Consumer Search Behavior Research Articles

A Study on Information Search Behavior Using AI-Powered Engines: Evidence From Chatbots on Online Shopping Platforms

The development of Artificial Intelligence (AI) has significantly influenced how consumers search for information. However, there is a lack of comprehensive models based on theoretical foundations that specifically address AI-powered information search behavior. This study draws on psychological motivation, information processing, and information economics theories to develop a theoretical model of consumer AI-powered information search behavior. The study aims to identify the main factors affecting consumer search behavior, offering a more holistic understanding of consumer behavior in the context of AI. Analyzing 512 valid questionnaires, the study shows that search motivation not only had the most significant impact on search behavior but also served as a mediator between other variables and search intensity. Additionally, perceived search ability had a direct and the greatest indirect impact on search behavior, while other variables such as perceived search costs and benefits also had indirect effects on search behavior. Practically, the study offers valuable insights for businesses and AI developers. Understanding the factors that drive AI-powered search behavior can inform the design of more effective AI systems and marketing strategies.

To Hinder or to Facilitate: Retailers’ Strategy of Consumer Information Sharing

Consumer information sharing is considered an effective strategy to attract consumers, yet certain high-end retailers, such as Bergdorf Goodman and Farfetch, tend to hinder consumers from sharing information through online reviews. We study a retailer’s strategy for consumer information sharing in a supply chain. We find that a retailer’s information sharing strategy can prevent manufacturers from extracting excessive profit when consumers are heterogeneous in their valuations of the selling product. Specifically, a retailer can achieve a higher profit margin by targeting all consumer segments. By strategically choosing the information sharing strategy to influence consumer beliefs, the retailer can induce the manufacturer to conform to the retailer’s preferred targeting segment through a low wholesale price. Thus, a high-end retailer, whose consumers have a high ex-ante quality belief, favors hindering information sharing among consumers because it enables the retailer to target all consumer segments. Interestingly, deterring consumers from learning about the product quality may generate more consumer surplus. Our main results are robust under extensions such as consumer search behavior, consumer waiting, and multiple product selling. When selling multiple products, a retailer with a large quality variation is better off hindering consumers from sharing information. Our work shows that strategically choosing a consumer information sharing strategy enables retailers to enhance profit margins in their interactions with upstream manufacturers.

The Brushing Game in Online Marketplaces

ABSTRACT We study a pervasive fraudulent strategy, called “brushing,” employed by retailers operating in e-commerce platforms: Retailers place fake orders of their own products to inflate sales and boost the visibility of their store links in search results. Using a duopolistic pricing game with heterogeneous consumer search behavior, we show that the retailer at a more prominent position in product listing enjoys greater profit as well as an advantage in future position allocation if the platform charges a per-sale commission fee and uses a sales-based ranking mechanism to display store-link positions. To fight for better visibility, retailers may employ brushing to boost their sales metrics. We characterize the equilibrium brushing behavior for the position competition game between the retailers. Under a sufficiently high commission rate, the game has a pure-strategy equilibrium and entails no-brushing outcome. Otherwise, retailers fully engage in brushing activity and the brushing equilibrium exists only in mixed strategies. Model extensions incorporating heterogeneities in wholesale cost and product quality show that retailers with cost and quality advantages are able to gain profit improvement at a less prominent position and thereby have less incentive to invest in brushing. Our results provide platform managers and retailers with economic insights to better understand the logic for brushing fraud and strategic decisions in online marketplaces.

This meat or that alternative? How masculinity stress influences food choice when goals are conflicted.

This research integrates literature on masculinity stress-the distress experienced as the result of a perceived discrepancy with male gender norms-with research on goal conflict to examine preferences for plant-based meat alternatives (PBMAs). Men experiencing masculinity stress are likely to hold salient a goal of being masculine, which should lead to less preference for PBMAs. However, many of these men simultaneously hold competing goals, such as making ethical food choices, which remain inhibited in favor of the focal masculinity goal. We argue that once men experiencing masculinity stress highlight their masculinity through the selection of a manly product, they satisfy that higher-order goal and are then free to pursue previously inhibited goals, such as making an ethical choice through the selection of PBMAs. We present the results of three studies supporting these expectations. Study 1 tests the link between masculinity stress and meat (alternative) consumption using consumer search behavior collected from Google Trends, showing that masculinity stress is positively (negatively) correlated with searches for red meat (PBMAs). Study 2 shows that men experiencing masculinity stress are more inclined to choose PBMAs, provided they are presented within a masculine product context. Study 3 presents a parallel mediation model, showing that ethical considerations (as opposed to masculine goals) shape the choice of PBMA preference. We conclude with a discussion of theoretical implications for the impression management strategies utilized by men experiencing masculinity stress and practical implications for the growing PBMA industry.

