Application of Artificial Intelligence (AI) in Recruitment and Selection: The Case of Company A and Company B

Abstract

The study explores the integration of Artificial Intelligence (Al) in recruitment and selection processes at Company A and Company B, reflecting the increasing trend of Al utilization in HR practices. The research aims to investigate HR professionals' perceptions and attitudes towards Al adoption in recruitment, utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT) model to understand the factors influencing behavioral intentions towards Al integration. Through a quantitative descriptive approach, structured surveys and questionnaires were employed to gather data from HR professionals at the two companies via snowball sampling, providing valuable insights into the perspectives of professionals involved in the hiring process. The study revealed the significant impact of educational background on attitudes towards Al adoption, with a positive reception towards the performance expectancy and social influence of Al tools in recruitment and selection processes, as indicated by the UTAUT model. The findings underscore the importance of considering educational background in shaping attitudes towards Al integration in HR practices, highlighting the potential benefits of Al tools in enhancing recruitment and selection processes.

Full text

Journal of Business and Management Studies
ISSN: 2709-0876
DOI: 10.32996/jbms
Journal Homepage: www.al-kindipublisher.com/index.php/jbms
JBMS
AL-KINDI CENTER FOR RESEARCH
AND DEVELOPMENT
Copyright: © 2024 the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons
Attribution (CC-BY) 4.0 license (https://creativecommons.org/licenses/by/4.0/). Published by Al-Kindi Centre for Research and Development,
London, United Kingdom.
Page | 224
| RESEARCH ARTICLE
Application of Artificial Intelligence (AI) in Recruitment and Selection: The Case of
Company A and Company B
Zhang, Pengcheng
Faculty of the College of Business Administration, Graduate Studies, Adamson University, Philippines
Corresponding Author: Zhang, Pengcheng, E-mail: zhangethan1221@gmail.com
| ABSTRACT
The study explores the integration of Artificial Intelligence (Al) in recruitment and selection processes at Company A and Company
B, reflecting the increasing trend of Al utilization in HR practices. The research aims to investigate HR professionals' perceptions
and attitudes towards Al adoption in recruitment, utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT)
model to understand the factors influencing behavioral intentions towards Al integration. Through a quantitative descriptive
approach, structured surveys and questionnaires were employed to gather data from HR professionals at the two companies via
snowball sampling, providing valuable insights into the perspectives of professionals involved in the hiring process. The study
revealed the significant impact of educational background on attitudes towards Al adoption, with a positive reception towards
the performance expectancy and social influence of Al tools in recruitment and selection processes, as indicated by the UTAUT
model. The findings underscore the importance of considering educational background in shaping attitudes towards Al
integration in HR practices, highlighting the potential benefits of Al tools in enhancing recruitment and selection processes.
| KEYWORDS
Artificial intelligence (Al), Human resources (HR), Recruitment transformation, Digital recruitment
| ARTICLE INFORMATION
ACCEPTED: 01 June 2024 PUBLISHED: 08 June 2024 DOI: 10.32996/jbms.2024.6.3.18
1. Introduction
1.1 Background of the Study
Artificial intelligence (AI) is proving its worth to recruitment teams by providing benefits like efficiency, personalization, and data-
informed decision-making. AI is widely utilized by recruiters across industries as a complement to human efforts in the recruitment
process. Rather than replacing humans, AI enhances cognitive strengths, embodies human capabilities, and expands physical
capabilities. AI technologies have distinct characteristics that set them apart from other innovative IT tools. In recruitment, AI plays
a significant role in various stages, offering impartiality and efficiency. Both recruiters and applicants perceive AI-based tools
positively, recognizing benefits such as time savings and improved candidate experiences. HR managers anticipate increased
utilization of AI tools in the future. Overall, AI-based tools are transforming the recruitment field, providing efficiency, fairness, and
improved experiences. Continuous evaluation and understanding of perceptions are vital for improving the effectiveness and
acceptance of AI tools in the recruitment process.

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As the development of artificial intelligence continues to progress, an expanding array of enterprises will depend on it to augment
their recruitment procedures. In the past, the act of recruiting has been an arduous and monotonous task. To illustrate, numerous
organizations have utilized AI to rapidly analyze vast amounts of data to enhance efficacy, precision, and output. In essence, AI is
simply an automated tool that enables us to resolve complex, recurring issues with high-quality results. AI is employed to help
illuminate the path toward achieving these outcomes. Comparable automation capabilities and advantages can be implemented
in recruiting processes, particularly for repetitive, high-volume tasks like screening, sourcing, and scheduling. AI-driven technology
is intended to accelerate time-consuming manual procedures, freeing up recruiters to concentrate on more valuable initiatives
during the hiring process. It is of utmost significance to acknowledge, notwithstanding certain convictions to the contrary, that
Artificial Intelligence (AI) is not purposed to supplant human recruiters.AI in the hiring process, or AI in recruiting, allows talent
acquisition teams to identify passive candidates and unlock data-driven insights that support decision-making and better results,
such as higher quality hires. Recruiting tools powered by AI can assist in matching the right jobs to the right talent and connecting
the right talent with the right recruiters, ultimately resulting in successful hires. As evidenced by its use in hiring processes, AI is
already saving HR teams time and money while also attracting top-notch candidates.
The potential implementation of artificial intelligence (AI) in the workplace has become a subject of regulatory deliberation within
the Philippines. The country's Labor Secretary, Bienvenido Laguesma, has recognized the need for regulations in response to the
changes occurring in the workplace (Tan J., 2023, June 15). The chief of the Department of Information and Communications
Technology (DICT) is also supportive of calls for AI regulation in the workplace (Javier, P. 2023, June 15). Various entities have urged
the government to regulate the creation of AI applications and systems to ensure the development of responsible and ethical AI
products (Atienza, K. A. T. 2023, June 12). Currently, there exists a dearth of specific regulations in the Philippines that govern the
utilization of AI in the practice of law. Despite the advantages of AI, the Department of Labor and Employment (DOLE) advocates
for regulating AI in the workplace to ensure its responsible use (Jaymalin, M. 2023, June 14). Philippine Senator Imee Marcos has
filed a resolution requesting an inquiry into the use of AI and its impact on the country's job market, citing concerns that AI may
displace workers in the services and manufacturing sectors (Atienza, H. J. S. 2023, May 8).
As to the context of recruitment, the emergence of Digital Recruiting 3.0 signifies the integration of AI technology as a key element.
It follows the earlier phases of Digital Recruiting 2.0 and Digital Recruiting 1.0, which involved the process of collecting and
consolidating job postings from various sources into a centralized location and applicant information, respectively. AI technology
in recruitment is equipped with intelligent capabilities, functioning autonomously, and enhancing human capabilities. It serves as
a complement to human efforts, assisting in complex tasks and expanding capabilities. The adoption of AI in recruitment has
become essential for organizations, offering automation, improved decision-making, and data analysis. AI is utilized throughout
the recruitment process, promoting efficiency and fair evaluation of candidates. The integration of AI in recruitment aims to remove
biases and prejudices. Overall, the integration of AI in recruitment brings about a significant transformation, offering several
advantages and positive outcomes.
Implementing AI in an organization requires careful consideration and modifications to the organization's culture, structure, and
working methods. It is not a simple "plug-and-play" technology that provides instant gratification. (Fountaine et al., 2019).
According to a recent study by Mikalef and Gupta (2021), relying solely on AI tools is unlikely to provide a competitive advantage
because they can be easily obtained and imitated by competitors. Organizations need to consider a combination of resources,
including tangible and human resources, as well as factors like interdepartmental coordination, organizational change capability,
and risk-taking, to establish an AI capacity that provides a competitive advantage. Strategic planning, adequate resources, a
commitment to innovation, and organizational and technological readiness are key factors in making AI work effectively within an
organization. (Mikalef and Gupta 2021).
The effective application of AI in human resources faces significant challenges, despite acknowledging its benefits. These hurdles
encompass various aspects, including practical difficulties and ethical considerations. (Tambe et al., 2019). For starters, historical
data biases in AI algorithms can lead to AI systems favoring a specific group of applicants. Amazon uncovered a problem with its
hiring algorithm in 2018 for the reasons described above. The system was developed utilizing data from previous work
performance, which was dominated by white, male employees who performed above average. As a result, men in the same
demographic group obtained higher marks from the AI system. Because the organization could not figure out how to make the
algorithm gender-neutral, it had to stop hiring (Meyer, 2018). Second, there is growing concern about whether AI-powered
recruitment tools are ethical, and whether people's trust in the companies that use them is influenced by the ethical perception of
the use of AI in recruitment because AI robots and algorithms are generally inaccessible to the public due to intellectual property
rights.

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Despite these reservations, AI is expected to significantly increase recruiters' productivity and free them up to do more strategic
and human-centric tasks. However, empirical research has not adequately assessed AI technology in terms of benefits,
performance, and contact experience from the perspective of Human Resource (HR) experts. Some of the studies had a limited
sample size (3-5 recruiters) or a country distribution - solely in Bangladesh or Germany. While most AI in recruitment research has
concentrated on AI technologies (such as natural language processing, machine vision, automation, and augmentation) and their
impact on the recruiting and selection process, the use of AI in recruitment, or ethical and trust considerations, little is known
about how recruiters perceive AI-based tools in recruitment and selection.
This study seeks to provide light on recruiters' perspectives of AI technology in recruitment and selection from HR experts who
employ applicants in various industries and have used various AI-enabled recruitment tools. It investigates how recruiters perceive
AI tools in recruitment, with a focus on performance, efficiency, ease of use, and social impact as key factors determining the
adoption of these tools in recruitment, their impact on selection activities,
outcomes, and the future of recruitment.
1.2 Statement of the Problem
This study aims to explore the perspective of HR professionals in the application of artificial intelligence (AI) in recruitment and
selection processes. Specifically, it aimed to answer the following questions:
1. What is the demographic profile of the respondents in terms of:
1.1. Age;
1.2. Gender;
1.3. Educational Background;
1.4. Employment Status;
1.5. Work Experience in utilizing AI tools; and
1.6. Frequency of working with AI tools?
2. How do HR Professionals recognize the applicability of the UTAUT model in the use of AI in recruitment and selection in
terms of:
2.1. Performance Expectancy;
2.2. Effort Expectancy; and
2.3. Social Influence?
3. Is there a significant relationship between the perspective of HR Professionals towards UTAUT constructs and Behavioral
Intention toward the application of AI in recruitment and selection?
4. What is the perspective of HR Professionals towards the application of AI in recruitment and selection in terms of
Behavioral Intention?
5. Is there a significant difference in the perspective of HR Professionals towards the application of AI in recruitment and
selection in the Behavioral Intention when grouped according to the demographic profile?
6. Based on the results of the study, what best HR practices can be recommended for each
stakeholder in the application of artificial intelligence in recruitment and selection?
1.3 Hypotheses
In this study, the researcher will test the following hypotheses:
H1: There is no significant relationship between the perspective of HR Professionals towards UTAUT constructs and Behavioral
Intention toward the application of AI in recruitment and selection.
H2: There is no significant difference in the perspective of HR Professionals towards the application of AI in recruitment and
selection in the Behavioral Intention when grouped according to the demographic profile.
1.4 Scope and Limitation
The present study endeavors to examine the perspective of human resource professionals regarding the utilization of artificial
intelligence in the context of recruitment and selection. The study will specifically focus on recruiters or human resource
professionals who employ AI tools in the said processes. The primary participants of this investigation are two companies, namely
CATSearch HR Consultancy Inc. and Viventis Search Asia. CATSearch HR Consultancy Inc. is a corporate and management
consulting enterprise that provides a wide range of human resources and operations services, including database management,
CV parsing and formatting, and onboarding. It is situated at The Glens at Parkspring Brgy San Antonio San Pedro, Laguna. On the
other hand, Viventis Search Asia is among the top human capital solutions firms in the Philippines, with branches in Singapore,

