Main and sub-themes
| Main themes | Sub-themes | Concepts | References | Repetition of themes |
|---|---|---|---|---|
| Organizational and strategic factors | Organizational strategies for facilitating AI adoption | Training and skill development (1): increasing employee knowledge and reducing anxiety/Participation in decision-making (1): Creating opportunities for employee involvement/Reassurance about Job Security (1): reducing employee concerns/Improving employee selection criteria (1): focusing on adaptability to change/Developing AI-specific resources (1): encouraging managers to implement relevant resources/Developing AI literacy (1): enhancing knowledge of HR professionals/Strategic leadership (1): collaboration between leaders and HR development specialists/Stakeholder engagement (1): involving a wide range of stakeholders in the adoption process | Ghosh et al. (2024), Palos-Sánchez et al. (2022), Suseno et al. (2021) | 8 |
| Organizational and leadership factors | Senior management support (3): Supporting AI adoption/Organizational culture (2): Supporting change and innovation/Integrated competency development (2): continuous improvement to address integration/HR managers’ understanding (1): facilitating adoption through increased comprehension/Competitive pressure (1): impact of competition in talent acquisition/Vision and strategy (1): connection with AI implementation/High-performance work systems (1): Reducing anxiety and increasing readiness/Reward system (1): motivation for AI adoption/Continuous learning (1): adapting to AI-induced changes | Pillai and Sivathanu (2020), Joshi et al. (2024), Suseno et al. (2021), Merhi and Harfouche (2024), Tuffaha and Perello-Marin (2022), Dima et al. (2024), Alkudah et al. (2024), Ghosh et al. (2024) | 13 | |
| Strategic and competitive advantages | Creating competitive advantage (2): assisting in talent attraction and retention/Improving recruitment and hiring processes (2): enhancing interview quality and employee matching/Improving performance management (2): automated analysis of performance data/Modernizing processes (1): digitalization of HR/Human-AI integration (1): creating effective interaction models/Focus on human development (1): maintaining focus on employee well-being/Increasing fairness in compensation management (1): creating a more equitable evaluation system | Pillai and Sivathanu (2020), Alkudah et al. (2024), Joshi et al. (2024), Jia et al. (2018), Rathi (2018), Ghosh et al. (2024) | 10 | |
| Technological and operational factors | Technical and infrastructure factors | HR readiness (1): ensuring readiness of personnel and technological resources/Vendor support (1): assistance at each stage of adoption and use/Technology competence (1): positive relationship with AI adoption/Technology complexity (1): negative relationship with AI adoption/Computational power and capacity (1): impact on data processing and productivity/Real-time experience (1): positive impact on rapid decision-making/IT infrastructure (1): prerequisite for digital technologies/Perceived compatibility (1): impact on implementation success/Data quality and complexity (1): importance in output accuracy/Security and privacy (1): necessity of data protection | Pillai and Sivathanu (2020), Pan et al. (2022), Panda et al. (2023), Merhi and Harfouche (2024) | 10 |
| Technology characteristics affecting AI adoption | Perceived usefulness (1): extent of user belief in performance improvement with AI use/Perceived ease of use (1): employees’ belief in the ease of interacting with AI/Compatibility (1): degree of AI alignment with organizational values and needs/Relative advantage (1): degree of perceiving AI as value-adding/Complexity (1): level of difficulty in understanding and using AI/Performance speed (1): high processing and performance speed of AI | Lichtenthaler (2020), Tuffaha and Perello-Marin (2022), Tabor-Błażewicz (2023) | 6 | |
| Operational and functional benefits of AI in HR | Automation of repetitive tasks (3): reducing fatigue and errors, improving variety/Optimization of HR data usage (1): aiding data-driven decision-making/Enhancement of human capabilities (1): increasing specialists’ abilities/Improvement of organizational efficiency (1): enhancing performance by combining procedures and tools/Increasing efficiency of recruitment processes (1): eliminating 75% of related tasks/Effective candidate screening (1): assisting in initial screening and engagement/Automated scheduling (1): automatic booking of meetings and interviews/Big data analysis (1): processing and analyzing large volumes of data/Predicting employee turnover (1): preventing productivity decline | Alkudah et al. (2024), Dima et al. (2024), Geetha and Bhanu (2018), George and Thomas (2019), Palos-Sánchez et al., (2022), Tabor-Błażewicz (2023) | 11 | |
