Research Article |
Corresponding author: Leen Paape ( l.paape@nyenrode.nl ) Academic editor: Annemarie Oord
© 2022 Wouter Kolk, Leen Paape, Igor Nikolic, Ron de Korte.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits to copy and distribute the article for non-commercial purposes, provided that the article is not altered or modified and the original author and source are credited.
Citation:
Kolk W, Paape L, Nikolic I, de Korte R (2022) Theorizing Participatory Control Systems: an organizational control concept for enabling and guiding adaptivity in complex situations. Maandblad voor Accountancy en Bedrijfseconomie 96(7/8): 267-277. https://doi.org/10.5117/mab.96.90745
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This paper presents a theoretical framework for a new concept of organizational control, that stimulates organizational change and adaptation. It introduces Participatory Control Systems (PCS) as a distinct type of control based on the complex adaptive system literature. These control systems are fundamentally different from the traditional notion of (management) control. Building on the notion of complex systems and the concept of social learning, PCS increase organizational adaptivity by enabling and facilitating social learning processes that may emerge to transformational change over time. To illustrate the PCS concept in practice, three examples are given in this paper. Moreover, some key implications for internal auditors and suggestions for future research are provided.
Control systems, complex adaptive systems, transformation, social learning, internal audit
Contemporary internal control instruments and frameworks are based on a paradigm that is increasingly ill-suited for the main challenges that organizations face in the 21st Century. As a result, there is a strong need for new theories, mental models, tools, and frameworks to help internal auditors and others involved in issues of control and governance. In this paper, we provide a new, yet robustly theorized concept that provides in this need.
In a world that we call Volatile, Uncertain, Complex, and Ambiguous (VUCA) (
Our claim is that Participatory Control Systems (PCS) are needed to help overcome these shortcomings. These PCS enable continuous learning in organisations and although this sounds all too familiar, the application is in its infancy. This paper elaborates on the outline of such systems and thereby offers another perspective on organisational control. It might help further the longevity of organisations because traditional MCS do not enable adaptivity sufficiently.
In this paper we present a new theoretical framework for control systems that will allow adaptivity and innovation. This framework uses ideas from Complex Adaptive Systems (CAS), social (learning)system theory, strategic management, and behavioural theory of the firm. It will provide Internal Audit practitioners with new ideas for the way they look at their organisation.
This paper is structured as follows. First, since PCS is built on a different paradigm, we explain why organizational adaptation and innovation is not well addressed by management control. Second, we elaborate on the lens we applied for conceptualizing PCS, which embodies a view on the organization as a complex system of learning systems. Third, we introduce the basic concept of PCS. Fourth, we provide three different practical examples of PCS to illustrate its range in application. Fifth, we briefly discuss possible implications for internal audit. Sixth, we provide several suggestions for future research. Lastly, we end with a concluding remark.
The notion of a VUCA world requires concepts of corporate governance, enterprise risk management and management control that explicitly consider the complex and unpredictable nature of the social and organizational domain.
Traditional MCS instruments primarily seek to implement predefined strategies effectively, efficiently, and predictively (
One of the most influential constructs of MCS that addresses the need for transformational change and innovation, is the construct of interactive control systems (ICS) (
First, it can be argued that a root-cause for this problem is that the traditional notion of management and internal control are ‘thing’-based and largely neglect the ‘flow’-based nature of relational sense-making (
A second shortcoming from a complex systems view is the conceptual negligence of the notion of emergence in the MCS literature. A common property for all CAS is that, if such systems manage to successfully adapt and transform themselves, this happens by means of self-organized emergence. Emergence refers to a process of adaptation by which “at some time the architecture of information processing has changed in such a way that a distinct and more powerful level of intrinsic computation has appeared that was not present in earlier conditions” (
A third problem with ICS theory and research is that it disregards the fact that organizations are path-dependent, meaning that a company is unable to capture a new market and produce new products fast enough. In this regard,
The key issue concerns what people in the organisation do to make sense of signals of disruption and how they explore new ideas to find out whether and how to develop them into innovations.
Moreover, social systems- and CAS theory states that a system’s degree of freedom and adaptivity is related to the degree of integration in that system. Loosely coupled systems have a larger range of possible states and therefore a large adaptive capacity than tightly coupled systems (
In this paper, we take a system-of-systems view of the organization (
As a flip side of their identity, social learning systems consist of boundaries (
Figure
Social learning as a relational process of constructing understanding and practice (source:
The effectiveness of social learning processes depends on several factors. Derived from
Participation can be both intra- and interorganizational.
