Abstract
Background: Corporate governance is evolving and shifting, becoming more outcome-focused to address a more complex and uncertain world, rooted in ethics, accountability and integrated thinking. However, governance is complex, so there needs to be an approach to governance observation that manages this paradox.
Objectives: This article proposed a pragmatic and innovative model that represents and makes the complex nature of governance accessible to boards, policymakers and regulators.
Method: This exploratory and developmental article followed the stages of literature review, conceptual design, model development and visualisation.
Results: Corporate governance is shifting as the modern world moves towards greater uncertainty and unpredictability, and along with this, corporate failure is not abating. The science of complexity has been proposed as an approach to observe and enhance understanding of complex systems, and some authors have connected the fields of governance and complexity. A new governance model (GOVIndicia®) was conceptually designed, constructed and visualised. The model proposed three core domains represented by a Venn diagram: culture, decision-making and implementation and oversight. The model inferred functional operation in both a cyclical and interconnected action. The three domains each had three categories, and each of those three indices made 27 indices overall.
Conclusion: The critical importance of governance is clear, and yet corporate failures keep occurring. Modern governance is operating in an increasingly complex environment, and governance itself is a complex system and should be modelled as such.
Contribution: The complexity governance model proposed supports boards and their organisations, as well as policymakers and regulators.
Keywords: governance; corporate governance; complexity; modelling; governance complexity model; systems model.
Introduction
Corporate governance failures, along with other regulatory issues, have been identified by regulators around the world (Cole, 2021). There is much discussion and wide-ranging views over how governance assessments and board evaluations should be conducted to ensure good governance. One of the reasons for this is that governance, the act of governing, is not just a compliance exercise but is also a performance one that involves human interaction, discussion, decision-making and oversight. Therefore, with a key influencer of governance quality being culture and its inherent complexity, how to approach these assessments is a key challenge for regulators and organisations alike.
This study investigated the complexity of governance and how, by modelling governance through this lens, it might enhance the nature and validity of these reviews and improve the quality of governance practice. It emphasises the importance of endeavouring to reverse the current entropy of governance towards a linear compliance system and how this entropy might be reversed and embraced using the science of complexity.
Corporate governance
Corporate governance has become a mainstay within many of our business, economic and social systems; its purpose is to ensure a stable and ethical foundation upon which economies and society can rely. While its purpose might be clear, its nature is perhaps more difficult to determine. As with many constructs, to embed these into societal and business processes, they have been woven into legislation, regulations and codes of practice, and although this may seem to be appropriate, the nature of governance that is emerging suggests that this may not be the best way to nurture and embed good governance and ethical decision-making into organisations. Corporate governance was used as a field of reference because it most closely models the human activity of governing, which the proposed model addresses.
Many jurisdictions around the world have introduced codes of corporate governance, and these are applicable to different sectors and organisations, such as corporate, charitable and sporting (Walters & Tacon, 2018). Within these corporate governance code frameworks, there are expectations for organisations to measure and monitor the effectiveness of their governance systems through internal and external board and governance effectiveness reviews. These reviews have generally approached these assessments by adopting their respective regulatory frameworks and making assessments, using traditional linear dynamics and thinking, to observe and measure the compliance of an organisation against such frameworks.
This ignores the nature of governance altogether. With modern governance frameworks, such as ISO37000 (ISO, 2022) and King V (Draft) (King, 2025), suggesting a more outcomes-based approach to governance codification, and with modern scientific inquiry utilising interdisciplinary and transdisciplinary methodologies, such as complexity science (complexity), there is an urgent need to reimagine how we conduct governance reviews, especially in light of continued high-profile and high-impact governance failings. We must do better.
Governance and board evaluations
Many regulated organisations, such as those in the financial services, charities and governmental bodies, are expected to align themselves with governance frameworks or codes. Many are also obligated to assess, on a regular basis, their compliance with these frameworks, alongside their board effectiveness.
Board evaluations and governance effectiveness reviews have become common practice in most boardrooms and are considered by many to be essential for organisations aiming to enhance their operational performance and accountability (Kiel & Nicholson, 2005). These evaluations provide a structured framework for assessing the collective and individual effectiveness of board members, thereby identifying areas for improvement and development in decision-making, risk management and overall governance practice (Nicholson & Kiel, 2004).
Regular evaluations foster a culture of accountability, ensuring that board members are aware of their responsibilities and the impact of their decisions on the organisation’s objectives and purpose (World Bank, 2009). This proactive approach not only strengthens corporate governance but also enhances stakeholder trust and engagement, which are critical for long-term success in today’s competitive environment.
Regulatory bodies are increasingly emphasising the importance of not just compliance with existing regulations but also the performance and cultural dynamics of boards. The Financial Reporting Council (2024) has highlighted that effective governance goes beyond mere adherence to rules; it encompasses the ability of boards to adapt to changing business landscapes and stakeholder expectations. This shift reflects a broader recognition that strong governance is linked to organisational resilience and performance, prompting regulators and others (Klemash et al., 2018) to advocate for comprehensive evaluations that assess both quantitative outcomes and qualitative aspects, such as board culture and dynamics.
As organisations navigate this evolving regulatory landscape, the expectation is clear: boards must engage in meaningful self-assessments that drive continuous improvement (Kiel et al., 2018). This includes evaluating how well they fulfil their roles in overseeing management, addressing emerging risks and maintaining alignment with stakeholder interests. By adopting robust evaluation processes, boards can not only comply with regulatory demands but also position themselves as leaders in governance practice, ultimately enhancing their effectiveness and contributing to sustainable organisational growth and environmental sustainability (Kiel & Beck, 2018; Soininen et al., 2025).
Corporate governance is shifting
With modern corporate governance moving into its postmodern (CoSteer, 2022) era and with its theory and practice developing to cope with climate change and other social pressures (Abson et al., 2017; Boyd et al., 2015; Dietz et al., 2003; Geels, 2004), it is clear that corporate governance is having to adapt to support organisations in navigating these crises. Therefore, it appears we find governance often ill-defined and somewhat troublesome (CoSteer, 2022). Recently, regulators, international organisations and academics (Cuomo et al., 2016; Financial Times, 2022; Nordberg & McNulty, 2013; Sjåfjell, 2017; Tacon & Walters, 2021) around the world have recognised that current codification is adding little to resolve corporate failures, board performance challenges and corporate ethical issues.
