Orchestration: Dynamic Control from the Panopticon

Business ecosystems are distinct from random collections of companies for the high degree of interactivity between ecosystem participants. In a purely ecological sense, this is the difference between a random collection of penguins, chimpanzees, and grizzly bears in a zoo – who have little if any interaction with each other for being confined to separate enclosures and having no natural connection otherwise – and a collection of lesser long-nosed bats and the night-blooming cacti of the Sonoran Desert. In these more interesting cases, interactivity is beneficial to at least one party and ideally beneficial – but perhaps inert or even damaging – to the other. In the case of the bat and cactus, the relationship might even be a matter of survival.

Businesswise, we see this in, for instance, the smart lighting industry. Providers of smart lighting platforms provide the lights and a basic infrastructure for third parties to provide add-on services. Those complementors then come in and jazz up an otherwise mundane chandelier with services like music synchronization to make the entire ecosystem – platform, complementors, and all – more attractive to customers. Scale these systems up from individual users to smart lighting systems for entire cities, and the potential for ecosystem diversity becomes immense.

However, unreigned chaos – in the constructive rather than destructive sense – rarely provides efficient market outcomes. In the smart lighting example, platform providers have some sort of de facto control over the ecosystem of complementors that amass around their platforms, and they might use this to nudge certain outcomes. This is not always the case. Especially when the “platform” around which complementors come together is more conceptual rather than a tangible product, the roles of who provides the platform and who really guides where it’s going become disentangled. In these settings, a strategically advantageous position to take is that of an orchestrator, which involves putting one’s self or firm in the center of many others and attempting to order and interlace those others’ capabilities and offerings.

This either requires or hopefully provides a panoptical view of the ecosystem, which can then be harnessed to create things of value. Information asymmetry, which we won’t get into, and intentional ambiguity, which we will, can affect what one can do from that panopticon, but in this post, we’ll first discuss orchestration generally as a concept before getting into some literature that addresses the challenges that can burden the orchestrator. Don’t worry though: we’ll also get to some strategic fixes and recommendations to get around those, which will likely become more useful over time as we see ecosystem cooperation – and thus orchestration – rise in importance.

The panopticon is a dynamic literary symbol, i.e. commenting on cultures of surveillance and the vulnerability of those surveilled. Ideally, this post will strike a more positive tone. Artwork created by and used with permission from Adam Simpson.

Defining an Orchestrator

Rarely are business leaders and their stakeholders – employees concerned with career growth, investors looking for substantial returns, and so on – content with being in the passenger seat while unplanned chaos drives. Ecosystems, despite their difficulty to gain unilateral control of, are steerable. They can be steered by the institutions that oversee them from the beginning – such as regulatory authorities providing tax incentives for companies working towards certain Sustainable Development Goals – or they can be steered by actors within the ecosystem – such as firms attempting to establish themselves as industry leaders by enlisting other ecosystem actors to work towards a collaborative, groundbreaking innovation.

An orchestrator, to borrow and slightly adjust the oft-cited definition of Dhanaraj and Parkhe, are central firms that create value by ordering components of an ecosystem into sequences more valuable than the sum of their parts, and then extract value by selling those sequences as products or services. That this is exactly what happens in a professional orchestra is a painfully obvious statement, but also one I’ve not seen anywhere in all this literature, so there it finally is.

In a paper I recently submitted to a few conferences, I focus on Atos as an orchestrator amid the financial services ecosystem. Over the past five years or so, they’ve worked to create a system whereby they search for promising fintechs, daisy-chain those fintechs’ offerings alongside those of other fintechs as well as the ones Atos itself can provide, then sell those solutions to clients. The benefits are clear in this win-win-win situation: fintechs (those being orchestrated) gain market exposure especially to large clients, clients (those for whom the orchestrator is orchestrating) are able to purchase their innovation goals, and Atos (the orchestrator) draws in revenue as the broker of the deal with minimal costs in terms of production.

Orchestration as a theoretical event is not a necessarily new concept. The previously cited Dhanaraj and Parkhe article, which seems to be the root article in a lot of management literature concerning the topic, was published in 2006. It’s been a long time since then, with 2020 accounting for roughly half of it. However, one of our colleagues right here at Radboud University co-authored an article on orchestration that was published in Organization Studies this year, and the insights are particularly valuable for practitioners in likely any industry who seek to achieve a similar role. The above win-win-win dynamic, after all, is about more than generating profit for shareholders: it’s about improving the health of the ecosystem. Whether in an ecological or a market sense of ecosystems, it’s hard to argue against that. I’ll also reference an article co-authored by a partner of the FINDER project, Dr. Miriam Wilhelm, which relates in its discussion of how a central firm must apply different approaches and more specifically ambidextrous ones when dealing with other firms contributing to its outputs.

