Importance  Autonomy concerns represent one of many contributors to vaccine hesitancy, yet public health messaging often emphasizes government compliance. It is important to study whether alternative message framing is associated with stated vaccine acceptance among hesitant individuals.

Objective  To evaluate whether framing vaccines as freedom enhancing is associated with higher vaccine acceptance among vaccine hesitant adults.

Design, Setting, and Participants  This cross-sectional online discrete choice experiment was conducted from May 1 to 4, 2024. Each respondent completed 12 choice tasks comparing 2 vaccine options and a no-vaccine option, with vaccines described by 6 attributes, including infection prevention and severe disease protection efficacy, minor and major adverse effects, duration of protection, and additional reason to take (message framing).

Exposures  Three levels for message framing, including government compliance (reference), personal freedom, and protect others.

Main Outcomes and Measures  Hesitancy was measured using vaccine concerns and the Vaccine Adverse Belief Index (VABI). Main outcomes were vaccine preference shifts and estimated vaccine uptake associated with message framing variations examined using hierarchical bayesian analysis, with vaccine concerns and VABI serving as second-level factors.

Results  A total of 907 adults (mean [SD] age, 69.5 [11.0] years; 454 female [50.3%]) participated in the study. Among respondents with high vaccine concern, freedom framing was associated with a preference shift of 33.8 percentage points (95% credible interval [CrI], 21.5-45.8 percentage points) and an increase in uptake of 6.3 percentage points (95% CrI, 2.9-11.5 percentage points), assuming the most favorable vaccine profile (highest efficacy, lowest adverse effects, longest duration). Among respondents with a high VABI, freedom framing was associated with a preference shift of 27.7 percentage points (95% CrI, 14.5-40.5 percentage points) and an increase in uptake of 4.6 percentage points (95% CrI, 1.8-8.8), respectively. Respondents with low vaccine concern or VABI showed no significant preference shifts associated with framing. In contrast, protect-others framing was associated with positive preference shifts and uptake, with no evidence that these associations differed by vaccine hesitancy.

Conclusions and Relevance  This cross-sectional study found that compared with government recommendation framing, freedom framing was selectively associated with higher vaccine acceptance among adults who were vaccine hesitant. Protect-others framing was associated with higher vaccine acceptance regardless of hesitancy level. Absolute gains in acceptance were modest. These findings highlight how autonomy-consistent framing may influence stated vaccine preferences among hesitant adults.

The author explores how racial alignment between film critics and movie casts affects critic ratings and their market implications. Testing the hypothesis of cultural affinity, this research shows that critics’ ratings tend to decrease as the proportion of Black cast members increases, but this effect is mitigated when the critic is Black. A text analysis of critics’ review excerpts suggests the presence of two tenets of cultural affinity: cultural literacy and cultural identity. An online survey further demonstrates that, in the absence of self-selection, Black respondents perceive greater affinity toward movies featuring predominantly Black casts and assign higher ratings to trailers featuring racially similar casts. Two additional analyses show that critic ratings have market consequences: As the proportion of Black cast members rises, critic ratings diverge more from audience choices (box office revenue) and audience ratings, though this effect is mitigated when the critics are Black. These findings underscore the importance of racial diversity in promoting culturally representative content in the marketplace.

Conventional wisdom in casino gaming research suggests that gamblers are unable to identify slot machines with better odds. However, this study challenges that notion by examining whether experienced players, with years of casino play, can make informed decisions to optimize their chances of winning. With real gambling data, our findings reveal that seasoned players tend to favor slot machines with better odds. Interestingly, they refine their choices during less crowded hours when availability constraints are eased. Furthermore, consistent with the tradeoff between exploitation and exploration in reinforcement learning, more experienced players exhibit higher consistency in slot machine selection over time, suggesting genuine knowledge of which machines offer better odds. These results have significant implications for understanding casino player behavior and the potential for human learning to optimize complex decisions in a large-scale multi-armed bandit problem.

