The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part B

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.

Introduction: Many organisations nowadays use artificial intelligence (AI) in human resource (HR) activities like talent acquisition, onboarding of new employees, learning and development, succession planning, retention of employees, and automation of administrative tasks. When AI is integrated with HR practices, it helps HR personnel to focus more on the strategic aspects of the HR function and relieve them from routine HR activities.

Purpose: The readiness of employees to accept any change depends on organisational facilitation to change, employee willingness to accept the change, the requirement for change, situational factors, etc. This research studies the factors influencing employees’ change readiness towards acceptance of AI in HR practices. The researchers also strive to develop a conceptual technology adoption model for AI in HR practices by studying the earlier models. Finally, the research explores the acceptance of AI by various service sector employees and identifies whether there is any difference in their acceptance of AI based on demographic variables.

Methodology: A conceptual framework was derived using a combination of previous models, including the Technology Readiness Index (TRI), Change Readiness Scale, Technology Acceptance Model (TAM), Technology, Organization, and Environment (TOE) model, and change readiness scale. A structured questionnaire was designed and distributed to 228 respondents from the service sector based on the conceptual framework. An exploratory factor analysis (EFA) was used to determine the elements that influence employees’ level of change readiness.

Findings: The exploratory results on data collected from 228 respondents show that the model can be used for further research if a confirmatory factor analysis and validity and reliability test are performed. Employees are aware of AI and how it is used in HR practices, based on the study results. Moreover, while most respondents favour using AI in their company’s HR practices, they are wary of some aspects of AI.

Introduction: Technology and the environment remain uncertain for organisations that impose enormous challenges and opportunities to redesign policies and practices for human resources (HR). The use of technology is ubiquitous and pervasive. Technology has altered the way individuals and organisations seek knowledge, process information, instrument, and practice the learning outcomes.

Purpose: This conceptual paper highlights the change in technological and change nature of work impact on HR practices. Technology has changed the nature of work, which affects individuals and organisations. The dynamic change in technology forces organisations to rethink policies and procedures that fuel the organisation’s competence. The difference in HR practices (recruitment and selection, training and development, performance management, and turnover) is not a trend but rather a need for organisational survival. There is not only a transformation in technological implementation in an organisation but also in employee–organisation relations. The organisations install technology and replace employees.

On the contrary, employees leave an organisation and switch towards self-employed jobs entitled Gig-economy (World Bank, 2018). The individuals are moving towards a more flexible and self-employed relationship. Unfortunately, though, working flexibly create concern for an employee–employer relationship such as pension plan, health insurance, and paid leaves. It also creates income inequality.

Methodology: This is a conceptual paper.

Findings: Technology has a dual effect on the organisation and employees. Thus, technology affects employees, employers, and organisations. The change in technology moderates the psychological contract and career selection, leading to change in the policies and practices of the HR department. A research model is proposed in this conceptual research study which will further be tested to examine and confirm the impact of change.

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.

Need of the Study: Digitalisation, machine learning, and artificial intelligence (AI) is changing at a swift pace, significantly uplifting the role of information technology. The present human resource (HR) aspect transpires AI-based resolution, are gradually more effective with HR process, time-consumption and a complex tasks surrounded by the HRM functionalities.

Purpose: This study attempts to investigate the adoption and diffusion of human resource management (HRM) with the phenomenon of AI-based applications. Hence, this study has emphasised the predictors of AI adoption like competitive pressure, performance expectancy, top management support, strategic partner, employee champion, etc. Moreover, how the AI predictors are connected with HR practices. The research sample focused on 207 HR managers and senior managers from various industries.

Methodology: This study is based on a quantitative research technique encompassing mean, standard deviation, exploratory factor analysis (EFA), Confirmatory Factor Analysis (CFA), Average Variance Extracted (AVE), and Dependent Variable (DV).

Findings: The study’s empirical findings show that higher performance expectations and higher management support are both major predictors of AI adoption. In contrast, competitive pressure did not show a significant relationship with such an intention, and the ‘employee champion’ role has a negative impact on AI adoption.

Implications: AI diffusion and implementation show a significant research gap. In previous studies, adoption in HRM was overlooked. The study’s results provide a comprehensive picture of the situation. The framework and a major contribution to the study of the phenomenon in relation to its possible role in AI’s effectiveness and quality in HRM. The research inspires a debate among service providers, policy-makers, and stakeholders, and builds an efficient workplace.

Introduction: Artificial intelligence (AI) is being extensively used to solve complex problems in the industry. AI provides several benefits such as providing visibility in the processes, reducing time, improving accuracy, saving time, helping in the decision-making process, etc. Due to the range of benefits of AI technologies, organisations readily adopt this technology. However, there are several challenges that the organisation faces during the implementation of AI. These challenges are in context to human resource (HR) development for successful implementation of AI across different functions and are discussed in this chapter.

Purpose: Although we know that AI technology is widely accepted in human resource management (HRM) due to its various benefits. But the organisations face many challenges during the implementation of AI. The focus of the study is to explore the literature on AI in HRM, identify the challenges of implementing AI and provide potential future research direction based on a systematic literature review.

