Tip request messages are marketing communications that can help digital platforms balance customer spending with worker effort and compensation. We report the first field experiments testing how three appeals based on tipping motivations—injunctive norms (“It’s customary to leave a tip for the seller’s service”), personalized reciprocity, and impersonal reciprocity—affect tipping and future demand in a global freelance marketplace. Overall, the norms message increased tipping the most, and did so without reducing returning demand, showing that tip request messages can motivate customer tipping to better incentivize and compensate gig workers. , Tip request messages are marketing communications that can help digital platforms balance customer spending with worker effort and compensation. We report the first academic field experiments manipulating tip request message content, to our knowledge, analyzing 436,969 transactions. We test how three appeals based on tipping motivations—injunctive norms (“It’s customary to leave a tip for the seller’s service”), personalized reciprocity, and impersonal reciprocity—affect tipping and future demand in a global freelance marketplace. On the web, first exposures to the injunctive norms message increased new buyers’ tipping rate by 11.8 percentage points (p.p.) and repeat buyers’ tipping rate by 6.8 p.p. In the app, first exposures to the injunctive norms message increased tipping rate by 2.5 p.p. for new buyers and 1.4 p.p. for repeat buyers. Treatment effects on tipping increase with transaction order price. The norms and personalized reciprocity messages increased subsequent orders and spending in the web experiment and did not significantly change repatronage in the app experiment, suggesting that effective tipping prompts need not reduce future business. Overall, tip request messages can motivate customer tipping to better incentivize and compensate gig workers, but optimization requires careful testing. History: Hema Yoganarasimhan served as the senior editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mksc.2025.0392 .
Prosocial Compliance in P2P Lending: A Natural Field Experiment
We implement behavioral mechanisms in a natural field experiment to increase loan repayment rates on a peer-to-peer (P2P) lending website. The results show that text message reminders that convey lenders’ positive expectations considerably increase the likelihood that borrowers will repay their loans, whereas reminders emphasizing the adverse consequences of failure to repay loans do not have enduring effects. Our experiment results in an increase in loan repayments in the sample period. In addition, our reminders are cost-free to implement, showing the potential importance of such interventions in enhancing prosocial compliance in P2P lending. This paper was accepted by John List, behavioral economics.
The moderating role of income on consumers’ preferences and usage for online and offline payment methods
In this paper we examine consumer attitudes towards a payment method, which is a key factor affecting the probability of completing a transaction offline and online. More specifically, we constructed a model that surveyed the offline and online usage of prepaid e-cash, debit cards, credit cards and cash. User perceptions of the attractiveness of e-cash and various traditional payment means were also empirically assessed. Consumer attitudes towards a payment technology were found to be influential on users’ perceptions in both online and offline environments. User perceptions of offline purchases with a payment technology had significant and positive effects on the corresponding online usage perceptions. The effects of our research model are contingent on the income level of users. Our findings have significant implications, as they could help shed light on why consumers abandon their shopping carts and do not complete their transactions, which could potentially play a significant role when it comes to designing applications targeting sspecific consumer segments.
