Frontiers in Management Science

Emotional Intelligence and Customer Satisfaction in Deposit Money Banks: A Conceptual Review

Atimba Oboko I. — 2026-01-28

The study examined emotional intelligence and customer satisfaction in Akwa Ibom State. The specific objectives were to: examine the effect of self-awareness, self-management, and social awareness on customer satisfaction of deposit money banks in Akwa Ibom State. This study adopted conceptual Review methods. Information was obtained from secondary sources using published journals. Based on the conceptual review, it was revealed that self-awareness, self-management, social awareness have effect on customer satisfaction of deposit money banks in Akwa Ibom State. The study recommended that Managers should create an environment of confidence and equality, and their feature of self-regulation is crucial in terms of a competitive viewpoint. Managers need to effectively direct the relationships, but it is the social skills that make such relations possible. Managers should show a great deal of sympathy, as a key part of management, with their surrounding individuals.

A Study on the Impact of Cross-Departmental Data Collaboration on Marketing Campaign Efficiency in Fast-Moving Consumer Goods E-commerce: The Case of PepsiCo (China)’s 7UP and Mirinda Project

Yiyang Wu — 2026-01-28

In the digital ecosystem where platform algorithms embed real-time inventory, pricing, and creative performance directly into traffic allocation formulas, systematic micro-level evidence remains scarce on how FMCG brands can break through marketing efficiency bottlenecks via internal cross-departmental data collaboration. This study employs an embedded single-case research design, analyzing PepsiCo (China)’s 2024 “Summer Spark” e-commerce project for 7UP and Mirinda. Through a mixed-methods approach integrating 1,536 hours of high-frequency panel data with 12 semi-structured interviews, we systematically examine the transmission mechanism of “data collaboration → decision efficiency → marketing performance.” Findings reveal that each one-standard-deviation increase in cross-departmental data collaboration intensity reduces decision-making time by an average of 29%, subsequently driving ROI up by 0.42 standard deviations and decreasing inventory turnover days by 1.8 days, with decision efficiency mediating 55%-62% of this effect. The “E-commerce War Room” SOP developed through this case practice has been successfully replicated within the enterprise, yielding significant financial returns and green benefits. This study embeds decision efficiency within the information processing theory framework and develops a four-dimensional scale for collaboration intensity tailored to FMCG e-commerce contexts, offering both theoretical grounding and a replicable practical template for cross-departmental collaboration in the algorithm-driven era.

A Study on Resource Allocation Efficiency in Multinational Logistics Hubs

Xiaoying Nie — 2026-01-28

In the context of globalization, multinational logistics hubs face the paradox of “coexistence of transport capacity redundancy and shortage.” The average equipment idleness rate in North American hubs is 28%, while the container turnover rate gap exceeds 35% during peak seasons. Existing research lacks a three-dimensional collaborative framework of “space – time – transport capacity” for multinational networks, and the models are mostly static. This study constructs a multi-objective mixed-integer programming (MOMIP) model integrating spatial, temporal, and resource constraints. Taking the cross-border multimodal transport network from Los Angeles to Mexico City and Toronto as a case study, the model is verified with millions of operational data from the three major North American hubs from 2022 to 2023. The results show that the model increases transport capacity utilization from 62% to 89%, reduces equipment idleness costs by $4.3 million per year, shortens cross-border transport time by 11.2 hours, and reduces overall costs by 18.5%. The model also demonstrates robustness under fluctuations in oil prices and changes in cargo type structure. This study breaks through the limitations of single-hub research, providing support for corporate cost reduction and efficiency enhancement, as well as for regional logistics policy formulation. It also points out limitations such as the exclusion of air logistics and carbon neutrality goals, and proposes future directions for expanding low-carbon models and supplementing data from Asian hubs.

The Role of Customer Value Proposition in Digital Financial Literacy and Ai-Driven Finance Adoption

Ayuk Takemeyang — 2026-01-28

Purpose of the Study: The purpose of this study is to investigate the effect of digital financial literacy on customers’ intention to adopt AI-driven digital finance, with customer value proposition (CVP) serving as a mediating variable. Methodology: Surveys were employed to collect data from 466 banking customers in Cameroon, which were analyzed using partial least squares-based structural equation modeling (PLS-SEM). Main Findings: It was revealed that DFL influences CVP, including functional, benefit, symbolic and emotional value, which are involved in the intention to use digital finance. The mediation of the nexus between DFL and the intention to use digital finance was carried out using the four dimensions of CVP. The coefficient of intention to use of 0.669, which indicates that the direct contribution of the customer value proposition to intention to use amounted to approximately 66.9%. Therefore, this study revealed that the customer value proposition significantly predicted the intention to Use AI-driven banking services. Managerial Contributions: The study’s findings have significant implications for managers in the Cameroonian banking industry as the study contributes in developing targeted CVP strategies, investing in DFL initiatives, designing user-centric AI-driven services, fostering trust and transparency and monitoring and evaluating CVP and DFL initiatives. By implementing these strategies, managers in the Cameroonian banking industry can enhance AI-driven digital finance adoption, improve customer satisfaction, and drive business growth.

