Multi-unit organizations are a form of organizations where the geographically dispersed units provide similar products or services in different markets. Deciding on an appropriate level of centralization in such organizations presents a unique challenge. One the one hand the organizations want to maintain a consistent brand identity in all units through centralized control, but on the other hand, they want to provide the units with sufficient autonomy to respond to the challenges they face locally. Traditionally, this challenge was perceived to require a trade-off between performance and standardization, with performance demanding more decentralization and standardization requiring more centralized control. However, our research explores how organizations can potentially resolve this trade-off by promoting norms for knowledge-sharing and setting up the right communication channels, relying on the unit managers’ intrinsic tendency to conform to the behavior of their peers. We build an agent-based model of an organization with multiple interdependent units facing highly similar task environments to investigate how unit managers’ ability to communicate, share knowledge, and conform to peer practices might influence organizational dynamics. We find that, under specific communication network structures, increased decentralization can enhance both performance and standardization without sacrificing one or the other. Furthermore, we discover that centralization might still be preferable for standardization if the units are interdependent.

Despite numerous organizational commitments to equality, ethnoracial inequality remains persistent in practice. This paper investigates how performance evaluations distorted by ethnoracial bias and informal organizational networks jointly contribute to the emergence of inequality in individual capabilities. To analyze these dynamics, an agent-based model of a stylized organization in which performance evaluations (biased or unbiased) and informal peer interactions co-evolve over time is proposed. Simulation results reveal that, at the macro level, enabling informal communication can enhance overall organizational performance. However, at the micro level, informal exchanges substantially amplify inequality when evaluations are biased. In such cases, inequality becomes self-reinforcing through the interaction of biased assessments and informal resource sharing. This paper makes two contributions. First, it reproduces empirically observed patterns of inequality, offering a form of model validation. Second, it highlights a fundamental ethical dilemma organizations may face: performance improvements can come at the cost of equality.

Personalized news recommendations have become a standard feature of large news aggregation services, optimizing user engagement through automated content selection. In contrast, legacy news media often approach personalization cautiously, striving to balance technological innovation with core editorial values. As a result, online platforms of traditional news outlets typically combine editorially curated content with algorithmically selected articles - a strategy we term controlled personalization. In this industry paper, we evaluate the effectiveness of controlled personalization through an A/B test conducted on the website of a major Norwegian legacy news organization. Our findings indicate that even a modest level of personalization yields substantial benefits. Specifically, we observe that users exposed to personalized content demonstrate higher click-through rates and reduced navigation effort, suggesting improved discovery of relevant content. Moreover, our analysis reveals that controlled personalization contributes to greater content diversity and catalog coverage and in addition reduces popularity bias. Overall, our results suggest that controlled personalization can successfully align user needs with editorial goals, offering a viable path for legacy media to adopt personalization technologies while upholding journalistic values.

PurposeThis research seeks to explore the intersection between modularity and conformity in organizational contexts. Modularity, a cornerstone of organizational design, pertains to the decomposability of tasks within an organization into subtasks with internal interdependence and external independence. Conformity, on the other hand, is the adjustment of an individual’s behavior to match that of others, often driven by a desire to adhere to social norms.Design/methodology/approachWe employ agent-based modeling and simulation as a technique to model organizations as complex systems. This approach allows us to delve into the effects of modularity in organizational structures on organizational performance, with a particular emphasis on the role of conformity in this relationship. We treat conformity as exogenously given, which allows us to focus on its effects rather than its emergence.FindingsThe results demonstrate that a concentration of interdependent tasks within fewer departments can boost overall performance. Conformity decreases performance in all organizational structures except for cases when the departments work on highly similar tasks. This decline in performance can also explain why functional organizational structures are still being used in practice even though they are less modular than divisional structures — they feature lower levels of conformity and, thus, face smaller decline. Finally, we find that in highly complex settings, organizational performance can, surprisingly, be improved as complexity within departments increases.Originality/valueTo the best of our knowledge, this study is the first to explore the modularity in organizational structures in presence of conformity. Distinctively, we adapt the NKCS model from evolutionary biology to our study, and perform an exhaustive analysis by examining all possible combinations of parameters that refer to the task allocation within organizations. We thereby contribute a unique perspective to the discourse on organizational theory and behavior.