| Human-centric factors | Improving decision-making and reducing bias | Employee performance and sentiment Analysis (1): identifying psycho-emotional characteristics/Reducing bias in HR Processes (1): creating equal opportunities/Quality and unbiased hiring (1): utilizing extensive data/Precise talent matching (1): identifying and matching skills with job requirements/Increasing accuracy in analysis and prediction (1): Improving workforce planning/Relative advantage (1): improving decision-making and candidate selection | Geetha and Bhanu (2018), George and Thomas (2019), Panda et al. (2023), Pillai and Sivathanu (2020) | 6 |
| Enhancing employee and candidate experience | Personalization of training and development (2): creating customized training programs/Improving candidate interaction (1): using chatbots for responses/Facilitating access to information (1): providing easy access to company information/Improving request processes (1): using automated emails and messaging/Facilitating new employee onboarding (1): providing necessary information and resources/Offering career development programs (1): providing individualized training and coaching programs | Geetha and Bhanu (2018), George and Thomas (2019), Jia et al. (2018) | 7 | |
| Psychological and attitudinal factors | Employee attitudes (3): key role in adoption/Concerns about job automation (2): fear of unemployment and resistance to adoption/Trust-building (1): reducing psychological barriers to adoption/Adherence to traditional methods (1): obstacle to full AI utilization/AI anxiety (1): negative impact on adoption readiness/Knowledge and awareness (1): lack of knowledge as an adoption barrier/Skills and competencies (1): lack of skills as an adoption barrier/Technology inclination (1): more positive attitude towards AI/Comfort orientation (1): more positive attitude towards AI/Social acceptance of AI (1): essential condition for successful implementation | Dima et al. (2024), Joshi et al. (2024), Lichtenthaler (2020), Palos-Sánchez et al. (2022), Pillai and Sivathanu (2020), Suseno et al. (2021), Tabor-Błażewicz (2023) | 13 | |
| Challenges and opportunities | Ethical and legal considerations | Ethics (4): integrating ethical aspects for greater transparency and privacy/Security and privacy concerns (3): concern about candidate confidential data/Transparency and accountability (2): increasing trust and acceptance/Legal environment (2): positive relationship between supportive legal environment and adoption/Reducing discrimination and increasing diversity (1): helping identify and eliminate bias patterns/Ethical approach (1): creating ethical frameworks for responsible use/Transparency (1): creating transparency in decision-making processes/Data security and privacy (1): prioritizing employee privacy protection/Ethical and legal issues (1): responsibility for ethical implementation and compliance with laws and regulations | Alkudah et al. (2024), Dima et al. (2024), Ghosh et al. (2024), Joshi et al. (2024), Merhi and Harfouche (2023), Pan et al. (2022), Pillai and Sivathanu (2020), Rathi (2018) | 16 |
| Implementation and management challenges | Technical and systemic challenges (1): technical barriers can delay AI adoption/Lack of expertise (1): organizations often lack necessary expertise for automation adoption/Governance (1): current AI governance efforts are still in early stages/Accountability (1): inability to explain the reason for a particular action can create accountability issues/Lack of empathy and “Human” Approach (1): this deficiency can be a barrier to AI adoption in HR processes/Lack of understanding of complex issues (1): virtual assistants’ inability to understand complex issues can be an adoption barrier/Lack of Creativity (1): lack of creativity in AI can be a negative factor in its adoption/Implementation challenges (1): difficulty in understanding and implementing software or algorithms can be an adoption barrier/Complexity of HR phenomena (1): complexities in HR phenomena can be a barrier to AI adoption and implementation in this field/Transformation of HR procedures (1): AI profoundly transforms HRM and impacts hiring, employee management, and decision-making/Workplace redesign (1): AI changes the form and content of work, necessitating preparation of employees for higher value-added jobs | Alkudah et al. (2024), Dima et al. (2024), Joshi et al. (2024), Palos-Sánchez et al. (2022), Rathi (2018), Tabor-Błażewicz (2023) | 11 | |
