Firms using agentic AI see gains in circular procurement only when organizational groundwork exists: structured resource scanning, integrated procurement systems and active supplier collaboration unlock AI’s benefits.
Agentic AI and Circular Procurement Performance: An Empirical Study
openalex
correlational
low evidence
7/10 relevance
Summary only
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Adopting agentic AI is associated with better circular procurement performance, but benefits arise primarily when firms have structured resource scanning, integrated cross-functional procurement systems, and established supplier collaboration routines.
ABSTRACT Organizations that practice circular procurement in this rapidly changing world face many uncertainties. However, emerging digital technologies, such as agentic AI, can help manage resources and improve responsiveness to better serve both internal and external customers. Drawing on resource‐orchestration theory, this study aims to understand the impact of adopting agentic AI in industrial purchasing on circular procurement performance. Data for this study were collected from a developing nation, and the analysis was conducted using covariance‐based SEM and a Process analytical method. The results show that firms are most likely to benefit from agentic AI adoption when three prerequisites are present: (i) structured resource scanning and evaluation processes, (ii) integrated cross‐functional procurement systems, and (iii) established supplier collaboration routines. This study is the first to theorize the relationship between organizations' agentic AI adoption and circular procurement performance. The findings will help purchasing and supply managers formulate policies and standard operating procedures to effectively manage circular procurement performance in this rapidly changing world.
Summary
Main Finding
Adopting agentic AI in industrial purchasing improves circular procurement performance, but firms realize those benefits primarily when they have three organizational prerequisites: (1) structured resource scanning and evaluation processes, (2) integrated cross‑functional procurement systems, and (3) established supplier collaboration routines. The study frames these complementarities using resource‑orchestration theory.
Key Points
- Agentic AI can help manage resources and increase responsiveness in circular procurement, supporting internal and external customer needs.
- The positive impact of agentic AI is conditional — AI effectiveness depends on existing organizational capabilities and routines.
- Three prerequisites (structured scanning/evaluation, integrated procurement systems, supplier collaboration routines) are identified as necessary to unlock AI’s benefits for circular procurement.
- The paper claims to be the first to theorize the direct link between organizational adoption of agentic AI and circular procurement performance.
- Practical takeaway: managers should invest in processes, system integration, and collaborative supplier routines alongside AI adoption to improve circular outcomes.
Data & Methods
- Empirical setting: survey/data collected from firms in a developing nation (country and sample size not specified in the abstract).
- Theoretical framework: resource‑orchestration theory (focus on how firms assemble, structure, and leverage resources).
- Analytical approach:
- Covariance‑based structural equation modeling (CB‑SEM) used to test the hypothesized relationships.
- A Process analytical method (likely moderation/mediation analyses such as Hayes’ PROCESS framework) was used to examine conditional effects and interactions between AI adoption and organizational prerequisites.
- Study type: observational, cross‑sectional (based on abstract; causal claims should be treated cautiously).
Implications for AI Economics
- Complementarities matter: Economic models of AI adoption should account for firm‑level organizational capital and routines as required complements that determine productivity gains from agentic AI.
- Heterogeneous returns: The study implies large heterogeneity in economic returns to agentic AI across firms and countries depending on preexisting capabilities—important for diffusion and mismatch predictions.
- Policy design: Policies aimed at promoting AI for sustainability (e.g., circular economy) should pair technology subsidies with support for process redesign, system integration, and interfirm collaboration capabilities.
- Labor and capital effects: Gains from agentic AI in procurement likely change tasks (better resource allocation, coordination) rather than simple labor substitution—models should consider task reallocation and complementary human roles (procurement specialists, supplier managers).
- Measurement and evaluation: Researchers and policymakers should measure organizational routines and systems integration when estimating the macroeconomic impact of AI, to avoid overestimating benefits from technology deployment alone.
- Future empirical work needed to quantify magnitude of gains, establish causality (panel/experimental designs), and test generalizability across countries and sectors.
