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Traceability is predicted to usher in a fundamental shift in the way transactions in supply chains (SCs) are carried out. By reducing the negative aspects of trust-related issues in a SC, traceability enables improved visibility and transparency.

We advance research on traceability adoption in the perishable products supply chain by developing and validating an integrated model that combines the technology acceptance model (TAM), the technology readiness index (TRI) and the theory of planned behavior (TPB). A quantitative approach was employed, collecting data through an online survey of 174 supply chain professionals in major Indian cities using a five-point Likert scale. Participants were selected via LinkedIn, each with at least two years of SCM experience. Nonresponse bias was assessed by comparing early and late respondents, revealing no significant differences. Structural equation modeling (SEM) was used to test various research hypotheses derived from literature. Composite reliability and discriminant validity of constructs were verified before examining the relationships among the constructs within the structural model.

The study found that the TRI components of optimism and innovation did not impact perceived ease of use or perceived utility. Additionally, behavioral intention is shaped by perceived utility, attitude and perceived behavioral control.

This research provides valuable insights for managers aiming to adopt traceability in supply chains (SCs). It helps identify critical factors for effective traceability adoption, showing that perceived ease of use (PEU) and perceived usefulness are pivotal in shaping practitioners’ intentions. Managers should prioritize developing intuitive, user-friendly traceability applications that demonstrate clear value in optimizing SC efficiency. The study also reveals that while practitioners are generally optimistic about traceability, they may feel indifferent or lack a sense of control over it. Therefore, companies should focus on marketing strategies that empower decision-makers, highlighting the ease of use and practical benefits of traceability. Additionally, the findings suggest that perceived behavioral control, combined with intention, can effectively predict traceability adoption. By understanding these dynamics, managers can better guide their firms in successfully implementing traceability, ensuring both technological acceptance and operational efficiency.

This research offers a novel and in-depth exploration of traceability as an emerging concept in supply chains, particularly in India, where adoption remains limited. It highlights that while SC practitioners recognize traceability’s potential, they lack practical expertise, often driven by curiosity about decentralized databases. It underscores the critical role of artificial intelligence, IoT devices and big data in ensuring precise data collection and analytics, essential for successful traceability. The research also introduces a predictive model combining TAM, TRI and TPB constructs, identifying perceived usefulness, attitude and perceived behavioral control as key factors influencing traceability adoption.