A Study on Data-Driven Budget Optimization for U.S. Enterprises’ Cross-Border Marketing

Chunzi Wang — 2026-01-28

This study constructs an integrated theoretical framework of “Dynamic Institution-Capability-Resource Allocation,” employing a double machine learning dynamic panel model with 1.5 million project-level data points to systematically examine the optimization mechanisms of cross-border marketing budgets in U.S. enterprises. Based on 2015-2024 multinational operations data from 2,800 S&P 500 firms, complemented by in-depth case studies of six corporations including Nike and Tesla, empirical findings reveal that for every one-standard-deviation increase in the institutional distance friction index, marketing ROI deteriorates by 12.7%. However, when dynamic data processing capability surpasses the 0.73 threshold, 58% of efficiency losses can be reversed. This research pioneers the theoretical subfield of dynamic institutional marketing and develops an interpretable AI budget optimization system. Validated through A/B testing, the system yields an average ROI improvement of 22.3% with a 79% manager adoption rate. Theoretically, it introduces a new dimension of digital institutional distance and operationalizes dynamic capabilities into a three-stage mechanism of “real-time sensing-algorithmic seizing-agile reconfiguration.” Methodologically, it integrates causal machine learning with dynamic panel estimation to resolve the dual challenges of endogeneity and dynamic effects. Practically, it constructs an intelligent decision-making tool that balances predictive accuracy and interpretability, providing a paradigm transformation pathway for marketing strategy in the Globalization 4.0 era.

Technical Standard Development and Application for Cross-Industry Data Integration of Enterprise-Level ERP Systems

Yanmin Qiu — 2026-01-28

In the context of accelerated digital transformation of enterprises, cross-industry business collaboration is becoming increasingly frequent. ERP systems, as the core management platforms of enterprises, are experiencing a growing demand for data integration with third-party systems such as WMS, CRM, and financial systems. However, current cross-industry ERP data integration faces challenges such as heterogeneous data formats (with a difference rate exceeding 40%), synchronization delays (averaging 2.3 hours), and high integration failure rates (reaching 38%). Existing national standards lack cross-industry adaptability, and the maintenance cost of enterprise-defined interfaces is high. Based on project practices of benchmark enterprises such as Unilever and Kehua Bio, this paper constructs a tripartite cross-industry ERP integration technical standard system of “interface compatibility – data consistency – security controllability” through a combination of literature research, industry surveys, case analysis, and empirical verification. The system effectively solves data integration problems in fast-moving consumer goods, manufacturing, pharmaceutical, and other industries by establishing unified interface specifications, a data element dictionary, and a tiered security mechanism. Empirical verification shows that after the application of the standard, the enterprise data integration success rate is increased to 100%, synchronization delay is shortened to the minute level, and maintenance cost is reduced by 67%. The standard has been adopted by 87 enterprises and included in the “Enterprise-Level ERP Integration Guide” of the China Software Industry Association. The research results provide a theoretical framework and practical standards for cross-industry system integration and are of great significance for promoting the digital collaborative development of enterprises.

Research on the Impact of Corporate ESG Performance on ROA

Zhekai Liu — 2026-01-29

Against the backdrop of China’s transition towards high-quality economic development, the impact mechanism of corporate ESG performance on its market value has become a key proposition in deciphering the path of sustainable development. This article is based on data from A-share listed companies from 2015 to 2021, integrating stakeholder theory, resource-based theory, and signal transmission theory to construct a three-dimensional theoretical framework that includes a superlinear production function for green technology innovation, an exponential decay model for supply chain default probability, and a fractional function for governance signal efficiency. The mixed OLS model is used to empirically test the impact of ESG performance on corporate return on assets (ROA). Research has found that for every one level increase in a company’s ESG score, ROA can significantly increase by 1.617%, and the positive effect of non-state-owned enterprises is significantly higher than that of state-owned enterprise; Mechanism analysis shows that environmental responsibility improves production efficiency through the marginal revenue increase of green patents and the threshold triggering mechanism of policy subsidies. Social responsibility optimizes asset turnover by reducing the probability of supply chain default. Governance level relies on the degree of marketization to regulate signal transmission efficiency and reduce financing costs. Based on the conclusion, this article proposes a “four-dimensional synergy” policy framework: enterprises need to establish green patent portfolios and dynamic equity incentive mechanisms, the government should implement a tiered environmental subsidy and differentiated disclosure system, investors can develop ESG derivatives and improve risk management models, and the public can promote corporate transparency practices through a technology empowered supervision network, providing theoretical and empirical support for solving the “greenwashing” dilemma and addressing international ESG trade barriers.