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

The research team, led by Qingyu Gao and colleagues, combined Hofstede’s cultural scores with the Government AI Readiness Index published by Oxford Insights, which evaluates countries across government, technology sector, and data and infrastructure pillars. Because cultural values are time-invariant over the study window, the authors employed a random effects panel model, a standard econometric approach for handling characteristics that do not change over time. The final sample covered 61 countries with complete data on all five cultural dimensions examined: power distance, individualism, uncertainty avoidance, masculinity, and long-term orientation. Control variables included internet usage, government expenditure, GDP per capita, population, and unemployment, with missing values imputed using a random forest algorithm.

Three cultural dimensions emerged as decisive. Masculinity, which reflects a society’s emphasis on competition, assertiveness, and achievement, was negatively associated with government AI adoption, with each unit increase predicting a 0.082 unit decrease in readiness. Uncertainty avoidance, the degree to which societies feel threatened by ambiguity and unpredictability, showed a similar inhibitory effect, reducing readiness by 0.076 units per unit increase. In contrast, long-term orientation, the tendency to prioritize future goals over immediate gratification, was the strongest positive predictor, adding 0.13 units of AI readiness per unit increase and reaching statistical significance at the 0.001 level. Power distance and individualism, by comparison, showed no significant effects.

The masculinity result is perhaps the most counterintuitive. Earlier research on e-government had often found masculinity to be a minor or statistically insignificant factor, and one might expect performance-driven, competitive cultures to leap at technologies promising measurable gains. But the authors argue that AI is fundamentally different from earlier digital upgrades. Successful AI adoption demands long-term investment, cross-domain collaboration, and ethical co-governance, qualities that sit uneasily with a cultural orientation toward immediate, controllable competitive wins. AI also carries risks of bias, inequity, and unintended social consequences, and a governance ethos grounded in cooperation and concern for citizen well-being, characteristic of feminine cultures, appears better suited to managing them.

Uncertainty avoidance operates through a more intuitive channel. AI is a radical innovation that raises ethical dilemmas, regulatory ambiguity, and value conflicts, so governments must tolerate experimentation and learn from trial and error. Societies that prize stability and rules tend to perceive such technologies as risky or disruptive, and their decision makers emphasize control and predictability over bold experimentation. The study’s heterogeneity analysis sharpened this picture: the negative effect of uncertainty avoidance was most pronounced in countries with high internet usage, where mature technological infrastructure lowers perceived reliance on AI while heightening concerns about its potential to disrupt existing security frameworks.

Beyond direct effects, the researchers probed a mediating mechanism using Baron and Kenny’s causal step method, supplemented by Sobel tests. Governance effectiveness, drawn from the World Bank’s Worldwide Governance Indicators and lagged by two years to capture temporal effects, proved to be a key pathway. Masculinity and uncertainty avoidance both significantly reduced governance effectiveness, while long-term orientation enhanced it. Governance effectiveness, in turn, strongly promoted AI adoption. Notably, it fully mediated the relationships between masculinity and AI adoption and between uncertainty avoidance and AI adoption, and partially mediated the long-term orientation pathway. In other words, culture does not simply push governments toward or away from AI; it shapes the institutional quality that makes AI integration feasible in the first place.

The heterogeneity analyses revealed that cultural influence is context-dependent in surprising ways. Stratifying countries by GDP per capita at a 20,000 dollar threshold, the team found that the negative effects of masculinity and uncertainty avoidance were stronger in high-income countries, while the positive effect of long-term orientation was concentrated in low-income countries, where resource constraints make long-term strategic planning essential for rationalizing AI investment. The authors suggest that wealthy nations with robust welfare systems and egalitarian gender roles may find traditional masculine norms of competition and control particularly detrimental, and that their sophisticated technological expectations generate heightened concerns about AI security and privacy.

Employment and connectivity conditions further modulated the picture. In high-unemployment countries, power distance showed a significant negative effect on AI adoption, plausibly because governments preoccupied with joblessness and political stability can use centralized authority to reject disruptive technologies. In low-unemployment countries, individualism and long-term orientation turned significantly positive, while uncertainty avoidance remained a brake. Meanwhile, in low internet usage countries, power distance and long-term orientation showed positive effects, suggesting that nations with limited technical capacity may actually benefit from centralized strategic planning and sustained long-term guidance to jumpstart AI development. A significant positive effect of long-term orientation on AI adoption was observed specifically in the cohort combining low income, low unemployment, and low internet usage.

Methodologically, the study reframes AI adoption as a dynamic, process-oriented phenomenon rather than a binary adopt-or-not decision. By using AI readiness as the dependent variable, the authors align with recent scholarship showing that governments that appear well positioned to adopt AI do not always succeed, and that adoption unfolds across initial, assessment, decision, management, and optimization stages. This operationalization matters because failed AI deployments in the public sector can produce bureaucratic dysfunction or even systemic collapse, as documented in cases such as automated debt recovery systems. Readiness, encompassing tangible resources, human capital, and intangible institutional capacity, captures the preparedness that determines whether AI initiatives take root or wither.

The authors acknowledge limitations. Hofstede’s framework, while widely validated, does not cover all countries, restricting generalizability, and its static scores cannot capture cultural evolution over time. Cross-national regressions also face omitted variable bias, and factors such as religion or legal traditions were not modeled. Still, the practical implications are considerable. Policymakers seeking to accelerate government AI adoption may need culturally adapted strategies: institutional designs that reorient competitive, short-term policy cultures toward long-term strategic development, and governance reforms that build the effectiveness through which cultural values ultimately flow. As AI reshapes public administration worldwide, this study suggests that the most important infrastructure may not be silicon, but shared values.

Subject of Research: The influence of Hofstede's national culture dimensions on government artificial intelligence adoption across countries

Article Title: Sailing with the cultural winds: the impact of National culture on government AI adoption

Article References: Gao, Q., Wang, S., Liang, Z., Wang, G., & Guo, L. (2025). Sailing with the cultural winds: the impact of National culture on government AI adoption. Global Public Policy and Governance, 5(3), 251-273. https://doi.org/10.1007/s43508-025-00122-y

Image Credits: AI Generated

DOI: 10.1007/s43508-025-00122-y

Keywords: artificial intelligence, government AI adoption, national culture, Hofstede, AI readiness, governance effectiveness, public sector innovation, uncertainty avoidance, long-term orientation, cross-national study, e-government, public policy