Facilitation involves, according to
Social capital involves various items that collectively create a notion of community. Examples of such items are trust building among the people involved and a cultural environment that provides safety to enable open, sincere, and respectful dialogues and discussions.
Ecological constraints rely on the collective knowledge by the participants (and participatory learning systems) involved about the components, properties and processes of concerned ecosystems (
Lastly,
Figure
This model is a formal system as articulated by
Next, these new ideas and practices are enacted by the learning system in the environment, for instance by means of experimentation. This allows the learning system to construct feedback and select which features and assumptions is considered plausible. Those features and assumptions that are retained in boundary processes and the modelling objects, altering both the identity and the co-creations of the learning system. In turn, through its actions, the learning system also perturbates other systems in its environment making. As such, the processes that form a PCS are ongoing and dynamic in the sense that participants may be added to or removed from the learning system, altering boundary processes and understanding of the complex situation. This ongoing process flow is visualized in Figure
Moreover, upon positive feedback these features, and assumptions typically diffuse to other learning systems (
In this paragraph, we provide three examples that serve to illustrate how PCSs work. These examples are based on the authors own practical and scientific experiences.
The first example deals with an assumption-based operational and financial planning and analysis approach applied at Uber Technologies, Inc. (Uber) during the period 2013–2016. This approach is also known as driver-based, collaborative, or extended planning and analysis. In the period 2013–2016 Uber rapidly launched in over 60 countries and nearly 500 cities across the world. Moreover, it explored and launched new services such as ridesharing and food delivery. Evidently, Uber faced enormous complexity and uncertainty resulting from e.g., regional, and local differences in competitive landscapes, legal and regulatory landscapes, rider and driver preferences, cultural habits, and safety considerations. A way for Uber to continuously learn and adapt from a financial, operational, and strategic perspective was by creating interdisciplinary learning systems centred around a co-constructed model that integrates operational, tactical and strategies assumptions with operational and financial outcomes. Basically, these learning systems were informal teams that consisted of various domain specialists such as local business representatives, regional growth representatives, strategic planners, financial planners, and data analysts. Overall, several of such teams were created, all around a distinct domain (e.g., a specific market or a new business initiative). These teams were dynamic in the sense that the members were often changing, as well as the domain boundaries, depending on the issues at hand. The financial planning & analysis team were the ultimate owners of this planning approach and responsible for making sure all domains were sufficiently addressed from a corporate perspective.
On the one hand, the model serves as a boundary object in the sense that it facilitates constructive dialogues and planning processes across operational and financial domains. It helps local operational teams to understand how their operational assumptions result in expected operational and financial outcomes (or vice versa, trace back outcomes to underlying ‘business drivers’) and provides transparency about these assumptions to other internal stakeholders for the purpose of debate. On the other hand, the model is the outcome of a domain-spanning co-creation process:
The model facilitates ongoing learning as such, by continuously processing actual outcomes against expected outcomes and enabling the interdisciplinary domain-spanning participants to collectively make sense of its implications. For a broader theoretical background, we refer to
Our second example involves social learning at the energy industry level and, as such, provides an example of an interorganizational PCS. A robust energy infrastructure (transport and distribution of electricity, natural gas, hydrogen, etc.) is an essential part of the transition to new and sustainable energy sources. Such an infrastructure should ensure the right type of energy is present at the right location, at the right time and that also allows for the required de-carbonization. Given that energy infrastructures are highly path-dependent and complex socio-technical systems composed of many different yet interdependent parties, these parties face the complex and uncertain challenge of fundamentally transforming the infrastructure, while maintaining an uninterrupted delivery of services.
