Search results

This paper aims to review the applications of artificial intelligence (AI) in the hiring process and its practical implications. This paper highlights the strategic shift in recruitment industry caused due to the adoption of AI in the recruitment process.

This paper is prepared by independent academicians who have synthesized their views by a review of the latest reports, articles, research papers and other relevant literature.

This paper describes the impact of developments in the field of AI on the hiring process and the recruitment industry. The application of AI for managing the recruitment process is leading to efficiency as well as qualitative gains for both clients and candidates.

This paper offers strategic insights into automation of the recruitment process and presents practical ideas for implementation of AI in the recruitment industry. It also discusses the strategic implications of the usage of AI in the recruitment industry.

This article describes the role of technological advancements in AI and its application for creating value for the recruitment industry as well as the clients. It saves the valuable reading time of practitioners and researchers by highlighting the AI applications in the recruitment industry in a concise and simple format.

Introduction: Artificial intelligence (AI) assists recruiters in effectively and efficiently nominating applicants precisely and accurately. It helps in the screening of resumes without biasness. This chapter will identify different AI technology and various organisations using it fully or partially.

Purpose: This chapter aims to get insights about various AI tools that assist human recruiters, save time and cost, and provide modern experiences. It will help identify various applications that are currently in use and their features. It also helps in finding out the benefits and the challenges faced by the recruiters and the applicants while assimilating those applications in hiring.

Need for the Study: The study will be helpful to all those recruiting firms who are presently using AI or not using it to understand the benefits and challenges they might face.

Methodology: The chapter will be based on reviews and industry reports. This chapter will include a study related to human resource (HR) functions where AI is used. To give more insights into AI technology, this study mentions various applications like Mya, Brazen, etc., and their usefulness in recruitment. Also, special emphasis would be given to the recruitment functions as most companies use AI. Some companies like Deloitte and Oracle are using AI fully or partially will also be incorporated.

Findings: The study finds out that although many companies have started to use AI tools for recruitment, they have not explored all the algorithms that can be used to complete the whole recruitment and selection process. Companies like Loreal use AI for candidate applications and recruiter screening, but human recruiters stand strong for assessments and interviews. AI’s widespread use presents human resource management (HRM) practitioners with both opportunities and challenges.

Practical implications: The basic idea of the study is to scrutinise the related literature and find out the features, advantages and limitations/challenges of using AI which would be helpful for recruiters in better understanding of the technology-driven recruitment.

The purpose of this study is to contribute to the knowledge on the opportunities and risks in the use of artificial intelligence (AI) in recruitment and selection by exploring the perspectives of recruitment professionals in a multicultural multinational organisation.

A qualitative approach was used in this exploratory study. Face-to-face, semi-structured in-depth interviews were conducted with ten professional recruiters who worked for a multinational corporation.

The findings revealed that AI facilitates the effective performance of routine tasks through automation. However, the adoption of AI technology in recruitment and selection is also fraught with risks that engender fear and distrust among recruiters. The effective adoption of AI can improve recruitment strategies. However, cynicism exists because of the fears of job losses to automation, even though the participants thought that their jobs would continue to exist because recruiters should always be humans.

This paper provides a unique exploration of the opportunities and risks in the adoption of AI for the recruitment and selection function in human resource management. The benefits are the delegation of routine tasks to AI and the confirmation of the crucial role of professional recruiters.

Introduction: Traditional recruitment system relied heavily on the applicants’ curriculum vitae (CV). This system, besides becoming redundant, has proved to be a futile exercise leading to the hiring of candidates that eventually turn out to be ‘misfits’. CVs were the only source of candidates’ data available for the recruiters a few years back. Face-to-face interviews was considered to be the ultimate solution for hiring suitable candidates. However, evidence suggests that interview scores and job performances do not complement each other. Advancement in artificial intelligence (AI) has introduced several techniques in the recruitment process.

Purpose: This chapter underscores the drawbacks of the traditional recruitment process. Evidence suggests that the traditional recruitment process is prone to subjectivity and is time-consuming. Surprisingly, despite the disadvantages, the integration of AI into the recruitment process is still slow. This chapter highlights the need to harness AI and the advantage technology could bring to the recruitment process. Some of the techniques that are garnering attention and widely used by organisations, such as chatbots, gamification, virtual employment interviews, and resume screening are described to enable the readers to understand with less effort. Chatbots and gamification techniques are described through process flow charts. We also describe the various types of interviews that could be conducted through virtual platforms and the modality by which the resume screening technique operates. Today, we are at a juncture wherein it is pertinent to acknowledge the superiority of technology-driven processes over traditional ones. This chapter will help the readers to understand the modus operandi to implement chatbots, gamification, virtual interviews and online resume screening techniques besides their advantages.

