Knowledge that Transforms

Although multichannel sales strategies have become common due to the use of advanced information technologies, how one trading mechanism can influence the outcome of another, especially in the B2B market, remains largely underexplored. This paper investigates the effect of price and quantity information from an online posted-price presales channel on the performance of the century-old sequential Dutch auction system. Sellers can control the price paid and make a proportion of their stock available in auction presales. Anything left after presales is sold via auctions. Our analysis of nearly 1.5 million flower lots reveals a positive effect with higher auction prices and total revenue for lots listed in presales than for lots that are not. The result holds even for lots with no actual sales in the presales, indicating that buyers pay close attention to the additional information from the posted-price presales channel. By teasing out the information effect of presales prices and presales quantity on auction prices, we evaluate a number of pricing strategies. The results suggest that selling at a high price in presales is still more beneficial than selling more by discounting prices.

There is a growing interest in understanding the role of genetics in explaining heterogeneity in behaviors, including those related to information systems (IS). The majority of the recent genetics research focuses on searching the entire genome in genome-wide association studies (GWASs) to link DNA to human traits. The results of GWASs can be used on datasets to compute a measure of genetic propensity known as a polygenic score, or PGS. PGSs are widely viewed as the future of genetics research. We conducted an exploratory study, in the context of information technology (IT) use, to examine if the PGS approach can be used to better understand the role of genetics in IS research. Consistent with our hypotheses, genetic endowments associated with Educational Attainment and General Cognition positively predict technology use, and genetic endowments associated with Neuroticism, Depressive Symptoms, Myocardial Infarction, and Coronary Artery Disease negatively predict technology use more than half a century later (genetic endowments are established at conception and our sample consists of individuals aged 50 to 80). Many of the characteristics known to be associated with heterogeneity in IT use (e.g., trust, education) appear to be mediators linking PGSs to IT use. Nonetheless, a number of PGSs maintain meaningful direct effects.

In the U.S., multihospital systems (MHSs) charge significantly higher prices for hospital services than stand-alone hospitals. Rivalry restraint theory suggests that MHS with multimarket contact (MMC) can tacitly collude and mutually forebear from price competition to keep their prices above competitive levels. We posit that the success of such MMC-induced rivalry restraints (the truce) is affected by two conflicting roles of IT at the corporate level and market unit levels, respectively. The corporate parent seeks to standardize IT applications enterprise-wide to coordinate market units as a means of jointly implementing the rivalry restraint strategy and keeping prices high enterprise-wide. However, market units, i.e., the member hospitals of MHS clustered in geographic patient markets, face competitive pressures to reduce their service costs. Market units seek to use differentiated IT applications to achieve cost reductions, which then fuel price competition in local markets, jeopardize the sustainability of the truce, and weaken the enterprise-wide price effects of the corporate parent’s rivalry restraint strategy. In a longitudinal study of 195 multihospital systems in the U.S. in the 2005-2013 time period, we found support for these ideas. The corporate-wide standardization of the operational IT of MHS complements the rivalry restraint strategy to increase enterprise-wide prices. Market units’ use of differentiated analytical IT reduces costs in local markets and weakens the price effects of the rivalry restraint strategy. The study advances IS research and practice by theorizing how the corporate-level and the market unit-level IT of a multi-unit, multimarket (MUMM) organization can have opposing moderating effects on the link between MMC and the average prices charged by the MUMM organization.

Existing studies have found that online search is a revealed measure for investor attention and a useful predictor of stock returns. We study the heterogeneity in retail investor attention by comparing search conducted on weekdays vs. weekends and investigate the price pressure channel and information processing channel for stock return predictability. According to the information processing channel, weekends afford retail investors more time for the intensive cognitive analysis necessary to make better predictions. Alternatively, weekend search might better capture the price pressure from retail investors’ trading activities. We provide empirical results that support the information processing channel. We first show that weekend search, rather than weekday search, predicts large-cap stock returns in both the cross-section and time series. Additionally, our findings on retail trading activity contradict the price pressure channel in that weekday search, rather than weekend search, leads to a subsequent retail order imbalance. Overall, our study contributes to the literature on the predictive power of online search on stock returns, which has mainly focused on the price pressure channel, which yields significant results for small-cap stocks only.