Much of the focus of early corporate governance codes around the world (Shleifer & Vishny, 1997) was to negate the ‘agency problem’ identified by Meckling and Jensen (1976) (CoSteer, 2022). Fully negating or de-risking this ‘agency problem’, however, is a challenge, and other theories of corporate governance, such as Stewardship (Davis et al., 1997), considered this problem to be more human. Ghoshal and Bartlett (1994), along with Freeman (1994), pushed forward ‘organisational purpose’ and suggested that it could be the driver of effective strategic management. They noted a shift from the ‘old doctrine of strategy, structure and systems’ to ‘a softer, more organic model built on the development of purpose, process and people’. They argued that the principal responsibility of the board is not solely to develop strategy but to instil a unified sense of ‘purpose’ across and through an organisation. This theme around purpose and the role of culture has been developed by recent corporate governance codes such as ISO37000, King V and the United Kingdom (UK) Corporate Governance Code.
Governance into complexity
There are clear expectations, therefore, that boards explore the interconnections between organisational culture, the quality and ethics of decision-making and the operational efficacy and performance of their organisations, not just tick the proverbial box of code compliance (CoSteer, 2022). If organisations are but a group of humans gathered around a common purpose or goal, it would be understandable to presume that the arrangement and operation of that system would be predominantly linear. However, the governance of an organisation has been determined by many as a ‘complex’ system (Stacey, 2003; eds. Teisman et al., 2009) and, therefore, as explained below, it is inconceivable that a linear approach to its measurement and understanding be warranted, but this is largely what we see in the literature and in the practice of board and governance evaluations.
Linear approaches to solving complex problems have been shown to be ineffective (Mateos et al., 2002), and in the early 1970s, interdisciplinary research began to emerge. Whitney and colleagues (2015) describe it as ‘[where] … representatives from different disciplines confronted problem complexes together to solve them collaboratively’. Complexity and other systems approaches were born out of the need to develop solutions that were more interdisciplinary in nature and embraced the nonlinearity of systems. Complexity is a science that endeavours, amongst other aims, to unify frameworks across disciplines in a bid to understand a system or behaviour which previously has been very difficult to predict. There is a developing branch of complexity that specifically looks at governance, Complex System Governance (CSG), and Keating and Katina (2016) noted that there is a paucity of research in this area and nothing of rigorous depth (CoSteer, 2022).
This article reviewed recent developments in the field of corporate governance, theories around the science of complexity and how, by approaching governance through the lens of complexity, it might be possible to significantly improve our understanding of what makes good governance in practice. It proposed a model that can observe governance to better understand how different attributes of organisational factors, such as leadership quality, operational efficacy, decision-making and culture, have influence on each other and on overall organisation and board governance. If governance can be modelled as a complex system, how should governance and board assessments and evaluations adapt in order to then approach this issue?
In order to address the deficiencies of codification, the shifting sands of governance theories and the progress of the measurement and assessment of both governance and boards, a nonlinear emergent (complexity) approach to these evaluations needs to be created and conducted.
Research methods and design
This study employed recognised methods to conceptualise, design and visualise a new systems dynamics model of governance. The process of designing this new theoretical model required defining the problem and grounding the work in existing theories. This included identifying gaps and justifying the need for a new model by reviewing the relevant literature and understanding the system context (Jaccard & Jacoby, 2009). A conceptual core framework was then developed, which involved defining key constructs enriched by stakeholder input and qualitative insights to align theory with practice.
Lave and March (1975) highlight that a methodological approach to designing a model for social systems involves defining the problem and grounding it in existing theory to frame the system in context. This includes identifying key concepts and relationships relevant to the system and understanding the complex environment within which it operates.
The model development then entailed an iterative process of hypothesis generation, formalising constructs and selecting appropriate methods and visualisation to articulate causal and systemic linkages within the system model, close to that proposed by Sevaldson (2022) in systems-orientated design.
Appropriate methodologies were selected, including an extensive review of the literature, the identification of current issues and consideration of complexity and systems dynamics modelling as a solution.
Results
The discussion of results of this study is presented in four review sections considering the challenge of observing and measuring governance; complexity theory, social systems and modelling; the complexity of governance; and the Complexity Governance Model (GOVIndicia®).
The challenge of observing and measuring governance
Modern corporate governance, which probably emerged from the Financial Aspects of Corporate Governance, forever after known as the Cadbury Committee, in May 1991, is perhaps moving into its postmodern era. The Organisation for Economic Co-operation and Development’s (OECD) definition of governance is aligned with the one presented in the Cadbury Report, where governance is defined as the system and way organisations are both controlled and directed (Shah et al., 1992 [Cadbury Report]). The OECD goes on to describe the purpose of corporate governance as the creation of an environment characterised by trust, transparency and accountability and that this is essential for promoting long-term investment, ensuring financial stability and maintaining business integrity (OECD, 2023).
Many theories of what facilitates ‘good’ governance, such as stewardship, have emerged within the sphere of corporate governance, illustrating that governance is shifting, often ill-defined and somewhat troublesome to define and measure. As noted, regulators, international organisations and academics have been recognising that current codification is adding little to resolve corporate failures and corporate ethical issues. Regulators are also beginning to consider and place importance on purpose and values (Tassopoulou & Bert, 2019). Purpose has become increasingly used and connected not just with organisational performance but with governance. This was made most visually clear in the publication of the International Organisation for Standardisation’s (ISO) Guidance on Organisational Governance (ISO 37000) in 2022. In this model of governance, purpose sits at the core.
Governance has also been suggested to be inherently very difficult to measure (Tipurić et al., 2014), and the codification of corporate governance has also led organisations to slide towards a ‘tick box’ approach, despite the requirements for ‘comply or explain’ statements in certain countries (CoSteer, 2022). The measures that do exist are simply not enough, and, in fact, do not really get at the heart of the question, ‘how well are both the organisation, and the people within the organisation, “being steered” and how well are they “steering”?’.
