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Rapid development and adoption of AI, machine learning, and natural language processing applications challenge managers and policy makers to harness these transformative technologies. In this context, the authors provide evidence of a novel \"word-of-machine\" effect, the phenomenon by which utilitarian/hedonic attribute trade-offs determine preference for, or resistance to, AI-based recommendations compared with traditional word of mouth, or human-based recommendations. The word-of-machine effect stems from a lay belief that AI recommenders are more competent than human recommenders in the utilitarian realm and less competent than human recommenders in the hedonic realm. As a consequence, importance or salience of utilitarian attributes determine preference for AI recommenders over human ones, and importance or salience of hedonic attributes determine resistance to AI recommenders over human ones (Studies 1–4). The word-of machine effect is robust to attribute complexity, number of options considered, and transaction costs. The word-of-machine effect reverses for utilitarian goals if a recommendation needs matching to a person's unique preferences (Study 5) and is eliminated in the case of human–AI hybrid decision making (i.e., augmented rather than artificial intelligence; Study 6). An intervention based on the consider-the-opposite protocol attenuates the word-of-machine effect (Studies 7a–b).

The authors examine consumers’ information channel usage during the customer journey by employing a hedonic and utilitarian (H/U) perspective, an important categorization of consumption purpose. Taking a retailer-category viewpoint to measure the H/U characteristics of 20 product categories at 40 different retailers, this study combines large-scale secondary clickstream and primary survey data to offer actionable insights for retailers in a competitive landscape. The data reveal that, when making hedonic purchases (e.g., toys), consumers employ social media and on-site product pages as early as two weeks before the final purchase. By contrast, for utilitarian purchases (e.g., office supplies), consumers utilize third-party reviews up to two weeks before the final purchase and make relatively greater usage of search engines, deals, and competitors’ product pages closer to the time of purchase. Importantly, channel usage is different for sessions in which no purchase is made, indicating that consumers’ information channel choices vary significantly with the H/U characteristics of purchases. The article closes with an extensive discussion of the significant implications for managing customer touchpoints.

Manufacturers and online retailers are readily availing themselves of new technologies to present their merchandise using a variety of formats, including static (still image) and dynamic (video) portrayal. Building on vividness theory, the authors propose and demonstrate that presenting products and services using a dynamic visual format enhances consumer preference for hedonic options and willingness to pay for those options. The dynamic presentation format increases involvement with the product/service experience in a manner presumably similar to that of the actual product experience. The result is an increased preference for and valuation of hedonic options. This holds true for experiential and search products in single and joint evaluations and carries over to subsequent choices. Across all studies, the results demonstrate that a dynamic (relative to static) presentation format enhances choice of the hedonically superior (vs. utilitarian-superior) option by more than 79%.

Motivated by the prevalence of ride-sharing platforms, in “Spatial Pricing in Ride-Sharing Networks,” Bimpikis, Candogan, and Saban explore the impact of the demand pattern for rides across a network’s locations on a platform’s optimal pricing and compensation policy, profits, and consumer surplus. They explicitly account for the pricing problem’s spatial dimension and the fact that the drivers endogenously determine whether and where to provide service. Their first contribution is to develop a tractable model to study a platform operating on a network of locations that may differ in both the size of their potential demand and the destination preferences of riders. Second, they provide a characterization of the platform’s optimal policy and identify “balancedness” of the demand pattern as a property that captures the profit potential of a given network. Finally, they discuss the benefits and limitations of a number of alternative pricing and compensation schemes. We explore spatial price discrimination in the context of a ride-sharing platform that serves a network of locations. Riders are heterogeneous in terms of their destination preferences and their willingness to pay for receiving service. Drivers decide whether and where to provide service so as to maximize their expected earnings given the platform’s pricing and compensation policy. Our findings highlight the impact of the demand pattern on the platform’s prices, profits, and the induced consumer surplus. In particular, we establish that profits and consumer surplus at the equilibrium corresponding to the platform’s optimal pricing and compensation policy are maximized when the demand pattern is “balanced” across the network’s locations. In addition, we show that they both increase monotonically with the balancedness of the demand pattern (as formalized by its structural properties). Furthermore, if the demand pattern is not balanced, the platform can benefit substantially from pricing rides differently depending on the location from which they originate. Finally, we consider a number of alternative pricing and compensation schemes that are commonly used in practice and explore their performance for the platform. The e-companion is available at https://doi.org/10.1287/opre.2018.1800 .

