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The purpose of this study is to identify and analyze the key factors influencing stock market movements, using a multifactor hierarchical approach. By applying interpretive structural modeling (ISM) and Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) techniques, this study aims to uncover the interrelationships between these factors and provide a clearer understanding of their role in shaping market dynamics, with practical implications for investors and policymakers.

This study uses ISM and MICMAC analysis to explore the hierarchical relationships among key factors driving stock market movements. A panel of 25 financial market experts was used to develop the structural self-interaction matrix, and ISM was applied to structure the relationships between these factors. MICMAC analysis categorized the factors based on their driving power and dependence. The combined use of ISM and MICMAC provides a structured and quantitative approach to understanding the complexities of stock market dynamics.

The research identifies behavioral biases, corporate governance, interest rates, global events, investor sentiment and market volatility as pivotal factors influencing stock market movements. The hierarchical ISM model reveals that behavioral biases strongly drive investor sentiment, while global events and interest rates heavily impact market volatility. The MICMAC analysis categorizes these variables into autonomous, dependent and independent factors, providing a nuanced understanding of their influence on stock prices.

This study is limited by its reliance on expert judgments, which may introduce bias, and the sample size of 25 experts may not fully capture the diversity of financial market perspectives. In addition, the scope of the study is limited to generalized stock market factors, excluding regional or sector-specific analyses. These limitations affect the generalizability of the findings.

The findings of this research offer practical implications for investors, financial analysts and portfolio managers seeking to navigate the complexities of stock market behavior. By identifying key factors such as behavioral biases, corporate governance, currency fluctuations and regulatory changes, stakeholders can gain a deeper understanding of the dynamics driving stock prices. This structured approach can inform investment strategies, risk management practices and decision-making processes, enabling stakeholders to adapt to market fluctuations and make informed choices that align with their financial goals.

This study’s exploration of factors influencing stock market movements carries social implications that extend beyond financial markets. Understanding how global events, political stability and regulatory changes impact stock prices can shed light on the broader socio-economic landscape. By recognizing the interplay between these factors and their influence on investment decisions, policymakers, regulators and society at large can gain insights into the interconnectedness of financial markets with social and political dynamics. This awareness can inform policy decisions, economic strategies and initiatives aimed at fostering market stability and sustainable economic growth.

By using ISM and expert judgment, this research developed a comprehensive model that unveils the key factors influencing stock market movements. This model can potentially be used to inform investment decision-making and improve investment strategies, providing a structured approach for stakeholders to analyze and adapt to the complexities of stock market behavior.

The aim of this study is to investigate the application of advanced language models, particularly ChatGPT-4, in identifying and utilizing industrial symbiosis opportunities within the circular economy. It examines how the model can aid in promoting sustainable industrial practices by processing data from the MAESTRI project database, which includes various symbiotic relationships, as well as randomly selected waste codes not included in the database. The research involves structured queries related to industrial symbiosis, circular economy, waste codes and potential opportunities. By assessing the model’s accuracy in response generation, the study seeks to uncover both the capabilities and limitations of the language model in resource efficiency and waste reduction, emphasizing the need for ongoing refinement and expert oversight.

The study adopts a mixed-methods approach, combining qualitative and quantitative analyses to explore the potential of ChatGPT-4 in identifying industrial symbiosis opportunities. Data from the EU-funded MAESTRI project database, which includes existing symbiotic relationships, as well as randomly selected waste codes not included in the database, are used as the primary sources. The language model is queried with structured questions on industrial symbiosis, circular economy and specific waste codes utilizing the model’s advanced functions such as file upload. Responses are evaluated by comparing them with the MAESTRI database and official European Waste Catalogue (EWC) codes.

The study finds that ChatGPT-4 possesses a solid understanding of fundamental concepts related to industrial symbiosis and the circular economy. However, it encounters challenges in accurately describing EWC codes, with a notable portion of descriptions found to be incorrect. Despite these inaccuracies, the model shows potential in suggesting symbiotic opportunities, although its effectiveness is limited. Interestingly, the study reveals that the model can occasionally identify correct symbiotic relationships even with initial inaccuracies. These findings highlight the need for expert oversight and further development of the language model to improve its utility in complex, regulated fields like industrial symbiosis.

