How can we improve entrepreneurial dynamics in electric vehicle manufacturing for a sustainable future: insights using a deep learning-based hybrid PLS-SEM-ANN approach

International Entrepreneurship and Management Journal, Год журнала: 2025, Номер 21(1)

Опубликована: Янв. 30, 2025

Язык: Английский

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A deep learning-based hybrid PLS-SEM-ANN approach for predicting factors improving AI-driven decision-making proficiency for future leaders

Journal of International Education in Business, Год журнала: 2025, Номер unknown

Опубликована: Март 3, 2025

Purpose This study explores the factors influencing artificial intelligence (AI)-driven decision-making proficiency (AIDP) among management students, focusing on foundational AI knowledge, data literacy, problem-solving, ethical considerations and collaboration skills. The research examines how these competencies enhance self-efficacy engagement, with curriculum design, industry exposure faculty support as moderating factors. aims to provide actionable insights for educational strategies that prepare students AI-driven business environments. Design/methodology/approach adopts a hybrid methodology, integrating partial least squares structural equation modeling (PLS-SEM) neural networks (ANNs), using quantitative collected from 526 across five Indian universities. PLS-SEM model validates linear relationships, while ANN captures nonlinear complexities, complemented by sensitivity analyses deeper insights. Findings results highlight pivotal roles of literacy problem-solving in fostering self-efficacy. Behavioral, cognitive, emotional social engagement significantly influence AIDP. Moderation analysis underscores importance design enhancing efficacy constructs. identifies most critical predictors AIDP, respectively. Research limitations/implications is limited central universities may require contextual adaptation global applications. Future could explore longitudinal impacts AIDP development diverse cultural settings. Practical implications findings designers, policymakers educators integrate into education. Emphasis experiential learning, frameworks interdisciplinary preparing AI-centric landscapes. Social By equipping future leaders proficiency, this contributes societal readiness technological disruptions, promoting sustainable contexts. Originality/value To author’s best uniquely integrates analyze interplay shaping It advances theoretical models linking learning theories practical education strategies, offering comprehensive framework developing students.

Язык: Английский

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Environmental, social and governance-type investing: a multi-stakeholder machine learning analysis

Management Decision, Год журнала: 2025, Номер unknown

Опубликована: Март 26, 2025

Purpose This research delves into the determinants influencing adoption of environmental, social and governance (ESG) investing through an analysis media dialogs using uses gratification theory. Design/methodology/approach study employs a mixed-methods approach, integrating sentiment analysis, topic modeling, clustering, causal loop ethnography to examine ESG-related content on media. Analyzing data, identified key themes derived ten propositions about ESG investing. Industry professionals, financial advisors investors further validated these findings expert interviews. Combining data-driven qualitative insights provides comprehensive understanding how shapes investor preferences decision-making in domain. Findings Environmental aspects, such as conservation, preservation natural resources, renewable clean energy, biodiversity, restoration eco-friendly products technologies, shape attitudes toward Social considerations, including inclusivity, diversity, justice, human rights, stakeholder engagement, transparency, community development philanthropy, significantly influence sentiments. Governance elements accountability, ethical governance, compliance, risk management, regulatory compliance responsible leadership also play pivotal role shaping opinions. Practical implications presents actionable for policymakers organizations by identifying constructs proposing integrated framework that includes mediating factors like resource efficiency engagement alongside moderating environment preferences. Policymakers should establish standardized reporting frameworks, incentivize sustainable practices use data purposes. For businesses, can enhance communication strategies accountability. These measures will foster greater strengthen relations contribute more inclusive global economy. Originality/value To authors' best knowledge, this is first investigate improving based big mined from platforms.

Язык: Английский

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How can we improve AI competencies for tomorrow's leaders: Insights from multi-stakeholders’ interaction

The International Journal of Management Education, Год журнала: 2024, Номер 22(3), С. 101070 - 101070

Опубликована: Окт. 16, 2024

Язык: Английский

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How Can We Improve Hospitality Excellence for Sustainable Development Using Machine Learning

Journal of Hospitality & Tourism Education, Год журнала: 2024, Номер unknown, С. 1 - 13

Опубликована: Ноя. 26, 2024

This study uncovers a significant shift in the hospitality landscape by acknowledging users as stakeholders, transcending traditional consumer-business interactions. It investigates how sentiment analysis and topic modeling of hotel reviews can guide sustainable practices, leveraging stakeholder signaling theories. analyzes large dataset from online travel platforms; research identifies six key themes closely linked to SDGs, offering actionable insights for aligning operations with sustainability objectives. Predominantly positive guest sentiments reflect favorable experiences, highlighting potential enhance customer satisfaction through sustainability. The demonstrates effectiveness machine learning models, particularly Random forest, classification, while LDA effectively relevant themes. Despite limitations such data source constraints, this pioneers new approach integrating user feedback into business contributing valuable managers, policymakers, evolving conscious consumerism industry.

Язык: Английский

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Big data and machine learning-based decision support system to reshape the vaticination of insurance claims

Technological Forecasting and Social Change, Год журнала: 2024, Номер 209, С. 123829 - 123829

Опубликована: Окт. 19, 2024

Язык: Английский

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What we know and what should we know about the future of blockchain in finance

F1000Research, Год журнала: 2024, Номер 13, С. 1051 - 1051

Опубликована: Сен. 12, 2024

Background In response to the transformative impact of blockchain technology on economic and financial landscapes, there is a critical need for review study that analyses knowledge landscape from diverse perspectives. Methods This research VOSviewer, Bibliometrix undertake bibliometric analysis expanding literature related within sector. Through examination 500 published articles, identifies insightful trends, patterns, emerging domains global scale. Results The findings highlight advancing trajectory in finance, with notable concentration studies originating United States China, both terms total publications citations. Key thematic clusters identified include “smart contracts,” “financial institutions,” “initial coin offerings,” “big data analytics.” Intersections risk management, digital transformation, integration big analytics artificial intelligence machine learning are particularly noteworthy, marking focal points exploration. Conclusions While affirming potential blockchain, also sheds light persistent impediments hindering its widespread adoption utilization. not only contributes current understanding finance but serves as valuable resource future researchers. It guides systematic reviews by pinpointing prominent journals influential authors dynamic field thereby fostering deeper facilitating further exploration this evolving field.

Язык: Английский

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Research on Stock Selection and Quantitative Investment Decision-Making Based on Machine Learning

Опубликована: Июль 26, 2024

Язык: Английский

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