Why Shoppers Refuse to Scan: New Study Maps Consumer Resistance to QR Code Product Verification in India and France

Quick response codes have quietly become one of the most consequential technologies in the modern food and beverage supply chain. Printed on a bottle of wine or a packet of supplements, the humble black-and-white square now promises something that traditional labels never could: a direct, on-demand window into a product’s journey, from the farm where ingredients were grown to the shelf where the item is sold. Yet a new study published in Information Systems Frontiers reveals a stubborn paradox at the heart of this digital revolution. Although manufacturers are investing heavily in QR code-based verification to build consumer trust, many shoppers simply refuse to engage, and the reasons behind that refusal differ sharply between countries.

The research, conducted by Subhadeep Mandal of FORE School of Management in New Delhi, Arpan Kumar Kar of the Indian Institute of Technology Delhi, and Shivam Gupta of NEOMA Business School in Reims, compares consumer resistance to QR code verification systems in India and France. The two countries were chosen deliberately because they sit at opposite ends of one of the most influential dimensions in cross-cultural psychology: uncertainty avoidance. According to Hofstede’s cultural framework, France scores high on this dimension, meaning its citizens tend to feel threatened by ambiguous situations and prefer rules, structure, and predictability. India, by contrast, scores low, reflecting a greater tolerance for ambiguity and a more pragmatic, situational approach to risk. The researchers reasoned that these deep-seated cultural orientations should shape how shoppers react when a code on a package asks them to verify a product’s authenticity or trace its origins.

The study distinguishes between two fundamentally different verification architectures that manufacturers can deploy behind a QR code. The first is traceability, typically built on blockchain-based distributed ledgers that record every step of a product’s farm-to-fork journey in an immutable, decentralized database. No single company controls the record; instead, verification depends on a consensus mechanism spread across multiple supply chain actors. The second is authentication, a company-managed centralized database that confirms whether a specific product is genuine. Here, the manufacturer itself acts as the arbiter of truth, and the consumer must trust not only the technology but also the firm operating it. Both mechanisms are designed to combat the same enemies: counterfeiting and misinformation, problems that cost the food, beverage, and pharmaceutical sectors billions each year and have triggered high-profile scandals, from the European horsemeat affair to raids on counterfeit medicine operations in India.

To understand why consumers resist these systems, the researchers drew on two complementary theoretical lenses. Innovation resistance theory, developed originally by Ram and Sheth in the late 1980s, holds that resistance is not merely the absence of adoption but an active psychological process driven by functional barriers, such as perceived complexity, lack of perceived value, and doubts about usefulness, and psychological barriers, including habits, perceived risk, and attachment to tradition. Status quo bias theory adds a second layer: people systematically prefer their current state of affairs, and the inertia, switching costs, and satisfaction with existing routines can outweigh even compelling reasons to change. The study also separates active resistance, in which consumers consciously oppose or criticize the technology, from passive resistance, in which they simply ignore it and continue shopping as before.

What makes the methodological approach particularly interesting is the analytical tool the authors chose. Rather than fitting a traditional regression model that estimates the average effect of each barrier, the team applied fuzzy set Qualitative Comparative Analysis, or fsQCA, to survey data from 581 retail shoppers. FsQCA is a set-theoretic technique rooted in the work of Charles Ragin, which treats conditions such as perceived risk or system quality as fuzzy sets that respondents can belong to partially, rather than as continuous variables. The method then searches for combinations, or causal recipes, of conditions that are jointly sufficient to produce an outcome, in this case high consumer resistance. This configurational logic acknowledges a truth that averaging techniques often obscure: different shoppers may resist the same technology for entirely different reasons, and multiple distinct pathways can lead to the same resistant behavior.

