Knowledge that Transforms

The literature on institutional voids examines how intermediaries, such as business groups and business incubators, address such voids in emerging economies. However, it remains unclear whether and how digital multisided platforms fill these voids given their unique features. This study focuses on mobile money platforms, which allow users without bank accounts or credit cards to perform financial transactions. We propose that these platforms fill institutional voids in three ways by (i) enabling data-based certification, (ii) providing unified access to distributed services, and (iii) scaling through network effects to reach previously excluded market participants. We argue that these novel mechanisms enable mobile money platforms to expand credit access to end users from formal financial institutions and thereby act as stepping stones to financial inclusion. Our analysis is based on a difference-in-difference design that leverages regulatory changes that allowed nonbanks to operate as mobile money operators and data from a representative random sample of 151,771 individuals in 78 countries. We supplement our quantitative analysis with rich, hand-collected qualitative evidence to illustrate the mechanisms underlying our findings.

The rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative problem-solving through human-guided AI partnerships. To explore this potential, we initiated a crowdsourcing challenge focused on sustainable, circular economy business ideas generated by the human crowd (HC) and collaborative human-AI efforts using two alternative forms of solution search. The challenge attracted 125 global solvers from various industries, and we used strategic prompt engineering to generate the human-AI solutions. We recruited 300 external human evaluators to judge a randomized selection of 13 out of 234 solutions, totaling 3,900 evaluator-solution pairs. Our results indicate that while human crowd solutions exhibited higher novelty—both on average and for highly novel outcomes—human-AI solutions demonstrated superior strategic viability, financial and environmental value, and overall quality. Notably, human-AI solutions cocreated through differentiated search, where human-guided prompts instructed the large language model to sequentially generate outputs distinct from previous iterations, outperformed solutions generated through independent search. By incorporating “AI in the loop” into human-centered creative problem-solving, our study demonstrates a scalable, cost-effective approach to augment the early innovation phases and lays the groundwork for investigating how integrating human-AI solution search processes can drive more impactful innovations.

In creativity research, time is rarely conceptualized as a multidimensional phenomenon. Instead, it is conceived either as an external variable, for coordinating successive phases of an idea journey, interaction patterns, and moments of insight—or as an individual experience, encompassing aspects like stress or timelessness. Based on an ethnography of a music studio, I show how these temporalities coexist and how time is organized as a linear coordination process as well as an experience to enable and align individual and collective creativity. Time is thereby available in three dimensions, as planned time for linear sequencing of collective work steps, as assigned meantime for the spontaneous and parallel allocation of tasks to free time slots, and as idle meantime for indeterminate waiting periods afforded by the material temporality of artifacts and bodies. My findings elucidate that organizing the interplay of all three temporal dimensions favors both individual ideation in indeterminate situations of idleness and collective creative work on predefined tasks in planned phases and ad hoc structured situations. Importantly, I found how the time afforded by artifacts and bodies in creative work is key to enabling and aligning individual creative processes by providing opportunities for relaxation, defocusing, and humor during collective creative processes, based on coordinated interaction. My findings contribute to a social process perspective on creativity by reconsidering the role of individual experiences in creative collaboration from a temporal perspective.

Moods and emotions are an important influence on creativity at work, and recent developments point to emotional complexity as a particularly relevant influence in this respect. We develop this line of research by shifting focus from emotional complexity as an intrapersonal influence to emotional complexity as an interpersonal influence between leader and subordinate. Specifically, we integrate the social-functional approach to emotions with theory on self-regulation to shed light on the effects of leader emotional complexity (LEC), operationalized as alternations between leader displays of happiness and anger, on follower creativity. Three studies, two video experiments (Studies 1 and 2) and a multisource experience sampling study (Study 3), revealed that, on one hand, LEC stimulated creativity by enhancing the cognitive flexibility of followers; on the other hand, LEC led to heightened self-regulatory resource depletion, which compromised follower creativity. Our results also showed that trait epistemic motivation strengthened the positive effects of LEC on creativity via cognitive flexibility, the negative effects via self-regulatory resource depletion were also stronger for followers with higher trait epistemic motivation. Combined, results suggest that leader displays of emotional complexity can be tiring but are even more inspiring.

College is regarded as the great equalizer. People with four-year degrees expect to reap the rewards of their education. This paper examines the pivotal transition from college to the labor market. How do candidates fare when they reveal to prospective employers that they are “first-gen”? Based on the literature, one may advance two competing predictions. One perspective predicts the possibility of a first-gen advantage. This view predicts that revealing one’s first-gen status can help applicants, by making them seem motivated, committed, responsible, and hardworking. It also makes for a compelling narrative; many Americans love stories of “bootstrapped” success. In contrast, a competing perspective predicts the possibility of a first-generation disadvantage. According to this view, there are forces that block decision makers from recognizing the strengths of first-gen students. We tested these two perspectives with an audit study (n = 1,783) and four follow-up studies (n = 4,920). The results supported the first-gen disadvantage hypothesis. Even in the mainstream labor market, first-gen students were evaluated less favorably. We traced this bias to the impact of one possible mechanism: deficit thinking. Despite overcoming hardships, first-gen students were often viewed through the lens of deficits. As a consequence, they were often denied opportunities to gain entry into organizations. Importantly, we found that a mindset shift can help ameliorate the problem. When we nudged decision makers to adopt a strengths-based lens, they became more receptive to hiring first-gen applicants. This work extends knowledge on the mechanisms that drive social class gaps in hiring. It also invites a reassessment of how to study social class in organizations. Deficit models dominate the study of social class. However, as we demonstrated, focusing on deficits can exacerbate inequality. It is important to consider people’s experiences and humanity holistically.

