Digital nomads: a systematic literature review and future research agenda

Tourism Review, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 3, 2024

Purpose This study aims to address the need for robust conceptual foundations in digital nomadism discourse through a systematic literature review (SLR). It delves into within sustainable tourism, providing valuable insights foster community development. Design/methodology/approach uses rigorous eight-step process that combines an SLR and system dynamics approach. In phase, authors use theory, context, characteristics methodology framework identify key variables dynamic model of tourism nomadism, following detailed search selection criteria based on PRISMA guidelines. The second phase causal loop diagrams (CLDs) from visualize relationships inform future research directions. CLD is validated literature-based stakeholder interaction processes, focusing social, economic environmental dimensions, resulting development model. Findings identified 28 theories, including agency–structure travel career ladder theory grounded among others. Semi-structured interviews were primary method. Major themes explored encompass interactions, work lifestyles, cultural aspects, financial considerations, infrastructure policy. proposed scrutinize across economic, social foundation investigating promoting tourism. Research limitations/implications Policy implications involve refining taxation policies maintain tax bases, collaborative models enforcing regulations. Additionally, integrating planning implementing demographic strategies manage potential population shifts are crucial. Policies supporting education, skill transfer, health well-being initiatives contribute significantly fostering practices enhancing vitality. Originality/value To best authors’ knowledge, this first space propose adoption.

Language: Английский

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Decoding mood of the Twitterverse on ESG investing: opinion mining and key themes using machine learning

Management Research Review, Journal Year: 2024, Volume and Issue: 47(8), P. 1221 - 1252

Published: March 21, 2024

Purpose Grounded in the stakeholder theory and signaling theory, this study aims to broaden research agenda on environmental, social governance (ESG) investing by uncovering public sentiments key themes using Twitter data spanning from 2009 2022. Design/methodology/approach Using various machine learning models for text tonality analysis topic modeling, scrutinizes 1,842,985 texts extract prevalent ESG trends gauge their sentiment. Findings Gibbs Sampling Dirichlet Multinomial Mixture emerges as optimal modeling method, unveiling significant topics such “Physical risk of climate change,” “Employee Health, Safety well-being” “Water management Scarcity.” RoBERTa, an attention-based model, outperforms other sentiment analysis, revealing a predominantly positive shift toward over past five years. Research limitations/implications This establishes framework alternative data, offering foundation future research. Prospective studies can enhance insights incorporating additional media platforms like LinkedIn Facebook. Practical implications Leveraging unstructured provides novel avenue capture company-related information, supplementing traditional self-reported sustainability disclosures. approach opens new possibilities understanding company’s standing. Social By shedding light perceptions investing, uncovers influential factors that often elude corporate reporting. The findings empower both investors general public, aiding managers refining strategies. Originality/value marks groundbreaking contribution scholarly exploration, best authors’ knowledge, being first analyze context unique advancing emerging field.

Language: Английский

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

Management Decision, Journal Year: 2025, Volume and Issue: unknown

Published: March 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.

Language: 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, Journal Year: 2024, Volume and Issue: 22(3), P. 101070 - 101070

Published: Oct. 16, 2024

Language: Английский

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

Journal of Hospitality & Tourism Education, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 13

Published: Nov. 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.

Language: Английский

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Accuracy Comparison of Machine Learning Algorithms on World Happiness Index Data

Mathematics, Journal Year: 2025, Volume and Issue: 13(7), P. 1176 - 1176

Published: April 2, 2025

This study aims to compare the accuracy performances of different machine learning algorithms (Logistic Regression, Decision Tree, Support Vector Machines (SVMs), Random Forest, Artificial Neural Network, and XGBoost) using World Happiness Index data. The is based on 2024 Report data employs indicators such as Ladder Score, GDP Per Capita, Social Support, Healthy Life Expectancy, Freedom Determine Choices, Generosity, Perception Corruption. Initially, K-Means clustering algorithm applied group countries into four main clusters representing distinct happiness levels their socioeconomic profiles. Subsequently, classification are used predict cluster membership scores obtained serve an indirect measure quality. As a result analysis, Logistic SVM, Network achieve high rates 86.2%, whereas XGBoost exhibits lowest performance at 79.3%. Furthermore, practical implications these findings significant, they provide policymakers with actionable insights develop targeted strategies for enhancing national improving well-being. In conclusion, this offers valuable information more effective analysis by comparing various algorithms.

Language: 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, Journal Year: 2024, Volume and Issue: 209, P. 123829 - 123829

Published: Oct. 19, 2024

Language: Английский

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Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis

Transportation Research Part A Policy and Practice, Journal Year: 2023, Volume and Issue: 179, P. 103947 - 103947

Published: Dec. 29, 2023

Language: Английский

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The Exploration of Predictors for Peruvian Teachers’ Life Satisfaction through an Ensemble of Feature Selection Methods and Machine Learning

Sustainability, Journal Year: 2024, Volume and Issue: 16(17), P. 7532 - 7532

Published: Aug. 30, 2024

Teacher life satisfaction is crucial for their well-being and the educational success of students, both essential elements sustainable development. This study identifies most relevant predictors among Peruvian teachers using machine learning. We analyzed data from National Survey Teachers Public Basic Education Institutions (ENDO-2020) conducted by Ministry Peru, filtering methods (mutual information, analysis variance, chi-square, Spearman’s correlation coefficient) along with embedded (Classification Regression Trees—CART; Random Forest; Gradient Boosting; XGBoost; LightGBM; CatBoost). Subsequently, we generated learning models Decision CatBoost; Support Vector Machine; Multilayer Perceptron. The results reveal that main are health, employment in an institution, living conditions can be provided family, performing teaching duties, as well age, degree confidence Local Management Unit (UGEL), participation continuous training programs, reflection on outcomes practice, work–life balance, number hours dedicated to lesson preparation administrative tasks. Among algorithms used, LightGBM Forest achieved best terms accuracy (0.68), precision (0.55), F1-Score Cohen’s kappa (0.42), Jaccard Score (0.41) LightGBM, (0.67), (0.54), (0.41), (0.41). These have important implications management public policy implementation. By identifying dissatisfied teachers, strategies developed improve and, consequently, quality education, contributing sustainability system. Algorithms such valuable tools management, enabling identification areas improvement optimizing decision-making.

Language: Английский

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

F1000Research, Journal Year: 2024, Volume and Issue: 13, P. 1051 - 1051

Published: Sept. 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.

Language: Английский

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