Many of us have run into the same problem in supply chain research. The theory is relational, but the data come from one side only. A buyer reports on trust, collaboration, or governance, and we treat that account as if it captures the relationship itself. Sometimes that is the best we can do. But often it is not enough. 

This is the central idea behind dyadic, triadic, and network research, or DTN research. Dyadic designs capture both sides of a focal relationship. Triadic designs bring in a third actor when intermediation, coordination across tiers, or three-way value creation is part of the mechanism. Network designs examine how ties among multiple firms shape outcomes such as diffusion, resilience, spillovers, and structural advantage. The key point is not that "larger designs are always better" but that the data structure should match the question. 

That distinction matters because supply chain phenomena are often asymmetric. Buyers and suppliers may not perceive trust, fairness, or dependence in the same way. A third actor, such as a logistics provider or a lower-tier supplier, can shape outcomes even when it sits outside the focal dyad. Network position can affect risk exposure or innovation even when a firm’s direct partner relationships look stable. When we study these mechanisms with one-sided data, we can miss misalignment, hidden dependencies, and indirect effects.

This issue sits at the heart of our new International Journal of Production Economics special issue, Revamping multi-tier supply chain relationships: Using dyadic, triadic, and network data to explore uncharted territory and discover new frontiers, and of our introductory review paper, Why one perspective is not enough: theory-data alignment for dyadic, triadic, and network studies in supply chain management. In our review of 111 empirical studies published between 2018 and 2025 across the eight journals in the SCM Journal List, we found that DTN research is growing, but not evenly. Dyadic studies are common. Network studies are becoming more visible. Triadic studies remain relatively rare, even when the mechanism clearly involves mediated coordination or second-tier effects.

The more striking take-away regards theory-data misalignment. Some studies theorize mutual constructs, such as trust or reciprocity, but measure them from only one side. Others make network claims without network measures. Many of us recognize how easy it is to fall into these compromises, especially when access to multi-party data is hard. But once theory, construct, and unit of analysis pull in different directions, the study becomes harder to interpret and harder to defend. 

For researchers interested in DTN work, that leads to a simple but demanding lesson. Start with the mechanism. If the mechanism is bilateral, a dyadic design may be enough. If a third actor enables, constrains, or buffers the process, then a triadic design is warranted. If the outcome depends on indirect ties, diffusion, spillovers, or structural position, then a network design is needed. From there, the design choices become more concrete. Be explicit about where the construct lives. Define the unit of analysis carefully. Show how actors are matched. Address asymmetry and interdependence directly. And when full multi-party coverage is not feasible, be clear about what your data can and cannot support. 

That is the goal of the special issue and the review paper. We wanted to do more than encourage researchers to collect data from more actors and also offer a clearer path for building DTN studies that are theoretically grounded, methodologically rigorous, and still feasible in practice. If you are interested in reading the full paper and the collection of papers in the special issue, the links are below!