Impact of Political Television Advertisements on Viewers’ Response to Subsequent Advertisements
Beth Fossen, Girish Mallapragada & Anwesha De
Marketing Science, forthcoming
Political advertisements on television can affect viewers and may, consequently, influence the effectiveness of subsequent ads. Such ad-to-ad spillover effects -- where one ad influences how viewers respond to a subsequent ad -- have drawn concerns from nonpolitical advertisers, raising the question: how do political ads on television impact viewers’ response to subsequent ads? We empirically investigate this question using two outcomes of ad response: ad viewership and online conversations about ads. We use data on 849 national political television ads from the 2016 election and leverage a quasi-experimental design to delineate the effect that a political ad has on the subsequent ad. We show that, counterintuitively, political ads spur positive spillover effects. Specifically, ads that follow a political ad, compared with ads that follow a nonpolitical ad, experience an 89% reduction in audience decline and thus air to larger audiences. Additionally, we find evidence that viewers engage in more positive online ad chatter about ads that air after political ads, with these ads experiencing a 3% increase in positive chatter after the ad. Our investigation contributes to research on advertising that has yet to explore ad-to-ad spillover effects and, more broadly, provides insights into how political messages influence consumers.
Robots at work: People prefer -- and forgive -- service robots with perceived feelings
Kai Chi Yam et al.
Journal of Applied Psychology, forthcoming
Organizations are increasingly relying on service robots to improve efficiency, but these robots often make mistakes, which can aggravate customers and negatively affect organizations. How can organizations mitigate the frontline impact of these robotic blunders? Drawing from theories of anthropomorphism and mind perception, we propose that people evaluate service robots more positively when they are anthropomorphized and seem more humanlike -- capable of both agency (the ability to think) and experience (the ability to feel). We further propose that in the face of robot service failures, increased perceptions of experience should attenuate the negative effects of service failures, whereas increased perceptions of agency should amplify the negative effects of service failures on customer satisfaction. In a field study conducted in the world’s first robot-staffed hotel (Study 1), we find that anthropomorphism generally leads to higher customer satisfaction and that perceived experience, but not agency, mediates this effect. Perceived experience (but not agency) also interacts with robot service failures to predict customer satisfaction such that high levels of perceived experience attenuate the negative impacts of service failures on customer satisfaction. We replicate these results in a lab experiment with a service robot (Study 2). Theoretical and practical implications are discussed.
“Tell all the truth, but tell it slant”: Documenting media bias
Collin Raymond & Sarah Taylor
Journal of Economic Behavior & Organization, forthcoming
Media outlets often appear to bias news reports, but it is often difficult to document bias cleanly and to ascribe motivation when bias is detected. We test for media bias in a novel setting. We show that in the late 19th century The New York Times weather reports were correlated with the schedule of the Giants, the local professional baseball team: On home game days weather reports were more “optimistic” (i.e., relatively more accurate at predicting sunny weather). The size of optimism is large — when producing in-house forecasts, The New York Times predicted fair weather 14.2 percentage points more often on days when the Giants were scheduled to play at home. We provide a framework for evaluating such bias, and show that the bias is consistent with a demand-driven motivation.
Affiliation bias in the online market for rental accommodation
Barbara Bliss, Joseph Engelberg & Mitch Warachka
Real Estate Economics, forthcoming
We find evidence of taste‐based discrimination against rival affiliations in the online market for rental accommodation. Airbnb hosts in college towns increase their listing prices more than hotels on home football games against rival teams. By setting listing prices too high as a result of their affiliation bias against rival fans, hosts experience a 30% reduction in rental income. The overestimation of demand, the cost (inconvenience) of temporary relocation, and the likelihood of incurring damage cannot explain the inverse relation between listing price increases, and rental incomes that is limited to games against rival teams. Instead, greater financial constraints are associated with smaller listing price increases, and higher rental incomes on rival games, suggesting that taste‐based discrimination is a luxury.
Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The “Word-of-Machine” Effect
Chiara Longoni & Luca Cian
Journal of Marketing, forthcoming
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).
Brand Aid and coffee value chain development interventions: Is Starbucks working aid out of business?
