A UTAUT-Based Framework for Analyzing Users’ Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand

 Introduction

A startling, 62% of organizations in Thailand still struggle to encourage the usage of artificial intelligence, despite the field's growing recognition of its potential in HR recruitment (Tanatorn et al., 2024). An all-encompassing approach is required to effectively address the obstacles associated with the deployment of AI. To successfully integrate AI into a variety of contexts and optimize its benefits, this strategy should take into account several factors, including organizational preparedness, user acceptance, technology improvements, and regulatory considerations. Successful implementation of AI in HR recruitment requires both cultivating trust and acceptance of AI applications and analyzing users' intention to adopt AI using a UTAUT-based framework (Annapoorani et al., 2023; Shevtsova et al., 2022; Tanatorn et al., 2024).

Literature Review

Introduction to Literature Review

The use of a UTAUT-based framework to analyze users' desire to employ artificial intelligence in HR recruiting, with a particular emphasis on the Thailand case (Tanatorn et al., 2024). For organizations in Thailand to successfully integrate AI technologies and improve their recruiting processes, it is imperative to comprehend users' desire to utilize AI in human resource recruitment. To emphasize the primary issue of technology adoption in organizational contexts, the literature review only includes research publications that are specifically focused on factors influencing users' intention to embrace artificial intelligence in human resource recruiting. The study looked at several factors including organizational culture, perceived utility, perceived simplicity of use, and social impact, that affected users' propensity to utilize artificial intelligence in HR recruitment in the setting of Thailand. ProQuest and EBSCO Host from the Monroe College Library were the databases used for this literature research to investigate the variables impacting the adoption of AI in HR procedures. Keywords like UTAUT framework, artificial intelligence adoption, human resource recruitment, and Thailand were among the search terms used to locate the research article on users' intention to adopt AI in HR recruiting.

Review of Literature

Understanding AI Adoption in HR Recruitment: Insights from Thailand

Tanatorn et al. (2024) study was centered on figuring out what variables users would be most likely to employ when deciding whether or not to recruit human resources using artificial intelligence (AI) in Thailand in 2024. By creating a framework based on the widely used Unified Theory of Acceptance and Use of Technology (UTAUT), which attempts to explain technology adoption behaviors, the researchers hope to contribute to the area. Employing a quantitative methodology, the investigators gathered information via surveys or organized interviews with a subset of human resources specialists or those engaged in the hiring procedure in diverse establishments around Thailand. The UTAUT framework's constructs—performance expectancy, effort expectancy, social influence, enabling conditions, and behavioral intention—were the foundation for the survey's design. The study revealed information on the variables impacting users' plans to use AI for hiring human resources in Thailand. This entails pinpointing particular factors that influence people's readiness to use AI technology for hiring purposes in the Thai environment, such as perceived utility, usability, organizational support, and outside influences. The findings of the research offer insightful information to organizations and policymakers wishing to introduce AI-based hiring practices in Thailand. This information helps them comprehend the most important aspects to take into account as well as possible obstacles to overcome to encourage the uptake and acceptance of these technologies. It's critical to recognize some limitations even if the study offers insightful information about the elements driving Thailand's intention to use AI in HR recruitment. These include potential biases in the sample population, the findings' generalizability outside of Thailand, and the inherent limitations of the UTAUT paradigm. The study's cross-sectional design makes it more difficult to prove a relationship between variables, and its reliance on self-reported data from surveys or interviews introduces response biases. Understanding of AI adoption in recruitment practices, future research could overcome these constraints by utilizing alternative methodological techniques, undertaking comparison studies across cultural contexts, and utilizing longitudinal designs (Tanatorn et al., 2024).

