Knowledge that Transforms

Past research has primarily focused on what happens after a merger. This research attempts to determine whether anticipated benefits from the merger actually accrue. We characterize the effects of observed variables on whether pairs of firms merge, vis-à-vis roommate matching, and then link these factors to post-merger innovation (i.e., number of patents). We jointly estimate the two models using Markov Chain Monte Carlo methods with a unique panel data set of 1,979 mergers between 4,444 firms across industries and countries from 1992 to 2008. We find that similarity in national culture and technical knowledge has a positive effect on partner selection and post-merger innovation. Anticipated synergy from subindustry similarity, however, is not realized in post-merger innovation. Furthermore, some key synergy sources are unanticipated when selecting a merger partner. For example, financial synergy from higher total assets and complementarity in total assets and debt leverage as well as knowledge synergy from breadth and depth of knowledge positively influence innovation but not partner selection. Furthermore, factors that dilute synergy (e.g., higher debt levels) are unanticipated, and firms merge with firms that detract from their innovation potential. Overall, the results reveal some incongruity between anticipated and realized synergy.Data, as supplemental material, are available at https://doi.org/10.1287/mksc.2016.0978 .

In this paper, we propose an automated and scalable garment recommender system using real-time in-store videos that can improve the experiences of garment shoppers and increase product sales. The video-based automated recommender (VAR) system is based on observations that garment shoppers tend to try on garments and evaluate themselves in front of store mirrors. Combining state-of-the-art computer vision techniques with marketing models of consumer preferences, the system automatically identifies shoppers’ preferences based on their reactions and uses that information to make meaningful personalized recommendations. First, the system uses a camera to capture a shopper’s behavior in front of the mirror to make inferences about her preferences based on her facial expressions and the part of the garment she is examining at each time point. Second, the system identifies shoppers with preferences similar to the focal customer from a database of shoppers whose preferences, purchasing, and/or consideration decisions are known. Finally, recommendations are made to the focal customer based on the preferences, purchasing, and/or consideration decisions of these like-minded shoppers. Each of the three steps can be implemented with several variations, and a retailing chain can choose the specific configuration that best serves its purpose. In this paper, we present an empirical test that compares one specific type of VAR system implementation against two alternative, nonautomated personal recommender systems: self-explicated conjoint (SEC) and self-evaluation after try-on (SET). The results show that VAR consistently outperforms SEC and SET. A second empirical study demonstrates the feasibility of VAR in real-time applications. Participants in the second study enjoyed the VAR experience, and almost all of them tried on the recommended garments. VAR should prove to be a valuable tool for both garment retailers and shoppers.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2016.0984 .

This research examines the effects of hyper-contextual targeting with physical crowdedness on consumer responses to mobile ads. It relies on rich field data from one of the world’s largest telecom providers who can gauge physical crowdedness in real-time in terms of the number of active mobile users in subway trains. The telecom provider randomly sent targeted mobile ads to individual users, measured purchase rates, and surveyed purchasers and nonpurchasers. Based on a sample of 14,972 mobile phone users, the results suggest that, counterintuitively, commuters in crowded subway trains are about twice as likely to respond to a mobile offer by making a purchase vis-à-vis those in noncrowded trains. On average, the purchase rates measured 2.1% with fewer than two people per square meter, and increased to 4.3% with five people per square meter, after controlling for peak and off-peak times, weekdays and weekends, mobile use behaviors, and randomly sending mobile ads to users. The effects are robust to exploiting sudden variations in crowdedness induced by unanticipated train delays underground and street closures aboveground. Follow-up surveys provide insights into the causal mechanism driving this result. A plausible explanation is mobile immersion: As increased crowding invades one’s physical space, people adaptively turn inwards and become more susceptible to mobile ads. Because crowding is often associated with negative emotions such as anxiety and risk-avoidance, the findings reveal an intriguing, positive aspect of crowding: Mobile ads can be a welcome relief in a crowded subway environment. The findings have economic significance because people living in cities commute 48 minutes each way on average, and global mobile ad spending is projected to exceed $100 billion. Marketers may consider the crowdedness of a consumer’s environment as a new way to boost the effectiveness of hyper-contextual mobile advertising. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0905 .

This paper examines how firms should allocate their advertising budgets between consumers who have a high preference for their products (i.e., strong segment) and those who prefer competing products (i.e., weak segment). Targeted advertising transmits relevant information to otherwise uninformed consumers and it is used as a price discrimination device. With targeted advertising and price discrimination, we find that, when the attractiveness of the weak segment is low, each firm advertises more intensively in its strong segment. The same result arises when the attractiveness of the weak segment is high and advertising is sufficiently expensive. Interestingly, when the attractiveness of the weak segment is high but advertising costs are sufficiently low, it is optimal for each firm to advertise more intensively in its weak segment. The paper also investigates how advertising strategies and equilibrium profits are affected by price discrimination. Compared with uniform pricing, firms can increase or reduce the intensity of advertising targeted to each segment when price discrimination is allowed. Furthermore, when the attractiveness of the weak market is high, price discrimination boosts firms’ profits provided that advertising costs are sufficiently low. The reverse happens when advertising costs are high.