Social media listening has become an integral part of many companies marketing strategies. Using a unique dataset of social media comments to 413 movie trailers, we document the systematic differences in sentiments expressed on Facebook and YouTube. First, Facebook comments are less likely to involve sentiments. Second, when sentiments are expressed, Facebook comments tend to be more positive than those on YouTube. Third, on both platforms, comments are more likely to express sentiments after a movie’s release than before it. Furthermore, the sentiment gap between Facebook and YouTube diminishes after a movie’s release. We propose a behavioral explanation for our findings based on network structure and social desirability bias and test our hypothesis with an experiment. Finally, we demonstrate that cross-platform sentiment divergence is significantly associated with box office revenue.

Herding behavior refers to the behavior of individuals behaving similarly as a group without directions to coordinate. Herding can demonstrate rational characteristics. When consumers believe that others may have private information about a product, they infer unobserved information through other people’s behaviors, thereby engaging in similar actions themselves. While rational herding behavior has been found mostly in high involvement environments such as the financial markets, this paper provides evidence that such behavior may also occur in a comparatively lower involvement environment such as retailing. To demonstrate herding behavior and test shoppers’ rationality in such, the authors employ a unique dataset from a major TV shopping channel. In this setting, information about other buyers’ purchase decisions is only sometimes observed by shoppers. Evidence suggests that herding happens among shoppers and the herding behavior appears to exhibit rationality. The authors find that herding effects (1) are stronger when relative price discount is smaller, (2) are more prominent for a product category with less digitalizable attributes, and (3) appear to happen mainly in the earlier part of a sales pitch when shoppers have less information about a product and are more uncertain about their product valuation.

Research Summary: This study investigates incumbent responses to a main rival's exit. We argue that longtime rivals have developed an equilibrium by offering a mix of overlapping and unique products and by choosing geographic proximity to each other. A rival's exit, however, disrupts this equilibrium and motivates surviving firms to expand in both product and geographic spaces to seek a new equilibrium. Using data from all U.S. Best Buy stores before and after the exit of Circuit City, we find that Best Buy uses product variety expansion as its major response in markets where Circuit City was colocated, but it more often responds by opening new stores in non-colocated markets. Regardless of preexisting market structures, the magnitude of product variety expansion decreases with the opening of new stores.

Managerial Summary: How do surviving firms respond to a major rival's exit? By studying Best Buy's responses to Circuit City's withdrawal, we find the survivor expands in both product space (increasing product variety) and geographic space (opening new stores), due to two motives. First, the survivor strives to fill in “holes” left in the market. Second, the survivor experiences uncertainty in the postexit world wherein its reference point is gone, threat of potential entry looms, and it lacks information about new entrants. Thus, it must deter potential entry ex ante by preempting many prime product and geographic locations. Best Buy also responds according to preexisting market structures, primarily through product variety expansion in markets wherein Circuit City was colocated and through opening new stores in non-colocated markets.

Consumers often consider multiple alternatives from the same product category prior to making a purchase. Uncovering the predominant patterns of such co-considerations can help businesses learn more about the competitive structure of the market in the mind of the consumer. Extant research has shown that various types of online and offline consumer activity data (e.g., shopping baskets, search and browsing histories, social media mentions) can be used to infer product co-considerations. In this paper, we study a case of uncovering co-consideration patterns using a massive dataset of online price quote requests from U.S. auto shoppers. The main challenge we face is that, for privacy protection, no unique individual identifier (anonymous or otherwise) is contained in the data. Such a data deficiency prevents us from using existing methods such as affinity analysis for inferring co-considerations. However, by leveraging spatiotemporal patterns in the data, we manage to probabilistically uncover the predominant patterns of co-considerations in the U.S. auto market. As a validation and illustration of its usefulness, we embed the inferred market structure in a sales response model and show a substantial improvement in predictive performance.

Current practice of TV advertising campaign generation usually starts with a small number of concepts and ends up with a final copy on TV, a funneling process that narrows down quickly without reliably testing the ad concepts’ effectiveness in market. Is this practice optimal? Should more ad copies be generated for testing? We propose a model and evaluation procedure to improve the ad copy generation and evaluation process. We conceptualize the process as first generating a number of alternative advertising campaigns from advertising creative and production sources, screening those campaigns with some advertising pretest methodologies which have specified validity and reliability, and then picking the best campaign based upon the screening. Our method involves variability and distribution of campaign profits, which makes it possible for ad executives to take the risk of investment into consideration. As an empirical illustration, based on estimates of the variability of profitability of alternative TV campaigns by a small sample of senior marketing executives for consumer products, we show a large sum of incremental profit could potentially be obtained if TV advertisers would screen alternative TV campaigns with pre-tests of modest reliability and validity. We also show how thepretesting community can estimate the validity and reliabilityof their tests.

