Abstract Expectancy-disconfirmation has been the dominant paradigm to explain the formation of consumer satisfaction for over 40 years. Within this paradigm, it is possible for expectations to have opposing effects on consumer satisfaction depending on the underlying psychological processes presupposed. In general, assimilation processes predict positive effects, while contrast processes predict negative effects. A comprehensive assessment of the empirical evidence for these positions is missing. Hence, we provide a meta-analysis of expectancy-disconfirmation research, using 150 records ( N = 58,597), to test the direct effects of perceived performance and performance expectations on consumer satisfaction, while also including disconfirmation as a mediator in each path (using meta-analytical path analysis). We found evidence for an overall positive relationship between expectations and consumer satisfaction ( r = .29 [0.24, 0.34]) and no evidence supporting contrast effects. Moderator analyses revealed that the positive correlation between performance expectations and consumer satisfaction was significantly stronger for predictive (vs. normative) expectations, for services (vs. goods), and for cross-sectional (vs. longitudinal and experimental) studies. Furthermore, we found an unexpected downward publication bias, which suggests that the true correlation between disconfirmation and consumer satisfaction is higher than the (already high) estimate we found. We discuss how future research can empirically scrutinize popular practitioner views and promote the development of causal explanations, account for non-linear effects, and elucidate the anomalous publication bias found here.

Abstract What are the channels through which monetary policy affects financial stability? Can (and should) central banks prevent financial crises by deviating from price stability? To what extent may monetary policy itself unintentionally breed financial vulnerabilities? We answer these questions using a New Keynesian model with capital accumulation and endogenous financial crises due to adverse selection and moral hazard in credit markets. Our findings are threefold. First, monetary policy affects the probability of a crisis both in the short run (via aggregate demand) and in the medium run (via capital accumulation). Second, the central bank can reduce the incidence of crises in the medium run by tolerating higher inflation volatility in the short run. Third, prolonged periods of loose monetary policy followed by a sharp tightening can lead to financial crises.

As artificial intelligence (AI) tools have become pervasive in business applications, so too have interactions between AI and humans in business processes and decision-making. A growing area of research has focused on human decision and task delegation to AI assistants. Simultaneously, extensive research on algorithm aversion—humans’ resistance to algorithm-based decision tools—has demonstrated potential barriers and issues with AI applications in business. In this paper, we test a simple strategy for mitigating algorithm aversion in the context of AI task delegation. We show that simply changing the framing of decision tasks can allay algorithm aversion. Through multiple studies, we found that participants exhibited a strong preference for human assistance over AI assistance when they were rewarded for task performance (i.e., money was gained for good performance), even when the AI had been shown to outperform the human assistant on the task. Alternatively, when we reframed the task such that the participant experienced losses for poor performance (i.e., money was taken from their endowment for poor performance), the bias for preferring human assistance was removed. Under loss framing, participants delegated the decision task to human and AI assistants at similar rates. We demonstrate this finding across tasks at differing levels of complexity and at different incentive sizes. We also provide evidence that loss framing increases situational awareness, which drives the observed effects. Our results offer useful insights on reducing algorithm aversion that extend the literature and provide actionable suggestions for practitioners and managers. This paper was accepted by Dongjun Wu, Special Issue on the Human-Algorithm Connection. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05585 .

Abstract Global phenomena, such as climate change, often have local impacts that are spatially correlated. We show that greater spatial correlation of productivities can increase international inequality by increasing the correlation between a country's productivity and its gains from trade. We confirm this prediction using a half-century of exogenous variation in the spatial correlation of agricultural productivities induced by a global climatic phenomenon. We introduce this general-equilibrium effect into projections of climate-change impacts that typically omit spatial linkages and therefore do not account for the global scope of climate change. We project greater international inequality, with higher welfare losses across Africa.

Abstract Extremeness aversion—the tendency for consumers to prefer middling options in a choice set—is an incredibly robust and well-studied phenomenon. However, it has primarily been studied in the context of two- or three-option choice sets. In six studies (Ntotal = 9,377), we suggest that consumers’ aversion to extreme options depends on the frequency of similar options in the choice set. In particular, we find that consumers are relatively more likely to choose an option that is in an extreme relative position when they are exposed to multiple extreme options, an effect not predicted by standard theories of context-dependent choice. This occurs because consumers perceive objectively extreme (vs. intermediate) options as relatively more typical of the product category. We demonstrate that this effect is robust across different types of compositions, hypothetical and incentive-compatible studies, and in a variety of decision contexts (e.g., purchasing an item vs. choosing an activity to complete). Furthermore, we identify boundary conditions, such as the type of occasion consumers are choosing for.

This paper develops the concept of a “crypto multiplier,” which measures the equilibrium response of a cryptocurrency’s market capitalization to aggregate inflows and outflows of investors’ funds. The crypto multiplier takes high values when a large share of a cryptocurrency’s coins is held as an investment rather than being used as a means of payment. Blockchain data show that the share of coins held for the purpose of making payments is rather small for major cryptocurrencies suggesting large crypto multipliers. Our results highlight the need for market participants to be vigilant when accepting block holdings of a cryptocurrency as collateral or as compensation for seed funding. The crypto multiplier indicates that the liquidation value of block holdings of cryptocurrencies can be substantially below their prevailing market values.

ABSTRACT The annual proportion of U.S. public firms that reported a financial covenant violation fell roughly 70% between 1997 and 2019. To understand this trend, we develop an estimable model of covenant design that depends on the ability to differentiate between distressed and nondistressed borrowers and the relative costs associated with screening incorrectly. We find that the drop in violations is best explained by an increased willingness to forgo early detection of distressed borrowers in exchange for fewer inconsequential violations, which we attribute largely to a shift in the composition of public borrowers and partly to heightened investor sentiment during the 2010s.