The Adoption and Effect of Artificial Intelligence on Human Resources Management:
Emerald Studies In Finance, Insurance, And Risk Management 7 explores how AI and Automation enhance the basic functions of human resource management
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Bingley
Emerald Publishing Limited
2023
|
| Ausgabe: | 1st ed |
| Schriftenreihe: | Emerald Studies in Finance, Insurance, and Risk Management Series
v.7, Part A |
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Zusammenfassung: | Emerald Studies In Finance, Insurance, And Risk Management 7 explores how AI and Automation enhance the basic functions of human resource management |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (313 Seiten) |
| ISBN: | 9781803820293 |
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| 245 | 1 | 0 | |a The Adoption and Effect of Artificial Intelligence on Human Resources Management |
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| 505 | 8 | |a Intro -- Half Title Page -- Emerald Studies in Finance, Insurance, and Risk Management -- Title Page -- Emerald Publishing Limited -- Contents -- List of Figures and Tables -- About the Editors -- About the Contributors -- Foreword -- Preface -- Chapter 1: Redefining HRM with Artificial Intelligence and Machine Learning -- Introduction -- Artificial Intelligence -- Machine Learning -- HRM With AI and ML -- Literature Review -- Objectives of this Study -- Research Methodology -- Data Analysis -- Sampling -- Conclusion -- References -- Chapter 2: Employee Engagement in the New Normal: Artificial Intelligence as a Buzzword or a Game Changer? -- Introduction -- The Theoretical Framework of the Study -- Research Methodology -- Drivers of Employee Engagement in the New Normal in the Context of AI -- Quality of Work Life -- Diversity and Inclusion -- Communication -- Outcomes of Employee Engagement in the New Normal in the Context of AI -- Decreases Intention to Quit the Organisation -- Increases Productivity -- Employee Well-being -- Conceptual Framework -- Discussion -- Managerial Implication -- Conclusion and Future Work -- References -- Chapter 3: Impact of Artificial Intelligence on Human Capital in Healthcare Sector Post-COVID-19 -- Introduction -- Background of the Study -- Methods -- Result and Discussion -- Realistic Difficulties in the Implementation of Efficient Healthcare Resource Management -- Most Important Issues in the Healthcare Sector During COVID-19 -- Healthcare Personnel -- Follow-up With the Patient -- Shortage of Medical Resources -- AI's Principal Use in Virtual Health Care -- AI-connected Consultation Services and Remote Monitoring Services Delivered Through AI Devices -- The Earlier a Diagnosis Is Detected, The Earlier a Treatment May Be Started -- Connected Electronic Health Records (EHRs) | |
| 505 | 8 | |a Highly Prioritised Patient Scheduling -- Participation in the Formulation of Policy -- Discussion -- How can AI Assist? -- Recommendation of AI Approaches for Implementing in Health Sector -- Customised Programmes for Patient Care and Management -- How Does AI Have the Potential to Aid? -- Giving Policy Advice While Helping to Ensure Successful Cooperation -- What Can AI Do? -- What Are the Differing Policies for Diagnosing and Treating? -- According to Which Policies Are the Priority Shown? -- What Are the Most Effective Policy Reforms? -- What Amount of Faith Do You Have in Policies That Have Been Learned? -- More Quickly to Expedite Clinical Studies -- AI Has the Potential to Aid -- Research Challenges: Accounting for Uncertainty -- AI Can Assist -- What Method Should Be Used to Apply the Approaches Recommended? -- Conclusion -- Scope for Future Research -- References -- Chapter 4: Healthcare Employee Engagement Using the Internet of Things: A Systematic Overview -- 1. Introduction -- 2. Methodology -- 2.1. Selection of Articles -- 2.2. Effectiveness of Theory-based Method -- 3. Theoretical Perspective for Literature Review -- 4. Improve Healthcare Professional Engagement Through IOT -- 4.1. Traditional HRM to AI Integration in the Healthcare Industry -- 5. Impact of the IOT and AI on the Healthcare Industry -- 6. Discussion -- 7. Future Research Directions -- 7.1. Innovative Thoughts/Propositions -- 8. Conclusion -- Conflict of Interest -- References -- Chapter 5: Factor Affecting the Successful Digitalisation of Human Resources -- 1. Introduction -- 2. Review of Literature -- 3. Discussion -- 3.1. Factors Affecting Digitalisation of HR. | |
| 505 | 8 | |a 3.1.1. Technological Factors. A study conducted by Njoku (2019) revealed that the attitude of top management towards digital tools is positively impacted by perceived usefulness and ease of use. However, perceived usefulness was found to have a substantia -- 3.1.2. Organisational Factors. Qualities of the association, its capacities and accessible assets likewise influence the fruitful digitalisation of executives' human support. A review led by Ketolainen (2018) uncovered that size, area, business region and -- 3.1.3. People Factors. Individual's factors incorporate different sub-factors, including top administration support, client acknowledgement, correspondence and cooperation among units, human asset abilities and skill, authority and culture (Bondarouk et a -- 3.2. Consequences of HRM Digitalisation -- 3.2.1. Positive Consequences of HRM Digitalisation. -- 3.2.2. Negative Consequences of HR Digitalisation. -- 4. Conclusion -- References -- Chapter 6: Challenges and Path Ahead for Artificial Intelligence-aided Human Resource Management -- Introduction -- 2. Methodology -- 3. Confluence of AI and HRM -- 4. Challenges of AI Application in HRM -- 5. Findings and Discussion -- 5.1. Conceptual Model for Sustainable Use of AI in HRM -- 5.1.1. Explain and Educate. The HR staff, HR managers, and the top-level management should be educated on the basics of AI algorithms. The end-users of the algorithm should not consider AI as a black box where they can provide the inputs and get the outpu -- 5.2. Empower and Execute -- 5.3. Empathy and Emotions -- 5.4. Ethics and Encapsulation -- 5.5. Evaluate and Evolve -- 6. Conclusion -- References -- Chapter 7: Navigating the Paradigm Shift in HRM Practices Through the Lens of Artificial Intelligence: A Post-pandemic Perspective -- Introduction -- Research Methodology | |
