Exploring community pharmacist's psychological intentions to adopt generative artificial intelligence (GenAI) chatbots for patient information, education, and counseling

Neuroscience informatics Pub Date : 2025-06-05 DOI:10.1016/j.neuri.2025.100213

Hafidz Ihsan Hidayatullah , Muhammad Taufiq Saifullah , Muhammad Thesa Ghozali , Ayesha Aziz

{"title":"Exploring community pharmacist's psychological intentions to adopt generative artificial intelligence (GenAI) chatbots for patient information, education, and counseling","authors":"Hafidz Ihsan Hidayatullah , Muhammad Taufiq Saifullah , Muhammad Thesa Ghozali , Ayesha Aziz","doi":"10.1016/j.neuri.2025.100213","DOIUrl":null,"url":null,"abstract":"<div><div>Generative AI (GenAI) chatbots, driven by advanced machine learning algorithms, are emerging as transformative tools for enhancing patient education, information dissemination, and counseling (EIC) in healthcare. This study investigated the psychological determinants of community pharmacists' intentions to adopt GenAI chatbots using the Extended Technology Acceptance Model (ETAM). A cross-sectional survey of 240 licensed community pharmacists across several Indonesian provinces assessed key constructs, including self-efficacy (SE), perceived usefulness (PU), perceived ease of use (PEU), attitude toward technology (ATT), trust (TT), and behavioral intention (BI). Structural equation modeling revealed that SE significantly influenced PU (<span><math><mi>β</mi><mo>=</mo><mn>0.37</mn></math></span>) and PEU (<span><math><mi>β</mi><mo>=</mo><mn>0.57</mn></math></span>), indicating that confidence in using technology positively affects perceived utility and usability. PU further predicted ATT (<span><math><mi>β</mi><mo>=</mo><mn>0.39</mn></math></span>) and BI (<span><math><mi>β</mi><mo>=</mo><mn>0.236</mn></math></span>), emphasizing the motivational role of perceived benefits. Trust emerged as a crucial mediator, channeling favorable attitudes into actionable behavioral intentions (indirect <span><math><mi>β</mi><mo>=</mo><mn>0.148</mn></math></span>). The model demonstrated strong fit indices (<span><math><msup><mrow><mi>χ</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>263.09</mn></math></span>, RMSEA = 0.019, GFI = 0.915, CFI = 0.991), supporting the psychological framework. These findings highlight the importance of fostering trust, improving perceived usability, and enhancing self-efficacy through targeted training to promote GenAI chatbot adoption. Future research should explore longitudinal behavioral changes and contextual influences to support sustainable AI integration in pharmacy practice.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 3","pages":"Article 100213"},"PeriodicalIF":0.0000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuroscience informatics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772528625000287","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

Generative AI (GenAI) chatbots, driven by advanced machine learning algorithms, are emerging as transformative tools for enhancing patient education, information dissemination, and counseling (EIC) in healthcare. This study investigated the psychological determinants of community pharmacists' intentions to adopt GenAI chatbots using the Extended Technology Acceptance Model (ETAM). A cross-sectional survey of 240 licensed community pharmacists across several Indonesian provinces assessed key constructs, including self-efficacy (SE), perceived usefulness (PU), perceived ease of use (PEU), attitude toward technology (ATT), trust (TT), and behavioral intention (BI). Structural equation modeling revealed that SE significantly influenced PU (

β = 0.37

) and PEU (

β = 0.57

), indicating that confidence in using technology positively affects perceived utility and usability. PU further predicted ATT (

β = 0.39

) and BI (

β = 0.236

), emphasizing the motivational role of perceived benefits. Trust emerged as a crucial mediator, channeling favorable attitudes into actionable behavioral intentions (indirect

β = 0.148

). The model demonstrated strong fit indices (

χ^{2} = 263.09

, RMSEA = 0.019, GFI = 0.915, CFI = 0.991), supporting the psychological framework. These findings highlight the importance of fostering trust, improving perceived usability, and enhancing self-efficacy through targeted training to promote GenAI chatbot adoption. Future research should explore longitudinal behavioral changes and contextual influences to support sustainable AI integration in pharmacy practice.

查看原文本刊更多论文

探索社区药剂师采用生成式人工智能（GenAI）聊天机器人进行患者信息、教育和咨询的心理意向

由先进机器学习算法驱动的生成式人工智能（GenAI）聊天机器人正在成为增强医疗保健领域患者教育、信息传播和咨询（EIC）的变革性工具。本研究使用扩展技术接受模型（ETAM）调查了社区药剂师采用GenAI聊天机器人意图的心理决定因素。对印度尼西亚几个省的240名有执照的社区药剂师进行了横断面调查，评估了关键结构，包括自我效能感（SE）、感知有用性（PU）、感知易用性（PEU）、对技术的态度（ATT）、信任（TT）和行为意向（BI）。结构方程模型显示，SE显著影响PU （β=0.37）和PEU (β=0.57)，表明使用技术的信心正向影响感知效用和可用性。PU进一步预测了ATT （β=0.39）和BI (β=0.236)，强调了感知利益的激励作用。信任是一个重要的中介，将有利的态度转化为可操作的行为意图（间接β=0.148）。模型拟合指数较强（χ2=263.09, RMSEA = 0.019, GFI = 0.915, CFI = 0.991），支持心理框架。这些发现强调了通过有针对性的培训来促进GenAI聊天机器人的采用，培养信任、提高感知可用性和增强自我效能的重要性。未来的研究应该探索纵向行为变化和环境影响，以支持人工智能在药学实践中的可持续整合。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Neuroscience informatics Surgery, Radiology and Imaging, Information Systems, Neurology, Artificial Intelligence, Computer Science Applications, Signal Processing, Critical Care and Intensive Care Medicine, Health Informatics, Clinical Neurology, Pathology and Medical Technology

自引率

0.00%

发文量

审稿时长

57 days