The Ecosystem Operating System: How Agreement Infrastructure Predicts Business Model Performance

Written By Christoph Hinske

  • In my PhD, I do not treat “agreemets” as a metaphor. I treat them as real in their effects: they shape what people can do, who carries costs, and how value and power flow through an SME ecosystem. From a systems-and-complexity lens, reality is not a fixed external thing waiting to be discovered, and not only shared meaning-making. Instead, it emerges from patterned interactions, feedback loops, and agreements. The patterns are dynamic, layered, and historical, and I am part of the system I study, not outside it. Agreement fields are therefore ontologically legitimate for me as relational configurations that constrain/enable action and evolve over time through recurrent coordination and adaptive responses (Hinske, 2025a).

  • Because I treat reality as emergent and pattern-generated, I treat knowing as participatory and recursive. I do not assume that knowledge is “discovered” by stepping outside the system, and I do not rely on dialogue alone. Instead, knowledge is produced through feedback-informed interaction with patterns over time: iterative cycles of mapping, modeling/simulation, stakeholder engagement, and reflective synthesis. I use triangulation not to “confirm a single truth,” but to locate alignment, conflict, and deeper structural tensions, and I keep claims provisional as the inquiry loops and updates. Methodologically, this is why I work in an interpretive, systems-informed, action-engaged way (CAESI), guided by the question: does this help the system see itself more clearly and navigate uncertainty more coherently (Hinske, 2025b)?

Why I’m writing this note now

I keep getting a polite version of the same question: “So… your PhD is basically business models, right?” The question is understandable; business model research has become the default language for explaining how value is created and captured (Teece, 2010; Zott et al., 2011). But my epistemology and ontology don’t let me stop there.

Epistemically, I’m strict about decision-grade evidence: claims must survive contact with practice through triangulation, not just coherent narrative. Ontologically, my unit of analysis is the agreement infrastructure that governs coordination across stakeholder relationships within an ecosystem, rather than a single focal organization in isolation. This stance is not abstract. It’s a reaction to recurring conversations with bankers, SME owners, and an SME federation, where the same gap keeps surfacing: coordination reality matters, but it is hard to make legible in ways people can actually use (Hinske, 2026a).

Ecosystems: mainstream concept, incomplete mechanism + measurement backbone

Ecosystem thinking has gained major traction over the past decade (Audretsch et al., 2018). Strategy and management research increasingly treats ecosystems as a serious unit of analysis for understanding interdependence, complementarity, and value creation beyond firm boundaries (Adner, 2017; Jacobides et al., 2018). Entrepreneurial ecosystem work similarly frames multi-actor diversity and complementarity as a source of innovation, productivity, and growth dynamics (Spigel & Harrison, 2018). One way to say it plainly: what counts as competitive advantage has been moving outward, from firm-internal optimization toward ecosystem coordination, where the test becomes whether stakeholders are better off because of participation (Ritchie-Dunham, 2023).

At the same time, ecosystem research keeps running into a clarity problem: the concept can become a weak metaphor unless it is grounded in mechanisms, boundaries, and researchable constructs (Neumeyer & Corbett, 2017; Wurth et al., 2022). Methodologically, that problem gets sharper because ecosystems behave like complex adaptive systems, dynamic, multi-level, and hard to study with single-snapshot measures (Phillips & Ritala, 2019).

Even when groups look “high performing,” the coordination advantage often gets described as intangible, something like “it just works.” A useful discipline is to treat that as a research problem: identify the repeatable processes and structures that make the advantage manageable rather than mystical (Hinske, 2013).

So here is the practical question I keep returning to:

What is the operational substrate that enables SME ecosystems to reliably produce ecosystem-wide flourishing over time?

Business models explain value logic, yet often under-specify coordination reality

Business model research provides strong language for value creation and capture (Teece, 2010) and a solid synthesis of how the field has developed (Zott et al., 2011). That work matters. It is part of the reason people assume my topic is “already covered.”

But the dominant business model discourse often assumes that coordination quality is either embedded in an activity architecture or handled via generic governance/capability/culture talk. In the field, especially in SME ecosystems, coordination doesn’t fail because people lack concepts. It fails because tacit rules break down under exceptions: who decides, how quality is negotiated, how conflicts escalate, who carries the burden, and what happens when constraints hit.

My core claim is narrow and testable: business model logic becomes viable in practice only when the agreement infrastructure can carry interdependent coordination across stakeholders.