The immense complexity of semiconductor production demands for advanced dispatching strategies that transcend traditional rule-based systems. This paper introduces a novel approach to dispatching rules derived from swarm intelligence techniques, specifically designed to tackle the intricate dynamics of large-scale semiconductor manufacturing processes. Our approach integrates simulation and optimization methods to investigate and enhance operational efficiency, addressing both the scalability of schedules and their practical implementation. We employ a customizable simulation framework to model a semiconductor manufacturing environment, wherein various dispatching rules as well as our proposed swarm intelligence-driven method are assessed. The effectiveness of these dispatching rules is quantitatively evaluated through a series of simulations that measure key performance indicators such as work-in-progress levels, throughput, and operational variability across different production scenarios. This study not only elucidates the potential of swarm intelligence techniques in refining production dispatching strategies, but also provides a simulation-based evaluation framework that can assist in the further development of intelligent dispatching systems.

This paper examines the influence of decision-making modes on organizational resilience, particularly how these modes affect an organization's capacity to withstand and recover from shocks. Amidst increasing occurrences of economic and operational shocks, understanding the efficacy of organizational structures in crisis situations is crucial. Using agent-based modeling, this study simulates various decision-making mode to evaluate their performance before and after induced shocks. The findings demonstrate that collaborative and hierarchical decision-making modes generally enable organizations to maintain or exceed pre-shock performance levels, especially in less complex task environments. In contrast, silo-based and sequential modes often hinder recovery in more complex settings.

Multi-unit organizations are a form of organizations where the geographically dispersed units provide similar products or services in different markets. Deciding on an appropriate level of centralization in such organizations presents a unique challenge. One the one hand the organizations want to maintain a consistent brand identity in all units through centralized control, but on the other hand, they want to provide the units with sufficient autonomy to respond to the challenges they face locally. Traditionally, this challenge was perceived to require a trade-off between performance and organizational synchrony, with performance demanding more decentralization and synchrony requiring more centralized control. However, our research explores how organizations can potentially resolve this trade-off by promoting norms for knowledge-sharing and setting up the right communication channels, relying on the unit managers' intrinsic tendency to conform to the behavior of their peers. We build an agent-based model of an organization with multiple interdependent units facing highly similar task environments to investigate how unit managers' ability to communicate, share knowledge, and conform to peer practices might influence organizational dynamics. We find that, under specific communication network structures, increased decentralization can enhance both performance and organizational synchrony without sacrificing one or the other. Furthermore, we discover that centralization might still be preferable for synchrony if the units are interdependent.

Abstract In the rapidly evolving field of e-commerce, conventional research methods struggle to keep pace with the dynamic landscape characterised by exponential growth and changing user behaviours. To address this challenge, agent-based modelling and simulation offer a promising research paradigm. This chapter explores the potential of agent-based modelling and simulation in capturing the intricate dynamics of the e-commerce environment and advancing our understanding of this complex domain. We provide an overview of agent-based modelling and simulation applications in various e-commerce domains and identify three compelling avenues for future research. Firstly, exploring the emergence of network structures helps actors understand communication and information-sharing patterns among them, revealing their impact on e-commerce dynamics. Secondly, considering individual differences in personality and culture unveils how these factors influence behaviours, preferences, and decision-making processes in e-commerce. Lastly, analysing longitudinal dynamics and asynchronous timelines captures evolving patterns and long-term effects seen in e-commerce phenomena. Agent-based modelling allows researchers to track the evolution of these dynamics over time. To showcase the power of agent-based modelling and simulation in e-commerce, the chapter presents a case study that focuses on the longitudinal dynamics of multistakeholder recommendation systems. It highlights the versatility and effectiveness of agent-based modelling in capturing heterogeneous consumer preferences, diverse objectives of recommendation providers and users of recommendation services, and the longitudinal dynamics of a set of recommendation strategies.

This paper examines the interactions between selected coordination modes and dynamic team composition, and their joint effects on task performance under different task complexity and individual learning conditions. Prior research often treats dynamic team composition as a consequence of suboptimal organizational design choices. The emergence of new organizational forms that consciously employ teams that change their composition periodically challenges this perspective. In this paper, we follow the contingency theory and characterize dynamic team composition as a design choice that interacts with other choices such as the coordination mode, and with additional contextual factors such as individual learning and task complexity. We employ an agent-based modeling approach based on the NK framework, which includes a reinforcement learning mechanism, a recurring team formation mechanism based on signaling, and three different coordination modes. Our results suggest that by implementing lateral communication or sequential decision-making, teams may exploit the benefits of dynamic composition more than if decision-making is fully autonomous. The choice of a proper coordination mode, however, is partly moderated by the task complexity and individual learning. Additionally, we show that only a coordination mode based on lateral communication may prevent the negative effects of individual learning.

