{"title":"A UTAUT-Based Framework for Analyzing Users’ Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand","authors":"Tanatorn Tanantong, Piriyapong Wongras","doi":"10.3390/systems12010028","DOIUrl":null,"url":null,"abstract":"Recruitment is a fundamental aspect of Human Resource Management to drive organizational performance. Traditional recruitment processes, with manual stages, are time-consuming and inefficient. Artificial Intelligence (AI), which demonstrates its potential in various sectors such as healthcare, education, and notable cases of ChatGPT, is currently reshaping recruitment by automating tasks to improve efficiency. However, in Thailand, where there is a growing demand for talents, the application of AI in recruitment remains relatively limited. This study focuses on human resources (HR) and recruitment professionals in Thailand, aiming to understand their perspectives on the integration of AI in recruitment. It utilized the Unified Theory for Acceptance and Use of Technology (UTAUT) model, customized to suit the specific requirements of Thailand recruitment practices. The study explores the factors influencing users’ intention to adopt AI in recruitment. Survey questionnaire items were created based on prior literature and refined with insights from HR and recruitment experts to ensure applicability in the context of recruitment in Thailand. A survey involving 364 HR and recruiting professionals in the Bangkok metropolitan area supplied comprehensive responses. The study reveals that several factors, including perceived value, perceived autonomy, effort expectancy, and facilitating conditions, significantly impact the intention to adopt AI for recruitment. While social influence and trust in AI technology do not have a direct influence on intention, social influence directly affects perceived value. Trust in AI technology positively influences Effort Expectancy. This study provides valuable benefits for HR and recruitment professionals, organizations, and AI developers by offering insights into AI adoption and sustainability, enhancing recruitment processes and promoting the effective use of AI tools in this sector.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"64 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.3390/systems12010028","RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SOCIAL SCIENCES, INTERDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Recruitment is a fundamental aspect of Human Resource Management to drive organizational performance. Traditional recruitment processes, with manual stages, are time-consuming and inefficient. Artificial Intelligence (AI), which demonstrates its potential in various sectors such as healthcare, education, and notable cases of ChatGPT, is currently reshaping recruitment by automating tasks to improve efficiency. However, in Thailand, where there is a growing demand for talents, the application of AI in recruitment remains relatively limited. This study focuses on human resources (HR) and recruitment professionals in Thailand, aiming to understand their perspectives on the integration of AI in recruitment. It utilized the Unified Theory for Acceptance and Use of Technology (UTAUT) model, customized to suit the specific requirements of Thailand recruitment practices. The study explores the factors influencing users’ intention to adopt AI in recruitment. Survey questionnaire items were created based on prior literature and refined with insights from HR and recruitment experts to ensure applicability in the context of recruitment in Thailand. A survey involving 364 HR and recruiting professionals in the Bangkok metropolitan area supplied comprehensive responses. The study reveals that several factors, including perceived value, perceived autonomy, effort expectancy, and facilitating conditions, significantly impact the intention to adopt AI for recruitment. While social influence and trust in AI technology do not have a direct influence on intention, social influence directly affects perceived value. Trust in AI technology positively influences Effort Expectancy. This study provides valuable benefits for HR and recruitment professionals, organizations, and AI developers by offering insights into AI adoption and sustainability, enhancing recruitment processes and promoting the effective use of AI tools in this sector.