Drivers,barriers and social considerations for AI adoption in business and management: A tertiary study

The number of academic papers in the area of Artificial Intelligence (AI) and its applications across business and management domains has risen significantly in the last decade, and that rise has been followed by an increase in the number of systematic literature reviews.The aim of this study is to provide an overview of existing systematic reviews in this growing area of research and to synthesise the findings related to drivers, barriers and social implications of the AI adoption in business and management.The methodology used for this tertiary study is based on Kitchenham and Charter's guidelines [14], resulting in a selection of 30 reviews published between 2005 and 2019 which are reporting results of 2021 primary studies.These reviews cover the AI adoption across various business sectors (healthcare, information technology, energy, agriculture, apparel industry, engineering, smart cities, tourism and transport), management and business functions (HR, customer services, supply chain, health and safety, project management, decision-support, systems management and technology adoption).While the drivers for the AI adoption in these areas are mainly economic, the barriers are related to the technical aspects (e.g. availability of data, reusability of models) as well as the social considerations such as, increased dependence on non-humans, job security, lack of knowledge, safety, trust and lack of multiple stakeholders’perspectives.Very few reviews outside of the healthcare management domain consider human, organisational and wider societal factors of the AI adoption.In addition to increased focus on social implications of AI, the reviews are recommending more rigorous evaluation, increased use of hybrid solutions (AI and non-AI) and multidisciplinary approach to AI design and evaluation.Furthermore, this study found that there is a lack of systematic reviews in some of the early AI adoption sectors such as financial industry and retail.     

A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application

More and more products in everyday life are using artificial intelligence (AI). The purpose of this research is to investigate influence factors in an acceptance model on behavioral intention and use behavior for products containing AI in an everyday life environment. Using PLS-Analysis, this study analyzes additional influence factors to the UTAUT2 model in the three application segments mobility, household, and health, using a sample of 21,841 respondents. Except for safety security, all additional factors to the UTAUT2 model play a relevant role in explaining behavioral intention and use behavior of products containing AI. This study answers the applicability of an established acceptance model for products that incorporate AI, extended by five additional influencing factors.