The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization
Online platforms have a wealth of data, run countless experiments, and use industrial-scale algorithms to optimize user experience. Despite this, many users seem to regret the time they spend on these platforms. One possible explanation is that incentives are misaligned: platforms are not optimizing for user happiness. We suggest the problem runs deeper, transcending the specific incentives of any particular platform, and instead stems from a mistaken foundational assumption. To understand what users want, platforms look at what users do. This is a kind of revealed-preference assumption that is ubiquitous in the way user models are built. Yet research has demonstrated, and personal experience affirms, that we often make choices in the moment that are inconsistent with what we actually want. The behavioral economics and psychology literatures suggest, for example, that we can choose mindlessly or that we can be too myopic in our choices, behaviors that feel entirely familiar on online platforms. In this work, we develop a model of media consumption where users have inconsistent preferences. We consider a platform which wants to maximize user utility, but only observes behavioral data in the form of the user’s engagement. We show how our model of users’ preference inconsistencies produces phenomena that are familiar from everyday experience but difficult to capture in traditional user interaction models. These phenomena include users who have long sessions on a platform but derive very little utility from it, and platform changes that steadily raise user engagement before abruptly causing users to go “cold turkey” and quit. A key ingredient in our model is a formulation for how platforms determine what to show users: they optimize over a large set of potential content (the content manifold) parametrized by underlying features of the content. Whether improving engagement improves user welfare depends on the direction of movement in the content manifold: For certain directions of change, increasing engagement makes users less happy, whereas in other directions on the same manifold, increasing engagement makes users happier. We provide a characterization of the structure of content manifolds for which increasing engagement fails to increase user utility. By linking these effects to abstractions of platform design choices, our model thus creates a theoretical framework and vocabulary in which to explore interactions between design, behavioral science, and social media. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This work was supported by the Vannevar Bush Faculty Fellowship and Multidisciplinary University Research Initiative [Grant W911NF-19-0217]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.03683 .
Always Leave Home Without It: A Further Investigation of the Credit-Card Effect on Willingness to Pay
In studies involving genuine transactions of potentially high value we show that willingness-to-pay can be increased when customers are instructed to use a credit card rather than cash. The effect may be large (up to 100%) and it appears unlikely that it arises due solely to liquidity constraints. In addition to demonstrating the effect, we provide a methodology for detecting it, and our findings suggest a source of variance to test alternative explanations.
Small Probabilistic Discounts Stimulate Spending: Pain of Paying in Price Promotions
AbstractWe find that small probabilistic price promotions effectively stimulate demand, even more so than comparable fixed price promotions (e.g., “1% chance it’s free” vs. “1% off,” respectively), because they more effectively reduce the pain of paying. In three field experiments at a grocer, we exogenously and endogenously manipulated the salience of pain of paying via elicitation timing (e.g., at entrance or checkout) and payment method (i.e., cash/debit cards or credit cards). This modulated the attractiveness of probabilistic discounts and their ability to stimulate spending. Shoppers paying with cash or debit cards, for example, spent 54% more if they received a 1% probabilistic discount than a 1% fixed discount (experiment 2). A fourth experiment showed that consumers’ sensitivity to pain of paying modulates the greater comparative efficacy of small probabilistic than fixed discounts. More broadly, the results elucidate a novel affective route through which price promotions stimulate demand––pain of paying.
Persuasion: Empirical Evidence
We provide a selective survey of empirical evidence on the effects as well as the drivers of persuasive communication. We consider persuasion directed at consumers, voters, donors, and investors. We organize our review around four questions. First, to what extent does persuasion affect the behavior of each of these groups? Second, what models best capture the response to persuasive communication? Third, what are persuaders' incentives, and what limits their ability to distort communications? Finally, what evidence exists on the way persuasion affects equilibrium outcomes in economics and politics?
Are Large Language Models Sensitive to the Motives Behind Communication?
Human communication is $\textit{motivated}$: people speak, write, and create content with a particular communicative intent in mind. As a result, information that large language models (LLMs) and AI agents process is inherently framed by humans' intentions and incentives. People are adept at navigating such nuanced information: we routinely identify benevolent or self-serving motives in order to decide what statements to trust. For LLMs to be effective in the real world, they too must critically evaluate content by factoring in the motivations of the source---for instance, weighing the credibility of claims made in a sales pitch. In this paper, we undertake a comprehensive study of whether LLMs have this capacity for $\textit{motivational vigilance}$. We first employ controlled experiments from cognitive science to verify that LLMs' behavior is consistent with rational models of learning from motivated testimony, and find they successfully discount information from biased sources in a human-like manner. We then extend our evaluation to sponsored online adverts, a more naturalistic reflection of LLM agents' information ecosystems. In these settings, we find that LLMs' inferences do not track the rational models' predictions nearly as closely---partly due to additional information that distracts them from vigilance-relevant considerations. However, a simple steering intervention that boosts the salience of intentions and incentives substantially increases the correspondence between LLMs and the rational model. These results suggest that LLMs possess a basic sensitivity to the motivations of others, but generalizing to novel real-world settings will require further improvements to these models.
