High-quality agricultural products from the Lambayeque region have contributed to the growth of Peru’s agro-export sector and increased international trade. However, the need for agricultural exports to be more resilient and sustainable is demonstrated by the fact that markets are still concentrated, logistical costs are high, and global demand is constantly shifting. The purpose of this study is to use a gravity-based trade model and market intelligence techniques to analyse the agricultural exports from the Lambayeque region between 2015 and 2024. Using official data from the World Bank, AZATRADE, CEPII, and MINCETUR, we employed a quantitative explanatory approach. The results show that the concentration of businesses has significantly decreased while the value of exports has increased steadily. The Herfindahl–Hirschman Index increased from 6209 in 2015 to 1349 in 2024, and export destinations have become slightly more diverse. Exports are negatively impacted by geographic distance, but free trade agreements greatly benefit them. There is a lot of export potential in markets like Finland, Indonesia, Austria, Bolivia, and Vietnam. However, Israel and Hong Kong appear to be full. Overall, the findings indicate that Lambayeque’s export performance has improved, but it still runs the risk of becoming overly focused on a single sector. Long-term sustainability of the region’s agricultural exports depends on enhancing logistical infrastructure, bolstering market intelligence, and promoting regional diversity.

This article introduces a research framework for studying emotional artificial intelligence (AI) used to create marketing content and the behaviour of cultural events consumers. The research we propose would assess consumer responses to AI-generated content, employing models such as the Six Emotional Dimension and the Technology Acceptance Model. We propose a mixed-methods approach: interviews, eye-tracking, experimental exposure, and social media analysis. Key objectives would include comparing perceptions of AI- and human-generated content and examining generational responses in cultural contexts. The findings aim to provide insights for cultural organizations to improve promotional effectiveness by integrating Emotional AI into marketing strategies.

The implementation of survival strategy is challenging because of the complexity of making strategic decisions that would ensure the continuous existence of the organization. For quantity surveying firms, several strategies have been suggested to survive challenging and turbulent economic environment. However, these firms are yet to gain insight into the factors that can influence or drive the implementation of appropriate survival strategies. Hence, the need for the study to examine the drivers to implementing survival strategy in quantity surveying firms in Nigeria. The study adopted the survey research approach and through the questionnaire, One Hundred and Thirty (130) quantity surveying firms were purposively selected in South-west, Nigeria. The 99 responses received from the principal partner or senior management personnel of these firms were analysed using descriptive and inferential statistics. From the result, it was revealed that the decision to implement survival strategies in quantity surveying firms is based on the desire of the firm to improve performance, operational efficiency, anticipating changes in firm, innovativeness, technical edge and so on. Additionally, the study categorized the drivers into five distinct factors driving the implementation of survival strategy thereby indicating that quantity surveying firms must focus on both internal and external driving forces. The prioritized drivers are strategic leadership and market intelligence, innovation and competitive edge, policy alignment and performance, cost discipline and project security and growth and competitive positioning. The study concluded that the implementation of survival strategies in quantity surveying firms would be successful when factors that can drive its implementation are carefully considered. Hence, this study suggests that the partners or top management of quantity surveying firms in Nigeria and worldwide should organize summit to deliberate on the pros and cons of implementing strategies as well as the driving force for each strategy.

The rapid advancement of large language models (LLMs) has opened new possibilities for business analytics and market intelligence. While traditional single-model systems such as ChatGPT can analyze text and summarize insights, they lack collaborative specialization and workflow coordination. This study explores a no-code experimental framework using a multi-agent system built upon GPT to perform automated market analysis. The experiment compares a baseline single-LLM configuration with a three-agent structure composed of a Data Agent, an Analysis Agent, and an Auditor Agent. A dataset of 200 publicly available product reviews was used to evaluate performance across quantitative metrics (accuracy, precision, recall, F1-score) and qualitative metrics (report structure, insight quality, information coverage). Results show that the multi-agent workflow produced clearer, more structured market reports with marginally higher accuracy and significantly improved interpretability (accuracy = 0.86 vs. 0.81; macro-F1 = 0.86 vs. 0.81). This exploratory research highlights the potential of GPT-based agents for business decision support and demonstrates a reproducible no-code approach accessible to non-technical practitioners.