Using data from Pennsylvania and an array of empirical techniques to control for confounding factors, we recover hedonic estimates of property value impacts from nearby shale gas development that vary with water source, well productivity, and visibility. Results indicate large negative impacts on nearby groundwater-dependent homes, while piped-water-dependent homes exhibit smaller positive impacts, suggesting benefits from lease payments. Results have implications for the debate over regulation of shale gas development.

We estimate the aggregate productivity gains from reducing barriers to internal labor migration in Indonesia, accounting for worker selection and spatial differences in human capital. We distinguish between movement costs, which mean workers will move only if they expect higher wages, and amenity differences, which mean some locations must pay more to attract workers. We find modest but important aggregate impacts. We estimate a 22 percent increase in labor productivity from removing all barriers. Reducing migration costs to the US level, a high-mobility benchmark, leads to a 7.1 percent productivity boost. These figures hide substantial heterogeneity. The origin population that benefits most sees a 104 percent increase in average earnings froma complete barrier removal, or a 25 percent gain from moving to the US benchmark.

Real estate platforms provide a new source of data which has already been used as a substitute for transaction data in hedonic regression applications. This paper asks whether it is valid to do so in the established research areas of (1) willingness to pay estimation, (2) automated valuations, and (3) price index construction. It therefore compares listings and transaction data and regression results derived from them. We find that ask prices stochastically dominate sale prices, mainly because the composition of characteristics differs between the two data sets. But estimates of implicit prices also differ. As a result, willingness to pay estimates from listings data can be widely off when compared with estimates from transaction data. Listings data are not very useful to predict market values of individual houses either, as these predictions suffer from upward bias and large error variance. We find, however, that an ask price index complements a sale price index, as it is useful for nowcasting.

This paper extends the unified theory of acceptance and use of technology (UTAUT) to study acceptance and use of technology in a consumer context. Our proposed UTAUT2 incorporates three constructs into UTAUT: hedonic motivation, price value, and habit. Individual differences — namely, age, gender, and experience — are hypothesized to moderate the effects of these constructs on behavioral intention and technology use. Results from a two-stage online survey, with technology use data collected four months after the first survey, of 1,512 mobile Internet consumers supported our model Compared to UTAUT, the extensions proposed in UTAUT2 produced a substantial improvement in the variance explained in behavioral intention (56 percent to 74 percent) and technology use (40 percent to 52 percent). The theoretical and managerial implications of these results are discussed.

We construct housing price indices for 120 major cities in China in 2003–2013 based on sequential sales of new homes within the same housing developments. By using these indices and detailed information on mortgage borrowers across these cities, we find enormous housing price appreciation during the decade, which was accompanied by equally impressive growth in household income, except in a few first-tier cities. While bottom-income mortgage borrowers endured severe financial burdens by using price-to-income ratios over eight to buy homes, their participation in the housing market remained steady and their mortgage loans were protected by down payments commonly in excess of 35%. As such, the housing market is unlikely to trigger an imminent financial crisis in China, even though it may crash with a sudden stop in the Chinese economy and act as an amplifier of the initial shock.

We rely on novel textual analysis of real estate listings and identify renovated dwellings in a dataset of Norwegian transactions to estimate the renovation premium in an urban housing market. The renovation premium is estimated in a hedonic framework by classical regression approaches and a random forest algorithm. The strength of the latter is that it allows for a more complex interplay between the renovation premium and explanatory variables. We estimate a significant positive renovation premium of 5–7 percent for renovated dwellings and a negative premium of 9–10 percent for unmaintained/neglected dwellings. These averages mask significant variations in these premiums over time, particularly, a counter-cyclical effect. Omitting renovation information also has implications for estimated short-term house price growth. Unmaintained dwellings tend to transact more in the fourth quarter, indicating that parts of the seasonal price variation reported in the literature are due to compositional variation with respect to renovation. This composition effect bias price movement estimates downward, if uncontrolled for, as unmaintained dwellings transact at significantly lower prices.