| Environmental and economic factors | Environmental and institutional factors | Legal regulations (2): necessity of complying with current and evolving laws as a challenge to AI adoption/Vendor support (2): technical and human resource support from vendors/External pressure (1): competitive pressure for faster AI adoption/Government involvement (1): impact of government policies and laws on encouraging AI dissemination/Vendor participation (1): reducing costs of managing and maintaining technical assets through vendor collaboration/Asset specificity (1): moderating effect on technology complexity and competence/Uncertainty (1): moderating effect on technology competence | Tabor-Błażewicz (2023), Merhi and Harfouche (2024), Tuffaha and Perello-Marin (2023), Pan et al. (2022) | 9 |
| Economic factors | Economic viability (2): reducing labor costs and time required for repetitive tasks/Reducing recruitment time and cost (1): reducing costs by up to 71% and increasing recruiter efficiency | Joshi et al. (2024), Pillai and Sivathanu (2020), George and Thomas (2019) | 3 |
| Main themes | Sub-themes | Concepts | References | Repetition of themes |
|---|---|---|---|---|
| Organizational and strategic factors | Organizational strategies for facilitating AI adoption | Training and skill development (1): increasing employee knowledge and reducing anxiety/Participation in decision-making (1): Creating opportunities for employee involvement/Reassurance about Job Security (1): reducing employee concerns/Improving employee selection criteria (1): focusing on adaptability to change/Developing AI-specific resources (1): encouraging managers to implement relevant resources/Developing AI literacy (1): enhancing knowledge of HR professionals/Strategic leadership (1): collaboration between leaders and HR development specialists/Stakeholder engagement (1): involving a wide range of stakeholders in the adoption process | 8 | |
| Organizational and leadership factors | Senior management support (3): Supporting AI adoption/Organizational culture (2): Supporting change and innovation/Integrated competency development (2): continuous improvement to address integration/HR managers’ understanding (1): facilitating adoption through increased comprehension/Competitive pressure (1): impact of competition in talent acquisition/Vision and strategy (1): connection with AI implementation/High-performance work systems (1): Reducing anxiety and increasing readiness/Reward system (1): motivation for AI adoption/Continuous learning (1): adapting to AI-induced changes | 13 | ||
| Strategic and competitive advantages | Creating competitive advantage (2): assisting in talent attraction and retention/Improving recruitment and hiring processes (2): enhancing interview quality and employee matching/Improving performance management (2): automated analysis of performance data/Modernizing processes (1): digitalization of HR/Human-AI integration (1): creating effective interaction models/Focus on human development (1): maintaining focus on employee well-being/Increasing fairness in compensation management (1): creating a more equitable evaluation system | 10 | ||
| Technological and operational factors | Technical and infrastructure factors | HR readiness (1): ensuring readiness of personnel and technological resources/Vendor support (1): assistance at each stage of adoption and use/Technology competence (1): positive relationship with AI adoption/Technology complexity (1): negative relationship with AI adoption/Computational power and capacity (1): impact on data processing and productivity/Real-time experience (1): positive impact on rapid decision-making/IT infrastructure (1): prerequisite for digital technologies/Perceived compatibility (1): impact on implementation success/Data quality and complexity (1): importance in output accuracy/Security and privacy (1): necessity of data protection | 10 | |
| Technology characteristics affecting AI adoption | Perceived usefulness (1): extent of user belief in performance improvement with AI use/Perceived ease of use (1): employees’ belief in the ease of interacting with AI/Compatibility (1): degree of AI alignment with organizational values and needs/Relative advantage (1): degree of perceiving AI as value-adding/Complexity (1): level of difficulty in understanding and using AI/Performance speed (1): high processing and performance speed of AI | 6 | ||
| Operational and functional benefits of AI in HR | Automation of repetitive tasks (3): reducing fatigue and errors, improving variety/Optimization of HR data usage (1): aiding data-driven decision-making/Enhancement of human capabilities (1): increasing specialists’ abilities/Improvement of organizational efficiency (1): enhancing performance by combining procedures and tools/Increasing efficiency of recruitment processes (1): eliminating 75% of related tasks/Effective candidate screening (1): assisting in initial screening and engagement/Automated scheduling (1): automatic booking of meetings and interviews/Big data analysis (1): processing and analyzing large volumes of data/Predicting employee turnover (1): preventing productivity decline | 11 | ||
