Amid ongoing pressures for efficiency, accountability, and equity in health service governance, this longitudinal case study examined the five‑year impacts of a centralized allied health structure in a large public hospital setting. Building on earlier findings that demonstrated short‑term improvements in governance clarity, workforce flexibility, and professional identity, the five‑year follow‑up explored whether these benefits were sustained and what new challenges emerged as the model matured. Qualitative data were collected from 169 allied health professionals (25
Research studying how Information Technology (IT) adoption affects managers has typically focused on how IT can complement and substitute for manager work, leading to increases in span of control. However, previous work has not accounted for how work is coordinated in the adopting organization. Some managers spend relatively little time processing and communicating information prior to IT adoption. As such managers process and communicate new information provided by IT, they may spend more time coordinating with subordinates based on this new performance feedback. Thus, in some cases IT adoption could lead to managers supervising fewer subordinates, contrary to conventional wisdom and prior results. Using data on hospital divisions, IT adoption, and occupational activities, I find evidence that span of control does indeed decrease for some managers with IT adoption. The analysis indicates that changes in the number of front-line managers primarily drive the overall results.
Organization redesign often raises a central question: Where are managers needed? A potential substitute for formal supervision by managers is interpersonal cultural alignment, which can ease coordination between colleagues. Yet prior research suggests cultural alignment may also complement formal structure. We theorize that the balance depends on coordinative complexity: Whether culturally aligned individuals benefit from common supervision hinges on how broadly they depend on others to get work done. Cultural alignment can function as a substitute for formal structure when coordinative complexity is low and as a complement when coordinative complexity is high. Focusing on one of the most tangible and consequential forms of cultural alignment – the degree to which individuals are linguistically aligned with their peers in everyday communications – we make predictions about which pairs of colleagues organizational designers will tend to bring together under, versus separate from, a shared supervisor. Using archival data from a design firm, we find support for our theory, with especially strong effects when colleagues are highly visible to their senior managers.
In Greek mythology, Callisto was a nymph who survived being raped by Zeus only to be tortured and ultimately murdered by Zeus's partner for her unchastity. Thousands of years later, Callisto's story continues to reflect a persistent issue in society. Survivors of sexual assault still face punishment, not by vengeful gods, but by a justice system that silences, retraumatizes, and fails them. This failure is systemic: Over 75% of sexual assault survivors choose not to report their experiences due to institutional hostility, legal risk, and the absence of safe channels for action, leaving the bulk of sex crimes unaddressed. To change this, a solution must reduce the burden on survivors and rethink their path to justice. We examine how the nonprofit organization Callisto does just that by enabling a novel form of coordination: concealed flash organizing. Using a trauma-informed approach, Callisto allows survivors to store encrypted accounts of their assaults and fosters collective action. In doing so, Callisto replaces risky public disclosure with conditional, concealed coordination. This empowers survivors while minimizing harm and enables the formation of organizations that are too risky to emerge publicly but have the potential to improve the world. We argue that this approach not only redefines the conditions for organizing under threat but also offers a new pathway to justice in contexts where traditional systems have failed.
This paper studies alternative empirical strategies for estimating the effects of organization design practices on performance, as well as the factors which determine organizational design, in a cross-section of firms. Our economic model is based on a firm where multiple organizational design practices are en endogenously determined, and these organizational design practices affect output through an 'organizational design production function.' The econometric model includes unobserved exogenous variation in the costs and returns to each of the individual practices. The model is used to evaluate how different econometric strategies for testing theories about complementarity can be interpreted under alternative assumptions about the economic and statistical environment. We identify plausible hypotheses about the joint distribution of the unobservables under which several different approaches from the existing literature will yield biased and inconsistent estimates. We show that the sign of the bias depends on two factors: whether the organzational design practices are complements, and the correlation between the unobserved returns to each practice. We find several sets of conditions under which the sign of the bias can be determined, and we provide economic interpretations. Our analysis shows that for a particular set of hypotheses, a variety of different procedures may all yield qualitatively similar biases, presenting a challenge for the identification of complementarity. We then propose a structural approach, which is based on a system of simultaneous equations describing productivity and the demand for organizational design practices. As long as exogenous variables are observed which are uncorrelated with the unobserved returns to practices, the structural parameters are identified, yielding consistent tests for complementarity as well as the cross-equation restrictions implied by static optimization of the organizatin's profit function.
