Online Search Behavior Research Articles

Measuring the Impact of a Single Negative Customer Review on Online Search and Purchase Decisions

How much does a single negative customer review impact online search and purchase behavior? To answer this question, the authors compare choices made by consumers searching for a product that had a negative review posted on its product-page with those made when the same review had “moved” to a second page as additional reviews were posted. By focusing on where the review appears rather than whether it was submitted, our approach tackles concerns of spurious correlation between customer reviews and unobserved demand shocks. Using data from a large online retailer, we find that the discovery of a negative review – one that has 3-stars or less out of 5 – leads to an average drop in purchase probability of 51.4%, an increase in continuing search for a competing item of 11.4%, and a 15.8% increase in the price paid if a purchase occurs. Our findings are presented on a two-dimensional product map – an innovative way to evaluate the vulnerability of products to a single negative review.

395P - Filipinos and lung cancer: An infodemiological assessment using Google trends from 2009 to 2019

BackgroundGoogle Trends, a free web-based tracking system of Google search volumes, can be used to monitor online interest in various health conditions, including lung cancer. This is particularly useful in the Philippines, the country named as the world's number one internet user in 2018. Understanding the pattern of information-seeking behavior of Filipinos with regards to lung cancer is crucial because it may inform public policy and potentially make lung cancer awareness campaigns more directed and effective in the population-based setting. Here we described patterns in online search behaviors related to lung cancer using Google Trends data on Google web and YouTube searches in the Philippines for the past ten years. MethodsRelative search volumes on the term “lung cancer” from 2009 to 2019 were extracted from Google Trends. Both Google web search and YouTube search data were downloaded. The data were saved in a comma-separated values file then imported to Microsoft Excel where a graph was plotted and a polynomial trendline (second order) was added to estimate search trends over a time period. Regions and cities in the Philippines where the term “lung cancer” was most popular were identified. Related topics and queries were also enumerated. ResultsTrends in Google web search showed a steady interest in lung cancer for both Google web search and YouTube searches from 2009 to 2019, with peaks in 2014. No spikes were observed in August, the Philippines’ Lung Cancer Awareness month. Online search behaviour for lung cancer was greatest in Metro Manila and areas in Northern Philippines. Top related and rising topics and queries in lung cancer, both on Google web search and YouTube, were related to etiology, symptoms, and management of lung cancer. ConclusionsFilipinos had a steady interest in lung cancer, as shown by Google Trends data for both Google web search and YouTube searches from 2009 to 2019. Cancer diagnoses of Filipino personalities in 2014 may have led to peaks in online interest. Filipinos were also interested to learn about etiology, symptoms, and management of lung cancer. These data may be used to leverage online interest to optimize lung cancer awareness campaigns in the Philippines. Legal entity responsible for the studyThe authors. FundingHas not received any funding. DisclosureAll authors have declared no conflicts of interest.

Algorithmic futures

In the last few years, tracking systems that harvest web data to identify trends, calculate predictions, and warn about potential epidemic outbreaks have proliferated. These systems integrate crowdsourced data and digital traces, collecting information from a variety of online sources, and they promise to change the way governments, institutions, and individuals understand and respond to health concerns. This article examines some of the conceptual and practical challenges raised by the online algorithmic tracking of disease by focusing on the case of Google Flu Trends (GFT). Launched in 2008, GFT was Google’s flagship syndromic surveillance system, specializing in ‘real-time’ tracking of outbreaks of influenza. GFT mined massive amounts of data about online search behavior to extract patterns and anticipate the future of viral activity. But it did a poor job, and Google shut the system down in 2015. This paper focuses on GFT’s shortcomings, which were particularly severe during flu epidemics, when GFT struggled to make sense of the unexpected surges in the number of search queries. I suggest two reasons for GFT’s difficulties. First, it failed to keep track of the dynamics of contagion, at once biological and digital, as it affected what I call here the ‘googling crowds’. Search behavior during epidemics in part stems from a sort of viral anxiety not easily amenable to algorithmic anticipation, to the extent that the algorithm’s predictive capacity remains dependent on past data and patterns. Second, I suggest that GFT’s troubles were the result of how it collected data and performed what I call ‘epidemic reality’. GFT’s data became severed from the processes Google aimed to track, and the data took on a life of their own: a trackable life, in which there was little flu left. The story of GFT, I suggest, offers insight into contemporary tensions between the indomitable intensity of collective life and stubborn attempts at its algorithmic formalization.