| Human-centric factors | Improving decision-making and reducing bias | Employee performance and sentiment Analysis (1): identifying psycho-emotional characteristics/Reducing bias in HR Processes (1): creating equal opportunities/Quality and unbiased hiring (1): utilizing extensive data/Precise talent matching (1): identifying and matching skills with job requirements/Increasing accuracy in analysis and prediction (1): Improving workforce planning/Relative advantage (1): improving decision-making and candidate selection | 6 | |
| Enhancing employee and candidate experience | Personalization of training and development (2): creating customized training programs/Improving candidate interaction (1): using chatbots for responses/Facilitating access to information (1): providing easy access to company information/Improving request processes (1): using automated emails and messaging/Facilitating new employee onboarding (1): providing necessary information and resources/Offering career development programs (1): providing individualized training and coaching programs | 7 | ||
| Psychological and attitudinal factors | Employee attitudes (3): key role in adoption/Concerns about job automation (2): fear of unemployment and resistance to adoption/Trust-building (1): reducing psychological barriers to adoption/Adherence to traditional methods (1): obstacle to full AI utilization/AI anxiety (1): negative impact on adoption readiness/Knowledge and awareness (1): lack of knowledge as an adoption barrier/Skills and competencies (1): lack of skills as an adoption barrier/Technology inclination (1): more positive attitude towards AI/Comfort orientation (1): more positive attitude towards AI/Social acceptance of AI (1): essential condition for successful implementation | 13 | ||
| Challenges and opportunities | Ethical and legal considerations | Ethics (4): integrating ethical aspects for greater transparency and privacy/Security and privacy concerns (3): concern about candidate confidential data/Transparency and accountability (2): increasing trust and acceptance/Legal environment (2): positive relationship between supportive legal environment and adoption/Reducing discrimination and increasing diversity (1): helping identify and eliminate bias patterns/Ethical approach (1): creating ethical frameworks for responsible use/Transparency (1): creating transparency in decision-making processes/Data security and privacy (1): prioritizing employee privacy protection/Ethical and legal issues (1): responsibility for ethical implementation and compliance with laws and regulations | 16 | |
| Implementation and management challenges | Technical and systemic challenges (1): technical barriers can delay AI adoption/Lack of expertise (1): organizations often lack necessary expertise for automation adoption/Governance (1): current AI governance efforts are still in early stages/Accountability (1): inability to explain the reason for a particular action can create accountability issues/Lack of empathy and “Human” Approach (1): this deficiency can be a barrier to AI adoption in HR processes/Lack of understanding of complex issues (1): virtual assistants’ inability to understand complex issues can be an adoption barrier/Lack of Creativity (1): lack of creativity in AI can be a negative factor in its adoption/Implementation challenges (1): difficulty in understanding and implementing software or algorithms can be an adoption barrier/Complexity of HR phenomena (1): complexities in HR phenomena can be a barrier to AI adoption and implementation in this field/Transformation of HR procedures (1): AI profoundly transforms HRM and impacts hiring, employee management, and decision-making/Workplace redesign (1): AI changes the form and content of work, necessitating preparation of employees for higher value-added jobs | 11 | ||
| Environmental and economic factors | Environmental and institutional factors | Legal regulations (2): necessity of complying with current and evolving laws as a challenge to AI adoption/Vendor support (2): technical and human resource support from vendors/External pressure (1): competitive pressure for faster AI adoption/Government involvement (1): impact of government policies and laws on encouraging AI dissemination/Vendor participation (1): reducing costs of managing and maintaining technical assets through vendor collaboration/Asset specificity (1): moderating effect on technology complexity and competence/Uncertainty (1): moderating effect on technology competence | 9 | |
| Economic factors | Economic viability (2): reducing labor costs and time required for repetitive tasks/Reducing recruitment time and cost (1): reducing costs by up to 71% and increasing recruiter efficiency | 3 |
Source(s): Authors’ own work
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