| 505 | 8 | |a HRM Practices Post-pandemic and the Role of AI -- Remote Workforce Management -- Contingent Workforce Management Through AI -- Mindfulness Through AI -- Social Capital Through AI -- The Increasing Role of Employee Engagement -- Reskilling and Upskilling Workforce Towards New Competencies -- Proposed Conceptual Framework -- Discussion -- Managerial Implication -- Future Direction -- Conclusion -- References -- Chapter 8: Recruitment Analytics: Hiring in the Era of Artificial Intelligence -- Introduction -- Technology Organization Environment Theory -- Methodology -- Automated Recruitment Techniques -- Chatbots -- Gamification -- Virtual Environment Interviews -- Resume Screening -- Conclusion -- Future Studies -- References -- Chapter 9: Artificial Intelligence in HRM: Role of Emotional-Social Intelligence and Future Work Skill -- Introduction -- Literature Review -- FWS and the Global Scenario -- FWS and AI -- ESI and Organisational Practices -- ESI and AI -- Purpose -- Methodology -- What is AI? -- Role of FWS in AI for HRM -- Role of ESI in AI for HRM -- Required Skills for the Future -- Data Inquisitiveness -- Business Expertise -- Critical Thinking -- Consistency -- Emotional and Social Intelligence -- Six Functions of HRM -- AI and FWS Model for HRM -- HR - Data Analytics -- Recruiting and Selection Development -- Training and Development -- Appraising Performance Management -- Employee Relations -- Compensation and Providing Benefits -- Findings and Conclusions -- Practical Implications -- References -- Chapter 10: Implementation of Artificial Intelligence in Diagnosing Employees Attrition and Elevation: A Case Study on the IBM Employee Dataset -- 1. Introduction -- 1.1. Objectives -- 2. Literature Review -- 3. Methodology -- 3.1. Classification Algorithms -- 3.1.1. Decision Trees. A decision tree (Duda, Hart, & | |
| 505 | 8 | |a Stork, 2001) is a graphical depiction of certain choice scenarios utilised in structured decisions when complicated branching occurs. Decision trees are used to extract knowledge from a significant qua -- 3.1.2. K-Nearest Neighbor. The KNN algorithm classifies new data based on the category of its nearest neighbours. The nearest neighbour technique is considered lazy since it does not learn a compact model for the data. It is the most fundamental machine l -- 3.1.3. Naive Bayes. According to Miha˘escu (2011), Bayesian classification is a supervised learning approach and a statistical classification method. These classifiers are especially useful when the dimensionality of the input data is high, and the maximu -- 3.1.4. Random Forest. RF, a tree-based method, is well-known in machine learning issues. For classification issues, RF is used. The RF provides numerous random training subsets by constructing multiple decision trees. After that, it builds a tree using ra -- 3.1.5. Logistic Regression. The term 'logistic regression' refers to a subset of linear regression models. When LR creates fundamental possibility classification formulae, it uses the greatest likelihood ratio to do so. LR represents the statistical signi -- 3.1.6. AdaBoost. One of the earliest boosting algorithms to solve problems was AdaBoost. Adaboost makes it possible to merge several 'weak classifiers' into a single 'strong classifier'. The AdaBoost offers a few benefits: robust theoretical underpinnings -- 3.1.7. XG Boost. XGB is a distributed gradient boosting algorithm that has been improved. It is a library that's designed to be very efficient, adaptable, and portable. Many data science challenges can be solved using XGB's parallel tree boosting. XGB is | |
| 505 | 8 | |a 3.1.8. Support Vector Machines. The SVM algorithm is an optimisation-based method. The SVM classifier was first described by Vapnik (1992) and quickly established itself as a powerful tool in the study and resolution of classification problems. Several st | |
| 520 | |a Emerald Studies In Finance, Insurance, And Risk Management 7 explores how AI and Automation enhance the basic functions of human resource management | ||
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| author | Tyagi, Pallavi |
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| contents | Intro -- Half Title Page -- Emerald Studies in Finance, Insurance, and Risk Management -- Title Page -- Emerald Publishing Limited -- Contents -- List of Figures and Tables -- About the Editors -- About the Contributors -- Foreword -- Preface -- Chapter 1: Redefining HRM with Artificial Intelligence and Machine Learning -- Introduction -- Artificial Intelligence -- Machine Learning -- HRM With AI and ML -- Literature Review -- Objectives of this Study -- Research Methodology -- Data Analysis -- Sampling -- Conclusion -- References -- Chapter 2: Employee Engagement in the New Normal: Artificial Intelligence as a Buzzword or a Game Changer? -- Introduction -- The Theoretical Framework of the Study -- Research Methodology -- Drivers of Employee Engagement in the New Normal in the Context of AI -- Quality of Work Life -- Diversity and Inclusion -- Communication -- Outcomes of Employee Engagement in the New Normal in the Context of AI -- Decreases Intention to Quit the Organisation -- Increases Productivity -- Employee Well-being -- Conceptual Framework -- Discussion -- Managerial Implication -- Conclusion and Future Work -- References -- Chapter 3: Impact of Artificial Intelligence on Human Capital in Healthcare Sector Post-COVID-19 -- Introduction -- Background of the Study -- Methods -- Result and Discussion -- Realistic Difficulties in the Implementation of Efficient Healthcare Resource Management -- Most Important Issues in the Healthcare Sector During COVID-19 -- Healthcare Personnel -- Follow-up With the Patient -- Shortage of Medical Resources -- AI's Principal Use in Virtual Health Care -- AI-connected Consultation Services and Remote Monitoring Services Delivered Through AI Devices -- The Earlier a Diagnosis Is Detected, The Earlier a Treatment May Be Started -- Connected Electronic Health Records (EHRs) Highly Prioritised Patient Scheduling -- Participation in the Formulation of Policy -- Discussion -- How can AI Assist? -- Recommendation of AI Approaches for Implementing in Health Sector -- Customised Programmes for Patient Care and Management -- How Does AI Have the Potential to Aid? -- Giving Policy Advice While Helping to Ensure Successful Cooperation -- What Can AI Do? -- What Are the Differing Policies for Diagnosing and Treating? -- According to Which Policies Are the Priority Shown? -- What Are the Most Effective Policy Reforms? -- What Amount of Faith Do You Have in Policies That Have Been Learned? -- More Quickly to Expedite Clinical Studies -- AI Has the Potential to Aid -- Research Challenges: Accounting for Uncertainty -- AI Can Assist -- What Method Should Be Used to Apply the Approaches Recommended? -- Conclusion -- Scope for Future Research -- References -- Chapter 4: Healthcare Employee Engagement Using the Internet of Things: A Systematic Overview -- 1. Introduction -- 2. Methodology -- 2.1. Selection of Articles -- 2.2. Effectiveness of Theory-based Method -- 3. Theoretical Perspective for Literature Review -- 4. Improve Healthcare Professional Engagement Through IOT -- 4.1. Traditional HRM to AI Integration in the Healthcare Industry -- 5. Impact of the IOT and AI on the Healthcare Industry -- 6. Discussion -- 7. Future Research Directions -- 7.1. Innovative Thoughts/Propositions -- 8. Conclusion -- Conflict of Interest -- References -- Chapter 5: Factor Affecting the Successful Digitalisation of Human Resources -- 1. Introduction -- 2. Review of Literature -- 3. Discussion -- 3.1. Factors Affecting Digitalisation of HR. 3.1.1. Technological Factors. A study conducted by Njoku (2019) revealed that the attitude of top management towards digital tools is positively impacted by perceived usefulness and ease of use. However, perceived usefulness was found to have a substantia -- 3.1.2. Organisational Factors. Qualities of the association, its capacities and accessible assets likewise influence the fruitful digitalisation of executives' human support. A review led by Ketolainen (2018) uncovered that size, area, business region and -- 3.1.3. People Factors. Individual's factors incorporate different sub-factors, including top administration support, client acknowledgement, correspondence and cooperation among units, human asset abilities and skill, authority and culture (Bondarouk et a -- 3.2. Consequences of HRM Digitalisation -- 3.2.1. Positive Consequences of HRM Digitalisation. -- 3.2.2. Negative Consequences of HR Digitalisation. -- 4. Conclusion -- References -- Chapter 6: Challenges and Path Ahead for Artificial Intelligence-aided Human Resource Management -- Introduction -- 2. Methodology -- 3. Confluence of AI and HRM -- 4. Challenges of AI Application in HRM -- 5. Findings and Discussion -- 5.1. Conceptual Model for Sustainable Use of AI in HRM -- 5.1.1. Explain and Educate. The HR staff, HR managers, and the top-level management should be educated on the basics of AI algorithms. The end-users of the algorithm should not consider AI as a black box where they can provide the inputs and get the outpu -- 5.2. Empower and Execute -- 5.3. Empathy and Emotions -- 5.4. Ethics and Encapsulation -- 5.5. Evaluate and Evolve -- 6. Conclusion -- References -- Chapter 7: Navigating the Paradigm Shift in HRM Practices Through the Lens of Artificial Intelligence: A Post-pandemic Perspective -- Introduction -- Research Methodology HRM Practices Post-pandemic and the Role of AI -- Remote Workforce Management -- Contingent Workforce Management Through AI -- Mindfulness Through AI -- Social Capital Through AI -- The Increasing Role of Employee Engagement -- Reskilling and Upskilling Workforce Towards New Competencies -- Proposed Conceptual Framework -- Discussion -- Managerial Implication -- Future Direction -- Conclusion -- References -- Chapter 8: Recruitment Analytics: Hiring in the Era of Artificial Intelligence -- Introduction -- Technology Organization Environment Theory -- Methodology -- Automated Recruitment Techniques -- Chatbots -- Gamification -- Virtual Environment Interviews -- Resume Screening -- Conclusion -- Future Studies -- References -- Chapter 9: Artificial Intelligence in HRM: Role of Emotional-Social Intelligence and Future Work Skill -- Introduction -- Literature Review -- FWS and the Global Scenario -- FWS and AI -- ESI and Organisational Practices -- ESI and AI -- Purpose -- Methodology -- What is AI? -- Role of FWS in AI for HRM -- Role of ESI in AI for HRM -- Required Skills for the Future -- Data Inquisitiveness -- Business Expertise -- Critical Thinking -- Consistency -- Emotional and Social Intelligence -- Six Functions of HRM -- AI and FWS Model for HRM -- HR - Data Analytics -- Recruiting and Selection Development -- Training and Development -- Appraising Performance Management -- Employee Relations -- Compensation and Providing Benefits -- Findings and Conclusions -- Practical Implications -- References -- Chapter 10: Implementation of Artificial Intelligence in Diagnosing Employees Attrition and Elevation: A Case Study on the IBM Employee Dataset -- 1. Introduction -- 1.1. Objectives -- 2. Literature Review -- 3. Methodology -- 3.1. Classification Algorithms -- 3.1.1. Decision Trees. A decision tree (Duda, Hart, & Stork, 2001) is a graphical depiction of certain choice scenarios utilised in structured decisions when complicated branching occurs. Decision trees are used to extract knowledge from a significant qua -- 3.1.2. K-Nearest Neighbor. The