This study’s originality lies in its exploration of advanced language models, particularly ChatGPT-4, for identifying industrial symbiosis opportunities within the circular economy framework. Unlike previous research, which primarily focuses on specific sectors and AI’s role in general resource efficiency, this study specifically examines the capabilities and limitations of the language model in handling specialized and regulated information, such as EWC codes across various sectors. It employs a novel approach by comparing AI-generated responses with an established symbiosis database, which is comprehensive and spans all sectors rather than being limited to a single industry, as well as with randomly selected waste codes not included in the database. The study contributes to understanding how AI tools can support sustainable industrial practices, emphasizing the importance of refining these models for practical applications in environmental and industrial contexts.

The escalating integration of AI tools like ChatGPT within academia poses a critical challenge regarding their impact on faculty members’ and researchers’ academic performance levels. This paper aims to delve into academic performance within the context of the ChatGPT era by exploring the influence of several pivotal predictors, such as academic integrity, academic competence, personal best goals and perceived stress, as well as the moderating effect of ChatGPT adoption on academic performance.

This study uses a quantitative method to investigate the impact of essential variables on academic integrity, academic competence, perceived stress and personal best goals by analysing 402 responses gathered from ResearchGate and Academia.edu sites.

While affirming the established direct positive relationship between academic integrity and performance since adopting AI tools, this research revealed a significant moderating role of ChatGPT adoption on this relationship. Additionally, the authors shed light on the positive relationship between academic competence and performance in the ChatGPT era and the ChatGPT adoption-moderated interaction of competence and performance. Surprisingly, a negative association emerges between personal best goals and academic performance within ChatGPT-assisted environments. Notably, the study underscores a significant relationship between heightened performance through ChatGPT and increased perceived stress among academicians.

The research advocates formulating clear ethical guidelines, robust support mechanisms and stress-management interventions to maintain academic integrity, enhance competence and prioritise academic professionals’ well-being in navigating the integration of AI tools in modern academia.

This research stands out for its timeliness and the apparent gaps in current literature. There is notably little research on the use of ChatGPT in academic settings, making this investigation among the first to delve into how faculty and researchers in education use OpenAI.

This study aims to examine the ability of Generative Pre-trained Transformer 4 (GPT-4), one of the most powerful large language models, to generate a literature review for peer-reviewed journal publications. The objective is to determine whether business scholars can rely on GPT-4’s assistance with literature reviews and how the nature of human–artificial intelligence (AI) interaction may affect the quality of the reviews generated by GPT-4.

A survey of 30 experienced researchers was conducted to assess the quality of the literature reviews generated by GPT-4 in comparison with a human-authored literature review published in a Social Science Citation Index (SSCI) journal. The data collected were then analyzed with analysis of variance to ascertain whether we may trust GPT-4’s assistance in writing literature reviews.

The statistical analysis reveals that when a highly structured approach being used, GPT-4 can generate a high-quality review comparable to that found in an SSCI journal publication. However, when a less structured approach is used, the generated review lacks comprehensive understating and critical analysis, and is unable to identify literature gaps for future research, although it performed well in adequate synthesis and quality writing. The findings suggest that we may trust GPT-4 to generate literature reviews that align with the publication standards of a peer-reviewed journal when using a structured approach to human–AI interaction.

The findings suggest that we may trust GPT-4 to generate literature reviews that align with the publication standards of a peer-reviewed journal when using a structured approach to human–AI interaction. Nonetheless, cautions should be taken due to the limitations of this study discussed in the text.

By breaking down the specific tasks of a literature review and using a quantitative rather than qualitative assessment method, this study provides robust and more objective findings about the ability of GPT-4 to assist us with a very important research task. The findings of this study should enhance our understanding of how GPT-4 may change our research endeavor and how we may take a full advantage of the advancement in AI technology in the future research.