The version effect of apps and operating systems in mobile commerce

Different versions of mobile operating systems or shopping apps enable different functionalities and information flows, thus creating various mobile shopping environments. In general, up‐to‐date versions provide better information flows and richer functionalities. However, mobile operating systems and shopping apps affect consumer behavior through different mechanisms. An up‐to‐date mobile operating system reduces the system response time, while an up‐to‐date shopping app improves algorithms for better search accuracy. The former encourages consumer explore and search, whereas the latter improves consumer search efficiency and reduces chances for consumers to discover more products. Using a unique large‐scale clickstream data set from a mobile commerce retailer, we examine the effect of mobile operating system and app versions on consumer search and impulse purchase behaviors in mobile commerce. Our results show that consumers with an up‐to‐date mobile operating system or a previous version of a shopping app conduct more searches in terms of increased product page views and time spent on product pages, which results in a higher probability of consumer impulse purchase. However, consumers' search for individual products is not affected by versions. Surprisingly, though more page views or time spent may boost purchases, we find that consumer search affects impulse purchases nonlinearly in that page views and time spent have a decreasing rate of impact. Our computations show that using an up‐to‐date operating system increases consumer search activities by 60.45 product pages or 521.30 seconds spent browsing compared to using a previous version of an operation system. Using an up‐to‐date app decreases consumer search activities by 129.34 product pages or 1446.48 seconds spent browsing compared to using a previous version of a mobile app.

Store expensiveness and consumer saving: Insights from a new decomposition of price dispersion

We build on recent work that analyzes consumers’ ability to save by exploiting price dispersion in grocery stores. We show that store expensiveness varies across consumers depending on the basket they consume, meaning that consumers can save more by shopping at a store that is cheaper for their basket rather than at a store that is cheaper overall. We incorporate this insight into a new price variance decomposition that is a refinement of existing approaches. Our results show that the ability to buy products from the store where they are cheapest is much less important than previous work had found; rather, the ability to choose the cheapest stores for one’s basket is a more important source of variation in the prices consumers pay. Our approach also provides an informal test for competing theories modeling consumers as either shopping for products or shopping for categories, and finds support for both. We conclude that the idea of consumers choosing the right store for their basket has substantial traction and is a useful addition to our arsenal of models of consumer search behavior.

Bidding for an optimal portfolio of keywords in sponsored search advertising: From generic to branded keywords

With the increasing prominence of digital media, retailers attempt to attract consumers to their websites by investing in sponsored search advertising. However, due to stiff competition among retailers, sponsored search advertising can be expensive. This paper develops a multi-period, dynamic programming model that provides a retailer with an optimal portfolio of generic and branded bids. We model two critical aspects of consumer search behavior: (i) the spillover effect of generic searches leading to branded search arrivals in subsequent periods and (ii) the memory effect that leads to a decline of consumer awareness of a brand over time. We find that the retailer can effectively shuffle his investments on generic and branded keywords depending on several consumer parameters, e.g., awareness level, brand retention and reservation price variances. We develop a bidding policy framework to highlight the shift in bid shares from generic to branded at different levels of consumer awareness. We find that harnessing the benefits of spillover from generic to branded keywords allows the retailer to save on generic bids at higher awareness and retention levels and lower variance in consumers’ reservation prices. Further, we extend our model to different consumer purchase situations/ product classifications, viz., Convenience, Shopping and Specialty purchasing. Our analysis suggests prevalence of generic bids for certain purchase/product situations, whereas branded bids remain salient in other situations.

Information provision and consumer search behavior for products with asymmetric uncertainty

When selling multiple products with asymmetric uncertainty, should the seller disclose product information so that customers do not have to incur any cost to resolve their uncertainties; if so, which product should the seller choose? To address these questions, we consider a monopolist selling two substitutable products to a group of consumers. Each consumer has asymmetric uncertainty regarding the two products. A total of four different information provision structures are considered based on whether the seller discloses information about each product with the aim of determining which strategy provides the seller with the greatest revenue. We derive several interesting results. First, the optimal information provision strategy depends on the magnitude of uncertainty in relation to the product with lower uncertainty. Specifically, if the uncertainty regarding the product with lower uncertainty is sufficiently small, it is optimal for the seller to provide information about the product with higher uncertainty, otherwise, the seller should provide information about both products. Second, when only one product's information should be revealed, it is optimal for the seller to choose the product with higher uncertainty and charge a higher price. Third, withholding information on both products is never optimal for the seller. Finally, our main model is extended by examining the Mean-Preserving Spread setting, and the robustness of our main results is confirmed. Furthermore, we examine the situation in which a monopolist sells a single product with two main attributes. We find that each of the four information provision strategies can be optimal under various scenarios.

