Effectiveness of Chatbots in the Recruitment Process for IT Industry
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This study examines the effectiveness of chatbots in IT recruitment from HR professionals perspectives. A survey of 150 HR specialists assessed chatbot advantages and challenges in candidate screening, engagement, and surveys. Using the Smart PLS software program and structural equation modeling (SEM), the study analyzed key elements including efficiency, candidate experience, price-effectiveness, and time financial savings. Findings reveal that HR specialists view chatbots as effective in automating repetitive obligations, improving recruitment performance, and improving candidate experience. Automation in early hiring levels reduces administrative burdens and speeds up recruitment. However, chatbots face demanding situations in handling complicated queries and maintaining human-like interactions. While they significantly reduce fees, their capacity to assess soft talents and make the very last hiring selections is limited. This research gives insights for IT firms aiming to combine or enhance the chatbot era, presenting both its blessings and constraints. The study presents a foundation for destiny research and practical implementations to optimize recruitment techniques inside the evolving IT sector.
The growth in the technology industry across business organizations reflects differently in most aspects of organizations including Human Resource Management (HRM) as regards recruitment (Wahdaniah et al., 2023). Conventional recruitment in the past used techniques like job advertisements, resumes, and interviews among others (Anand & Radha, 2020). However, these traditional approaches are now outdated because of the increasing need for quick and efficient solutions in hiring (Ebrahim & Rajab, 2025). Among these technologies, Artificial Intelligence (AI) has been found to play a significant role in the field of recruitment (Meshram, 2023). Therefore, chatbots using natural language processing (NLP) and machine learning (ML) have been increasingly employed in the recruitment process to enhance the efficiency of accomplishing recruitment goals (Shenoy et al., 2022). These chatbots are important in those companies that receive many applications and require precise skills, such as the IT industry (Meshram, 2023).
Recruitment for years has been a highly human intervention activity as the use of chatbots has become prevalent across industries for resume screening, candidate conversations, and scheduling of interviews (Swapna & Arpana, 2021). Chatbot is an artificial intelligence (AI) system that is designed to interact with the customer through text and speech and help companies perform routine work more efficiently (Shenoy et al., 2022). According to the research, candidates prefer to use artificial intelligence during the recruitment process since it saves time and gives more precise results (Horodyski, 2023). Most of the perceived factors for the Human-Centered Technology Acceptance Model (HTAM) play a significant role in understanding the influence of perceived usefulness and ease on users satisfaction and the adoption of chatbots in recruitment (Akram et al., 2024; Davis, 1989). Chatbots make HR more organized and efficient in candidate management and help to increase the effectiveness of the whole hiring process (Rathore, 2023; Islam MT., 2020).
The lack of skilled professionals remains one of the greatest concerns in Information Technology Recruitment. According to the World Economic Forum, (2025) the number of available jobs in technology is expected to reach around 170 million by 2030 implying more competition for talent (Forum, 2025). Traditional methods of recruitment like resume shortlisting and job advertising cannot meet these demands, hence resulting in organizations lagging on quality candidates through lengthy processes (Nyathani, 2022). AI-based chatbots mitigate this problem by eliminating time-consuming and repetitive tasks, responding promptly to the candidates, and enabling HR professionals to review them more critically (Murugesan et al., 2023). In addition, chatbots are available all day, enabling applicants to receive up-to-date information as well as enhancing the candidate experience (Murugesan et al., 2023).
Nevertheless, there are issues with AI in the recruitment process. One of these is algorithm bias where AI models trained from biased datasets develop a bias in their vision as the one that discriminates based on gender, race, or education level (Chen, 2023). Prejudiced AI recruitment technologies are misaligned with diversity and inclusion principles and can negatively impact the organizations image (Shams et al., 2023). Moreover, although chatbots excel at evaluating technical competencies, they are currently unable to assess intangible, interpersonal skills, emotional intelligence, or cultural fit that are important in IT positions (Yanamala, 2021). However, practical teamwork, interpersonal and soft skills, which are incredibly important for IT professionals cannot be evaluated by AI.
