International Journal of Management

Cross-national sentiment analysis models aim to evaluate emotional tone, public opinion, and behavioral patterns across multiple linguistic and cultural contexts. Unlike monolingual sentiment models trained on single-language datasets, multi-country sentiment models integrate multilingual corpora, cultural lexicons, machine translation pipelines, embedding alignment, and cross-regional semantic calibration. This paper analyzes theoretical foundations, model architectures, and real-world challenges in constructing scalable global sentiment systems. Using conceptual models and hypothetical datasets covering five countries, results illustrate accuracy variations caused by cultural idioms, lexicon bias, sarcasm interpretation, political framing differences, and morphological complexity. A Global Multi-Country Sentiment Model Framework is proposed for future research and real-time global analytics.

Artificial Intelligence has transformed market segmentation by enabling large-scale processing of behavioral, demographic, psychographic, and cultural data. However, traditional segmentation models often treat global markets homogenously, failing to account for cultural variation in beliefs, identity, purchasing attitudes, and communication norms. This study introduces an AI-powered cross-cultural segmentation framework that integrates machine learning, natural language processing, and cultural analytics to derive culturally sensitive consumer clusters across regions. Using social media datasets from five global markets and interviews with AI marketing experts, the analysis reveals that cultural values significantly shape consumer responses to advertising even when observable behaviors appear similar. A multi-layered segmentation model is proposed to optimize communication across culturally diverse audiences.

Big data systems enable large-scale collection, storage, processing, and analysis of personal information across global digital ecosystems. As data travels across jurisdictions with different legal norms, international privacy frameworks become essential to protect user rights, regulate data flows, and reduce cyber risks. This study analyzes various international privacy regimes, including GDPR (EU), CCPA (USA), PDPB (India), PDPA (Singapore), LGPD (Brazil), PIPL (China), and OECD cross-border principles. Using comparative analysis, regulatory interpretation, and hypothetical datasets, the paper evaluates how differing legal definitions, enforcement mechanisms, and cultural norms shape global data governance. Results show that global harmonization is hindered by political sovereignty, economic interests, and technological complexity. A unified policy model is proposed to balance innovation, security, and human rights.

Market intelligence systems (MIS) enable transnational firms to identify emerging opportunities, competitive threats, regulatory shifts, and cultural variations across markets. These systems capture and analyze multi-country data streams to guide strategic decision-making related to pricing, product adaptation, supply chain routing, and political risk management. Using theoretical foundations from information systems, global strategy, and data governance, this paper proposes a Transnational Market Intelligence Framework (TMIF) integrating data infrastructure, knowledge systems, analytics, and localized decision loops. Findings suggest that advanced MIS enhance global competitiveness by improving forecasting accuracy, enabling real-time response, and supporting multi-regional strategy alignment. However, challenges persist in cross-border data regulation, cultural interpretation, interoperability, and bias in automated models.

The complexity of forecasting global demand has increased due to volatile geopolitical conditions, pandemic disruptions, climate shocks, and changing consumer patterns. Traditional econometric models often fail to capture nonlinear relationships, multi-source data streams, and dynamic causal interactions. Machine learning (ML) has emerged as a powerful alternative for predictive analytics, enabling more accurate scenario modeling using high-dimensional data, real-time updates, and cross-market integration. This paper evaluates ML-based forecasting approaches for global demand, comparing statistical, hybrid, and deep-learning models. The study proposes a new Global Demand Forecasting ML Framework (G-DFMLF), integrating feature engineering, multi-modal data fusion, cloud-based pipelines, and explainability protocols. Applications span energy markets, consumer goods, agriculture, pharmaceuticals, and automotive industries.