We examine how entrepreneurs transition to angel investing through the lens of generalized reciprocity and embeddedness. Using a multimethod approach combining archival analysis with three experiments, we reveal how psychological and social mechanisms shape this transition. We find that entrepreneurs who have received angel funding are more likely to become angel investors themselves, particularly when they share educational, professional, or geographic ties with their initial investors. While angel investors, like all investors, seek financial returns, we theorize that choosing angel investing over alternative investment vehicles reflects prosocial motivations rooted in reciprocity. Our mediation analyses demonstrate that gratitude, rather than reputation concerns, drives this initial transition. Furthermore, entrepreneurs who received embedded funding preferentially direct their investments toward entrepreneurs with whom they share embedded ties. This reveals a two-part process: emotional mechanisms drive the decision to become an angel investor, while structural embeddedness guides investment targeting. These findings integrate psychological accounts of generalized reciprocity with sociological perspectives on embeddedness, linking individual-level and structural explanations of prosocial behavior in economic contexts. Our study highlights how angel investing perpetuates itself through self-reinforcing cycles that create both opportunities and constraints for ecosystem development.

When crises strike, new forms of emergent organizing often arise to address urgent societal needs that formal institutions struggle to meet. Among these, emergent response groups (ERGs)—self-organized communities that form to respond to unexpected and extreme events—offer a particularly salient example of decentralized and nonhierarchical organizing. This multicase study investigates eight ERGs that formed during the COVID-19 pandemic to design and distribute critical medical supplies. Drawing on sensemaking theory, we show how bricolage—making do with at-hand resources—supports coordination and community structuring by reducing equivocality caused by distributed actors. Our findings describe how these ERGs grew rapidly by using bricolage to reduce action, goal, and resource equivocality, enabling coordinated and scalable crisis response efforts. We contribute to research on emergent organizing in crisis contexts by revealing how bricolage fosters coherence and rapid scaling in the absence of formal hierarchies. Our study also challenges the dominant assumption that bricolage is inherently limiting to organizational growth, showing that—in the context of self-organizing collectives—it offers a novel solution to the problem of coordinating action among distributed agents.

How can a firm increase its chances of spotting an up-and-coming competitor? I examine how the experience of top management teams (TMTs) relates to the foresight needed to spot “rising stars”—that is, competitors that are currently ignored by other firms in a market but later grow to become prominent threats. My theory hinges on the primacy of relational versus categorical reasoning in mental models of competition. Firm mental models conceptualize competition through a relational lens, such that deep experience within the focal firm encourages fixation on familiar, proximate rivals while varied experience across firms redirects attention to the competitive periphery. In contrast, industry mental models rely on categorical reasoning, such that varied experience across industries yields a shallow understanding of divergent—and often irrelevant—categorical logics while deep experience in the focal industry builds the expertise to see which competitors will break out in the future. I further show that deep industry experience enables the TMT to draw more useful insights from varied firm experience, while a combination of varied firm and industry experience creates harmful information overload. I find support for my theory using a hand-collected dataset on competitor identification and TMTs in the enterprise software industry.

Investors perceive a CEO’s regulatory foci based on the language they use. Complementing earlier studies on the direct effects of CEO regulatory focus, we propose a novel theoretical paradigm in which CEOs’ regulatory-focused language act as interpretive frames shaping investors’ reactions to earnings deviations—the positive or negative earnings deviations from analysts’ expectations. We propose that investor reactions to earnings deviations will be amplified when investors perceive a CEO to have higher promotion focus, and dampened when investors perceive a CEO to have higher prevention focus. We employ a multimethod approach to test our theorizing; we use an archival study of S&P 500 firms from 2003 to 2017 to test our hypotheses, and two preregistered randomized vignette experiments to explore the underlying investor attributions theorized in our framework. Across all studies, we find broad support for our theorization regarding how CEO regulatory-focused language acts as interpretive frames for investors, which has important implications for research on the social view of financial markets and CEO regulatory focus.