The critical approach that combines automation via artificial intelligence with competent human skills will be ideal. Thus, the study aims to evaluate the level of perceived ease of use, perceived usefulness, transparency, and trust in recruitment chatbots, especially in the IT industry. It should also identify the effectiveness of such decisions on candidates, hiring procedures, and overall hiring decision-making.
Research Question
Conceptual Framework
This conceptual framework highlights the important variables and relationships that decide the effectiveness of chatbots in the recruitment procedure. By knowledge these interactions, IT generation agencies can optimize their chatbot implementations to improve recruitment efficiency, candidate engagement, and HR productiveness. By focusing on elements like ease of use, believe, transparency, and comments collection, groups can make certain that chatbots function treasured belongings of their recruitment strategies. The conceptual framework for knowledge the effectiveness of chatbots within the recruitment system for IT generation groups integrates several key variables that impact both the candidate and recruiter studies. This framework explores how different factors - which includes ease of use, accept as true with, transparency, and perceived usefulness - interact to beautify recruitment performance, enhance candidate engagement, and optimize HR operations.
Fig. 1: Conceptual Framework.
Hypothesis
Hypotheses for testing the effectiveness of chatbots in the recruitment process for an IT technology company, using the provided variables:
Research Design
A quantitative research approach was conducted to determine the efficiency of chatbots in IT sector recruitment. Data were collected to understand the participants satisfaction with the chatbots usability, usefulness, transparency and ethical considerations.
Research Approach
The approach carried out in this study consists of a deductive studies approach suitable for analyzing records gathered from relevant respondents and the consequences acquired by linking them with present theories. The deductive method uses a theoretical prediction or hypothesis; information collection and evaluation are generally executed with the use of predesigned tools and strategies. This approach is especially relevant to the current study since it aims to evaluate the efficiency of the application of chatbots in recruitment for IT technology firms on a quantitative basis.
Data Collection
A structured questionnaire was designed using Google Forms and administered electronically. To evaluate the participants perceptions of the chatbot application in recruitment, a five- Likert scale was used (1 = Strongly Disagree to 5 = Strongly Agree). The survey questions also covered respondents age, gender, education, their prior interaction with chatbots, and their work background and experience in the IT industry. Further, it also highlighted several other concepts like Ease of Use, Perceived Usefulness, Higher Transparency, Improved Trust, Enhanced Personalization, Candidate Screening, Employee Onboarding & Training, FAQ Responding, Automation of Routine Processes, and Collecting Feedback.
Sample and Sampling Technique
The study involved 150 participants including HR professionals, IT recruiters and candidates who have been through the interface of chatbots. Purposive sampling was used to ensure that only participants with prior experience in recruitment using the chatbot were included. This method will help in gathering specific data from the professionals and the job seekers who have firsthand experience of chatbots in recruitment.
Data Analysis Technique
Data analysis was done using SmartPLS, a well-known software for managing and analyzing quantitative data. SmartPLS was considered due to its applicability in handling large samples. Besides, it offers a wide variety of tests that give accurate results. Descriptive and inferential examination procedures, including correlation and regression, were used to assess the relationships between variables, such as the effects of chatbots when used for recruitment and selection on efficiency, candidate experience, and decision-making processes. The integrative approach used for the study helped fulfil the research objectives and gain valuable insights concerning the efficiency of chatbots in the recruitment process of IT companies HR departments.
Pilot Study Results
The pilot study results are comprised of the results taken from the smart PLS software with the help of a variety of statistical operations. Major operations evaluated in this context include path coefficient, outer loading, RHO values, R adjustment value and average variance review. It has been supported within the variables and their effects on one another according to the feedback provided by the respondents in the Google survey form.
Table 1: Path Coefficient.
Notes: Increase Ease of Use (IEU), Perceived Usefulness (PU), Higher Transparency (HT), Improved Trust (IT), Reduced Effort (RE), Enhanced Personalization (EP), Better Ethical Perception (BEP), Candidate Screening (CS), Employee Onboarding & Training (EOT), FAQ Responding (FAQ), Automate Routine Processes (ARP), Collecting Feedback (CF).