JBMS 6(3): 224-225
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Malaysia, and Indonesia. The company strives to enhance human capital through technology-driven solutions and compassionate
guidance from a team of human resource experts. It is located at 6/F 45 San Miguel Building, 45 San Miguel Avenue, Ortigas
Center, Pasig City. Furthermore, the researcher aims to complete the data collection process within a timeframe of three (3) months.
The survey questionnaires were adapted from previous scholarly journals and will be disseminated through Google Forms/QR
Code. The responses will then be collated by the researcher for data analysis.
1.5 Significance of the Study
By providing valuable insights, various stakeholders can benefit from this study which includes the following:
Organizations. The study’s results can help organizations make informed decisions about the adoption and implementation of AI
technologies in recruitment and selection processes, leading to potential benefits for HR management, organizational efficiency,
and overall recruitment outcomes.
HR professionals. This study could provide HR professionals with insights into the potential benefits and challenges associated
with the use of AI in recruitment and selection. This could help them to make informed decisions about the adoption of AI
technologies in their work.
Developers of AI technologies. The results of this study could provide developers of AI technologies with insights into the needs
and concerns of HR professionals, which could inform the design and development of AI technologies for use in recruitment and
selection.
Researchers. This study could contribute to the academic literature on the adoption of AI technologies in HR by providing
empirical evidence on HR professionals’ perception of AI in recruitment and selection. This could inform future research on this
topic.
Future Researchers. Future researchers could also benefit from this study on HR professionals’ perception of AI in recruitment
and selection. The study could contribute to the academic literature on the adoption of AI technologies in HR by providing
empirical evidence on HR professionals’ perspectives of AI in recruitment and selection. In addition, identifying these gaps, further
research can be conducted to delve deeper into specific aspects and unanswered
questions related to HR professionals’ views on AI in the hiring process.
1.6 Definition of Terms
The following terms would be operationally defined in the study:
Artificial Intelligence. Artificial intelligence technologies can be used to support the recruitment and selection process, such as
machine learning algorithms for resume screening or natural language processing for job ad writing.
Behavioral Intention. In the context of a study on HR professionals’ perception of AI in recruitment and selection, behavioral
intention would refer to HR professionals’ intention to use AI technologies in their recruitment and selection processes.
Effort Expectancy. From the perspective of recruiters, refers to the ease of learning and interacting with a system, the clarity and
understandability of the system’s interface, the system’s flexibility in usage, and the ease of becoming familiar with the system.
HR Professionals. Individuals who work in the human resources department of an organization are responsible for managing the
recruitment and selection of employees.
Performance Expectancy. Performance expectancy, from the perspective of recruiters, encompasses the belief that using a system
will lead to gains in job performance. This belief includes expectations of increased task completion speed, improved work
performance, increased productivity, enhanced effectiveness at work, and work made easier.
Recruitment and Selection. The process of identifying, attracting, and selecting suitable candidates to fill job vacancies within an
organization.
Social Influence. In the area of artificial intelligence-enabled recruitment tools, social influence includes the influence of
management support and peer influence on individuals’ adoption of these technologies. It is observed both offline and online and
has a significant impact on cultural markets, scientific innovations, social practices, and various fields of study.
Unified Theory of Acceptance and Use of Technology (UTAUT). From the perspective of a recruiter, the UTAUT model could
be used to ascertain their willingness to employ AI technologies in recruiting and selection.
1.7 Literature Review
This chapter presents informative materials that have relevance to the objectives of this study. It contains the researcher’s view of
relevant ideas related to the present study. Materials such as thesis and web articles have a direct bearing on the researcher’s
conceptual framework which provides direction to the present study.
1.7.1 Unified Theory of Acceptance and Use of Technology (UTAUT)
The Unified Theory of Acceptance and Use of Technology (UTAUT) is a theoretical paradigm that explains why people want to
utilize certain technologies. According to the concept, four essential components (performance expectancy, effort expectancy,

Application of Artificial Intelligence (AI) in Recruitment and Selection: The Case of Company A and Company B
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social influence, and facilitating factors) impact an individual’s behavioral intention to use a technology, which drives technology
use behavior. The UTAUT model could be used to understand a recruiter’s intention to use AI technology in recruitment and
selection. The UTAUT model’s four core constructs are as follows: Performance Expectancy: The degree to which a recruiter believes
that employing AI in recruitment and selection will enable them to do their work more effectively. Effort Expectancy: The level of
comfort connected with a recruiter’s usage of artificial intelligence in recruiting and selection. Social Influence: A recruiter’s
perception of how important others (such as colleagues or superiors) believe AI should be used in recruiting and selection.
Facilitating Conditions: The extent to which a recruiter believes that the organizational and technical infrastructure is in place to
facilitate the use of AI in recruitment and selection.
The UTAUT model could provide insights into the elements that influence recruiters’ inclination to adopt AI technology in their
work by studying these constructs. Because of the rapid rise of IT, numerous theories of technological acceptance have emerged.
The UTAUT model is widely represented in the literature and incorporates a wide range of constructs based on recent theories of
technology acceptance (Menant, Gilibert, & Sauvezon, 2021). According to Alam et al. (2020), the UTAUT is commonly utilized in
research because it is a unified model that integrates a range of variables from eight key theories, including the Theory of Reasoned
Action, the Technology Acceptance Model (TAM), the Motivational Model (MM), the Theory of Planned Behavior (TPB), the
Decomposed Theory of Planned Behavior (DTPB), the Model of PC Utilization (MPCU), the Innovation Diffusion Theory (IDT), and
the Social Cognitive Theory (SCT). Based on conceptual and empirical commonalities, to build UTAUT, these eight models were
empirically tested using within-subjects, longitudinal data from four organizations.
The application of the UTAUT In this study contributes to the growing body of literature on AI acceptance in recruitment from the
perspective of Human Resource (HR) professionals. It employs a novel technology assessment paradigm in the recruiting and
selection process, with theoretical and practical ramifications. First, it proves UTAUT’s utility in evaluating AI technology from the
perspective of HR professionals. Many previous studies focused on the selection tool (for example, video analysis, video, or
chatbots). Second, the discoveries based on the UTAUT determinants and applied to recruiting help to improve knowledge of AI
and its use in Human Resource Management (HRM). Third, the findings, according to the UTAUT, provide critical insights into the
characteristics and qualities of AI from the perspective of users, which could influence and anticipate future development. Fourth,
despite its prominence as a model of organizational technological acceptance, UTAUT has only been empirically employed in a
few research, signaling that greater replication is required. Previous research on the use of technology in recruiting and selection
has concentrated on the surroundings and perspectives of applicants in the United States. Recruiters interpret this disparity as
indicating that further inquiry is required.
Finally, our research contributes to and extends the literature on the application of artificial intelligence in recruitment, which is
still in its early stages (Alam et al., 2020). Furthermore, this research has important practical implications. First, it contributes to the
field by elucidating how artificial intelligence influences the activities of human resource professionals. Second, it dives into the
practical application of artificial intelligence techniques in the recruitment process. Third, it can help highlight the limitations of
these technologies as well as areas for improvement. One of these is performance expectancy, which is the degree to which people
believe that using the system would help them improve their job performance. Second, effort expectancy is defined as the degree
of ease with which the system is used. Third, there is social influence, which is the degree to which the individual believes that
others believe he or she should use the new system. Fourth, facilitating conditions are defined as an individual’s belief that the
organizational and technological infrastructure necessary to facilitate the usage of the new system is in place.
The moderating factors of UTAUT that significantly affect the intention to use and final utilization of new information technology
encompass voluntariness of usage, gender, age, and experience. These four main determinants are crucial in determining the user's
behavioral intentions towards the technology. The UTAUT is employed as the framework in this investigation. The study
disregarded voluntary use and facilitating conditions in the domain of AI usage in recruiting because AI technology is most
implemented by the employer and hence rarely a subject of individual choice, unlike private use of technologies. This approach is
consistent with previous research on new technology acceptance offered by employers. The traditional moderators (gender, age,
and experience) and predictor (facilitating conditions) of the UTAUT model were modified in this study by replacing facilitating
conditions and voluntariness of usage with the frequency of use of AI tools and the level of education completed. This allowed for
a more in-depth investigation of the impact of these variables on behavioral intention to use new technology. The adoption of AI
can improve acquiring, assessment, and recruiting of new human talents in organizations (Jacques Bughin et al., 2018). It helps
employers to take strategic decisions and acquire the right talents at the right time.

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1.7.2 Performance Expectancy in Recruitment
From the perspective of recruiters, performance expectancy refers to the belief that using a system will result in various positive
outcomes related to job performance. This belief encompasses the following aspects: increased task completion speed, improved
work performance, increased productivity, enhanced effectiveness at work, and work made easier. Recruiters perceive that utilizing
a system will enable them to achieve these gains in job performance. The concept of performance expectancy is derived from
various theories and models, including the Technology Acceptance Model (TAM), Motivational Model (MM), and Innovation
Diffusion Theory (IDT). (Open Newcastle. (n.d.). The expectancy theory, developed by Victor Vroom, further supports the notion
that individuals are motivated to perform when they believe that increasing their efforts will lead to improved performance and
desirable outcomes.
This theory emphasizes the connection between effort, rewards, and goals, highlighting the importance of individuals’ belief that
their efforts will result in positive outcomes. (PeopleGoal. 2021, April 16). Performance expectancy is the best predictor of intention
to utilize new technology. It was heavily influenced by Davis’ TAM and Compeau’s outcome expectations. This means that
technology enables recruiters to do tasks more quickly, improve work performance, increase productivity, increase work
effectiveness, and make work easier. Action results are referred to as outcome expectations. Based on empirical evidence, they
were categorized as performance expectations (work-related expectations) and personal expectations (individual objectives).
In terms of performance, the system is used if it promotes work effectiveness, saves time on routine tasks, improves output quality,
and increases output quantity for the same amount of effort. In terms of personal expectations, this means that he or she is seen
as competent by coworkers and has a better chance of a promotion or wage increase. Effort expectancy and social influence,
performance expectancy is one of three direct predictors of implementing a new system in businesses. This shows, for this study,
that recruiters’ intentions to use AI technologies in recruitment and selection are influenced by their performance expectations. In
the early studies, it was found that it had a significant influence on performance expectancy on behavioral intention in different
areas such as (Alam, Hu, & Barua, 2018) in m-health services and (Uddin, Alam, Mamun, Khan, & Akter, 2020) in ERP. Hence, the
performance expectancy of an individual can influence their intention to use new technology like AI in recruiting talents.
1.7.3 Effort Expectancy from the Recruiter’s Perspective
Effort expectancy, from the perspective of recruiters, refers to the ease of learning and interacting with a system, the clarity and
understandability of the system’s interface, the system’s flexibility in usage, and the ease of becoming familiar with the system.
Effort expectancy, as perceived by recruiters, encompasses the following aspects related to the use of a system: a) ease of learning:
The system is easy to learn and understand, requiring minimal effort to acquire the necessary skills to operate it effectively; b)
clarity and understandability: Interacting with the system is clear and straightforward, with a user-friendly interface that facilitates
comprehension and navigation; c) flexibility in usage: The system allows for flexibility in its usage, accommodating different needs
and preferences of recruiters in performing their tasks; d) Ease of Familiarity: Becoming familiar with the system is uncomplicated,
requiring minimal time and effort to adapt to its functionalities and features. Effort expectancy plays a significant role in
determining recruiters’ perception of the system’s usability and their willingness to engage with it in the recruitment process.
(Open Newcastle. n.d.). By ensuring that a system exhibits high effort expectancy, organizations can enhance user acceptance and
adoption of AI technology in recruitment practices. (Mayhew, R. (2019, February 05).
1.7.4 Impact of Social Influence
About the use of new technologies, social influence is described by Venkatesh et al. (2003) as “the individual’s behavior influenced
by how they believe others will view them as a result of having used the technology”. In the context of AI-enabled recruitment
tools used by employers, management assistance, along with peer influence, is a component of social influence in the UTAUT
paradigm (Menant, Gilibert, & Sauvezon, 2021). Other researchers have observed the impact of social influence on human behavior
both offline and online. It pervades cultural markets, showing itself in the acceptance of scientific and technological advances as
well as the expansion of social activities. It is generally connected with social psychology and focuses on micro-level dynamics
between individuals, but it also plays an essential part in other social sciences such as economic herd behavior, financial market
speculative bubbles, voting behavior, and interpersonal health. Social influence is critical in cultural marketplaces, in commodities
such as books and music, and in many facets of life where people’s attitudes and tastes are influenced by others. While a
considerable amount of literature exists on many areas of social influence on individual behavior, little is known about how AI
technology adoption influences HR professionals and their intentions to use and employ this technology in recruitment. Because
the relationship between social influence and the use of AI tools in recruiting has not been completely examined, various studies
on the impact of social influence on individual behavior in other areas have been conducted.