Evolving tastes can change the relative importance of product features in shaping consumers’ purchase decisions, which in turn can shift the relative attractiveness of products with different feature levels. The challenge lies in finding a reliable yet cost-effective way to monitor the weights consumers place on various product features. In the context of the U.S. automotive market, the authors explore the potential of using trends in online searches for feature-related keywords as indicators of trends in the relative importance of the corresponding features (e.g., fuel economy, acceleration, cost to buy, cost to operate, body type). By augmenting marketing-mix data with feature search data in a market response model, they show substantial improvements in goodness-of-fit both in and out of sample. The authors find empirical support for the hypothesis that feature search trends are positively correlated with feature importance trends. They discuss how managers may make better decisions by monitoring feature search trends and leveraging those trends strategically.

Unlike sales data, data on intermediate stages of the purchase funnel (e.g., how many consumers have searched for information about a product before purchase) are much more difficult to acquire. Consequently, most advertising response models have focused directly on sales and ignored other purchase funnel activities. The authors demonstrate, in the context of the U.S. automotive market, how consumer online search volume data from Google Trends can be combined with sales data to decompose advertising’s overall impact into two underlying components: its impacts on (1) generating consumer interest in prepurchase information search and (2) converting that interest into sales. The authors show that this decompositional approach, implemented through a novel state-space model that simultaneously examines sales and search volumes, offers important advantages over a benchmark model that considers sales data alone. First, the approach improves goodness-of-fit, both in and out of sample. Second, it improves diagnosticity by distinguishing advertising effectiveness in interest generation from its effectiveness in interest conversion. Third, the authors find that overall advertising elasticity can be biased if researchers consider only sales data.

The authors examine the role of external reference prices in a unique form of gift giving behaviour—online gift registries. A Hierarchical Bayesian analysis of fulfilment data from 555 online wedding registries reveals the probability of a gift fulfilment follows a bimodal distribution around the gift registry’s average price. This is consistent with the hypothesis that online gift purchases are driven by the average price of the registry, which serves as the reference price, and two competing motivations among gift givers. These motivations are a desire for social benefits (e.g., to enhance the relationship) and a desire to limit monetary costs (e.g., to save money). The former motivation favours gifts with higher than average prices; whereas the latter favours those with lower than average prices. Because an average priced gift does not appeal to either segment, its fulfilment probability is relatively low. Finally, because gifts with extremely high or extremely low prices have less appeal for each segment, the fulfilment probability curve is bimodal.

Product variety is an important strategic tool that firms can use to attract customers and respond to competition. This study focuses on the retail industry and investigates how stores manage their product variety, contingent on the presence of competition and their actual distance from rivals. Using a unique data set that contains all Best Buy and Circuit City stores in the United States, the authors find that a store’s product variety (i.e., number of stock-keeping units) increases if a rival store exists in its market but, in the presence of such competition, decreases when the rival store is collocated (within one mile of the focal store). Moreover, collocated rival stores tend to differentiate themselves by overlapping less in product range than do noncollocated rivals. This smaller and more differentiated product variety may be because of coordinated interactions between collocated stores. In summary, this paper presents evidence of both coordination and competition in retailers’ use of product variety.

Using book review data on Amazon.com, the authors extend current research into online consumer reviews by empirically investigating the context dependence effect in the review writing process. They find that when product quality remains constant, later reviews tend to differ from previously posted ones, and the difference is moderated by the popularity of the product, the variance of previous reviews, whether later reviews explicitly refer to previous reviews, and the age of the product and the reviews. This phenomenon can be explained by both consumer expectation and self-selection effects in review writing. The implications of this research can help practitioners understand the reviewing process and provide some guidelines for improving the objectivity of online product reviews.