'Dr Google': trends in online interest in prostate cancer screening, diagnosis and treatment.

To examine trends in online search behaviours related to prostate cancer on a national and regional scale using a dominant major search engine. Google Trends was queried using the terms 'prostate cancer', 'prostate-specific antigen' (PSA), and 'prostate biopsy' between January 2004 and January 2019. Search volume index (SVI), a measure of relative search volume on Google, was obtained for all terms and examined by region and time period: pre-US Preventive Services Task Force (USPSTF) Grade D draft recommendation on PSA screening; during the active Grade D recommendation; and after publication of the recent Grade C draft recommendation. Online interest in PSA screening differed by time period (P < 0.01). The SVI for PSA screening was greater pre-Grade D draft recommendation (82.7) compared to during the recommendation (74.5), while the SVI for PSA screening was higher post-Grade C draft recommendation (90.4) compared to both prior time periods. Similar results were observed for prostate biopsy and prostate cancer searches. At the US state level, online interest in prostate cancer was highest in South Carolina (SVI 100) and lowest in Hawaii (SVI 64). For prostate cancer treatment options, online interest in cryotherapy, prostatectomy and prostate cancer surgery overall increased, while searches for active surveillance, external beam radiation, brachytherapy and high-intensity focused ultrasonography remained stable. Online interest in prostate cancer has changed over time, particularly in accordance with USPSTF screening guidelines. Google Trends may be a useful tool in tracking public interest in prostate cancer screening, diagnosis, and treatment, especially as it relates to major shifts in practice guidelines.

Does Online Search Behavior Coincide with Candida auris Cases? An Exploratory Study.

Candida auris is an emerging multidrug resistant infectious yeast which is challenging to eradicate and despite available laboratory methods is still difficult to identify especially in less developed countries. To limit the rapid spread of C. auris, quick and accurate detection is essential. From the perspective of disease surveillance, additional methods of tracking this yeast are needed. In order to increase global preparedness, we explored the use of online search behavior to monitor the recent global spread of C. auris. We used Google Trends to assess online search behavior on C. auris from January 2016 until August 2018. Weekly Google Trends results were counted as hits and compared to confirmed C. auris cases obtained via publications and a global expert network of key opinion leaders. A total of 44 countries generated a hit, of which 30% (13/44) were confirmed known cases, 34% (15/44) were missed known cases, 34% (15/44) were hits for unknown cases, and 2% (1/44) were confirmed unknown cases. Conclusions: Google Trends searches is rapidly able to provide information on countries with an increased search interest in C. auris. However, Google Trends search results do not generally coincide with C. auris cases or clusters. This study did show that using Google Trends provides both insight into the known and highlights the unknown, providing potential for surveillance and tracking and hence aid in taking timely precautionary measures.

Can Google search data help predict macroeconomic series?

We make use of Google search data in an attempt to predict unemployment, CPI and consumer confidence for the US, UK, Canada, Germany and Japan. Google search queries have previously proven valuable in predicting macroeconomic variables in an in-sample context. However, to the best of our knowledge, the more challenging question of whether such data have out-of-sample predictive value has not yet been answered satisfactorily. We focus on out-of-sample nowcasting, and extend the Bayesian structural time series model using the Hamiltonian sampler for variable selection. We find that the search data retain their value in an out-of-sample predictive context for unemployment, but not for CPI or consumer confidence. It is possible that online search behaviours are a relatively reliable gauge of an individual’s personal situation (employment status), but less reliable when it comes to variables that are unknown to the individual (CPI) or too general to be linked to specific search terms (consumer confidence).

Noise traders and smart money: Evidence from online searches

Traditional finance theory considers that the impact of noise traders' attention on asset prices is offset by attention from smart investors. This paper uses online search data to study the influence of noise traders and smart investors on stock returns and volatility. Adopting an original approach, we construct a proxy for smart investor attention based on investors' online search behavior provided by Wikipedia Page Traffic. We combine this new measure with a standard measure of noise traders' attention as proxied by Google Search Volume Index. We show for a sample of 87 French firms over the period 2008–2018 that only noise traders' attention influences stock returns. Noise traders' attention increases volatility by creating an extra risk that is priced into the market. Conversely, smart investors' attention decreases volatility because their presence stabilizes stock prices by reducing uncertainty. Our empirical results support a behavioral explanation of stock prices.