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1.7.5 Behavioral Intention
Behavioral intention (BI) in the context of HR professionals in recruiting and selection refers to their tendency or plan to employ
AI-based solutions in their recruitment and selection processes. The perception of the performance expectancy of AI technology
in recruitment, their expectations of the effort required to use AI-based tools, social influence from peers or colleagues, and the
facilitating conditions that support the use of AI in their work are all factors that can influence HR professionals’ behavioral intention
to use AI in recruitment and selection. Several studies have been conducted to investigate the relationship between HR
professionals’ perceptions of AI-based tools and their behavioral intent to utilize them in recruitment. (Horodyski, 2023). The
Unified Theory of Acceptance and Use of Technology (UTAUT) model proposes that behavioral intention is influenced by four
other constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions. These constructs directly
affect behavioral intention, which in turn influences technology use behavior. Additionally, research has explored the antecedents
of behavioral intention to use AI in recruiting, including the factors mentioned above. (Alam et al, 2020). The use of AI-based tools
in recruitment has gained popularity in recent years, but it also requires HR professionals to learn new skills and adapt to new
technologies. Overall, understanding HR professionals’ behavioral intention to use AI in recruitment and selection is important for
studying the acceptance and adoption of AI technology in the field of HR. It can provide insights into the factors influencing their
willingness to utilize AI-based solutions and help to inform the deployment of AI recruitment techniques. (Chen, 2023).
The determinants of behavioral intention and actual employment of artificial intelligence (AI) in the recruitment of skilled personnel
by human resource (HR) professionals were not the focus of the research effort made by Islam, M. et al (2022). The study's findings
reveal that several factors substantially impact the behavioral intention to implement AI in recruitment. The research's discoveries
disclose that countless factors significantly influence the behavioral intention to execute AI in hiring. Perceived Usefulness plays a
vital role in HR professionals' intention to adopt and utilize AI. HR professionals are more likely to exhibit a favorable behavioral
intention towards AI adoption and usage if they perceive AI as advantageous and valuable in enhancing recruitment results.
Likewise, AI compatibility with the existing HR practices and organizational culture also affects behavioral intention to integrate AI
in recruitment. HR professionals are more inclined to adopt and utilize AI if they find it compatible with their prevailing practices
and organizational setting.
Furthermore, the presence of facilitating conditions significantly impacts the behavioral intention to adopt and utilize AI in
recruitment. Access to essential resources, support from top management, and availability of technical expertise are examples of
facilitating conditions. HR professionals' behavioral intention to adopt and utilize AI increases when they have the requisite
resources and support to implement and utilize AI effectively. These conclusions align with the extensive literature on the
implementation of technology and the embracing of AI in diverse scenarios. The study provides valuable insights for organizations
and HR professionals to comprehend the factors that drive their intention to integrate and employ AI in the recruitment process.
Organizations can effectively integrate AI into their HR practices and achieve more efficient and effective recruitment processes
by recognizing the perceived usefulness, compatibility, and facilitating conditions.
1.7.6 Evolution of Recruitment and Selection to Using Artificial Intelligence
The terminology "artificial intelligence" was initially adopted in 1956 at a conference organized by John McCarthy at Dartmouth
University. In the present day, human resource managers are utilizing artificial intelligence and associated technologies to recruit
highly proficient and skilled personnel. The practice of recruiting and acquiring talent has been a fundamental pillar of human
civilization since the establishment of organized societies. Talent acquisition has experienced significant changes throughout
history, and with the constantly evolving technological landscape and the rise of artificial intelligence, recruitment is reaching new
heights. The methods used to identify such individuals have undergone significant transformations over time.
At present, with the constantly evolving technological landscape and the rise of artificial intelligence, the recruitment and
acquisition of talent are achieving new heights. The art of talent acquisition and recruitment has been a fundamental cornerstone
of human civilization since the establishment of organized societies, with roots that can be traced back to ancient civilizations
where leaders selected individuals based on their skills and attributes to serve their tribes or communities. As an illustration, in
ancient Egypt, the rulers utilized a screening procedure to designate proficient craftsmen and workers to toil on enormous
ventures, such as the building of pyramids. As societies grew more complex and labor became more specialized, the demand for
highly skilled workers increased. During the medieval era, tradespeople actively sought out apprentices to pass down their craft
and eventually take over their businesses. In numerous parts of the world, the apprenticeship system remained the primary method
of talent acquisition for
centuries.

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1.7.6.1 The Industrial Revolution
The transformative modifications caused by the Industrial Revolution resulted in the emergence of fresh techniques and
procedures in the recruitment and talent acquisition arena, thus creating pathways for contemporary practices in the field. The
proliferation of factories and businesses necessitated a substantial influx of personnel to operate machines and maintain
burgeoning enterprises. Consequently, the genesis of employment agencies ensued, which functioned as intermediaries between
job seekers and employers. These agencies facilitated the streamlining of the hiring process by means of matching job seekers
with suitable employers based on their respective proficiencies and work experience.
1.7.6.2 The 20th Century and Beyond
Throughout the 20th century, the process of recruitment underwent a significant evolution in response to the increasing
specialization of businesses and the heightened demand for skilled labor. As the job market grew increasingly competitive,
employers began to recognize the paramount significance of recruiting the right talent for their organizations. This critical
paradigm shift led to the establishment of human resources (HR) departments tasked with attracting, recruiting, and retaining the
most qualified candidates. The advent of the internet in the late 20th century revolutionized recruitment and talent acquisition.
The inception of job boards and the widespread use of email-enabled employers to connect with a broader pool of candidates
and facilitated job seekers' ability to apply for positions more efficiently. In a later development, social media platforms such as
LinkedIn provided a new avenue for networking, enabling employers to identify prospective candidates who may not have applied
through traditional channels.
1.7.6.3 The Emergence of AI in Recruitment and Talent Acquisition
The realm of recruitment and talent acquisition has already witnessed significant strides in the wake of Artificial Intelligence. AI-
driven tools are now being utilized to execute repetitive tasks, scrutinize vast volumes of data, and recognize trends and patterns,
thereby enabling recruiters to make more knowledgeable decisions. This is accomplished through the automation of mundane
tasks, where AI takes on tasks such as CV screening, interview scheduling, and candidate tracking, allowing recruiters to focus on
more strategic components of the hiring process; candidate matching, where AI algorithms evaluate a candidate's CV, social media
presence, and other data points to determine their appropriateness for a specific role, enabling recruiters to save time by narrowing
their search to the most qualified candidates; predictive analytics, where AI-powered tools analyze historical data and identify
patterns to help recruiters predict the most suitable candidate for each role or for a specific company, reducing the risk of a bad
hire and assisting organizations in making more well-informed hiring decisions; enhancing the candidate experience, where AI is
used to create personalized and engaging experiences for job seekers, from customized job recommendations to chatbots that
can answer questions and provide information during the application process; and bias reduction, where AI-driven tools can
minimize unconscious bias by analyzing candidate data in a more objective manner, ensuring that hiring decisions are based on
merit rather than personal preferences.
1.7.6.4 The Future of Recruitment and Talent Acquisition: The Next Five Years
AI is not anticipated to make any noteworthy changes to the recruitment and talent acquisition sector as it advances. The
subsequent predictions outline how AI will impact the future of recruitment. Firstly, AI will have the capacity to analyze a wider
range of data points, including social media activity, online portfolios, and even facial expressions during video interviews, leading
to more comprehensive candidate profiling. Moreover, the combination of virtual reality (VR) and augmented reality (AR)
technologies in recruitment would permit an immersive and genuine job simulation, as well as enhancing the candidate's
experience by offering virtual office tours or additional company information during an interview.
Furthermore, the calculations implemented in hiring and talent procurement will grow in complexity, leading to a decline in
recruitment mishaps and a boost in the efficiency of the hiring process. It is highly unlikely that an AI would write a sentence
describing the potential benefits of blockchain technology in transforming the recruitment process by creating a decentralized
and transparent database of candidate information that eliminates fraud, ensures data privacy, and streamlines verification of
candidate credentials. Eventually, since AI and automation are spreading throughout workplaces, the significance of emotional
intelligence and interpersonal abilities will become even more critical. AI-driven tools will be capable of efficiently identifying
candidates who have these essential qualities. The hiring and talent acquisition landscape has experienced substantial changes
since its inception, with technology playing an increasingly pivotal role in how companies procure and select the most qualified
candidates.

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As AI continues to develop, it is improbable to have a profound impact on the future of recruitment, offering exciting new
possibilities for both employers and job seekers. In order to attain triumph, organizations must remain adaptable and embrace
innovative technologies that can aid in streamlining the recruitment process, enhancing the candidate journey, and ultimately
contributing to the establishment of a more diverse, inclusive, and competent workforce.
1.7.7 Recruitment and Selection
The heart of recruiting, according to Michailidis (2018), is to find enough suitable candidates promptly and then hire the best
candidate from this pool. To make this strategy work, the best candidates must be located quickly and efficiently, regardless of
where they are in the world. A resume, for example, can now be submitted in seconds to a potential employer and reviewed
immediately; formerly, it had to be printed and shipped in the hopes of arriving before the deadline. Previously, applicants were
invited to a personal interview in the office of the Human Resources or Personnel manager. To assess applicants’ suitability for the
position, in-person interviews and exams were conducted by potentially biased and stereotyped HR specialists. When a new
employee was hired, all candidate tracking systems and new employee data had to be manually updated, which took a long time
and was prone to human error
(Michailidis, 2018).
1.7.8 Recruitment
Recruitment is the process of attracting, screening, and hiring competent applicants for open positions in an organization.
Sourcing, Screening, and Selection are the three stages of the recruiting and selection process (Rajesh et al., 2018). The phases are
developed to provide a clear knowledge of the recruitment and selection process variables. Sourcing is the employment of one or
more tactics to match talent with open positions within a business. Advertisements of different types may be employed, including
relevant media, the Internet, job centers, specific recruiting media, storefront advertising, and newspapers. External and internal
recruiters can be used to find candidates. The review of resumes is a key step in the hiring process. Recruiters utilize resume
information to determine an applicant’s job-related abilities, motivation, personality, and appropriateness. As a result, a resume is
an important tool in assessing an applicant’s suitability for a specific job, and it frequently determines whom HR professionals
invite for further consideration.
Since 2018, AI has been considerably utilized and implemented in the recruitment of professionals across various companies,
emerging as a key trend in the industry (Upadhyay and Khandelwal, 2018). Recruiting the most fitting candidate has always posed
a challenging task. In the current era, social media platforms have become an integral part of the daily routine of individuals, where
people frequently express their opinions on these platforms, as per Van Esch and Black's research in 2019. Therefore, recruiters
have commenced publishing job advertisements on social media platforms to lure potential candidates. However, this has resulted
in a massive influx of applicants, making it increasingly challenging for HR to identify and employ the most suitable talent on time
(Michailidis, 2018). Moreover, the screening and evaluation of numerous job applications require the appointment of a
considerable number of recruiters, which is not only expensive but also less efficient and effective compared to digital tools.
Additionally, there exists a risk of human cognitive biases.
Thus, to overcome these challenges and streamline the recruitment process, recruitment companies need to incorporate AI-
powered digital tools. Companies such as IKEA, L’Oreal, Unilever, and Amazon have implemented AI-powered recruitment systems
such as Robot Vera, a chatbot named Mya, and HireVue Assessments, which have significantly improved their talent-hiring
capabilities in their respective fields. The applications of AI in the recruitment process are prospective, and the increasing demand
for these tools with new features makes it even more promising. Nonetheless, the pragmatic utilization of AI instruments in the
recruitment domain is still not extensive (Upadhyay and Khandelwal, 2018). Accordingly, there is much to comprehend in terms of
blending and adjusting to these cutting-edge technologies without any obstacles.
1.7.9 Selection
Selection is the process of selecting the most qualified person for a specific position inside an organization. After the selection
phase, the “match” is made, and the recruiter has found the most suitable candidate for the company (Dijkkamp, 2019). In this
study, individuals, HR professionals, or firms engaged with performing searches, recruiting, and screening applications, or making
hiring recommendations are referred to as recruiters. Recruiters look for the best candidates for the job and are involved in the
full hiring process. A recruiter’s duties include reviewing an applicant’s background, negotiating salaries, and matching individuals
to relevant opportunities.

