{"id":119201,"date":"2025-09-24T10:20:09","date_gmt":"2025-09-24T10:20:09","guid":{"rendered":""},"modified":"-0001-11-30T00:00:00","modified_gmt":"-0001-11-30T00:00:00","slug":"the-importance-of-defining-artificial-intelligence-for-participants-in-studies-investigating-ai-acceptance-and-its-implications-for-cross-industry-applications-1000072","status":"publish","type":"post","link":"https:\/\/www.simbo.ai\/blog\/the-importance-of-defining-artificial-intelligence-for-participants-in-studies-investigating-ai-acceptance-and-its-implications-for-cross-industry-applications-1000072\/","title":{"rendered":"The Importance of Defining Artificial Intelligence for Participants in Studies Investigating AI Acceptance and Its Implications for Cross-Industry Applications"},"content":{"rendered":"

A recent review by scholars including Sage Kelly, Sherrie-Anne Kaye, and Oscar Oviedo-Trespalacios looked at 60 studies about AI acceptance from many industries. These 60 were chosen out of almost 8,000 articles. The review showed that almost half of these studies (31 out of 60) did not clearly say what AI means in their papers. Also, 38 studies did not explain AI to the participants who took part.<\/p>\n

This unclear definition causes big problems in understanding the results. If people do not agree on what AI means, their answers about using AI might be mixed up or wrong. For example, some might think AI is just automated software, while others believe it includes smart things like machine learning or natural language processing. This mix-up can change how much people trust AI and how useful they think it is. These feelings are important for whether they accept AI or not.<\/p>\n

Not defining AI well makes it hard to trust these studies. This weakens their value, especially for decisions about using AI in healthcare and other fields.<\/p>\n

Why Defining AI Matters for Medical Practices in the United States<\/h2>\n

In healthcare, privacy, accuracy, and reliability are very important. So, it is important to know how staff and patients see AI tools. Medical leaders and IT managers in clinics and hospitals often use studies about AI acceptance to decide on AI tools like virtual front-office helpers, phone answering automation, or AI chatbots for managing appointments.<\/p>\n

If a study says people are ready to use AI answering services but does not explain what AI is or how it works, healthcare leaders may make wrong decisions based on wrong ideas. Clear information about AI\u2019s abilities and limits helps leaders choose the right tools and prepare the staff better.<\/p>\n

Also, using AI in healthcare has special challenges like following privacy laws (such as HIPAA), keeping data safe, and keeping personal care where human feelings matter. Without a clear AI definition, staff may fear or not trust new technology. Giving everyone the same clear idea about AI in studies helps find real reasons why people might not want AI. This way, training and communication can be improved to help acceptance.<\/p>\n

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Key Predictors Influencing AI Acceptance Across Industries<\/h2>\n

The most used model to study AI acceptance is called the extended Technology Acceptance Model (TAM). This model looks at different factors that explain if a person wants to use AI or likes it. These factors include:<\/p>\n