There has been little research on how market disruptions affect customer–brand relationships and how firms can sustain brand loyalty when disruptions occur. Drawing from social identity theory and the brand loyalty literature, the authors propose a conceptual framework to examine these issues in a specific market disruption, namely, the introduction of a radically new brand. The framework focuses on the time-varying effects of customers’ identification with and perceived value of the incumbent relative to the new brand on switching behavior. The authors divert from the conventional economic perspective of treating brand switching as functional utility maximization to propose that brand switching can also result from customers’ social mobility between brand identities. The results from longitudinal data of 679 customers during the launch of the iPhone in Spain show that both relative customer–brand identification and relative perceived value of the incumbent inhibit switching behavior, but their effects vary over time. Relative customer–brand identification with the incumbent apparently exerts a stronger longitudinal restraint on switching behavior than relative perceived value of the incumbent. The study has important strategic implications for devising customer relationship strategies and brand investment.

Using real-world transaction prices in the Internet auction Web site eBay’s U.S., U.K., and global markets, the authors study the price dispersion of homogeneous products related to the sellers’ country-of-origin. For both tangible and intangible products and services, sellers from the United States enjoy a price premium. This premium appears to stem from country-of-origin equity instead of trading risk or product quality. The findings of this research suggest potential profitable opportunities in international trade by employing the retailer’s country-of-origin as an arbitrage tool.

On the basis of the bidding history of a panel of new eBay bidders, we examine the impact of different types of experiences on bidding behavior evolution. Accounting for unobserved bidder heterogeneity, the results indicate that losing experiences make the bidders’ bidding behavior evolve toward the normative predictions of auction theory, in that they submit fewer bids and bid later. Winning experiences, however, do not have such an effect. Moreover, the experience effect pertains to the bidder’s entire previous bidding experience regardless of product categories. We also assess the potential bias introduced by using feedback ratings (compared with actual participation) as experience measures.

An analysis is performed on the results of 241 real world TV advertising tests conducted by Information Resources, Inc. between 1989 and 2003 to partially update the findings of Lodish et al. [Journal of Marketing Research 32, 2 (1995): 125–39]. Two types of market test results, BehaviorScan and Matched-Market, are analyzed. Overall, the improvement of TV advertising sales effectiveness because of media weight increase is significantly larger than zero for established products, which is different from Lodish et al.’s finding. A further analysis indicates that such significance is mainly driven by more recent tests. A comparison between the new results and Lodish et al. reveals a significant increase in the average advertising effectiveness for tests completed after 1995. The new data still suggest (as did the original data) that it is of great managerial interest to identify advertising effectiveness before launching

advertising campaigns.

In this note, the authors propose several extensions of the model of consumer learning in conjoint analysis that Bradlow, Hu, and Ho (2004) develop. They present a clarification of the original model, propose an integration of several new imputation rules, add new measurement metrics for pattern matching, and draw a roadmap for further real-world tests. The authors also discuss general modeling challenges when researchers want to mathematically define and integrate behavioral regularities into traditional quantitative domains. They conclude by suggesting several critical success factors for modeling behavioral regularities in marketing.

Respondents in a conjoint experiment sometimes are presented with successive partial product profiles. First, the authors model how respondents infer missing levels of product attributes in a partial conjoint profile by developing a learning-based imputation model that nests several extant models. The advantage of this approach over previous research is that it infers missing levels of an attribute not only from prior levels of the same attribute but also from prior levels of other attributes, especially ones that match the attribute levels of the current product profile. Second, the authors provide an empirical demonstration of their approach and test whether learning in conjoint studies occurs; to what extent; and in what manner it affects responses, partworths, and the relative importance of attributes. They show that the relative importance of attribute partworths can shift when subjects evaluate partial profiles, which suggests that consumers may construct rather than retrieve partworths and are sensitive to the order in which the profiles are presented. Finally, the results show that consumers’ imputation processes can be influenced by manipulating their prior information about a product category. This research is of both theoretical and practical importance. Theoretically, this research sheds light on how customers integrate different sources of information in evaluating products with incomplete attribute information; practically, it highlights the potential pitfalls of imputing missing attribute levels using simple rules and develops a better behavioral model for describing and predicting customers’ ratings for partial conjoint profiles.