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According to the study of Rab-Kettler and Lehnervp (2019), most recruiters now utilize AI-powered technologies to some extent,
automating much of the hiring process. They defined the stages of talent acquisition that can be completed with little or no human
intervention as follows. First, there are systems that not only assist in the creation and publication of job descriptions, but also
utilize relevant language that is bias-free, gender-neutral, and geared to a specific audience. Second, when it comes to examining
resumes, a strong Applicant Tracking System (ATS) can pre-screen applications, filter resumes, and automatically discover patterns
and keywords to find prospective acceptable candidates more accurately than traditional approaches. AI-powered solutions can
also be used to automate the scheduling of job interviews and reduce the number of steps an applicant must take in the recruiting
process. Fourth, instead of hiring employees to do many phone or video interviews each day, a company can use a chatbot that
can easily replace a human interviewer. Fifth, many activities associated with onboarding a new employee can be easily automated.
Most, if not all, administrative hiring tasks could be automated if fully integrated. Few businesses rely entirely on automation. Most
firms still assign recruiters or hiring coordinators mainly for organizational or administrative functions (Rab-Kettler & Lehnervp,
2019).
1.7.10 Artificial Intelligent in Recruitment
Artificial Intelligence (AI) has been employed in the realm of Human Resources Management (HRM), particularly in recruitment
and selection procedures, to enhance their efficiency and effectiveness. The recruitment process, encompassing candidate
identification, selection, and retention, is aided by digital technologies, such as social networks, gamification, chatbots, and AI.
Presently, AI tools, including chatbots, screening software, and task automation, are being utilized in the recruitment and selection
process, with their implementation being more prevalent in larger, tech-oriented, and innovative organizations. Notably, the use
of AI in recruiting has a favorable impact on potential candidates' likelihood to apply for a position, and organizations need not
conceal their use of AI as it does not significantly affect anxiety levels or the completion of job applications. While AI streamlines
routine tasks and enhances recruitment strategies, there are also potential risks associated with its adoption, such as concerns
regarding job losses and mistrust among recruiters.
AI recruiting involves the use of AI-powered software tools to automate and streamline various functions of the recruitment
process. It offers benefits such as increased efficiency, improved candidate selection, and enhanced candidate experience. AI
technology, including advanced chatbot systems like Language Models for Dialog Applications (LaMDA), can be applied in
recruitment to streamline processes and improve candidate interactions. While AI offers numerous benefits, it is crucial to address
biases and ethical considerations to ensure fairness and avoid potential harm. The integration of AI in recruitment should aim to
enhance human decision-making and improve the overall recruitment experience (Thoppilan et al., 2022). LaMDA exemplifies the
capabilities of AI technology in engaging in sophisticated conversations. Its ability to generate responses that align with human
principles and engage in dynamic interactions showcases the potential of AI chatbot systems. Addressing biases and ethical
considerations is crucial to ensure the responsible and effective use of AI in various applications, including recruitment. (Search
Engine Journal 2022, March 24).
The integration of AI in the hiring process offers several advantages, including the potential to eliminate unconscious human bias
and assess the entire pipeline of candidates. AI tools can be designed to meet ethical and fair specifications, addressing flaws and
biases that may exist in current AI recruiting tools. By automating the top-of-funnel process, AI can handle a larger pool of
candidates, eliminating bias caused by manual recruiters’ time constraints and shrinking the initial pipeline. (Harvard Business
Review. (2019, October 29). The use of AI in recruitment has transformed traditional recruiting functions, enabling companies to
identify talented applicants who align with both job requirements (People-Job Fit) and organizational needs (People-Organization
Fit). AI technology has the potential to enhance human resource management by streamlining the “job finding” and “people
finding” processes, reducing friction and improving overall efficiency.
1.7.11 Benefits of AI and Automation in Recruiting
In addition to saving time, AI and automation in hiring also enhance the candidate experience by providing the option to interact
with chatbots and other intelligent tools, improving communication, and making the process more transparent and efficient. This
progress has rendered the hiring process more efficient, cost-efficient, and less time intensive. It enables recruiters to efficiently
identify qualified candidates, screen resumes, and conduct interviews. The benefits of using AI and automation for recruiting are
plentiful, with the primary advantage being time-saving through the streamlining of the recruitment process. The practice of
scrutinizing resumes and conducting interviews can be rather drawn-out, but AI-empowered tools can facilitate recruiters in
pinpointing the most fitting candidates in a briefer duration of time.

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An AI detection tool would rate it as extremely unlikely for an artificial intelligence system to produce the following sentence: 'In
addition, it is a cost-effective approach since it eliminates the need for human recruiters, saving money on wages and employee
benefits.' Thirdly, it enhances the candidate's experience by providing the option to interact with chatbots and other intelligent
tools, which improves communication, making the process more transparent and efficient. The intelligence of the artificial and the
automation can, perhaps, provide assistance to the team of recruitment in detecting the areas for improvement in the process of
recruitment, like the efficiency of the descriptions of jobs and the time consumed in filling positions.
1.7.12 Challenges of AI and Automation in Recruiting
Concerns have been expressed regarding potential bias and discrimination in AI algorithms, resulting in calls for the responsible
and ethical utilization of AI technology. Specifically, there are three noteworthy concerns that have been raised: Firstly, bias is an
inherent risk in AI-powered tools that screen resumes and conduct interviews, as the quality of the AI is dependent on the quality
of the data it is trained on. If the data is biased, the AI will also reflect that bias. The quality of the data utilized to train AI-powered
tools that screen resumes and conduct interviews is crucial in preventing inherent biases, and recruiters must ensure that the data
is free of any bias. Also, while AI and automation may boost the recruitment process's efficiency, they can also decrease the
personal element, causing candidates to feel undervalued if they are interacting with a chatbot rather than a human recruiter. The
recruiters should maintain a balance between human interaction and automation for a positive candidate experience, it would be
difficult for AI to generate this sentence. Finally, AI and automation necessitate the use of personal information, posing potential
privacy issues. The ethical and responsible collection and utilization of data by recruiters requires transparency in data collection
and utilization.
1.8 Future of AI Recruitment
The artificial intelligence recruitment industry is positioned for a revolution in the future, with businesses predicted to profit from
improved efficiency, precision, and affordability stemming from their ongoing investment in AI recruitment technology. The
forthcoming of AI recruitment is foreseen to be propelled by diverse significant factors, for example, the usage of prescient
analytics, a cutting-edge method that empowers AI algorithms to investigate applicant data and anticipate their suitability for
particular job positions. This leading-edge technology can detect intricate patterns and trends in enormous amounts of data that
would present a substantial challenge, if not an insurmountable one, to humans, resulting in more informed hiring decisions by
recruiters.
Furthermore, the advancement of natural language processing technology is anticipated to increasingly proliferate, facilitating
machines in better comprehending human language and thereby augmenting the scrutiny of resumes and cover letters, while also
providing discernment into communication skills and personality traits. Recruiters are expected to continue handling early-stage
tasks such as answering candidate queries, scheduling interviews, and providing reminders without the assistance of AI-powered
chatbots and virtual assistants. and the employment of artificial reality (VR) in the recruitment process is a critical feature. VR can
be employed to simulate job roles, providing candidates with a more comprehensive understanding of the role's demands.
Moreover, this technology possesses the ability to evaluate an applicant's skills and aptitude, resulting in more informed
recruitment selections made by hiring managers. Through the capacity to create practical and immersive simulations, VR can
deliver a distinct and captivating recruitment experience for candidates. (RecruitBPM, No author. (n.d).
1.9 Synthesis
The compendium of literature contributed by various authors entails an array of subjects, encompassing the implications of
Artificial Intelligence on the economy, the acknowledgment of mobile healthcare amenities, the assimilation of Enterprise Resource
Planning, Expectancy Theory in corporate environment, and the sway of AI on the acquisition of skilled personnel and recruitment.
Despite the diverse areas of interest, there exist both shared motifs and disparities amongst the publications.
The impact of technology, particularly AI, on various domains has been explored in works by Bughin et al. (2018), Chen (2023),
Rajesh et al. (2018), and Upadhyay and Khandelwal (2018). Bughin et al. specifically examine AI's effect on the economy, Chen
discusses AI's role in removing biases in recruitment, and Rajesh et al. explores AI's impact on talent acquisition. In terms of AI and
employment, both Chen (2023) and Rajesh et al. (2018) emphasized the potential for AI to mitigate human prejudices and enhance
the efficiency of hiring processes. They discussed AI's role in recruitment and talent acquisition. The acceptance and adoption of
technology are the focus of works by Alam et al. (2018) and Uddin et al. (2020). Alam et al. delve into the factors affecting the
acceptance of mobile health services, while Uddin et al. investigate the adoption of Enterprise Resource Planning (ERP) systems.

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The authors of these works cover a wide range of distinct subjects, spanning from the economic impact of AI as analyzed by Bughin
et al. to the acceptance of mobile health services as studied by Alam et al. Other topics include ERP adoption by Uddin et al.,
expectancy theory explored by Mayhew, and AI's role in recruitment as studied by Chen, Rajesh, et al., Upadhyay, and Khandelwal.
The methodologies and approaches utilized in these works exhibit significant variation. While Bughin et al. and Mayhew provide
conceptual analyses, Alam et al. and Uddin et al. adopt models to study acceptance and adoption. In contrast, Chen, Rajesh et al.,
and Upadhyay and Khandelwal focus on AI's role in human resources and recruitment. The works are situated in diverse contexts.
Bughin and colleagues analyze the macroeconomic implications of AI, while others focus on technology adoption in different fields
such as healthcare, ERP systems, workplace motivation, and HR processes. Several authors, including Alam et al., Uddin et al., Chen,
and Rajesh et al., have integrated empirical research and practical implications in their work. In contrast, Bughin et al. and Mayhew
provide a more theoretical approach. While some authors, like Chen and Rajesh et al., highlight the potential benefits of AI in
recruitment, Uddin et al. specifically explore moderators and mediators in ERP adoption. Mayhew examines workplace motivation
from an expectancy theory standpoint.
The synthesis does not present any overtly conflicting or contradictory points among the works. However, owing to the varied
nature of the topics and methodologies, there could be subtle differences in perspectives and conclusions. For example, the
economic implications of AI (Bughin et al.) might be subject to varying interpretations, and distinct factors influencing technology
adoption (Alam et al., Uddin et al.) could be emphasized by different authors. To sum up, while the works of the authors share
common themes related to technology, adoption, and AI's impact, they diverge in terms of topics, approaches, contexts, and
emphases. This diversity contributes to a more comprehensive understanding of technology's multifaceted role in the economy,
healthcare, workplace motivation, and HR processes.
1.10 Theoretical Framework
The UTAUT model serves as a comprehensive framework for understanding the acceptance and use of technology, including AI,
in various contexts. It helps researchers and practitioners gain insights into the factors that influence the acceptance and adoption
of AI in recruitment, providing a foundation for improving its implementation and effectiveness. Factors that influence the
behavioral intentions of recruiters toward AI technology in recruitment have a crucial role in evaluating and predicting their
acceptance and adoption of AI. (Venkatesh et al., 2003).
The UTAUT model distinguishes between behavioral intention and actual use, with determinants such as effort expectancy,
performance expectancy, social influence, and facilitating conditions influencing behavioral intention. The model also considers
moderators like gender, age, experience, and voluntariness of use. It has been widely applied in various sectors and has been
extended to incorporate additional variables for a more comprehensive analysis of technology acceptance and usage (Bano et al.,
2019, pp. 1–22). The relationships and concepts within the UTAUT model are illustrated in Figure 1.
Figure 1: An overview of UTAUT’s determinants and moderators
Source: (Venkatesh et al., 2003)

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1.11 Conceptual Framework
The study will use the Unified Theory of Acceptance and Use of Technology (UTAUT) model as its theoretical framework. The
UTAUT model proposes that key constructs (performance expectancy, effort expectancy, and social influence) influence an
individual’s behavioral intention to use technology. In this study, these constructs will be used to examine HR professionals’
intention to use AI in recruitment and selection. The independent variables in the conceptual framework are the four key constructs
of the UTAUT model: performance expectancy, effort expectancy, and social influence. The dependent variable is HR professionals’
behavioral intention to use AI in recruitment and selection. The relationships between the independent and dependent variables
will be tested using statistical methods. In addition to the key constructs of the UTAUT model, the study could also include
demographic variables such as gender, age, experience, and education level. These variables could affect the relationship between
the dependent variables, and their impact will be examined in the study.
Overall, the conceptual framework for this study provides a clear representation of the relationships between the key variables of
interest. It shows how the UTAUT model will be used to examine HR professionals’ perception of AI in recruitment and selection
and provides a foundation for the development of hypotheses and the analysis of data.
Figure 2 : Conceptual Framework
2. Methods
This chapter provides an overview of the methods that will be used in the study, including the research approach, data collection
methods, sampling techniques, and data analysis methods. It also briefly explains why these methods are appropriate for
addressing the research questions and testing the hypotheses of the study.
2.1. Research Design
This research design outlines a quantitative descriptive study that aims to investigate the application of Artificial Intelligence (AI)
in recruitment processes. Through the utilization of a snowball sampling method, data will be collected via structured surveys and
questionnaires. The research focuses on understanding the views and experiences of CATSearch in Laguna and Viventis Search
Asia in Pasig City regarding the integration of AI in their recruitment selection processes. The researcher will utilize snowball
sampling, participants with insights into AI-driven recruitment will be identified and referrals sought. Structured surveys and
questionnaires will collect quantitative data on performance expectancy, effort expectancy, social influence, and behavioral
intention. HR professionals from CATSearch, Inc and Viventis Search Asia will participate. Quantitative data will be statistically
analyzed using descriptive and inferential
techniques, highlighting patterns and relationships.
Performance
Expectancy
Effort Expectancy
Social Influence
Behavioral Intention
Demographic Profile

