Towards an AI-Driven Marketplace for Small Businesses During COVID-19

Coltey, Erik; Alonso, Daniela; Vassigh, Shahin; Chen, Shu‐Ching

doi:10.1007/s42979-022-01349-w

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…This is interpreted positively within the framework of sustainable development, as it supports the practical implementation of Sustainable Development Goal 9(SDG 9), which is formulated as “industrialization, innovation and infrastructure”. It also provides high-tech economic growth, but decreases the need of enterprises for workers and pushes them to reduce staff (Åström et al, 2022 ; Coltey et al, 2022 ; Gloor et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

“Smart” outsourcing in support of the humanization of entrepreneurship in the artificial intelligence economy

Matytsin

Dzedik

Makeeva

et al. 2023

Humanit Soc Sci Commun

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The article focuses on the problem of optimizing human resource management with systematic coverage of economic efficiency and corporate social responsibility. The purpose of the article is to study the role of outsourcing in the humanization of entrepreneurship in the AI economy. The potential of outsourcing in the field of entrepreneurship development in the AI economy is justified through econometric modeling using the regression analysis method on the example of companies from the “Global-500” in 2022. Promising directions for the development of “smart” outsourcing in support of the humanization of entrepreneurship in the economy using the method of comparative analysis are determined through the identification of its advantages in comparison with traditional outsourcing for the humanization of entrepreneurship. Using the case study method, successful examples of the use of “smart” outsourcing in various business operations of entrepreneurship are systematized. The key conclusion is that in the AI economy, the humanization of entrepreneurship can be provided with the help of “smart” outsourcing, which is more preferable than traditional outsourcing due to its increased flexibility, rationality, and efficiency. The theoretical significance lies in clarifying the specifics of outsourcing in the AI economy, as well as in justifying the preference for “smart” outsourcing and in providing a scientific methodology for its implementation. The practical significance is connected with the fact that the proposed practical recommendations on the implementation of “smart” outsourcing allow to improve the efficiency of entrepreneurial activity and strengthen the resilience of businesses to economic crises, supporting economic growth through “smart” outsourcing.

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Section: Introductionmentioning

confidence: 99%

“Smart” outsourcing in support of the humanization of entrepreneurship in the artificial intelligence economy

Matytsin

Dzedik

Makeeva

et al. 2023

Humanit Soc Sci Commun

View full text Add to dashboard Cite

show abstract

“…This section explores these major themes, drawing on recent scholarly research. Coltey et al (2022) discuss the development of an AI-driven marketplace tailored for small businesses during the COVID-19 pandemic. This research highlights the theme of AI's adaptability and responsiveness to market changes and crises.…”

Section: Major Themes Identified In Ai-driven Business Intelligencementioning

confidence: 99%

Business Intelligence Transformation Through Ai and Data Analytics

Emmanuel Osamuyimen Eboigbe,

Oluwatoyin Ajoke Farayola,

Funmilola Olatundun Olatoye

et al. 2023

Eng. sci. technol. j.

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This paper delves into the transformative role of Artificial Intelligence (AI) and Data Analytics in the realm of Business Intelligence (BI), marking a significant shift in the landscape of business decision-making and strategic planning. The study's purpose was to comprehensively explore the evolution of BI, underscored by the integration of AI and advanced data analytics, and to project the future trajectory of these technologies within the business context. Adopting a systematic literature review as its methodology, the study meticulously analyzed a wide array of scholarly articles and industry reports. This approach facilitated a deep understanding of the historical development of BI, the current synergy between AI, Data Analytics, and BI, and the emerging trends shaping their future. The inclusion and exclusion criteria for sources were rigorously applied to ensure the relevance and quality of the information gathered. The findings of the study highlighted a paradigm shift from traditional data processing methods to AI-driven predictive analytics, significantly enhancing the efficiency, accuracy, and predictive capabilities of BI tools. This evolution has redefined business operations, offering unprecedented insights and fostering more informed decision-making processes. Conclusively, the study posits that the integration of AI and Data Analytics into BI is a fundamental, rather than a transient, shift in business operations. It recommends further exploration into the ethical implications of AI in BI, the development of user-friendly AI tools for non-technical users, and an examination of the long-term impacts of AI-driven BI across various industries. The study's classical and engaging tone aims to captivate and inform a diverse audience, from academic researchers to industry practitioners. Keywords: Artificial Intelligence, Business Intelligence, Data Analytics, Predictive Analytics.

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“…The impact of AI on small businesses, particularly during challenging times such as the COVID-19 pandemic, has been profound. Coltey et al (2022) highlight the role of AI in enabling small businesses to navigate rapidly changing market conditions. By leveraging AI-driven tools, such as natural language processing (NLP), businesses can automate market research, adapt to changing conditions, and maintain operational efficiency even in times of crisis.…”

Section: Introduction the Emergence Of Ai In Modern Business: An Over...mentioning

confidence: 99%

Innovative Business Models Driven by Ai Technologies: A Review

Oluwatoyin Ajoke Farayola,

Adekunle Abiola Abdul,

Blessing Otohan Irabor

et al. 2023

Comput. sci. IT res. j.

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In an era where artificial intelligence (AI) is revolutionizing business paradigms, this study delves into the intricacies of AI-driven business models, offering a nuanced understanding of their emergence, evolution, and impact on traditional business strategies. This scholarly inquiry aims to dissect the role of AI in reshaping business models, highlighting the interplay between technological innovation and business strategy. The study meticulously examines the integration of AI into various business facets by employing a systematic and thematic analysis of a diverse range of literature, including academic journals, industry reports, and case studies. This methodological approach facilitates a comprehensive understanding of AI's role in business innovation, addressing both the opportunities and challenges it presents. The findings reveal that AI-driven business models are characterized by enhanced operational efficiency, data-driven decision-making, and customer-centric approaches. These models signify a transformative shift from conventional business strategies, demanding a reevaluation of leadership roles and ethical considerations in the digital age. The study identifies key challenges in AI implementation, such as technical complexities and ethical dilemmas, while uncovering AI's vast opportunities for business growth and competitive advantage. Conclusively, the study recommends a balanced approach to AI integration, emphasizing the need for ethical AI practices, continuous adaptation, and a synergy between AI capabilities and human insights. It advocates for business leaders to embrace AI not just as a technological tool, but as a catalyst for sustainable and innovative business growth. This scholarly work contributes significantly to the discourse on AI in business, providing a foundational framework for future research and practical application in AI-driven business innovation. Keywords: Artificial Intelligence, Business Models, Digital Transformation, AI Integration, Leadership in AI, Ethical AI Practices.

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Towards an AI-Driven Marketplace for Small Businesses During COVID-19

Cited by 3 publications

References 20 publications

“Smart” outsourcing in support of the humanization of entrepreneurship in the artificial intelligence economy

“Smart” outsourcing in support of the humanization of entrepreneurship in the artificial intelligence economy

Business Intelligence Transformation Through Ai and Data Analytics

Innovative Business Models Driven by Ai Technologies: A Review

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