Online presence is fast becoming a marketing hub for contemporary businesses. Often known as digital marketing, the phenomenon offers several opportunities to businesses. Small and medium enterprises (SMEs) are using their online presence to launch stern competitive promotions and interact with consumers. Against the backdrop of the stern competition, digital marketing is being utilized to drive sustainable strategies for SMEs. This study leverages the theory of planned behavior to explore the impact of digital marketing adoption on the sustainable growth of SMEs in Ghana. Using a structured questionnaire and SmartPLS version 3.3 for the data analysis, 533 owners/managers of SMEs in Ghana were drawn to administer the questionnaire. Our findings suggest that, while attitudes toward digital marketing did not influence the intention to use digital marketing, perceived behavior control and subjective norms were found to affect individuals’ intentions to use digital marketing. Additionally, the results proved a direct positive link between subjective norms and actual behavioral use of digital marketing. Finally, the relationship between the actual use of digital marketing and SMEs’ sustainable growth was also proven positive, affirming that digital marketing significantly improved the sustainable growth of SMEs in developing countries. This study contributes to the multiplicity of factors that influence the behavioral tendencies of managers of firms in their quest to adopt digital platforms to enhance their sustainable growth. The study’s results serve as guidelines for prospective adopters of digital platforms as they develop their sustainability strategies.

This paper examines the risks linked to E-business collaboration between China and West Africa, with particular emphasis on Ghana as a pivotal digital commerce centre. This research employs the Hesitant Fuzzy Best–Worst Method (HF-BWM) to systematically identify and prioritise the institutional, technological, sociocultural, and legal issues affecting cross-border e-business operations. This study combines Transaction Cost Theory (TCT), the Technology Acceptance Model (TAM), and Commitment–Trust Theory to create a comprehensive framework for analysing the interplay of these risks and their effects on transaction costs and company sustainability. The findings indicate that institutional risks constitute the most substantial obstacles, with deficient digital transaction legislation and inadequate data governance recognised as the principal drivers of uncertainty and increased transaction costs. The research indicates that these institutional challenges necessitate immediate focus, as they immediately affect corporate operations, especially in international digital commerce. Technological risks, such as cybersecurity vulnerabilities, insufficient IT skills, and deficiencies in digital infrastructure, were identified as the second most critical factors, leading to considerable operational disruptions and heightened expenses. Sociocultural hazards, such as language difficulties and varying consumer behaviours, were recognised as moderate concerns that, although significant, exert a weaker cumulative impact than technological and institutional challenges. Eventually, legal risks, especially concerning cybercrime legislation and the protection of intellectual property, were identified as substantial complicators of e-business activities, increasing the intricacy of legal compliance and cross-border contract enforcement. The results underscore the imperative for regulatory reforms, investments in cybersecurity, and methods for cultural adaptation to alleviate the identified risks and promote sustainable growth in China–West Africa e-business relationships. This study offers practical insights for governments, business leaders, and investors to effectively manage the intricate risk landscape and make educated decisions that foster enduring collaboration and trust between China and West Africa in digital trade.

The rapid expansion of digital trade presents transformative opportunities for South-South cooperation, particularly between China and West Africa. However, emerging new risks in technological, institutional, and sociocultural domains pose significant challenges to sustainable e-business collaboration. This study proposes a Pythagorean Fuzzy Analytic Hierarchy Process (PF-AHP) framework to evaluate and prioritize these risks under conditions of uncertainty and expert judgment ambiguity. By integrating fuzzy logic with AHP, the model effectively captures the vagueness and imprecision inherent in multi-criteria decision-making (MCDM). Expert assessments from professionals in digital trade, cybersecurity, and risk management were used to conduct pairwise comparisons across three primary risk categories: Technological, Institutional, and Sociocultural Risks. The results indicate that Institutional Risk (weight: 0.3488) holds the highest priority, followed by Sociocultural Risk (weight: 0.3268) and Technological Risk (weight: 0.3244), highlighting the critical influence of cultural alignment, consumer trust, and behavioral norms in cross-border digital collaboration. The PF-AHP approach enhances decision reliability by incorporating membership, non-membership, and indeterminacy degrees, offering a robust tool for risk assessment in complex digital supply chains. This research contributes to the discourse on equitable digital globalization and provides strategic insights for policymakers and stakeholders aiming to build inclusive, resilient, and mutually beneficial digital trade ecosystems between China and West Africa.

Accurate forecasting of agricultural commodity prices is essential for managing market volatility, improving supply chain coordination, and supporting food security-related decision-making. Recent advances in deep learning have demonstrated strong potential for capturing nonlinear and temporal dependencies in commodity price dynamics. In this study, we propose a hybrid long short-term memory–multi-head attention (LSTM–MHA) framework for agricultural commodity price forecasting using global futures market data. The model is trained and evaluated on multivariate global commodity futures prices, reflecting internationally traded benchmark markets rather than region-specific domestic prices. While the empirical analysis is based on global data, the study is motivated by the relevance of international price movements for import-dependent regions, particularly West Africa, where global price transmission plays a critical role in domestic market dynamics. The experimental results demonstrate that the proposed model effectively captures short-term temporal dependencies and provides interpretable attention-based insights into lag relevance. An ablation study further highlights the trade-offs between forecasting accuracy and interpretability across different model configurations. The hybrid architecture combines the time-based pattern identification and weighting capabilities of multi-head attention with the sequential learning capabilities of LSTM. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the model’s performance. With an MSE of 0.0124, an RMSE of 0.1114, and an MAE of 0.1097, the model outperformed conventional models like ARIMA and standalone LSTM by three to four times in error reduction. The findings suggest that attention-enhanced deep learning models can serve as valuable analytical tools for understanding global price dynamics and informing policy analysis and risk management in West African agricultural markets.

The rapid expansion of digital trade presents transformative opportunities for South-South cooperation, particularly between China and West Africa. However, emerging risks in technological, institutional, and sociocultural domains pose significant challenges to sustainable e-business collaboration. This study proposes a Pythagorean Fuzzy Analytic Hierarchy Process (PF-AHP) framework to evaluate and prioritize these risks under conditions of uncertainty and expert judgment ambiguity. By integrating fuzzy logic with AHP, the model effectively captures the vagueness and imprecision inherent in multi-criteria decision-making (MCDM). Expert assessments from professionals in digital trade, cybersecurity, and risk management were used to conduct pairwise comparisons across three primary risk categories: Technological, Institutional, and Sociocultural Risks. The results indicate that Sociocultural Risk holds the highest priority (weight: 0.3268), followed by Institutional Risk (0.3488) and Technological Risk (0.3244), highlighting the critical influence of cultural alignment, consumer trust, and behavioral norms in cross-border digital collaboration. The PF-AHP approach enhances decision reliability by incorporating membership, non-membership, and indeterminacy degrees, offering a robust tool for risk assessment in complex digital supply chains. This research contributes to the discourse on equitable digital globalization and provides strategic insights for policymakers and stakeholders aiming to build inclusive, resilient, and mutually beneficial digital trade ecosystems between China and West Africa.