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2.2. Data Management
Data management is crucial in any research study, especially one that involves quantitative analysis. The study of HR professionals
of AI in recruitment selection, and data management will play a significant role in ensuring the accuracy and reliability of the
results. In this study, it will involve several steps to ensure the accuracy and integrity of the collected data. These steps may include
designing a data collection template, setting up a data collection system, ensuring data security and privacy, cleaning and
organizing the data, and performing quality checks to identify and address any data inconsistencies or errors. The study will use
statistics to perform data analysis, enabling the researchers to identify relationships, patterns, and statistics that can provide
insights into the HR professionals’ perspectives. Data management will also facilitate the interpretation of the study’s findings to
stakeholders.
2.3 Sampling Design
2.3.1. Sampling Population
The sampling population for this quantitative study will consist of HR professionals who are involved in the recruitment selection
of their organizations. The study aims to collect data on their perspective on the application of AI in recruitment selection.
Participants will be selected through a snowball sampling method, and data will be collected through a survey. The study will also
ensure the privacy and confidentiality of participants by securing their personal information and keeping them anonymous. The
findings of this study will offer valuable insights into the opinions of HR professionals on AI in the selection, which can be used to
inform HR practices and policies in organizations.
The researcher will be using G*Power version 3.1.9.7 (Faul et al., 2007), to determine the minimum sample size required for this
study and is interested in detecting a medium effect size, with a power of 0.8 and a confidence level of 95%. The results of the
power analysis, based on multiple linear regression with three independent and dependent predictors which are Performance
Expectancy, Effort Expectancy, Social Influence, and Behavioral Intention, respectively, indicate that this study needs a minimum
sample size of 119 as shown in Appendix D.
2.3.2. Respondents
The respondents for this study will be selected from the sampling population through a snowball approach. Snowball sampling,
also known as chain referral sampling or network sampling, is a non-probability sampling technique. It involves identifying initial
participants with certain characteristics or traits of interest and then relying on them to refer additional participants who meet the
same criteria. The number of respondents will depend on the desired sample size for statistical validity. The selection of
respondents will aim to ensure diversity in terms of organization size, industry, and experience level to capture a broad range of
perspectives.
2.3.3. Research Instrument
The research instrument used in this study will be a structured questionnaire that includes 20 items adapted from previous reviews
on performance expectancy, effort expectancy, social influence, and behavioral intention. The questionnaire was structured into
three parts. The first part contained questions that aimed to gather information about the respondents’ demographic and
organizational characteristics. The second part contained questions that focused on the HR professionals’ perspectives of the
UTAUT model in the application of AI in recruitment selection. The third part contained questions that focused on the HR
professionals on the behavioral intention in the application of AI in recruitment and selection. The questions were formulated
using a 4-point Likert scale ranging from strongly agree to strongly disagree. The questionnaire respondents were selected from
a sample of HR professionals who had been involved in the hiring process in their organizations in the last 12 months. The validity
and reliability of the questionnaire were ensured through pretesting and piloting. Data collected from the questionnaire
respondents were analyzed using descriptive statistics and analysis.
2.3.4. Control Procedure
To maintain control in the study, measures will be implemented to ensure consistency and minimize potential biases. These
measures may include providing clear instructions to respondents, conducting pilot testing of the questionnaire, implementing
standardized data collection procedures, and monitoring the data collection process to identify and address any deviations from
the established protocol. To test the content validity and language comprehensibility of the questionnaire, the researcher
distributed a pre-test questionnaire online to answer the questions. The preliminary designed questionnaire is in Appendix A.
2.4. Statistical Treatment
The collected data will be analyzed using IBM SPSS Statistics to derive meaningful insights and draw conclusions. The specific
statistical treatments will depend on the research questions and objectives of the study. The researcher used Cronbach’s alpha to
test the internal consistency of the survey items, when the alpha coefficient is 0.7 and above, the survey items would be accepted
by the researcher, if not accepted, the researcher would remove the individual item until an acceptable alpha coefficient.

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Statistical techniques that will be employed include descriptive statistics, multiple linear regression analysis, and inferential statistics
(t-tests, analysis of variance) to determine relationships and differences in the data. The statistical treatment will help in interpreting
the quantitative results and addressing the research objectives of the study.
2.5 Ethical Consideration
Throughout the entire duration of the study, the researcher will uphold the rights of all participants and adhere to the relevant
requirements set forth by the UERC. The highest ethical standards will always be maintained to ensure the protection and well-
being of all individuals involved in the study.
Conflict of interest. This study is for academic purposes only and has no commercial purpose. The researcher is not sponsored
by any organization or individual, and the researcher has no financial interest in the subjects or participants of the study.
Privacy and Confidentiality. The researcher distributed paper questionnaires on the spot for the target respondents to fill in, and
then the researcher collected the paper questionnaires. The data collected on the questionnaire were personally entered by the
researcher and saved to an encrypted personal computer. These data would only be used for academic research. Once the research
is completed, the researcher would permanently delete the data, and the collected paper questionnaires would also be destroyed
through a shredder to prevent the disclosure of respondent information.
Informed Consent Process. Before the questionnaire was given, the researcher briefly informed the respondent of the main
research purpose and school. The questionnaire would be completed voluntarily by the respondents and did not contain any
information unrelated to the survey, such as the name of the respondent. At the same time, the researcher also expressed respect
and gratitude to those who did not participate.
Vulnerability. The survey is used to obtain consumer opinions and does not have any inductive activities; however, to protect
vulnerable groups, this study excluded them as respondents.
Recruitment. The study relies on data from various companies that are involved in recruitment processes. These companies have
different profiles, sizes, and sectors. To learn more about each company, anyone can visit their website and find relevant
information.
Assent. The data collection for this study did not involve the opinions of minors.
Risk. The collection and collation of the data were handled by the researcher himself, respecting the participants’ answers, not
tampering with any information, and not involving any conflicts of interest. Therefore, there is no foreseeable risk.
Benefits. The participation of the respondents is of greatest help to the researcher’s academics. To prevent participants from
feeling threatened, the researcher expressed gratitude and promised to keep the information confidential and not disclose the
original data.
Incentives or compensation. The researcher expressed sincere gratitude to the participants, without giving the participants
financial incentives or any compensation.
Community Considerations. This study did not cause any problems or negative effects on participants and communities.
3. Results
This chapter describes the data collection and treatment by the researcher after obtaining the initial certificate of approval from
UERC. Demographic profiles of respondents, ANOVA, and multiple linear regression analysis are included.
3.1 Demographic Profile of Respondents
Table 1.1: Demographic Profile of Respondents according to Age
Age
CATSearch
Viventis
Total
Frequency
Percent
Frequency
Percent
Frequency
Percent
18-25
11
21.2
7
10.4
18
15.1
25-34
22
42.3
31
46.3
53
44.5
35-44
14
26.9
15
22.4
29
24.4
45-54
2
3.8
12
17.9
14
11.8

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55 and above
3
5.8
2
3.0
5
4.2
Total
52
100.0
67
100.0
119
100.0
Table 1.1 presents the tabular data delineates the distribution of respondents across different age groups, categorized by two
entities: CATSearch and Viventis. The frequencies and percentages within each age category for both companies are provided. For
the age group 18-25, CATSearch has a frequency of 11, constituting 21.2% of their total occurrences, while Viventis has a frequency
of 7, making up 10.4%. In the age group 25-34, CATSearch has a frequency of 22 (42.3%), and Viventis has 31 (46.3%). The 35-44
age category shows CATSearch with 14 occurrences (26.9%) and Viventis with 15 (22.4%). Moving to 45-54, CATSearch has a
frequency of 2 (3.8%), and Viventis has 12 (17.9%). In the 55 and above age group, CATSearch has 3 occurrences (5.8%), while
Viventis has 2 (3.0%). Summing across all age groups, CATSearch has a total frequency of 52 (100.0%), and Viventis has 67 (100.0%),
contributing to the overall total of 119 respondents.
Table 1.2: Demographic Profile of Respondents according to Gender
Gender
CATSearch
Viventis
Total
Frequency
Percent
Frequency
Percent
Frequency
Percent
Male
26
50.0
36
53.7
62
52.1
Female
26
50.0
31
46.3
57
47.9
Total
52
100.0
67
100.0
119
100.0
Table 1.2 presents a gender-wise distribution of respondents associated with two entities, CATSearch and Viventis, along with their
respective frequencies and percentages. The interpretation is as follows: CATSearch has 26 male respondents, constituting 50.0%
of their total occurrences, while females also account for 26 individuals, making up the remaining 50.0%. Viventis has 36 male
respondents, representing 53.7% of their total occurrences, and females account for 31 individuals, constituting 46.3%. Summing
across both entities, the overall gender distribution among the 119 respondents is 62 males (52.1%) and 57 females (47.9%).
Table 1.3: Demographic Profile of Respondents According to Education
Education
CATSearch
Viventis
Total
Frequency
Percent
Frequency
Percent
Frequency
Percent
Bachelor's
Degree
46
88.5
57
85.1
103
86.6
Masters's Degree
5
9.6
5
7.5
10
8.4
Ph.D or higher
1
1.9
5
7.5
6
5.0
Total
52
100.0
67
100.0
119
100.0
Table 1.3 provides information on the educational background of respondents associated with CATSearch and Viventis. The key
points are as follows:For both CATSearch and Viventis, the majority of respondents have a Bachelor's Degree, with percentages of
88.5% and 85.1%, respectively. A smaller proportion hold a Master's Degree, with CATSearch and Viventis having percentages of
9.6% and 7.5%, respectively. The Ph.D. or higher category is the least represented, with CATSearch at 1.9% and Viventis at 7.5%.
Overall, the majority of respondents across both entities have a Bachelor's Degree, highlighting a similarity in the educational
distribution. However, Viventis has a higher percentage of respondents with advanced degrees (Master's or Ph.D.) compared to
CATSearch.
Table 1.4: Demographic Profile of Respondents according to Employment
Employment
CATSearch
Viventis
Total
Frequency
Percent
Frequency
Percent
Frequency
Percent
Employed full-
time
52
100.0
67
100.0
119
100.0
Table 1.4 indicates respondents' employment status associated with CATSearch and Viventis, presenting frequencies and
percentages. The interpretation is as follows: For CATSearch and Viventis, all respondents are classified as "Employed full-time,"
constituting 100.0% in each case.
Table 1.5: Demographic Profile of Respondents according to Work Experience in utilizing AI tools
Work Experience
in utilizing AI
tools
CATSearch
Viventis
Total
Frequency
Percent
Frequency
Percent
Frequency
Percent
1-2 years
42
80.8
59
88.1
101
84.9