In-Store Spending Dynamics: How Budgets Invert Relative-Spending Patterns
Abstract The authors conduct four controlled lab experiments and one field study in a brick-and-mortar grocery store to demonstrate that relative spending—the price of the purchased item relative to the mean price of the product category—evolves nonlinearly and distinctly for budget and nonbudget shoppers. While the relative spending of budget shoppers evolves in a concave manner, the relative spending of nonbudget shoppers evolves inversely in a convex manner. Thus, budget (nonbudget) shoppers spend relatively more (less) in the middle than at the beginning and toward the end of their shopping trip. Mediation analyses confirm that the pain of paying experienced while shopping drives price salience, which then drives relative spending. Moreover, manipulating shoppers’ pain of paying, by altering the opportunity costs associated with their spending or drawing shoppers’ attention to their spending via real-time spending feedback, is shown to influence these spending patterns. The research offers theoretical contributions to the in-store decision-making, budgeting, and pain-of-paying literature and has important implications for marketing and promotion strategies in retail and mobile technology environments, as it suggests when a shopper may be more sensitive to price-related factors.
Demand characteristics in human–computer experiments
Demand characteristics refer to cues that can inform participants in experiments about the hypothesis and influence their behavior. They lead researchers to erroneously infer non-existing effects, undermining the experimental integrity of empirical studies. Despite a widespread acknowledgment of their confounding influence in experimental psychology, experiments involving humans and computers to a lesser extent consider effects of demand characteristics, as computerized protocols are thought to be immune to some experimenter biases. Furthermore, demand characteristics are considered to mainly effect subjective measures. As a result, demand characteristics often remain uncontrolled in studies involving computers, and in particular for objective measures such as performance. In this paper, we present two experiments that underline the importance of demand characteristics in human–computer interaction experiments. In a text-entry study, we made participants believe they were evaluating a research-based keyboard. This belief led to increased performance and self-reported user experience. In a second study, we conducted a thought experiment on the illusion of body ownership in virtual reality, where the experimental design indicated the study hypothesis. We found hypothesis-compliant responses from participants, even when they did not experience the illusion. We conclude that demand characteristics pose a significant challenge to the interpretation and validity of human–computer experiments, even when they are fully automated. We discuss the implications and offer guidelines to mitigate effects of demand characteristics.
Mis-Nudging Morality
Morals constrain self-serving behavior. Yet, self-regulation failures in the face of monetary temptation are common at the workplace. To limit such failures, organizations can design environments that limit the temptation to behave self-servingly, nudging workers to uphold their morals. In a series of experiments where participants may be tempted to take excessive pay after exerting effort, we study whether a simple intervention—asking individuals to state the wage they believe should be paid ex ante, before facing the temptation to take excessive compensation—prevents self-serving behavior. In contrast to lay beliefs and the predictions from prior work, we find that such an intervention is not effective, leading to self-serving behavior. However, a more realistic elicitation procedure of the appropriate wage mitigates this effect. These findings contribute to work on the malleability of moral behavior showing that simple interventions thought to effectively mitigate self-serving behavior can prompt individuals to stretch their moral boundaries. They also stress the importance of properly testing interventions that might seem intuitive. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: Financial support from the Israel Science Foundation [Grant 766/19] is gratefully acknowledged. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4344 .