The value of the path coefficient has been reviewed for a variety of variables in collaboration. It has been outlined that most of the variables show a significance value of less than 0.7. However, there are some variables which are showing a value close to 0.7 that justifies their collaborative significance in the context of comparisons. The value of EOT -> EP is traced as 0.034 while the value of CS -> IEU is about 0.029.
Outer loading value as Supplementary Material file has been revealing a significant impact where maximum values are recorded above 0.8 for most of the collaborative variables. It shows a high value for some of the variables including RE 1 <- RE and IEU 1 <- IEU. It is showing a value of 1 for these two variables however it is showing a value of above 0.9 for IT 1 <- IT.
Table 2: R Square.
The R square value shows the individual dominance of some of the variables independently in the context of an overland review. It has been found that EP has a value of 0.626. RE has also recorded about 0.636.
Table 3: R Square Adjustment.
R square adjustment value are you for the significant impact of various variables in the context of research. Most of the values are below 0.7 which shows that the values are not able to have a dominant impact in the scenario of Chatbots. It is found that HT has a value of 0.417 as compared to another dominant value including RE of 0.626. The two values narrate that organizational efficiency has an essential role in addition to work agility because the two agents are emphasizing a value with a high significance above 0.7.
Table 4: Average Variance.
The average variance shows the efficiency of most of the variables. IEU has a value of 0.501, OF has 0.827, and BEP has 0.723. The value of CS and CF are equal to 1. It is narrating that the dominant value for maintaining confidentiality and time-saving has been following the previous patterns as narrated by the outer loading where both the agents had mentioned high values.
Table 5: Cronbach Alpha.
The Cronbach alpha value is how the individual value is independent but it is still showing an impact on the collaborative effort. The value of CS and EOT are equal to 1. It is narrating that the dominant value for Candidate Screening and Employee Onboarding & Training has been following the previous patterns as narrated by the outer loading where both the agents had mentioned high values. RE provides a value of 0.836 while EP shows 0.838. The other dominant values include FAQ of 0.841 and HT of 0.743. IT and PU are also significant for 0.9 and above. HTMT shows as Supplementary Material file that various variables have a dominant value of about 0.7 in linkage. IT <-> CS is showing a value above 1. This shows its dominance and significance in the context of the growing timeline and dominance of the impacts. The link shows the collaboration of the two variables in the context of dominance.
Hypotheses Testing
The analysis has been done to understand the importance of using the Effectiveness of Chatbots in the Recruitment Process for IT Technology Companies in the country. Some hypotheses are outlined based on the conceptual framework to understand how the IT Technology Company is becoming innovative with the support of chatbot use and facilitation of a better organizational recruitment process under the perspective of the use of a human resource management plan. The hypotheses are critically evaluated and tested with the help of a conceptual framework outlining the hypothesis and their impact and linkage to one another. Hypothesis and their impact and linkage to one another have been valued with the support of a smart PLS software where comparison and contrast have been done to test the hypothesis.
H1: Increase in Ease of Use (IEU) positively influences the Perceived Usefulness (PU) of chatbots in the recruitment process.
The effectiveness of chatbots within the recruitment process in large part relies upon their usability and functionality. The speculation (H1) states that an increase in ease of use (IEU) positively affects the perceived usefulness (PU) of chatbots in recruitment. This relationship is vital because if chatbots are easy to use, candidates and recruiters are more likely to understand them as beneficial equipment inside the hiring process. This phase explores how the usability of chatbots influences their perceived usefulness and contributes to a green recruitment system. Ease of use in chatbots refers to how intuitive, handy, and user-pleasant they are in interacting with candidates and HR experts. A chatbot with a nicely designed interface, natural language processing (NLP) skills, and seamless integration with recruitment structures enhances the consumer experience. When applicants can effortlessly navigate via chatbot interactions, time table respondents, or acquire task application updates, theyre much more likely to locate the chatbot useful (Bondarouk et al., 2017).