Corporate venturing modes (CVMs)—organizational formats such as incubators and accelerators—are commonly employed by firms to engage with startups for mutual benefit. Existing research, however, has mainly produced typological classifications that, while offering structure and orientation, provide limited insight into how specific design choices shape CVMs’ effectiveness. This point of view article proposes reframing CVM research through a design science lens: building on the descriptive foundations of prior literature, yet moving further to generate empirically evaluated and generalizable insights into effective CVM design. The article further diagnoses key epistemic challenges—understood as fundamental obstacles to discovering and validating knowledge about what constitutes effective CVM design—that have so far hindered such progress. Moreover, it outlines a research agenda integrating novel empirical, methodological, and theoretical approaches to address these challenges. Overall, the article aims to foster a more comprehensive, empirically grounded approach to studying effective CVM design—one that links organizational design principles with outcomes across varying contexts. Accumulating insights on these matters may both advance theory development and inform practice, helping to improve the often-disappointing results observed in current CVM initiatives.
In the evolving organizational landscape, innovation plays a crucial role in driving economic and social development. Over the past decade, various initiatives, including incubators, accelerators, and corporate venture capital, have played pivotal roles in supporting emerging startups. A recent addition to this support ecosystem is the venture builder, a prominent approach to scale the innovation fuzzy front-end, emphasizing the growth of startups and fostering strategic partnerships for the creation of a portfolio of ventures. However, the lack of literature on these models limits the possibilities to fully understand its academic and practical implications, leading to challenges in their theoretical conceptualization and concrete applications within entrepreneurial ecosystems. Addressing this gap, this work introduces a robust theoretical foundation for venture builders, articulated through a research prime. It differentiates venture builders from traditional startup support mechanisms and proposes a new analytical framework based on five dimensions: activities, selection processes, assessment processes, financing mechanisms and temporal scope program. Therefore, theses dimensions enhance the understanding of venture builders and stimulate further academic and practical inquiry into their impact on economic and social development.
Generative Artificial Intelligence (AI) and Large Language Models (LLMs) will have an enormous impact on how organizations make decisions. On many operational decisions, AI and LLMs will replace humans, while on higher stakes, more subjective tactical and strategic decisions, humans and AI will likely work together as thought-partners. AI will also, occasionally, arrive at disruptive, potentially breakthrough ideas. Those ideas will be less understandable by humans given how differently AI reasons. This disruptive reasoning opacity creates an inferential trilemma: is an AI-generated innovative idea a true breakthrough, a hallucination, or the product of misalignment? True breakthroughs will often generate cascading improvements owing again to differences in how AI and humans think. The organizations that survive and thrive will be those that best navigate the replace-augment boundary and that also develop structures and protocols that resolve the inferential trilemma and drive cascading improvements.
Generative AI and autonomous AI agents are reshaping both the legal employment relationship and the broader social contract between employers and employees. This contract reflects society's shared expectations about what employers and employees owe each other, including how value is created and distributed and who has authority and accountability for various activities. Unlike prior waves of automation that primarily affected routine tasks, generative AI now touches high-wage, high-status "knowledge" work, altering how expertise is recognized, how decisions are made, and how relationships are formed at work. This paper examines the historical foundations of technological change and employment relations, highlighting how generative AI intensifies existing trends while also introducing new dynamics across six key domains: (1) the role of employee judgment and authority, (2) the value of employee expertise and human-created data, (3) appropriate organizational control and employee autonomy, (4) the nature of work relationships, particularly in the context of AI agents, (5) responsibility for reskilling and career development, and (6) worker collective power. By examining these changing domains, organizational scholars can help explain how generative AI and AI agents are fundamentally altering the social contract of modern employment.
