI.

[49]  C. Carter,et al.  The Timing of Action-Monitoring Processes in the Anterior Cingulate Cortex , 2002, Journal of Cognitive Neuroscience.

[50]  Hyun Hee Kim,et al.  Toward video semantic search based on a structured folksonomy , 2011, J. Assoc. Inf. Sci. Technol..

[51]  Barry Smyth,et al.  Combining similarity and sentiment in opinion mining for product recommendation , 2015, Journal of Intelligent Information Systems.

[52]  Zhong Yao,et al.  How Social Ties Influence Consumer: Evidence from Event-Related Potentials , 2017, PloS one.

[53]  K. Rayner Eye movements in reading and information processing: 20 years of research. , 1998, Psychological bulletin.

[54]  Jay F. Nunamaker,et al.  Embodied Agents and the Predictive Elaboration Model of Persuasion--The Ability to Tailor Embodied Agents to Users' Need for Cognition , 2012, 2012 45th Hawaii International Conference on System Sciences.

[55]  FATIH GEDIKLI,et al.  Improving recommendation accuracy based on item-specific tag preferences , 2013, TIST.

[56]  Dinkar Sharma,et al.  Neural correlates of intrusion of emotion words in a modified Stroop task. , 2008, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[57]  Andreas Dengel,et al.  Attentive documents: Eye tracking as implicit feedback for information retrieval and beyond , 2012, TIIS.

[58]  Alan H. S. Chan,et al.  Perceptions of implied hazard for visual and auditory alerting signals , 2009 .

[59]  Thorsten Joachims,et al.  Eye-tracking analysis of user behavior in WWW search , 2004, SIGIR '04.

[60]  Melika Husic-Mehmedovic,et al.  Seeing is not necessarily liking: Advancing research on package design with eye-tracking ☆ , 2017 .

[61]  N. Hagemann,et al.  Influence of red jersey color on physical parameters in combat sports. , 2013, Journal of sport & exercise psychology.

[62]  K Grune,et al.  Updating of working memory in a running memory task: an event-related potential study. , 2000, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[63]  Ed H. Chi,et al.  Perception and understanding of social annotations in web search , 2013, WWW.

[64]  Lei Mo,et al.  Blue or red? The effects of colour on the emotions of Chinese people , 2014 .

[65]  Gayle Kerr,et al.  The elaboration likelihood model: review, critique and research agenda , 2014 .

[66]  O. Hue,et al.  How Red, Blue, and Green are Affectively Judged , 2016 .

[67]  Li Chen,et al.  An Eye-Tracking Study: Implication to Implicit Critiquing Feedback Elicitation in Recommender Systems , 2016, UMAP.

[68]  Michael Burch,et al.  Concentri Cloud: Word Cloud Visualization for Multiple Text Documents , 2015, 2015 19th International Conference on Information Visualisation.

[69]  Tamás D. Gedeon,et al.  Understanding eye movements on mobile devices for better presentation of search results , 2015, J. Assoc. Inf. Sci. Technol..

[70]  Fengyu Cong,et al.  Event-related potentials elicited by social commerce and electronic-commerce reviews , 2015, Cognitive Neurodynamics.

[71]  Yue-jia Luo,et al.  Emotional conflict occurs at an early stage: Evidence from the emotional face–word Stroop task , 2010, Neuroscience Letters.