Knowledge that Transforms

Reference EPFL-ARTICLE-178583doi:10.2753/MIS0742-1222280201View record in Web of Science Record created on 2012-06-25, modified on 2017-05-12

Historically, inaccurate credibility assessments have resulted in tremendous costs to businesses and to society. Recent research offers unobtrusive credibility assessment aids as a solution; however, the accuracy of these decision aids is inadequate, and users often resist accepting the aids’ recommendations. We follow the principles of signal detection theory to improve the accuracy of recommendations in computer-aided credibility assessment by combining automated and participatory decision support. We also leverage participation in decision-making theory to explain and predict an increased acceptance of assessment aid recommendations when perceptual cues are elicited from users. Based on these two theories, we design and test a hybrid decision aid to perform automated linguistic analysis and to elicit and analyze perceptual cues from an observer. Results from a laboratory experiment indicate that decision aids that use linguistic and perceptual cues offer more accurate recommendations than aids that use only one type of cue. Automatic analysis of linguistic cues improved both the decision aid’s recommendations and the users’ credibility assessment accuracy. Challenging the generalizability of past findings, the elicitation of perceptual cues did not improve the decision aid’s recommendations or the users’ assessment accuracy. Elicitation of perceptual cues, however, did improve user acceptance of the decision aid’s recommendations. These findings provide guidance for future development of credibility assessment decision aids.

Digital intermediaries and Internet search technologies have commoditized many products, resulting in intense price competition and channel conflict. Firms use decommoditization strategies to regain control over distribution channels, as well as to implement resonance marketing and hyperdifferentiation, which allows them to improve margins through differentiation. We test two hypotheses: the decommoditization hypothesis and the resonance marketing hypothesis. We use data from an airline with a new à la carte pricing mechanism, which allows consumers to tailor airline ticket bundles to suit their individual preferences. We compare à la carte ticket pricing, whose features can be modified by the purchaser, and fixed (bundled offer) sales, which cannot be modified. We found that a significant number of travelers do use à la carte pricing, which allows the airlines to regain some control over distribution. We find that travelers customized standard bundles when it was possible for them to make à la carte ticket bookings, but mainly for low-feature standard bundles. Frequent-flyer members purchased higher-feature bundles more often when they had the opportunity. The findings support the proposed hypotheses. We discuss the implications for distribution strategy and channel conflict management.

This paper examines how information provided by online reviews influences firms' pricing strategy for repeat purchase products. It is commonly understood that online reviews can reduce consumer uncertainty about product characteristics and, therefore, have the potential to increase product demand and firm profits. However, when considering repeat purchase products, online reviews have an additional effect in that they can alter consumers' propensity to switch among products, which can intensify price competition and lead to lower profits. The strength of these potentially offsetting effects depends on the informativeness of consumer reviews, which is a function of both objective review accuracy and the ability of consumers to obtain information from reviews when their idiosyncratic preferences over product characteristics might differ from the preferences of reviewers. The interplay of these competing effects results in an S-shaped relationship between the quality of reviews and firm profits. There exists an optimal level of consumer informedness from the firms' perspective, and competing firms may have incentives to facilitate consumer reviews in some markets but not in others. Given firms' strategic pricing, consumers may also be worse off as review informativeness increases.

We examine a platform's optimal two-sided pricing strategy while considering seller-side innovation decisions and price competition. We model the innovation race among sellers in both finite and infinite horizons. In the finite case, we analytically show that the platform's optimal seller-side access fee fully extracts the sellers' surplus, and that the optimal buyer-side access fee mitigates price competition among sellers. The platform's optimal strategy may be to charge or subsidize buyers depending on the degree of variation in the buyers' willingness to pay for quality; this optimal strategy induces full participation on both sides. Furthermore, a wider quality gap among sellers' products lowers the optimal buyer-side fee but leads to a higher optimal seller-side fee. In the infinite innovation race, we perform computations to find the stationary Markov equilibrium of sellers' innovation rate. Our results show that when all sellers innovate, there exists a parameterization under which a higher seller-side access fee stimulates innovation.

Measuring peer influence in social networks is an important business and policy question that has become increasingly salient with the development of globally interconnected information and communication technology networks. However, in spite of the new data sources available today, researchers still face many of the same measurement challenges that have been present in the literature for over four decades: homophily, reflection and selection problems, identifying the source of influence, and determining preexisting knowledge. The goal of this paper is to develop an empirical approach for measuring information diffusion and discovery in online social networks that have these measurement challenges. We develop such an approach and apply it to data collected from 4,000 users of an online music community. We show that peers on such networks significantly increase music discovery. Moreover, we demonstrate how future research can use this method to measure information discovery and diffusion using data from other online social networks.