Click-through Behavior Research Articles

Environmental or Personal Benefit? The Role of Message Appeal and Information Type in Destination Social Media Advertisements

Destinations are increasingly focusing on sustainability awareness through tourism campaigns, but effective communication remains challenging. This research, rooted in consumption value theory, delves into the influence of message appeal (environmental vs. personal benefit) and information type (objective vs. subjective) on tourists’ behavioral intentions through two online experiments. Study 1 demonstrates the positive impact of environmental benefit message appeals on behavioral intentions toward destinations, both directly and indirectly, by enhancing destination brand quality and perceived green value. Study 2 validates these findings and extends the analysis to include click-through behavior as a proxy for real behavior. Our research emphasizes the importance of environmental benefits alongside objective information in destinations’ sustainability messages within social media advertisements. This approach serves to bolster destination brand quality, perceived green value, and ultimately, positive behavioral intentions.

Consumer Click Responses to Paid Ad Set Strategies of Search Platforms

Search platforms are contemporary digital services that create value on multi-sided markets through providing services to each side of their platforms—indexing contents for all website operators on the internet, placing pages and tracking performance for paid advertisers or sellers, and searching market offerings to match inquiries for web users. Search platforms are interested in developing strategies at the sponsored ad set level to optimize consumer clicks. Drawing literature supports from hospitality, search engine advertising, and product assortment areas, our study examines the relationships between search platforms’ paid ad strategies (in paid ad set size and set attractiveness) and consumer’s single or multiple clicks on the sponsored ad section. We empirically test the relationships in a multinomial logit modeling approach using a large-scale disaggregated, hotel-search dataset collected from a leading Chinese search engine. Our results suggest a positive but diminishing impact of paid ad set size on a consumer’s set selection and multiple click-through behavior. Our study also spotlights the distinct and often divergent moderating effects of two types of ad attractiveness, preference-based and social-based, on the relationship between paid ad set size and consumer responses. We discuss the theoretical and managerial implications of this research to search platforms and advertisers in hospitality alike.

PREDICTIVE MODELING OF CLICK-THROUGH RATES: A REGRESSION ANALYSIS APPROACH

This research uses advanced regression techniques to develop a robust predictive model for Click-Through Rates (CTR) in online advertising. The study leverages a diverse dataset encompassing various advertising campaigns and user interactions to uncover patterns and relationships influencing click-through behavior. The goal is to provide advertisers with a tool for accurate CTR prediction, enabling them to optimize campaigns and allocate resources effectively.

What makes customers “Click”? An analysis of hotel list content using deep learning

Understanding what motivates customers to click through a hotel on the search results page for more details under the influence of COVID-19 is important since it is a prerequisite step before booking a hotel. This study aims to examine how visual and textual information cues shown on each hotel listing influence customers’ click-through behavior using real-time viewing times as an indicator. Using the deep learning DenseNet model, this study analyzes both visual and textual information cues from 3617 hotel listings and reveals that: 1) Four hotel cover photograph features (building outside view, pool view, street view, property detail) extracted significantly influence click-through behavior except for pool view; 2) Presenting social proof badges (guest favorite, very clean) and booking terms and conditions (pay later, free cancellation) are effective marketing tactics. This study enriches the literature on hotel information search behavior and assists hotel practitioners to optimize their listing information.

Does scarcity increase or decrease donation behaviors? An investigation considering resource-specific scarcity and individual person-thing orientation.

The online version contains supplementary material available at 10.1007/s11747-023-00938-2.

Who Should Own the Data? The Impact of Data Ownership Shift from the Service Provider to Consumers

ABSTRACT With the wide use of information technologies including Big Data and artificial intelligence (AI), consumers’ personal actions (their search history, transaction records, click-through behaviors, etc.) can be tracked, recorded and analyzed by the service provider (e.g., Google) to provide personalized services. Under the current regime, consumers usually hand over their personal data for free in exchange for high-quality services. As it becomes more and more commonly accepted that “data is property,” should consumers be entitled to claim their property rights over their personal data? New technologies emerge to empower consumers to control their own data, and the service provider may need to compensate for the usage of such data. How consumers and the service provider should react to such technologies, however, is not clear. We build a theoretical model in which consumers have different sensitivities towards their data ownership. We show that the impact of the data ownership shift depends not only on the service provider’s revenue structure and the discount in the service quality offered to non-data-providing consumers, but also on whether and how consumers are compensated. More importantly, if the service provider can endogenously adjust the qualities of services provided to consumers, the shift of data ownership may not necessarily benefit consumers, or harm the service provider. We also offer guidelines for data regulation policy designs.