Most measures of corporate governance focus on compliance-related issues, and numerous rating models also seem to focus on the inputs of governance, such as the composition of boards and the separation of the Chief Executive Officer and Chairperson roles and diversity (Argüden, 2010). All admirable; however, he argues that these measures simply do not pay sufficient attention to the quality of information or decision-making processes, nor do they link the effectiveness of governance to output measures such as the brand image, employee and customer satisfaction indices, or profitability and value creation.
The measurement and assessment of governance is deeply complex and is perhaps like culture, intangible; however, this does not mean it is not important. As the Financial Reporting Council (2020), in their Review of Corporate Governance Reporting, November 2020, suggest, there is a need for better assessment and monitoring of culture, including the consideration of methods and metrics used.
Complexity theory, social systems and modelling
Introduction to complexity theory
Complexity, as a science, first emerged in the late 1940s, although it has been studied by humanity for 1000s of years (Castellani & Gerrits, 2024). The term ‘complexity’ is rooted in the Greek word plektös, which means woven together. Historically, the concepts of simplicity and complexity have been linked to perceived ease or difficulty of understanding (Flood & Carson, 2013). Such interpretations, however, are inherently subjective and vary according to individual perception.
‘Complexity’ describes a phenomenon whose interconnected nature surpasses the explanatory scope of mechanistic or linear models. While certain individual elements may be understood in isolation, the behaviour of larger and more intricately related systems requires a focus on emergent patterns, learning, innovation and adaptive processes (The Santa Fe Group, 1996).
The principles governing the emergence of both knower and known, the boundedly rational yet optimally complex biological and social agents, form the foundation of complexity science (Kauffman, 1991). This body of thought contends that the fragile coherence and stability of biological and social systems are precarious and vulnerable to sudden disruptions or phase transitions. Systems positioned at the edge of these thresholds, sometimes referred to as the edge of chaos, serve as paradigmatic expressions of adaptive order (Kauffman, 1991). That said, systems do find positions of stability, the defining characteristic being that complex systems can be disrupted (go through a phase transition of exponential change) with relatively small disrupters.
The study of the behaviour of collectives, like groups of people and organisations, in terms of how they structure themselves and their dynamics is endowed innately with the potential to evolve over time; they remain emergent (Coveney & Highfield, 1996).
A common understanding around the definition of complexity (Gleick, 1989; Parker & Stacey, 1994; Stacey, 1996; Wilding, 1998) highlights that complex behaviours occur in different kinds of systems, such as biological, ecological, economic and social, encompassing chaos because of its nonlinearity. Chaotic dynamics are evident in business environments, where system sensitivity and nonlinear relationships can separate causes and their effects across time and space. Such conditions render future outcomes inherently uncertain, posing substantial challenges for organisational decision-making and strategy, and therefore governance (Mena, 2003).
Complexity and modelling
Complexity theory offers a conceptual foundation and methodological rubric for applying study through disciplines, such as both traditional, agent-based modelling and systems dynamics, to understand complex environments.
Traditional modelling: Traditional modelling often relies on linear, reductionist approaches that emphasise equilibrium and predictability. It typically adopts linear and reductionist assumptions, focusing on aggregated patterns and controllable structures to explain organisational outcomes (Hoogervorst, 2009).
Complexity theory, however, uses traditional modelling approaches, such as formal system models and information theory, to develop understandings of systems by capturing their interconnected components and dynamic behaviours (Varley, 2023). These models provide frameworks to represent complex systems’ emergent properties and interactions. By integrating various data sources and managing uncertainty, traditional modelling helps to bridge metaphorical descriptions and precise representations, supporting better insights into systemic complexity and decision-making (Batty & Torrens, 2001). Traditional models offer a structured way to explore complex systems beyond linear causality.
Agent-based modelling: In agent-based modelling, a system can be understood as an assemblage of autonomous agents (these could be individuals, organisations or systems within systems) that interact with one another and with their environment (Janssen, 2005; Chu et al., 2003). Each agent functions as an independent decision-maker, adapting its behaviour to internal environmental conditions and the actions of other agents within the system in accordance with specified rules. Inevitably, these dynamics can extend beyond the explicit boundaries of the model. Agent-based modelling generally does not incorporate open systems for this reason, as they exchange energy, matter or information with an external environment. Agent-based modelling focuses on the internal dynamics within a system, simulating the behaviours and interactions of individual agents, rather than the system’s interactions with the external world (Deguchi, 2011).
Agent-based models can therefore be used to understand closed complex systems that evolve under conditions far from equilibrium and are shaped by historical trajectories. They consist of numerous interacting agents, whose relationships with each other are dynamic, nonlinear and often localised (Bonabeau, 2002). Individual agents typically lack awareness of the overall system behaviour, while their interactions generate both reinforcing and balancing feedback loops, giving rise to emergent properties and adaptive dynamics within the system (Maguire et al., 2006).
System dynamics modelling: System dynamics modelling is a prominent technique within the complexity theory framework used to understand systems and their interactions by capturing feedback loops, delays and nonlinear relationships (Bala et al., 2017; Sterman, 2002). It enables simulation of system behaviour over time, linking structure to dynamic outcomes, which helps in identifying leverage points and testing scenarios for better decision-making. System dynamics modelling treats systems as a whole rather than isolated parts, emphasising interdependencies and adaptation. This approach supports the exploration of emergent properties and dynamic complexity in social, environmental and organisational contexts (Coyle, 1997; Sterman, 2002).
Complexity and social systems
Much of the complexity research has been in physical and biological systems, although there is a growing body of research in social systems (Kauffman, 2022; Sawyer, 2005). Generally, the difference identified is that of independent free will, which is not generally present in biological and physical systems. It has been suggested that purposeful elements and adaptive responses, such as those used in biological systems, can also be extended to social organisations, with complexity theory providing a framework that accommodates free will (ed. Kane, 2011).