Methodology: To explore the literature on AI in HRM, the study undertakes a systematic literature review. The study identifies, analyse and classifies the literature to provide a holistic view of HR challenges in implementing AI. The study is built on a review of 47 documents, including the articles, book chapters and conference papers using the Scopus database for the past 10 years (2012–27 January 2022).

Findings: The study provides an overview of the documents published in Scopus in this area through a systematic literature review. The study reveals that a significant amount of growth in the publication has been shown in the past 10 years. The maximum and continuous growth is shown after 2017. The maximum number of papers are published in India, the USA and China. The study identifies major eight challenges of AI implementation in HRM. The study also provides a secondary case to deep dive in this area based on a systematic literature review.

Research Limitation/Implication: The challenges identified in the study are not empirically tested. Each of the identified challenges should be empirically examined. This study has expanded the body of knowledge of AI in HRM. This study will help the academicians and practitioners work on the identified challenges and help the organisations ease in adopting AI.

Originality/Value: This study represents the first work that integrates AI implementation challenges in HRM.

Introduction: AI technologies are transforming the industrial sectors, and the impact of AI technologies does not leave behind human resource management (HRM). From recruitment to development and payroll, AI has its own impact. In recruitment, selection, training and development, compensation, and remuneration AI play an important role.

Purpose: The main purpose of this chapter is to analyse the challenges in adopting AI in HRM. This chapter investigates the various strategies to overcome challenges encountered by companies while adopting AI in HR practices. Moreover, this chapter also examines the role of AI in HR practices.

Methodology: To achieve the purpose of this chapter, various case studies were analysed and literature studies with emphasis on what types of challenges are face in adopting AI in HR practices. The authors of various newspaper articles, books, published journals, and websites on AI analyse the various newspaper articles, books, published journals, and websites on AI in human resources (HR). Electronic databases are used as the most effective technique to begin a literature search for the current overview, specifically Science Direct, Google Scholar, and Emerald. In addition to this, the application of AI in HRM practices used by leading organisations globally is also reviewed. The analysis used four keywords: HRM, AI, challenges and adopting. This research has been conducted by searching scholarly papers and relevant studies using similar keywords.

Findings: The adoption of AI technologies is continuously increasing in HR practices. AI performs various HR functions such as recruitment, selection, training and development practices, scanning resume, etc. Organisations can be benefitted in many ways by adopting AI in HRM practices, such as better employees engagement and relations, accelerating competitive advantages and effectively utilising HR budgeting. The findings show that many companies like IBM, Deloitte, Amazon, etc., adopt AI in their HR practices.

Purpose: To analyse the acceleration of artificial intelligence (AI) operations and robotic process automation (RPA) by comparing its market size and revenue worldwide during the pandemic and, measuring the impact of AI investment levels on jobs human resource functions, and analysing the role of AI in future work.

Design/Methodology: The archival data analysis technique is used to fetch data from sources like the Centre for Monitoring Indian Economy (CMIE), Statista, Deloitte, Mc Kinsey, Strata, Tractica, and IDC. Descriptive analysis with supporting literature has been contextually used for each objective which further establishes practical and theoretical implications of AI, intelligent process automation (IPA), and RPA in different industries during Covid-19 pandemic. This study analysed active scholarly articles from the Scopus database and presented results and findings.

Findings: The findings of the study state that emerging technologies such as AI, IPA, and RPA have a strong potential impact on market size, revenue, number of jobs, and investments levels during the pandemic. The global investment in AI is projected to witness an upsurge from 2018 to 2027, which significantly impacts the human workforce in various industries. The results of the study state that AI/RPA seems to be a crucial technological intervention, especially in times of the pandemic.

Originality/Value: This study contributes to the body of knowledge by constructing a base for understanding the pace of AI/RPA/IPA intervention and its significant impact on organisation process, structure, and people in different sectors. The timeline and forecast of this study intend to make industry consultants future to prepare to align themselves in an era of digital disruption.

Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.

Introduction: Artificial intelligence (AI) has progressed significantly over the past few years, evolving into a collection of innovative tools that provide a competitive advantage to businesses. The acceptance and investment in AI are skyrocketing over the globe. The entry of AI in the workplace automates tasks and impacts making a timely decisions. At the same time, the workforce is not ready to welcome the new technology due to the skill gap. The organisation has to face many challenges in reskilling and convincing the workforce to incorporate AI in their work.

Purpose: With this study, the authors aim to analyse and highlight the introduction of AI in the organisation and the call for the reskilling of the workforce. To figure out what skills are most important for employees to learn to advance their careers.

Methodology: Given the deductive nature of the study, the researchers used secondary data collected and compiled from research papers, publications, websites, HR blogs, survey reports, etc. Research papers from reputed journals, reports of consultancies and agencies have been considered to synthesise the information and present it in a systematic manner and to derive the conclusion.