Response to a phishing attack: persuasion and protection motivation in an organizational context

PurposeThis study aims to examine the effect of cybersecurity threat and efficacy upon click-through, response to a phishing attack: persuasion and protection motivation in an organizational context.Design/methodology/approachIn a simulated field trial conducted in a financial institute, via PhishMe, employees were randomly sent one of five possible emails using a set persuasion strategy. Participants were then invited to complete an online survey to identify possible protective factors associated with clicking and reporting behavior (N = 2,918). The items of interest included perceived threat severity, threat susceptibility, response efficacy and personal efficacy.FindingsThe results indicate that response behaviors vary significantly across different persuasion strategies. Perceptions of threat susceptibility increased the likelihood of reporting behavior beyond clicking behavior. Threat susceptibility and organizational response efficacy were also associated with increased odds of not responding to the simulated phishing email attack.Practical implicationsThis study again highlights human susceptibility to phishing attacks in the presence of social engineering strategies. The results suggest heightened awareness of phishing threats and responsibility to personal cybersecurity are key to ensuring secure business environments.Originality/valueThe authors extend existing phishing literature by investigating not only click-through behavior, but also no-response and reporting behaviors. Furthermore, the authors observed the relative effectiveness of persuasion strategies used in phishing emails as they compete to manipulate unsafe email behavior.

A Strategic Group Analysis of Competitor Behavior in Search Advertising

Firms compete intensely in sponsored search. Their bidding strategies hinge on understanding who competes with whom, how they compete, and how consumers react to competing advertisements. In this context, we investigate how firm competition impacts consumers’ click-through behaviors in search advertising from a strategic group perspective. Using search results from Google and consumers’ clickstream data, we found strong negative externality for competitors within the same strategic group relative to competitors across strategic groups: firms reap fewer click-throughs when an advertisement of another firm from the same strategic group is also displayed in search results, relative to when other displayed advertisers are not from the same group. This indicates that when competitors from the same strategic group are likely to appear in the results of a sponsored search auction, the focal firm would be better off avoiding head-to-head competition in the auction. However, we did not find empirical evidence of such firm behaviors, suggesting myopia or the inability of firms to avoid such competition. We also show that when multiple firms from the same strategic group appear in search results, the closer the focal firm is located to such competing firms, the more click-throughs the firm accrues. This suggests that firms should stay close to their within-group competitors when they compete in the same search auction. Further, our empirical results indicate that firms are indeed doing so. Using another set of data from Google AdWords reports, we show that our findings are also robust to multi-keyword bidding scenarios. These findings represent the first attempt to understand the impact of strategic groups in search advertising and provide interesting implications for advertisers and search engines.

Search Engine Consumer Journeys: Exploring and Segmenting Click-Through Behaviors

The consumer online search journey is viewed as one of the key determinants toward consumer purchase decisions. Using a unique disaggregated query-level data set collected from a leading Chinese search engine, we employ a latent-class, two-stage logistic regression model and identify the existence of three distinct consumer segments in the hospitality industry. Each group represents a unique stage of the consumer experience along a typical search journey, leading to a set of click-through behaviors. The consumer search and click-through insights garnered from this study provide hotel marketers with deeper understandings and actionable guidelines to the development of their search advertising campaign and (re)targeting strategies. The segmentation model and the empirical insights also demonstrate the possibility that a segment-specific ad assortment approach could improve advertising efficiency, enrich consumer’s search experience, and eventually create a win-win situation that benefit all entities involved in the hospitality-related, search advertising domain.

