Table 1 Summary and synthesis of the... | Emerald Publishing

Table 1

Summary and synthesis of the literature

Literature domian	Sample references	Implications for the current study
Computational studies of AI in T&L	Balster et al. (2020), Tu et al. (2021)	Identify opportunities and potential areas of AI for T&L but not real-world applications
Behavioural studies of AI in T&L	Koreis et al. (2025), Loske and Klumpp (2021), Li et al. (2025), El Bhilat et al. (2024), Fatorachian and Kazemi (2025), Mishra et al. (2024), Shen and Sun (2023), Garg et al. (2025), Richey et al. (2023)	Offer either conceptual or adoption-focused narratives highlighting the benefits of different AI types in T&L, but not the capabilities organisations need to implement and harness AI and the socio-technical foundations
AI capabilities	Bawack et al. (2021), Schuetz and Venkatesh (2020), (Fosso-Wamba et al., 2024a, b), Mikalef and Gupta (2021), Abou-Foul et al. (2023), Shen and Sun (2023)	Offer insights about the technological and organisational perspective of AI capabilities. Our work aligns with organisational perspective and builds on AI-dynamic capabilities
Dynamic capabilities microfoundations	Schilke et al. (2018) Chen et al. (2023), Chatterjee et al. (2022), Mikalef et al. (2021), Steininger et al. (2022), Teece (2007), Warner and Wäger (2019), Teece (2007), Black et al. (2024), Fisher and Aguinis (2017)	Provides a theoretical foundation on how firms develop sensing, seizing and transforming abilities to gain and maintain competitive advantage. We elaborate on the theory of DCs to conceptualise AI-based DCs, as a subset of a firm's overall dynamic capabilities, that reflect the advent of AI and its embeddedness in organisational routines, as well as the sociotechnical microfoundations that explain the AI-business value chain in the T&L domain

Literature domian	Sample references	Implications for the current study
Computational studies of AI in T&L	Balster et al. (2020), Tu et al. (2021)	Identify opportunities and potential areas of AI for T&L but not real-world applications
Behavioural studies of AI in T&L	Koreis et al. (2025), Loske and Klumpp (2021), Li et al. (2025), El Bhilat et al. (2024), Fatorachian and Kazemi (2025), Mishra et al. (2024), Shen and Sun (2023), Garg et al. (2025), Richey et al. (2023)	Offer either conceptual or adoption-focused narratives highlighting the benefits of different AI types in T&L, but not the capabilities organisations need to implement and harness AI and the socio-technical foundations
AI capabilities	Bawack et al. (2021), Schuetz and Venkatesh (2020), (Fosso-Wamba et al., 2024a, b), Mikalef and Gupta (2021), Abou-Foul et al. (2023), Shen and Sun (2023)	Offer insights about the technological and organisational perspective of AI capabilities. Our work aligns with organisational perspective and builds on AI-dynamic capabilities
Dynamic capabilities microfoundations	Schilke et al. (2018)Chen et al. (2023), Chatterjee et al. (2022), Mikalef et al. (2021), Steininger et al. (2022), Teece (2007), Warner and Wäger (2019), Teece (2007), Black et al. (2024), Fisher and Aguinis (2017)	Provides a theoretical foundation on how firms develop sensing, seizing and transforming abilities to gain and maintain competitive advantage. We elaborate on the theory of DCs to conceptualise AI-based DCs, as a subset of a firm's overall dynamic capabilities, that reflect the advent of AI and its embeddedness in organisational routines, as well as the sociotechnical microfoundations that explain the AI-business value chain in the T&L domain

Source(s): Authors’ own work