Lisa Ann Richey & Stefano Ponte
World Development, forthcoming
In spring 2016, Starbucks launched its first single-origin specialty coffee from South Kivu, Democratic Republic of Congo (DRC). This coffee was produced with support from a partnership known as Kahawa Bora — a value chain development intervention (VCDI) combining a coffee corporation (Starbucks), a celebrity (Ben Affleck) and a development agency (USAID). Moving from disengaged cause-marketing to engaged development interventions, these types of partnerships promise to help beneficiaries, provide good feelings to consumers and promote the brands of corporations and NGOs. This paper applies a value chain approach to the concept of ‘Brand Aid’ as a modality of development intervention to parse the possibilities and limitations of involving corporations and celebrities in development interventions and to address a considerable research gap on the local effects that Brand Aid partnerships have on their intended beneficiaries in the global South. On the basis of original data from fieldwork in DRC, a desk study and interviews with stakeholders of the project, we compare Kahawa Bora’s formation, development and outcomes to those of a more traditional and less glitzy VCDI that has been operating in the same areas of Eastern Congo. We find that Kahawa Bora has attracted considerably more attention than other VCDIs, with little to show in terms of coffee supply and tangible benefits to farmers. We conclude that while Brand Aid forms of VCDIs promise to ‘work aid out of business’, they actually serve the interests of business and celebrities, while their actual impact on the ground is limited and uncertain.
Shrouded Prices and Firm Reputation: Evidence from the U.S. Hotel Industry
Management Science, forthcoming
Firms in many industries engage in price obfuscation — tactics that intentionally make prices more difficult for consumers to discern. Although existing research has focused on the short-term financial gains that motivate firms to obfuscate, reputational concerns may at least partially counteract these incentives if consumers punish deceptive firms via loss of loyalty in future transactions and/or publicly observable negative feedback. This paper addresses the latter possibility, exploring the impact of mandatory shrouded surcharges on firm reputation in the U.S. hotel industry. Using data collected from two major online travel sites, I exploit differences in surcharge disclosure across booking channels to identify the causal effect of hidden “resort fees” on traveler ratings. I find that hidden fees decrease ratings by roughly 0.15 points (on a rating scale ranging from 1 to 5). The magnitude of this effect varies based on firm characteristics, and this variation is consistent with observed heterogeneity in resort fee adoption patterns: when the expected punishment is more severe, firms are substantially less likely to adopt shrouded surcharges. Results shed light on the extent to which reputational mechanisms may act as a check against price obfuscation and other similar practices intended to exploit boundedly rational consumers.
Price Fairness and Strategic Obfuscation
William Allender et al.
Marketing Science, forthcoming
Firms are increasingly using technology to enable targeted, or “personalized,” pricing strategies. In settings where prices are transparent to all consumers, however, there is the potential for interpersonal price differences to be perceived as inherently unfair. In response, firms may strategically obfuscate their prices so that direct interpersonal comparisons are more difficult. The feasibility of such a pricing strategy is not well understood. In this paper, we investigate the conditions under which it is profitable for firms to engage in price obfuscation, given the potential fairness concerns of consumers. We study how price obfuscation affects consumer fairness concerns, consumer demand, and equilibrium pricing strategies. The findings suggest that if obfuscation mitigates fairness concerns, it can arise as an equilibrium outcome, even if consumers are aware of the seller’s strategic behavior and are able to update their beliefs and expectations about the prices offered to their peers accordingly. To test the theoretical predictions, an experiment is conducted in which price obfuscation is varied both exogenously and endogenously. The results confirm that buyers have intrinsic distributional (based on the seller’s margins) and peer-induced fairness (due to others being charged different prices) concerns when prices are transparent. In particular, disadvantaged peer-induced fairness concerns enter utility as an intrinsic cost that the seller has to compensate for through lower prices. Obfuscation effectively reduces peer-induced fairness concerns and increases sellers’ pricing power. However, this pricing power is constrained by distributive inequity becoming more salient when prices are obfuscated.
Outsourcing Tasks Online: Matching Supply and Demand on Peer-to-Peer Internet Platforms
Zoë Cullen & Chiara Farronato
Management Science, forthcoming
We study the growth of online peer-to-peer markets. Using data from TaskRabbit, an expanding marketplace for domestic tasks at the time of our study, we show that growth varies considerably across cities. To disentangle the potential drivers of growth, we look separately at demand and supply imbalances, network effects, and geographic heterogeneity. First, we find that supply is highly elastic: in periods when demand doubles, sellers perform almost twice as many tasks, prices hardly increase, and the probability of requested tasks being matched falls only slightly. The first result implies that in markets where supply can accommodate demand fluctuations, growth relies on attracting buyers at a faster rate than sellers. Second and perhaps most surprisingly, we find no evidence of network effects in matching: doubling the number of buyers and sellers only doubles the number of matches. Third, we show that the cities where market fundamentals promote efficient matching of buyers and sellers are also those that are the fastest growing. This heterogeneity in matching efficiency is related to two measures of market thickness: geographic density (buyers and sellers living close together) and level of task standardization (buyers requesting homogeneous tasks). Our results have two main implications for peer-to-peer markets in which network effects are limited by the local and time-sensitive nature of the services exchanged. First, marketplace growth largely depends on strategic geographic expansion. Second, a competitive rather than winner-take-all equilibrium may arise in the long run.