ABSTRACT In Ghana, Climate-Smart Agriculture (CSA) is essential to improving the production, resilience, and sustainability of high-value perennial crops like mangos. However, there are no empirical data regarding the variables influencing mango growers’ acceptance of CSA, especially in the climate-sensitive Yilo Krobo Municipality. Using survey data from 112 farmers and a convergent parallel mixed-methods design, this study used a Tobit regression model to investigate the institutional and socioeconomic factors that influence the intensity of CSA adoption. This is the first study to use an integrated theoretical framework that combines the Technology Acceptance Model (TAM), Diffusion of Innovations (DOI), and Sustainable Livelihoods Framework (SLF) to measure the degree of CSA adoption among Ghanaian mango growers. The findings indicate that while larger household size decreases CSA adoption intensity, education, agricultural experience, farm size, financial availability, and market information considerably increase it. These show the impact of information dissemination pathways (DOI), perceived utility and ease of use (TAM), and livelihood assets (SLF). Disaggregated research reveals differences in farm size, loan availability, and gender. Scaling CSA adoption requires targeted loan programmes, digital extension platforms, gender-responsive programming, and enhanced market intelligence systems. The findings offer evidence-based strategies for improving climate resilience, food security, and sustainable development.

With the sustained expansion of the global athleisure trend, Lululemon has emerged as a paradigmatic case of a data-driven, high-end sports lifestyle brand. This paper examines Lululemons brand development from a dual lens of brand strategy and data analytics. The research explores how Lululemon incorporates consumer data, digital marketing intelligence, and predictive analytics in pursuit of brand differentiation and sustainable growth. Through a review of financial reports, industry data, and relevant literature, this paper constructs a systematic analytical framework centered on four dimensions: data-driven brand management, market intelligence systems, community engagement analytics, and future AI-driven decision-making. The findings suggest that Lululemons success derives from its ability to translate massive datasets into actionable insights, forming a self-reinforcing cycle among data intelligence, consumer loyalty, and product innovation. However, challenges remain, including algorithmic bias, data silo effects, and the imperative for ethical data governance. The study concludes that the future of brand competitiveness will hinge on integrating quantitative data modeling with emotional branding strategies, a balance Lululemon has begun to master. This paper contributes to advancing understanding how analytical approaches can redefine brand management in the age of intelligent retail.

Abstract Artificial intelligence (AI) has emerged as a transformative force in digital and performance marketing, enabling data-driven decision-making, real-time optimization, and measurable performance outcomes. While digital marketing focuses on customer engagement across online platforms, performance marketing emphasizes accountability through metrics such as click-through rates, conversion rates, customer acquisition cost, and return on investment. This study empirically examines the impact of AI on digital and performance marketing using a secondary-data-based research design, drawing evidence from academic literature, industry reports, and AI-enabled advertising platforms. The findings indicate that AI-driven personalization, predictive analytics, automation, and real-time optimization significantly enhance marketing efficiency and effectiveness. The study also discusses ethical challenges related to data privacy, algorithmic bias, and organizational readiness. By providing empirical insights and a structured analytical framework, this paper contributes to the growing body of research on AI-driven marketing and offers practical implications for marketers and technology-driven organizations. Keywords: Artificial Intelligence, Digital Marketing, Performance Marketing, Predictive Analytics, Marketing Automation, Empirical Study