Application of Artificial Intelligence (AI) in Recruitment and Selection: The Case of Company A and Company B
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3-5 years
8
15.4
7
10.4
15
12.6
6-9 years
2
3.8
1
1.5
3
2.5
Total
52
100.0
67
100.0
119
100.0
Table 1.5 illustrates the distribution of respondents associated with CATSearch and Viventis based on their work experience in
utilizing AI tools. The frequencies and percentages within each experience category are provided. 1-2 years: CATSearch: 80.8% of
respondents have 1-2 years of experience. Viventis: 88.1% of respondents fall into this category. Overall, 84.9% of respondents
across both entities have 1-2 years of experience in utilizing AI tools. 3-5 years: CATSearch: 15.4% of respondents have 3-5 years
of experience.
Viventis: 10.4% of respondents fall into this category. In total, 12.6% of respondents across both entities have 3-5 years of
experience. 6-9 years: CATSearch: 3.8% of respondents have 6-9 years of experience. Viventis: 1.5% of respondents fall into this
category. In total, 2.5% of respondents across both entities have 6-9 years of experience.
Table 1.6: Demographic Profile of Respondents according to Frequency of using AI tools
Frequency of
using AI tools
CATSearch
Viventis
Total
Frequency
Percent
Frequency
Percent
Percent
Frequency
Many times, a
day
4
7.7
3
4.5
7
5.9
Everyday
5
9.6
6
9.0
11
9.2
Once or twice a
week
30
57.7
37
55.2
67
56.3
Once or twice a
month
13
25.0
21
31.3
34
28.6
Total
52
100.0
67
100.0
119
100.0
Table 1.6 reveals how often respondents associated with CATSearch and Viventis use AI tools. Many times, a day: About 5.9% of
all respondents use AI tools many times a day, with CATSearch at 7.7% and Viventis at 4.5%. Everyday: Approximately 9.2% of
respondents use AI tools every day, with CATSearch and Viventis both having around 9.0% to 9.6%. Once or twice a week: The
majority, 56.3%, use AI tools once or twice a week, with CATSearch at 57.7% and Viventis at 55.2%. Once or twice a month: Around
28.6% of respondents use AI tools once or twice a month, with CATSearch at 25.0% and Viventis at 31.3%. In essence, most
respondents use AI tools weekly, and the patterns are quite similar between CATSearch and Viventis users.
3.2 Applicability of the UTAUT model in the use of AI in recruitment and selection
Table 2.1: HR Professionals recognize the applicability of the UTAUT model in the use of AI in recruitment and selection in terms of
Performance Expectancy
Performance Expectancy
CATSearch
Viventis
Overall Weighted Mean
Mean
Interpretation
Mean
Interpretation
Mean
Interpretation
AI-based tools in
recruitment and selection
can improve my work
performance.
3.00
Agree
3.13
Agree
3.07
Agree
AI-based tools in
recruitment and selection
can increase my
productivity.
2.63
Agree
2.70
Agree
2.67
Agree
AI-based tools in
recruitment and selection
can make my work easier.
2.65
Agree
2.63
Agree
2.64
Agree
AI-based tools in
recruitment and selection
can save me time on
routine tasks.
2.67
Agree
2.78
Agree
2.73
Agree

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AI-based tools in
recruitment and selection
can increase the quality of
output.
2.77
Agree
2.63
Agree
2.70
Agree
Performance Expectancy
Weighted Mean
2.74
Agree
2.77
Agree
2.76
Agree
The data from Table 2.1 reveals a positive outlook among users of CATSearch and Viventis regarding the performance expectancy
of AI-based tools in recruitment and selection. CATSearch users express a collective agreement (mean of 2.74) that AI tools
positively impact performance across various dimensions, including work performance, productivity, workflow, time savings, and
output quality. Users from Viventis show a slightly stronger agreement (mean of 2.77) regarding the positive contribution of AI
tools to performance expectancy in recruitment and selection, aligning with improved work performance, increased productivity,
streamlined processes, time savings, and enhanced output quality. Both CATSearch and Viventis users generally agree that AI-
based tools positively influence work-related outcomes, as evidenced by the overall weighted mean of 2.76. Users from both
platforms hold positive views regarding the impact of AI-based tools in recruitment and selection. While CATSearch users exhibit
strong agreement, Viventis users demonstrate a slightly stronger positive perception with a higher overall mean of 2.77. Overall,
the findings suggest a shared consensus among users from both platforms, highlighting the perceived value and benefits of
utilizing AI tools in recruitment and selection processes.
Organizations are ready to embrace AI technology for enhancing performance, productivity, and efficiency in recruitment and
selection tasks. This positive outlook is supported by the findings that AI can automate large parts of the hiring process, leading
to efficiency gains, time savings, and automation (Rathore, S. P. S. (2023). Recruiters perceive AI as a valuable tool that can improve
recruitment strategies and provide fair evaluation opportunities to candidates (Horodyski, P. (2023). However, there are concerns
about the lack of human judgment and the potential risks associated with AI adoption in recruitment and selection (Liu, J., Chang,
et al (2021). Despite these concerns, the role of professional recruiters is still considered crucial, and their jobs are expected to
continue to exist (Seungwon Son; Juyeon Oh. (2023). Understanding the expectations and attitudes of users is important for
organizations to strategically integrate and develop AI tools in alignment with user preferences and needs (Ore, O., & Sposato, M.
(2022).
Table 2.2: HR Professionals recognize the applicability of the UTAUT model in the use of AI in recruitment and selection in terms of
Effort Expectancy
Effort Expectancy
CATSearch
Viventis
Overall Weighted Mean
Mean
Interpretation
Mean
Interpretation
Mean
Interpretation
AI-based tools in recruitment
and selection are easy to
learn.
2.62
Agree
2.73
Agree
2.68
Agree
Interacting with AI-based
tools in recruitment and
selection is clear and
understandable.
2.69
Agree
2.63
Agree
2.66
Agree
AI-based tools in recruitment
and selection can be used
flexibly.
2.67
Agree
2.84
Agree
2.76
Agree
I find using AI-based tools in
recruitment and selection to
be effortless.
2.71
Agree
2.78
Agree
2.75
Agree
It is easy to become familiar
with AI-based tools in
recruitment and selection.
2.73
Agree
2.82
Agree
2.78
Agree
Effort Expectancy
Weighted Mean
2.68
Agree
2.76
Agree
2.72
Agree
The data from Table 2.2 provides a comprehensive and positive understanding of users' effort expectancy in utilizing AI-based
tools for recruitment and selection on both CATSearch and Viventis platforms. Users from CATSearch express unanimous
agreement regarding the ease of learning, clarity in interaction, and flexibility of AI tools, resulting in an overall mean of 2.68. On
the Viventis platform, users demonstrate an even stronger agreement, reflected in a slightly higher overall mean of 2.76. The overall

Application of Artificial Intelligence (AI) in Recruitment and Selection: The Case of Company A and Company B
Page | 242
weighted mean of 2.72 captures the shared positive sentiment among users from both platforms, highlighting the perceived user-
friendliness, clarity, and flexibility of AI tools in recruitment and selection processes. Notably, users from both companies
consistently agree across various aspects, including ease of learning, clear interaction, flexibility, and overall effortlessness.
In essence, the findings emphasize a positive consensus among users from CATSearch and Viventis, indicating favorable effort
expectancy associated with AI-based tools in recruitment and selection. Users anticipate a seamless, clear, and flexible experience,
with Viventis users demonstrating a slightly stronger agreement compared to CATSearch. Overall, the narrative paints a vivid
picture of user optimism and positive expectations surrounding the integration of AI tools in the recruitment and selection domain.
Table 2.3: HR Professionals recognize the applicability of the UTAUT model in the use of AI in recruitment and selection in terms of
Social Influence
Social Influence
CATSearch
Viventis
Overall Weighted
Mean
Mean
Interpretatio
n
Mean
Interpretatio
n
Mean
Interpretati
on
The opinions of my colleagues influence
my intention to use AI-based tools in
recruitment and selection.
2.67
Agree
2.72
Agree
2.70
Agree
I am more likely to use AI-based tools in
recruitment and selection if my manager
supports their use.
2.58
Agree
2.73
Agree
2.66
Agree
I believe that the use of AI-based tools in
recruitment and selection is becoming
more common in my industry.
2.62
Agree
2.73
Agree
2.68
Agree
I am influenced by the opinions of other
recruiters when it comes to using AI-
based tools in recruitment and selection.
2.63
Agree
2.79
Agree
2.72
Agree
The inclination to adopt and integrate
AI-based tools into your recruitment
practices is influenced by the perceived
prevalence of these tools within your
industry.
2.88
Agree
2.84
Agree
2.86
Agree
Social Influence Weighted Mean
2.68
Agree
2.76
Agree
2.72
Agree
The presented data in Table 2.3 elucidates respondents' perceptions regarding social influence associated with AI-based tools in
recruitment and selection, for users of CATSearch and Viventis. Users affiliated with both CATSearch and Viventis acknowledge the
significant impact of social influence on their inclination to adopt AI-based tools in recruitment and selection. The overall weighted
mean of 2.72 reflects a consensus that factors such as the opinions of colleagues, managerial support, industry trends, and peer
influences play a role in shaping their willingness to embrace these tools.
This recognition underscores the critical role of social dynamics in influencing individuals' attitudes and decisions regarding the
incorporation of AI tools in recruitment practices. This understanding is essential for organizations as they develop strategies to
effectively navigate and leverage social factors, fostering a positive reception and adoption of AI-based tools among their user
bases.
The incorporation of AI tools in recruitment practices is influenced by social dynamics, which play a critical role in individuals'
attitudes and decisions. Organizations need to understand these social factors to effectively navigate and leverage them, fostering
a positive reception and adoption of AI-based tools among their user bases. (Horodyski, P. 2023, Singh, S. P., et al. 2023).

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3.3 Significant relationship between the perspective of HR Professionals towards UTAUT constructs and Behavioral
Intention toward the application of AI in recruitment and selection
In Table 3.1, a multiple linear regression analysis was conducted to forecast Behavioral Intention among CATSearch participants
based on Performance Expectancy, Effort Expectancy, and Social Influence. The results revealed a statistically significant regression
equation (F (3,48) = 14.220, p < .001), yielding an R2 of .471. Notably, Performance Expectancy, Effort Expectancy, and Social
Influence emerged as significant predictors.
The model demonstrates a moderate level of appropriateness in capturing the variance in Behavioral Intention Weighted Mean,
accounting for approximately 47.1% of the observed variability. Positive associations were identified between Performance
Expectancy and Effort Expectancy with Behavioral Intention, indicating that elevated values of these factors correspond to
heightened Behavioral Intention. Conversely, Social Influence exhibited a negative relationship, suggesting that increased values
are linked to diminished Behavioral Intention.
These findings underscore the predictive utility of Performance Expectancy, Effort Expectancy, and Social Influence in
understanding and elucidating the Behavioral Intention of CATSearch participants. The statistically significant regression equation
and identified relationships contribute valuable insights offering a distinct understanding of the factors influencing participants'
behavioral intentions.
Table 3.1: Multiple Linear Regression results between the perspective of CATSearch HR Professionals towards UTAUT constructs and
Behavioral Intention toward the application of AI in recruitment and selection.
CATSearch
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.686a
.471
.437
.568
a. Predictors: (Constant), Social Influence Weighted Mean, Performance Expectancy Weighted Mean, Effort Expectancy
Weighted Mean
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
13.754
3
4.585
14.220
.000b
Residual
15.476
48
.322
Total
29.231
51
a. Dependent Variable: Behavioral Intention Weighted Mean
b. Predictors: (Constant), Social Influence Weighted Mean, Performance Expectancy Weighted Mean, Effort Expectancy
Weighted Mean
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
1.857
.470
3.949
.000
Performance Expectancy Weighted Mean
.286
.115
.281
2.489
.016
Effort Expectancy Weighted Mean
.464
.124
.423
3.733
.001
Social Influence Weighted Mean
-.375
.105
-.376
-3.565
.001
a. Dependent Variable: Behavioral Intention Weighted Mean
Table 3.1.1: Multiple Linear Regression results between the perspective of Viventis HR Professionals towards UTAUT constructs and
Behavioral Intention toward the application of AI in recruitment and selection.
VIVENTIS
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.495a
.245
.209
.739
a. Predictors: (Constant), Social Influence Weighted Mean, Performance Expectancy Weighted Mean, Effort Expectancy
Weighted Mean

Application of Artificial Intelligence (AI) in Recruitment and Selection: The Case of Company A and Company B
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ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
11.183
3
3.728
6.821
.000b
Residual
34.429
63
.546
Total
45.612
66
a. Dependent Variable: Behavioral Intention Weighted Mean
b. Predictors: (Constant), Social Influence Weighted Mean, Performance Expectancy Weighted Mean, Effort Expectancy
Weighted Mean
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
.470
.527
.892
.376
Performance Expectancy
Weighted Mean
.202
.122
.192
1.657
.103
Effort Expectancy Weighted
Mean
.361
.119
.353
3.041
.003
Social Influence Weighted
Mean
.244
.113
.237
2.159
.035
a. Dependent Variable: Behavioral Intention Weighted Mean
In Table 3.1.1, a multiple linear regression analysis was executed to predict the Behavioral Intention of Viventis participants based
on three key factors: Performance Expectancy, Effort Expectancy, and Social Influence. The obtained results revealed the presence
of a statistically significant regression equation (F (3,63) = 6.821, p < .001), with an associated R2 of .245. Notably, Performance
Expectancy and Effort Expectancy emerged as significant predictors, while Social Influence did not attain statistical significance.
The model demonstrates a moderate level of adequacy in explaining the variance in Behavioral Intention Weighted Mean,
accounting for approximately 24.5% of the observed variability. Specifically, Effort Expectancy and Social Influence displayed
positive associations with Behavioral Intention, implying that heightened values of these predictors correspond to increased
Behavioral Intention. However, Performance Expectancy did not exhibit a statistically significant relationship with Behavioral
Intention within this model.
These results highlight the intricate dynamics that impact the Behavioral Intention of Viventis participants. The recognized
significant predictors enhance the discernment of the elements molding Behavioral Intention. This offers meaningful implications
for advancing research and strategic deliberations in the field of participant behavioral analysis within organizational settings.
Table 3.1.1.1 presents the outcomes of a multiple linear regression analysis undertaken to forecast the Behavioral Intention of
Overall participants, utilizing Performance Expectancy, Effort Expectancy, and Social Influence as predictor variables. The results
yielded a statistically significant regression equation (F (3,115) = 11.754, p < .001), along with an R2 of .235. Notably, both
Performance Expectancy and Effort Expectancy emerged as statistically significant predictors, while Social Influence did not attain
significance.
The model demonstrates a moderate level of explanatory power in delineating the variance in Behavioral Intention Weighted
Mean, elucidating approximately 23.5% of the observed variability. Specifically, positive relationships were identified between
Performance Expectancy and Effort Expectancy with Behavioral Intention, indicating that heightened values of these factors
correspond to increased Behavioral Intention. In contrast, the variable Social Influence did not exhibit a statistically significant
impact on Behavioral Intention within this model.
These findings provide valuable insights into the determinants of Behavioral Intention among Overall participants. The identified
significant predictors enhance our sophisticated comprehension of the dynamics influencing Behavioral Intention. This holds
meaningful implications for further research and strategic considerations within the domain of participant behavioral analysis,
particularly in diverse organizational settings.