KNN algorithm classifies new data based on the category of its nearest neighbours. The nearest neighbour technique is considered lazy since it does not learn a compact model for the data. It is the most fundamental machine l -- 3.1.3. Naive Bayes. According to Miha˘escu (2011), Bayesian classification is a supervised learning approach and a statistical classification method. These classifiers are especially useful when the dimensionality of the input data is high, and the maximu -- 3.1.4. Random Forest. RF, a tree-based method, is well-known in machine learning issues. For classification issues, RF is used. The RF provides numerous random training subsets by constructing multiple decision trees. After that, it builds a tree using ra -- 3.1.5. Logistic Regression. The term 'logistic regression' refers to a subset of linear regression models. When LR creates fundamental possibility classification formulae, it uses the greatest likelihood ratio to do so. LR represents the statistical signi -- 3.1.6. AdaBoost. One of the earliest boosting algorithms to solve problems was AdaBoost. Adaboost makes it possible to merge several 'weak classifiers' into a single 'strong classifier'. The AdaBoost offers a few benefits: robust theoretical underpinnings -- 3.1.7. XG Boost. XGB is a distributed gradient boosting algorithm that has been improved. It is a library that's designed to be very efficient, adaptable, and portable. Many data science challenges can be solved using XGB's parallel tree boosting. XGB is 3.1.8. Support Vector Machines. The SVM algorithm is an optimisation-based method. The SVM classifier was first described by Vapnik (1992) and quickly established itself as a powerful tool in the study and resolution of classification problems. Several st |
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| discipline | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
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Introduction -- 2. Methodology -- 2.1. Selection of Articles -- 2.2. Effectiveness of Theory-based Method -- 3. Theoretical Perspective for Literature Review -- 4. Improve Healthcare Professional Engagement Through IOT -- 4.1. Traditional HRM to AI Integration in the Healthcare Industry -- 5. Impact of the IOT and AI on the Healthcare Industry -- 6. Discussion -- 7. Future Research Directions -- 7.1. Innovative Thoughts/Propositions -- 8. Conclusion -- Conflict of Interest -- References -- Chapter 5: Factor Affecting the Successful Digitalisation of Human Resources -- 1. Introduction -- 2. Review of Literature -- 3. Discussion -- 3.1. Factors Affecting Digitalisation of HR.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.1.1. Technological Factors. A study conducted by Njoku (2019) revealed that the attitude of top management towards digital tools is positively impacted by perceived usefulness and ease of use. However, perceived usefulness was found to have a substantia -- 3.1.2. Organisational Factors. Qualities of the association, its capacities and accessible assets likewise influence the fruitful digitalisation of executives' human support. A review led by Ketolainen (2018) uncovered that size, area, business region and -- 3.1.3. People Factors. Individual's factors incorporate different sub-factors, including top administration support, client acknowledgement, correspondence and cooperation among units, human asset abilities and skill, authority and culture (Bondarouk et a -- 3.2. Consequences of HRM Digitalisation -- 3.2.1. Positive Consequences of HRM Digitalisation. -- 3.2.2. Negative Consequences of HR Digitalisation. -- 4. Conclusion -- References -- Chapter 6: Challenges and Path Ahead for Artificial Intelligence-aided Human Resource Management -- Introduction -- 2. Methodology -- 3. Confluence of AI and HRM -- 4. Challenges of AI Application in HRM -- 5. Findings and Discussion -- 5.1. Conceptual Model for Sustainable Use of AI in HRM -- 5.1.1. Explain and Educate. The HR staff, HR managers, and the top-level management should be educated on the basics of AI algorithms. The end-users of the algorithm should not consider AI as a black box where they can provide the inputs and get the outpu -- 5.2. Empower and Execute -- 5.3. Empathy and Emotions -- 5.4. Ethics and Encapsulation -- 5.5. Evaluate and Evolve -- 6. Conclusion -- References -- Chapter 7: Navigating the Paradigm Shift in HRM Practices Through the Lens of Artificial Intelligence: A Post-pandemic Perspective -- Introduction -- Research Methodology</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">HRM Practices Post-pandemic and the Role of AI -- Remote Workforce Management -- Contingent Workforce Management Through AI -- Mindfulness Through AI -- Social Capital Through AI -- The Increasing Role of Employee Engagement -- Reskilling and Upskilling Workforce Towards New Competencies -- Proposed Conceptual Framework -- Discussion -- Managerial Implication -- Future Direction -- Conclusion -- References -- Chapter 8: Recruitment Analytics: Hiring in the Era of Artificial Intelligence -- Introduction -- Technology Organization Environment Theory -- Methodology -- Automated Recruitment Techniques -- Chatbots -- Gamification -- Virtual Environment Interviews -- Resume Screening -- Conclusion -- Future Studies -- References -- Chapter 9: Artificial Intelligence in HRM: Role of Emotional-Social Intelligence and Future Work Skill -- Introduction -- Literature Review -- FWS and the Global Scenario -- FWS and AI -- ESI and Organisational Practices -- ESI and AI -- Purpose -- Methodology -- What is AI? -- Role of FWS in AI for HRM -- Role of ESI in AI for HRM -- Required Skills for the Future -- Data Inquisitiveness -- Business Expertise -- Critical Thinking -- Consistency -- Emotional and Social Intelligence -- Six Functions of HRM -- AI and FWS Model for HRM -- HR - Data Analytics -- Recruiting and Selection Development -- Training and Development -- Appraising Performance Management -- Employee Relations -- Compensation