Purpose The mud crab sector within the broader seafood industry in Bangladesh is witnessing considerable growth, driven by increasing global demand for seafood. This sector primarily comprises small and medium-sized enterprises (SMEs), which significantly contribute to the national economy. The study aims to explore the relationship between market intelligence (MI) and export intensity (EI), taking mud crab SMEs as a case study of the seafood industry, with a specific focus on the moderating role of the vibrant ecosystem. Design/methodology/approach This study uses a hypothetico-deductive quantitative research design. A total of 120 participants from the SME mud crab sector in the southwestern region of Bangladesh were selected using convenience sampling methods. Data were collected through a structured survey questionnaire and analysed using structural equation modelling. Findings The analysis results highlight two significant relationships in the context of mud crab entrepreneurs in Bangladesh: the direct impact of MI on EI and the moderating effect of the vibrant ecosystem on this relationship. The moderating effect of the vibrant ecosystem on the relationship between MI and EI is also statistically significant. Research limitations/implications This study sheds light on the crab sector as a potential sector for SMEs in Bangladesh to provide policymakers and entrepreneurs with practical insights to improve exports and economic growth. However, the study is limited to the southwestern region and the findings may not be directly applicable to other regions or sectors within Bangladesh or other countries. Originality/value To the best of the authors’ knowledge, this study is one of the first to examine the role of MI in enhancing EI within the context of the emerging mud crab sector in Bangladesh. Additionally, it contributes by addressing the gap in the literature regarding the role of the vibrant ecosystem in moderating this relationship, providing unique insights for both academic and practical applications.

This paper examines the current and future trends in integrating Artificial Intelligence (AI) and automation in marketing and international trade. The main objective is understanding how these technologies transform these fields, identifying the predominant discussions, methodologies employed, and reported results since 2010. To this end, a rigorous and systematic scoping review was conducted using the Scopus and Google Scholar databases. A total of 35 studies published between 2010 and 2023 were selected following a multi-phase screening process based on thematic alignment, methodological rigor, and publication quality. Only peer-reviewed journal articles and conference proceedings published in English were considered. Studies included had to explicitly address AI and automation in marketing or international trade, while descriptive reports or articles lacking analytical depth were excluded. The findings show a trend towards qualitative approaches in studies, emphasising theoretical and conceptual understanding over generating new data. Key themes identified include efficiency in cross-border e-commerce, user knowledge, adaptation to the era of big data and its effects on logistics, and regulatory challenges in the face of AI. These findings underscore the growing importance of AI and automation in marketing and international trade, highlighting opportunities and challenges for businesses and governments in a globalised environment.

ABSTRACT Circular Procurement (CP) integrates Circular Economy ( CE ) principles into purchasing decisions to close material loops and retain value across product life cycles. Yet, its adoption remains limited due to persistent barriers within procurement processes. This study investigates where these barriers emerge across procurement phases and how firms develop capabilities to overcome them. Drawing on Dynamic Capabilities (DCs) theory and the microfoundational perspective, we conduct a multiple‐case study of nine Italian manufacturing firms recognized for leadership in CP. Our analysis identifies five key DCs–demand intelligence, market intelligence, strategic design, reconfiguring supplier selection, reconfiguring contracts–each underpinned by distinct microfoundations at individual, process, and structural levels. Building on these insights, this study offers a process‐based framework linking CP barriers to DCs and microfoundations addressing them. The findings aim to advance theoretical understanding of capability building for CP and offer practical guidance for managers seeking to embed circularity into purchasing routines.

Timely and reliable labor market intelligence is crucial for evidence-based policymaking, workforce planning, and economic forecasting. However, traditional data collection and centralized analytics raise growing concerns about privacy, scalability, and institutional data governance. This paper presents a large language model (LLM)-powered framework for privacy-preserving and scalable labor market analysis, designed to extract, structure, and interpret occupation, skill, and salary information from distributed textual sources. Our framework integrates domain-adapted LLMs with federated learning (FL) and differential privacy (DP) to enable collaborative model training across organizations without exposing sensitive data. The architecture employs secure aggregation and privacy budgets to prevent information leakage during parameter exchange, while maintaining analytical accuracy and interpretability. The system performs multi-task inference—including job classification, skill extraction, and salary estimation—and aligns outputs to standardized taxonomies (e.g., SOC, ISCO, ESCO). Empirical evaluations on both public and semi-private datasets demonstrate that our approach achieves superior performance compared to centralized baselines, while ensuring compliance with privacy and data-sharing regulations. Expert review further confirms that the generated trend analyses are accurate, explainable, and actionable for policy and research. Our results illustrate a practical pathway toward decentralized, privacy-conscious, and large-scale labor market intelligence.

