Publications
Mourali, M., Novakowski, D., Pogacar, R., & Brigden, N. (2025) Public perception of accuracy-fairness trade-offs in algorithmic decisions in the United States. PLOS ONE, 20(3), e0319861 https://doi.org/10.1371/journal.pone.0319861
ABSTRACT
The naive approach to preventing discrimination in algorithmic decision-making is to exclude protected attributes from the model’s inputs. This approach, known as “equal treatment,” aims to treat all individuals equally regardless of their demographic characteristics. However, this practice can still result in unequal impacts across different groups. Recently, alternative notions of fairness have been proposed to reduce unequal impact. However, these alternative approaches may require sacrificing predictive accuracy. The present research investigates public attitudes toward these trade-offs in the United States. When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. Additionally, preferences polarized along partisan identities, with Democrats favoring impact parity over accuracy maximization while Republicans displayed the reverse preference. Gender and social justice orientations also significantly predicted EIA support. Overall, findings demonstrate multidimensional drivers of algorithmic fairness attitudes, underscoring divisions around equality versus equity principles. Achieving standards around fair AI requires addressing conflicting human values through good governance.
Mourali, M., Novakowski, D., Pogacar, R., & Brigden, N. (2025) Post hoc explanations improve consumer responses to algorithmic decisions. Journal of Business Research, 189, 114981 https://doi.org/10.1016/j.jbusres.2024.114981
ABSTRACT
Algorithms are capable of assisting with, or making, critical decisions in many areas of consumers’ lives. Algorithms have consistently outperformed human decision-makers in multiple domains, and the list of cases where algorithms can make superior decisions will only grow as the technology evolves. Nevertheless, many people distrust algorithmic decisions. One concern is their lack of transparency. For instance, it is often unclear how a machine learning algorithm produces a given prediction. To address the problem, organizations have started providing post-hoc explanations of the logic behind their algorithmic decisions. However, it remains unclear to what extent explanations can improve consumer attitudes and intentions. Five experiments demonstrate that algorithmic explanations can improve perceptions of transparency, attitudes, and behavioral intentions – or they can backfire, depending on the explanation method used. The most effective explanations highlight concrete and feasible steps consumers can take to positively influence their future decision outcomes.
Brigden, N. (2024) Participant Multitasking in Online Studies. Marketing Letters. https://doi.org/10.1007/s11002-024-09718-6
ABSTRACT
Do online research participants complete studies as continuous tasks, or do they switch back and forth between the study and other online activities? While researchers generally prefer for participants to complete online studies continuously, participants may choose to multitask and complete other activities simultaneous to the study, potentially impacting their responses. This research directly measures the prevalence of online participant multitasking across three studies, examines the impact of multitasking on participant responses, and explores solutions for reducing multitasking. Findings indicate that multitasking is common, is dramatically understated in participant self-reports, can be observed unobtrusively, significantly affects participant responses, and is difficult to reduce. I also find age and gender differences in the frequency of multitasking. The appendices include new code, making it easy for other researchers to measure multitasking on multiple platforms.
Pogacar, R., Brigden, N., Plant, E., Kardes, F. R., & Kellaris, J. (2023). The reference dependence roots of inaction inertia: A query theory account. PLOS ONE, 18(3), e0282876. https://doi.org/10.1371/journal.pone.0282876
ABSTRACT
Inaction inertia is the tendency to forego an opportunity after missing a significantly better opportunity. We show that inaction inertia is rooted in reference dependence. This is consistent with prior work finding that smaller discounts are devalued and inertia is motivated by avoidance of loss. We further illuminate the process by showing that consumers treat the missed discount (rather than the regular price) as a reference point relative to which a smaller discount feels like a loss. Missing a significantly better deal causes people to focus first and foremost on thoughts critical of the current deal. Notably, consumers who miss a smaller discount also construe the second deal as a loss, even if they take it. This research integrates inaction inertia and reference dependence theory using query theory analysis to contextualize inaction inertia with biases such as loss aversion, anchoring, and the default effect.