Scope: Although chatbots, resume screening, virtual interviews, and gamification are used in other areas, too, such as training and development, marketing, etc., in this chapter, we restrict solely to employee recruitment processes.

Methodology: Scoping review is used to examine the existing literature from various databases such as Google Scholar, IEEE, Proquest, Emerald, Elsevier, and JSTOR databases are used for extracting relevant articles.

Findings: Automation and analytics in recruitment and selection remove bias which is otherwise increasingly found in manual hiring processes. Also, previous studies have observed that candidates engage in impression management tactics in traditional face-to-face interviews. However, through automated recruitment processes, the influence of these tactics can be eliminated. AI-based virtual interviews reduce human bias. It also helps recruiters to hire talents across the globe. Gamification improves the candidate’s perception of the work and work environments. Through gamified techniques, the recruiters can understand whether a candidate possesses the required job skills. Chatbots are an interactive technique that can respond to interviewees’ queries. Resume screening techniques can save the recruiter’s time by screening and selecting the most appropriate candidates from a large pool. Hence, the chosen candidates alone can be referred to the next stage of the recruitment cycle. AI improves the efficiency of the recruitment process. It reduces mundane tasks. It saves time for the human resources (HR) team.

The number of candidates applying to public contests (PC) is increasing compared to the number of human resources employees required for selecting them for the Police Force (PF). This work intends to perceive how those public institutions can evaluate and select their candidates efficiently during the different phases of the recruitment process. To achieve this purpose, artificial intelligence (AI) was studied. This paper aims to focus on analysing the AI technologies most used and appropriate to the PF as a complementary recruitment strategy of the National Criminal Investigation police agency of Portugal – Polícia Judiciária.

Using design science research as a methodological approach, the authors suggest a theoretical framework in pair with the segmentation of the candidates and comprehend the most important facts facing public institutions regarding the usage of AI technologies to make decisions about evaluating and selecting candidates. Following the preferred reporting items for systematic reviews and meta-analyses methodology guidelines, a systematic literature review and meta-analyses method was adopted to identify how the usage and exploitation of transparent AI positively impact the recruitment process of a public institution, resulting in an analysis of 34 papers between 2017 and 2021.

Results suggest that the conceptual pairing of evaluation and selection problems of candidates who apply to PC with applicable AI technology such as K-means, hierarchical clustering, artificial neural network and convolutional neural network algorithms can support the recruitment process and could help reduce the workload in the entire process while maintaining the standard of responsibility. The combination of AI and human decision-making is a fair, objective and unbiased process emphasising a decision-making process free of nepotism and favouritism when carefully developed. Innovative and modern as a category, group the statements that emphasise the innovative and contemporary nature of the process.

There are two main limitations in this study that should be considered. Firstly, the difficulty regarding the timetable, privacy and legal issues associated with public institutions. Secondly, a small group of experts served as the validation group for the new framework. Individual semi-structured interviews were conducted to alleviate this constraint. They provide additional insights into an interviewee’s opinions and beliefs.

Ensure that the system is fair, transparent and facilitates their application process.

The main contribution is the AI-based theoretical framework, applicable within the analysis of literature papers, focusing on the problem of how the institutions can gain insights about their candidates while profiling them, how to obtain more accurate information from the interview phase and how to reach a more rigorous assessment of their emotional intelligence providing a better alignment of moral values. This work aims to improve the decision-making process of a PF institution recruiter by turning it into a more automated and evidence-based decision when recruiting an adequate candidate for the job vacancy.

This study aims to investigate the configurational effects of five rules – artificial intelligence (AI)-based hiring decision transparency, consistency, voice, explainability and human involvement – on applicants' procedural justice perception (APJP) and applicants' interactional justice perception (AIJP). In addition, this study examines whether the identified configurations could further enhance applicants' organisational commitment (OC).

Drawing on the justice model of applicants' reactions, the authors conducted a longitudinal survey of 254 newly recruited employees from 36 Chinese companies that utilise AI in their hiring. The authors employed fuzzy-set qualitative comparative analysis (fsQCA) to determine which configurations could improve APJP and AIJP, and the authors used propensity score matching (PSM) to analyse the effects of these configurations on OC.

The fsQCA generates three patterns involving five configurations that could improve APJP and AIJP. For pattern 1, when AI-based recruitment with high interpersonal rule (AI human involvement) aims for applicants' justice perception (AJP) through the combination of high informational rule (AI explainability) and high procedural rule (AI voice), there must be high levels of AI consistency and AI voice to complement AI explainability, and only this pattern of configurations can further enhance OC. In pattern 2, for the combination of high informational rule (AI explainability) and low procedural rule (absent AI voice), AI recruitment with high interpersonal rule (AI human involvement) should focus on AI transparency and AI explainability rather than the implementation of AI voice. In pattern 3, a mere combination of procedural rules could sufficiently improve AIJP.