Validation of the information processing theory of consumer choice: evidence from travel search engine clickstream data

PurposeThis paper aims to demonstrate how consumer clickstream data from a leading hotel search engine can be used to validate two hidden information processing stages – first eliminate alternatives, then choose – proposed by the revered information processing theory of consumer choice.Design/methodology/approachThis study models the two hidden information processing stages as hidden states in a hidden Markov model, estimated on consumer search behavior, product attributes and diversity of alternatives in the consideration set.FindingsFirst, the stage of information processing can be statistically characterized in terms of consumer search covariates, including trip characteristics, use of search tools and the diversity of the consideration set, operationalized in terms of: number of brands, dispersion of price and dispersion of quality. Second, users are more sensitive to price and quality in the first rather than the second stage, which is closer to purchase.Research limitations/implicationsThe results suggest practical implications for how search engine managers can target consumers with appropriate marketing-mix actions, based on which information processing stage consumers might be in.Originality/valueMost previous studies on validating the information processing theory of consumer choice have used laboratory experiments, subjects and information display boards comprising hypothetical product alternatives and attributes. Only a few studies use observational data. In contrast, this study uniquely uses point-of-purchase clickstream data on actual visitors at a leading hotel search engine and tests the theory based on real products, attributes and diversity of the consideration set.

The hidden conversion funnel of mobile vs. desktop consumers

We compare mobile and desktop user progress through the conversion funnel. Using detailed log-files of an online flight search engine, we analyze consumer search behavior and model the stages consumers go through along the funnel in both mobile and desktop platforms. We ask: Do mobile and desktop users go through similar funnel stages? Are the funnel stages of both platforms characterized differently? To analyze the progress through the funnel, we use hidden Markov models (HMM) that capture latent funnel stages based on observable search-behavior variables indicative of convergence towards a purchase. We find that while similar stochastic processes characterize funnel stages on both platforms, the distribution of visits across stages in the two platforms is significantly different: a larger percentage of mobile visits resides in more advanced stages, indicating that mobile consumers use the website when they are more advanced in the funnel, where less cognitive effort is required.

Mobile App Adoption and Its Differential Impact on Consumer Shopping Behavior

The mobile application (app) market is one of the fastest growing markets in recent years. In this study, we investigate how app adoption changes customers’ hotel booking behaviors using a dataset of around 16,000 randomly selected customers’ hotel booking and stay information from one of the largest hotel chains in China. Our analyses find that customer purchase frequency and total sales increased after app adoption. Additional investigation shows that customers’ search behaviors also evolved post‐adoption. Specifically, customers tended to book rooms earlier and select options with less discount, which indicates that app users reduced their search intensity after app adoption. We further investigate the differential impact of mobile app adoption on sales, taking into account consumer heterogeneity in price sensitivity and purchase frequency. We find that both price‐sensitive and ‐insensitive customers increased the number of bookings, and they tended to book earlier post‐adoption. Furthermore, price‐sensitive customers on average booked more expensive rooms with less discount after app adoption, suggesting that these customers likely decreased their search effort. In terms of hotel stay frequency, app adoption increased low‐frequency customers’ purchase frequency but did not have any significant effect on high‐frequency customers. These analyses provide a more nuanced view of how mobile apps change consumer search and purchase behaviors. Our findings have important managerial implications. With a better understanding of customer behaviors, firms can provide incentives to customers who are more likely to be affected by mobile app adoption.

Comparing health consumer search behavior and scientific research productivity related to COVID-19 vaccines in the USA: an infodemiology study