and Providing Benefits -- Findings and Conclusions -- Practical Implications -- References -- Chapter 10: Implementation of Artificial Intelligence in Diagnosing Employees Attrition and Elevation: A Case Study on the IBM Employee Dataset -- 1. Introduction -- 1.1. Objectives -- 2. Literature Review -- 3. Methodology -- 3.1. Classification Algorithms -- 3.1.1. Decision Trees. A decision tree (Duda, Hart, &amp</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Stork, 2001) is a graphical depiction of certain choice scenarios utilised in structured decisions when complicated branching occurs. Decision trees are used to extract knowledge from a significant qua -- 3.1.2. K-Nearest Neighbor. The KNN algorithm classifies new data based on the category of its nearest neighbours. The nearest neighbour technique is considered lazy since it does not learn a compact model for the data. It is the most fundamental machine l -- 3.1.3. Naive Bayes. According to Miha˘escu (2011), Bayesian classification is a supervised learning approach and a statistical classification method. These classifiers are especially useful when the dimensionality of the input data is high, and the maximu -- 3.1.4. Random Forest. RF, a tree-based method, is well-known in machine learning issues. For classification issues, RF is used. The RF provides numerous random training subsets by constructing multiple decision trees. After that, it builds a tree using ra -- 3.1.5. Logistic Regression. The term 'logistic regression' refers to a subset of linear regression models. When LR creates fundamental possibility classification formulae, it uses the greatest likelihood ratio to do so. LR represents the statistical signi -- 3.1.6. AdaBoost. One of the earliest boosting algorithms to solve problems was AdaBoost. Adaboost makes it possible to merge several 'weak classifiers' into a single 'strong classifier'. The AdaBoost offers a few benefits: robust theoretical underpinnings -- 3.1.7. XG Boost. XGB is a distributed gradient boosting algorithm that has been improved. It is a library that's designed to be very efficient, adaptable, and portable. 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| id | DE-604.BV049874501 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-05T17:02:58Z |
| institution | BVB |
| isbn | 9781803820293 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035213959 |
| oclc_num | 1369650132 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (313 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Emerald Publishing Limited |
| record_format | marc |
| series2 | Emerald Studies in Finance, Insurance, and Risk Management Series |
| spelling | Tyagi, Pallavi Verfasser aut The Adoption and Effect of Artificial Intelligence on Human Resources Management 1st ed Bingley Emerald Publishing Limited 2023 ©2023 1 Online-Ressource (313 Seiten) txt rdacontent c rdamedia cr rdacarrier Emerald Studies in Finance, Insurance, and Risk Management Series v.7, Part A Description based on publisher supplied metadata and other sources Intro -- Half Title Page -- Emerald Studies in Finance, Insurance, and Risk Management -- Title Page -- Emerald Publishing Limited -- Contents -- List of Figures and Tables -- About the Editors -- About the Contributors -- Foreword -- Preface -- Chapter 1: Redefining HRM with Artificial Intelligence and Machine Learning -- Introduction -- Artificial Intelligence -- Machine Learning -- HRM With AI and ML -- Literature Review -- Objectives of this Study -- Research Methodology -- Data Analysis -- Sampling -- Conclusion -- References -- Chapter 2: Employee Engagement in the New Normal: Artificial Intelligence as a Buzzword or a Game Changer? -- Introduction -- The Theoretical Framework of the Study -- Research Methodology -- Drivers of Employee Engagement in the New Normal in the Context of AI -- Quality of Work Life -- Diversity and Inclusion -- Communication -- Outcomes of Employee Engagement in the New Normal in the Context of AI -- Decreases Intention to Quit the Organisation -- Increases Productivity -- Employee Well-being -- Conceptual Framework -- Discussion -- Managerial Implication -- Conclusion and Future Work -- References -- Chapter 3: Impact of Artificial Intelligence on Human Capital in Healthcare Sector Post-COVID-19 -- Introduction -- Background of the Study -- Methods -- Result and Discussion -- Realistic Difficulties in the Implementation of Efficient Healthcare Resource Management -- Most Important Issues in the Healthcare Sector During COVID-19 -- Healthcare Personnel -- Follow-up With the Patient -- Shortage of Medical Resources -- AI's Principal Use in Virtual Health Care -- AI-connected Consultation Services and Remote Monitoring Services Delivered Through AI Devices -- The Earlier a Diagnosis Is Detected, The Earlier a Treatment May Be Started -- Connected Electronic Health Records (EHRs) Highly Prioritised Patient Scheduling -- Participation in the Formulation of Policy -- Discussion -- How can AI Assist? -- Recommendation of AI Approaches for Implementing in Health Sector -- Customised Programmes for Patient Care and Management -- How Does AI Have the Potential to Aid? -- Giving Policy Advice While Helping to Ensure Successful Cooperation -- What Can AI Do? -- What Are the Differing Policies for Diagnosing and Treating? -- According to Which Policies Are the Priority Shown? -- What Are the Most Effective Policy Reforms? -- What Amount of Faith Do You Have in Policies That Have Been Learned? -- More Quickly to Expedite Clinical Studies -- AI Has the Potential to Aid -- Research Challenges: Accounting for Uncertainty -- AI Can Assist -- What Method Should Be Used to Apply the Approaches Recommended? -- Conclusion -- Scope for Future Research -- References -- Chapter 4: Healthcare Employee Engagement Using the Internet of Things: A Systematic Overview -- 1. Introduction -- 2. Methodology -- 2.1. Selection of Articles -- 2.2. Effectiveness of Theory-based Method -- 3. Theoretical