Artificial Intelligence acceptance means how willing users—like healthcare workers, managers, or patients—are to use AI technology. A review of 60 studies on AI use in many industries found that factors like usefulness, expected performance, trust, positive attitudes toward AI, and ease of use affect acceptance.
The Technology Acceptance Model (TAM) is a main way to study this. TAM looks at how people see usefulness and ease of use, which affects if they want to use the technology. For hospital staff and IT managers in the U.S., this means AI tools like Simbo AI’s phone system will be accepted when they clearly help with work and are easy for people to use.
However, many studies had problems. More than half did not clearly define what AI means, and most did not explain AI to their participants. This makes it hard for people to understand AI, which can lead to less reliable answers about their feelings toward it.
Most research on AI acceptance uses self-reported data, like surveys and interviews, where people say how they feel or what they plan to do. This method has some problems:
These issues matter in healthcare because actual use depends on how well AI fits with daily work and patient needs. An administrator might say they would use phone AI, but the system also has to work well and not cause problems.
Because self-reports have limits, some researchers suggest new ways to study AI acceptance in real-life settings. Rodion Sorokin introduced the Federated Longitudinal Studies (FLS) method. This way uses mobile devices and sensors to collect real behavior data while protecting privacy.
FLS is different because it:
For healthcare leaders examining AI tools like Simbo AI’s system, methods like FLS can offer a clearer picture of how the AI really works day to day. This helps with making smart tech choices based on actual results rather than just opinions.
The review also found that culture affects how people accept AI. In many U.S. healthcare places, people still want live human contact. Some patients and staff prefer talking to real people for certain needs. This is stronger in some communities or with older patients who trust face-to-face communication more.
For example, older patients might not like AI phone answering systems replacing human receptionists. Practice managers need to plan AI tools that add to human help rather than fully replace it. This way, patients get human attention when needed and the practice can still gain efficiency.
For hospital leaders and practice managers, one reason to use AI like Simbo AI is to automate front-office phone tasks. These jobs take a lot of time and human staff can make mistakes. Using AI for booking appointments, reminders, or answering basics saves time and cuts costs.
Simbo AI’s system fits into phone lines and uses natural language processing to understand callers, direct calls, and respond without human help. This lowers wait times and lets staff focus on harder tasks that need human judgment.
In the U.S., healthcare rules like HIPAA require careful handling of patient info. AI call systems have to keep data safe. The Federated Learning setup in research shows how AI can protect privacy by handling sensitive info on the device instead of sending it out.
AI automation also matches healthcare’s push to go digital, which aims to make patients happier and ease admin work on clinical teams. When AI is seen as easy to use and helpful, more staff and administrators will accept it.
Trust is very important for accepting AI in healthcare. Many managers worry if AI will be accurate, secure, and work well all the time. Building trust means explaining clearly how AI works, what data it collects, and how it keeps info private.
Effort expectancy means how easy the AI is to use. Systems like Simbo AI try to make their tools simple, needing little training for staff and easy menus for patients. If people think AI is easy to use, they will resist it less and start using it more.
By focusing on trust and effort expectancy, healthcare managers in the U.S. can bring AI automation into their work smoothly. This helps get staff support and improves patient services.
The research also shows that worries about job security can block AI adoption. Staff might fear automation means losing jobs or fewer roles. Lack of clear knowledge or wrong ideas about AI can cause doubt or rejection too.
Managers need to talk openly about these worries, educate staff, involve them in AI plans, and explain how AI will help, not replace, human jobs. Training and pilot tests can make staff feel more comfortable and less afraid.
Research on AI acceptance points to key things for adopting AI tools like Simbo AI’s services in U.S. medical offices:
For practice owners and IT managers thinking about AI phone automation, these points highlight the need for a full approach. This includes looking at how AI works in real life, building trust, making sure it is easy to use, and fitting AI into healthcare values and work routines in the U.S.
With well-planned adoption beyond just surveys, healthcare providers can use AI systems like Simbo AI to improve front-office work, cut costs, and better serve patients, while keeping respect for human contact and ethical rules. As AI tools change, future studies and practices should also keep changing to properly track how AI fits into healthcare settings.
The review focused on user acceptance of artificial intelligence (AI) technology across multiple industries, investigating behavioral intention or willingness to use, buy, or try AI-based goods or services.
A total of 60 articles were included in the review after screening 7912 articles from multiple databases.
The extended Technology Acceptance Model (TAM) was the most frequently employed theory for evaluating user acceptance of AI technologies.
Perceived usefulness, performance expectancy, attitudes, trust, and effort expectancy were significant positive predictors of behavioral intention, willingness, and use of AI.
Yes, in some cultural situations, the intrinsic need for human contact could not be replaced or replicated by AI, regardless of its perceived usefulness or ease of use.
There is a lack of systematic synthesis and definition of AI in studies, and most rely on self-reported data, limiting understanding of actual AI technology adoption.
Future studies should use naturalistic methods to validate theoretical models predicting AI adoption and examine biases such as job security concerns and pre-existing knowledge influencing user intentions.
Acceptance is defined as the behavioral intention or willingness to use, buy, or try an AI good or service.
Only 22 out of the 60 studies defined AI for their participants; 38 studies did not provide a definition.
The acceptance factors applied across multiple industries, though the article does not specify particular sectors but implies broad applicability in personal, industrial, and social contexts.
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