Technology-augmented choice-making impacts many facets of business. The use of economic incentives under the ubiquitous mobile ecosystem for prosocial behavior has been shown to be particularly effective. We build on the previous work on this topic and study how mobile-based economic incentives and environments influence charitable giving behavior. In contrast to traditional fund-raising, we consider the use of mobile devices to generate giving in small denominations, which we term microgiving. In collaboration with a US-based mobile app provider, we incorporated a functionality that allowed users to contribute their in-app reward points to charity. To encourage donations, we used economic incentives in the form of monetary subsidies, i.e., rebates or matching grants, as well as digital nudges in the form of push notifications. We studied the effects of these factors on giving behavior across two large-scale field experiments. Focusing on the different aspects of smartphones that could differentially impact charitable giving behavior—namely the intensely private and personal nature of smartphones—we examined how the visibility of donation decisions affects giving behavior by toggling audience effects. Our results show that the effectiveness of incentives is contingent upon the magnitude of the incentive as well as the extent to which individual decisions are visible to others. To situate our results in relation to the traditional medium of charitable giving, we propose an analytical model that internalizes the subsidy rates and the audience effect. This study provides initial empirical evidence and an analytical model to advance technology-augmented charitable giving that can provide insights to organizations and service providers.

We study how firms respond to online customer reviews in a competitive market where they jostle with one another for sales based on online ratings. The focus of this paper is on how firms can optimally manage their ratings through management response and how review ratings affect the sales and profits of competing firms. We develop a controlled diffusion process to model the coevolution of sales and ratings as a function of the response strategy chosen to maximize profit over time. Our model considers a variety of factors, such as profit margin and customer rating sensitivity, that influence a firm’s effort to manage ratings and subsequently its sales and profits. More response effort needs to be exerted to manage ratings when either the profit margin of a tour is very high or customers are very sensitive to ratings. We estimate our model using data on Ctrip’s tours that include each tour’s sales, reviews, prices, and tour features. We find that consumers anchor their beliefs in the mean market rating and that their purchase decisions depend on the tour’s rating relative to this anchor. Thus, relative, rather than absolute, ratings matter. Our study informs firms on how competition and other primitives impact their efforts to manage ratings and hence profit. Our methodology allowed us to conduct “what-if” analyses, for example, to study what would happen to the review ratings, sales, and profits of a tour if a firm adopted a different response strategy. We were also able to provide turnaround strategies for struggling tours, i.e., factors that a loss-making tour should change if it wishes to make a positive profit. Ultimately, we conducted a competitive analysis that allowed us to modify certain parameters that affect the intensity of competition and hence the sales and the profits of competing tours. Finally, we demonstrate the flexibility of the model by extending it to incorporate multiple state variables that might affect the response strategy.

E-commerce platforms often use collaborative filtering (CF) algorithms to recommend products to consumers. What recommendations consumers receive and how they respond to the recommendations largely depend on the design of CF algorithms. However, the extant empirical research on recommender systems has primarily focused on how the presence of recommendations affects product demand, without considering the underlying algorithm design. Leveraging a field experiment on a major e-commerce platform, we examine the differential impact of two widely used CF designs: view-also-view (VAV) and purchase-also-purchase (PAP). We found several striking differences between the impact of these two designs on individual products. First, VAV is about seven times more effective in generating additional product views than PAP but only about twice as effective in generating sales due to a lower conversion rate. Second, VAV is more effective in increasing views for more expensive products, whereas PAP is more effective in increasing the sales of cheaper products. Third, VAV is less effective in increasing the views but more effective in increasing the sales of products with higher purchase incidence rates (PIRs). Finally, when aggregated over all products with the same levels of price or PIRs, VAV dominates PAP in generating views and the difference is more striking for products with higher prices or lower PIRs. Interestingly, PAP is more effective than VAV in increasing the sales of products with low prices or moderate PIRs, though VAV generates more sales than PAP overall. Our findings suggest that platforms may benefit from employing different CF designs for different types of products.