Literature review synthesis
| Sources | Key findings |
|---|---|
| Brynjolfsson and McAfee (2017), Barile et al. (2019) | AI is a transformative, general-purpose technology driving marketing and value creation |
| Teece (2010), Teece and Linden (2017) | Technological innovation needs strategic business models to create value; value capture is complex |
| Huang and Rust (2021, 2022), Volkmar et al. (2022), Davenport et al. (2020) | AI supports automation, decision-making, data interpretation, real-time personalization and service improvement |
| Akter et al. (2023), Füller et al. (2022), Belanche et al. (2020) | AI adoption faces organizational challenges, including uncertainty and resistance |
| Davenport and Ronanki (2018), Camisón and Villar-López (2014), Gama and Magistretti (2025), Raisch and Krakowski (2021), Kellogg et al. (2020) | AI adoption requires strong innovation capabilities and can enhance decision-making and product development |
| Rasheed et al. (2023), Tóth et al. (2022) | Adoption is influenced by perceived benefits and barriers; ethical concerns and accountability drive resistance |
| Macnish et al. (2019), Stahl et al. (2022), Horvath et al. (2023) | Organizations are aware of AI's ethical risks and promote human oversight and responsible implementation |
| Leung et al. (2018) | Excessive or improper AI use may create managerial tension and alienate key stakeholders |
| Kshetri et al. (2024), Stahl and Eke (2024) | AI is triggering a paradigm shift beyond technological innovation through its rapid growth and social acceptance |
| Zheng et al. (2017), Barile et al. (2024) | AI becomes a learning instrument; value is co-created through human–machine interaction |
| Corsaro and D’Amico (2022) | Human expertise is needed to interpret AI-generated data; integration of human and tech factors is essential |
| Holzer (2024), Plaisance (2025), Fine and Kanter (2020) | AI in NPOs brings opportunities (e.g. efficiency, collaboration) and risks (e.g. data quality, ethical concerns, for-profit mimicry) |
| Bouschery et al. (2023), Sedkaoui and Benaichouba (2024) | Hybrid intelligence and human–AI collaboration can unlock synergies |
| Mikalef and Gupta (2021), Saenz et al. (2020), Hauptman et al. (2023), Simòn et al. (2024) | Human–AI integration demands redesigning human operations to align with AI's strengths and organizational needs |
| Simòn et al. (2024) | Value increases when human–AI dialogue enhances interoperability, trust, transparency and organizational alignment |
| Faruq et al. (2024), Jaskyte et al. (2018), Du and Xie (2021), Gaczek et al. (2023) | AI supports marketing in NPOs by improving data analysis and decision-making; effectiveness depends on human input and training |
| Castelo et al. (2019), Newman et al. (2020), Davenport and Kirby (2016), Kolbjørnsrud et al. (2016) | Trust in AI varies; hesitations persist due to lack of transparency and explainability of AI decisions |
| Volkmar et al. (2022), Hoffman and Novak (2018) | AI integration requires reconsidering human–tech interactions and a redesign of roles, strategy, ethics and responsibilities |
| Baharmand et al. (2021), Comes et al. (2018) | Digital tools in NPOs enhance efficiency and accountability but must align with ethical principles due to financial constraints |
| Vogelsang et al. (2021), Brink et al. (2020), Cipriano and Za (2023) | Digital transformation's strategic value for NPOs' marketing is underexplored due to knowledge gaps in frameworks and prerequisites |
| Sources | Key findings |
|---|---|
| AI is a transformative, general-purpose technology driving marketing and value creation | |
| Technological innovation needs strategic business models to create value; value capture is complex | |
| AI supports automation, decision-making, data interpretation, real-time personalization and service improvement | |
| AI adoption faces organizational challenges, including uncertainty and resistance | |
| AI adoption requires strong innovation capabilities and can enhance decision-making and product development | |
| Adoption is influenced by perceived benefits and barriers; ethical concerns and accountability drive resistance | |
| Organizations are aware of AI's ethical risks and promote human oversight and responsible implementation | |
| Excessive or improper AI use may create managerial tension and alienate key stakeholders | |
| AI is triggering a paradigm shift beyond technological innovation through its rapid growth and social acceptance | |
| AI becomes a learning instrument; value is co-created through human–machine interaction | |
| Human expertise is needed to interpret AI-generated data; integration of human and tech factors is essential | |
| AI in NPOs brings opportunities (e.g. efficiency, collaboration) and risks (e.g. data quality, ethical concerns, for-profit mimicry) | |
| Hybrid intelligence and human–AI collaboration can unlock synergies | |
| Human–AI integration demands redesigning human operations to align with AI's strengths and organizational needs | |
| Value increases when human–AI dialogue enhances interoperability, trust, transparency and organizational alignment | |
| AI supports marketing in NPOs by improving data analysis and decision-making; effectiveness depends on human input and training | |
| Trust in AI varies; hesitations persist due to lack of transparency and explainability of AI decisions | |
| AI integration requires reconsidering human–tech interactions and a redesign of roles, strategy, ethics and responsibilities | |
| Digital tools in NPOs enhance efficiency and accountability but must align with ethical principles due to financial constraints | |
| Digital transformation's strategic value for NPOs' marketing is underexplored due to knowledge gaps in frameworks and prerequisites |
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