The difficulty of constantly being in tune with all orchestrated components – being in the panopticon – is no small factor, and it not only requires many sets of eyes to monitor what’s going on in many places at once, but it also involves many sets of hands to address various issues and concerns among the various project participants. Even more importantly, it requires an intuition for when to apply hands-on, dominant solutions and when to only provide a gentle nudge before letting the consensus figure out the rest.


Two Modes

Broadly speaking, the study focuses on interfirm orchestration. This is in contrast to orchestration that occurs between different units within a firm, which I’ll cover in a future post. In their paper, Reypens, Lieven, and Blazevic assess a project with a large collection of stakeholders to explore how orchestrators go about mobilizing agents in a variety of firms to work towards the same objective. They adopt the view from previous literature that there are two modes of orchestration: dominant and consensus-based. These are fairly self-explanatory: in the former, one entity attempts to centrally govern most processes that happen within the endeavor, putting other entities in a de facto subordinate role. In the latter, governance and management are decentralized or revolving. The authors then assert that both of these modes can be employed in a given project and by a given entity dynamically.

It is along this line of thought that they commence their analysis, and their study lays out in great detail the dynamics that occurred between stakeholders through a four-year project. Specifically, they narrate how orchestrators of the project danced between dominant and consensus-based orchestration based on environmental conditions, the growing capabilities and interaction of the network participants, and so on. The paper – cited in full at the bottom – contains insights that would likely be useful for any manager at the head of a collaborative project, and thus is worth a fuller read. I’ll use the remainder of this piece to discuss how key aspects of their abstraction can turn into specific strategic methods for practitioners. In the following section, I’ll refer to orchestrated projects, but keep in mind that this can be scaled up to long-term, international events or scaled down to embedded units within a single company. If you find exceptions to that, feel free to engage in the comments section of whatever medium through which you found this post. For the speed-readers out there, I’ve put the main takeaways in bold.


Strategic Recommendations

This section briefly extracts a few points that are practically relevant for managers finding themselves at the beginning of or in the midst of populated projects. The authors also included a chart in their work for this, which I’ve included below, that discusses specific orchestration practices that address the plurality as well as the diversity of stakeholders – again, the paper is worth a look for a more comprehensive explanation.

To start, I’m going to momentarily reach out to a different theoretical topic before coming back to this paper. You might’ve heard the team “ambidexterity” in contexts not referring to what people can do with their hands lately; as a theoretical topic, it’s a contemporary darling in management literature and not for no reason. At its core, it refers to the basic idea of doing two different things (well) at once. In this paper, the authors suggest that orchestrating dominantly and orchestrating harmoniously must be dynamically balanced over time to account for stakeholder diversity. The link between these concepts is clear, but we can make it clearer if we compress the four-year period they researched the medical project of their focus into one event1. As such, these occur ambidextrously and through three episodes the authors define: connecting members, facilitating their work, and governing the process.

To tie this in with the article co-authored by Dr. Wilhelm, the orchestrator should make it a goal from the beginning to gain a comprehensive understanding of each orchestrated member’s own capabilities and how motivated they are of their own accord to accelerate or modify those capabilities. While a complex task to pull off, it can really pay dividends: having an in-depth knowledge of how certain, KPI-driven members respond to ambiguous versus very specific task guidance sounds intuitive but is also overlooked to a disappointing extent. Consider, if nothing else, how this knowledge might be used to motivate those members to optimize their own processes without repetitive external pressure (from the orchestrator).

To borrow an example from the above-cited paper, Toyota sought cost-reduction behaviors from its supply network partners. However, Toyota also was interested in maintaining quality of parts delivered. While on one hand demarcating clear, measurable cost-reduction goals to all of its suppliers, Toyota on the other hand offered coaching in the production-optimization practice of kaizen2 to individual suppliers without explicitly forcing them to follow it or micromanaging how those suppliers optimize their practices. This lateral freedom allows those suppliers to explore their own potential for improvement, and giving that to members of an orchestration project at every ripe opportunity is a key strategy managers should keep at the top of their toolboxes.