Organizational survival and success depend on having members who have a shared understanding about the enterprise's purpose and strategy. Organizations therefore invest heavily in the selection and socialization of new members. Since the public release of generative artificial intelligence based on large language models (GAI) in 2022, organizational leaders have been grappling with foundational questions about how this new technology will reshape these core activities. Although it is difficult to make precise predictions amid ongoing technological ferment, here we offer informed guesses about the trajectory of GAI-driven change in organizational selection and socialization. To organize our predictions, we draw on three key conceptual distinctions. First, we distinguish between the ability of GAI to select and socialize individuals who are internally committed to organizationally desirable values, versus individuals who only perform these values. Our second distinction pertains to the cross-pressures of fitting in versus standing out within organizations. Third, we distinguish between how GAI is adopted initially, and responses to these configurations by strategic actors, which we refer to as "second order effects". Based on these distinctions, we array our predictions across three phases, with each new phase a response to the tensions and dissatisfactions of a preceding one.
This essay examines how generative AI is transforming organizational hiring processes. It maps out key issues and proposes research questions to strengthen our understanding of AI's impact on hiring. By conceptualizing hiring as a multi-stage funnel, the essay highlights generative AI's influence on decisions about which roles to hire for, how candidates are sorted into pools, and how applicants are selected. It underscores the interplay between organizational decisions and candidate behaviors, examining how AI can shape outcomes at each stage. The article also highlights opportunities and challenges that generative AI presents for individuals, organizations, and policymakers.
This article presents an exploratory, inductive study of unconventional organization design in the context of small and medium-sized enterprises. Drawing on a multi-case study of five Spanish firms, the study identifies six components that recurrently appeared across the cases: (1) responsible freedom; (2) collaborative and boundaryless workflows; (3) ample transparency; (4) adaptive governance; (5) disruptive and continuous innovation; and (6) sustainable impact. Rather than offering a prescriptive or exhaustive model, the study proposes an empirically informed account of how certain features of unconventional organization designs were enacted and interrelated in the selected organizations. These findings contribute to the literature by illustrating how unconventional organizational configurations may emerge and operate in practice, and by highlighting the trade-offs and tensions associated with each component. While the results are context-specific, they may serve as a useful point of departure for further inquiry into alternative design logics. For practitioners, the identified components offer conceptual guidance that may inform reflection and experimentation in the pursuit of more adaptive and participatory organizational forms.
The transformative potential of artificial intelligence (AI) is reshaping collaboration within organizations, evoking both excitement and concern. As an individual production technology, AI risks fragmenting workflows, isolating workers, and undermining the human-centered collaboration vital for creativity and innovation. However, as a coordination technology, AI holds enormous promise for enhancing collective intelligence. By considering the fundamental processes underlying intelligence in any system-including reasoning, memory, and attention-we can envision ways AI can overcome traditional barriers to effective collaboration to elevate collective intelligence. This paper explores how AI can help clarify team goals and resolve conflicting motives to enhance collective reasoning; provide personalized knowledge assistance, connect complementary expertise, and mitigate biases to augment collective memory; and facilitate attention to priorities and manage asynchronous and synchronous coordination to optimize the use of collective attention. Despite these opportunities, integrating AI into collaboration presents challenges, including potential impacts on trust, cohesion, and ethical concerns. The paper outlines a research agenda to address these challenges and explore AI's role in fostering inclusive, efficient, and innovative collaboration. By leveraging generative AI responsibly, organizations can amplify human abilities, creating synergistic outcomes that redefine the future of work.