H2: Higher Transparency (HT) in chatbot interactions positively impacts Improved Trust (IT) among candidates.
The effectiveness of chatbots in recruitment depends no longer handiest on their capability but additionally on how an awful lot of candidates and recruiters accept as true with them. The hypothesis (H2) states that Higher Transparency (HT) in chatbot interactions impacts Improved Trust (IT) amongst applicants. Transparency in chatbot layout, decision-making approaches, and communique performs a crucial position in building accept as true with, which is vital for candidates to engage with and depend upon chatbot-driven recruitment structures. This phase explores the relationship between transparency and acceptance as trust, highlighting why greater openness in chatbot operations complements a person self-belief in the recruitment procedure (Skjuve et al., 2021). Transparency in chatbots refers to how openly they speak their role, boundaries, and rationale judgment at the back of their responses. Candidates interacting with a chatbot during activity applications ought to be aware of whether or not theyre talking with an AI or a human, how their information is getting used, and the way the chatbot reaches conclusions (e.g., shortlisting applicants or scheduling respondents). When chatbots honestly divulge those factors, candidates are more likely to trust the device.
H3: Reduced Effort (RE) required by candidates increases Enhanced Personalization (EP) in chatbot-based recruitment.
The effectiveness of chatbots in recruitment relies upon on their capacity to streamline the hiring system whilst providing a personalized experience for candidates. The speculation (H3) states that Reduced Effort (RE) required through applicants increases Enhanced Personalization (EP) in chatbot-based recruitment. This courting is vital due to the fact when chatbots reduce the attempt candidates want to use for jobs, ask questions, or receive updates, they can attention more on personalizing interactions based on person alternatives. This section explores how reducing effort results in more suitable personalization, in the end improving the candidate experience and ordinary recruitment performance. Reduced attempt in chatbot-based recruitment refers to minimizing the manual work, time, and cognitive load required from candidates to finish job applications, get records, and obtain updates (Brands & Fernandez-Mateo, 2017). When chatbots are designed to fast manual customers through job listings, robotically fill out utility paperwork, and provide instant responses, candidates revel in a smoother and more enticing method. This reduction in attempt permits chatbots to cognizance on turning in customized interactions, consisting of tailoring process tips based on a candidates experience, talents, and alternatives.
H4: Better Ethical Perception (BEP) of chatbots positively affects Improved Trust (IT) in the recruitment process.
The adoption of chatbots in the recruitment technique has added new challenges associated with ethics and agree with. The hypothesis (H4) states that Better Ethical Perception (BEP) of chatbots definitely influences Improved Trust (IT) within the recruitment procedure. Ethical worries, which includes fairness, bias discount, transparency, and responsible data handling, play a vast role in shaping applicants accept as true with in AI-driven recruitment. When applicants perceive chatbots as ethical and unbiased, they are more likely to trust them, interact with them hopefully, and consider the hiring method as truthful and reliable. Ethical notion refers to how applicants and recruiters perceive the chatbots fairness, impartiality, and adherence to ethical requirements. A chatbot designed with moral issues, consisting of independent candidate assessment, obvious selection-making, and stable information dealing with, fosters agree with in the hiring system (Boella & Goss-Turner, 2013).
H5: Increase in Perceived Usefulness (PU) of chatbots improves Candidate Screening (CS) efficiency.
The efficiency of chatbot-driven recruitment is predicated closely on applicants and recruiters perceptions of its usefulness. The hypothesis (H5) states that Increase in Perceived Usefulness (PU) of chatbots improves Candidate Screening (CS) performance. This relationship is crucial due to the fact if recruiters and applicants perceive chatbots as treasured equipment in the hiring system, they are more likely to agree with and rely upon them for screening duties. This phase explores how perceived usefulness influences candidate screening, main to a more streamlined, truthful, and effective recruitment process. Perceived usefulness refers back to the quantity to which users agree with that a chatbot enhances their recruitment experience by using saving time, lowering workload, and improving decision-making. In candidate screening, chatbots can analyze resumes, assess talents, behavior preliminary respondents, and rank candidates based totally on task requirements. When recruiters locate these functionalities beneficial in figuring out the first-class applicants speedy and accurately, theyre more likely to undertake chatbot-based totally screening answers (Bertillo & Salando, 2013).