Click-Through Behavior across Devices In Paid Search Advertising

This study investigated differences in consumer click-through behavior with paid search advertisement across devices—smartphone versus desktop versus tablet. The authors examined how different device users behave in terms of their tendency to click on the top paid search advertisement and their sensitivity to advertisement position change, and whether tablet users are more similar to smartphone or desktop users when clicking through paid search advertisements. By leveraging Google AdWords data from 13 paid search advertisers, the authors developed empirical findings that provide insights into paid search advertising strategies across devices.

Brain buzz for Facebook? Neural indicators of SNS content engagement

Marketing in social media is gaining importance among young consumers. Because of the clutter in social network services (SNS) like Facebook, creating engaging content that can generate click-through and sharing behaviors is becoming a key success factor for SNS marketing. We investigate the neural indicators of engagement with content as a driver of consumers′ click-through behavior in social media. Using a novel neuroscientific research methodology called event-related potentials (ERP), we find unique brainwave signals that combine P3 and late positive potential (LPP), increasing power signals with multiple exposures while young consumers are exposed to highly engaging Facebook posts. These unique brainwave signals in response to engagement with online content, which occur within 300 ms, are detected in the bilateral temporal brain areas. A new neuromarketing method will soon allow marketers to predict consumers′ online click-through behaviors from micro-second brain processes that occur almost simultaneously with the time of content exposure.

Personalized Sales Targets with Customer Choices

Advertisement, as a major revenue source of e-commerce platforms, is an important online marketing tool for sellers thereof. In this paper, we explore the dynamic ad allocation with limited slots upon each customer arrival for e-commerce platforms when the advertisers specify, for each ad, the budget constraint, the time periods when the ad should be displayed, and the click-through (lower-limit) constraint for certain customer segments. The goal of the platform is to maximize its payoff over the entire horizon. We propose a two-stage stochastic program framework in which the platform first decides the click-through goals for each ad/customer-type pair, and then devises the ad allocation policy to satisfy these goals in the second stage. We show that the optimal click-through goals can be achieved efficiently by solving a convex program, which can further reduce to a scalable linear program if the customer click-through behavior follows the multinomial logit model. Moreover, we provide a family of debt-weighted algorithms to achieve the optimal click-through goals, and prove that they are asymptotically optimal when the problem size scales to infinity. Compared to choice-based linear programming and its variant, our approach has better scalability and can deplete the ad budgets more smoothly throughout the horizon, which is very much desirable for the online advertising business in practice.

R-RNN: Extracting User Recent Behavior Sequence for Click-Through Rate Prediction

Click-through rate (CTR) prediction is of great importance in such web applications as recommender systems, web search, and online advertising. Exploring the interest of a user through feature interactions behind massive behaviors of users is essential for CTR prediction. Recently, deep learning-based models have been proposed, which follow a similar embedding and multi-layer perception (MLP) paradigm and achieve great success in both business and research fields. However, existing studies ignore the sequential characteristics of click-through behaviors of users. To overcome this limitation, we propose a novel model named recent recurrent neural network (R-RNN) to adaptively learn the representation of the interest of a user from his or her overall historical click-through behaviors. R-RNN puts an emphasis on recent click-through behaviors via a novel neural network architecture. Compared to the state-of-the-art methods including the deep interest network (DIN) model recently proposed by Alibaba, R-RNN not only applies the attention mechanism to help capture the representation of the main interest of a user but also incorporates a long short-term memory (LSTM) unit for exploring the trend of the change of the interest of a user behind his or her recent click-through behaviors. Comprehensive experiments are conducted to demonstrate the effectiveness of R-RNN for CTR prediction on a benchmark data set. The results show that R-RNN significantly outperforms existing deep learning models. The results also demonstrate that the length of the recent click-through behavior sequence has an important effect on the prediction performance of the model.