The concept of free will implies that conscious beings possess sufficient autonomy from physical constraints to intentionally shape future outcomes through goal-directed action, a form of intrinsic intentionality that is unique solely to conscious organisms (Shepherd, 2012). Therefore, organisations, which are groups of humans with free will, cannot be reduced to transformation processes or control systems; their survival requires mechanisms for interpreting ambiguity and generating meaning and direction for participants (Kauffman, 1995). Organisations are human and therefore ‘meaning’ systems; this distinguishes them from lower level biological systems (Daft & Weick, 1984). It is perhaps Kauffman’s (2022) definition of complexity which bridges this divide between biological and social. He describes complexity as a systemic condition marked by both stability and adaptability. Such systems exhibit a high degree of internal organisation that ensures coherence while simultaneously maintaining the capacity for flexibility and unexpected outcomes. The relative autonomy of individual components, coupled with their diverse patterns of interaction, gives rise to nonlinearity, unpredictability and emergent properties. In this sense, complexity emerges from the dynamic tension between order and indeterminacy. Perhaps, this is where we find governance.
The complexity of governance
The interconnection between governance and complexity
Complex systems, therefore, are distinguished by their interconnected nature, fundamental unpredictability and controllability, as well as nonlinearity. Within such contexts, scientific inquiry, including modelling, moves beyond the pursuit of prediction and control, instead prioritising an enhanced understanding of systemic dynamics and interrelationships. This shift in focus enables more informed and contextually appropriate forms of engagement within the systems to which we belong and allows us to choose to participate in the system with more strategic intelligence (Wahl, 2019), perhaps precisely what is required in the management of governance.
Governance structures and systems, therefore, can be seen as inherently complex and refer to the intricate and multifaceted nature of organisational and institutional systems through which authority is exercised, decisions are made, resources are mobilised and the way in which these challenges are addressed (Commission on Global Governance, 1995). It is an ongoing process that allows for the reconciliation of diverse interests and the facilitation of cooperative action. Governance includes formal institutions and regulatory frameworks with the authority to enforce compliance, as well as informal arrangements that actors recognise or adopt based on perceived mutual benefit (Carlsson & Ramphal, 1995).
Within global governance, Fuß et al. (2021) describe this complexity in structural terms, highlighting dimensions such as scale, diversity and density. Scale captures the proliferation of governance elements across three dimensions: the growing number of actors (e.g. governments, firms, non-governmental organisations (NGOs) and civil society groups), the expanding range of institutions (formal and informal rule systems and the organisations that embody them) and the interaction of multiple levels (local, national, regional and global). Diversity reflects the heterogeneity of these actors and institutional forms, while density refers to the intensity and overlap of their interconnections. These aspects underscore why governance is irreducible to linear processes of control and therefore must be understood as a dynamic and adaptive system, shaped by interdependence and emergent outcomes (Klijn & Koppenjan, 2016).
The complexity of governance also involves the perspectives of simple view, thin complexity and thick complexity (Strand & Fjelland, 2000). They distinguish between three ways of understanding complexity in governance. The first one is a simple view, which observes the system as linear and reducible, where outcomes can be predicted through stable causal relationships. By contrast, a thin conception of complexity acknowledges uncertainty and nonlinearity yet seeks to manage this by simplifying these challenges into models or heuristics. A thick conception of complexity describes the system as irreducible, constitutive of reality and that uncertainty, emergence and dynamic interactions are inherent features of governance systems. They further emphasise the emergent qualities of governance processes, recognising that interactions among actors, institutions, organisations and environments cannot be fully controlled or eliminated. This perspective shows the limits of prediction and highlights the importance of embracing adaptive, reflexive and participatory forms of governance.
Strand and Fjelland further indicate the importance of complementarity and contextuality when understanding the complexity of governance. The principle of complementarity suggests that different perspectives on governance complexity can coexist but are not mutually exclusive. Contextuality stresses that the appropriateness of any governance approach depends on its context. Complex systems cannot be governed with a ‘one-size-fits-all’ model; instead, responses must be sensitive to cultural, institutional and situational factors.
Stacey (2000) also suggests that the worldview of thin complexity must be enriched with the understanding of complementarity and contextuality. This implication is faithful to complex systems theory, where the question is not ‘How can I govern the system into a new attractor’ (a desired state)? but rather ‘What is my role in this system, and how do the actions of me and others affect the system?’. This aligns closely with Wahl’s (2019) perspective, where he suggests that it is the participation with the system that is critical in influencing the performance of the system.
Modelling and operationalising governance through complexity
To operationalise effective governance in such contexts, specifically within organisations, involves fostering collaboration across multiple levels and sectors, integrating diverse knowledge sources and understanding the aspects of uncertainty and unpredictability. This perspective aligns with Kreienkamp’s and Pegram (2021) assertion that existing global governance structures are often ill-equipped to manage complex global catastrophic risks, advocating for a design model that accommodates complexity through iterative processes and adaptive mechanisms. Therefore, contemporary governance must evolve to embrace complexity, within and outside of the organisational environment, to ensure resilience and responsiveness in the face of global challenges (Kreienkamp & Pegram, 2021).
Modelling and understanding governance as a complex system necessitates a dynamic, adaptive approach that transcends traditional hierarchical models, suggested Duit and Galaz (2008). They go on to suggest that conventional governance frameworks often struggle to address the nonlinear dynamics, threshold effects and cascading events characteristic of complex adaptive systems. These systems, marked by interdependencies and emergent behaviours, require governance structures, models and measures that are flexible, decentralised and capable of learning and evolving in response to changing conditions.
Complexity governance model – GOVIndicia®
Building the model – The core components
Modelling governance from the perspective of complexity required the consideration of how the complexity governance model (Model) would be both pragmatic and theoretical. Given the continued challenges facing governance and board evaluations and the expectation from regulators and other stakeholders to approach and measure aspects of governance, such as culture, decision-making and oversight, these factors should be central to the model. The model also needed to account for both the circularity of governance and decision-making, as well as oversight processes commonly found in organisations and their interconnected nature.
