In this reflection paper, I trace what shaped my thinking: forests and earth systems, complexity science, ecosynomics and years of field conversations in business contexts. I argue that the real constraint on my doctoral freedom is not external constraints, but my choice to remain intelligible inside finance and economics. That is why I translate ecological and systemic concerns into the vocabulary of risk, performance, and creditworthiness. My PhD is not about “saving” organizations at all costs. It is about making the conditions and assumptions that shape coordination visible through agreements and agreement footprints, so actors can see what they are doing and choose with clearer sight. (Burroughs, 1978; Heikkurinen, 2019; Matin, 2011; Ritchie-Dunham & Pruitt, 2014; Ritchie-Dunham, 2024; Ritchie-Dunham et al., 2025)

My research impacts business practice by changing what decision-makers treat as “real evidence” about coordination quality, hidden risk, and value creation in SME ecosystems. Instead of advice or glossy narratives, I operationalize the enacted agreement layer through observable “agreement footprints” and conservative translation into decision-grade risk and performance signals. The critical challenge is to keep this lens ethically safe and scientifically breakable: explicit value-awareness in every dialogue, clear boundary conditions, rival explanations, and published disconfirming cases to prevent my concepts from becoming the next unfalsifiable management ideology.

Geopolitics is not “background noise” for SMEs. It quietly shapes what coordination is possible: which suppliers are allowed, which data can cross borders, which technologies become sensitive, and how fast rules can change. This post links great-power regime logics (Mearsheimer) and critical geopolitical awareness in management (Belhoste & Dimitrova) to my core research move: making the invisible agreement infrastructure of SMEs measurable. Starting from a concrete bank scenario (a strong SME rejected because its real risk-prevention engine is not legible), I argue that fragility shows up first as agreement debt, and that geopolitical regimes influence which agreement patterns are feasible, rewarded, or punished. The takeaway: business research is not politically neutral when it enters real decision contexts, and treating geopolitics as a “silent stakeholder” helps explain coordination risk before it becomes a financial event.

Business model frameworks are useful maps of value creation and capture, but they fail at the exact moment practitioners ask: “Why does this work here, but not there?” This post argues the missing layer is agreement infrastructure: the lived rules of coordination that decide who can decide, how exceptions move, how quality is negotiated, and where costs and risks land across an SME ecosystem. I explain why I treat agreements as real, measurable infrastructure (not “soft culture”), and why business model logic only becomes viable when the agreement layer can carry interdependence under stress. The note also connects outcome measurement (ecosystem-wide flourishing and TVG) with mechanism evidence (agreement footprints) so performance talk does not turn into vibes or slogans.

This post is the counterpart to my banking-side notes. It shows what becomes visible when you start from the SME support system and the businesses it represents: a field hypothesis about coordination health, translated into measurable claims and then stress-tested through triangulation—leadership pattern recognition, in-house SME workshops using the Agreements Health Check, and quick-scan signals at scale. The result anchors a practical gap: resilient firms often don’t feel seen in formal risk routines, while fragile firms can look “fine” until evidence makes the difference legible.

Sustainable development and growth-critical science seem to argue about the “right camp.” I start somewhere else: when SMEs hit real constraints, cooperation breaks first—not strategy. The weak point is the agreement infrastructure: who can decide, how exceptions are handled, what is safe to say, and where costs and risks end up. This note explains why I use constraint realism as a discipline against wishful talk, why I locate myself in the transformative corner of SD, and how my PhD contribution becomes concrete: making “agreement footprints” visible enough to compare across cases and test which patterns reduce cost-shifting and build regenerative capacity under limits.

Low-risk SMEs manage risk through how they coordinate work—within the firm and across customers, suppliers, and partners—yet that effort doesn’t always become legible in standard credit workflows. Many bankers recognize this gap too: it’s not about “not caring,” but about missing decision-grade ways to capture and use coordination quality without resorting to storytelling or fake precision.

Most risk frameworks focus on artifacts: plans, policies, and governance charts. I keep finding the real early-warning signals in coordination: decision rights, exceptions, conflict handling, or learning loops. This note reframes agreements as risk-relevant infrastructure—not “culture.”

