Knowledge that Transforms

Evaluating the attractiveness of startup employment requires an understanding of both what startups pay and the implications of these jobs for earnings trajectories. Analyzing Danish registry data, we find that employees hired by startups earn roughly 17% less over the next 10 years than those hired by large, established firms. About half of this earnings differential stems from sorting—from the fact that startup employees have less human capital. Long-term earnings also vary depending on when individuals are hired. Although the earliest employees of startups suffer an earnings penalty, those hired by already-successful startups earn a small premium. Two factors appear to account for the earnings penalties for the early employees: Startups fail at high rates, creating costly spells of unemployment for their (former) employees. Job-mobility patterns also diverge: After being employed by a small startup, individuals rarely return to the large employers that pay more.

It is well established that the effectiveness of pay-for-performance (PfP) schemes depends on employee- and organization-specific factors. However, less is known about the moderating role of external forces such as market competition. Our theory posits that competition generates two counteracting effects—the residual market and competitor response effects—that vary with competition and jointly generate a curvilinear relationship between PfP effectiveness and competition. Weak competition discourages effort response to PfP because there is little residual market to gain from rivals, whereas strong competition weakens incentives because an offsetting response from competitors becomes more likely. PfP hence has the strongest effect under moderate competition. Field data from a bakery chain and its competitive environment confirm our theory and let us refute several alternative interpretations.

Members of global teams are often dispersed across time zones. This paper introduces the construct of temporal brokerage, which we define as being in a position within a team’s temporal structure that bridges subgroups that have little or no temporal overlap with each other. Although temporal brokerage is not a formal role, we argue that occupying such a position makes an individual more likely to take on more coordination work than other members on the team. We suggest that, while engaging in such coordination work has advantages in the form of enhanced integrative complexity, it also comes with costs in the form of a greater workload relative to other members. We further argue that the increased integrative complexity and workload that result from occupying a position of temporal brokerage have implications that go beyond the boundaries of the focal team, spilling over into other projects the individual is engaged in. Specifically, we predict that being in positions of temporal brokerage on global teams decreases the quantity but increases the quality of an individual’s total productive output. We find support for these predictions across two studies comprising 4,553 individuals participating in global student project teams and 123,586 individuals participating in global academic research teams, respectively. The framework and findings presented in this paper contribute to theories of global teamwork, pivotal roles and leadership emergence in global teams, and social network theory.

Social movement organizations (SMOs) are increasingly using collaborative tactics to engage firms. Implications of this are not well understood by researchers. This study investigates one risk that looms over such collaborations: if the corporate partner is later implicated in an industry scandal. Ideas are investigated in the context of the Deepwater Horizon oil spill. First, we find that industry scandals differentially affect contentious and collaborative SMOs’ ability to mobilize resources. SMOs that had collaborated with the oil and gas industry before the spill suffered from reduced public support after the spill, and those that had contentiously interacted with the industry enjoyed increased contributions. Second, we find that industry scandals affect SMOs’ willingness to collaborate with firms in the future. We show that the Horizon oil spill produced a broad chilling effect on environmental SMOs’ collaborations with firms both within and outside of the oil and gas industry. Our findings show that there are risks inherent to a collaborative strategy that cannot be fully mitigated. Further, we demonstrate that industry scandals represent critical exogenous events that affect social activists’ tactical repertoires for engaging in private politics.

In this paper, we investigate the attentional engagement of chief executive officers (CEOs) of large healthcare organizations in England. We study attention ethnographically as something managers do—at different times, in context, and in relation to others. We find that CEOs match the challenges of volume, fragmentation, and variety of attentional demands with a bundle of practices to activate attention, regulate the quantity and quality of information, stay focused over time, and prioritize attention. We call this bundle of practices the CEO’s attentional infrastructure. The practices that compose the attentional infrastructure work together to ensure that CEOs balance paying too much with paying too little attention, sustain attention on multiple issues over time, and allocate attention to the issues that matter, while avoiding becoming swamped by too many other concerns. The attentional infrastructure and its component practices are constantly revised and adapted to match the changes in the environment and ensure that managers remain on top of the things that matter to them. The idea of a practice-based attentional infrastructure advances theory by expanding and articulating the concept of attentional engagement, a central element in the attention-based view of the firm. We also demonstrate the benefits of studying attention as practice, rather than as an exclusively mental phenomenon. Finally, we contribute to managerial practice by introducing a set of categories that managers can use to interrogate their existing attentional practices and address attentional traps and difficulties.