Perspective for Literature Review -- 4. Improve Healthcare Professional Engagement Through IOT -- 4.1. Traditional HRM to AI Integration in the Healthcare Industry -- 5. Impact of the IOT and AI on the Healthcare Industry -- 6. Discussion -- 7. Future Research Directions -- 7.1. Innovative Thoughts/Propositions -- 8. Conclusion -- Conflict of Interest -- References -- Chapter 5: Factor Affecting the Successful Digitalisation of Human Resources -- 1. Introduction -- 2. Review of Literature -- 3. Discussion -- 3.1. Factors Affecting Digitalisation of HR. 3.1.1. Technological Factors. A study conducted by Njoku (2019) revealed that the attitude of top management towards digital tools is positively impacted by perceived usefulness and ease of use. However, perceived usefulness was found to have a substantia -- 3.1.2. Organisational Factors. Qualities of the association, its capacities and accessible assets likewise influence the fruitful digitalisation of executives' human support. A review led by Ketolainen (2018) uncovered that size, area, business region and -- 3.1.3. People Factors. Individual's factors incorporate different sub-factors, including top administration support, client acknowledgement, correspondence and cooperation among units, human asset abilities and skill, authority and culture (Bondarouk et a -- 3.2. Consequences of HRM Digitalisation -- 3.2.1. Positive Consequences of HRM Digitalisation. -- 3.2.2. Negative Consequences of HR Digitalisation. -- 4. Conclusion -- References -- Chapter 6: Challenges and Path Ahead for Artificial Intelligence-aided Human Resource Management -- Introduction -- 2. Methodology -- 3. Confluence of AI and HRM -- 4. Challenges of AI Application in HRM -- 5. Findings and Discussion -- 5.1. Conceptual Model for Sustainable Use of AI in HRM -- 5.1.1. Explain and Educate. The HR staff, HR managers, and the top-level management should be educated on the basics of AI algorithms. The end-users of the algorithm should not consider AI as a black box where they can provide the inputs and get the outpu -- 5.2. Empower and Execute -- 5.3. Empathy and Emotions -- 5.4. Ethics and Encapsulation -- 5.5. Evaluate and Evolve -- 6. Conclusion -- References -- Chapter 7: Navigating the Paradigm Shift in HRM Practices Through the Lens of Artificial Intelligence: A Post-pandemic Perspective -- Introduction -- Research Methodology HRM Practices Post-pandemic and the Role of AI -- Remote Workforce Management -- Contingent Workforce Management Through AI -- Mindfulness Through AI -- Social Capital Through AI -- The Increasing Role of Employee Engagement -- Reskilling and Upskilling Workforce Towards New Competencies -- Proposed Conceptual Framework -- Discussion -- Managerial Implication -- Future Direction -- Conclusion -- References -- Chapter 8: Recruitment Analytics: Hiring in the Era of Artificial Intelligence -- Introduction -- Technology Organization Environment Theory -- Methodology -- Automated Recruitment Techniques -- Chatbots -- Gamification -- Virtual Environment Interviews -- Resume Screening -- Conclusion -- Future Studies -- References -- Chapter 9: Artificial Intelligence in HRM: Role of Emotional-Social Intelligence and Future Work Skill -- Introduction -- Literature Review -- FWS and the Global Scenario -- FWS and AI -- ESI and Organisational Practices -- ESI and AI -- Purpose -- Methodology -- What is AI? -- Role of FWS in AI for HRM -- Role of ESI in AI for HRM -- Required Skills for the Future -- Data Inquisitiveness -- Business Expertise -- Critical Thinking -- Consistency -- Emotional and Social Intelligence -- Six Functions of HRM -- AI and FWS Model for HRM -- HR - Data Analytics -- Recruiting and Selection Development -- Training and Development -- Appraising Performance Management -- Employee Relations -- Compensation and Providing Benefits -- Findings and Conclusions -- Practical Implications -- References -- Chapter 10: Implementation of Artificial Intelligence in Diagnosing Employees Attrition and Elevation: A Case Study on the IBM Employee Dataset -- 1. Introduction -- 1.1. Objectives -- 2. Literature Review -- 3. Methodology -- 3.1. Classification Algorithms -- 3.1.1. Decision Trees. A decision tree (Duda, Hart, & Stork, 2001) is a graphical depiction of certain choice scenarios utilised in structured decisions when complicated branching occurs. Decision trees are used to extract knowledge from a significant qua -- 3.1.2. K-Nearest Neighbor. The KNN algorithm classifies new data based on the category of its nearest neighbours. The nearest neighbour technique is considered lazy since it does not learn a compact model for the data. It is the most fundamental machine l -- 3.1.3. Naive Bayes. According to Miha˘escu (2011), Bayesian classification is a supervised learning approach and a statistical classification method. These classifiers are especially useful when the dimensionality of the input data is high, and the maximu -- 3.1.4. Random Forest. RF, a tree-based method, is well-known in machine learning issues. For classification issues, RF is used. The RF provides numerous random training subsets by constructing multiple decision trees. After that, it builds a tree using ra -- 3.1.5. Logistic Regression. The term 'logistic regression' refers to a subset of linear regression models. When LR creates fundamental possibility classification formulae, it uses the greatest likelihood ratio to do so. LR represents the statistical signi -- 3.1.6. AdaBoost. One of the earliest boosting algorithms to solve problems was AdaBoost. Adaboost makes it possible to merge several 'weak classifiers' into a single 'strong classifier'. The AdaBoost offers a few benefits: robust theoretical underpinnings -- 3.1.7. XG Boost. XGB is a distributed gradient boosting algorithm that has been improved. It is a library that's designed to be very efficient, adaptable, and portable. Many data science challenges can be solved using XGB's parallel tree boosting. XGB is 3.1.8. Support