Crowdsourcing has evolved as an organizational approach to distributed problem solving and innovation. As contests are embedded in online communities and evaluation rights are assigned to the crowd, community members face a tension: They find themselves exposed to both competitive motives to win the contest prize and collaborative participation motives in the community. The competitive motive suggests they may evaluate rivals strategically according to their self-interest, the collaborative motive suggests they may evaluate their peers truthfully according to mutual interest. Using field data from Threadless on 38 million peer evaluations of more than 150,000 submissions across 75,000 individuals over 10 years and two natural experiments to rule out alternative explanations, we answer the question of how community members resolve this tension. We show that as their skill level increases, they become increasingly competitive and shift from using self-promotion to sabotaging their closest competitors. However, we also find signs of collaborative behavior when high-skilled members show leniency toward those community members who do not directly threaten their chance of winning. We explain how the individual-level use of strategic evaluations translates into important organizational-level outcomes by affecting the community structure through individuals’ long-term participation. Although low-skill targets of sabotage are less likely to participate in future contests, high-skill targets are more likely. This suggests a feedback loop between competitive evaluation behavior and future participation. These findings have important implications for the literature on crowdsourcing design, and the evolution and sustainability of crowdsourcing communities.

This paper develops and tests a theory of organizational learning, proposing that prior competitive interaction improves coordination among teammates. We test the theory using millions of experiments in the formation of eSports teams. The results show that exogenously assigned teams of former competitors are highly effective—The marginal returns to prior competitive interaction are even larger than the returns to prior collaborative interaction. The evidence suggests that teammates learn to coordinate by competing, a finding with implications for organizational design and the management of human capital.

Women experience chronically inferior returns in organizations. One common recommendation is to form instrumental network ties with high-status others in groups. We integrate research on social status, social perceptions, and gender issues in social networks to suggest that, despite the theoretical and empirical appeal of this approach, instrumental ties to high-status network contacts (versus ties to lower-status network contacts) in groups may incur hidden social status costs for women in intragroup status-conferral processes. Instrumental ties to high-status network contacts may be perceived as a sign of agency of the focal person, which violates feminine gender norms. Women with these high-status network contacts in groups may therefore be perceived as less communal, thus subsequently lowering their status in the eyes of other group members compared with women with lower-status network contacts. Studies 1–4, across cross-sectional, longitudinal, and experimental designs, support our model. Study 5 suggests that signaling a group-oriented goal may mitigate the interpersonal, social perceptual costs of instrumental ties to high-status network contacts for women. The effect of ties with high-status network contacts for men is relatively inconsistent. This research reveals a potential social-network dilemma for women: Instrumental ties to high-status network contacts in groups and organizations are necessary for success and should be encouraged, yet they may also create an extra social perceptual hurdle for women. Organizations need to investigate social and structural solutions that harness the benefits of high-status network contacts for women, while minimizing any potential social perceptual costs.

By integrating Herbert Simon’s theory of artifact and Brian Arthur’s concept of functionality in technology, we propose a novel conceptual framework for analyzing the process by which shadow options embedded in a firm’s existing technologies emerge and are converted into real options. We apply this framework to examine the shadow options of Corning’s photosensitive glass technologies. The core of our model is that technologies subsume options, most of which are shadow because they refer to functions of those resources that were unknown when they were originally acquired or created. Shadow options are hidden in functionalities. Functionalities and functions are relational entities that exist at the interface between an artifact’s inner and outer environments. The transformation of shadow into real options occurs along a continuum, with two distinct types represented at its extremes: the Schumpeterian and Kirznerian types. We examine organizational attributes that enable Schumpeterian and Kirznerian shadow options and explain how firms can systematically pursue an innovation strategy that exploits the shadow options embedded in their technologies.

We study how restrictive immigration policies that result in the unexpected loss of coworkers affect the performance of skilled migrants employed in organizations. Specifically, we examine the impact of the loss of team members on their coworkers’ performance in response to the unexpectedly increased denials of extensions of H-1B work visas in the United States beginning in 2017. Losing a team member generally has a positive, albeit economically insignificant, effect on the performance of workers left behind. However, we find that individuals who lost peers of the same ethnic background experience a substantial decrease in their performance. To confirm that our results are not plagued by the presence of unobservable team or individual features that might impact visa denial decisions, we build an instrumental variable that exploits the fixed duration of the H-1B visa. Heterogeneity analyses suggest that our result is driven by workers in small teams, teams working on atypical tasks, and ethnically homogeneous teams. These analyses hint at the fact that ethnic ties may boost individual performance through preferential channels of knowledge and information spillovers.