The development of Artificial Intelligence (AI) is significantly impacting the global economy, transforming corporate strategies and enhancing operational efficiency. This study aims to analyze the relative efficiency of the Generative AI (GenAI) market, considering the market size of chips, servers, and data center infrastructure required for its operation, and comparing these market sizes with the market size of AI solutions. The study hypothesizes that the current AI market, despite its rapid development, is characterized by a catching-up nature compared to the component market and does not yet fully reflect the proportional relationship between the volumes of these markets (the hardware market and the AI solutions market). It is emphasized that the capital expenditures of technology giants on the creation of AI infrastructure have significantly increased, which may require decades to achieve a balance between the size of the hardware market that supports AI and the size of the AI solutions market itself. To assess the efficiency of the AI market, the Data Envelopment Analysis (DEA) methodology is applied, considering «inputs» (the market size of components) and "outputs" (the market size of AI solutions). The results of the DEA analysis of the GenAI market dynamics from 2016 to 2024 reveal a non-linear nature of development, starting in 2021, with a trend reversal and a decrease in efficiency indicators, which confirms the hypothesis of the catching-up nature of AI technologies compared to the component market. It is shown that fluctuations in efficiency begin three years after the deployment of the first large language models, indicating their significance for the demand for hardware, but not yet demonstrating sufficient returns in the form of a comparable growth of the AI solutions market. The limitations of the study are associated with the time interval of analysis (2016-2024) and the composition of the companies included in the analysis, which covers a majority, but not the entire, market. The novelty of the study lies in the application of DEA analysis for a comprehensive assessment of the AI market considering, but divides the component market and the technological solutions market of AI usage. The results obtained provide a critical assessment of the prospects for the development of the AI market and identify an imbalance between the «soft» (technological solutions) and «hard» (components) markets, identifying the potential for more efficient exploration and use of generative models. However, the results require further development in terms of describing the effects in different sectors of the economy.

The Nigerian used car market is characterized by significant price variability, lack of transparency, and inconsistent valuation mechanisms, posing challenges to both buyers and sellers. This research aimed to develop a robust, data-driven predictive model tailored to the specific dynamics of the Nigerian automotive ecosystem using machine learning algorithms. The study employed a comprehensive dataset of used vehicles listed in Nigeria, incorporating features such as make, model, year, mileage, engine size, fuel type, transmission, condition, and location. Extensive data preprocessing, exploratory analysis, and feature engineering were conducted to uncover the most influential variables affecting vehicle prices. Six machine learning models— Linear Regression, Ridge Regression, Lasso Regression, Decision Tree Regressor, Support Vector Regression, and Random Forest Regressor were trained and evaluated using performance metrics including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²) Score. The Random Forest Regressor outperformed other models, achieving the highest prediction accuracy with an R² score of 0.91 and the lowest RMSE, making it the most suitable algorithm for this context when compared with previous work done by Pudaruth (2023) and Breen et al. (2024). The study identified vehicle year, mileage, brand, engine size, and geographic location as key determinants of price. The resulting model provides a practical framework for real-time price prediction and can be integrated into digital platforms for use by dealerships, private sellers, and online marketplaces. This research contributes to local automotive market intelligence, promotes pricing transparency, and underscores the transformative potential of machine learning in emerging economies.

ABSTRACT This study focused on examining the ethical and legal challenges of using artificial intelligence in marketing. It also provided valuable insights into small and medium-sized enterprises (SMEs) in Jordan by exploring several key areas, like: Personal data concerns, Systematic algorithmic unfairness, User trust, Legal sufficiency, Policy design. Methodology consisted of analyses, descriptive and analytical. This study collects information from 342 employees of 49 Jordanian SMEs in the marketing sector that had used Artificial Intelligence in their marketing strategies. We distributed 342 questionnaires to these companies, and as result received 327 back. Of these, 318 were valid for analysis, representing 92.9 percent. The results shown in this research highlight that use of Artificial Intelligence (AI) has clearly shown positive impacts on the Ethical and Legal Challenges faced in today’s business environment. All structures related to Ethical and Legal Challenges had statistically significant results, Personal data concerns were the most significant one then followed by Policy design, User trust, Systematic algorithmic unfairness, Legal sufficiency. According to these results the authors make a recommendation for the small and medium-sized businesses (SMEs) to start establishing Data Protection Protocols which will protect their customers’ data, like but not limited to, Strong Encryption, Routine Audits, and Transparency in Data Handling.