For business leaders finding themselves near the starting line of projects that resonate so far, the connection step is important. Especially as the ongoing COVID-19 pandemic has largely scattered the workforce out of centralized working locations such as corporate offices or construction jobsites, bringing members back together is necessary to prevent a situation where project members feel like they’re disconnected from their peers. In a material sense, this can have resounding consequences for the serendipitous generation of new ideas that could make a good project even greater. I beg of you, however, to mitigate effects such as “Zoom fatigue” (a review of that linked article being a good first step).

Shifting tracks slightly from connection of members to facilitation of their work, being a present and connected orchestrator goes a long way. “Work” of course means different things in different arenas, but I focus here on the type of work where various members of a larger project have relative freedom in the ways in which they go about performing their tasks. In other words, they’re able to deliberate, think of alternative methods, and perhaps implement them even if it slightly shifts the course of the entire project. This stands in contrast to, for instance, assembly line work, where workers (be they human or machine) perform highly specialized tasks without much room for on-the-spot improvisation.

Members of an orchestrated project – especially due to the tendency for these workers to get into states where their field of vision narrows to what they and only the direct links in the project’s system are concerned with on a daily basis – might find themselves hitting the proverbial “writer’s block,” or perhaps straying away from original objectives. Especially when given ambiguous guidance per the earlier recommendation, this is likely in large projects with a diversity of stakeholders. Orchestrators, however, have an extremely valuable bird’s-eye view of the project even when it might seem chaotically dense. How can they leverage this to refuel, restart, and realign their agents? By making the objectives and especially the interdependencies of other components in the project chain known to straying or stalled participants, giving them a reference point to guide their own way forward.

The nexus of this paper, and the final point I’ll discuss here despite there being much more that’s worth a look in the paper itself, is in discussing the orchestration mode as dynamic through time. Sounds intuitive, doesn’t it? But considering the reasons why that might need an entire research paper to cover alludes to the instinctive and perhaps counterproductive nature of projects with too many cooks in the kitchen, so to speak.

The project they researched showed that orchestration moved from dominating to consensus-based because “as ambiguity decreases and relationships form, the reliance on formal structures decreases.” It’s not difficult to imagine why this crucial step goes missed in, for example, old-school dinosaur companies that have opted for a community-based innovation approach in trying to leapfrog past their advancing competitors. Relinquishing control, even if for the health of the initiative itself, is a difficult thing to do for high-level managers in these companies who might perceive doing so as jeopardizing their professional reputation.

– S. James Ellis, ESR


The original paper co-authored by our Radboud colleague, Dr. Vera Blazevic:

Reypens, C., Lievens, A., & Blazevic, V. (2019). Hybrid Orchestration in Multi-stakeholder Innovation Networks: Practices of mobilizing multiple, diverse stakeholders across organizational boundaries. Organization Studies, 42(1), 61–83. https://doi.org/10.1177/0170840619868268

The paper co-authored by Dr. Miriam Wilhelm, a member of the broader FINDER team:

Aoki, K., & Wilhelm, M. (2017). The Role of Ambidexterity in Managing Buyer–Supplier Relationships: The Toyota Case. Organization Science, 28(6), 1080–1097. https://doi.org/10.1287/orsc.2017.1156


1: “Why would you do that though?” Good question. In process research methods, and more specifically in researching Markov processes (which I do not claim to be an expert about, so take the following with a grain of salt), occurrences (such as the collaborative writing of one work package that is a small component of a larger project) stack into events (such as the combination, assignment, and fulfillment of these work packages to achieve project outcomes); events then stack into states (such as the project shifting from incomplete to complete). This is not absolute, but rather a good framework through which one can comprehend how long-term processes can be systematically divided up for incremental analysis.

2: Kaizen, per Dr. Katsuki Aoki (the co-author of Dr. Wilhelm’s paper), is “a term generally and broadly used in Japanese manufacturing industries to refer to activity that is implemented onsite by recognizing and bridging the gap between ideal and actual conditions and applying ideas to improve a production situation.”

Volatility Precedes Standardization

The financial services ecosystem is experiencing innovation at breakneck speed, as can be seen within the walls of fintech-heavy startup incubators such as TechQuartier. Regulations – PSD2 and MiFID II for instance – from the topside constrain the direction of innovation, usually with consumer protection as the driving force. However, a third force is equally in play: standards. Standards are independent from regulations, in that “regulations stem primarily from a top-down approach, while formal standards are typically the result of a market-driven process (Büthe and Mattli, 2011)”1.

Ecosystems are groups of interacting firms, where interaction is largely of collaborative and/or interdependent natures2. The standards of interaction especially in digital ecosystems are critical: APIs must be able to interact, programming languages must be mutually intelligible, the data that certain services rely on to provide value must be created and packaged in workable ways, and so forth. A lack of adherence to these standards would mean, for instance, that the smartphone in your pocket that you use for mobile banking, equities trading, and payment processing would figuratively fall apart.