The Windmaster (
In practice, these projects are having a profound impact on the ways how infrastructure providers approach their own work. Traditionally, two to four scenarios are selected from a two-by-two matrix, and individual models are constructed by infrastructure operators in isolation. If multiple models are used, the process is manual, and limited or no attention is paid to deep uncertainty aspects. In this case multi-models are applied to consider different infrastructure systems in concert, resulting in scenario spaces with more than 10^36 plausible pathways. Specific decision-making under deep uncertainty methods (
Scientifically, these projects have pushed the boundaries of transdisciplinary knowledge through deep integration of participatory process design, modelling methodology, model analysis and collective sense-making process design. These different strands are not merely put together and applied at the same time but have been tightly integrated through the concept of a co-evolving boundary object ecology (
To illustrate that PCS may also consist of qualitative models, our third example is a hackathon. Hackathons are commonly practiced in the software development industry. They enable people to explore their potentially fruitful ideas together with people from other disciplines, which is typically required to develop an idea into a tangible innovation such as a new product feature. In this example, models of co-creation may range from flowcharts to create insight into technical or architectural interdependencies, to product prototypes (e.g., minimum viable products). These models help to develop understanding and practices within a participating hackathon team, but also helps to share this knowledge and innovative ideas in a more ‘tangible’ manner with others (i.e., across boundaries of other learning systems).
First and foremost, the PCS concept provides a framework that can help internal auditors in understanding and evaluating how their organizations systematically pursue change and adaptation. For example, internal auditors should evaluate whether a sufficient and dynamic domain-spanning learning system is operating around an issue of complexity and uncertainty. By identifying boundary processes and boundary objects, internal auditors can judge whether a productive interdisciplinary understanding is being facilitated to enable social learning. Additionally, other aforementioned factors shaping social learning outcomes should be considered: participation, social capital, ecological constraints, and institutional frameworks. Moreover, the internal auditor should pay attention to ensure the sense-making process of enactment, selection and retention is an ongoing process and not a one-off exercise. The frequency by which feedback is being measured, constructed, and debated could be a helpful indicator.
Secondly, we believe that the PCS concept helps internal auditors to understand the need for a different paradigm in complex environments. Although standards and objective norm setting – a prerequisite conform the standards of practice – are not really feasible, PCSs require a more comprehensive discussion with auditees to understand complexity and the need for adaptation and to come to grips with any situation. It will allow internal auditors to come to different conclusions that are more helpful for the long-term value creation of the organization and even its longevity. Furthermore, internal auditors tend to adhere to a more positivistic perspective based on an objectivist ontology. Words often used are objective, assurance, formal, documented, etc. This way of communicating fits nicely into the categories ‘Clear’ and ‘Best Practice’ of
Our basic concept of PCSs as a way for organizations to systematically learn and adapt is based on practical experience and informed by a study to identify and select theoretical components from the CAS and social learning literature. As such, the PCS concept has a robust foundation, clearly more research is needed to understand PCSs to its full extent. For example, there is a need for more research to understand how PCSs originate and evolve over time. Moreover, we need to better understand how they can be activated by people in the organization and how they provide access to participants. To which extent do PCSs spontaneously emerge over time, and do they require a formal status in the organization to be effective? Empirical research is needed to understand the relationship between PCS, social learning, and adaption. Moreover, future research could focus on better understanding the relationship between PCSs and MCSs. In alignment with
For internal auditors to be able to identify and evaluate PCSs in their organization, more research is needed. In the Complex space of Figure
There is still (a lot of) work in progress to develop the concept of PCSs. Nevertheless, we truly believe that the complex nature of organizations and their environments are not sufficiently addressed by current MCSs, potentially leading managers and internal auditors to take the wrong direction and diminish adaptivity and flexibility of their organizations. In turn, this will lead to shortening the organization’s longevity, hampering long-term value creation and ultimately even bankruptcy. That doesn’t need to be, if we extend our notion of control with the notion of complex systems and concepts such as PCS. Let us join efforts, both scientists and practitioners.
Wouter Kolk MSc RC is a PhD candidate at Nyenrode Business Universiteit (Center for Entrepreneurship, Governance & Stewardship) and TU Delft (Systems Engineering section, Faculty of Technology, Policy & Management).
Prof dr Leen Paape RA RO CIA Leen Paape is professor of corporate governance at Nyenrode Business University. Next to that he is a non executive board/audit committee member for Univé Dichtbij, SNS Reaal Pensioenfonds, ABP, Stichting BOOR and the IMF in Washington.
Dr. Ir. Igor Nikolic is an Associate Professor Participatory Multi-Modelling for Decision Making under Deep Uncertainty at TU Delft (Multi-Actor Systems department, Faculty of Technology, Policy & Management).
Ron de Korte RA RE RO is a partner at ACS Partners based in Doorn and co-designer and teacher of the postmaster education program ‘Internal Auditing and Advisory’ (RO) of Erasmus School of Accounting and Assurance. Ron is also the author of books and articles related to internal or management control auditing.