A validated four-part scale now measures firms' analytics and collaboration capabilities in supply‑chain risk management, based on a survey of Sri Lankan manufacturers; the tool separates analytics-driven assessment and monitoring from partner information‑sharing and strategic collaboration, enabling future empirical work on returns to analytics investments.

Main Finding

The authors develop and validate a new four-dimensional measurement instrument for analytics-and-collaboration-integrated supply chain risk management (SCRM) tailored to Sri Lanka’s industrial product manufacturing sector. Using exploratory and confirmatory factor analysis on survey data from 205 supply chain managers, they identify and validate four factors: (1) analytics-based risk assessment, (2) information sharing–based risk mitigation, (3) analytics-based risk identification and monitoring, and (4) collaboration-based strategic risk mitigation.

Key Points

• Problem: Existing SCRM measurement tools inadequately integrate supply chain analytics (SCA) as practiced in the Sri Lankan manufacturing context, though they typically include supply chain collaboration (SCC).
• Theoretical lenses: Organizational Information Processing Theory (OIPT) (emphasizes information/analytics to reduce uncertainty) and the Relational View (RV) (emphasizes interfirm relational resources and collaborative rents).
• Item development: Items came from prior literature on SCRM measurement and a preceding qualitative study in the same context.
• Data: Online survey of 205 randomly selected supply chain managers from Sri Lankan industrial product manufacturers engaged in global business.
• Methods: Exploratory factor analysis (EFA) to discover underlying dimensions, followed by confirmatory factor analysis (CFA) to validate the factor structure.
• Resulting dimensions:
• Analytics-based risk assessment — use of analytics to estimate and prioritize risks.
• Information sharing–based risk mitigation — active exchange of risk-relevant information with partners.
• Analytics-based risk identification and monitoring — analytic tools for detection and ongoing surveillance of risks.
• Collaboration-based strategic risk mitigation — joint strategic actions with partners to mitigate risks.
• Contribution: A validated, contextually grounded measurement instrument that integrates both analytics and collaboration aspects of SCRM, suitable for future empirical research.

Data & Methods

• Design: Exploratory quantitative study based on survey research.
• Sample: N = 205 supply chain managers randomly selected from Sri Lankan industrial product manufacturers involved in international trade.
• Instrument development: Items synthesized from existing SCRM measurement scales and findings from a prior qualitative study in the Sri Lankan manufacturing context.
• Statistical analysis:
  • Exploratory Factor Analysis (EFA) to identify factor structure and reduce items.
  • Confirmatory Factor Analysis (CFA) to validate the four-factor measurement model (psychometric validation reported in the study).
• Limitations (implied by methods): context-specific to Sri Lanka and industrial product manufacturing; cross‑sectional self-reported survey data; replication and longitudinal validation warranted.

Implications for AI Economics

• Measurement infrastructure for empirical AI/analytics research: The instrument operationalizes firm-level SCA capabilities and collaborative practices, enabling economists to quantify variation in analytics adoption and integration into risk management. This supports causal and correlational studies on returns to analytics investments in supply chains.
• Evaluating productivity and risk-return trade-offs: Researchers can link the instrument scores to firm outcomes (e.g., disruption losses, inventory costs, delivery performance, profitability) to estimate marginal returns to SCA and collaborative investments and to assess whether analytics reduce downside risk exposure.
• Complementarities and strategic incentives: The four-dimensional construct lets economists test complementarity between analytics and collaboration (e.g., whether analytics yield greater returns when paired with active information sharing), informing models of investment complementarities and joint adoption decisions.
• Data-sharing and governance economics: By measuring information-sharing–based mitigation separately, the instrument can help study incentives, private vs. social returns, and policy interventions (data standards, privacy rules, market design) that affect interfirm data exchange and platform-mediated coordination.
• Diffusion and inequality in AI capabilities: Applied across firms and regions, the measure can document heterogeneity in SCA capability diffusion, enabling work on digital divides, comparative advantage in global supply chains, and the role of firm size or foreign linkages in analytics adoption.
• Policy and development relevance: For developing-country contexts like Sri Lanka, the instrument allows evaluation of whether analytics and collaboration bolster resilience to shocks (e.g., pandemics, trade disruptions), informing industrial policy, subsidies for digital adoption, and international trade negotiations.
• Suggestions for future empirical work:
  • Link instrument scores to objective performance and disruption outcome data (to address self-report bias).
  • Use panel or quasi-experimental designs to identify causal effects of analytics investments or collaboration initiatives.
  • Test external validity by applying and validating the instrument in other countries, sectors, and firm sizes.
  • Incorporate cost data to perform cost-benefit and welfare analyses of SCA adoption and collaborative mechanisms.

Overall, this validated instrument creates a practical bridge between micro-level analytics capabilities and macro/firm-level economic questions about returns to AI, strategic complementarities, and policy levers that shape the digital transformation of supply chains.