Table 2. Success factors emanating from... | Emerald Publishing

Table 2.

Success factors emanating from existing literature review

SN	CSF	Supported factor theme	Reference
1	Perceived usefulness (PU)	Technologies that demonstrate their usefulness by improving productivity, efficiency and overall operational effectiveness are more likely to be adopted by manufacturing SMEs	Davis (1989), Gao et al. (2008), Gobin-Rahimbux et al. (2017), Chale and Mbamba (2015), Lubua and Semlambo (2017), Mandari et al. (2021)
2	Perceived ease of use (PEoU)	The ease with which manufacturing SMEs can interact with and seamlessly incorporate these services into their daily tasks determines their adoption	Davis (1989), Gao et al. (2008), Gobin-Rahimbux et al. (2017), Lubua and Semlambo (2017), Shah et al. (2020), Tongora and Ndume (2020), Mandari et al. (2021)
3	Context	AI services must align with contextual factors such as the nature of manufacturing processes, supply chain complexities and market dynamics	Ndesaulwa et al. (2017), Lubua and Semlambo (2017), Msuya et al. (2017), Kabanda and Brown (2017)
4	Personal initiatives and characteristics	The adoption of mobile-based AI services in SMEs relies on personal initiatives and characteristics of individuals within the manufacturing SMEs	Mpunga (2016), Anderson (2017), Ndesaulwa et al. (2017), Ishengoma et al. (2018), Mdasha et al. (2018), (Ishengoma, 2022)
5	Trust	Adopting technologically advanced services, such as mobile-based AI services in SMEs, heavily relies on trust	Mwangi et al. (2014), Chale and Mbamba (2015), Kabanda and Brown (2015, 2017), Amoako (2018), Gamba (2019), Mabula et al. (2020), Nkwabi and Fallon (2020)
6	Infrastructure	The technological infrastructure within manufacturing SMEs is a vital determinant of the feasibility of adopting mobile-based AI services	Mohamed and Mnguu (2014), Kuzilwa and Nyamsogoro (2016), Ndesaulwa et al. (2017), Lubua and Semlambo (2017), Msuya et al. (2017), Kabanda and Brown (2017), Nkwabi and Fallon (2020), Shah et al. (2020), Tongora and Ndume (2020), Ye and Tekka (2020)
7	Cost	In African developing economies, SMEs are more likely to adopt solutions that offer tangible benefits while being affordable and functional	Marwa (2014), Mohamed and Mnguu (2014), Kuzilwa and Nyamsogoro (2016), Mpunga (2016), Anderson (2017), Ndesaulwa et al. (2017), Ishengoma et al. (2018), Mdasha et al. (2018), Gamba (2019), Tongora and Ndume (2020), Bigambo et al. (2023)
8	Mobility	The prevalence of mobility among the SME workforce significantly contributes to creating an optimal environment for adopting technologically advanced innovations such as mobile-based AI services	Donner and Escobari (2010), Aikaeli (2012), Chale and Mbamba (2015), Kabanda and Brown (2017), Ndekwa (2017), Ishengoma et al. (2018),Tongora and Ndume (2020)
9	Observability	The degree to which the results and benefits of an innovation are visible to others	Ndesaulwa et al. (2017), Msuya et al. (2017), Kabanda and Brown (2017), Ye and Tekka, 2020, Mandari et al. (2021)
10	Triability	The degree to which an innovation can be tested on a limited basis	Rogers (1983), Mamun (2018), Ndesaulwa et al. (2017), Wang and Lin (2019), Shah et al. (2020), Ben Hamadi and Fournès (2023)
11	Compatibility	The extent to which an innovation is consistent with the existing values, past experiences and needs of potential adopters	Rogers (1983), Mdasha et al. (2018), Wang and Lin (2019), Gharaibeh et al. (2020), Tongora and Ndume (2020), Bigambo et al. (2023)

SN	CSF	Supported factor theme	Reference
1	Perceived usefulness (PU)	Technologies that demonstrate their usefulness by improving productivity, efficiency and overall operational effectiveness are more likely to be adopted by manufacturing SMEs	Davis (1989), Gao et al. (2008), Gobin-Rahimbux et al. (2017), Chale and Mbamba (2015), Lubua and Semlambo (2017), Mandari et al. (2021)
2	Perceived ease of use (PEoU)	The ease with which manufacturing SMEs can interact with and seamlessly incorporate these services into their daily tasks determines their adoption	Davis (1989), Gao et al. (2008), Gobin-Rahimbux et al. (2017), Lubua and Semlambo (2017), Shah et al. (2020), Tongora and Ndume (2020), Mandari et al. (2021)
3	Context	AI services must align with contextual factors such as the nature of manufacturing processes, supply chain complexities and market dynamics	Ndesaulwa et al. (2017), Lubua and Semlambo (2017), Msuya et al. (2017), Kabanda and Brown (2017)
4	Personal initiatives and characteristics	The adoption of mobile-based AI services in SMEs relies on personal initiatives and characteristics of individuals within the manufacturing SMEs	Mpunga (2016), Anderson (2017), Ndesaulwa et al. (2017), Ishengoma et al. (2018), Mdasha et al. (2018), (Ishengoma, 2022)
5	Trust	Adopting technologically advanced services, such as mobile-based AI services in SMEs, heavily relies on trust	Mwangi et al. (2014), Chale and Mbamba (2015), Kabanda and Brown (2015, 2017), Amoako (2018), Gamba (2019), Mabula et al. (2020), Nkwabi and Fallon (2020)
6	Infrastructure	The technological infrastructure within manufacturing SMEs is a vital determinant of the feasibility of adopting mobile-based AI services	Mohamed and Mnguu (2014), Kuzilwa and Nyamsogoro (2016), Ndesaulwa et al. (2017), Lubua and Semlambo (2017), Msuya et al. (2017), Kabanda and Brown (2017), Nkwabi and Fallon (2020), Shah et al. (2020), Tongora and Ndume (2020), Ye and Tekka (2020)
7	Cost	In African developing economies, SMEs are more likely to adopt solutions that offer tangible benefits while being affordable and functional	Marwa (2014), Mohamed and Mnguu (2014), Kuzilwa and Nyamsogoro (2016), Mpunga (2016), Anderson (2017), Ndesaulwa et al. (2017), Ishengoma et al. (2018), Mdasha et al. (2018), Gamba (2019), Tongora and Ndume (2020), Bigambo et al. (2023)
8	Mobility	The prevalence of mobility among the SME workforce significantly contributes to creating an optimal environment for adopting technologically advanced innovations such as mobile-based AI services	Donner and Escobari (2010), Aikaeli (2012), Chale and Mbamba (2015), Kabanda and Brown (2017), Ndekwa (2017), Ishengoma et al. (2018),Tongora and Ndume (2020)
9	Observability	The degree to which the results and benefits of an innovation are visible to others	Ndesaulwa et al. (2017), Msuya et al. (2017), Kabanda and Brown (2017), Ye and Tekka, 2020, Mandari et al. (2021)
10	Triability	The degree to which an innovation can be tested on a limited basis	Rogers (1983), Mamun (2018), Ndesaulwa et al. (2017), Wang and Lin (2019), Shah et al. (2020), Ben Hamadi and Fournès (2023)
11	Compatibility	The extent to which an innovation is consistent with the existing values, past experiences and needs of potential adopters	Rogers (1983), Mdasha et al. (2018), Wang and Lin (2019), Gharaibeh et al. (2020), Tongora and Ndume (2020), Bigambo et al. (2023)

Source: Authors’ work