Connect With Us










 

IoT savvy people want more government regulation

Steve Rogerson
February 15, 2018



People with experience in the IoT want to see more government regulation in connected technologies than those with no or little IoT knowledge, according to a study by Market Strategies International.
 
The study identified two distinct groups: the IoT Haves and the IoT Have Nots. The Haves are defined as people who have worked with IoT technologies in their workplace, whether in an office, home office, retail space, factory or other work setting. They comprise one in ten US workers. The Have Nots have not had exposure to IoT technologies at work.
 
The study revealed clear attitudinal and behavioural differences between these two groups when it comes to desire for and concerns about the IoT, but the most striking difference is that IoT Haves and Have Nots disagree on whether the government should regulate connected technologies.
 
“We expected the Have Nots to agree that the US government should regulate IoT, given their limited understanding of and experience with IoT, but it was the exact opposite,” said Erin Leedy, senior vice president of the technology research division at Market Strategies. “Surprisingly, the Haves are twice as likely to agree that the US government should regulate IoT. We believe these workers have already seen the massive potential of the IoT and recognise that the risks – data security, privacy and environmental – are very real and merit the government getting involved to set some guardrails.”
 
Market Strategies interviewed a national sample of 1007 consumers age 18 and older. Respondents were recruited from the e-Rewards opt-in online panel of US adults and were interviewed online. To qualify, each survey respondent had to confirm they have either primary or shared responsibility for making household financial and purchase decisions.
 
The data were weighted by age and gender to match the demographics of the US population. Due to its opt-in nature, this online panel does not yield a random probability sample of the target population. As such, it is not possible to compute a margin of error or to quantify statistically the accuracy of projections.