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Open research questions in Impact of AI and Big Data on Business and Society

91 gap statements mined from Impact of AI and Big Data on Business and Society papers in our 4.5M-paper local library, which holds 364 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • The gap is attributed to the lack of standardized reporting frameworks and the use of “redundant narratives” to mask a “governance vacuum”.

    Pathways to Green AI: Information Disclosure of Artificial Intelligence Within the ESG Framework of Commercial Entities · 2026 · DOI

  • Finally, a key limitation of this study is the measurement of artificial intelligence (AI) adoption using a disclosure-based textual review of corporate reports.

    The role of artificial intelligence in shaping ESG disclosure evidence from listed companies in Saudi Arabia · 2026 · DOI

  • There is a paucity of research on transitional economies like Croatia. The study aims to address this gap by investigating the social foundations of AI adoption in Croatia.

    Attitudes, Risks and Regulation: The Social Foundations of AI Adoption in Croatia · 2026 · DOI

  • The study notes the challenge of limited resources for research and development and limited access to innovation networks.

    Adoption of Artificial Intelligence and SME Performance in Digital Ecosystems · 2026 · DOI

  • The study does not mention any specific limitations. However, it can be inferred that the study may be limited by its focus on small and medium-sized enterprises in Romania.

    Adoption of Artificial Intelligence and SME Performance in Digital Ecosystems · 2026 · DOI

  • Future research should examine the long-term effects of AI on employee performance. Future research should also examine the impact of AI on employee performance in other sectors and countries.

    The Impact of Artificial Intelligence on Job Performance in Tunisian Telecommunications Companies · 2026 · DOI

  • Existing studies have mostly assessed the emission reduction effect of AI based on linear frameworks. There is a need to explore the nonlinear effects of AI use levels on corporate carbon emission intensity.

    A study on the nonlinear impact and mechanism of artificial intelligence application level on corporate carbon emission intensity · 2026 · DOI

  • The lack of standardised definitions for social metrics. The gap between what organisations disclose and what they actually do. The need for more credible and effective corporate climate action.

    Harnessing Artificial Intelligence for Climate-Aligned Corporate Sustainability: Integrating ESG Frameworks, CSR Mandates, and SDGs toward a Resilient Low-Carbon Economy · 2026 · DOI

  • Existing literature offers limited insight into how AI reshapes the underlying processes of green credit. There is a lack of understanding of the specific mechanisms through which technology influences green credit efficiency.

    The Impact of Artificial Intelligence on Green Credit Efficiency in Commercial Banks: Mechanisms and Evidence · 2026 · DOI

  • The study highlights the need for more research on the relationship between sustainability and financial performance.

    The The Impact of Sustainable Development on Financial Performance with The Role of Artificial Neural Networks as A Moderate Variable in Iraq · 2026 · DOI

  • The study needs to address the complexity of the relationships between AIT, digital economy, and environmental regulation.

    How does artificial intelligence technology promote industrial structure upgrading? The moderating role of environmental regulation · 2026 · DOI

  • The study does not provide a comprehensive analysis of the mechanisms by which AIT affects industrial structure upgrading. The data are limited to Chinese prefecture-level cities.

    How does artificial intelligence technology promote industrial structure upgrading? The moderating role of environmental regulation · 2026 · DOI

  • Further empirical research is needed, particularly regarding leader-AI interactions and the examination of long-term performance effects. In-depth analysis of the organizational impacts, adherence to ethical and regulatory standards, and transparency principles is required.

    Key Factors in the Effective Integration of AI into Executive Decision-Making · 2026 · DOI

  • Existing studies predominantly adopt a technological perspective, while integrated analyses of managerial and organizational implementation dimensions remain limited. The study aims to fill this gap by identifying the key organizational, managerial, and regulatory factors that influence the effectiveness of AI-based decision support.

    Key Factors in the Effective Integration of AI into Executive Decision-Making · 2026 · DOI

  • The results indicate that motivation alone is insufficient; risk awareness and governance determine whether AI leads to measurable improvements in supplier performance.

    Buying artificial intelligence for the public sector: the interplay of digital sourcing governance, motivation and risk awareness · 2026 · DOI

  • All relationships were interpreted as associative rather than strictly causal. The study encourages future studies to apply longitudinal designs and objective productivity indicators.

