The study, published in the journal Research Policy, analysed personality data from more than 3.2 million Americans across 349 metropolitan regions and tracked it against the early spread of three digital platforms that reshaped everyday life: Airbnb, Uber and Flickr. The result was strikingly consistent. Regions where residents scored higher on psychological openness were reliably more likely to be early adopters of all three innovations, even after the researchers statistically accounted for economic conditions, demographics, infrastructure, social characteristics and geography.

Psychological openness is one of the Big Five personality traits, the broad dimensions that psychologists have used for decades to characterise human temperament alongside conscientiousness, extraversion, agreeableness and neuroticism. Openness captures curiosity, receptiveness to new ideas and experiences, and a willingness to engage with the unfamiliar or unconventional. What the researchers propose is that this trait, aggregated across a population, functions as a kind of invisible social infrastructure. It cannot be photographed the way a highway or a university campus can, yet it may shape how readily an entire community engages with something genuinely new.

What stood out was the consistency of the pattern, says Prof. Tobias Ebert of the University of St Gallen. Psychological openness was the only factor reliably associated with adoption across all of the three innovations studied. In an era when innovation research often chases fragmented, technology-specific explanations, a single psychological variable holding steady across home-sharing, ride-hailing and photo-sharing platforms is a notable finding.

The scale of the regional differences was substantial. The proportion of residents classified as highly open ranged from roughly 21 percent to 48 percent across the metropolitan areas examined. Early adoption varied just as sharply. Cities such as San Francisco, Portland and Austin repeatedly appeared among the strongest adopters, while El Paso, Flint and Jackson ranked among the lowest. These are not marginal differences; they represent dramatically different starting conditions for any company or technology hoping to gain a foothold in a new market.

Why would openness matter so much? The answer may lie in what these early platforms actually demanded of their first users. When Airbnb launched, participants had to be willing to let strangers sleep in their homes. Uber asked people to offer rides in their own cars to unseen passengers. Early social-media platforms such as Flickr depended on people creating and publicly sharing content long before such behaviour was commonplace. In each case, users were not simply buying a new product. They were being asked to adopt unfamiliar behaviours, and in the case of platforms, their participation helped create the service itself.

Someone has to be willing to try a new behaviour before everyone else regards it as normal, says Prof. Martin Obschonka of the Amsterdam Business School at the University of Amsterdam. This can be especially important for platforms, which need enough local users, hosts, drivers or content creators to generate the network effects that make them useful and viable. A ride-hailing app with no drivers is useless; a home-sharing site with no hosts is an empty shell. The earliest adopters are not merely customers but co-builders of the technology, and their psychological profile may determine whether the network effects that sustain a platform ever take hold in a given city.

The findings add a behavioural dimension to how innovation ecosystems are usually understood. Regions seeking to encourage innovation typically focus on infrastructure, investment, universities, entrepreneurs, skilled workers and access to capital. The study does not suggest that these factors are unimportant. Instead, it suggests that the psychological composition of a population may also shape how readily a radically new activity gains an early foothold. Two cities with comparable funding, talent pools and connectivity may still differ markedly in their appetite for the unfamiliar, and that difference may only become visible when a genuinely disruptive technology arrives.

The practical implications cut in two directions. For businesses, local behavioural receptivity could offer an additional clue when deciding where to introduce unfamiliar technologies or business models, complementing the usual market analyses of income, competition and regulation. For policymakers, the message is more sobering: differences in adoption may not always be explained by infrastructure or investment alone. A region that wants to be an early adopter of the next transformative technology may need to think not only about broadband and capital but about cultivating the cultural and psychological conditions that make experimentation feel acceptable.

The researchers are careful about the limits of their work. The study did not examine artificial intelligence, and its results should not be directly extrapolated to AI adoption, which raises its own distinct social and economic questions. But they argue that the broader question is increasingly relevant as new technologies emerge. We did not study AI adoption, so we should be careful not to extrapolate, says Obschonka. But when a genuinely new technology arrives, its future does not depend only on the technology itself. It also depends on whether people are ready to experiment with it. As the next wave of radical innovations moves from laboratory to marketplace, the places that embrace them first may be decided less by what those places build than by who they already are.

Subject of Research: Regional psychological openness as a predictor of early adoption of radical digital innovations

Article Title: What makes a place ready for radical innovation?

Article References: What makes a place ready for radical innovation?. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: psychological openness, radical innovation, early adoption, Big Five personality traits, innovation ecosystems, Airbnb, Uber, Flickr, network effects, Research Policy, metropolitan regions, technology diffusion