Physician Perspectives on AI in Otolaryngology and Rhinology

In a similar vein, Annapoorani et al. (2023) carried out a study in 2023 that concentrated on physicians' perceptions of artificial intelligence (AI) in otolaryngology and rhinology, further exploring the use of AI.  The viewpoints of physicians on the use of AI in particular medical disciplines are explored in this study. A mixed-methods strategy was used in the study to collect and analyze data from both qualitative and quantitative sources. To collect qualitative information on the attitudes, convictions, and experiences of doctors with AI in otolaryngology and rhinology, surveys and interviews were conducted. Quantitative methods have been used to evaluate variables like perceived usefulness, adoption hurdles, and AI trust. The findings of the research cover a variety of topics, such as how doctors in different specialties feel about artificial intelligence (AI), as well as the advantages and difficulties of implementing AI in clinical settings. The study's findings help develop methods for resolving issues, maximizing the advantages of AI technology in enhancing patient care and outcomes, and successfully integrating AI into medical practice. It is imperative to acknowledge the possible constraints of the research, including sample size, participant demographics, and the applicability of results to alternative medical specializations or healthcare environments. Due to the inherent limits of mixed-methods research, the study has difficulties combining qualitative and quantitative data as well as guaranteeing the validity and reliability of the findings. When interpreting the study's findings and consequences, these limitations should be taken into account (Annapoorani et al., 2023).

Trust and Acceptance of AI in Healthcare: Exploring People's Attitudes and Opinions

Unlike the previous study, which examined users' intentions to use AI in Thailand for HR recruitment, this one looks into people's attitudes and opinions of AI technology in the healthcare industry, with a focus on medical applications. Shevtsova et al. (2022) carried out a study in 2022 concentrating on trust in and acceptance of artificial intelligence (AI) applications in medicine, building on the exploration of AI adoption in multiple fields. The goal of the study was to find out how much people trusted and accepted AI applications in medicine. They made an effort to comprehend people's views, attitudes, and other elements affecting their acceptance and level of trust in AI in healthcare settings. A mixed-methods strategy was used in the study, including quantitative and qualitative data collecting and analysis strategies. This involves techniques like questionnaires, interviews, and sometimes observational research. While the quantitative component can involve using formal surveys or scales to measure acceptability and trust levels, the qualitative component might involve investigating participants' attitudes, opinions, and experiences using AI technology in medicine. The study participants' degree of trust and acceptance of AI applications in medicine was revealed to the researchers. This entails determining the variables that affect trust, such as perceived dependability and accuracy, privacy issues, and the advantages of AI in healthcare. The report also reveals obstacles to acceptance, including patients' and healthcare professionals' ignorance of AI technology, worries about job displacement, and ethical concerns. To improve patient care and outcomes, legislators, healthcare professionals, and technology developers can benefit greatly from the study's findings, which will facilitate the adoption and integration of AI in medical settings. must recognize the study's possible shortcomings, including sample size, participant demographics, and the applicability of findings in different healthcare settings. Integrating qualitative and quantitative data and guaranteeing the validity and reliability of findings can be difficult for mixed-methods research. When interpreting the study's findings and consequences, these limitations should be taken into account (Shevtsova et al., 2022).

Analysis of Literature

The study conducted by Annapoorani et al. (2023) was carried out in 2023, whilst Shevtsova et al. (2022) and Tanatorn et al. (2024) carried out their studies in 2022 and 2024. The three studies were carried out in a comparatively brief period of two years. This suggests that there is a growing interest in and attention to the topic of artificial intelligence (AI) adoption across a range of industries, including human resource recruitment, otolaryngology, and rhinology, among others. While Annapoorani et al. (2023) and Shevtsova et al. (2022) carried out their research in various areas, Tanatorn et al. (2024) concentrated on Thailand when they studied the use of AI in human resource recruiting. Even though the precise locations vary, all three included people from different organizations or environments pertinent to their respective fields of study, demonstrating a wide range of investigation.

The study's main driving forces differ. Annapoorani et al. (2023) sought to evaluate attitudes, perceptions, and concerns about artificial intelligence (AI) in these medical disciplines by examining physician views on AI integration in otolaryngology and rhinology. Conversely, Shevtsova et al. (2022) investigated the variables impacting people's trust in AI technologies in healthcare settings and their adoption of AI applications in medicine more generally. Tanatorn et al. (2024) conducted a study with a focus on Thailand to investigate the elements that influence users' inclination to embrace artificial intelligence (AI) in human resource recruiting.