The paper explores trends in the use of artificial intelligence in marketing, such as predictive analytics, personalization, targeted advertising, automation, and chatbots. It also covers the psychosocial dynamics of AI and keyword co-occurrence analysis. Examples from companies like Amazon, Netflix, Spotify, Canva, and Raiffeisen Bank are included.

The implementation of Artificial Intelligence (AI) in digital marketing has become a major driver of business efficiency, yet its strategic implications and the role of human involvement still require in-depth study. This qualitative case study research aims to analyze the effectiveness of adopting generative AI (Gemini AI and Claude AI) in the content creation process at PT. Gaivo Solusi Manajemen, focusing on perceived usefulness, the role of human-in-the-loop, and the perspective of competitive advantage. Data were collected through semi-structured interviews with key informants and analyzed using Thematic Analysis. The findings indicate that AI substantially increases process efficiency, particularly in drafting content and SEO Meta packages, which boosts production volume and speed. However, key findings emphasize that AI is merely a supporting tool and necessitates mandatory supervision by expert staff (human-in-the-loop) to ensure information integrity and quality that complies with professional service industry regulations. Strategically, AI is not considered a source of hardly imitable competitive advantage (it is a commodity), but rather an enabler. The true competitive advantage lies in the staff’s ability in prompt engineering, supported by the company’s relevant internal data. This study provides managerial contributions by recommending a focus on investment in human resource skill development rather than solely on the acquisition of AI tools.

For high-value perishable goods such as cherries, accurate price forecasting is crucial for the supply chain, stabilizing producer income, informing policy interventions & real-time decision support at various stakeholder levels. This study enquires whether Deep Learning (DL) architectures can be implemented in real time for daily advisory tools while simultaneously surpassing conventional statistical and Machine Learning (ML) models in predicting cherry prices. To tackle this issue, daily cherry price data from five wholesale markets in India were utilized for the period 2012-2024 to evaluate six forecasting methodologies, i.e., Seasonal Auto-regressive Integrated Moving Average (SARIMA), Prophet, Random Forest (RF), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Transformer. The results show that deep learning models are better at capturing non-linear price changes than statistical or tree-based methods. The LSTM and Transformer models consistently performed better across all error metrics (MAE, RMSE, sMAPE, MFE, NMBE, and DA). During the 2025 cherry season, the best-performing LSTM was used as a live, web-based forecasting system that accepted real-time submissions from market officials and produced daily predictions based on field conditions. After the season, we checked the actual prices against the forecasted values and found that they were correct with more than 92 per cent accuracy for market-variety-grade combinations. In major markets like Azadpur, Narwal, and Parimpora, the sMAPE was often below 5-10 per cent, and the error margins (MAE 5-8; RMSE 8-12) were low. Further, the Diebold-Mariano (DM) test showed that deep learning was statistically superior to the baselines. The current study presents a novel and comprehensive operational methodology for advanced Artificial Intelligence (AI) models in the agriculture & allied sectors of India, establishing a scalable framework for incorporating ML/DL into agricultural market intelligence and advisory systems.