This article aims to shift the ongoing debate from how leaders affect individuals through interpersonal influence at the micro-level to how they influence organizations through processes at the meso-level. We show how leaders, rather than solely relying on interpersonal influence or strategic management, can develop the human resource management (HRM) infrastructure needed for effective strategy implementation and organizational value creation. In so doing, we address the gap between micro- and macro-perspectives in the HRM and the leadership literature. We draw on an integration of the emerging theory of architectural leadership with research on structuring HRM processes. An integrative model is developed that illustrates how senior executives, as architectural leaders, can lead the structuring of HRM processes (at the meso-level) to harness people’s potential (at the micro-level) to enable effective implementation of the organization’s strategy (at the macro-level). We elaborate on the theoretical foundations of the seven dimensions of structuring, and outline the supra-process that underpins the influence of leadership employed by architectural leaders to structure HRM processes. The theoretical implications of this study are relevant not only to public sector organizations but also to any company aiming to improve HRM practices and align them with its strategic goals.
This paper develops a multidimensional, mezzo model of uncertainty to pave the way for interdisciplinary theory development. We propose an information-processing model, where perceptions of eco-system uncertainty are shaped by its complexity, expressed by the number of elements that are embedded in it, their diversity and interconnectedness, and by the eco-system changeability in terms of degree, pace and consistency. In contrast to traditional approaches, the proposed model posits that what affects the behavior of organizational decision makers are the transient feelings of uncertainty. These feelings emanate not only from the way decision-makers perceive and interpret uncertainty triggers embedded in their environment, but also from their cognitive capabilities, epistemic motivation and tolerance for ambiguity. Implications for management and organization theories are discussed.
We developed an experimental method to investigate organization design and grouping decisions more specifically. We demonstrate the method in a study with 285 participants. The participants were asked to group a set of nine roles into units using card-sorting. The role descriptions indicated that there were interdependencies between some of the roles. Participants’ grouping decisions were quantified and compared against an algorithmic solution that minimized coordination costs. It was found that a relatively small difference in task complexity between groups greatly affected participants’ performance. We discuss how the method can be extended to study a range of variables related to decision-making about organization design.
Buyers and suppliers engage in relationship exchange, which may lead to value creation for both parties in the supply chain activities. The relational view in operations research points to the importance of these work relationships, but has thus far overlooked the process whereby they emerge. We advance this line of research and theory by delineating a process model that explicates how relationships develop during the early stages of the exchange between buyers and suppliers. Using a grounded theorizing effort, based on open, in-depth interviews with 88 buyer and supplier agents from a variety of organizations, our research reveals a multi-level process by which work relationships emerge and enable formalized cooperative understandings. First, the findings shed light on how early stage work relationships in markets of customizable differentiated products/services are designed and delineate that the genesis of buyer–supplier relationships lies in early interaction between individual agents of buyers and suppliers. Second, relationships between individuals, amplified by relational affect, can lead to authenticity, conductive through sharing of rich-context information to alignment of interests between the respective parties. Third, relational authenticity and the alignment of interests can serve as the foundation for understanding between buyers and suppliers at the organizational level, attained through co-active issue-selling efforts.
The development of Bitcoin and its underlying technology blockchain has enabled a new phenomenon called decentralized autonomous organizations (DAOs). DAOs can be perceived as self-governing organizations whose management is based on programmed and encoded rules on a decentralized and distributed peer-to-peer network. These DAOs typically manage and allocate funds, often in the form of cryptocurrencies. However, in recent years, a variety of DAOs have been established to provide services (e.g., currency exchange, project financing), curate collections (e.g., art collections), or own and manage real assets (e.g., land). Currently, DAO literature focuses mainly on online communities managing digital assets; however, DAOs owning physical properties differ from them in localized communities, asset indivisibility, and additional complexity in collective acquisition, ownership, limited physical capacity, and decentralized governance. Such property-owning DAOs are interesting, because they fuel the transition from purely online organizations into organizations integrating with the physical world. From an organizational system theory perspective this article explores how a DAO owning properties could be designed by exploring three DAO projects that own properties. Applying a conceptual research design, we first identify DAO Design Principles obtained by traditional organizational system theory, followed by examining and describing the core organizational principles for property-owning DAOs. Based on a comprehensive discussion of the conceptual findings, we present a research agenda for further studies on DAOs owning properties.