This study, which involved real applicants, is one of the few empirical studies to explore the mechanisms behind the impact of AI hiring decisions on AJP and OC, and the findings may inform researchers and managers on how to best utilise AI to make hiring decisions.

The purpose of this paper is to examine the use of artificial intelligence (AI) in human resource management (HRM) in the Global South.

Multiple case studies of AI tools used in HRM in these countries in recruiting and selecting as well as developing, retaining and productively utilizing employees have been used.

With AI deployment in HRM, organizations can enhance efficiency in recruitment and selection and gain access to a larger recruitment pool. With AI deployment in HRM, subjective criteria such as nepotism and favoritism are less likely to come into play in recruitment and selection of employees. AI deployment in HRM also has a potentially positive impact on the development, retainment and productive utilization of employees.

AI is an evolving technology. Most HRM apps have not gained enough machine learning capabilities with real-world experience. Some of them lack a scientific basis. AI in HRM thus currently affects only a tiny proportion of the population in the GS.

The paper explores the roles of AI in expanding recruitment pools. It also advances our understanding of how AI-based HIRM tools can help reduce biases in selecting candidates, which is especially important in the Global South. It also delves into various mechanisms by which AI helps in the development, retainment and productive utilization of employees.

We provide details of various mechanisms by which AI brings input and output efficiencies in recruitment and selection in these countries.

The purpose of our study was two-fold: (1) to examine the effect of perceived value derived from perceived benefits and sacrifices in the adoption of artificial intelligence (AI) in talent acquisition and (2) to investigate the moderating role of human resource (HR) readiness in the association between perceived value and AI adoption intention.

A structured questionnaire was administered to 198 talent acquisition executives and HR professionals of Indian IT companies based on a purposive sampling technique. Partial least squares structural equation modeling (PLS-SEM) was used on the Smart PLS 2.0 platform to analyse the data and test the model.

Results revealed that perceived benefits and sacrifices significantly predict perceived value which significantly affects the HR professional’s AI adoption intention. The study further found that HR readiness moderates the link between perceived value and the intention of HR professionals to adopt AI in the talent acquisition process in the Indian IT industry.

IT companies are advised to continuously monitor and evaluate the performance of AI tools to ensure that they are meeting the recruitment process needs to leverage AI’s benefits in talent acquisition. This study seeks to provide the impetus for a planned AI adoption in talent acquisition.

This research provides ample evidence for the existing technology adoption theories. It explored the predictors of adoption by validating the value-based adoption model in the Indian context. It provides valuable insights into the practice of acquiring talents in the IT sector using artificial intelligence.

This study is dedicated to critically analysing research addressing human resource management (HRM) and the adoption of artificial intelligence (AI) with the purpose of driving development in the field of human resources (HR) at the strategic and managerial level.

A systematic literature review (SLR) was conducted using the Scopus database, which gathered 61 articles between 2002 and 2022. The SLR process has the potential to, in addition to generating knowledge and theories, support and guide policy development and practice in many disciplines.

The results of this study allowed the author to identify three main conclusions: (a) there are four thematic clusters – (i) Strategic HR and AI, (ii) Recruitment and AI, (iii) Training and AI and (iv) Future of work; (b) there is a growing academic interest in studying the implementation of AI to develop the HR sector and (c) the application of AI stands out in the strategic HR and AI cluster as a means of achieving profit maximisation and the overall development of the organisation.

This study is the first SLR to present a strategic and managerial view on AI applications associated with specific HRM dimensions. The study is also the first SLR to identify key trends in the literature, drivers and obstacles to the development of AI in HRM and then place them within the landscape of positive and negative approaches in a framework. Also, as a contribution, the study has practical implications for HR managers and practitioners in adopting AI as a decision support in the area's processes.

Need of the Study: In an ever-changing environment, the use of artificial intelligence (AI) to accelerate the business is inevitable. By introducing various advanced technologies to improve productivity, technology users are well aware of the challenges ahead.

Purpose: This chapter aims to understand AI technology and the challenges it faces in noted domains.

Methodology: This chapter is based on secondary research, and relevant information has been gathered from various secondary sources such as research articles, newspaper articles, books, and websites. There is a considerable gap between the expected outcomes of AI and the reality of AI in human resource (HR) practice.

Findings: The study’s outcome focuses on AI challenges in human resource management (HRM) functions such as recruitment and selection, learning and development, and performance appraisal. Considering the numerous benefits, it becomes essential to understand these issues/challenges so that they can be adequately addressed.

Practical Implications: This study highlights the issues such as complexity of HR practices, organisation readiness, staff acceptability, and responsibility for AI implementation in HRM, and other related issues and proposes prudent response to these challenges that will be embraced by both employees and employers, thereby adding novelty to this research.