Paper Type

Complete

Abstract

The emergence of GenAI has become a key driver of change across many industries, significantly enhancing performance and the efficiency of business processes. The healthcare sector is no exception, and the necessity of GenAI in practical healthcare fields has become evident. The goal of our research is to explore healthcare providers' awareness of GenAI, its potential implementation in their practices, and the factors influencing their eventual adoption of GenAI in their service areas. Our research employed Behavior Reasons Theory (BRT) as a theoretical framework to achieve the research goals and to explain the adoption behaviors of healthcare providers. To garner immediate attention from industries and practitioners, we collected public opinions from discussion forums, industry reports, and news articles as data sources. We analyzed these using Latent Dirichlet Allocation (LDA). Our findings revealed people's reasons for and against adoption based on the BRT, and we have suggested several propositions.

Paper Number

1647

Author Connect URL

https://authorconnect.aisnet.org/conferences/AMCIS2024/papers/1647

Comments

SIGHEALTH

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Aug 16th, 12:00 AM

What Are the Driving Factors of Using the Generative Artificial Intelligence Service for the Healthcare Workforce?

The emergence of GenAI has become a key driver of change across many industries, significantly enhancing performance and the efficiency of business processes. The healthcare sector is no exception, and the necessity of GenAI in practical healthcare fields has become evident. The goal of our research is to explore healthcare providers' awareness of GenAI, its potential implementation in their practices, and the factors influencing their eventual adoption of GenAI in their service areas. Our research employed Behavior Reasons Theory (BRT) as a theoretical framework to achieve the research goals and to explain the adoption behaviors of healthcare providers. To garner immediate attention from industries and practitioners, we collected public opinions from discussion forums, industry reports, and news articles as data sources. We analyzed these using Latent Dirichlet Allocation (LDA). Our findings revealed people's reasons for and against adoption based on the BRT, and we have suggested several propositions.

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