Abstract This chapter explores the significance of agent-based modelling and simulation in advancing theories in the field of corporate investment. It sheds light on three primary contributions of agent-based techniques: testing assumptions in analytical models, modelling human behaviour realistically, and analysing contingency effects and resulting dynamic and nonlinear effects. First, agent-based modelling enables the rigorous testing of assumptions made in analytical models. By employing agent-based simulations alongside analytical approaches, researchers can critically evaluate the robustness of existing theories. This comparative analysis helps identify the limitations and refine the assumptions underlying corporate investment models, leading to more accurate and reliable theoretical frameworks. Second, agent-based techniques offer a more flexible and realistic means of modelling human behaviour in corporate investment. By incorporating factors such as biases, adaptive behaviour, learning processes, and social norms, agent-based models can capture the complexities of decision-making processes. This enhanced representation of human behaviour facilitates the development of a comprehensive behavioural theory of corporate investment, providing valuable insights into the factors influencing investment decisions and outcomes. Lastly, agent-based modelling allows for the systematic exploration of contingency effects on corporate investment theories. Contingency factors, including firm characteristics, industry dynamics, and economic conditions, significantly influence investment strategies. By integrating these contingencies into agent-based models, researchers can analyse their interactions and understand how they shape investment behaviour, leading to a deeper understanding of the dynamic nature of corporate investment decisions.

In recent times, organizations have increasingly adopted structures in which decision making is distributed rather than centralized. This approach often leads to task allocation emerging from the bottom up, moving away from strict top-down control. This shift raises a key question: How can we guide this emergent task allocation to form an effective organizational structure? To address this question, this paper introduces a model of an organization where task assignment is influenced by agents acting based on either long-term or short-term motivations, facilitating a bottom-up approach. The model incorporates an incentive mechanism designed to steer the emergent task allocation process, offering rewards that range from group-based to individual-focused. The analysis reveals that when task allocation is driven by short-term objectives and aligned with specific incentive systems, it leads to improved organizational performance compared to traditional, top-down organizational designs. Furthermore, the findings suggest that the presence of group-based rewards reduces the necessity of mirroring, i.e., for a precise matching of the organizational structure to task characteristics.

Abstract This chapter examines the role of agent-based modelling and simulation in advancing theories and addressing operational and design issues within the realm of management science. We specifically explore the potential contributions of agent-based modelling and simulation to abductive and deductive reasoning. Through a theoretical lens, we argue that agent-based modelling and simulation is useful in exploring complex dynamics, and it thereby emerges as a potent tool for generating, refining, and selecting hypotheses. It facilitates a deeper understanding of the behaviours within complex adaptive systems—ranging from organizations to networks—thereby enabling the formulation of causally plausible explanations for observed phenomena. This capability is particularly crucial in environments where traditional analytical methods fall short due to inherent complexities. Furthermore, we discuss the role of agent-based modelling and simulation in operationalizing hypotheses for empirical testing, emphasizing its utility in translating theoretical concepts into testable models and identifying the boundary conditions under which these models hold true. In the context of operational and design issues, we argue that agent-based modelling and simulation supports the exploration of means–ends relations and the operationalization of design choices, thereby contributing to the development of innovative solutions and potentially better decision-making within organizations. Thereby, the method substantially contributes to bridging the gap between theory and practice within the field of management science, presenting it as an essential approach for navigating the complexities of contemporary management challenges.

Open Strategy is a novel strategy-making approach that considers the inclusion of stakeholders as one of its main principles. While it has the potential to enhance the strategy-making process, there also is a lack of studies examining its long-term effectiveness. To address this gap, we conduct a simulation study to explore the impact of stakeholders’ participation in the idea-generation phase on the performance of strategies, considering the complexity of the strategic task, the number of participants, and their objectives’ alignment with the organization’s objectives. We find that Open Strategy initially outperforms closed strategy-making, but not in the long-term, particularly if the objectives of the participants differ from those of the organization. Additionally, Open Strategy leads to better performance when more participants are involved, and complexity is lower. Our study challenges the prevailing views about Open Strategy as a superior approach to the strategy-making process.

