List of key drivers for AI incorporation in HSCM
| Area | Key drivers | Explanation | References |
|---|---|---|---|
| Technology (T) | Interoperability (T1) | Indicates the handling and evaluation of the collected data to ensure better communication among stakeholders | Rojas Trejos et al. (2023), Samad et al. (2022) |
| Data quality and integrity (T2) | Refers to usability and credibility of data in terms of reliability and validity | Beduschi (2022), Patil et al. (2023) | |
| Technology advancement (T3) | Issues such as latency and security have been addressed with technological maturity | Kord and Samouei (2023), Samad et al. (2022) | |
| Improved performance output (T4) | Well-defined testing models and tools enhance the performance in the applied area | Kumar et al. (2023), Patil et al. (2023) | |
| Perceived advantages (T5) | It necessitates the adoption of technological advancements owing to anticipated benefits | Hosseini et al. (2023), Patil et al. (2023) | |
| Organization (O) | Organization capacity (O1) | The capability of the organization to invest and handle risks while adopting AI | Cadden et al. (2022) |
| Organizational involvement (O2) | Indicates the support extended by the organization in embracing AI | Cadden et al. (2022), Rahman et al. (2022) | |
| Competitive advantage (O3) | Provides a competitive advantage edge among the competitors in the dynamic business environment | Kumar et al. (2023), Modgil et al. (2022) | |
| Organizational culture (O4) | Refers to consensus among the stakeholders in accepting the adoption of new technology | Cadden et al. (2022) | |
| Organizational preparedness (O5) | Related to the foresightedness of the organization in adopting new technologies for the development of the organization | Dicuonzo et al. (2023), Samad et al. (2022) | |
| Human (H) | Ethical Considerations (H1) | Ensuring transparency and accountability in decision-making with AI | Kamran et al. (2023), Petersson et al. (2022) |
| Data literacy and interpretation (H2) | Humanitarian workers need analytical skills and domain knowledge to interpret data-driven insights from AI outputs | Petersson et al. (2022) | |
| Technical expertise (H3) | Availability of workers with technical competence | Cadden et al. (2022), Pournader et al. (2021) | |
| Training facility (H4) | Organizations must be able to train workers in AI | Beduschi (2022), Dohale et al. (2022) | |
| Guarantee of job security for technologically semi-skilled workers (H5) | AI adoption poses a threat to the job security of technically semi-skilled workers | Beduschi (2022), Kabra et al. (2023) | |
| Institution (I) | Government support (I1) | Financial and infrastructure assistance by the government helps in AI adoption | Kumar et al. (2023), Pournader et al. (2021) |
| Establishment of business collaboration (I2) | Helps in knowledge transfer and mutual benefit for the stakeholders involved | Dubey et al. (2022), Kumar et al. (2023) | |
| Expanded business viability (I3) | Helps in business expansion | Dubey et al. (2022), Kumar et al. (2023) | |
| Improves the functioning of health care sector (I4) | Addresses the problems involved in the healthcare sector | Cadden et al. (2022), Kumar et al. (2023) | |
| Business ecosystem management (I5) | Intricate relationship among the stakeholders and participants needs to be managed | Modgil et al. (2022), Di Vaio et al. (2023) |
| Area | Key drivers | Explanation | References |
|---|---|---|---|
| Technology (T) | Interoperability (T1) | Indicates the handling and evaluation of the collected data to ensure better communication among stakeholders | |
| Data quality and integrity (T2) | Refers to usability and credibility of data in terms of reliability and validity | ||
| Technology advancement (T3) | Issues such as latency and security have been addressed with technological maturity | ||
| Improved performance output (T4) | Well-defined testing models and tools enhance the performance in the applied area | ||
| Perceived advantages (T5) | It necessitates the adoption of technological advancements owing to anticipated benefits | ||
| Organization (O) | Organization capacity (O1) | The capability of the organization to invest and handle risks while adopting AI | |
| Organizational involvement (O2) | Indicates the support extended by the organization in embracing AI | ||
| Competitive advantage (O3) | Provides a competitive advantage edge among the competitors in the dynamic business environment | ||
| Organizational culture (O4) | Refers to consensus among the stakeholders in accepting the adoption of new technology | ||
| Organizational preparedness (O5) | Related to the foresightedness of the organization in adopting new technologies for the development of the organization | ||
| Human (H) | Ethical Considerations (H1) | Ensuring transparency and accountability in decision-making with AI | |
| Data literacy and interpretation (H2) | Humanitarian workers need analytical skills and domain knowledge to interpret data-driven insights from AI outputs | ||
| Technical expertise (H3) | Availability of workers with technical competence | ||
| Training facility (H4) | Organizations must be able to train workers in AI | ||
| Guarantee of job security for technologically semi-skilled workers (H5) | AI adoption poses a threat to the job security of technically semi-skilled workers | ||
| Institution (I) | Government support (I1) | Financial and infrastructure assistance by the government helps in AI adoption | |
| Establishment of business collaboration (I2) | Helps in knowledge transfer and mutual benefit for the stakeholders involved | ||
| Expanded business viability (I3) | Helps in business expansion | ||
| Improves the functioning of health care sector (I4) | Addresses the problems involved in the healthcare sector | ||
| Business ecosystem management (I5) | Intricate relationship among the stakeholders and participants needs to be managed |
Source(s): Authors’ own contributions
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