Considering the escalating prevalence of cyber threats, data breaches and unauthorized access incidents, the implementation of real-time network monitoring has become imperative for the enhancement of cyber-security protocols in different sectors. The objective of this research was to strengthen cyber-security by delivering real-time traffic visualization with packet classification (TCP, UDP, ICMP) units and automated anomaly detection capacity. The integration of digital technologies in agriculture has increased the reliance on real-time data exchange for market intelligence, precision farming and value chain management. Thus the study developed a real-time network traffic monitoring dashboard utilizing Python and Streamlit, which effectively captures, analyzes and visualizes network traffic to facilitate improved threat detection. The methodology involved Network monitoring, analysis process, real time visualization, anomaly detection and logging which involved online real time monitoring and offline storage repository for archival and analysis where significant research deficiency is identified in the realms of scalability and adaptive anomaly detection. With the growing adoption of Internet of Things (IoT) devices, drones and cloud-based platforms, network traffic monitoring has become critical to ensure reliability, security and efficiency in agricultural operations. Leveraging open-source Python libraries, the framework provides a user-friendly dashboard with low computational overhead, making it accessible to rural and resourceconstrained settings. Results demonstrate the feasibility of real-time monitoring for enhancing data security, minimizing downtime and improving the resilience of digital agriculture systems. Moreover, the consoles architecture will be optimized to handle even the largest networks and highest traffic volumes ensuring it remains effective and efficient, even in the most demandingenvironments such as monitoring of agricultural value chains efficiency.

In In today’s fast-paced e-commerce landscape, pricing strategies play a pivotal role in driving customer acquisition, maximizing revenue, and adapting to volatile market conditions. This paper introduces an AI-powered dynamic pricing framework that combines supervised machine learning with real-time market intelligence to recommend optimal product prices. The system integrates Random Forest and Artificial Neural Network (ANN) classifiers to predict consumer purchase behavior, achieving an average accuracy of 94.6% and an F1-score of 0.92. While Random Forest delivered a precision of 93.1%, ANN exhibited superior recall, underscoring their complementary classification capabilities. For precise price estimation, a multivariate linear regression model was implemented, attaining a high coefficient of determination (R² = 0.917) and a low Mean Absolute Error (MAE) of 2.87. The framework maintains prediction latency under 300 ms, ensuring suitability for real-time applications. Market data ingestion from platforms such as BigBasket was automated using Python-based web scraping, while an intuitive Gradio-powered GUI enables seamless user interaction. Designed to be scalable and modular, the system adapts effectively to evolving market dynamics and product-specific attributes. This research demonstrates how the synergy of machine learning, real-time analytics, and automation can drive intelligent pricing strategies that enhance competitiveness, customer targeting, and operational efficiency in modern e-commerce ecosystems.

Engagement with the illicit street tablet market in Middlesbrough, North-East England, has been shown to impact treatment engagement, and the area appears to have a unique z-drug market, potentially not observed in other areas. When combined with high levels of injecting opioid and polydrug usage, this context may account for the steep increases in drug-related deaths reported locally. However, little is known about the volume, dosage, frequency, drivers, and dislikes of street tablet usage from the perspective of those who use drugs. In areas of high drug-related harm, local drug market intelligence is crucial to co-developing acceptable harm reduction and treatment offers that are attractive to those most at risk from a drug-related death. This project used a peer research model. The peer researcher engaged a group of high-risk individuals (traditionally underserved by standard research processes) in a survey regarding street tablet usage, drivers, dislikes, and desired treatment options. A small but notable sample of 38 individuals engaged with the survey, the majority of whom (60%) were not involved in drug treatment, with a high proportion of street homelessness (38%). Street tablets were perceived as a multifunctional remedy to achieve a range of perceived mental and physical effects or for pragmatic purposes, such as cost. Most (89% and 87%, respectively) respondents [with current or historical use] (n = 38) reported extensive illicit pregabalin and zopiclone usage. When only looking at those currently using street tablets (n = 32), 100% were using zopiclone, with nearly all reporting this in combination with pregabalin (n = 31, 96%). Reported drivers of tablet usage included attempts to self-medicate due to untreated mental health conditions. Self-dosage rates were substantially higher than the recommended therapeutic dose rates. Collaborating with the peer workforce in areas of high need relating to drug harms was an acceptable way of engaging some of the most vulnerable individuals at risk from mortality and morbidity in the research process. Our data highlights this population's complex, high-dose, polydrug use. There was an appetite to develop harm reduction interventions for illicit street tablet usage amongst the population sampled. Further work should develop tailored harm reduction advice for complex issues such as street tablets and opioid co-use.