This paper studies the optimal product and pricing decisions in a crowdfunding mechanism by which a project between a creator and many buyers will be realized only if the total funds committed by the buyers reach a specified goal. When the buyers are sufficiently heterogeneous in their product valuations, the creator should offer a line of products with different levels of product quality. Compared to the traditional situation where orders are placed and fulfilled individually, with the crowdfunding mechanism, a product line is more likely than a single product to be optimal and the quality gap between products is smaller. This paper also shows the effect of the crowdfunding mechanism on pricing dynamics over time. Together, these results underscore the substantial influence of the emerging crowdfunding mechanisms on common marketing decisions.
Persuasion success is often related to hard-to-measure characteristics, such as the way the persuader speaks. To examine how vocal tones impact persuasion in an online appeal, this research measures persuaders’ vocal tones in Kickstarter video pitches using novel audio mining technology. Connecting vocal tone dimensions with real-world funding outcomes offers insight into the impact of vocal tones on receivers’ actions. The core hypothesis of this paper is that a successful persuasion attempt is associated with vocal tones denoting (1) focus, (2) low stress, and (3) stable emotions. These three vocal tone dimensions—which are in line with the stereotype content model—matter because they allow receivers to make inferences about a persuader’s competence. The hypotheses are tested with a large-scale empirical study using Kickstarter data, which is then replicated in a different category. In addition, two controlled experiments provide evidence that perceptions of competence mediate the impact of the three vocal tones on persuasion attempt success. The results identify key indicators of persuasion attempt success and suggest a greater role for audio mining in academic consumer research.
This research investigates reviewing experts on online review platforms. The main hypothesis is that greater expertise in generating reviews leads to greater restraint from extreme summary evaluations. The authors argue that greater experience generating reviews facilitates processing and elaboration, and enhances the number of attributes implicitly considered in evaluations, which reduces the likelihood of assigning extreme summary ratings. This restraint-of-expertise hypothesis is tested across different review platforms (TripAdvisor, Qunar, and Yelp), shown for both assigned ratings and review text sentiment, and demonstrated both between (experts vs. novices) and within reviewers (expert vs. pre-expert). Two experiments replicate the main effect and provide support for the attributes-based explanation. Field studies demonstrate two major consequences of the restraint-of-expertise effect. (i) Reviewing experts (vs. novices), as a whole, have less impact on the aggregate valence metric, which is known to affect page-rank and consumer consideration. (ii) Experts systematically benefit and harm service providers with their ratings. For service providers that generally provide mediocre (excellent) experiences, reviewing experts assign significantly higher (lower) ratings than novices. This research provides important caveats to the existing marketing practice of service providers incentivizing reviewing experts, and provides strategic implications for how platforms should adopt rating scales and aggregate ratings.
Firms in many industries obtain superior knowledge of customer preferences through industry experience or data analytics, whereas customers often need costly efforts to learn their match values. In this paper, we examine the situations under which a customer chooses whether to inspect upon observing her personalized price from a firm with superior knowledge. On the surface, it seems that the firm can use personalized prices to directly communicate the customers’ match value, and thus there is no need for customers to expend inspection efforts. However, we find that in equilibrium the firm may trick low-preference customers into overpaying more than their match value, even when the inspection cost is low. The opportunistic incentives induce customer suspicions, which may lead to excessive customer inspection that would be avoided if the firm were not capable of price discrimination. Therefore, personalized pricing cannot obviate customer inspection. Since inspection cost raises a deadweight loss in social welfare, public policies that prevent firms from price-discriminating against customers may benefit both firms and customers.
Problem definition: Firms heavily invest in big data technologies to collect consumer data and infer consumer preferences for price discrimination. However, consumers can use technological devices to manipulate their data and fool firms to obtain better deals. We examine how a firm invests in collecting consumer data and makes pricing decisions and whether it should disclose its scope of data collection to consumers who can manipulate their data. Methodology/results: We develop a game-theoretic model to consider a market in which a firm caters to consumers with heterogeneous preferences for a product. The firm collects consumer data to identify their types and issue an individualized price, whereas consumers can incur a cost to manipulate data and mimic the other type. We find that when the firm does not disclose its scope of data collection to consumers, it collects more consumer data. When the firm discloses its scope of data collection, it reduces data collection even when collecting more data is costless. The optimal scope of data collection increases when it is more costly for consumers to manipulate data but decreases when consumer demand becomes more heterogeneous. Moreover, a lower cost for consumers to manipulate data can be detrimental to both the firm and consumers. Lastly, disclosure of data collection scope increases firm profit, consumer surplus, and social welfare. Managerial implications: Our findings suggest that a firm should adjust its scope of data collection and prices based on whether the firm discloses the data collection scope, consumers’ manipulation cost, and demand heterogeneity. Public policies should require firms to disclose their data collection scope to increase consumer surplus and social welfare. Even without such a mandatory disclosure policy, firms should voluntarily disclose their data collection scope to increase profit. Moreover, public educational programs that train consumers to manipulate their data or raise their awareness of manipulation tools can ultimately hurt consumers and firms.
The technology trend is rapidly reshaping the retail industry. With digitalization, some traditional business rules have been altered, removing many physical barriers while at the same time adding more challenges. This chapter first discusses the fundamental changes brought by the digital trend. To begin, it investigates traditional business wisdom and the underlying mechanisms of traditional business operations. The chapter examines the barriers to retailers in the traditional business world. Then, it focuses on how the digitalization trend can transform the realization of those mechanisms in order to remove the barriers and how retailers can best adjust to the changes. The implications of digitalization apply to both current and future marketing operations. When facing the new opportunities and challenges created by the digitalization trend, retailers cannot rely only on the traditional offline channel; they also need to seek omnichannel retailing. However, merging into omnichannel retailing from a traditional offline mode is not easy. The second part of the chapter thus discusses several specific risks that omnichannel retailers must tackle.
This paper investigates how a manufacturer should respond to the retailer’s and consumers’ stockpiling ability by contracting with the retailer.
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