Climate (in)action by small business owners is not driven only by policy or economic considerations but also by sociological and psychological factors, as leaders mimic the behaviors they perceive among their peers. We find that these social inferences about what others believe are often incorrect. In this registered report of 4,432 small-to-medium business owners and leaders across seven countries, we observe a persistent gap between perceived and actual beliefs. And this gap matters: small-to-medium enterprise owners exhibiting a larger gap have fewer sustainability policies in place. These results highlight how peer effects can spur climate action among business leaders.
Although trust is critical for entrepreneurs in all markets, it likely differs in gray markets from that in white or black markets. In this inductive study, we find a coherent configuration of trust mechanisms in a gray market that offers new insights into how entrepreneurs can use their network (i.e., referrals and “lineaging”) and heuristics (i.e., “villageing”) to build trust in new stakeholders when trust formation is challenging and violations are costly. We also offer new insights into the practices for verifying (i.e., impromptu tests and extensive probationary periods) and maintaining (i.e., financial support) trustworthiness, as well as repairing trust.
This article investigates whether institutional conditions that limit employees to pursue hybrid entrepreneurship reduce potential high-growth entrepreneurship. Using the Alcatel v. Brown ruling, as an institutional change that curtailed hybrid entrepreneurial activity, we show that affected states experienced a marked decline in indicators of ventures with high-growth potential. Effects are strongest among early-stage ventures, consistent with hybrid entrepreneurship enabling low-cost experimentation. Venture capital investors also respond to reduced experimentation by increasing syndication and staging. Overall, the results show that institutional arrangements shape experimentation through hybrid entrepreneurship and, consequently, the supply of ventures capable of achieving high growth.
Entrepreneurship outcomes tend to be right-skewed and heavy-tailed, with a small fraction of “star” firms often accounting for a disproportionate share of value creation. Yet, how to define the “star” entrepreneurs driving these outcomes remains highly contested. In this paper, we argue that star identification is best understood as a modeling choice that depends on the research objective—no threshold rule can be evaluated as “optimal” or more “precise” independent of what the rule it is supposed to serve—and introduce a precision–recall framework that makes the trade-offs of threshold-based approaches more explicit. Using 13 years of Inc. 5000 data linked to subsequent public listings, we then compare nine threshold-based methods, including the recently proposed quantile absolute deviation procedure by Gala and Schwab (hereafter GS-QAD) which uses a bootstrap to set a cutoff tailored to the tail of the observed distribution. Three findings emerge. First, using simulations, we show that GS-QAD’s bootstrap procedure introduces systematic sample-size bias that can confound cross-industry comparisons. At the same time, as sample size grows, the method converges to a near-universal 17% to 20% rule. Second, firms classified as stars are 10 to 20 times more likely to become publicly traded and account for the majority of current market capitalization among publicly traded Inc. 5000 alumni. However, the choice of threshold determines a precision–recall trade-off in which no method dominates on all metrics. Finally, the probability of becoming a publicly traded company rises smoothly with revenue, with no detectable discontinuity at any threshold. We conclude that stars are where you draw the line—thresholds should be treated as modeling choices fit to the research or policy objective, validated externally where feasible, and reported with their explicit trade-offs.
Integrating family firm innovation research with signaling theory, we develop a framework explaining market reactions to patents granted to family versus nonfamily firms. Using a U.S. dataset of 52,241 patents from 2014 to 2018, we find the stock market reacts more positively to patents granted to family firms than nonfamily firms. We then examine reactions to radical versus incremental patents. Patents reflecting radical innovation, measured by novelty and pedigree, create greater market value for family firms than nonfamily firms, although the pedigree effect is only marginally significant. By contrast, patents reflecting incremental innovation, measured by temporal depth, have little effect on nonfamily firms’ market value but reduce family firms’ market value.