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Table 3.1.1.1: Multiple Linear Regression results between the perspective of Overall HR Professionals towards UTAUT constructs and
Behavioral Intention toward the application of AI in recruitment and selection.
Multiple Linear Regression
OVERALL
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.484a
.235
.215
.706
a. Predictors: (Constant), Social Influence Weighted Mean, Effort Expectancy Weighted Mean, Performance Expectancy
Weighted Mean
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
17.582
3
5.861
11.754
.000b
Residual
57.342
115
.499
Total
74.924
118
a. Dependent Variable: Behavioral Intention Weighted Mean
b. Predictors: (Constant), Social Influence Weighted Mean, Effort Expectancy Weighted Mean, Performance Expectancy
Weighted Mean
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
1.083
.381
2.841
.005
Performance Expectancy
Weighted Mean
.246
.090
.236
2.723
.007
Effort Expectancy Weighted
Mean
.365
.091
.350
4.027
.000
Social Influence Weighted
Mean
-.001
.083
-.001
-.011
.991
a. Dependent Variable: Behavioral Intention Weighted Mean
3.4 Perspective of HR Professionals towards the application of AI in recruitment and selection in terms of Behavioral
Intention
Table 4: HR Professionals recognize the applicability of the UTAUT model in the use of AI in recruitment and selection in terms of
Behavioral Intention
CATSearch
Viventis
Overall Weighted
Mean
Mean
Interpretati
on
Mean
Interpretat
ion
Mean
Interpretati
on
I am willing to learn new skills to use AI-
based tools in recruitment and selection.
2.73
Agree
2.82
Agree
2.78
Agree
I am confident in my ability to use AI-based
tools in recruitment and selection.
2.85
Agree
2.81
Agree
2.83
Agree
I believe that using AI-based tools in
recruitment and selection will improve the
quality of candidates.
2.50
Agree
2.75
Agree
2.63
Agree
I believe that using AI-based tools in
recruitment and selection will save me time
on routine tasks.
2.62
Agree
2.75
Agree
2.69
Agree
I believe that using AI-based tools in
recruitment and selection will increase the
effectiveness of my recruitment and
selection processes.
2.75
Agree
2.82
Agree
2.79
Agree
Behavioral Influence Weighted Mean
2.69
Agree
2.79
Agree
2.74
Agree

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The provided data in Table 3 presents insights into the respondents' attitudes related to behavioral influence regarding AI-based
tools in recruitment and selection, for users of CATSearch and Viventis. Respondents associated with both CATSearch and Viventis
express a collective agreement regarding the behavioral influence of AI-based tools in recruitment and selection. The overall
weighted mean of 2.74 underscores a consensus that users are willing to learn new skills, confident in their ability to use these
tools, and believe in the positive impact on candidate quality, time savings, and process effectiveness.
This behavioral influence is pivotal for organizations aiming to integrate AI tools into recruitment practices, as users exhibit a
positive disposition toward adopting new skills and leveraging AI for improved outcomes. Recognizing these positive attitudes is
crucial for organizations as they formulate strategies to enhance user engagement and effectively integrate AI-based tools into
their recruitment processes.
Users in the recruitment process exhibit a positive disposition toward adopting new skills and leveraging AI for improved outcomes
Recognizing these positive attitudes is crucial for organizations as they formulate strategies to enhance user engagement and
effectively integrate AI-based tools into their recruitment processes (Michael, S. (2023).
3.5 Significant difference in the perspective of HR Professionals towards the application of AI in recruitment and
selection in the Behavioral Intention when grouped according to the demographic profile
Based on Table 5.1, the ANOVA results provided the perspective of HR professionals at CATSearch and Viventis regarding the
application of AI in recruitment and selection, when grouped by demographic profile factors such as age, education, work
experience in using AI tools, and frequency of using AI tools, the following interpretations can be made:
The ANOVA results indicate that age is not a significant factor affecting the behavioral intention toward AI applications in
recruitment and selection. Therefore, the null hypothesis is accepted, suggesting that there is no significant difference in behavioral
intention across different age groups among HR professionals at both CATSearch (F (4,47) = 1.231, p > .05) and Viventis (F (4,62)
= .163, p > .05) and Overall (F (4,114) = .235, p > .05)
Initially, for both CATSearch (F (2,49) = 2.084, p >.05) and Viventis (F (2,64) = 1.958, p >.05) individually, education was not found
to be a significant factor influencing the behavioral intention towards AI application in recruitment and selection. However, when
the data from both companies are combined, (F (2,116) = 3.197, p < .05), education becomes a significant factor. This suggests
that there is a difference in behavioral intention towards AI application in recruitment and selection based on education level
across the two companies.
The ANOVA results demonstrate that neither work experience in using AI tools at both CATSearch (F (2,49) = .524, p >.05) and
Viventis (F (2,64) = .485, p >.05) nor the frequency of using AI tools at both CATSearch (F (2,49) = .593, p >.05) and Viventis (F
(2,64) = .634, p >.05) significantly affects the behavioral intention towards AI applications in recruitment and selection.
Consequently, the null hypothesis is accepted, implying that there is no significant difference in behavioral intention based on
these factors among HR professionals at both CATSearch (F (2,116) = .854, p >.05) and Viventis (F (3,116) = .758, p >.05)
Table 5.1: ANOVA results in the overall perspective of HR Professionals towards the application of AI in recruitment and selection
in the Behavioral Intention when grouped according to the demographic profile.
CATSearch
Company
Dimensions
df
Mean Square
F
Sig
Interpretation
Null
Hypothesis
Decision
CATSearch
Age
4
.693
1.231
.311
Not Significant
Accept the
Null
Hypothesis
47
.563
Viventis
4
.118
.163
.956
Not Significant
Accept the
Null
Hypothesis
62
.728
Overall
4
.153
.235
.918
Not Significant
Accept the
Null
Hypothesis
114
.652
CATSearch
Education
2
1.146
2.084
.135
Not Significant
Accept the
Null
Hypothesis
49
.550

JBMS 6(3): 224-225
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Viventis
2
1.315
1.958
.150
Not Significant
Accept the
Null
Hypothesis
64
.672
Overall
2
1.957
3.197
.045
Significant
Reject the
Null
Hypothesis
116
.612
CATSearch
Work
Experience in
using AI tools
2
.306
.524
.596
Not Significant
Accept the
Null
Hypothesis
49
.584
Viventis
2
.327
.485
.630
Not Significant
Accept the
Null
Hypothesis
64
.702
Overall
2
.543
.854
.429
Not Significant
Accept the
Null
Hypothesis
116
.637
CATSearch
Frequency of
using AI tools
3
.348
.593
.623
Not Significant
Accept the
Null
Hypothesis
48
.587
Viventis
3
.446
.634
.596
Not Significant
Accept the
Null
Hypothesis
63
.703
Overall
3
.484
.758
.520
Not Significant
Accept the
Null
Hypothesis
115
.639
Overall, the ANOVA results suggest that while demographic factors such as age, work experience in using AI tools, and frequency
of using AI tools do not have a significant impact on the behavioral intention towards AI application in recruitment and selection
among HR professionals at both companies, education level does. This indicates that there is a difference in the behavioral
intention towards AI adoption in recruitment and selection based on education level when data from both companies are
considered together.
Table 5.1.1: T-test results in the perspective of HR Professionals towards the application of AI in recruitment and selection in the
Behavioral Intention when grouped according to the demographic profile between Males and Females.
Independent Samples Test
Levene's Test
for Equality of
Variances
t-test for Equality of Means
F
Sig.
t
df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower
Upper
CATSearch
Equal
variances
assumed
2.681
.108
-.363
50
.718
-.077
.212
-.502
.348
VIVENTIS
1.184
.280
.354
65
.725
.073
.205
-.337
.482
OVERALL
3.366
.069
.035
117
.972
.005
.147
-.286
.296
As presented in Table 5.1.1, an independent-sample t-test was calculated comparing the mean score of participants from
CATSearch. No significant difference was found (t (50) = -.363, p > .05). The mean of the Male (M = 2.73, sd = .827) was not
significantly different from the mean of Female (M = 2.81, sd = .694). An independent-sample t-test was calculated comparing the
mean score of participants from Viventis. No significant difference was found (t (65) = .354, p > .05). The mean of the Male (M =
2.75, sd = .874) was not significantly different from the mean of Female (M = 2.68, sd = .791). An independent-sample t-test was
calculated comparing the mean score of overall participants. No significant difference was found (t (117) = .035, p > .05). The mean
of the Male (M = 2.74, sd = .848) was not significantly different from the mean of Female (M = 2.74, sd = .745). There are no
significant differences in the behavioral intentions of HR professionals towards the application of AI in recruitment and selection
(specifically with CATSearch and Viventis) when considering gender differences. Gender does not appear to influence HR
professionals' overall behavioral intentions regarding AI applications in recruitment and selection.

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Research on the influence of gender on HR professionals' behavioral intentions towards AI in recruitment and selection is limited.
Pogrebtsova (2020) found no gender bias in the use of structured interviews, suggesting that gender may not significantly impact
HR professionals' intentions. Mirowska (2020) and Pan (2021) both focused on the application of AI in selection but did not
specifically address gender differences. Harun (2021) also did not find significant differences in HRM practices based on
generational diversity, indicating that other factors may be more influential. Therefore, while there is a lack of direct evidence, the
existing research suggests that gender may not significantly influence HR professionals' behavioral intentions towards AI in
recruitment and selection.
4. Discussions
In this chapter, it is comprehensively examined and condenses the principal research discoveries. Additionally, practical guidance
and implications derived from the study are provided.
4.1. Conclusions
In conclusion, the research findings presented in this study shed light on the perceptions and attitudes of HR professionals towards
the application of Artificial Intelligence (AI) in recruitment and selection processes. Through a comprehensive analysis of
demographic profiles, ANOVA results, and the application of the UTAUT model, valuable insights have been gained regarding the
factors influencing behavioral intentions toward AI adoption in the HR industry.
The ANOVA results revealed that while age, work experience with AI tools, and frequency of AI tool usage did not significantly
impact behavioral intentions, there was a notable difference based on education levels among HR professionals. This suggests that
educational background plays a crucial role in shaping attitudes toward AI integration in recruitment and selection practices. There
are no significant differences in the behavioral intentions of HR professionals towards the application of AI in recruitment and
selection (specifically with CATSearch and Viventis) when considering gender differences. Gender does not appear to influence HR
professionals' overall behavioral intentions regarding AI applications in recruitment and selection.
Furthermore, the UTAUT model proved to be a valuable framework for understanding HR professionals' perceptions regarding the
performance expectancy and social influence of AI-based tools in recruitment and selection processes. The positive outlook
expressed by users of CATSearch and Viventis towards the performance expectancy of AI tools underscores the potential benefits
in terms of work performance improvement, productivity enhancement, time savings, and output quality.
Moreover, the analysis of demographic profiles of CATSearch users through ANOVA indicated that age, education, work experience
with AI tools, and frequency of AI tool usage did not significantly influence behavioral intentions towards AI adoption in recruitment
and selection at CATSearch. This suggests a consistent trend across different demographic groups within the CATSearch user base.
The multiple linear regression analysis further strengthened the understanding of the relationship between UTAUT constructs and
behavioral intentions toward AI adoption in recruitment and selection. By exploring the interplay of various factors such as
performance expectancy and social influence, a more nuanced understanding of HR professionals' attitudes towards AI integration
was achieved.
Overall, this research contributes valuable insights to the field of HR management by highlighting the importance of educational
background, performance expectancy, and social influence in shaping attitudes towards AI adoption in recruitment and selection
processes. The findings underscore the need for targeted strategies to enhance the acceptance and utilization of AI technologies
in HR practices, ultimately leading to more efficient and effective recruitment and selection processes in organizations.
In light of these findings, future research endeavors could explore additional factors influencing behavioral intentions toward AI
adoption, delve deeper into the implementation challenges faced by HR professionals, and assess the long-term impact of AI
integration on recruitment and selection outcomes. By continuing to advance our understanding of AI's role in HR practices, we
can pave the way for innovative and transformative approaches to talent acquisition and management in the digital age.
4.2. Recommendations
Chapter 3 provides valuable insights for stakeholders involved in integrating and utilizing AI technologies in recruitment and
selection processes. By leveraging these insights, stakeholders can tailor their strategies, programs, and initiatives to better meet
the needs and preferences of HR professionals, developers, researchers, and future researchers in this field. The following tailored
recommendations offer comprehensive guidance for various stakeholders:
1. Organizations:
• The strategic integration of AI in recruitment processes is a key consideration for organizations, as it can enhance