However, standards, especially in uncertain environments, take time to formulate. During this process, multiple parties might attempt to control the outcome of standardization, likely in their and their stakeholders’ interests. As Dr. Philipp Tuertscher commented in a recent FINDER meeting, standards are fairly mundane once enacted, but their formation is highly political and an interesting phenomenon to observe.

An easy opportunity to watch this process is in the standardization of corporate ESG data reporting for investors. In the financial services ecosystem, this is a huge step ahead of MiFID II’s full implementation. In this essay, we’ll briefly cover these terms and discuss what’s happened thus far, which will set a baseline for a future series of essays covering key events, lessons learned, and theoretical takeaways from data collection during the ESG data standards-setting process.

ESG Data

ESG stands for environmental, social, and governance. This category of data has experienced a proliferation of importance alongside corporate social responsibility, or CSR, initiatives. The three subcategories of data, when considering a company, cover aspects such as gender wage gaps, environmental waste protocols, and anti-corruption protections.

Until recently, the disclosure of ESG data has been generally voluntary, with some exceptions. As such, industrialized ESG data production itself has not been a heavily regimented practice, so it’s largely been the efforts of NGOs, watchdog groups, investor discretion, and so forth that have pushed companies to publish ESG data. Since ESG data has not been directly monetizable (a familiar trait of all so-called “alternative data,” a category to which ESG has historically been ascribed),

However, self-generated reports of CSR performance are riddled with inconsistencies and gaps for obvious reasons. To address this, agencies and companies such as MSCI and Sustainalytics began publishing independent ESG ratings on mostly publicly listed companies, and over time, this practice has gained enough importance with institutional investors and asset managers such that there are even ecosystems of sustainability ratings agencies, most of which having their own unique methodologies and outputs.

While the proliferation of ESG reporting is generally good, there are obvious problems. Asset managers on the hunt for comprehensive data regarding a given publicly listed firm’s CSR performance are confronted with a blurry landscape of reporting and rating methodologies. Even as the agencies consolidate over time, the lack of industry-wide standards in ESG data reporting has asset managers significantly concerned over the loss of reporting and rating quality.3

Regulations & Standards

No classification system currently exists at EU level which clarifies what constitutes an environmentally-sustainable economic activity. Market-led initiatives that have emerged in recent years are not comprehensive enough and do not sufficiently reflect all EU environmental sustainability priorities.” – European Commission

The European Commission has introduced MiFID II, a sustainability-incorporating revision of the original Markets in Financial Instruments Directive from earlier this century, and a battery of sustainable finance directives installing, among other things, a taxonomy of sustainable economic activity. This combination pushes asset managers and institutional investors to bring ESG data closer to the core of their and their clients’ financial decision-making and affairs.

The benefits of a clear and concise data reporting methodology, which is only one of the foci of this push, are clear. It takes the burden of figuring out what important metrics are off of asset managers and institutional investors, it allows companies all along a value chain to assess each other and exclude any proverbial bad apples, which in turn gives the end consumer the ability to knowledgeably avoid below-threshold products and services.

However, as these initiatives come from a regulating body, they’re a bit top-down, and therefore the problem of standardization remains: who is in control? Who gains from the way this will eventually pan out? Who loses? These are just a few of the vivid questions that we ask as this process wears on.

Through interviews, participant observation, and content analysis, interesting angles of the standardization process will become apparent. We hope that these will have theoretical implications that go beyond sustainable finance, so please follow the FINDER blog and feel free to weigh in with your own insights – all perspectives are welcome. I can be reached for questions and comments at s.ellis@fm.ru.nl.

S. James Ellis, ESR


  1. Blind, K., Petersen, S. S., & Riillo, C. A. F. (2017). The impact of standards and regulation on innovation in uncertain markets. Research Policy, 46(1), 249–264. https://doi.org/10.1016/j.respol.2016.11.003
  2. Jacobides, M. G., Cennamo, C., & Gawer, A. (2018). Towards a theory of ecosystems. Strategic Management Journal, 39(8), 2255–2276. https://doi.org/10.1002/smj.2904
  3. Avetisyan, E., & Hockerts, K. (2017). The Consolidation of the ESG Rating Industry as an Enactment of Institutional Retrogression. Business Strategy and the Environment, 26(3), 316–330. https://doi.org/10.1002/bse.1919