PurposeWhile vaccines are an effective preventative measure to defend against the spread and harmful symptoms of COVID-19, information about COVID vaccines can be difficult to find and conflicting in its coverage of vaccines’ benefits and risks. This study aims to examine the extent to which Americans are searching for information about the three major vaccine producers (Pfizer-BioNTech, Moderna and Johnson & Johnson’s Janssen) in relation to the amount of reliable scholarly information that has been produced about each one.Design/methodology/approachData were retrieved from Google Trends for the US Web users alongside scientific research output of the US scientists toward three Centers for Disease Control and Prevention (CDC)-authorized COVID-19 vaccines in Web of Science, Scopus and PubMed. The authors searched for descriptive statistical analyses to detect coronavirus-seeking behavior versus coronavirus releases in the USA from May 1, 2020, to April 30, 2021.FindingsOf the three COVID-19 vaccines, Pfizer has attracted more attention from the US population. However, the greatest number of articles about COVID-19 vaccines published by the US scholars belonged to Moderna (M = 8.17), with Pfizer (M = 7.75) having slightly less, and Janssen (M = 0.83) well behind. A positive association was found between COVID-19 vaccine information-seeking behavior (ISB) on Google and the amount of research produced about that vaccine (P <0.001).Research limitations/implicationsAs the researchers use the single search engine, Google, to retrieve data from the USA, thus, selection bias will be existing as Google only gathers the data of people who chose to get the information by using this search engine.Practical implicationsIf the policymakers in the US Department of Health and Human Services or the US CDC desire to improve the country’s health ISB and the scientific publication behavior (SPB) of the US researchers regarding COVID-19 vaccines studies, they should reference the results of such a study.Originality/valueFrom an infodemiological viewpoint, these findings may support the health policymakers, as well as researchers who work on COVID-19 vaccines in the USA.

Exploring consumer online purchase and search behavior: An FCB grid perspective

Among the various types of online advertising, keyword advertising brings the business to consumers' attention effectively with little effort. Extant studies on consumer search behavior and keyword advertising have paid attention to the relationship between the types of keywords and ad effectiveness. However, we remain unclear about how consumers select keywords in different stages of the purchase and how marketers can best devise advertising strategies in accordance with consumers' online purchase and search behavior. This study focuses on this unexplored issue by conducting a contextual inquiry into consumers’ search and purchase behavior, their selected keywords, and their purchase process across different types of products. We base our inquiry on the “FCB grid” designed by Foote, Cone and Belding Advertising. The results show that consumers follow a certain sequence of stages, depending on the product type, as follows: (1) Learn-Learn-Feel-Do for high involvement and thinking products, (2) Feel-Learn-Do for high involvement and feeling products, (3) Do-Learn-Do for low involvement and thinking products, and (4) Do-Feel-Do stages for low involvement and feeling products. We identify the types of keywords searched in each of these stages. We also develop a planning model for keyword advertising to guide marketers bidding on appropriate keywords for various types of products. This study provides marketers and researchers with a better understanding of consumers' online buying decision process and the keywords that are searched in different stages of the process based on product types and prior product knowledge.

Consumer Online Search with Partially Revealed Information

Modern-day search platforms generally have two layers of information presentation. The outer layer displays the collection of search results with attributes selected by platforms, and consumers click on a product to reveal all its attributes in the inner layer. The information revealed in the outer layer affects the search costs and the probability of finding a match. To address the managerial question of optimal information layout, we create an information complexity measure of the outer layer, namely orderedness entropy, and study the consumer search process for information at the expense of time and cognitive costs. We first conduct online random experiments to show that consumers respond to and actively reduce cognitive cost for which our information complexity measure provides a representation. Then, using a unique and rich panel tracking consumer search behaviors at a large online travel agency (OTA), we specify a novel sequential search model that jointly describes the refinement search and product clicking decisions. We find that cognitive cost is a major component of search cost, while loading time cost has a much smaller share. By varying the information revealed in the outer layer, we propose information layouts that Pareto-improve both revenue and consumer welfare for our OTA. This paper was accepted by Juanjuan Zhang, marketing.

Mobile App Adoption and Its Differential Impact on Consumer Shopping Behavior

The mobile application (app) market is one of the fastest growing markets in recent years. In this study, we investigate how app adoption changes customers' hotel booking behaviors using a dataset of around 16,000 randomly selected customers' hotel booking and stay information from one of the largest hotel chains in China. Our analyses find that customer purchase frequency and total sales increased after app adoption. Additional investigation shows that 'customers' search behaviors also evolved post-adoption. Specifically, customers tended to book rooms earlier and select options with less discount, which indicates that app users reduced their search intensity after app adoption. We further investigate the differential impact of mobile app adoption on sales, taking into account consumer heterogeneity in price sensitivity and purchase frequency. We find that both price sensitive and insensitive customers increased the number of bookings, and they tended to book earlier post-adoption. Furthermore, price sensitive customers on average booked more expensive rooms with less discount after app adoption, suggesting that these customers likely decreased their search effort. In terms of hotel stay frequency, app adoption increased low-frequency customers' purchase frequency but did not have any significant effect on high-frequency customers. These analyses provide a more nuanced view of how mobile apps change consumer search and purchase behaviors. Our findings have important managerial implications. With a better understanding of customer behaviors, firms can provide incentives to customers who are more likely to be affected by mobile app adoption.

