Findings

Selling Feedback

Kevin Lewis

December 15, 2024

How Should Time Estimates be Structured to Increase Customer Satisfaction?
Beidi Hu, Celia Gaertig & Berkeley Dietvorst
Management Science, forthcoming

Abstract:
Businesses across industries, such as food delivery apps and GPS navigation systems, routinely provide customers with time estimates in inherently uncertain contexts. How does the format of these time estimates affect customers' satisfaction? In particular, should companies provide customers with a point estimate representing the best estimate, or should they communicate the inherent uncertainty in outcomes by providing a range estimate? In eight pre-registered experiments (N = 5,323), participants observed time estimates provided by an app, and we manipulated whether the app presented the time estimates as a point estimate (e.g., "Your food will arrive in 45 minutes.") or a range (e.g., "Your food will arrive in 40-50 minutes."). After participants learned about the app's prediction performance by sampling a set of past outcomes, we measured participants' evaluation of the app. We find that participants judged the app more positively when it provided a range rather than a point estimate. These results held across different domains, different time durations, different underlying outcome distributions, and an incentive-compatible design. We also find that this preference is not simply due to people's dislike of late outcomes, as participants also rated ranges more positively than conservative point estimates corresponding to the upper (i.e., later) bound of the range. These findings suggest that companies can increase customer satisfaction with realized time estimates by communicating the uncertainty inherent in these time estimates.


Navigating the Notches: Charity Responses to Ratings
Jennifer Mayo
Journal of Political Economy Microeconomics, forthcoming

Abstract:
This paper studies donor and nonprofit responses to the star rating system designed by Charity Navigator. I find that charities respond to the rating system by changing their behavior to move above the star thresholds, leading to "bunching." This response is equivalent to charities reducing spending on administration by half, partially driven by charity misreporting. Moreover, donors reward charities based on these ratings, such that crossing the threshold from 3- to the highest 4-star rating raises contributions by 6%. These results highlight the importance of optimal rating design, including charity incentives, cognitive shortcuts, and the need for monitoring.


Automating Abercrombie: Machine-learning trademark distinctiveness
Shivam Adarsh et al.
Journal of Empirical Legal Studies, December 2024, Pages 826-860

Abstract:
Trademark law protects marks to enable firms to signal their products' qualities to consumers. To qualify for protection, a mark must be able to identify and distinguish goods. US courts typically locate a mark on a "spectrum of distinctiveness" -- known as the Abercrombie spectrum -- that categorizes marks as fanciful, arbitrary, or suggestive, and thus as "inherently distinctive," or as descriptive or generic, and thus as not inherently distinctive. This article explores whether locating trademarks on the Abercrombie spectrum can be automated using current natural-language processing techniques. Using about 1.5 million US trademark registrations between 2012 and 2019 as well as 2.2 million related USPTO office actions, the article presents a machine-learning model that learns semantic features of trademark applications and predicts whether a mark is inherently distinctive. Our model can predict trademark actions with 86% accuracy overall, and it can identify subsets of trademark applications where it is highly certain in its predictions of distinctiveness. Using an eXplainable AI (XAI) algorithm, we further analyze which features in trademark applications drive our model's predictions. We then explore the practical and normative implications of our approach. On a practical level, we outline a decision-support system that could, as a "robot trademark clerk," assist trademark experts in their determination of a trademark's distinctiveness. Such a system could also help trademark experts understand which features of a trademark application contribute the most toward a trademark's distinctiveness. On a theoretical level, we discuss the normative limits of the Abercrombie spectrum and propose to move beyond Abercrombie for trademarks whose distinctiveness is uncertain. We discuss how machine-learning projects in the law not only inform us about the aspects of the legal system that may be automated in the future, but also force us to tackle normative tradeoffs that may be invisible otherwise.


Penalty or Reward? The Effect of Social Disincentives on Online Users' Contributions
Wenqi Shen, Yan (Lucy) Liu & Yun (Alicia) Wang
Management Science, forthcoming

Abstract:
Existing research on online communities has primarily demonstrated that users are motivated by social rewards or social incentives such as positive social feedback and enhanced reputation. In contrast, this study examines how social penalties, or social disincentives, including negative social feedback and loss in reputation, influence online users' voluntary contributions in the short and long term. We develop empirical models to investigate reviewers' decisions on whether to contribute (i.e., review incidence decision) and how much to contribute (i.e., quality-adjusted review effort decision) over time. Using a state-space model, we capture the dynamics in reviewers' latent review motivation that stems from both social incentives and social disincentives. Based on a unique data set from the Amazon review system, our analysis shows that, surprisingly, social disincentives increase reviewers' motivations to contribute more frequently and devote more effort to contributions. Such effects prevail in both the short and long term. In addition, a reviewer's real name identity and experience moderate the impact of social disincentives on a reviewer's contribution decisions. We find that loss in reputation has more impact on less experienced reviewers and anonymous reviewers. Our policy simulation exercise suggests that not providing negative social feedback could diminish reviewers' review propensity by 7.85% (2.39%) and review effort by 14.82% (4.50%) in the short term (long term). Not allowing reputation loss on a review site decreases reviewers' review propensity by 2.82% (1.40%) and review effort by 7.52% (3.45%) in the short term (long term).


Kingdom or fandom? YouTube and the changing role of gatekeeping in digital cultural markets
Sumeet Malik et al.
Strategic Management Journal, forthcoming

Abstract:
How should novelty-driven entrepreneurs best position themselves in digital cultural markets? On platforms like YouTube, cultural entrepreneurs may bypass classic gatekeepers to reach novelty-seeking audiences through algorithms that search and recommend. However, this positive view of disintermediation ignores that novelty often needs curation by and consecration from professional audiences. We explore what these potentially conflicting dynamics imply for cultural entrepreneurs in the arena of YouTubers nominated for acclaimed Streamy Awards. Merging fine-grained datasets and qualitative evidence, we find that general audiences value novelty they grasp well, that professional audiences undervalue novelty they should easily spot, and that cross-audience spillovers prove negative. We discuss the implications of our results for theories toward markets serving fragmented audiences, cultural markets going online, and digital platforms.


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