Vector Machines. The SVM algorithm is an optimisation-based method. The SVM classifier was first described by Vapnik (1992) and quickly established itself as a powerful tool in the study and resolution of classification problems. Several st Emerald Studies In Finance, Insurance, And Risk Management 7 explores how AI and Automation enhance the basic functions of human resource management Artificial intelligence Digitalisierung (DE-588)4123065-6 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Personalwesen (DE-588)4076000-5 gnd rswk-swf Humanvermögen (DE-588)4240300-5 gnd rswk-swf Personalentwicklung (DE-588)4121465-1 gnd rswk-swf Humanvermögen (DE-588)4240300-5 s Personalentwicklung (DE-588)4121465-1 s Personalwesen (DE-588)4076000-5 s Künstliche Intelligenz (DE-588)4033447-8 s Digitalisierung (DE-588)4123065-6 s DE-604 Chilamkurti, Naveen Sonstige oth Grima, Simon Sonstige oth Sood, Kiran Sonstige oth Balusamy, Balamurugan Sonstige oth Erscheint auch als Druck-Ausgabe Tyagi, Pallavi The Adoption and Effect of Artificial Intelligence on Human Resources Management Bingley : Emerald Publishing Limited,c2023 9781803820286 |
| spellingShingle | Tyagi, Pallavi The Adoption and Effect of Artificial Intelligence on Human Resources Management Intro -- Half Title Page -- Emerald Studies in Finance, Insurance, and Risk Management -- Title Page -- Emerald Publishing Limited -- Contents -- List of Figures and Tables -- About the Editors -- About the Contributors -- Foreword -- Preface -- Chapter 1: Redefining HRM with Artificial Intelligence and Machine Learning -- Introduction -- Artificial Intelligence -- Machine Learning -- HRM With AI and ML -- Literature Review -- Objectives of this Study -- Research Methodology -- Data Analysis -- Sampling -- Conclusion -- References -- Chapter 2: Employee Engagement in the New Normal: Artificial Intelligence as a Buzzword or a Game Changer? -- Introduction -- The Theoretical Framework of the Study -- Research Methodology -- Drivers of Employee Engagement in the New Normal in the Context of AI -- Quality of Work Life -- Diversity and Inclusion -- Communication -- Outcomes of Employee Engagement in the New Normal in the Context of AI -- Decreases Intention to Quit the Organisation -- Increases Productivity -- Employee Well-being -- Conceptual Framework -- Discussion -- Managerial Implication -- Conclusion and Future Work -- References -- Chapter 3: Impact of Artificial Intelligence on Human Capital in Healthcare Sector Post-COVID-19 -- Introduction -- Background of the Study -- Methods -- Result and Discussion -- Realistic Difficulties in the Implementation of Efficient Healthcare Resource Management -- Most Important Issues in the Healthcare Sector During COVID-19 -- Healthcare Personnel -- Follow-up With the Patient -- Shortage of Medical Resources -- AI's Principal Use in Virtual Health Care -- AI-connected Consultation Services and Remote Monitoring Services Delivered Through AI Devices -- The Earlier a Diagnosis Is Detected, The Earlier a Treatment May Be Started -- Connected Electronic Health Records (EHRs) Highly Prioritised Patient Scheduling -- Participation in the Formulation of Policy -- Discussion -- How can AI Assist? -- Recommendation of AI Approaches for Implementing in Health Sector -- Customised Programmes for Patient Care and Management -- How Does AI Have the Potential to Aid? -- Giving Policy Advice While Helping to Ensure Successful Cooperation -- What Can AI Do? -- What Are the Differing Policies for Diagnosing and Treating? -- According to Which Policies Are the Priority Shown? -- What Are the Most Effective Policy Reforms? -- What Amount of Faith Do You Have in Policies That Have Been Learned? -- More Quickly to Expedite Clinical Studies -- AI Has the Potential to Aid -- Research Challenges: Accounting for Uncertainty -- AI Can Assist -- What Method Should Be Used to Apply the Approaches Recommended? -- Conclusion -- Scope for Future Research -- References -- Chapter 4: Healthcare Employee Engagement Using the Internet of Things: A Systematic Overview -- 1. Introduction -- 2. Methodology -- 2.1. Selection of Articles -- 2.2. Effectiveness of Theory-based Method -- 3. Theoretical Perspective for Literature Review -- 4. Improve Healthcare Professional Engagement Through IOT -- 4.1. Traditional HRM to AI Integration in the Healthcare Industry -- 5. Impact of the IOT and AI on the Healthcare Industry -- 6. Discussion -- 7. Future Research Directions -- 7.1. Innovative Thoughts/Propositions -- 8. Conclusion -- Conflict of Interest -- References -- Chapter 5: Factor Affecting the Successful Digitalisation of Human Resources -- 1. Introduction -- 2. Review of Literature -- 3. Discussion -- 3.1. Factors Affecting Digitalisation of HR. 3.1.1. Technological Factors. A study conducted by Njoku (2019) revealed that the attitude of top management towards digital tools is positively impacted by perceived usefulness and ease of use. However, perceived usefulness was found to have a substantia -- 3.1.2. Organisational Factors. Qualities of the association, its capacities and accessible assets likewise influence the fruitful digitalisation of executives' human support. A review led by Ketolainen (2018) uncovered that size, area, business region and -- 3.1.3. People Factors. Individual's factors incorporate different sub-factors, including top administration support, client acknowledgement, correspondence and cooperation among units, human asset abilities and skill, authority and culture (Bondarouk et a -- 3.2. Consequences of HRM Digitalisation -- 3.2.1. Positive Consequences of HRM Digitalisation. -- 3.2.2. Negative Consequences of HR Digitalisation. -- 4. Conclusion -- References -- Chapter 6: Challenges and Path Ahead for Artificial Intelligence-aided Human Resource Management -- Introduction -- 2. Methodology -- 3. Confluence of AI and HRM -- 4. Challenges of AI Application in HRM -- 5. Findings and Discussion -- 5.1. Conceptual Model for Sustainable Use