Effects of Advocacy Banners after Abandoning Products in Online Shopping Carts

Aim/Purpose: This study empirically analyzed and examined the effectiveness of the online advocacy banners on customers’ reactions to make replacements with the similar products in their shopping carts. Background: When a product in a shopping cart is removed, it might be put back into the cart again during the same purchase or it may be bought in the future. Otherwise, it might be abandoned and replaced with a similar item based on the customer’s enquiry list or on the recommendation of banners. There is a lack of understanding of this phenomenon in the existing literature, pointing to the need for this study. Methodology: With a database from a Taiwanese e-retailer, data were the tracks of empirical webpage clickstreams. The used data for analyses were particularly that the products were purchased again or replaced with the similar ones upon the advocacy banners being shown when they were removed from customers’ shopping carts. Few pre-defined Apriori rules as well as similarity algorithm, Jaccard index, were applied to derive the effectiveness. Contribution: This study addressed a measurement challenge by leveraging the information from clickstream data – particularly clickstream data behavior. These data are most useful to observe the real-time behavior of consumers on websites and also are applied to studying click-through behavior, but not click-through rates, for web banners. The study develops a new methodology to aid advertisers in evaluating the effectiveness of their banner campaign. Findings: The recommending/advocating titles of “you probably are interested” and “the most viewed” are not significantly effective on saving back customers’ removed products or repurchasing similar items. For the banners entitled “most buy”, “the most viewed” might only show popularity of the items, but is not enough to convince them to buy. At the current stage on the host website, customers may either not trust in the host e-retailer or in such mechanism. Additionally, the advocating/recommending banners only are effective on the same customer visits and their effects fade over time. As time passes, customers’ impressions of these banners may become vague. Recommendations for Practitioners: One managerial implication is more effective adoption of advocacy/recommendation banners on e-retailing websites. Another managerial implication is the evaluation of the advocacy/recommendation banners. By using a data mining technique to find the association between removed products and restored ones in e-shoppers’ shopping carts, the approach and findings of this study, which are important for e-retailing marketers, reflect the connection between the usage of banners and the personalized purchase changes in an individual customer’s shopping cart. Recommendation for Researchers: This study addressed a new measurement which challenges to leverage the information from clickstream data instead of click-through rates – particularly retailing webpages browsing behavior. These data are most useful to observe the real-time behavior of consumers on websites and also are applied to studying click-through behavior. Impact on Society: Personalization has become an important technique that allows businesses to improve both sales and service relationships with their online customers. This personalization gives e-marketers the ability to deliver real effectiveness in the use of banners. Future Research: The effectiveness is time- and case-sensible. Business practitioners and academic researchers are encouraged to apply the mining methodology to longevity studies, specific marketing campaigns of advertising and personal recommendations, and any further recommendation algorithms.

Large-Scale Adversarial Sports Play Retrieval with Learning to Rank

As teams of professional leagues are becoming more and more analytically driven, the interest in effective data management and access of sports plays has dramatically increased. In this article, we present a retrieval system that can quickly find the most relevant plays from historical games given an input query. To search through a large number of games at an interactive speed, our system is built upon a distributed framework so that each query-result pair is evaluated in parallel. We also propose a pairwise learning to rank approach to improve search ranking based on users’ clickthrough behavior. The similarity metric in training the rank function is based on automatically learnt features from a convolutional autoencoder. Finally, we showcase the efficacy of our learning to rank approach by demonstrating rank quality in a user study.