Three core foundational domains to the model emerged through the review of literature: Decision-making, Implementation and Oversight, and Organisational Culture. To acknowledge and visualise the intersecting nature of these three, they were drawn as a Venn diagram, assisting with the immediate recognition that these foundational elements are interconnected; see Figure 1. The decisions made (Crivelli & Balconi, 2023; Morrison, 2025) and the actioning of those decisions (Ellinas et al., 2017; Franca & Hollnagel, 2023; Zheng et al., 2024), individually and/or collectively, are influenced by the prevailing culture of any group, and the culture of the group influences the manner in which decisions are made and the nature of those decisions. Organisational governance is more than the sum of its parts; it is more than the aggregate.
 |
FIGURE 1: The core domains of the complexity governance model – GOVIndicia®. |
|
Governance Quality (GovQ) is the outcome of the three interconnecting domains, each of which has a reciprocal relationship with the others, the outcome being the interplay between the domains. The domains themselves are also constantly in flux individually, feeding back into the overall system, resulting in a complexity model with observable emergent properties and internal feedback mechanisms. The ‘dashed’ arrows indicate the interconnectedness and that this is multidirectional.
Building the model – The nine categories
Each of the three (n = 3) foundational domains of GOVIndicia® has nine (n = 9) categories that contribute towards and influence them. These categories are also intimately connected to each other, and so the model recognised this by interconnecting each of these groups of three categories and by interconnecting them to their respective core domain. The categories are noted in Table 1 – The Domains, Categories and Indices of the Governance Complexity Model, GOVIndicia®.
Building the model – The 27-indices
The nine categories themselves also have three indices that influence them. The model, therefore, has three (n = 3) foundational domains, nine (n = 9) categories and 27 indices, as shown in Figure 2. The indices are noted in Online Appendix 1 – The Domains, Categories and Indices of the Governance Complexity Model, GOVIndicia®.
 |
FIGURE 2: The complexity governance model – GOVIndicia®. |
|
Complexity governance model: Operation and function
All models are a distillation of the real world, and not all functionally operate. However, if they do, they should inform the model user of something about the world that it represents. Therefore, observing and visualising the way or ways a model operates helps in developing an understanding of how the different facets within a model interact with each other. Models rarely operate in isolation, as they tend to be the representation of a part of a larger system. It was a requirement to acknowledge that this model interacts with its environment (everything outside the system), noting that this interaction was not part of this model. This issue was acknowledged earlier when system dynamics and agent-based modelling were discussed, and while this is not an agent-based model, agents (individuals within an organisation) are, in fact, those acting out many of the categories and indices within the model. This model is not the whole system; it is a part of a bigger, even more complex system. This model sought to model in a functional and pragmatic way the complex nature of interactions between aspects of governance in an internal setting (organisational governance) where social interactions, behavioural dynamics and institutional norms converge to influence collective outcomes.
The main essence of ‘modelling’ any system is to try and draw out key themes and present them in a way that is meaningful and will support better understanding. In this case, the model seeks to further understanding around intra-organisational governance. Governance practice, in fact, most practices within organisations are cyclical, to provide for decision, action and then review; this model accommodates this. It also accommodates the innate complexity and interconnectedness of governance as highlighted in the literature review.
Model dynamics: A cycle
The model is cyclical. It models the process of decision-making from an internal governance perspective rotating in a clockwise direction, and although there may be no specific start or end point, for want of illustration, by way of example, it starts with the category Purpose & Values.
Organisations (ISO 37000) to practise good governance should congregate around a sense of purpose and values (this might not always be an aspirational purpose, but a purpose, nonetheless). Leaders and leadership emerge, and along with that certain behaviours and an organisational ethic. At this point in the cycle the true nature of governing occurs; making decisions towards the purpose. This process includes the gathering of information, the flow of information and the quality of the decision-makers (experiences, knowledge and understandings of the world and its affect on decision-making). Once any decision has been made the decision may need to be cascaded or communicated. The methods of communication, their clarity and how emotional intelligence is used to develop and nurture communication feeds into the overall communication of any decision. This will also impact how well the decision is implemented; its operational efficacy (the efficiency and effectiveness of an organisation’s processes, those that are formal and informal). Finally, to establish the outcome of our decision and implementation, most organisations have some mechanisms to detect this measure of success in the cycle through feedback and learning, which the model calls ‘reasoned persistence’ (do we persist with the current decision and implementation, or do we need to adjust, reconsider or take an alternative course – we reason our persistence with a particular decision). Then, of course, it starts all over again. As with all models, this cycle is by no means fixed, as sometimes we might miss a stage or two in the process.
Model dynamics: Interconnection and interaction
The proposed model moves towards the conceptual idea of complexity by visualising the interconnected nature of the three core domains of the organisational governance system. To illustrate how this manifests itself in the model, it can be seen that there are clear relationships that cross the cycle. The purpose and values of an organisation are likely to have some level of connection with the nature of the decisions made and the nature of the implementation. The behaviours and ethics that emerge through the nature of leadership are likely to have an impact on the communication.
The model indicates these two operational aspects through the dotted and solid arrows. The solid arrow indicates the cyclical nature, and the dotted arrow indicates the interconnected nature.
Significance of the research study
This article intended to emphasise the importance of reversing the current entropy of governance to a linear compliance-orientated system, and how this entropy might be reversed and embrace a more complex view of governance. Additionally, it would be a catalyst for people to start considering other ways to address the apparent problem, where organisations practise perfunctory compliance as a way of demonstrating effective governance, which eludes accountability and hinders performance and social impact.
Discussion
Recommendations and future study
It is vital that any new proposed model then be applied to real-world contexts to evaluate its explanatory power and to enhance theoretical understanding. We recommend that further research design an assessment framework, perhaps through a survey instrument or interviews, collect qualitative and/or quantitative data and analyse those data to assess the applicability and validity of the model. This could also investigate, through the interpretation of power law relationships, whether there is evidence to suggest that this model can observe organisational governance as a complex system.
In addition, further research could consider, through appropriate methodologies, combining qualitative and quantitative approaches such as participatory modelling, surveys and structural equation modelling, whether there are emerging patterns, as one might see in complex systems, indicating key influencers on governance quality.