Annapoorani et al. (2023) employed a mixed-methods strategy to learn about physician opinions on artificial intelligence in otolaryngology and rhinology. They combined qualitative and quantitative data gathering and analysis methodologies. Similarly, Shevtsova et al. (2022) used surveys, interviews, or both to collect data as part of a mixed-methods study design to investigate acceptability and confidence in AI applications in medicine. Tanatorn et al. (2024) also used a UTAUT-based framework in their study on the adoption of AI in human resource recruiting, measuring users' intention to use AI technology through quantitative techniques like surveys.

Annapoorani et al. (2023) discovered characteristics impacting acceptability and potential barriers to adoption in various medical specialties, as well as insights on physicians' perspectives on AI integration in otolaryngology and rhinology. Similarly, Shevtsova et al. (2022) found elements impacting people's confidence in AI technology in healthcare settings and revealed insights into acceptability and trust in AI applications in medicine. Tanatorn et al. (2024), on the other hand, discovered information about users' intentions to utilize AI in human resource recruiting, in Thailand. They also identified characteristics that influence adoption decisions in this context, which help develop strategies for improving the use of AI in HR practices. Shevtsova et al. (2022) concentrated on the acceptability of AI applications in medicine, whilst Tanatorn et al. (2024) and Annapoorani et al. (2023) looked at the adoption of AI in two particular fields: otolaryngology/rhinology and human resource recruiting. Though in distinct circumstances, the research done by Shevtsova et al. (2022), Annapoorani et al. (2023), and Tanatorn et al. (2024) all explore the topic of artificial intelligence (AI) adoption.

Discussion

Introduction to Discussion

One of the main concerns is the application of artificial intelligence (AI) in a variety of industries, such as healthcare and hiring for human resources. An extensive investigation is underway into the factors that impact experts' and users' acceptance and confidence in AI technologies, as well as the obstacles that come with them. Research on this subject is being done in specialized fields like rhinology and otolaryngology as well as internationally, especially in countries like Thailand. AI technology integration can drastically change hiring practices and employment prospects, affecting companies and candidates alike. Understanding the elements driving AI adoption in this environment will help to support well-informed decision-making and successful implementation methods (Annapoorani et al., 2023; Shevtsova et al., 2022; Tanatorn et al., 2024).

Evidence-Based Recommendations

Recommendations from Literature Review

The research articles' recommendations provide insightful guidance on how to promote artificial intelligence (AI) acceptance and adoption across a range of industries. To improve diagnostic and therapeutic capacities, Annapoorani et al. (2023) recommend incorporating artificial intelligence (AI) tools into otolaryngology and rhinology practices. This suggestion places a strong emphasis on using AI's analytical capabilities to enhance medical decision-making procedures, which result in more precise diagnosis and individualized treatment regimens. Shevtsova et al. (2022) suggest that collaboration, transparency, and education between AI developers and healthcare practitioners can help to build trust and adoption of AI applications in medicine. In order to win stakeholders' trust and support for implementing AI technology in healthcare settings, this proposal emphasizes how critical it is to address worries about the safety, ethical implications, and dependability of AI. Tanatorn et al. (2024) recommend the usage of a UTAUT-based methodology to assess users' intent to use AI for hiring in human resources. This suggestion emphasizes how important it is to comprehend the elements—such as perceived performance expectancy, effort expectancy, social influence, and enabling conditions—that affect people's willingness to adopt AI solutions in organizational environments.