H6: Enhanced Personalization (EP) in chatbot interactions improves Employee Onboarding & Training (EOT) effectiveness.
The effectiveness of worker onboarding and schooling is an essential aspect in making sure a smooth transition for brand new hires in an IT agency. The hypothesis (H6) states that Enhanced Personalization (EP) in chatbot interactions improves Employee Onboarding & Training (EOT) effectiveness. Personalized onboarding and education reports help personnel integrate faster, experience extra engaged, and collect essential skills efficiently. This segment explores how chatbot-pushed personalization contributes to a greater established, interactive, and powerful onboarding and training system (Behera et al., 2024). Personalization in chatbot interactions refers back to the capability of AI-pushed systems to tailor responses, recommendations, and training substances primarily based on a man or womans function, experience, and gaining knowledge of possibilities. A chatbot that may customize onboarding reviews for specific task positions ensures that employees obtain relevant facts without being crushed via customary content material.
H7: Higher Transparency (HT) of chatbots improves FAQ Responding (FAQ) accuracy and candidate satisfaction.
The effectiveness of worker onboarding and education is notably prompted by means of the availability of well-timed and correct facts. The speculation (H7) states that FAQ Responding (FAQ) via chatbots improves Employee Onboarding & Training (EOT) performance. Providing new employees with short and relevant solutions to their questions ensures a smoother transition into their roles, decreasing confusion and growing engagement. This segment explores how chatbot-powered FAQ structures make contributions to more effective onboarding and education in IT companies. An important challenge in onboarding is making sure that new personnel acquire the facts they need without overwhelming them with immoderate substances (EG Bateson et al., 2014). FAQ chatbots assist via imparting instantaneous responses to common queries about agency regulations, advantages, IT gadget get admission to, and process-specific tasks. Instead of awaiting HR representatives or managers to answer questions, personnel can interact with chatbots to get on the spot solutions, enhancing their onboarding revel in. For example, a brand new software developer joining an IT employer may additionally have questions about gaining access to development gear, software licenses, or enterprise coding requirements.
H8: Automating Routine Processes (ARP) using chatbots reduces effort (RE) required in recruitment operations.
In recent years, the mixing of synthetic intelligence (AI) and automation into numerous enterprise features has revolutionized strategies, and recruitment is no exception. One giant development is the use of chatbots in the recruitment method. These automated structures can cope with numerous tasks historically controlled by means of human assets (HR) employees, resulting in widespread efficiency enhancements. The hypothesis, H8: Automating habitual methods of the use of chatbots reduces the effort required in recruitment operations, seeks to research the specific effect that chatbots have on reducing the time and guide labour had to execute repetitive and time-consuming obligations in recruitment (Nawaz et al., 2024). One of the middle functions of chatbots in recruitment is to automate ordinary administrative responsibilities consisting of candidate screening, answering frequently asked questions, scheduling respondents, or even sending compliance emails. These approaches, even as important, frequently contain considerable manual time and effort consumption, that may overwhelm HR teams, particularly in large organizations or all through high hiring seasons.
H9: Collecting Feedback (CF) through chatbots improves the Perceived Usefulness (PU) of chatbots in recruitment.
