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We develop and evaluate experimentally a novel tool that redesigns the job search process by providing tailored advice at lowcost. We invited jobseekers to our computer facilities for twelve consecutive weekly sessions to search for real jobs on our web interface. For one-half, instead of relying on their own search criteria, we use readily available labour market data to display relevant alternative occupations and associated jobs. The data indicate that this broadens the set of jobs they consider and increases their job interviews especially for participants who otherwise search narrowly and have been unemployed for a few months.

Firms typically use query-based search to help consumers find information/products on their websites. We consider the problem of optimally ranking a set of results shown in response to a query. We propose a personalized ranking mechanism based on a user’s search and click history. Our machine-learning framework consists of three modules: (a) feature generation, (b) normalized discounted cumulative gain–based LambdaMART algorithm, and (c) feature selection wrapper. We deploy our framework on large-scale data from a leading search engine using Amazon EC2 servers and present results from a series of counterfactual analyses. We find that personalization improves clicks to the top position by 3.5% and reduces the average error in rank of a click by 9.43% over the baseline. Personalization based on short-term history or within-session behavior is shown to be less valuable than long-term or across-session personalization. We find that there is significant heterogeneity in returns to personalization as a function of user history and query type. The quality of personalized results increases monotonically with the length of a user’s history. Queries can be classified based on user intent as transactional, informational, or navigational, and the former two benefit more from personalization. We also find that returns to personalization are negatively correlated with a query’s past average performance. Finally, we demonstrate the scalability of our framework and derive the set of optimal features that maximizes accuracy while minimizing computing time. This paper was accepted by Juanjuan Zhang, marketing.

Labor markets become more efficient in theory if job seekers direct their search. Using online job board data, we show that high-wage ads attract more applicants as in directed search models. Due to distinctive data features, we also estimate significant but milder directed search for hidden (or implicit) wages, suggesting that ad texts and requirements tacitly convey wage information. Since explicit-wage ads often target unskilled workers, other estimates in the literature ignoring hidden-wage ads may suffer from selection bias. Moreover, job ad requirements are aligned with their applicants’ traits, as predicted in directed search models with heterogeneity.

The objective of this study is to investigate factors that influence investor information demand around earnings announcements and to provide insights into how variation in information demand impacts the capital market response to earnings. The Internet is one channel through which public information is disseminated to investors and we propose that one way that investors express their demand for public information is via Google searches. We find that abnormal Google search increases about two weeks prior to the earnings announcement, spikes markedly at the announcement, and continues at high levels for a period after the announcement. This finding suggests that information diffusion is not instantaneous with the release of the earnings information, but rather is spread over a period surrounding the announcement. We also find that information demand is positively associated with media attention and news, and is negatively associated with investor distraction. When investors search for more information in the days just prior to the announcement, preannouncement price and volume changes reflect more of the upcoming earnings news and there is less of a price and volume response when the news is announced. This result suggests that, when investors demand more information about a firm, the information content of the earnings announcement is partially preempted.

Using Internet search volume for lottery to capture gambling sentiment shifts, we show that when the overall gambling sentiment is strong, investor demand for lottery stocks increases, these stocks earn positive short-run abnormal returns, managers are more likely to split stocks to cater to the increased demand for low-priced lottery stocks, and initial public offerings (IPOs) earn higher first day returns. Further, the sentiment-return relation is stronger among low institutional ownership firms, headquartered in regions where gambling is more acceptable and local bias is stronger. These results suggest that gambling sentiment has a spillover effect on the stock market.

We extend latent Dirichlet allocation by introducing a topic model, hierarchically dual latent Dirichlet allocation, the output of which provides a basis for estimating consumers’ content preferences on the fly from their search queries. We extend latent Dirichlet allocation by introducing a topic model, hierarchically dual latent Dirichlet allocation (HDLDA), for contexts in which one type of document (e.g., search queries) are semantically related to another type of document (e.g., search results). In the context of online search engines, HDLDA identifies not only topics in short search queries and web pages, but also how the topics in search queries relate to the topics in the corresponding top search results. The output of HDLDA provides a basis for estimating consumers’ content preferences on the fly from their search queries given a set of assumptions on how consumers translate their content preferences into search queries. We apply HDLDA and explore its use in the estimation of content preferences in two studies. The first is a lab experiment in which we manipulate participants’ content preferences and observe the queries they formulate and their browsing behavior across different product categories. The second is a field study, which allows us to explore whether the content preferences estimated based on HDLDA may be used to explain and predict click-through rates in online search advertising.

Metadata such as the hashtag is an important dimension of social media communication. Despite its important role in practices such as curating, tagging, and searching content, there has been little research into how meanings are made with social metadata. This book considers how hashtags have expanded their reach from an information-locating resource to an interpersonal resource for coordinating social relationships and expressing solidarity, affinity, and affiliation. It adopts a social semiotic perspective to investigate the communicative functions of hashtags in relation to both language and images. This book is a follow up to Zappavigna's 2012 model of ambient affiliation, providing an extended analytical framework for exploring how affiliation occurs, bond by bond, in online discourse. It focuses in particular on the communing function of hashtags in metacommentary and ridicule, using recent Twitter discourse about US President Donald Trump as a case study. It is essential reading for researchers as well as undergraduates studying social media on any academic course.

Despite a 20-year trend toward integrated marketing communications, advertisers seldom coordinate television and search advertising campaigns. We find that television advertising for financial services brands increases both the number of related Google searches and searchers' tendency to use branded keywords in place of generic keywords. The elasticity of a brand's total searches with respect to its TV advertising is 0.17, an effect that peaks in the morning. These results suggest that practitioners should account for cross-media effects when planning, executing, and evaluating both television and search advertising campaigns. This paper was accepted by Pradeep Chintagunta, marketing.

In sponsored search advertising, advertisers need to make a series of keyword decisions. Grouping these keywords to form several adgroups within a campaign is a challenging task because of the highly uncertain environment of search advertising. This paper proposes a stochastic programming model for keywords grouping, taking click-through rate and conversion rate as random variables, with consideration of budget constraints and advertisers' risk-tolerance. A branch-and-bound algorithm is developed to solve our model. Furthermore, we conduct computational experiments to evaluate the effectiveness of our model and solution, with two real-world data sets collected from reports and logs of search advertising campaigns. Experimental results illustrated that our keywords grouping approach outperforms five baselines, and it can approximately and steadily approach the optimal solution. This research generates several interesting findings that illuminate critical managerial insights for advertisers in sponsored search advertising. First, keywords grouping does matter for advertisers, especially with a large number of keywords. Second, in keywords grouping decisions, the marginal profit does not necessarily show the marginal diminishing phenomenon as the budget increases. Therefore, advertisers should try to increase their budget in keywords grouping decisions to garner additional profit. Third, the optimal keywords grouping solution is the result of a multifaceted trade-off among various advertising factors. In particular, assigning more keywords into adgroups or having a larger budget will not definitely lead to higher profits. This study suggests a warning for advertisers: It is not wise to use the number of keywords as a single criterion for keywords grouping decisions.

The authors study consumers' click behavior on organic and sponsored links after a keyword search on an Internet search engine. Using a data set of individual-level click activity after keyword searches from a leading search engine in Korea, the authors find that consumers' click activity after a keyword search is low and heavily concentrated on the organic list. However, searches of less popular keywords (i.e., keywords with lower search volume) are associated with more clicks per search and a larger fraction of sponsored clicks. This indicates that, compared with more popular keywords, consumers who search for less popular keywords expend more effort in their search for information and are closer to a purchase, which makes them more targetable for sponsored search advertising.