The recent emergence of blockchains may be considered a critical turning point in organizing collaborations. We outline the historical background and the fundamental features of blockchains and present an analysis with a focus on their role as governance mechanisms. Specifically, we argue that blockchains offer a way to enforce agreements and achieve cooperation and coordination that is distinct from both traditional contractual and relational governance as well as from other information technology solutions. We also examine the scope of blockchains as efficient governance mechanisms and highlight the tacitness of the transaction as a key boundary condition. We then discuss how blockchain governance interacts with traditional governance mechanisms in both substitutive and complementary ways. We pay particular attention to blockchains’ social implications as well as their inherent challenges and limitations. Our analysis culminates in a research agenda that explores how blockchains may change the way to organize collaborations, including issues of what different types of blockchains may emerge, who is involved and impacted by blockchain governance, why actors may want blockchains, when and where blockchains can be more (versus less) effective, and how blockchains influence a number of important organizational outcomes.

Organizations often learn vicariously by observing what other organizations do. Our study examines vicarious learning–related communication through which individuals share their observations with other organizational members. Most students and members of present-day organizations would expect that this communication is driven by a prodevelopment logic—that communication serves the purpose of organizational improvement and competitiveness. Our unique historical evidence on learning-related communication over multiple decades shows that the subjective and collective attitude toward prodevelopment communication may be ideologically conditioned. Prodevelopment communication is the norm in capitalist organizations, but competing ideologies may emphasize other goals higher than organizational development. Consequently, increasing challenges to capitalism as the ideological basis of economic organization can have deep impacts on how organizations learn and produce innovations in the future.

We investigate why employee-friendly firms often benefit from lower costs of debt financing. We theorize that banks use employee treatment as a screen to assess firms’ trustworthiness, which encompasses not only confidence in firms’ ability to perform well but also the belief that they will act with good intent toward their creditors. We integrate screening theory and stakeholder theory to explain the—oftentimes unintended—consequences that firms’ actions toward employees have on their relationships with other stakeholders. An analysis of U.S. firms between 2003 and 2010 shows that favorable employee treatment reduces the cost of bank loans, and this relationship is stronger when banks cannot infer firms’ intent from their relations with stakeholders other than employees. A policy-capturing study provides further support that employee treatment serves as a screen for intent. We discuss the implications of our stakeholder-screening perspective as a novel way to understand the second-order, unintended effects of a focal stakeholder relationship on firms’ relations with other stakeholders.

Across many fields of social science, machine learning (ML) algorithms are rapidly advancing research as tools to support traditional hypothesis testing research (e.g., through data reduction and automation of data coding or for improving matching on observable features of a phenomenon or constructing instrumental variables). In this paper, we argue that researchers are yet to recognize the value of ML techniques for theory building from data. This may be in part because of scholars’ inherent distaste for predictions without explanations that ML algorithms are known to produce. However, precisely because of this property, we argue that ML techniques can be very useful in theory construction during a key step of inductive theorizing—pattern detection. ML can facilitate algorithm supported induction, yielding conclusions about patterns in data that are likely to be robustly replicable by other analysts and in other samples from the same population. These patterns can then be used as inputs to abductive reasoning for building or developing theories that explain them. We propose that algorithm-supported induction is valuable for researchers interested in using quantitative data to both develop and test theories in a transparent and reproducible manner, and we illustrate our arguments using simulations.

This paper studies how audience members categorize and evaluate ambiguous offerings. Depending on whether audience members categorize ambiguous offerings based on prototypes or goals, they activate two distinct cognitive mechanisms and evaluate differently ambiguous offerings. We expect that when audiences engage in goal- versus prototype-based categorization, their evaluation of ambiguous products increases. We theorize that, under goal-based categorization, the perceived utility of unclear attributes increases for audiences, which leads them to evaluate more positively ambiguous product offerings. We test and find support for these direct and mediated relationships through a series of laboratory, online, and field experiments. Overall, this study offers important implications for research on product and market categories, optimal distinctiveness, and market agents’ cognitive ascription of value.