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Search Ad and Auction-Based Advertising
Published in Peng Liu, Wang Chao, Computational Advertising, 2020
With higher relevance, advertisers will be able to win more clicks at a lower price, so they have an incentive to increase CTR with more precise terms and better creatives. In 2013, the revenue of Google websites in Google financial report reached US$37.4 billion,2 which mainly came from Google AdWords.
Attention Grabbing through Forward Reference: An ERP Study on Clickbait and Top News Stories
Published in International Journal of Human–Computer Interaction, 2022
Xin Li, Jia Zhou, Honglian Xiang, Jingjing Cao
To understand what is at play, previous studies have revealed that clickbait headlines use many strategies, such as forward reference, puns, emotional words, hyperboles, and provoking content (Molek-Kozakowska, 2013; Molina et al., 2021; Palau-Sampio, 2016; Wallberg, 2013). Among these strategies, forward reference is widely used to create information gaps in clickbait headlines (Blom & Hansen, 2015). Forward reference is defined as a reference to upcoming discourse or to a word or a phrase later in the text (Blom & Hansen, 2015; Yang, 2011). Unlike traditional headlines that summarize news stories, forward-reference headlines conceal a lot of information from news stories and arouse curiosity through information gaps (Blom & Hansen, 2015; Yang, 2011). One example of forward reference in a headline is “The universe will end in this way, at this time, researcher says.” In this headline, the demonstrative pronoun “this” refers to an upcoming discourse in the content. The discourse describes exactly how and when the universe will end, while these messages were not disclosed in the headline. Readers may be attracted to further read the content of the article to understand it in detail. Therefore, forward reference is often used in clickbait headlines to boost the click-through rate (Kuiken et al., 2017; Lagerwerf & Govaert, 2018, 2021). In previous studies of forward-reference clickbait, researchers have focused on readers’ clicking behavior and self-reported perception (Kuiken et al., 2017; Lagerwerf & Govaert, 2018, 2021). However, little attention has been paid to how forward-reference headlines affect the attention allocation of readers.
Natural language processing (NLP) in management research: A literature review
Published in Journal of Management Analytics, 2020
Yue Kang, Zhao Cai, Chee-Wee Tan, Qian Huang, Hefu Liu
Within the discipline of information systems, the most investigated topics regarding NLP application are organization research and consumer behavior. Studies on organization have encompassed issues regarding R&D investment, innovation, and more. New entry threats in the fast-changing information technology industry have been shown to have significant impacts on firm decision making, such as in the case of R&D investment and the characteristics of industry that differentiate the mechanism (Pan, Huang, & Gopal, 2019). Moreover, researchers have proven that the capabilities of data analytics have contributed to firms’ innovation, a finding that has been classified as process improvement and new technology improvement, or centralized innovation and decentralized innovation (Wu, Hitt, & Lou, 2019; Wu, Lou, & Hitt, 2019). In online communities, interpersonal communication reflects a user’s personality and how it can influence other members’ identification and how similarities between two users can accentuate purchasing behavior (Adamopoulos, Ghose, & Todri, 2018; Hong & Pavlou, 2017). Interpersonal interactions among community members enable them to share insights and spur innovative ideas (Hwang, Singh, & Argote, 2019). Such interactions first require searching for specific information. For instance, consumers’ preferences and search habits can be ascertained via keyword, which demonstrates how search engine selection and design is crucial in consumers’ subsequent purchasing and search behavior and crucial to understanding search performance (e.g. click-through rate) and advertising performance (e.g. ad position) (Ghose, Ipeirotis, & Li, 2019; Gong, Abhisek, & Li, 2018).