Program Recommendation

 Further investigation revealed that Google is a model company that has made great progress towards addressing the adoption hurdles associated with artificial intelligence (AI) in the hiring process. In line with the suggestions made in the research publications, Google has put a number of tactics into place to encourage the use of AI in its hiring practices. Google's usage of AI-powered solutions like Hire by Google, which automates duties like candidate screening, interview scheduling, and fit assessment, is one specific endeavor that is worth mentioning. It streamlines the recruitment process. Google shows that it is committed to improving the efficacy and efficiency of hiring human resources while simultaneously addressing customer concerns and adoption barriers by utilizing AI in this way. Based on the research articles' recommendations, two crucial techniques are particularly pertinent for organizations seeking to encourage the use of AI in hiring. Tanatorn et al. (2024) suggestion to apply a UTAUT-based framework for assessing consumers' intention to adopt AI is very important. To resolve concerns and promote acceptance, the suggestion highlights how critical it is to comprehend consumer attitudes and beliefs around AI technology. It is crucial to cultivate trust and adoption of AI applications, as proposed by Shevtsova et al. (2022). Organizations can lessen opposition to the deployment of AI and create a climate of trust and acceptance by encouraging collaboration amongst stakeholders, educating the public, and promoting transparency.

Before putting these tactics into practice, leaders should first evaluate how well AI is already being used in their company and pinpoint areas where the hiring process needs to be improved. Then, using a framework based on UTAUT, they ought to employ a methodical analysis of users' intention to adopt AI, taking into account variables like social influence, effort expectancy, performance expectancy, and facilitating conditions. Leaders will be able to adjust interventions in light of the analysis's insightful findings regarding the factors promoting and impeding AI adoption in the company. Transparency and communication should be given top priority by leaders in order to promote confidence and acceptance of AI applications. They need to address any worries or misunderstandings, explain the reasoning for the deployment of AI in detail, and involve staff members in the decision-making process. It's also crucial to give staff members the guidance and assistance they need to become acquainted with AI technology and its advantages. It is imperative for leaders to foster a collaborative work atmosphere that gives employees the confidence to offer input and participate in the ongoing enhancement of AI systems.

It is anticipated that the workforce and the organization will benefit from the implementation of these measures. Organizations can increase hiring outcomes and employee happiness by utilizing a UTAUT-based framework to streamline their recruitment procedures and cultivate a culture of trust and acceptance. Encouraging AI usage in HR recruitment will help businesses draw in top people and maintain their competitiveness in the quickly changing digital market. All things considered, by embracing the advantages of AI technology, these tactics have the potential to promote good change in the workforce and propel organizational success.

Conclusion

Developing trust and acceptance of AI applications and using a UTAUT-based framework to assess users' intention to adopt AI are two crucial pillars that are necessary for the successful integration of artificial intelligence (AI) into human resource recruitment (Annapoorani et al., 2023; Shevtsova et al., 2022; Tanatorn et al., 2024). Through the resolution of these crucial elements, establishments can facilitate the successful integration of AI, enhance their hiring procedures, and set themselves up for prosperity in a progressively digitalized environment. A complete approach that takes into account a number of aspects, including organizational readiness, user acceptance, technological improvements, and regulatory compliance, is required to address the obstacles associated with the adoption of AI. Organizations can successfully integrate AI into a variety of situations and optimize its potential benefits across many industries by using this all-encompassing strategy. The research reveals a significant obstacle that Thai organizations face: a significant fraction of them continuously struggle to encourage the adoption of AI in HR recruitment, despite the technology's well-known ability to improve procedures and results.

 

 

 

References

Annapoorani, A., Massey, C J., Tietbohl, C., Kroenke, K., Morris, M., &  Ramakrishnan. V, (2023). Physician views of artificial intelligence in otolaryngology and rhinology: A mixed methods study. Larygoscope Investigative Otolarngology, 8(6), 1468-1475. https://doi.org/10.1002/lio2.1177

Shevtsova, D., Ahmed, A., Boot, I., Sanges, Carmen., Hudecek, M., Jacobs, J., Hort, S., & Vrijhoef, H. (2022). Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study. JMIR Human Factors, 11, e47031. https://doi.org/10.2196/47031

Tanatorn, T., & Piriyapong, W. (2024). A UTAUT-Based Framework for Analyzing Users’ Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand. Systems, 12(1), 28. https://doi.org/10.3390/systems12010028