Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model

Chatterjee, Sheshadri; Rana, Nripendra P.; Dwivedi, Yogesh K.; Baabdullah, Abdullah M.

doi:10.1016/j.techfore.2021.120880

Cited by 296 publications

(205 citation statements)

References 83 publications

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“…ORX also positively impacted the adoption of remote work systems, consistent with previous studies ( Chatterjee, Rana, Dwivedi, & Baabdullah, 2021 ). This concept reveals that when users perceive RWS as not complex and challenging, they perceive the technology as useful and effortless.…”

Section: Discussionsupporting

confidence: 89%

“…This study found that compatibility is not a significant factor for decision-makers to use when implementing RWS in Ghanaian industries. Although, the nonsignificant relationship is consistent with Low et al (2011) and Chatterjee et al (2021) results. There are many reasons to account for the non-significance of organisational compatibility.…”

Section: Discussionsupporting

confidence: 84%

See 1 more Smart Citation

Adoption of contactless technologies for remote work in Ghana post-Covid-19: Insights from technology-organisation-environment framework

Ofosu-Ampong

Acheampong

2022

Digital Business

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 84%

Adoption of contactless technologies for remote work in Ghana post-Covid-19: Insights from technology-organisation-environment framework

Ofosu-Ampong

Acheampong

2022

Digital Business

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“…Previous literature has used technology adoption theories to study AI adoption across various industrial sectors. For example, UTAUT is used in analyzing the adoption of AI tools (Venkatesh, 2021), the technology acceptance model (TAM) is used in AI adoption in the hospitality industry (Pillai & Sivathanu, 2020), and TAM–technology–organizations–environment is used in AI adoption in manufacturing firms (Chatterjee et al, 2021). The role of trust in AI adoption is studied using the valence framework (Bedué & Fritzsche, 2021), and AHP is used in analyzing barriers to AI adoption in healthcare (Al Badi et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective

Kar¹,

Kar

Gupta

2021

Intell Sys Acc Fin Mgmt

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Summary Artificial intelligence (AI) in business processes and academic research in AI has significantly increased. However, the adoption of AI in organizational strategy is yet to be explored in extant literature. This study proposes two conceptual frameworks showing hierarchical relationships among the various drivers and barriers to AI adoption in organizational strategy. In a two‐step approach, the literature study is first done to identify eight drivers of and nine barriers to AI adoption and validated by academic and industry experts. In the second step, MICMAC (matrice d'impacts croises‐multiplication appliqúe a un classment or cross‐impact matrix multiplication applied to classification) analysis categorizes the drivers and barriers to AI adoption in organizational strategy. Total interpretive structural modeling (TISM) is developed to understand the complex and hierarchical associations among the drivers and barriers. This is the first attempt to model the drivers and barriers using a methodology like TISM, which provides a comprehensive conceptual framework with hierarchical relationships and relative importance of the drivers and barriers to AI adoption. AI solutions' decision‐making ability and accuracy are the most influential drivers that influence other driving factors. Lack of an AI adoption strategy, lack of AI talent, and lack of leadership commitment are the most significant barriers that affect other barriers. Recommendations for senior leadership are discussed to focus on the leading drivers and barriers. Also, the limitations and future research scope are addressed.

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“…In addition, other exogenous PEOU variables include technological simplicity and self-efficacy. A study conducted by (Chatterjee et al 2021) with regards to the arising Industry 4.0 shows that the integrated TOE-TAM model had an effect on competitive advantage (Muller et al, 2018). The TAMs studied include PEOU and PU.…”

Section: Perceived Ease Of Use and Adoption E-commercementioning

confidence: 99%

Impact of e-Commerce Adoption on Marketing Performance

Sugandini

Suwardi

Ghofar

2021

bmss

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This review plans to examine the impact of relative advantage, perceived ease of use, and perceived usefulness on the marketing performance of SMEs mediated by the adoption of e-commerce studies on SMEs in Sleman Regency. This study uses a sample of 100 respondents from Prima Village SMEs who have used e-commerce. The sampling technique utilized in this examination is purposive sampling, and the information investigation technique utilized is Partial Least Square (PLS). The results of this study indicate that relative advantage, perceived ease of use, and perceived usefulness have a positive impact on e-commerce adoption. The relative advantage and adoption of e-commerce have an effect on marketing performance.

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Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model

Cited by 296 publications

References 83 publications

Adoption of contactless technologies for remote work in Ghana post-Covid-19: Insights from technology-organisation-environment framework

Adoption of contactless technologies for remote work in Ghana post-Covid-19: Insights from technology-organisation-environment framework

Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective

Impact of e-Commerce Adoption on Marketing Performance

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