Spatial-temporal response patterns of tourist flow under impulse pre-trip information search: From online to arrival

Search engines have become the main sources for tourists to obtain trip- and destination-related information, and valuable data sources for marketers to better understand the spatial-temporal patterns of tourist online search behaviours across cities. This paper utilises co-integration and Granger causality tests, taking Tianmu Lake as an example, to examine the relationship between daily tourist arrivals and search indexes from 13 cites. It further tests their spatial and temporal correlations using the impulse function in a vector auto-regressive model. Our findings are as follows. First, a long-term equilibrium relationship exists between the daily number of tourists and search volume index (SVI), with the SVI Granger causing tourist arrivals. Second, SVI is a useful predictor of tourist flows 1–2 days prior to visits for cities within 100 km driving distance, and the lag period of response increases as distance increases. Finally, SVI and tourist volume are inversely proportional to distance, while travel information demand is directly proportional.

Google Trends data for analysing tourists’ online search behaviour and improving demand forecasting: the case of Åre, Sweden

Accurate forecasting of tourism demand is of utmost relevance for the success of tourism businesses. This paper presents a novel approach that extends autoregressive forecasting models by considering travellers’ web search behaviour as additional input for predicting tourist arrivals. More precisely, the study presents a method with the capacity to identify relevant search terms and time lags (i.e. time difference between web search activities and tourist arrivals), and to aggregate these time series into an overall web search index with maximal forecasting power on tourism arrivals. The proposed approach enables a thorough analysis of temporal relationships between search terms and tourist arrivals, thus, identifying patterns that reflect online planning behaviour of travellers before visiting a destination. The study is conducted at the leading Swedish mountain destination, Are, using arrival data and Google web search data for the period 2005–2012. Findings demonstrate the ability of the proposed approach to outperform traditional autoregressive approaches, by increasing the predictive power in forecasting tourism demand.

Navigating the Online World: Gaze, Fixations, and Performance Differences between Younger and Older Users

ABSTRACTThe world’s populations and workforces are aging and older adults are now the fastest growing group of internet users. Unfortunately, age-related declines may prevent older adults from performing web-based tasks with the same ease as their younger counterparts. Recent studies have suggested that peripheral navigation may exacerbate age-related online performance gaps as older adults fail to adequately search peripheral elements. Such studies have however relied on the use of preexisting websites with no control for layout, complexity, or prior familiarity. This study tested whether age-related differences in online performance and search behavior exist across various navigational layouts. Participants (N = 47) completed four online tasks with purpose-built websites of equivalent difficulty, length, and content type, yet with varying navigational layouts (center; left-periphery; top-periphery; inconsistent). Participants’ performance (i.e., accuracy/efficiency), search behavior (i.e., eye gaze/fixation), and satisfaction were examined. Identifiable differences between age-groups and navigational layouts were found, with top-peripheral navigation proving most detrimental to the online performance and satisfaction of both younger and older adults. The results inform design principles that aim to increase older adults’ online functionality, and provide an extensive platform for further research.

Advertising strategy and its effectiveness on consumer online search in a defaming product-harm crisis

PurposeActual product-harm crises pose significant challenges to firms, but so can defaming product-harm crises, which are defined as crises caused by false or malicious rumors made by consumers or competing firms. Unlike typical product-harm crises, in defaming product-harm crises, the truth often emerges only after substantial damage has been done to the victim firm. Thus, crisis management strategies in these two cases may be different. The paper aims to discuss these issues.Design/methodology/approachUsing a defaming product-harm crisis that involved two competing firms, this paper examines how the firms changed their advertising strategies and how the changes affected consumers’ online search behavior regarding the two firms.FindingsThe analyses show that after the crisis, the offending firm sensitively reacted to its own and the victim firm’s advertising levels, but the victim firm did not react to the offending firm’s advertising as it had previously. The effectiveness of advertising on consumers’ online search weakened for both firms after the crisis.Originality/valueThe paper provides a new insight about marketing strategies and their effectiveness in the product-harm crisis literature.