Does “eureka” still ring true in the algorithmic age? This paper investigates algorithmic design and its impact on idea generation. The use of algorithms transforms knowledge work processes, redefining experts’ roles across industries. While experts are typically viewed as the cornerstone of knowledge work, an emerging debate is underway about whether expertise is still necessary in the idea generation process for creativity and innovation. Integrating creativity, expertise in knowledge work, and algorithmic design literatures, we theorize on the interplay between expertise and algorithmic design in idea generation. We suggest that the prevailing algorithmic design, focusing on information exploitation, is misaligned with the recombinant innovation processes needed for idea generation, especially for experts. We design an exploration-based modification to the prevalent exploitation-based algorithm—Google Search. We hypothesize that when using exploration-based algorithms, experts can overcome confirmation bias and generate more creative ideas through recombinant innovation. Moving beyond the individual level, experts are central to bursting “ideation bubbles”—clusters of similar ideas—thus enhancing idea diversity. We test our theory in a laboratory study and a field experiment through a global ideation challenge in sustainability launched on Freelancer. Findings offer insights into designing and using algorithms to augment human expertise for innovation.

Providing sponsorship, when senior colleagues (sponsors) mobilize resources in their social network on junior colleagues’ (protégés) behalf, is consequential to the effective functioning of organizations. Yet, the psychological processes underlying male and female sponsors’ goals, priorities, and strategies for activating resources in their networks for protégés are not well understood. We conduct four studies to examine male and female sponsors’ goals and the resulting impact on their approach to activating resources in their social network. Studies 1a and 1b provide converging, initial evidence that men approach sponsorship with less complexity than women, by setting fewer goals that focus more on benefiting themselves. Study 2 provides additional evidence: male sponsors are more likely than female sponsors to perceive sponsorship as an opportunity to benefit themselves while female sponsors perceive sponsorship as an opportunity to benefit their protégés. Study 3 experimentally links the differences in goal pursuit to sponsors’ social network strategies: when asked to balance protégé-centric and sponsor-centric goals, women cognitively activate networks higher in closure, but men activate sparse networks regardless of their goals. The implications of these network activation configurations for understanding the sponsorship process are discussed.

Despite using valid research designs informed by substantive and methodological theory, results across studies fail to converge at times. We introduce multiverse analysis as a tool to help catalyze theory development in situations with divergent theoretical explanations and associated evidence by (a) offering strong and precise inference testing, (b) identifying sources of variability across (dozens, hundreds, thousands, or even millions of) systematically and comprehensively derived valid alternative design decisions, and (c) allowing for the multifaceted nature of theoretical mechanisms. We use the theoretical and empirical divergence surrounding studies on how chief executive officer (CEO) gender relates to CEO pay to illustrate how multiverse analysis is a systematic and comprehensive method that is widely applicable, accessible, and flexible, making it a valuable catalyst for theory building.

This paper examines the process of designing interfaces between routine clusters in a world in flux in which interdependencies are emergent. We reveal that designing interfaces is an endogenous and iterative process of creating and harmonizing interdependencies. In our ethnographic study of the reorganization of agile software development, actors implemented a second routine cluster, which they envisioned to work autonomously. Because the teams shared resources, however, they created interdependencies across the two routine clusters that challenged their autonomy. Our findings contribute to research on routine dynamics, interdependence, and organizations design. They do so, first, by showing how the dynamics of designing interfaces are driven by the emergent nature of interdependencies, which routine participants may endogenously harmonize through routine performances. Second, our findings detail the practices through which “interface design work” is accomplished. Although some of these practices imply transitory variations in routine performances, others change the patterning of interfaces more sustainably. Third, resources play an important role in designing interfaces because making them readily available may require their reconfiguration. Moreover, the pooled interdependencies that flow from shared resources can escalate into more complex types of interdependence.