This paper studies information sharing in a distribution channel where the manufacturer possesses better demand-forecast information than the downstream retailer. We examine three information-sharing formats: no information sharing (i.e., the manufacturer ex ante commits to not sharing its forecast), voluntary information sharing (i.e., the manufacturer makes the sharing decision ex post after receiving the forecast), and mandatory information sharing (i.e., the manufacturer is mandated to share its forecast). We characterize the equilibrium outcomes under the three sharing formats and investigate the firms’ preferences regarding these formats. It is shown that when the retailer is risk-neutral, both firms are indifferent between voluntary and mandatory sharing. Among the three formats, ex ante, the retailer prefers the no-sharing format whereas the manufacturer prefers the mandatory-sharing format. In addition, we find that a more accurate forecast benefits both firms under voluntary- and mandatory-sharing formats, but may hurt both firms under the no-sharing format. Finally, we show that risk aversion plays a critical role in the firms’ sharing decisions and the impact of forecast accuracy. Specifically, when the retailer is risk-averse, the manufacturer may prefer the no-sharing format over the voluntary-sharing format, and improving forecast accuracy may hurt both firms even under voluntary sharing.

This paper examines the incentives of firms to invest in socially responsible product innovations. Our analysis connects the existence of socially responsible innovations to the presence of intrinsic and extrinsic social responsibility preferences. In addition to deriving economic value from the product, consumers have heterogeneous intrinsic needs to consume products that are socially responsible. They also have extrinsic social comparison preferences that are based on their meetings with others in social interactions. The frequency of these meetings are endogenous to the consumption choices of consumers. A consumer enjoys a social comparison benefit if her consumption decision is more socially responsible than the consumer that she meets in a social interaction and a social comparison cost if it is less socially responsible. The analysis reveals a nonmonotonic effect of social comparison effects on innovation incentives. When the economic value of a product is relatively small, the incentive to innovate decreases as social comparison effects increase. By contrast, when the economic value of a product is sufficiently large, increases in social comparison effects increase the incentive to innovate. Social comparison benefits and costs have different effects on competition between firms. In particular, social comparison benefits soften price competition, whereas social comparison costs tend to exacerbate price competition. We also identify market conditions where a monopoly invests more or less compared to a firm facing competition.

A seller often needs to determine the amount of product information to provide to consumers. We model costly consumer information search in the presence of limited information. We derive the consumer’s optimal stopping rule for the search process. We find that, in general, there is an intermediate amount of information that maximizes the likelihood of purchase. If too much information is provided, some of it is not as useful for the purchase decision, the average informativeness per search occasion is too low, and consumers end up choosing not to purchase the product. If too little information is provided, consumers may end up not having sufficient information to decide to purchase the product. The optimal amount of information increases with the consumer’s ex ante valuation of the product, because with greater ex ante valuation by the consumer, the firm wants to offer sufficient information for the consumer to be less likely to run out of information to check. One can also show that there is an intermediate amount of information that maximizes the consumer’s expected utility from the search problem (social welfare under some assumptions). Furthermore, this amount may be smaller than that which maximizes the probability of purchase; that is, the market outcome may lead to information overload with respect to the social welfare optimum. This paper can be seen as providing conditions under which too much information may hurt consumer decision making. Numerical analysis shows also that if consumers can choose to some extent which attributes to search through (but not perfectly), or if the firm can structure the information searched by consumers, the amount of information that maximizes the probability of purchase increases, but is close to the amount of information that maximizes the probability of purchase when the consumer cannot costlessly choose which attributes to search through.

We study three ways firms can communicate about their brands—paid media (advertising), earned media (word of mouth and online social media), and owned media (brand websites and other owned content)—and the roles these media types play in reminding (i.e., activating memory), informing (i.e., learning their tastes for the brand), or enhancing enjoyment (e.g., gaining additional utility from socializing about the brand). We do this for a new TV show setting using a data set that contains reported viewing, exposures, expectations, and experiences. We present descriptive analyses and results from a new structural model, which indicate that earned media is more impactful than paid and owned media per exposure. However, paid media has far more exposures, so for a given percentage increase, paid media’s influence dominates earned and owned media. Earned media operate primarily through enhancing enjoyment, whereas paid media operate through reminding and owned media through reminding, but discourage live viewing. We find that media exposures help consumers learn about how well they will like the program. However, this learning can either increase or decrease the expected liking, and in our data the average audience effects are negligible. Overall, we find that earned and paid media play a central role in developing and maintaining entertainment brands.