An Interactive Guide Through the Lens of Resource Dependence Theory (RDT)
Resource Dependence Theory (RDT) argues that organizations are not self-sufficient. To survive and thrive, they must acquire critical resources from their external environment, creating a web of dependencies.
The fundamental connection is this: Power is the inverse of dependence.
According to RDT, Party A has power over Party B if B depends on A for a critical resource. Power isn't a personality trait; it's a structural condition that arises from the pattern of resource flows between organizations.
Adjust the sliders to see how the balance of power shifts between a resource provider (Party A) and a resource recipient (Party B).
With moderately important resources and some alternatives, the power dynamic is balanced.
Placing power at the center of the investigation helps explain how and why AI is reconfiguring supply chain relationships. Here are some emergent questions framed through the RDT lens.
RQ1: How does control over AI-generated data on worker performance and supply chain compliance become a critical resource that enhances the power of certain stakeholders (e.g., management) over others (e.g., workers)?
Why ask this? It frames data as a tangible resource, allowing you to see if those who own and interpret AI have gained a new lever of control.
RQ2: In what ways do supply chain actors (e.g., SMEs) become newly dependent on AI tech providers or large clients with proprietary AI systems to ensure their own market survival?
Why ask this? It explores new dependencies. Power might shift not just between traditional actors but also to external tech giants.
RQ3: How do powerful downstream firms leverage AI-driven monitoring to enforce social sustainability standards, and how does this affect the autonomy and control of their upstream suppliers?
Why ask this? This examines AI as a mechanism of coercive power. Is it a tool for collaboration or for risk reduction by dominant firms?
RQ4: What strategies do less powerful stakeholders (e.g., unions, workers) use to counteract their increased dependence on AI-monitored systems? Are they creating "counter-resources"?
Why ask this? RDT isn't just about domination; it's about strategic response. This explores agency, resistance, and the dynamic nature of power.
The goal is to move beyond asking "Who has power?" and instead ask questions that reveal the underlying dependencies that create power imbalances. This blueprint is designed to uncover narratives about control, constraint, autonomy, and strategic responses related to the critical new resource: AI systems and the data they generate.
These questions set the stage and establish context before diving into RDT-specific themes.
This is the core of the investigation. Elicit stories and perceptions related to the three pillars of dependence: Criticality, Discretion, and Alternatives.
Perspective: They are often the agents implementing AI, managing dependencies on clients and tech providers, and exerting power over suppliers and workers.
Perspective: They are often the subjects of AI monitoring and experience the effects of power shifts directly. Their autonomy is a key variable.
Perspective: Their power depends on accessing information and influencing corporate behavior. AI can be both a threat and an opportunity.
Participants may not use the word "power." Using its proxies: autonomy, control, fairness, voice, constraint, and dependence.
"Tell me about a specific time when..." This grounds abstract concepts in concrete experiences.
When a manager says, "We had to because our client demanded it," probe further: "What gave them the leverage to make that demand?"
"How does this compare to how things were done before?" This highlights the shift in practices and power.
"What was your main concern when you heard about this new system?" This can reveal feelings of powerlessness or empowerment.