Balázs Kovács

We examine how political heterogeneity in groups affects individual performance in settings where people work alongside others. Leveraging the random assignment of golfers to groups in Professional Golfers’ Association Tour tournaments, we find that golfers score 0.2 strokes better per round when playing in politically homogeneous versus heterogeneous groups. This corresponds to a five-rank improvement before the tournament cut and an additional $13,000–$23,400 in tournament earnings. The effect intensifies during periods of high societal political polarization and diminishes when polarization is low. We propose that politically heterogeneous groups create a more stressful and less psychologically safe environment, reducing focus and leading to reduced performance. Consistent with this mechanism, analyses of shot-level data reveal that this effect is strongest during driving and putting shots when players are in close physical proximity. Our study contributes to the understanding of how political heterogeneity in groups affects individual performance in competitive settings, with implications for managing ideological differences in organizations. This paper was accepted by Sameer Srivastava, organizations. Funding: The authors appreciate financial help from Yale University and Fox International [fellowship]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.01798 .

Market categories can favor typical members over atypical ones, yet this “categorical imperative” operates inconsistently across contexts. We argue that typicality effects depend on how evaluators engage with categories during search. Using behavioral simulation in which participants search a large database of companies for competitors, we distinguish between searches that explicitly invoke industry classifications and those using alternative methods, and measure goal-category congruence through semantic alignment between evaluators’ objectives and the categories they employ. We find that typical companies become more likely to be selected in category-based searches; other searches produce no typicality effects. Among category-based searches, goal-category congruence moderates the effect: typicality strongly predicts selection when goals and categories misalign but becomes irrelevant when they align well. These findings identify a specific microprocess through which categorical effects become contingent, offering a process-level explanation for variation documented across audiences, producer characteristics, and evaluation contexts.

Research Summary: We examine political affiliation's role in venture team formation and success. Using data from Crunchbase and L2 on 1125 US‐based startups, we investigate political homophily in team assembly and its association with startup outcomes. Our analysis reveals strong political homogeneity in founding teams: teams with similar political views form more frequently than diverse teams, even after controlling for founders' gender, age, location, and industry. This political homophily relates to venture performance. Startups with politically heterogeneous founding teams are more likely to shut down. Across additional performance measures (capital funding, employee size, Crunchbase rankings), we observe directionally consistent associations with worse outcomes, though these secondary findings vary in robustness. These findings highlight the dual role of founders' political affiliations: their relationship with team composition and startup performance. Managerial Summary: Our study examines political diversity's association with startup team formation and venture success. Analyzing data from 1125 US‐based startups, we discovered a strong tendency for teams to form based on political similarity. Individuals with similar political views prefer starting companies together, even when accounting for age, gender, location, and industry. Startups with politically diverse founding teams are more likely to shut down. These teams also tend to receive less funding, have fewer employees, and exhibit worse Crunchbase rankings, although these patterns are less consistent than the survival effect. For entrepreneurs and investors, these findings highlight the need to balance team cohesion against diverse perspectives. Business practitioners should be aware of these dynamics when forming teams or developing management strategies, particularly in a politically charged environment.

Research Summary: In market networks, firms regularly seek partners with needed expertise, but these partners often work with the firms' competitors. How such second‐order competitive overlap affects partner selection is unclear. Prior theory assumes firms view networks as “pipes” and emphasizes flows of competitor information via the shared partner as key in partner selection. We propose that firms also view networks as “prisms” and use competitive overlap as a signal of a potential partner's expertise. Hence, firms may prefer partners with competitive overlap. We find support for our claims in the patent law firm selection context. Furthermore, higher competitive overlap leads to slower patent acceptance but results in broader patents, implying that the competitive overlap expertise signal reduces search costs without significant performance loss. Managerial Summary: When selecting expert partners like law firms or consultants, some companies may consider avoiding firms that also serve their competitor. However, when little is known about potential partners, this “competitive overlap” might be a way to assess their quality. We found that firms are more likely to choose patent law firms who work with their competitors. This occurs because a competitor's choice signals that partner's expertise. Companies rely on this signal most when they lack direct experience with potential partners or when entering new technological domains. While working with partners who serve competitors might slightly increase processing times, it can increase patent protection without compromising overall performance. These findings suggest that avoiding partners based solely on competitive overlap may limit access to valuable expertise.

Organizational research increasingly relies on online review data to gauge perceived valuation and reputation of organizations and products. Online review platforms typically collect ordinal ratings (e.g., 1 to 5 stars); however, researchers often treat them as a cardinal data, calculating aggregate statistics such as the average, the median, or the variance of ratings. In calculating these statistics, ratings are implicitly assumed to be equidistant. We test whether star ratings are equidistant using reviews from two large-scale online review platforms: Amazon.com and Yelp.com. We develop a deep learning framework to analyze the text of the reviews in order to assess their overall valuation. We find that 4 and 5-star ratings, as well as 1 and 2-star ratings, are closer to each other than 3-star ratings are to 2 and 4-star ratings. An additional online experiment corroborates this pattern. Using simulations, we show that the distortion by non-equidistant ratings is especially harmful in cases when organizations receive only a few reviews and when researchers are interested in estimating variance effects. We discuss potential solutions to solve the issue with rating non-equidistance.

Whenever someone posts an online review of a restaurant, museum, or barbershop, they also leave a trace of where they traveled. The author visualizes travel patterns in 11 North American metropolitan areas using geolocated review data. The data are based on approximately 7 million online Yelp.com reviews posted by 2 million reviewers between 2005 and 2020. First, the author demonstrates how individual travel patterns can be mapped using the review data and discusses the potential applications of such individual-level data. The author then turns to aggregate-level maps, creating establishment covisit networks in which two establishments are linked if multiple reviewers visit both. Maps of establishment covisits reveal various intriguing patterns related to consumption and geography, such as the connections between neighborhoods and the centralization and segregation within a metropolitan area. Establishment covisit maps can also inform researchers about the diffusion of ideas and practices, trends in crime, and gentrification.