The Effects of Financial Incentives in Experiments: A Review and Capital-Labor-Production Framework
We review 74 experiments with no, low, or high performance-based financial incentives. The modal result has no effect on mean performance (though variance is usually reduced by higher payment). Higher incentive does improve performance often, typically judgment tasks that are responsive to better effort. Incentives also reduce “presentation” effects (e.g., generosity and risk-seeking). Incentive effects are comparable to effects of other variables, particularly “cognitive capital” and task “production” demands, and interact with those variables, so a narrow-minded focus on incentives alone is misguided. We also note that no replicated study has made rationality violations disappear purely by raising incentives.
Variety Effects in Mobile Advertising
Mobile app users are often exposed to a sequence of short-lived marketing interventions (e.g., ads) within each usage session. This study examines how an increase in the variety of ads shown in a session affects a user's response to the next ad. The authors leverage the quasi-experimental variation in ad assignment in their data and propose an empirical framework that accounts for different types of confounding to isolate the effects of a unit increase in variety. Across a series of models, the authors consistently show that an increase in ad variety in a session results in a higher response rate to the next ad: holding all else fixed, a unit increase in variety of the prior sequence of ads can increase the click-through rate on the next ad by approximately 13%. The authors then explore the underlying mechanism and document empirical evidence for an attention-based account. The article offers important managerial implications by identifying a source of interdependence across ad exposures that is often ignored in the design of advertising auctions. Furthermore, the attention-based mechanism suggests that platforms can incorporate real-time attention measures to help advertisers with targeting dynamics.
Unwillingness to pay for privacy: A field experiment
We measure willingness to pay for privacy in a field experiment. Participants bought at most one DVD from one of two competing online stores. One store consistently required more sensitive personal data than the other, but otherwise the stores were identical. In one treatment, DVDs were one Euro cheaper at the store requesting more personal information, and almost all buyers chose the cheaper store. Surprisingly, in the second treatment when prices were identical, participants bought from both shops equally often.
Integrative experiments identify how punishment affects welfare in public goods games
Despite decades of research, the conditions under which punishment promotes cooperation remain unclear. Through an integrative experiment varying 14 design parameters of public goods games across 360 experimental conditions (147,618 decisions from 7100 participants), we reveal substantial heterogeneity in punishment effectiveness: Its impact on welfare ranges from 43% improvement to 44% reduction depending on the game parameters. To characterize these patterns, we developed models that outperformed human forecasters in predicting punishment effectiveness in new experiments. Communication emerges as the most important factor, followed by contribution framing (opt out versus opt in), contribution type (variable versus all-or-nothing), game length, and outcome visibility, though these factors often interact. The results reframe the debate from whether punishment works to when it does, demonstrating how integrative experiments enable discovery of generalizable patterns in social phenomena. , Editor’s summary People face conflicts between maximizing personal gain versus supporting collective interests. If we cooperatively recycle or donate to charities, it benefits society, but it also costs us time and resources that could be selfishly preserved for ourselves. We impose penalties to deter those undesirable or selfish behaviors, but under what conditions do punishments or penalties effectively modify behavior to benefit group welfare? Alsobay et al . systematically and simultaneously varied 14 factors together instead of in isolation. Punishment was unequivocally most effective when paired with consistent communication, particularly over time. Another effective factor was “opting out” or withdrawing some, but not all, endowments already in the public fund. These methodological advances revealed when, rather than whether, punishment works. —Ekeoma Uzogara , INTRODUCTION Human societies face many situations where individual and collective interests conflict, often referred to as social dilemmas. Costly peer punishment has been studied for more than 25 years in public goods games (stylized behavioral experiments in which individuals decide how much to contribute to a shared pool that benefits everyone) as a mechanism to promote cooperation. Prior research has identified many contextual factors that moderate punishment’s effectiveness, including game length, communication, group size, punishment cost, and so on. However, the specific conditions under which punishment improves group welfare remain unclear. RATIONALE We argue that this lack of clarity derives from the dominant experimental