Conclusion
Corporate governance is shifting as corporate failures continue and expectations from regulators continue to escalate. There was a need to explore further the growing body of discussion around the complexity of governance to support and develop this connection. Many authors, governing bodies and regulators inside and outside the academic sphere are calling for a more pragmatic, human and behavioural approach to the observation, measurement and development of corporate governance. It is suggested that this approach would enhance board evaluations, governance assessments and therefore the overall practice of governance, improving decision-making and corporate and societal accountability.
Complexity, as a scientific approach, has been shown to enhance understanding within complex social systems, and therefore is useful for developing the observation and measurement of governance, specifically the act of governing.
This article has proposed a new, practical system dynamics model of governance, which embraces its innate complexity, following a review of the literature. The process used for designing a new theoretical model for a system, like governance, involved defining the problem and grounding the work in existing theories, which this article has presented. This included identifying gaps and justifying the need for a new model by reviewing the relevant literature and understanding the corporate governance context. This article then developed a conceptual framework, which resulted in the proposal of a model formed of key constructs, domains, categories and indices. It went on to operationalise the model in two ways: cyclical and interconnected.
Further study should seek to validate the model through means such as qualitative and quantitative methods involving activities such as interviews, surveys and observation.
Corporate governance and its practice could benefit significantly from a more pragmatic, human-orientated model of governance that observes and facilitates the measurement of ‘governing’. The proposed model addresses this gap in the literature and in the practice of governance and board evaluations.
Acknowledgements
Competing interests
The authors declare that there are no financial or personal relationships that may have inappropriately influenced them in writing this article. The authors declare that the trademark and copyright for the GOVIndicia® – Complexity Governance Model are owned by CoSteer Limited, a private company incorporated in Guernsey. The lead author, P.J.G.C., is the majority shareholder. The author has disclosed this affiliation fully and confirmed that it has not influenced the design, conduct or reporting of the research presented in this article.
CRediT authorship contribution
Perrin J.G. Carey: Conceptualisatoin, Methodology, Investigation, Writing – original draft, Visualisation, Resources, Writing – review & editing. Divya Mahendran: Methodology, Investigation, Writing – original draft, Visualisation, Resouces, Writing – review & editing.
Ethical considerations
This article followed all ethical standards for research without direct contact with human or animal subjects.
Funding information
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Data availability
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. It does not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The authors are responsible for this article’s results, findings and content.
References
Abson, D.J., Fischer, J., Leventon, J., Newig, J., Schomerus, T., Vilsmaier, U., Von Wehrden, H., Abernethy, P., Ives, C., Jager, N., & Lang, D.J. (2017). Leverage points for sustainability transformation. Ambio, 46(1), 30–39. https://doi.org/10.1007/s13280-016-0800-y
Argüden, Y. (2010). Measuring the effectiveness of corporate governance. The Business School of the World.
Bala, B.K., Arshad, F.M., & Noh, K.M. (2017). System dynamics. Springer Singapore.
Batty, M., & Torrens, P.M. (2001). Modelling complexity: The limits to prediction. 12th European Colloquium of Theoretical and Quantitative Geography, St-Valéry-en-Caux, France, 7–11 September 2001 (p. 102). https://doi.org/10.4000/cybergeo.1035
Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(suppl_3), 7280–7287. https://doi.org/10.1073/pnas.082080899
Boyd, E., Nykvist, B., Borgström, S., & Stacewicz, I.A. (2015). Anticipatory governance for social-ecological resilience. Ambio, 44(Suppl. 1), 149–161. https://doi.org/10.1007/s13280-014-0604-x
Carlsson, I., & Ramphal, S. (1995). Co-Chairmen’s foreword. In Issues in global governance (pp. XI–XIII). Brill Nijhoff. https://doi.org/10.1163/9789004637467_004
Castellani, B., & Gerrits, L. (2024). The atlas of social complexity. Edward Elgar Publishing.
Chu, D., Strand, R., & Fjelland, R. (2003). Theories of complexity. Complexity, 8(3), 19–30. https://doi.org/10.1002/cplx.10059
Cole, R., Johan, S., & Schweizer, D. (2021). Corporate failures: Declines, collapses, and scandals. Journal of Corporate Finance, 67, 101872. https://doi.org/10.1016/j.jcorpfin.2020.101872
Commission on Global Governance. (1995). Our global neighborhood. Oxford University Press.
CoSteer Limited. (2022). Interconnecting governance and culture. Good Governance Academy. Retrieved from https://goodgovernance.academy/wp-content/uploads/2022/09/CoSteer-Interconnecting-governance-and-culture-Info.pdf
Coveney, P., & Highfield, R. (1996). Frontiers of complexity: The search for order in a chaotic world. London: Faber.
Coyle, R.G. (1997). System dynamics modelling: A practical approach. Journal of the Operational Research Society, 48(5), 544–544. https://doi.org/10.1057/palgrave.jors.2600682
Crivelli, D., & Balconi, M. (2023). Shared emotions, interpersonal syntonization, and group decision-making: A multi-agent perspective. Frontiers in Neuroscience, 17, 1251855. https://doi.org/10.3389/fnins.2023.1251855
Cuomo, F., Mallin, C., & Zattoni, A. (2016). Corporate governance codes: A review and research agenda. Corporate Governance: An International Review, 24(3), 222–241. https://doi.org/10.1111/corg.12148
Daft, R.L., & Weick, K.E. (1984). Toward a model of organizations as interpretation systems. Academy of Management Review, 9(2), 284–295. https://doi.org/10.2307/258441
Davis, J.H., Schoorman, F.D., & Donaldson, L. (1997). Toward a stewardship theory of management. Academy of Management Review, 22(1), 20–47. https://doi.org/10.2307/259223
Deguchi, H. (2011). Economics as an agent-based complex system: Toward agent-based social systems sciences. Springer Science & Business Media.