The barriers the researchers examined spanned four broad categories. Functional barriers included the perceived effort of scanning, doubts about whether the information provided is genuinely useful, and skepticism about the value the system adds to the shopping experience. Psychological barriers captured perceived privacy and security risks, the discomfort of changing established shopping habits, and resistance rooted in tradition. System-related barriers concerned the technical quality of the verification platform itself, including reliability, responsiveness, and the credibility of the information architecture. Individual barriers reflected personal characteristics such as technology readiness, product involvement, and demographic factors. By calibrating each of these conditions into fuzzy sets and analyzing them separately for traceability and authentication systems in each country, the team could identify which combinations of barriers most powerfully predicted resistance in each context.

The comparative design yielded striking insights into how national culture conditions technology acceptance. In France, where uncertainty avoidance runs high, resistance patterns were strongly influenced by psychological barriers and by concerns about the credibility and security of the systems, particularly for centralized authentication databases where the consumer must extend trust to a single corporate actor. French shoppers’ well-documented hesitancy toward government and corporate digital platforms, visible in earlier episodes such as the troubled StopCovid contact tracing application, appears to carry over into the retail verification domain. In India, where tolerance for ambiguity is greater but experience with large-scale data breaches, including the widely reported Aadhaar incident, has sharpened privacy anxieties, resistance was more closely tied to functional and system-related barriers, alongside privacy concerns. The same QR code, backed by the same blockchain architecture, can therefore encounter very different psychological obstacles depending on where it is deployed.

The distinction between traceability and authentication also mattered. Traceability systems, with their decentralized blockchain underpinnings, offer consumers a narrative of transparency: every hand that touched the product is recorded and verifiable. Authentication systems, by contrast, offer a simpler binary answer, genuine or fake, but concentrate trust in the manufacturer. The fsQCA results indicated that the recipes producing resistance differ between these two mechanisms, meaning that a barrier combination that dooms an authentication system in one market may leave a traceability system relatively unscathed, and vice versa. For manufacturers, this implies that the choice between blockchain-based distributed traceability and centralized authentication is not merely a technical or cost decision but a culturally loaded one that should be matched to the target market’s uncertainty profile and trust landscape.

The practical implications extend well beyond the food and beverage sector. Counterfeit goods infiltrate everything from pharmaceuticals to luxury products, and regulators in both the European Union and India are pushing toward digital labelling mandates. Yet the study’s central warning is that technology deployment without behavioral insight risks expensive failure. A system that ignores passive resistance, the quiet refusal to scan that never registers as a complaint, may appear successful on adoption dashboards while failing to change actual verification behavior. The authors suggest that mitigating resistance requires tailored strategies: emphasizing security and data protection where psychological barriers dominate, simplifying the scanning experience and demonstrating tangible value where functional barriers prevail, and ensuring system quality where technical doubts deter engagement.

Ultimately, the research reframes a question that the retail and technology industries have often treated as an afterthought. The success of QR code-based product verification does not hinge solely on the elegance of the blockchain or the robustness of the database, but on a messy constellation of cultural values, personal habits, privacy fears, and system perceptions that vary from one shopper, and one nation, to the next. By mapping the causal recipes behind resistance in two of the world’s largest and most culturally distinct consumer markets, the study offers a template for designing verification systems that people will actually use, and a reminder that in the battle against counterfeits, the consumer’s finger hovering over the camera lens is the final, decisive checkpoint.

Subject of Research: Consumer resistance to QR code-based product traceability and authentication systems in India and France

Article Title: Modelling Consumer Resistance Towards QR Code-Based Product Verification Systems: A Comparative Analysis of Traceability vs. Authentication in India and France

Article References: Mandal, S., Kar, A. K., & Gupta, S. (2026). Modelling Consumer Resistance Towards QR Code-Based Product Verification Systems: A Comparative Analysis of Traceability vs. Authentication in India and France. Information Systems Frontiers. https://doi.org/10.1007/s10796-026-10821-4

Image Credits: AI Generated

DOI: 10.1007/s10796-026-10821-4

Keywords: QR codes, consumer resistance, traceability, authentication, blockchain, fsQCA, uncertainty avoidance, status quo bias, counterfeiting, food supply chain, India, France