On the fourth or fifth slide of a typical product launch deck, a multinational tech company reveals its adoption forecast. The curve has the same appearance every time. Smooth, upbeat, with a generous upward slope. GDP, smartphone penetration, and possibly median age are used to categorize markets. When culture is mentioned at all, it receives a sentence. Perhaps two. The room then moves on.
The frequency with which that confidence crumbles within eighteen months of launch is difficult to ignore. In São Paulo, the app that took over Stockholm has stalls. In Osaka, the enterprise tool that swept through mid-market American businesses is quietly shelved. The UX is blamed by engineers. Pricing is blamed by marketers. The truth is typically older and stranger than that, buried in two decades of scholarly research that most operators never read. Culture is doing the work that no one wants to acknowledge.
One of the first indications that something more profound was at work was Mark Srite’s 2006 study, which used an extended Technology Acceptance Model to compare Chinese and American users. He conducted user surveys in both nations and discovered that while the model worked, the factors influencing acceptance differed. The traditional individualist calculus of perceived usefulness was heavily relied upon by American respondents. The original TAM, developed in Michigan in the late 1980s, was never intended to measure the weighting of social influence and group expectations by Chinese respondents.
Years later, Jeffy Jan and colleagues’ meta-analysis verified a long-held suspicion among practitioners. Hofstede’s cultural dimensions frequently appeared as direct, moderating, and mediating forces within acceptance models in research published between 1989 and 2019. Individualism, long-term orientation, power distance, and avoiding uncertainty were not background noise. Which features felt natural, which felt intrusive, and which never quite clicked were all being shaped by them.
The example that is still used in graduate seminars is Detlef Straub’s comparison of TAM in the US, Japan, and Switzerland. In two of the three nations, the model performed flawlessly. It just did not forecast behavior in Japan. Anyone promoting a cohesive worldwide product strategy will find the implication awkward. A framework with a quiet border problem is one that is predicated on the idea that people assess technology in the same way everywhere.
AI has fueled the entire debate, which is what makes this intriguing in 2026. When determining whether or not to trust an AI system, responsibility—an ethical category—may be just as important as usefulness or ease of use, according to a recent study by Lé Fel and his collaborators that polled over 2,000 university students. In earlier models, that variable was hardly present. In retrospect, it seems almost obvious. People are inquiring about more than just the tool’s functionality. When it doesn’t, they want to know who is responsible.
Walking through any technology conference in Jakarta, Lagos, or Riyadh gives the impression that the old Silicon Valley lexicon is becoming stale. The audience nods courteously rather than enthusiastically when founders continue to discuss viral loops and frictionless adoption. The local context continues to assert itself. Even when the survey question is the same, a Chinese teacher’s reluctance to use AI tools in the classroom is not the same as an American teacher’s reluctance.
The next generation of acceptance research might resemble a family of regional variations, each calibrated to the cultural weight of trust, hierarchy, risk, and obligation in that location, rather than a single universal model. Global product roadmaps would find that inconvenient. Additionally, it would be more in line with how the real world operates. Slowly, the frameworks are catching up to a reality that users have always recognized.