Organizations face numerous challenges posed by unexpected events such as energy price hikes, pandemic disruptions, terrorist attacks, and natural disasters, and the factors that contribute to organizational success in dealing with such disruptions often remain unclear. This paper analyzes the roles of top-down and bottom-up organizational structures in promoting organizational resilience. To do so, an agent-based model of stylized organizations is introduced that features learning, adaptation, different modes of organizing, and environmental disruptions. The results indicate that bottom-up designed organizations tend to have a higher ability to absorb the effects of environmental disruptions, and situations are identified in which either top-down or bottom-up designed organizations have an advantage in recovering from shocks.

Abstract In recent decades, management science has witnessed increasing interest in the reciprocal mechanisms between micro-level behaviours and macro-level patterns. At the same time, there are calls to bridge the field’s multidimensional divide in subdomains, theoretical foundations, or research methods that often address either the micro or macro level. The introductory chapter of the Handbook of Agent-based Computational Management Science outlines how the agent-based paradigm contributes to bridging the micro–macro divide in management science. For this, we build on the framework of causal mechanisms of Coleman’s boat. The dynamic variant of the framework, proposed in the philosophy of social science, allows for capturing the reciprocal connections between the micro and macro levels and translates into the agent-based paradigm and its generative approach. For the novice in agent-based modelling and simulation, we briefly outline some more ‘technical’ aspects. Finally, we provide a brief overview of the chapters in the handbook, organized into four parts as there are (1) the microfoundations of agent-based models in management, agent-based modelling and simulation for (2) theory building in management and (3) for operational issues in management, followed by some overarching, and (4) reflections and extensions.

Models of economic decision makers often include idealized assumptions, such as rationality, perfect foresight, and access to all relevant pieces of information. These assumptions often assure the models’ internal validity, but, at the same time, might limit the models’ power to explain empirical phenomena. This paper is particularly concerned with the model of the hidden action problem, which proposes an optimal performancebased sharing rule for situations in which a principal assigns a task to an agent, and the action taken to carry out this task is not observable by the principal. We follow the agentization approach and introduce an agent-based version of the hidden action problem, in which some of the idealized assumptions about the principal and the agent are relaxed so that they only have limited information access, are endowed with the ability to gain information, and store it in and retrieve it from their (limited) memory. We follow an evolutionary approach and analyze how the principal’s and the agent’s decisions affect the sharing rule, task performance, and their utility over time. The results indicate that the optimal sharing rule does not emerge. The principal’s utility is relatively robust to variations in intelligence, while the agent’s utility is highly sensitive to limitations in intelligence. The principal’s behavior appears to be driven by opportunism, as she withholds a premium from the agent to assure the optimal utility for herself.

Organisations rely upon group formation to solve complex tasks, and groups often adapt to the demands of the task they face by changing their composition periodically. Previous research has often employed experimental, survey-based, and fieldwork methods to study the effects of group adaptation on task performance. This paper, by contrast, employs an agent-based approach to study these effects. There are three reasons why we do so. First, agent-based modelling and simulation allows to take into account further factors that might moderate the relationship between group adaptation and task performance, such as individual learning and task complexity. Second, such an approach allows to study large variations in the variables of interest, which contributes to the generalisation of our results. Finally, by employing an agent-based approach, we are able to study the longitudinal effects of group adaptation on task performance. Longitudinal analyses are often missing in prior related research. Our results indicate that reorganising well-performing groups might be beneficial, but only if individual learning is restricted. However, there are also cases in which group adaptation might unfold adverse effects. We provide extensive analyses that shed additional light on and help explain the ambiguous results of previous research.

In recent years, various decentralized organizational forms have emerged, posing a challenge for organizational design. Some design elements, such as task allocation, become emergent properties that cannot be fully controlled from the top down. The central question that arises in this context is: How can bottom-up task allocation be guided towards an effective organizational structure? To address this question, this paper presents a novel agent-based model of an organization that features bottom-up task allocation that can be motivated by either long-term or short-term orientation on the agents' side. The model also includes an incentive mechanism to guide the bottom-up task allocation process and create incentives that range from altruistic to individualistic. Our analysis shows that when bottom-up task allocation is driven by short-term orientation and aligned with the incentive mechanisms, it leads to improved organizational performance that surpasses that of traditionally designed organizations. Additionally, we find that the presence of altruistic incentive mechanisms within the organization reduces the importance of mirroring in task allocation.