Kwawu’s entrepreneurial ecosystem (Ghana) didn’t scale because of “resources” alone — it scaled because of workable, renegotiable agreements that sustain trust, learning loops, and inclusion over time. I’m revisiting this earlier case to challenge my current PhD lens—and learn from what I saw back then.

A simple impact question often produces big talk and thin evidence. This post shares my 35-minute CAESI interview protocol that turns one real episode into something usable: three observable footprints, a clear view of where cost and risk land, and a few variables for later mapping and pattern coding. The Agreement Card Deck stays in the background as a quality checklist—so the conversation remains natural, case-specific, and CAESI-clean.

A bridge role like “Maya” becomes a hidden load-bearing wall when legitimacy is person-bound. This post shows a two-week micro-experiment cycle using the Agreement Card Deck to de-person legitimacy: make decision rights explicit, surface reliance and handoff gaps, name avoided mandate talk, and run small, reversible tests based on observable “agreement footprints” rather than advice or stories.

After writing twelve posts, I noticed I keep circling back to the same move: agreement footprints — the observable residues of how people coordinate, not what they say they value. So I did something slightly awkward on purpose: I put myself on the hot seat. In this meta-reflection, the Factor X editor interviews the PhD writer to surface what I’m actually doing here, what rules I’m enforcing, and what I’m becoming through this practice. The questions were co-developed with AI; the answers and accountability are mine.

When a team says “we can’t move without Maya,” it’s usually not about Maya—it’s about person-bound legitimacy. This field note shows one concrete episode and how the Agreement Card Deck turns a “bottleneck story” into traceable coordination data: decision rights, hidden reliance, weak handoffs, and avoided mandate talk—each ending in footprints and a two-week micro-experiment.

Some “low-risk” SMEs say banks don’t see how they prevent risk—because their resilience sits in ecosystem coordination and agreement health, not in paper-shaped evidence. This post lays out a working hypothesis (plus a possible incentive tension), defines what “risk-prevention capability” looks like in observable terms, and sketches a short, bounded practitioner check-in to stress-test the claim before building further.

A company once asked: “Can we gamify this and use it with our ecosystem?” This post explains why that’s a risky request if it turns into scoring—and why I use a card deck instead as a disciplined inquiry tool. I share the 10 (of 20) cards I start with, and the canonical flow that turns coordination talk into evidence-backed hypotheses, micro-experiments, and shared ownership across roles and stakeholders—without collapsing into recommendations.

This post shows how I translate agreement quality into financial risk language without fake precision. I distinguish cost from risk, outline two practical coordination modes (reactive vs anticipatory), and use three anchors—coordination overhead, decision latency, and talent drain—to make agreement footprints financially legible. The goal isn’t to corner entrepreneurs with numbers, but to create conservative, testable working assumptions that guide the next experiment.

Applied research has to give back to society—but recommendations turn inquiry into cheap consultancy and replace learning with borrowed certainty. In this post I explain why I refuse advice, how that protects truth-seeking under uncertainty, and what I give back instead: evidence-backed working explanations, explicit assumptions, conservative performance translation, and a next-step experiment. It’s engaged scholarship by design, not implementation support.

A validated survey is a powerful signal—but it’s not a diagnosis. In this post I explain why the Agreements Health Check must be treated as an entry point (especially when n is small), how intersubjective coverage changes what the data means, and why I validate survey signals through “agreement footprints” in the field. The goal isn’t recommendations. It’s conservative, testable working explanations that translate agreement quality into performance risks and opportunities.

I use “felt experience” (yes/no) as a starting signal—not as an explanation. This post shows the ladder I use to move from that signal to evidence-backed working explanations: shadowing, triangulation, and “agreement footprints” that make an SME’s agreement system legible. I also name three early footprint families—negotiability, identity-as-infrastructure, and embedded learning loops—not as a finished framework, but as a first set of testable distinctions that will expand as the evidence base grows.

This reflection draws on systems thinking to explore how everyday coordination failures reveal a system’s de facto goals. It also draws on insights from flourishing and self-determination theory to understand how these patterns shape people’s sense of agency and well-being.