Bridging Financial and Operational Gaps in Supply Chain Finance: An Information Processing Theory Perspective
Bibliographic record
Abstract
This paper explores the integration of financial and operational flows in Supply Chain Finance (SCF) through the lens of Information Processing Theory (IPT). Despite increasing adoption of SCF solutions like reverse factoring and trade credit, existing literature lacks a unified theoretical framework that captures both financial and organizational complexities. Drawing from 47 peer-reviewed articles in leading supply chain journals, this study identifies key SCF dimensions—task characteristics, environment, and interdependence—as primary sources of uncertainty and information processing needs. It then examines how IT systems, coordination mechanisms, and organizational design enhance processing capacity, enabling firms to build SCF capabilities such as risk assessment, supplier onboarding, and financial process standardization. These capabilities facilitate financial supply chain integration through data connectivity, embedded flows, and collaborative planning. The study contributes a comprehensive conceptual model that connects SCF uncertainties, processing strategies, and performance outcomes, addressing theoretical and managerial gaps. It further provides a foundation for future empirical research and strategic design of SCF systems to enhance supply chain resilience and financial efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.012 | 0.027 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".