Social media posts and online search behaviour as early-warning system for MRSA outbreaks

BackgroundDespite many preventive measures, outbreaks with multi-drug resistant micro-organisms (MDROs) still occur. Moreover, current alert systems from healthcare organizations have shortcomings due to delayed or incomplete notifications, which may amplify the spread of MDROs by introducing infected patients into a new healthcare setting and institutions. Additional sources of information about upcoming and current outbreaks, may help to prevent further spread of MDROs.The study objective was to evaluate whether methicillin-resistant Staphylococcus aureus (MRSA) outbreaks could be detected via social media posts or online search behaviour; if so, this might allow earlier detection than the official notifications by healthcare organizations.MethodsWe conducted an exploratory study in which we compared information about MRSA outbreaks in the Netherlands derived from two online sources, Coosto for Social Media, and Google Trends for search behaviour, to the mandatory Dutch outbreak notification system (SO-ZI/AMR). The latter provides information on MDRO outbreaks including the date of the outbreak, micro-organism involved, the region/location, and the type of health care organization.ResultsDuring the research period of 15 months (455 days), 49 notifications of outbreaks were recorded in SO-ZI/AMR. For Coosto, the number of unique potential outbreaks was 37 and for Google Trends 24. The use of social media and online search behaviour missed many of the hospital outbreaks that were reported to SO-ZI/AMR, but detected additional outbreaks in long-term care facilities.ConclusionsDespite several limitations, using information from social media and online search behaviour allows rapid identification of potential MRSA outbreaks, especially in healthcare settings with a low notification compliance. When combined in an automated system with real-time updates, this approach might increase early discovery and subsequent implementation of preventive measures.

Economic Expectations, Voting, and Economic Decisions around Elections

Survey respondents who associate themselves with the “winning team” in an election, substantially increase their expectations for their stock market, but only modestly increase their expected household economic well-being. The impact of the election outcome on the “losing team” is comparatively muted. Yet, the dramatic shifts in survey responses from “winning team” respondents do not manifest themselves in their actual behavior as revealed by their online search behavior, in contrast to members of the “losing team,” whose decline in durable goods purchases correlate with their stated economic expectations. Combining novel survey data and search, this study provides a uniquely meaningful comparison of stated attitudes with actual behaviors.

Inducing Behavioral Change in Seekers of Pro-Anorexia Content Using Internet Advertisements: Randomized Controlled Trial.

BackgroundThe influence of pro-anorexia (pro-ana) websites is debated, with studies indicating both negative and positive effects, as well as significant variation in the effects of different websites for those suffering from eating disorders (EDs) and the general population. Online advertising, known to induce behavioral change both online and in the physical world, has not been used so far to modify the search behavior of people seeking pro-ana content.ObjectiveThe objective of this randomized controlled trial (RCT) was to examine if online advertisements (ads) can change online search behaviors of users who are looking for online pro-ana content.MethodsUsing the Bing Ads system, we conducted an RCT to randomly expose the searchers for pro-ana content to 10 different ads referring people to one of the three websites: the National Eating Disorders Association, the National Institutes of Mental Health, and MyProAna. MyProAna is a pro-ana website that was found in a previous study to be associated with less pathological online behaviors than other pro-ana websites. We followed participants exposed and unexposed to the ads to explore their past and future online searches. The ads were shown 25,554 times and clicked on 217 times.ResultsExposure to the ads was associated with a decrease in searches for pro-ana and self-harm content. Reductions were greatest among those referred to MyProAna (reduction of 34.0% [73/215] and 37.2% [80/215] for pro-ana and self-harm, respectively) compared with users who were referred elsewhere (reduction of 15.47% [410/2650] and 3.21% [85/2650], respectively), and with users who were not shown the ads, who increased their behaviors (increase of 57.12% [6462/11,314] and 4.07% [461/11,314], respectively). In addition, those referred to MyProAna increased their search for treatment, as did control users, who did so to a lesser extent. However, users referred elsewhere decreased their searches for this content.ConclusionsWe found that referring users interested in ED-related content to specific pro-ana communities might lessen their maladaptive online search behavior. This suggests that those who are preoccupied with EDs can be redirected to less pathological online searches through appropriate pathways.Trial RegistrationClinicalTrials.gov NCT03439553; https://clinicaltrials.gov/show/NCT03439553 (Archived by WebCite at http://www.webcitation.org/6xNYnxYlw)