    Generative Artificial Intelligence Productivity in Indonesian Micro, Small, and Medium Enterprises · 2026 · DOI

  • Future studies are encouraged to apply longitudinal designs and objective productivity indicators. The study suggests examining GenAI adoption across enterprise scales nationally. The study highlights the importance of building capabilities to convert GenAI adoption into sustained performance.

    Generative Artificial Intelligence Productivity in Indonesian Micro, Small, and Medium Enterprises · 2026 · DOI

  • The coordination compression trap can lead to a decline in organizational resilience despite increased productivity. The accumulation of verification debt can reduce organizational resilience. The lack of assurance and verification can lead to fragility and negative outcomes.

    The Coordination Compression Trap: A Dynamic Model of AI-Enabled Productivity, Verification Debt, and Organisational Resilience in Global Knowledge Firms · 2026 · DOI

  • Future research should explore the empirical validity of the model's parameters and assumptions. Future research should investigate the impact of AI on organizational resilience in different contexts and industries. Future research should develop strategies to mitigate the coordination compression trap and promote verified autonomy and governance.

    The Coordination Compression Trap: A Dynamic Model of AI-Enabled Productivity, Verification Debt, and Organisational Resilience in Global Knowledge Firms · 2026 · DOI

  • The lack of AI-based solutions in sugarcane marketing systems. The need for improved market efficiency and transparency.

    Integrating Artificial Intelligence for Sustainable Development of Sugarcane Marketing Systems in Karnataka · 2026 · DOI

  • The study only examined the impact of AI on employee performance in the Tunisian telecommunications sector. The sample size was limited to 85 employees. The study did not examine the long-term effects of AI on employee performance.

    The Impact of Artificial Intelligence on Job Performance in Tunisian Telecommunications Companies · 2026 · DOI

  • Based on the findings of the study, the following recommendations are proposed: 1. AI Training Programs: 2. Conduct regular workshops and skill-development programs for entrepreneurs on AI tools and digital technologies. Increase awareness about government AI schemes, startup grants, and digital transformation programs. Awareness Campaigns: 4. Financial Support: 6.

    AI in Entrepreneurial Development: A Case Study of Dharwad · 2026 · DOI

  • Further research is needed to explore the standardized pathways of ESG-AI information disclosure, - Research should investigate the impact of AI on the environment, society, and governance, - Studies should examine the relationship between AI and the ESG framework in different industries…

    Pathways to Green AI: Information Disclosure of Artificial Intelligence Within the ESG Framework of Commercial Entities · 2026 · DOI

  • Mazkur yondashuv bank mahsulotlari samaradorligini baholashda texnologiyaning mavjudligini emas, balki uning mahsulot qiymat zanjirining qaysi bosqichida va qanday natija bilan qo'llanayotganini hisobga olish zarurligini ko'rsatadi.

    SUN’IY INTELLEKT TEXNOLOGIYALARI ASOSIDA BANK MAHSULOTLARI SAMARADORLIGINI OSHIRISHNING METODOLOGIK YONDASHUVLARI · 2026 · DOI

  • Further research on the adoption of AI in sugarcane marketing systems. Investigation of the impact of AI on farmer income stability and sustainable development.

    Integrating Artificial Intelligence for Sustainable Development of Sugarcane Marketing Systems in Karnataka · 2026 · DOI

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Open questions raised by papers listed on this page include: (1) The gap is attributed to the lack of standardized reporting frameworks and the use of “redundant narratives” to mask a “governance vacuum” (“Pathways to Green AI: Information Disclosure of Artificial Intelligence Within…”, 2026); (2) There is a paucity of research on transitional economies like Croatia (“Attitudes, Risks and Regulation: The Social Foundations of AI Adoption in…”, 2026); (3) Future research should examine the long-term effects of AI on employee performance (“The Impact of Artificial Intelligence on Job Performance in Tunisian…”, 2026); (4) There is a need to explore the nonlinear effects of AI use levels on corporate carbon emission intensity (“A study on the nonlinear impact and mechanism of artificial intelligence…”, 2026); (5) There is a lack of understanding of the specific mechanisms through which technology influences green credit efficiency (“The Impact of Artificial Intelligence on Green Credit Efficiency in Commercial…”, 2026). 91 gap statements have been mined from Impact of AI and Big Data on Business and Society papers in our 4.5M-paper local library, which holds 364 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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