CONSUMER CURIOSITY, ANTICIPATION, AND CLICK-THROUGH: NEURAL SIGNATURES OF DIGITAL BUZZ

Digital and social media has become an indispensable channel of promotion and marketing to the enterprises. For example, compared to other means of promotional channels, such as television advertising, newspapers, social network services (SNS) are more competitive, more convenient, and lower-cost. Because individuals compose or share those posts anytime, anywhere, information spreads fast and wide on social media. SNS posts can affect people's perception, thus affecting their purchase intention and behavior. A large number of researchers investigated academic issues involved SNS, such as the influence of eWOM in social media on consumers' purchase intentions (Erkan & Evans, 2016). According to Lin and Lu’s research (2011), which suggests that gender difference also exists in social media. In addition, a study identified attributes of social media messaging activities and examined the relationships among those perceived activities, value equity, relationship equity, brand equity, customer equity, and purchase intention through a structural equation model (Kim & Ko, 2012). In this study, we focus on the motivational antecedents of digital buzz on digital and social media. Among many performance metrics of digital marketing, the most important one that really matters to advertisers is click-through. Click-through is believed to reflect viewers’ interest in the product contents that are being shown on screen, and based on that assumption, it opens a new navigation tab, such as the advertiser’s web site that can provide information about products and services of an advertising company. However, there is a caveat here. Since advertisements in digital and social media are often placed on top of the contents that the viewers have already been reading, viewers can accidentally click on the internet advertisements in which they have no interests. Viewers are only annoyed by intrusive re-routing during their digital activities. Hence, it is important that the digital media marketer to be able to distinguish between the click-throughs, knowing the difference between the clicks that are genuinely reflective of a viewer’s interest, or the rest of clicks that are not. For this purpose, we propose consumer curiosity and anticipation as drivers of click-through that are meaningful to digital markers. Consumer curiosity is an important variable that results in click-through behavior, digital propagation or buzz, and further stimulates consumers' consumption activities. In recent years, there are many studies to verify this theory. Online atmospherics have an impact on customer emotions, and perceptual curiosity has a moderating effect existed in the two variables (Koo & Ju, 2010). However, the research about curiosity’s mediation effect on purchase intention aroused by SNS contents is still limited. In addition, the neural research in this field is still limited. The research seeks to understand how the SNS contents affect customer’s curiosity toward the product which the content mentioned. Furthermore, to find the relationship between curiosity and the purchase intention by using neuromarketing methodology. Our goal is to discover the neural signatures of consumers’ motivational state that leads to click-through, exploration of contents, and finally digital buzz behavior. EEG and fMRI are commonly research approaches used in the neuroscience field. EEG detect electric activities in the brain waves and fMRI measures hemodynamic events to discover individuals’ cognitive and motivational states. After content-analyzing top 10 most shared posts in the fashion brands Facebook official account which included luxury brands such as GUCCI, Luis Vuitton, and Hermes), we extract the attractive posts’ content attributes to be the level of accuracy, graphic quality, and information value. Then, we create two different simulations of social media activities that instigates viewer curiosity at differing levels. By designing an online experiment that examines how different arrays of information can create different levels viewer curiosity in user posts, and by measuring viewers’ intention to propagate the posts, we are able to discover neural signatures of digital buzz. Potential implications to digital marketing of this research in establishing meaningful performance metrics for digital marketing are discussed.

Search Neutrality and Search Bias: An Empirical Perspective on the Impact of Architecture and Labeling

As Google has moved from providing “ten blue links” to “universal search,” controversy has erupted over whether Google is favoring its own specialized search results over competing specialized results offered by other entities. Google’s competitors have complained about “search bias,” and demanded that antitrust enforcers should ensure “search neutrality.” Both the U.S. Federal Trade Commission (FTC) and the European Commission have considered these complaints. The FTC closed its investigation without taking any action, but the European Commission issued a formal statement of objections to Google in April 2015. This study empirically examines the impact of potential design remedies on search bias, including prominent links to rival specialized search services (“architectural remedies”) and clearer labeling of Google’s specialized search results (“labeling remedies”). This study finds that architectural remedies have much greater impact than labeling remedies. User awareness of labeling is low, and even labels far more explicit than those currently employed do not have much impact. Consumers have sticky expectations about how search results are presented, and their click-through behavior tracks those expectations irrespective of how the search results are labeled. However, major architectural changes can have a substantial impact on click-through rates. These findings suggest that the impact of architectural remedies will depend greatly on their design features, while labeling remedies are unlikely to have a significant impact. We explore the implications of these findings for other issues at the interface of Internet and intellectual property (IP) law.

Evaluation of an Online Campaign for Promoting Help-Seeking Attitudes for Depression Using a Facebook Advertisement: An Online Randomized Controlled Experiment.