Assessment
Paper Typecorrelational
Evidence Strengthlow — Findings are based on cross-sectional, self-reported survey data analyzed with SEM, which estimates associations and conditional relationships but cannot rule out reverse causality, omitted variables, or common-method bias; no natural experiment, instrumental variable, or longitudinal design is reported to bolster causal claims.
Methods Rigormedium — The study uses standard, appropriate multivariate techniques (covariance-based SEM and PROCESS) and is framed in resource-orchestration theory, which supports testing of mediated/moderated relationships, but the abstract provides no detail on sampling, measurement validity, sample size, or strategies to address common-method bias and endogeneity, limiting methodological credibility.
SampleSurvey data collected from firms in a developing nation (respondents likely procurement/purchasing managers); exact sample size, industry mix, sampling frame, and measurement items are not reported in the abstract.
Themesorg_design adoption human_ai_collab
IdentificationCross-sectional firm-level survey analyzed with covariance-based structural equation modeling (SEM) and PROCESS-style moderation/mediation analyses to estimate associations between self-reported agentic AI adoption, organizational routines, and circular procurement performance; no exogenous variation or quasi-experimental design, so causal interpretation depends on theoretical assumptions rather than identification from the data.
GeneralizabilitySingle developing-country sample limits transferability to advanced economies or different institutional contexts, Unknown industry composition—results may not apply across manufacturing, services, or public sector, Cross-sectional, self-reported measures risk bias and limit causal generalization, Likely reflects early adopters of agentic AI; outcomes may differ for later adopters or firms with different resource endowments, Definition and operationalization of ‘agentic AI’ and 'circular procurement performance' may vary across settings, limiting comparability
Claims (9)
| Claim | Direction | Outcome | Confidence & Evidence | Details |
|---|---|---|---|---|
| Firms are most likely to benefit from agentic AI adoption in industrial purchasing (with respect to circular procurement performance) when three prerequisites are present: (i) structured resource scanning and evaluation processes, (ii) integrated cross-functional procurement systems, and (iii) established supplier collaboration routines. Organizational Efficiency | positive | circular procurement performance |
Reading fidelity
high
Study strength
medium
|
not reported
|
| Structured resource scanning and evaluation processes (a firm-level capability) increase the likelihood that agentic AI adoption will produce benefits for circular procurement performance. Organizational Efficiency | positive | circular procurement performance |
Reading fidelity
high
Study strength
medium
|
not reported
|
| Integrated cross-functional procurement systems increase the likelihood that agentic AI adoption will produce benefits for circular procurement performance. Organizational Efficiency | positive | circular procurement performance |
Reading fidelity
high
Study strength
medium
|
not reported
|
| Established supplier collaboration routines increase the likelihood that agentic AI adoption will produce benefits for circular procurement performance. Organizational Efficiency | positive | circular procurement performance |
Reading fidelity
high
Study strength
medium
|
not reported
|
| Emerging digital technologies, such as agentic AI, can help manage resources and improve responsiveness to better serve both internal and external customers. Organizational Efficiency | positive | resource management and responsiveness (service to internal/external customers) |
Reading fidelity
high
Study strength
speculative
|
not reported
|
| Data for this study were collected from a developing nation. Other | null_result | study sample/source (geographic setting) |
Reading fidelity
high
Study strength
high
|
not reported
|
| The analysis in the paper was conducted using covariance-based structural equation modeling (CB-SEM) and a Process analytical method. Other | null_result | analytical methods used |
Reading fidelity
high
Study strength
high
|
not reported
|
| This study is the first to theorize the relationship between organizations' agentic AI adoption and circular procurement performance. Other | null_result | novelty of theoretical contribution |
Reading fidelity
high
Study strength
low
|
not reported
|
| The findings will help purchasing and supply managers formulate policies and standard operating procedures to effectively manage circular procurement performance. Organizational Efficiency | positive | managerial policy/SOP formulation effectiveness (implication) |
Reading fidelity
high
Study strength
speculative
|
not reported
|