Table 3 Summary of future research... | Emerald Publishing

Table 3

Summary of future research directions

Themes	Topics for future research	Related literature
Antecedents of AI adoption in BM	Factors influencing the motivation and feasibility of an organization to adopt AI Antecedents of AI adoption in different types of firms and industries The role of managerial support and AI education of employees in AI adoption Key drivers in developing AI capabilities The impact of government policy and regulation on AI adoption The role of crises in stimulating the automation of organizational processes	Battisti et al. (2022), Ivanov et al. (2022), Sjödin et al. (2021), Wang and Su (2021)
AI adoption in BM AI-enhanced BM components	The differences between AI adopters and non-adopters Augmentation and automation in various BM components Value co-creation in business ecosystems enhanced by AI AI agents in decision-making processes and customer relationships AI chatbots vs human customer services for customers’ problem-solving Deployment of configurations of AI technologies to optimize operations AI-based scalability of operations AI integration in product design and its impacts on product lifecycle AI’s influence on predictive maintenance AI and blockchain technology use for mapping supply chains Use of AI in employees’ routines and its impact on productivity AI’s impact on workforce skills	Chiu and Chuang (2021), Jin and Shin (2020), Helo et al. (2022), Manser Payne et al. (2021), Minbaeva (2021), Mithas et al. (2022), Rush et al. (2023), Haftor et al. (2021)
AI driving BMI	The role of AI capabilities in driving BMI The role of various stakeholders in the transformation of BM AI-driven BMI in B2B and B2C businesses AI-driven innovation addressing social issues Implications of generative AI for BMI across industries Autonomous solutions effectiveness of AI-based system	Battisti et al. (2022), Burström et al. (2021), Kanbach et al. (2023), Sjödin et al. (2021), Yun et al. (2016)
AI impacting circular economy BM	Choosing appropriate AI technologies and assessing their effectiveness for circular BM Resource efficiency and waste reduction solutions powered by AI AI-enabled recovery practices for end-of-life products Material recycling improvements by AI and blockchain Challenges associated with the implementation of AI-enabled circular BM	Chauhan et al. (2022), Fallahi et al. (2023), Ferreira et al. (2023), Sjödin et al. (2023)
AI as a part of digital transformation	Convergence of AI, IoT and big data analytics to improve BM digitalization Digital servitization BM transitions New intra and inter-organizational forms in the digital era The impact of digital technology on the fluidity of firms’ and ecosystems’ boundaries Managerial and organizational contingencies of the digital transformation process	Mariani and Nambisan (2021), Nguyen Dang Tuan et al. (2019), Sjödin et al. (2023), Volberda et al. (2021)
Outcomes of AI adoption in BM	First- and second-order effects of the implementation of AI in companies AI-enabled key performance indicators (KPI) of BM A payback period of investments in AI Pursuing both customization and cost efficiency with AI The influence of AI on employment, job security, compensation, satisfaction and employee well-being The role of AI in sustainability and resilience Ethical decision-making and corporate social responsibility (CSR) fostered by AI The dark side of AI technologies	Attaran (2020), Bilal et al. (2024), Breidbach and Maglio (2020), Budhwar et al. (2023), Cavazza et al. (2023), Ferreira et al. (2023), Morosan and Dursun-Cengizci (2023)

Themes	Topics for future research	Related literature
Antecedents of AI adoption in BM	Factors influencing the motivation and feasibility of an organization to adopt AI Antecedents of AI adoption in different types of firms and industries The role of managerial support and AI education of employees in AI adoption Key drivers in developing AI capabilities The impact of government policy and regulation on AI adoption The role of crises in stimulating the automation of organizational processes	Battisti et al. (2022), Ivanov et al. (2022), Sjödin et al. (2021), Wang and Su (2021)
AI adoption in BMAI-enhanced BM components	The differences between AI adopters and non-adopters Augmentation and automation in various BM components Value co-creation in business ecosystems enhanced by AI AI agents in decision-making processes and customer relationships AI chatbots vs human customer services for customers’ problem-solving Deployment of configurations of AI technologies to optimize operations AI-based scalability of operations AI integration in product design and its impacts on product lifecycle AI’s influence on predictive maintenance AI and blockchain technology use for mapping supply chains Use of AI in employees’ routines and its impact on productivity AI’s impact on workforce skills	Chiu and Chuang (2021), Jin and Shin (2020), Helo et al. (2022), Manser Payne et al. (2021), Minbaeva (2021), Mithas et al. (2022), Rush et al. (2023), Haftor et al. (2021)
AI driving BMI	The role of AI capabilities in driving BMI The role of various stakeholders in the transformation of BM AI-driven BMI in B2B and B2C businesses AI-driven innovation addressing social issues Implications of generative AI for BMI across industries Autonomous solutions effectiveness of AI-based system	Battisti et al. (2022), Burström et al. (2021), Kanbach et al. (2023), Sjödin et al. (2021), Yun et al. (2016)
AI impacting circular economy BM	Choosing appropriate AI technologies and assessing their effectiveness for circular BM Resource efficiency and waste reduction solutions powered by AI AI-enabled recovery practices for end-of-life products Material recycling improvements by AI and blockchain Challenges associated with the implementation of AI-enabled circular BM	Chauhan et al. (2022), Fallahi et al. (2023), Ferreira et al. (2023), Sjödin et al. (2023)
AI as a part of digital transformation	Convergence of AI, IoT and big data analytics to improve BM digitalization Digital servitization BM transitions New intra and inter-organizational forms in the digital era The impact of digital technology on the fluidity of firms’ and ecosystems’ boundaries Managerial and organizational contingencies of the digital transformation process	Mariani and Nambisan (2021), Nguyen Dang Tuan et al. (2019), Sjödin et al. (2023), Volberda et al. (2021)
Outcomes of AI adoption in BM	First- and second-order effects of the implementation of AI in companies AI-enabled key performance indicators (KPI) of BM A payback period of investments in AI Pursuing both customization and cost efficiency with AI The influence of AI on employment, job security, compensation, satisfaction and employee well-being The role of AI in sustainability and resilience Ethical decision-making and corporate social responsibility (CSR) fostered by AI The dark side of AI technologies	Attaran (2020), Bilal et al. (2024), Breidbach and Maglio (2020), Budhwar et al. (2023), Cavazza et al. (2023), Ferreira et al. (2023), Morosan and Dursun-Cengizci (2023)

Source(s): Authors’ own elaboration