paradigm, in which any given study manipulates only one or a few theoretically informed factors. Because such studies differ in many ways (different experimental procedures, populations), their results are often difficult to compare or integrate. Consequently, one can list many factors that have some effect, but cannot say how much each matters relative to the others, or how they work together, and as a result, cannot predict when punishment will help or harm welfare in new settings. To address this fundamental knowledge gap, we use an integrative experimental design and systematically vary 14 parameters across 360 conditions (147,618 decisions from 7100 participants) to elucidate when punishment improves versus undermines welfare in public goods games, which factors matter most, and how they interact. RESULTS The effect of punishment on welfare ranged from 43% improvement to 44% reduction depending on the specific combination of game parameters. To characterize this heterogeneity, we trained a model that outperformed all 553 human forecasters (laypeople and experts) in predicting whether punishment would help or harm welfare in new experiments. Communication emerged as roughly three times more important than any other factor, followed by contribution framing (opt in versus opt out), contribution type (variable versus all-or-nothing), game length, and peer outcome visibility (whether participants can see others’ earnings). These factors often interact. For example, longer games enhance punishment’s effectiveness only when communication is available, and contribution framing effects depend on both contribution type and outcome visibility. CONCLUSION Many phenomena in social science are shaped by many factors whose interactions are consequential, yet the dominant experimental paradigm often limits its inquiry to “does a given effect exist?” and examines hypothesized factors in isolation. As a result, research programs can accumulate many partial explanations without a clear picture of how they combine to determine outcomes across settings. Knowing that factors matter individually is fundamentally different from knowing how much each matters and how they interact. The integrative approach implemented here offers one way forward. It varies many factors simultaneously within a shared design space, evaluates models by their predictive accuracy on new experiments, and probes those models to constrain and develop theory. Our hope is that integrative experiment designs, combined with models that integrate prediction and explanation, represent a path toward more cumulative social science. Integrative experiment reveals when punishment helps versus harms. We systematically varied 14 design parameters across 360 experimental conditions. The effect of punishment on cooperation efficiency ranged from −44% to +43% depending on the specific game parameters. Communication emerged as three times more important than any other factor, followed by contribution framing, contribution type, and game length.
Mapping the online manipulation economy
A market perspective on digital manipulation may help improve online trust and safety , The internet is awash with inauthentic activity. Although some of this activity is harmless, a substantial proportion can be classified as malicious, including bots designed to steal personal data, networks of inauthentic social media accounts promoting crypto scams, and coordinated political influence operations ( 1 , 2 ). To enhance security, online platforms have implemented measures to counter inauthentic activity, with most requiring users to confirm account authenticity via a one-time password sent through short message service (SMS). However, there exists a thriving market for buying and selling on-demand SMS verifications, which forms a cornerstone of the wider online manipulation economy. Despite its centrality in the digital manipulation ecosystem, this SMS verifications market has not been systematically studied. To address this knowledge gap, we developed the Cambridge Online Trust and Safety Index (COTSI; cotsi.org), which tracks the daily price of SMS verifications for 197 countries and >500 platforms.
Mapping the online manipulation economy
A market perspective on digital manipulation may help improve online trust and safety , The internet is awash with inauthentic activity. Although some of this activity is harmless, a substantial proportion can be classified as malicious, including bots designed to steal personal data, networks of inauthentic social media accounts promoting crypto scams, and coordinated political influence operations ( 1 , 2 ). To enhance security, online platforms have implemented measures to counter inauthentic activity, with most requiring users to confirm account authenticity via a one-time password sent through short message service (SMS). However, there exists a thriving market for buying and selling on-demand SMS verifications, which forms a cornerstone of the wider online manipulation economy. Despite its centrality in the digital manipulation ecosystem, this SMS verifications market has not been systematically studied. To address this knowledge gap, we developed the Cambridge Online Trust and Safety Index (COTSI; cotsi.org), which tracks the daily price of SMS verifications for 197 countries and >500 platforms.