Comparison of Periodic Behavior of Consumer Online Searches for Restaurants in the U.S. and China Based on Search Engine Data

Increased knowledge about the online search behavior of restaurant consumers is valuable to restaurant management and marketing professionals. However, people in different countries may demonstrate distinctive online search behaviors. There has been a lack of cross-cultural research on the online search behavior of restaurant consumers. In this paper, the periodic nature of online search behavior demonstrated by restaurant consumers from U.S. and China is analyzed and compared using Fourier transform and Parseval’s theorem. The search interest records from Google and Baidu, respectively, are used. The results reveal that the online search behavior of restaurant consumers in the U.S. is strongly governed by weekly cycles but less dependent on annual cycles; however, the analogous consumer behavior in China exhibits less dependence on weekly cycles. The theoretical and practical implications of the research are discussed.

Does Information Mitigate Behavioral Gaps Due to Market Inexperience? Evidence from a Field Experiment

In this paper, we evaluate the effect of price information on consumer search and purchasing behavior. We collect detailed individual-level purchasing and search data on 2,449 consumers in the UNC-Chapel Hill textbook market. A random subset of these consumers are provided with information about the price of their assigned textbooks from various retailers before the semester begins. We use these data to compare the search and purchasing outcomes of consumers who receive information from their instructors (treatment group) with outcomes of consumers who do not receive this information (control group). We find that (i) information leads to more online search activity and online purchases, (ii) the informational treatment reduces the gap in online search behavior between consumers across experience levels, and (iii) the treatment increases take-up of the textbook at the extensive margin. Additionally, we solicit beliefs about the price distribution of general textbooks from the same participants and find that although the treatment helps inexperience consumers update on the mean of the textbooks, they do not update their beliefs about the distribution of prices.

Can Google Search Data Help Predict Macroeconomic Series?

We use Google search data with the aim of predicting unemployment, CPI and consumer confidence for the US, UK, Canada, Germany and Japan. Google search queries have previously proven valuable in predicting macroeconomic variables in an in-sample context. To our knowledge, the more challenging question of whether such data have out-of-sample predictive value has not yet been satisfactorily answered. We focus on out-of-sample nowcasting, and extend the Bayesian Structural Time Series model using the Hamiltonian sampler for variable selection. We find that the search data retain their value in an out- of-sample predictive context for unemployment, but not for CPI and consumer confidence. It may be that online search behaviour is a relatively reliable gauge of an individual's personal situation (employment status), but less reliable when it comes to variables that are unknown to the individual (CPI) or too general to be linked to specific search terms (consumer confidence).

网络搜索行为对旅游形象感知的影响—以韩国居民对中国目的地的信息搜索为例

网络搜索行为对旅游形象感知的影响—以韩国居民对中国目的地的信息搜索为例

‘At-risk’ individuals’ responses to direct to consumer advertising of prescription drugs: a nationally representative cross-sectional study

ObjectivesThe factors determining individuals’ self-reported behavioural responses to direct to consumer advertising of prescription drugs were explored with an emphasis on ‘at-risk’ individuals’ responses.DesignNationally representative cross-sectional survey.SettingCommunity living adults in...

Google Trends and reality: Do the proportions match?: Appraising the informational value of online search behavior: Evidence from Swiss tourism regions

This study examines the extent to which real-world economic activity is reflected in aggregate online search behavior on Google Search. As opposed to previous studies, being subject to potential mismeasurement problems when examining search queries along their longitudinal dimension, we apply an alternative investigative approach that exploits the cross-sectional instead of the longitudinal informational content embodied in Googles data. Moreover, while previous studies most often examine a single Google Trends series, our analyses are based on over 60 distinct series, which allow us to assess how informative the data are, not only within each series, but also between series. Finally, our Google Trends indices are based on the recently launched Google Knowledge Graph technology, allowing for a remarkably accurate measurement of relevant search query volumes. We assess the informational value of the data as strong, semi-strong, or weak based on unbiasedness and efficiency considerations in a Mincer–Zarnowitz-type regression model. Here, the context of (Swiss) tourism demand proves particularly useful, and we find that search-based tourism demand predictions are, on average, highly accurate approximations of reality. This indicates that search-based indicators may serve as valuable real-time complements for the guidance of economic policy.