Brigden, N. & Häubl, G. (2020) Inaction Traps in Consumer Response to Product Malfunctions. Journal of Marketing Research. https://doi.org/10.1177/0022243719889336
ABSTRACT
The authors develop and test a theory of consumer inaction traps in the domain of decisions to either address or endure product malfunctions. According to this theory, the magnitude of product malfunctions can have a paradoxical effect on consumption experience. In particular, the less severe a product malfunction is, the more inclined consumers are to defer the initial decision about whether to take corrective action. Subsequent opportunities for corrective action are devalued relative to previously forgone ones. This dynamic tends to trap consumers in a state of inaction, resulting in their enduring smaller malfunctions longer than larger ones. A consequence of these inaction traps is that minor product malfunctions may result in less enjoyable overall consumption experiences than more severe defects. Evidence from eight experiments and a survey provides support for this theorizing by demonstrating the inaction-trap phenomenon, examining its downstream consequences, shedding light on the psychological dynamics of inaction, and identifying boundary conditions that suggest interventions for counteracting consumers’ vulnerability to suffering disproportionately from relatively minor product malfunctions.
DelVecchio, D., Wang, J., & Brigden, N. (2020). All at Once or One at a Time? The Effect of Simultaneous Versus Sequential Discount Presentation on Store Patronage Intentions. Psychology and Marketing. https://doi.org/10.1002/mar.21336
ABSTRACT
Retailers often employ store flyers, be they in print or digital form, to drive store traffic. A fundamental difference in the presentation of multicomponent information, such as the multiple discounts presented in flyers, is whether the components are displayed simultaneously (all at once) of sequentially (one at a time). Yet a little extant research examines how these different presentations affect individuals' responses to retailer price promotions. Three experiments demonstrate that a sequential display of price discounts is associated with more positive store patronage intentions. Evidence, gleaned by both measuring and manipulating the process by which the discounts are evaluated, implicates a greater sense of accumulating benefit with each successive discount when presented sequentially as the driver of the cross‐format difference in patronage intentions.
Ge, X., Brigden, N., & Häubl, G. (2015). The Preference-Signaling Effect of Search. Journal of Consumer Psychology. https://doi.org/10.1016/j.jcps.2014.09.003
ABSTRACT
Consumers often make choices in settings where some alternatives are known and additional alternatives can be unveiled through search. When making a choice from a set of alternatives, the manner in which each of these was discovered should be irrelevant from a normative standpoint. By contrast, we propose that consumers infer from their own decisions to search for additional alternatives that previously known alternatives are comparatively less attractive, and that this results in an increase in preference for an alternative precisely because it was initially out of sight (rather than known). Evidence from four experiments provides support for this theorizing, demonstrating that — paradoxically placing an alternative out of sight (while providing the consumer with the opportunity to unveil it) can render that alternative more likely to be chosen. Moreover, the findings indicate that this shift in preferences is driven specifically by a devaluation of alternatives that were known prior to the decision to search. Finally, the preference-signaling effect of search is shown to be persistent in that it systematically influences a consumer's subsequent choices among new alternatives.
Swait, J., Brigden, N., & Johnson, R. D. (2014). Categories shape preferences: A model of taste heterogeneity arising from categorization of alternatives. Journal of Choice Modelling. https://doi.org/10.1016/j.jocm.2014.05.003
ABSTRACT
We propose a random utility model in which attribute importance weights used to evaluate a good is determined by the category to which that alternative is assigned. Although the weights associated with different categories may be stable, context effects can greatly influence categorization decisions. As a result, preferences may appear to be constructed when, in fact, they are driven by a finite number of category schemas. We present an experimental test of the model and demonstrate that it detects and describes explicit manipulations of product categorizations. Finally, we employ the model to analyze data from a discrete choice experiment and show that the results provide rich behavioral insights into the categorization mechanism. A key advantage of this approach is its ability to generate novel insights based on stated preference data of a form that is commonly available to decision researchers.