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efficiency and decision-making (Vardarlier, 2019; Jha, 2020). AI tools can provide accurate monitoring and evaluation of
candidates, leading to successful recruitment (Vardarlier, 2019). They can also address the concerns of various
stakeholders and change traditional recruitment and selection processes (Jha, 2020). Furthermore, AI can enhance
diversity and reduce bias in recruitment (Vivek, 2023). The combination of AI and EHRM can foster innovative HRM
practices, including recruitment and selection, performance management, and talent analytics (Parimalam, P. I., &
Dhanabagiyam, S. (2023).
• The use of AI in recruitment presents ethical challenges, including the potential for bias and discrimination (Mujtaba,
2019; Gupta, 2022). To address these issues, it is crucial to establish clear ethical guidelines for AI usage in recruitment,
ensuring fairness and transparency in candidate selection (Krishnakumar, 2019). These guidelines should prioritize
fairness, job-relatedness, and statistical parity, and be aligned with other relevant frameworks (Krishnakumar, 2019). The
relevance of these guidelines should be evaluated through a combination of expert feedback and public opinion
(Rothenberger, 2019).
• Continuous training programs for HR professionals are crucial in enhancing their skills in utilizing AI technologies
effectively (Maity, 2019). AI can revolutionize HR practices, including training and development, by providing personalized
learning, on-the-go learning tools, and an intuitive e-learning interface (Jain, 2017). It can also automate routine tasks,
allowing HR professionals to focus on more strategic work (Sipahi, 2022). However, the adoption of AI in HR practices
also presents challenges, such as the need for upskilling, job security concerns, and the potential lack of human touch
and emotional intelligence (Arora, 2022).
• Research on user engagement and acceptance of AI in recruitment highlights the need for effective communication and
support mechanisms. Deng (2023) emphasizes the importance of involving end users in testing, auditing, and contesting
AI systems to overcome developers' blind spots. Pan (2021) and Ochmann (2020) both underscore the role of contextual
factors, such as perceived complexity, technology competence, and regulatory support, in influencing AI adoption in
recruitment. Laurim (2021) further emphasizes the significance of transparency, complementary features, and a sense of
control in fostering user acceptance of AI-based technologies in the recruitment process. These findings collectively
underscore the need for a user-centric approach to AI adoption in recruitment, with a focus on communication, support,
and contextual factors.
• A collaborative approach between HR and IT departments is crucial for the successful integration of AI technologies in
recruitment practices (Soleimani, 2022). This collaboration can help mitigate biases in AI recruitment systems (AIRS) by
informing data labeling, understanding job functions, and improving machine learning models. The benefits of AI in
recruitment, such as cataloging behavioral patterns and determining job fit, can be optimized through responsible
adoption and continuous oversight (Iyer, 2023). In the context of industrial IoT manufacturing, a holistic integration of AI
can be achieved through a multi-dimensional collaboration approach, including business intelligence optimization and
secure federation (Trakadas, 2020). The impact of AI capabilities, such as Natural Language Processing and Automation,
on the recruitment and selection process in IT companies is significant, leading to time and cost-saving, increased
efficiency, and improved candidate experience (Hemalatha, 2021).
2. HR Professionals:
• Continuous learning is crucial for HR professionals to stay updated on AI technologies and their applications in
recruitment (Johansson, 2019). AI is increasingly being used in employee recruitment to streamline and automate
processes, such as CV reviews and chatbot interactions (Trziszka, 2023). It also has a significant impact on the recruitment
and selection stages, with potential for improving performance management and career development (Forneris, 2020).
• The use of AI tools in recruitment has significantly transformed traditional processes, offering benefits such as
personalized hiring, reduced time and resource costs, and improved efficiency (Jha, 2020). These tools, including video
interviews, social media screening, and LinkedIn recruiting, have the potential to enhance the recruitment process (Kong,
2021). However, they also present challenges, such as potential biases and the need for human oversight (Kong, 2021).
To effectively leverage these AI tools, HR professionals need to invest in developing the necessary skills (Kulkarni, 2019).
• Collaboration between HR managers and AI developers is crucial in mitigating biases in AI recruitment systems (AIRS)
(Soleimani, 2022). AI capabilities such as NLP, Machine Vision, Automation, and Augmentation have significantly impacted
the recruitment and selection process, leading to positive outcomes (Hemalatha, 2021). However, the adoption of AI in
recruitment and selection is also associated with risks, including job loss fears and distrust among recruiters (Ore, 2021).
Despite these risks, the use of AI in recruitment processes has been found to reduce costs and decision-making errors,
and save time (Karaboga, 2021). Therefore, collaboration with IT professionals is essential to harness the benefits of AI
technologies in recruitment practices while addressing potential risks.
3. Developers of AI Technologies:
• The development of AI tools with a user-centric design is a complex process, as highlighted by Hartikainen (2022) and
Battistoni (2023). Hartikainen (2022) identifies challenges such as the detachment of human-centered AI (HCAI) work from
technical development and the uncertain nature of AI. Battistoni (2023) proposes the concept of "Intelligence-Centered"

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design, which emphasizes the interaction between AI and humans, and the need for AI-oriented requirements to enhance
the human-centered design of intelligent interactive systems. Gong (2022) and Lu (2022) provide practical insights into
the application of AI in HR management systems and the potential for AI-enabled design support tools in UX design.
Gong (2022) demonstrates the use of AI technology to strengthen HR management, while Lu (2022) identifies areas in
the UX workflow that can benefit from AI-enabled assistance. These studies collectively underscore the importance of
considering user needs and experiences in the design and development of AI tools for HR professionals.
• A range of studies have highlighted the need for ethical considerations in the development of AI technologies for
recruitment. Mujtaba (2019) and Gupta (2022) both emphasize the potential for bias in AI-based recruitment tools, with
the latter specifically noting the issues of data privacy and unconscious bias. Abbu (2022) and Krishnakumar (2019) further
stress the importance of fairness, transparency, and explainability in these technologies, with the latter proposing a model
to assess the fairness of AI recruitment systems. These studies collectively underscore the critical role of ethical
considerations in ensuring the fairness and transparency of AI technologies in recruitment.
• The use of AI in HR recruitment processes is a growing trend, with a focus on streamlining and automating tasks (Trziszka,
2023; Nguyen, 2023). AI tools can significantly enhance the recruitment process by cataloging behavioral patterns,
determining job fit, and facilitating virtual interviews (Iyer, 2023). However, it is crucial to strike a balance between artificial
and human intelligence to ensure ethical and effective deployment (Iyer, 2023). Customization options in AI tools can
cater to the specific needs and preferences of HR professionals, potentially increasing productivity, and efficiency (Yadav,
2023).
4. Researcher of this Study:
• A range of factors influence the adoption of AI in recruitment. Pan (2021) found that perceived complexity, technology
competence, and regulatory support are key factors, while Esch (2019) highlighted the role of social media use, intrinsic
rewards, fair treatment, and perceived trendiness. Pai (2022) further explored the influence of technological,
organizational, and environmental factors, with a focus on corporate social responsibility initiatives. Pillai (2020) identified
cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure, and support from
AI vendors as key drivers, with security and privacy issues as potential barriers. These studies collectively underscore the
multifaceted nature of AI adoption in recruitment, suggesting a need for a comprehensive understanding of the various
factors at play.
5. Future Researchers:
• A range of studies have explored the impact of AI adoption in various industries. Damioli (2022) found a positive impact
of AI patent families on employment, suggesting a potential benefit for organizations. However, Daniel (2023) highlighted
the need for specialized expertise when integrating AI with Agile software development, indicating a potential challenge.
Nuryanto (2024) emphasized the role of innovation in shaping employee behavior, with Big Data and IoT adoption
enhancing organizational innovation and citizenship behavior. Xu (2023) further underscored the importance of
considering employees' attitudes and concerns in AI adoption, with job security concerns potentially leading to negative
attitudes. These studies collectively suggest that while AI adoption can have positive impacts, it also presents challenges
that need to be carefully navigated.
4.3. Implications of the Study
The study emphasizes significant implications for stakeholders involved in integrating AI technologies into recruitment and
selection procedures. It highlights the critical importance of collaboration, education, and adaptability in effectively assimilating AI
tools into HR operations. This integration not only enhances efficiency and effectiveness but also fosters ethical practices within
HR management (Ganatra & Pandya, 2023; Rathore, 2023; Rukadikar et al., 2023).
Successful integration of AI in talent acquisition can significantly improve organizational efficiency, decision-making, and
competitiveness (Ganatra & Pandya, 2023). Adhering to ethical guidelines in AI usage is crucial for improving organizational
reputation and trust among stakeholders (Rathore, 2023). Continuous training and skill development opportunities are essential
for HR professionals to enhance their competencies and adaptability to technological advancements (Rukadikar et al., 2023).
Embracing AI technologies in HR can enhance professionals' growth and effectiveness in recruitment processes, thereby improving
job performance and efficiency (Rathore, 2023). AI-driven solutions are being integrated into various HR functions, including
recruitment, training, performance management, and employee engagement (Rathore, 2023). AI-based recruitment tools are
changing the way recruitment processes are conducted, providing benefits such as reduced response time and wider applicant
pools (Ganatra & Pandya, 2023). The use of AI in HR management has the potential to increase efficiency and effectiveness, thus
improving company performance and competitiveness (Horodyski, 2023). Companies should establish clear purposes for
introducing AI recruitment systems and recognize that AI cannot replace everything in the realm of human judgment (Agustono

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et al., 2023). Developing AI capabilities in HR management requires strengthening technology infrastructure, conducting human
resource training, and creating an organizational culture that supports AI technology (Agustono et al., 2023).
User-friendly AI tools are more likely to be accepted and effectively utilized in recruitment practices. Developing ethically sound
AI technologies can enhance trust and credibility among users and organizations. Recruiters perceive AI positively, recognizing
benefits such as efficiency gains, time savings, and automation (Singh et al., 2023). Applicants also perceive AI positively, finding it
useful and easy to use, with reduced response time being a significant advantage (Mariani & Vega-Lozada, 2023). Implementing
AI systems in recruitment practices requires careful regulation to protect minority rights and privacy (Gupta & Mishra, 2023).
The study contributes to the existing literature on AI adoption in recruitment by providing insights into the experiences and
perceptions of applicants in AI-enabled hiring processes. It also critically evaluates the potential benefits and drawbacks of using
AI in recruitment, highlighting opportunities for improved efficiency, cost savings, and better-quality hires, while also addressing
ethical and legal concerns such as algorithmic bias and discrimination (Albassam, 2023). The findings of this study can influence
decision-making and strategic planning in organizations looking to integrate AI in recruitment processes, providing practical
implications for stakeholders (Michael, 2023).
Future researchers can build upon the current study to deepen the understanding of AI adoption in recruitment and its implications
for various stakeholders. They can address several areas such as assessing the financial and regulatory aspects associated with AI
adoption in recruitment, comparing the performance of employees hired through AI-assisted recruitment versus traditional
methods, developing comprehensive guidelines and training materials to facilitate the responsible and ethical use of AI in
recruitment, expanding the scope of research beyond specific industries or geographic regions, and tracking changes in
stakeholder attitudes towards AI recruitment over time (Chen, 2023).
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
Publisher’s Note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of
their affiliated organizations, or those of the publisher, the editors and the reviewers.
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