Dietz, T., Ostrom, E., & Stern, P.C. (2003). The struggle to govern the commons. Science, 302(5652), 1907–1912. https://doi.org/10.1126/science.1091015
Duit, A., & Galaz, V. (2008). Governance and complexity–Emerging issues for governance theory. Governance, 21(3), 311–335. https://doi.org/10.1111/j.1468-0491.2008.00402.x
Ellinas, C., Allan, N., & Johansson, A. (2017). Dynamics of organizational culture: Individual beliefs vs. social conformity. PLoS One, 12(6), e0180193. https://doi.org/10.1371/journal.pone.0180193
Financial Reporting Council. (2020). Review of corporate governance reporting. https://media.frc.org.uk/documents/Corporate_Governance_Review_2020.pdf
Financial Reporting Council. (2024). Corporate governance code guidance. Retrieved from https://www.frc.org.uk/library/standards-codes-policy/corporate-governance/corporate-governance-code-guidance/
Financial Times. (2022). Don’t strengthen the UK corporate governance code – abolish it. https://www.ft.com/content/057accea-5135-4a26-a5ed-e428184bc209
Flood, R.L., & Carson, E.R. (2013). Dealing with complexity: An introduction to the theory and application of systems science. Springer Science & Business Media.
Franca, J., & Hollnagel, E. (2023). Comparative analysis of Air France 447 (2009) and Costa Concordia (2012) using FRAM: How organizational culture influences cockpit/bridge decisions. In I.L. Nunes (Ed.), Human factors and systems interaction (AHFE 2023 International Conference; Vol. 84). AHFE International.
Freeman, R.E. (1994). The politics of stakeholder theory: Some future directions. In R. E. Freeman’s (Ed.), Selected works on stakeholder theory and business ethics (pp. 119–132). Springer International Publishing.
Fuß, J., Kreuder-Sonnen, C., Saravia, A., & Zürn, M. (2021). Managing regime complexity: Introducing the interface conflicts 1.0 dataset (Discussion Papers / Wissenschaftszentrum Berlin für Sozialforschung, Forschungsschwerpunkt Internationale Politik und Recht, Abteilung Global Governance, SP IV 2021-101). Wissenschaftszentrum Berlin für Sozialforschung gGmbH. Retrieved from http://hdl.handle.net/10419/233472
Geels, F.W. (2004). From sectoral systems of innovation to socio-technical systems: Insights about dynamics and change from sociology and institutional theory. Research Policy, 33(6–7), 897–920. https://doi.org/10.1016/j.respol.2004.01.015
Ghoshal, S., & Bartlett, C.A. (1994). Linking organizational context and managerial action: The dimensions of quality of management. Strategic Management Journal, 15(S2), 91–112. https://doi.org/10.1002/smj.4250151007
Gleick, J. (1989). Chaos: Making a new science. Cardinal.
Hoogervorst, J.A. (2009). Mechanistic and organismic perspectives on governance. In Enterprise governance and enterprise engineering (pp. 25–77). Sogeti Nederland B.V.
ISO Technical Committee. (2022). ISO 3700:2021 Governance of organizations – guidance. British Standards Institute, Retrieved November 20, 2022, from https://committee.iso.org/files/live/sites/tc309/files/ISO%2037000%20slides/ISO%2037000%20Governance%20of%20organizations%20-%20Guidance%20-%20v1%202022%20web.pdf
Jaccard, J., & Jacoby, J. (2009). Theory construction and model-building skills: A practical guide for social scientists. Guilford Publications.
Janssen, M.A. (2005). Agent-based modelling. Modelling in Ecological Economics, 155(1), 172–181. https://doi.org/10.4337/9781781958667.00015
Kane, R. (Ed.). (2011). The Oxford handbook of free will. OUP USA.
Kauffman, S. (1991). Antichaos and adaptation. Scientific American, 265(2), 78–85. https://doi.org/10.1038/scientificamerican0891-78
Kauffman, S. (2022). How complexity and emergence create a cosmos. Retrieved from https://www.youtube.com/watch?v=UiGG1UUk1EU
Kauffman, S.A. (1995). At home in the universe: The search for laws of self-organisation and complexity. Oxford University Press.
Keating, C.B., & Katina, P.F. (2016). Complex system governance development: A first generation methodology. International Journal of System of Systems Engineering, 7(1–3), 43–74. https://doi.org/10.1504/IJSSE.2016.076127
Kiel, G., & Beck, J. (2018). Board performance evaluations that add value. Harvard Law School Forum on Corporate Governance. Retrieved from https://corpgov.law.harvard.edu/2018/05/18/board-performance-evaluations-that-add-value/
Kiel, G., Nicholson, G., Tunny, J.A., & Beck, J. (2018). Reviewing your board: A guide to board and director evaluation. Australian Institute of Company Directors.
Kiel, G.C., & Nicholson, G.J. (2005). Evaluating boards and directors. Corporate Governance: An International Review, 13(5), 613–631. https://doi.org/10.1111/j.1467-8683.2005.00455.x
King. (2025). King V code on corporate governance for South Africa (Draft). Retrieved from https://cdn.ymaws.com/www.iodsa.co.za/resource/collection/7DAE15BF-07FA-4922-879E-6788368F5DB4/KingV_code.pdf
Klemash, S., Doyle, R., & Smith, J. (2018). Effective board evaluation. Harvard Law School Forum on Corporate Governance. Retrieved from https://corpgov.law.harvard.edu/2018/10/26/effective-board-evaluation/
Klijn, E.H., & Koppenjan, J. (2016). The shift toward network governance: Drivers, characteristics and manifestations. In Theory and practice of public sector reform (pp. 158–177). Routledge.
Kreienkamp, J., & Pegram, T. (2021). Governing complexity: Design principles for the governance of complex global catastrophic risks. International Studies Review, 23(3), 779–806. https://doi.org/10.1093/isr/viaa074
Lave, C.A., & March, J.G. (1993). An introduction to models in the social sciences. Bloomsbury Publishing PLC.
Maguire, S., McKelvey, B., Mirabeau, L., & Öztas, N. (2006). 1.5 complexity science and organization studies. In Sage handbook of organization studies (pp. 165–214). Sage Publications.