Agreements are not “soft”: they are infrastructure

In ecosynomics and agreements work, agreements are framed as implicit and explicit values that guide interaction and take effect the moment people enter an interaction, whether or not they are fully aware of them (Ritchie-Dunham, 2023, 2024). In practice, agreements function as enacted rules of engagement: what counts as legitimate, what quality means, who can decide what, how exceptions move, how conflict is handled, or how consequences are distributed (Ritchie-Dunham, 2024).

This is not just a conceptual preference. It aligns with what coordination research shows: coordination depends on creating integrative conditions, such as accountability, predictability, and common understanding, through concrete mechanisms, not through intention alone (Okhuysen & Bechky, 2009). It also aligns with relational contracting: much cooperation is governed by self-enforcing commitments sustained through ongoing relationships rather than formal contracts (Baker et al., 2002).

So I treat agreements as economic and social infrastructure, a measurable surface where performance, risk, and well-being outcomes begin to diverge.

The target variable is Ecosystem-Wide Flourishing, not sustainability

Therefore, my PhD is not “sustainability or business model canvas research.” It is ecosystem-wide flourishing (EWF).

EWF is defined as the flourishing of key stakeholders who interact within an ecosystem of relationships, not the “success” of a single focal organization in isolation (Ritchie-Dunham et al., 2025). In this framing, flourishing is whole-human well-being expressed through lived experience and observable stability/outcomes, shaped by the agreement infrastructure that governs how stakeholders actually coordinate (Ritchie-Dunham, 2024; Ritchie-Dunham et al., 2025). A broader practice framing of agreements and EWF is also developed in the World Agreements reporting work (Ritchie-Dunham, 2023).

This is where measurement changes. Instead of relying solely on internal output metrics, measurement shifts toward whether stakeholders are better off because of participation in the ecosystem, operationalized through multi-stakeholder baseline-and-change logics such as Total Value Generated (TVG) (Aguilera, 2025; Ritchie-Dunham et al., 2024).

From outcomes to mechanisms: how TVG and agreement footprints fit together

TVG-style measurement makes ecosystem impacts legible at the level of stakeholders and their lived outcomes (Aguilera, 2025; Ritchie-Dunham et al., 2024). But outcomes alone don’t explain why an ecosystem produces those outcomes, or why it stops producing them.

That is where my mechanism layer comes in:

  • TVG/EWF metrics capture the multi-stakeholder baseline and changes (Ritchie-Dunham, 2024; Ritchie-Dunham et al., 2024, 2025).

  • Agreement footprints capture the coordination mechanism plausibly generating those changes.

This pairing matters because it prevents two common failure modes: (1) well-being talk without operational discipline, and (2) performance talk that ignores where burdens and benefits actually land across stakeholders.

Agreement footprints: observable traces, not opinions

Because many agreements are tacit, I do not treat self-description as evidence. I treat it as a hypothesis. One reason self-description is unreliable is simple: people often don’t see the agreements they are enacting, life feels “just the way it is,” until friction reveals a rule that was always there (Ritchie-Dunham, 2014; Hinske & Drimmer, 2016). That is why I treat self-description as a hypothesis and look for footprints in recurring episodes.

My operationalization is agreement footprints: observable residues of enacted agreements, patterns you can see in how work actually moves. Examples:

  • how exceptions move (and where they get stuck),

  • escalation logic under pressure,

  • decision latency when responsibilities overlap,

  • rework loops that repeat without learning closure,

  • whether critique is invited and integrated into decision routines.

A practical way to make this inspectable is to look for stabilizers that prevent failure before it becomes visible in lagging metrics. Examples of footprint-friendly stabilizers include: (1) explicit sensemaking before commitment, (2) constraints treated as shared coordination information rather than hidden trade-offs, (3) identity used as a coordination reference point in decisions, and (4) reciprocity visible in the depth and follow-through of commitments (Hinske, 2026).

This is the “portable evidence” move: you can compare footprints across cases without turning the analysis into vibes. I sketch parts of this logic as a field memo in my “two lenses” post (Hinske, 2026a) and in method notes on evidence-oriented interviewing (Hinske, 2026c). A key constraint is signal integrity: signals can open inquiry, but they must not be treated as diagnosis (Hinske, 2025a). And when the conversation must connect to finance, the translation must remain conservative and explicit about inference steps, without fake precision (Hinske, 2025b).

What I’m contributing in one sentence

I propose agreement infrastructure as a missing micro-foundation for ecosystem research and operationalize it through agreement footprints (mechanisms) paired with multi-stakeholder baseline-and-change measurement (outcomes) (Aguilera, 2025; Ritchie-Dunham, 2024; Ritchie-Dunham et al., 2024, 2025).

That is the gap I want to make legible, without dismissing business-model work and without reducing flourishing to a slogan.

References

Christoph Hinske
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