What will ChatGPT revolutionize in the financial industry?

Ali, Hassnian; Aysan, Ahmet Faruk

doi:10.61351/mf.v1i1.67

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…For instance, the study by Dowling and Lucey [143] explores the potential of ChatGPT in finance research, underscoring its utility in idea generation and data identification, while also highlighting limitations in literature synthesis and developing testing frameworks. Additionally, the work of Ali and Aysan [144], published after our search deadline, delves into the diverse applications of ChatGPT in finance, ranging from customer engagement to stock forecasting. These studies indicate a growing recognition of GAI's potential within the financial sector and propose an expanding research horizon.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review

Al Naqbi,

Bahroun,

Ahmed

2024

Sustainability

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In this review, utilizing the PRISMA methodology, a comprehensive analysis of the use of Generative Artificial Intelligence (GAI) across diverse professional sectors is presented, drawing from 159 selected research publications. This study provides an insightful overview of the impact of GAI on enhancing institutional performance and work productivity, with a specific focus on sectors including academia, research, technology, communications, agriculture, government, and business. It highlights the critical role of GAI in navigating AI challenges, ethical considerations, and the importance of analytical thinking in these domains. The research conducts a detailed content analysis, uncovering significant trends and gaps in current GAI applications and projecting future prospects. A key aspect of this study is the bibliometric analysis, which identifies dominant tools like Chatbots and Conversational Agents, notably ChatGPT, as central to GAI’s evolution. The findings indicate a robust and accelerating trend in GAI research, expected to continue through 2024 and beyond. Additionally, this study points to potential future research directions, emphasizing the need for improved GAI design and strategic long-term planning, particularly in assessing its impact on user experience across various professional fields.

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Section: Conclusion and Future Researchmentioning

confidence: 99%

Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review

Al Naqbi,

Bahroun,

Ahmed

2024

Sustainability

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AI capabilities and export performance: the moderating role of province market development and cultural distance

Chen,

Dong

2024

IJOEM

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PurposeArtificial intelligence (AI) is a significant trend in digital technology that is revolutionizing the field of global business and internationalization. Based on institutional theory and resource-based view, this study examines the intricate relationship between AI capabilities and export performance, taking into account the different province market development within a country and cultural distance.Design/methodology/approachThis study gathered data from the websites of privately owned Chinese exporters and complemented it with a survey in 2023. In conducting the survey, we employed a simple random-sampling approach to select 1,500 exporters in China, with a focus on economic development indicators, particularly GDP contribution. We received 1,000 surveys, but only 749 were valid due to missing data. The study’s comprehensive coverage of regions ensured the inclusion of potential variations and subnational disparities within the country. This study conducted ordinary least squares (OLS) regression, and standardized variables before entering into the regressions.FindingsThe results demonstrate that AI capabilities have a significant positive impact on export performance. In addition, the influence of AI capabilities on export performance varies depending on the home-country and host-country institutional environment. The relationship between AI capabilities and export performance is strengthened by larger cultural distance, while province market development within a country has a negative moderating effect on this relationship. In less developed markets, the AI capabilities can drive significantly export performance. In developed markets with more advanced institutional development, the significance of AI capabilities in reducing transaction costs diminishes due to established institutions and market structures. AI capabilities serve as an intermediary institutional mechanism that connects the institutional context of the home country with the cultural environment of the host country.Originality/valueWhile the impact of AI on international business and internationalization performance is a growing area of study, further exploration of the moderating factors that influence this relationship is needed. Organizations operating in diverse global markets are profoundly shaped by institutional contexts in their operational environments. This research addresses the relatively unexplored role of institutional factors within the home country (provincial market development) and host country (cultural distance) in moderating the effects of AI capabilities on export performance. This study illuminates the intricate dynamics underlying the relationship between AI capabilities and export performance, with a specific focus on province-level market development and cultural distance. Employing institutional theory as the overarching framework, this research sheds light on how AI serves as an intermediary institutional mechanism, bridging the gaps related to cultural differences and varying market development levels. In doing so, it contributes to academia by advancing our understanding of how AI is shaping internationalization dynamics and the interaction between AI capabilities and institutional factors. Additionally, it offers insights for business managers and policymakers to make informed strategic decisions regarding AI capabilities.

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Incorporating Artificial Intelligence into Finance: A Bibliometric Analysis

Alcázar-Blanco,

Rangel-Preciado,

Portillo-Santos

2024

JRFM

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The aim of this study is to carry out an analysis of the intellectual structure of the introduction of AI into finance, in the period from 1995 to 2023, using SciMAT v.1.1.04 software. The results indicate how research on the incorporation of AI in finance has grown significantly, which shows the evolution and importance of this area of research. Eight main topics were obtained in this area: bank, prediction, impact, decision, valuesstock, genetic algorithm, big data analysis, and social data analysis. This study shows us how the incorporation of AI can strongly support the analysis of different financial situations such as decision making or the prediction of movements.

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What will ChatGPT revolutionize in the financial industry?

Cited by 4 publications

References 39 publications

Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review

Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review

AI capabilities and export performance: the moderating role of province market development and cultural distance

Incorporating Artificial Intelligence into Finance: A Bibliometric Analysis

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