Findings: The findings indicate AI’s capabilities and applications have grown considerably, which shows the importance of AI in a growing number of fields, yet several hurdles need to be overcome, the most prominent one being the issues concerning upskilling the workforce for the future of AI. This study reveals the change in the perceived importance of the skills in the present and future times. Reskilling and upskilling the workforce and creating new talent to meet the changing employment demands is becoming increasingly important.

Introduction: In previous years, we can witness an upsurge in the usage of different digital tools by the different corporates worldwide. Instead, it can be witnessed during the time of Covid-19, where most of this affects the various human resource management (HRM) practices. It became essential for the industries worldwide to shift through digitalisation and so for HRM functions.

Purpose: Understanding the present situation of extensive usage of different digital tools, this chapter aims to discover and comprehend how successful the various organisations were in digitalisation and explain its outcomes. The main objective of this chapter is to explore the various factors influencing the success of digitalisation in human resources (HR) and measure its outcomes. To fulfil this aim, authors have focused on exploring the literature on a similar concept in the last decade (2011–2021).

Methodology: To conduct the study, the authors have approached a systematic study of bibliographic search with a motive to achieve the available works about HRM digitalisation. The list of different resources was primarily created using Google Scholar’s information. The acquired resources were then analysed and, based on certain pre-defined criteria, filtered.

Findings: The result of this study indicated that most of the previous studies focused on digitalisation outcomes, but very few studies have explored the dimension of understanding the success of digitalisation. Authors have categorised the factors among technological, organisational, and people factors. So further to understand these in-depth outcomes, both positive and negative outcomes have been understood.

Implications: Lastly, the authors have also tried to explore the suitable settings required for HRM digitalisation by studying empirical articles. This chapter will provide the overall view of the crucial factors for successful digitalisation in the domain of HRM and evaluate the outcomes. The study’s findings can be further utilised to conduct an in-depth study of the phenomenon and explore how the organisation can manage these factors during the implementation of HR digitalisation.

Introduction: In a world characterised by volatility, uncertainty, complexity, and ambiguity, change is the only constant. Over the years, human resource management (HRM) has evolved from conventional functions of hiring and firing to being a strategic partner in organisations. Similarly, there has been a paradigm shift in the landscape of artificial intelligence (AI) from being a mere searching tool to the design and development of intelligent robots. Over the years, AI has emerged into a collection of powerful technologies re-inventing different functional areas, including HRM. The application of AI in HRM is perceived as an optimistic opportunity since it ought to bring maximum value at minimum cost. AI focuses on building tools that exhibit human-level intelligence and discernment in making decisions.

Purpose: The purpose of this chapter is to draw deeper insights into the relevance of AI in different functional areas of HRM. Integrating AI into HRM functions such as talent acquisition, training and development, performance management, employee engagement, and the like can help leverage efficiency and create an engaging employee experience. In the wake of Industry 4.0, where digitalisation has become imperative, this chapter explores the integration of AI into specific HR functions for a synergistic competitive advantage in companies. The purpose of this chapter is to signify the integration of AI into four vital functions of HRM, namely talent acquisition, training and development, performance management, and employee engagement. The objective is to chart how companies integrate various AI tools in four specific HRM functions to enhance efficiency. Also, the companies willing to implement AI in their HR functions can refer to the case studies used as exemplars in the chapter.

Methodology: This conceptual chapter is based on the secondary sources, which also build upon case studies of different companies that have implemented AI-enabled solutions and integrated them into different HRM functions and processes per needs. This chapter utilises the conceptual framework of both AI and HRM functions to give deeper insight into the challenges and implementation of technology-enabled solutions.

Findings: AI is used in HRM functions to automate repetitive and operational tasks to shift the focus to more strategic aspects. Despite many advantages of AI and machine learning, very few companies are using it, and companies may integrate technology-enabled solutions based on the size and nature of business.

Purpose: This study aims to review the existing literature on human resource management (HRM) as a major theme and sub-theme of human resource (HR) analytics. This chapter has the objective of analysing the trends in HR analytics.

Design/Methodology/Approach: It covered the publications between 2010 and 2021. There was a total of 500 articles sourced through ProQuest. The systematic literature review is applied as a research methodology. The metadata analysis was carried out to understand the trends, challenges, best practices, and scope of HR analytics. The authors have taken the help of keywords, journals, authors, domains, and topics for sourcing, screening, shortlisting, and finalising the article for review purposes.

Findings: It was found that research published in the early period concentrated on HRM’s theoretical and conceptual frameworks. In the middle phase, HR analytics gained momentum while the recent publications have reiterated on adopting various tools and technologies for optimum utilisation of resources and sustainable organisational development.

Practical Implications: This research will add to the literature in the HR analytics domain. It also provides a deeper insight to the researcher to explore and analyse more trends in the area that needs further exploration.

Originality: This study is relevant and comprehensive within the context of digitalisation, people analytics and competition management. It provides valuable insights to analyse the present scenario of the volatility, uncertainty, complexity, and ambiguity environment exploring new horizons for organisational efficiency and human capital.