In the digital age, remarks collection has emerged as a vital thing of continuous improvement and optimization in commercial enterprise processes. One region where remarks play a critical role is recruitment, in which the experience of candidates can without delay affect the effectiveness of the hiring technique. Hypothesis H9, which suggests that accumulating remarks via chatbots improves the Perceived Usefulness (PU) of chatbots in recruitment, explores how chatbots can decorate their value via obtaining and responding to candidate comments in real time. Feedback is an effective tool for information person delight, figuring out pain points, and riding enhancements. In the context of recruitment, candidate comments present treasured insights into the user experience with the chatbot interface, its functionalities, and the general recruitment manner (Koivunen et al., 2022). By integrating remarks collection into chatbot interactions, organizations can at once tap into candidate views, which can be neglected in conventional methods of feedback amassing, together with surveys or respondents. Collecting these remarks not handiest advantages applicants through giving them a possibility to percentage their reviews but also facilitates HR groups to excellent-tune the chatbots functions to higher meet consumer wishes. The procedure of amassing feedback thru chatbots can beautify Perceived Usefulness (PU) because it creates a comments loop that benefits each candidates and recruiters. When candidates offer feedback approximately the chatbot, consisting of feedback approximately the readability of its responses, ease of use, or the relevance of its questions, HR teams gain a deeper information of the chatbots strengths and areas for development (Akram et al., 2024).
H10: Higher levels of Perceived Usefulness (PU) and Improved Trust (IT) in chatbots enhance overall recruitment efficiency.
The rapid evolution of artificial intelligence (AI) and automation technology has had a profound impact on recruitment tactics. Chatbots, specially, have emerged as key gear to streamline recruitment obligations, enhance candidate engagement, and enhance universal efficiency. Hypothesis H10, which posits that higher levels of Perceived Usefulness (PU) and Improved Trust (IT) in chatbots result in greater recruitment performance, seeks to recognize the combined effect of these two factors on the overall performance of recruitment operations. Perceived Usefulness (PU) refers to the diploma to which a candidate or HR expert believes that using a chatbot complements the effectiveness and performance of the recruitment procedure. If applicants and recruiters locate the chatbots capabilities to be valuable - together with computerized candidate screening, answering FAQs, scheduling respondents, or offering on-the-spot updates - they are more likely to understand the device as an asset to the recruitment technique. Improved Trust (IT), however, increases the reliability and transparency of the chatbot. Candidates need to trust that the data presented by the chatbot is accurate, that their personal information is secure, and that the tool is designed to enhance their experience in preference to update human interaction absolutely (Skjuve et al., 2021).
Fig. 2: Diagrammatic Presentation of Data Variables (Author).
When each PU and IT are excessive, chatbots can significantly contribute to recruitment efficiency. Trust in a chatbot ensures that applicants feel cushty interacting with it, relying on it for vital obligations such as job software submission, interview scheduling, or receiving updates about the status in their applications. If candidates believe that the chatbot is handling these duties securely and effectively, theyre much more likely to have interaction with it and depend upon it to navigate through the recruitment method (Michaud, 2018).
Recruitment is a very time-consuming, long, laborious and expensive process for companies, which HR professionals hope to alleviate through new technology, such as automation and artificial intelligence. From the respondents, it can be concluded that currently the most useful topics that could be automated in recruitment processes are found in the tasks in the initial stages of the process. These are especially the time-consuming process stages that are currently handled manually, in which case efficiency would also increase with automation alone. The importance of the recruitment need and notification channels was not as great, and its possibilities in relation to artificial intelligence were of lower value. However, the aspect of web analytics related to the topic, which could, for example, be used to recommend suitable jobs to job seekers based on the applicant behavior in search channels and job advertisements. The recruitment need came up again in relation to predictive analytics, i.e. in the future, artificial intelligence may be able to predict the need for recruitment before the actual need has time to arise.
The authors participated in conceptualization, methodology, Software, Validation, Formal Analysis, Resources, data curation, Writing, visualization, Supervision, and Project Management, and approved the submitted version of the manuscript.
All data are included in the manuscript and will be available upon request.
The authors gratefully acknowledge the support and contributions of the institutions involved in this research.
The author declares no conflict of interest.
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Academic Editor
Dr. Liiza Gie, Head of the Department, Human Resources Management, Cape Peninsula University of Technology, Cape Town, South Africa
Karachi University Business School (KUBS), University of Karachi, Karachi, Pakistan
Khan N., and Waseem SN. (2025). Effectiveness of chatbots in the recruitment process for IT industry, Can. J. Bus. Inf. Stud., 7(2), 351-363. https://doi.org/10.34104/cjbis.025.03510363