Exploitation and Exploration: Improving Search Precision on E-commerce Platforms

E-commerce platforms match online buyers and sellers using their search technologies. Although a more precise search algorithm may improve search targetability, it may also reduce cross-selling opportunities, as consumers spend less time exploring different products. We empirically quantify these tradeoffs through a collaboration with Alibaba Group. Specifically, we take advantage of a 2019 quasi-experiment on Taobao.com, in which the platform refined some product categories into finer subgroups in order to return more-targeted search results to online shoppers. Using granular data on consumer search and purchase behaviors across multiple search sessions and product categories, we find that the improvement in search precision leads to a 37.3% increase in consumers’ click-through rates and a 64.4% increase in gross merchandise volume in the product category we study. The immediate improvement in matching outcomes, however, comes at the cost of a substantial decrease in consumer engagement and unplanned purchases in a longer time horizon for consumers prone to spending more time searching. On average, these consumers conduct 5.5% fewer searches, spend 4.1% less time on the platform, and decrease their spending on related categories by 2.2% in the week after the search precision increases. Our findings illustrate the tradeoff between exploitation and exploration in e-commerce search design that has not yet been previously documented in the literature.

Research on assistant decision-makings of E-commerce platform with refinement tools

ABSTRACT Most existing studies of consumer search behaviour focus on page-level analysis, and some scholars start to examine the effect of refinement tools and characteristics in terms of products. However, it still remains undeveloped on the product-level. To fill this gap, we reproduced the consumer shopping process in accordance with the topology of the Taobao platform from where we collected the clickstream data. We modelled consumers’ sequential decision-making behaviour based on the taxonomy with Bayesian approach and found that not all the refinement tools are utilised for optimising decisions by users and it’s surprising that there exists no significant impact of all sorting tools. Besides, consumers are highly concerned with the characteristics of products. On the basis of the findings, platform function announcement and platform design suggestions were provided for improving platform functionality and optimising consumer decision-making, which also points out the direction of future research.

Search and Bidding Costs

Abstract This paper studies consumer search behavior in markets where sellers must incur fixed costs to win a contract. We show that consumers face a commitment problem if their search intensities are not observed by sellers. Welfare is maximized when consumers can precommit to a limited number of searches, but in the absence of the ability to commit, consumers search more than the optimum. A decline in search costs can exacerbate the commitment problem and leave consumers potentially worse off.

Stochastic Dynamic Product Search

Before making purchase decisions, consumers often search for information on product attributes. In this paper, we incorporate a key feature of consumer search behavior---namely, search intensity---into a stochastic dynamic model. Specifically, motivated by industry evidence, we model search intensity as a mean-reverting square root process which is fed back to consumers' valuation. Due to this stochastic dynamic search intensity, the consumers' decision-making subjects to a two-dimensional (consumers' product valuation and search intensity) optimal stopping problem. We develop an asymptotic expansion technique to facilitate the solution procedure. We also prove that the value of consumers continuing a search is smaller, as (i) the search intensity becomes smaller, (ii) the search intensity decreases faster, (iii) the mean-reverting value of the search intensity becomes smaller, and (iv) the market is more volatile. Given this, we also evaluate the firm's pricing strategy of the product.

The Version Effect of Apps and Operating Systems in Mobile Commerce

Using a unique large-scale clickstream data set from a mobile commerce retailer, we examine the effect of the mobile shopping app and mobile operating system versions on consumer search and purchase behaviors in mobile commerce. We find that the consumers with an up-to-date shopping app or mobile operating system (but not both) compared to previous versions conduct more searches in terms of increased product page views and time spent on product pages, which in turn results in higher consumer purchase probability. Surprisingly, the consumers with both an up-to-date shopping app and mobile operating system have reduced search activities and lower purchase probabilities. Our results show that the mobile commerce environment affects the consumer shopping process in a non-linear fashion and indicate that there exists a sweet spot with the right level of smoothness in the mobile shopping environment. Our computations show that when compared to the baseline of using previous versions of both, using an up-to-date mobile shopping app alone increases consumer search activities by 336 more product pages or 1,975 more seconds spent, and using an up-to-date mobile operating system alone increases consumer search activities by 976 more product pages or 7,258 more seconds spent. However, using both an up-to-date mobile shopping app and mobile operating system only increases consumer search activities by 270 more product pages or 1,569 more seconds, compared to the baseline. One more product page viewed or one more second spent increases the average consumer purchase probability by 4.38% or 1.35%, respectively.