BackgroundA depression-awareness campaign delivered through the Internet has been recommended as a public health approach that would enhance mental health literacy and encourage help-seeking attitudes. However, the outcomes of such a campaign remain understudied.ObjectiveThe main aim of this study was to evaluate the effectiveness of an online depression awareness campaign, which was informed by the theory of planned behavior, to encourage help-seeking attitudes for depression and to enhance mental health literacy in Hong Kong. The second aim was to examine click-through behaviors by varying the affective facial expressions of people in the Facebook advertisements.MethodsPotential participants were recruited through Facebook advertisements, using either a happy or sad face illustration. Volunteer participants registered for the study by clicking on the advertisement and were invited to leave their personal email addresses to receive educational content about depression. The participants were randomly assigned into two groups (campaign or control), and over a four consecutive week period, received either the campaign material or official information developed by the Hospital Authority in Hong Kong. Pretests and posttests were conducted before and after the campaign to measure the differences in help-seeking attitudes and mental health literacy among the campaign and control groups.ResultsOf the 199 participants that registered and completed the pretest, 116 (55 campaign and 62 control) completed the campaign and the posttest. At the posttest, we found no significant changes in help-seeking attitudes between the campaign and control groups, but the campaign group participants demonstrated a statistically significant improvement in mental health literacy (P=.031) and a higher willingness to access additional information (P<.001) than the control group. Moreover, the happy face Facebook advertisement attracted more click-throughs by users into the website than did the sad face advertisement (P=.03).ConclusionsThe present study provides evidence that an online campaign can enhance people’s mental health literacy. It also demonstrates the practicality and effectiveness of an online depression awareness campaign using a Facebook-based recruitment strategy and distribution of educational materials through emails. It is important for future studies to take advantage of the popularity of online social media and conduct evaluative research on mental health promotion campaigns.

Social-Sensed Image Search

Although Web search techniques have greatly facilitate users’ information seeking, there are still quite a lot of search sessions that cannot provide satisfactory results, which are more serious in Web image search scenarios. How to understand user intent from observed data is a fundamental issue and of paramount significance in improving image search performance. Previous research efforts mostly focus on discovering user intent either from clickthrough behavior in user search logs (e.g., Google), or from social data to facilitate vertical image search in a few limited social media platforms (e.g., Flickr). This article aims to combine the virtues of these two information sources to complement each other, that is, sensing and understanding users’ interests from social media platforms and transferring this knowledge to rerank the image search results in general image search engines. Toward this goal, we first propose a novel social-sensed image search framework, where both social media and search engine are jointly considered. To effectively and efficiently leverage these two kinds of platforms, we propose an example-based user interest representation and modeling method, where we construct a hybrid graph from social media and propose a hybrid random-walk algorithm to derive the user-image interest graph. Moreover, we propose a social-sensed image reranking method to integrate the user-image interest graph from social media and search results from general image search engines to rerank the images by fusing their social relevance and visual relevance. We conducted extensive experiments on real-world data from Flickr and Google image search, and the results demonstrated that the proposed methods can significantly improve the social relevance of image search results while maintaining visual relevance well.

Captions and biases in diagnostic search

People frequently turn to the Web with the goal of diagnosing medical symptoms. Studies have shown that diagnostic search can often lead to anxiety about the possibility that symptoms are explained by the presence of rare, serious medical disorders, rather than far more common benign syndromes. We study the influence of the appearance of potentially-alarming content, such as severe illnesses or serious treatment options associated with the queried for symptoms, in captions comprising titles, snippets, and URLs. We explore whether users are drawn to results with potentially-alarming caption content, and if so, the implications of such attraction for the design of search engines. We specifically study the influence of the content of search result captions shown in response to symptom searches on search-result click-through behavior. We show that users are significantly more likely to examine and click on captions containing potentially-alarming medical terminology such as “heart attack” or “medical emergency” independent of result rank position and well-known positional biases in users' search examination behaviors. The findings provide insights about the possible effects of displaying implicit correlates of searchers' goals in search-result captions, such as unexpressed concerns and fears. As an illustration of the potential utility of these results, we developed and evaluated an enhanced click prediction model that incorporates potentially-alarming caption features and show that it significantly outperforms models that ignore caption content. Beyond providing additional understanding of the effects of Web content on medical concerns, the methods and findings have implications for search engine design. As part of our discussion on the implications of this research, we propose procedures for generating more representative captions that may be less likely to cause alarm, as well as methods for learning to more appropriately rank search results from logged search behavior, for examples, by also considering the presence of potentially-alarming content in the captions that motivate observed clicks and down-weighting clicks seemingly driven by searchers' health anxieties.