of AI in HRM -- 5.1.1. Explain and Educate. The HR staff, HR managers, and the top-level management should be educated on the basics of AI algorithms. The end-users of the algorithm should not consider AI as a black box where they can provide the inputs and get the outpu -- 5.2. Empower and Execute -- 5.3. Empathy and Emotions -- 5.4. Ethics and Encapsulation -- 5.5. Evaluate and Evolve -- 6. Conclusion -- References -- Chapter 7: Navigating the Paradigm Shift in HRM Practices Through the Lens of Artificial Intelligence: A Post-pandemic Perspective -- Introduction -- Research Methodology HRM Practices Post-pandemic and the Role of AI -- Remote Workforce Management -- Contingent Workforce Management Through AI -- Mindfulness Through AI -- Social Capital Through AI -- The Increasing Role of Employee Engagement -- Reskilling and Upskilling Workforce Towards New Competencies -- Proposed Conceptual Framework -- Discussion -- Managerial Implication -- Future Direction -- Conclusion -- References -- Chapter 8: Recruitment Analytics: Hiring in the Era of Artificial Intelligence -- Introduction -- Technology Organization Environment Theory -- Methodology -- Automated Recruitment Techniques -- Chatbots -- Gamification -- Virtual Environment Interviews -- Resume Screening -- Conclusion -- Future Studies -- References -- Chapter 9: Artificial Intelligence in HRM: Role of Emotional-Social Intelligence and Future Work Skill -- Introduction -- Literature Review -- FWS and the Global Scenario -- FWS and AI -- ESI and Organisational Practices -- ESI and AI -- Purpose -- Methodology -- What is AI? -- Role of FWS in AI for HRM -- Role of ESI in AI for HRM -- Required Skills for the Future -- Data Inquisitiveness -- Business Expertise -- Critical Thinking -- Consistency -- Emotional and Social Intelligence -- Six Functions of HRM -- AI and FWS Model for HRM -- HR - Data Analytics -- Recruiting and Selection Development -- Training and Development -- Appraising Performance Management -- Employee Relations -- Compensation and Providing Benefits -- Findings and Conclusions -- Practical Implications -- References -- Chapter 10: Implementation of Artificial Intelligence in Diagnosing Employees Attrition and Elevation: A Case Study on the IBM Employee Dataset -- 1. Introduction -- 1.1. Objectives -- 2. Literature Review -- 3. Methodology -- 3.1. Classification Algorithms -- 3.1.1. Decision Trees. A decision tree (Duda, Hart, & Stork, 2001) is a graphical depiction of certain choice scenarios utilised in structured decisions when complicated branching occurs. Decision trees are used to extract knowledge from a significant qua -- 3.1.2. K-Nearest Neighbor. The KNN algorithm classifies new data based on the category of its nearest neighbours. The nearest neighbour technique is considered lazy since it does not learn a compact model for the data. It is the most fundamental machine l -- 3.1.3. Naive Bayes. According to Miha˘escu (2011), Bayesian classification is a supervised learning approach and a statistical classification method. These classifiers are especially useful when the dimensionality of the input data is high, and the maximu -- 3.1.4. Random Forest. RF, a tree-based method, is well-known in machine learning issues. For classification issues, RF is used. The RF provides numerous random training subsets by constructing multiple decision trees. After that, it builds a tree using ra -- 3.1.5. Logistic Regression. The term 'logistic regression' refers to a subset of linear regression models. When LR creates fundamental possibility classification formulae, it uses the greatest likelihood ratio to do so. LR represents the statistical signi -- 3.1.6. AdaBoost. One of the earliest boosting algorithms to solve problems was AdaBoost. Adaboost makes it possible to merge several 'weak classifiers' into a single 'strong classifier'. The AdaBoost offers a few benefits: robust theoretical underpinnings -- 3.1.7. XG Boost. XGB is a distributed gradient boosting algorithm that has been improved. It is a library that's designed to be very efficient, adaptable, and portable. Many data science challenges can be solved using XGB's parallel tree boosting. XGB is 3.1.8. Support Vector Machines. The SVM algorithm is an optimisation-based method. The SVM classifier was first described by Vapnik (1992) and quickly established itself as a powerful tool in the study and resolution of classification problems. Several st Artificial intelligence Digitalisierung (DE-588)4123065-6 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Personalwesen (DE-588)4076000-5 gnd Humanvermögen (DE-588)4240300-5 gnd Personalentwicklung (DE-588)4121465-1 gnd |
| subject_GND | (DE-588)4123065-6 (DE-588)4033447-8 (DE-588)4076000-5 (DE-588)4240300-5 (DE-588)4121465-1 |
| title | The Adoption and Effect of Artificial Intelligence on Human Resources Management |
| title_auth | The Adoption and Effect of Artificial Intelligence on Human Resources Management |
| title_exact_search | The Adoption and Effect of Artificial Intelligence on Human Resources Management |
| title_full | The Adoption and Effect of Artificial Intelligence on Human Resources Management |
| title_fullStr | The Adoption and Effect of Artificial Intelligence on Human Resources Management |
| title_full_unstemmed | The Adoption and Effect of Artificial Intelligence on Human Resources Management |
| title_short | The Adoption and Effect of Artificial Intelligence on Human Resources Management |
| title_sort | the adoption and effect of artificial intelligence on human resources management |
| topic | Artificial intelligence Digitalisierung (DE-588)4123065-6 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Personalwesen (DE-588)4076000-5 gnd Humanvermögen (DE-588)4240300-5 gnd Personalentwicklung (DE-588)4121465-1 gnd |
| topic_facet | Artificial intelligence Digitalisierung Künstliche Intelligenz Personalwesen Humanvermögen Personalentwicklung |
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