Mateos, R., Olmedo, E., Sancho, M., & Valderas, J.M. (2002). From linearity to complexity: Towards a new economics. Complexity International, 10, 1–2.
Meckling, W.H., & Jensen, M.C. (1976). Theory of the firm. Managerial Behavior, Agency Costs and Ownership Structure, 3(4), 305–360. https://doi.org/10.1016/0304-405X(76)90026-X
Mena, C. (2003). Complexity in organisations: A conceptual model. PhD Submission dissertation, University of Warwick.
Morrison, A., Craswell, A., Bogossian, F., & Lord, B. (2025). The tensions between organisational culture and professionalisation and their influence on paramedic conveyance decision-making: A constructivist grounded theory framework. Paramedicine, 22(5), 27536386251335406. https://doi.org/10.1177/27536386251335406
Nicholson, G.J., & Kiel, G.C. (2004). A framework for diagnosing board effectiveness. Corporate Governance: An International Review, 12(4), 442–460. https://doi.org/10.1111/j.1467-8683.2004.00386.x
Nordberg, D., & McNulty, T. (2013). Creating better boards through codification: Possibilities and limitations in UK corporate governance, 1992–2010. Business History, 55(3), 348–374. https://doi.org/10.1080/00076791.2012.712964
OECD. (2023). G20/OECD Principles of Corporate Governance 2023. Retrieved from https://www.oecd.org/en/publications/g20-oecd-principles-of-corporate-governance-2023_ed750b30-en.html
Parker, D., & Stacey, R. (1994). Chaos, management and economics: The implications of nonlinear thinking. Institute of Economic Affairs.
Santa Fe Group (1996) cited by Battram (1998) Navigating complexity. The Industrial Society.
Sawyer, R.K. (2005). Social emergence: Societies as complex systems. Cambridge University Press.
Sevaldson, B. (2022). Designing complexity: The methodology and practice of systems oriented design. Common Ground Research Networks.
Shah, N., Napier, C.J., & Holloway, R. (1992). The Cadbury report 1992: Shared vision and beyond. United Kingdom.
Shepherd, J. (2012). Free will and consciousness: Experimental studies. Consciousness and Cognition, 21(2), 915–927. https://doi.org/10.1016/j.concog.2012.03.004
Shleifer, A., & Vishny, R.W. (1997). A survey of corporate governance. The Journal of Finance, 52(2), 737–783. https://doi.org/10.1111/j.1540-6261.1997.tb04820.x
Sjåfjell, B. (2017). When the solution becomes the problem: The triple failure of corporate governance codes. In Corporate governance codes for the 21st century: International perspectives and critical analyses (pp. 23–55). Springer International Publishing.
Soininen, N., Ruhl, J.B., Cosens, B., & Gunderson, L. (2025). Governing complexity: A comparative assessment of four governance models with applications to climate change mitigation and adaptation. Environmental Innovation and Societal Transitions, 57, 101020. https://doi.org/10.1016/j.eist.2025.101020
Stacey, R. (1996). One point of view: Management and the science of complexity: If organisational life is nonlinear, can business strategies prevail? Research-Technology Management, 39(3), 8–10. https://doi.org/10.1080/08956308.1996.11671056
Stacey, R. (2000). The emergence of knowledge in organisation. Emergence, A Journal of Complexity Issues in Organisations and Management, 2(4), 23–39. https://doi.org/10.1207/S15327000EM0204_05
Stacey, R. (2003). Complex responsive processes in organisations: Learning and knowledge creation. Routledge.
Sterman, J. (2002). Business dynamics: Systems thinking and modeling for a complex world. Irwin McGraw-Hill. Retrieved from https://dspace.mit.edu/bitstream/handle/1721.1/102741/esd-wp-2003-01.13.pdf
Strand, R. (2002). Complexity, ideology, and governance. Emergence, 4(1–2), 164–183. https://doi.org/10.1080/15213250.2002.9687743
Tacon, R., & Walters, G. (2021). The promises and pitfalls of codification of governance in sport as legitimacy work. In Good governance in sport (pp. 221–235). Routledge.
Tassopoulou, I., & Berti, E. (2019). The Wates Principles: Corporate Governance in Large Private Companies. International In-house Counsel Journal, 12(1).
Teisman, G., Van Buuren, A., & Gerrits, L.M. (Eds.). (2009). Managing complex governance systems (Vol. 3). Routledge.
Tipurić, D., Dvorski, K., & Delić, M. (2014). Measuring the quality of corporate governance—A review of corporate governance indices. Journal of Corporate Governance, Insurance and Risk Management, 1(1), 224–241.
Varley, T.F. (2023). Information theory for complex systems scientists. arXiv preprint arXiv:2304.12482.
Wahl, D. (2019). A brief history of systems science, chaos and complexity. Resilience.org. Retrieved from https://www.resilience.org/stories/2019-09-12/a-brief-history-of-systems-science-chaos-and-complexity/
Walters, G., & Tacon, R. (2018). The ‘codification’ of governance in the non-profit sport sector in the UK. European Sport Management Quarterly, 18(4), 482–500. https://doi.org/10.1080/16184742.2017.1418405
Whitney, K., Bradley, J.M., Baugh, D.E., & Chesterman, Jr., C.W. (2015). Systems theory as a foundation for governance of complex systems. International Journal of System of Systems Engineering, 6(1–2), 15–32. https://doi.org/10.1504/IJSSE.2015.068805
Wilding, R.D. (1998). Chaos theory: Implications for supply chain management. The International Journal of Logistics Management, 9(1), 43–56. https://doi.org/10.1108/09574099810805735
World Bank. (2009). Pakistan: Report on the Observance of Standards and Codes (ROSC): Corporate Governance Country Assessment. World Bank. Retrieved from http://documents.worldbank.org/curated/en/115971468059090429
Zheng, C., McKay, R., & Ciocirlan, C. (2024). Green waves: How leaders’ actions shape employee behaviours, a moderated mediation model of organizational green culture and personality traits. International Journal of Applied Research in Management and Economics, 7(4), 1–20. https://doi.org/10.33422/ijarme.v7i4.1429
|
Hide
Show all
Email this article (Login required)
