The American Journal of Management and Economics Innovations

Social and Behavioral Dimensions of Financial Inclusion: An Analysis

2025-07-01T06:57:23+00:00

Financial inclusion has emerged as a critical driver of economic development and social equity, yet much of the discourse focuses primarily on access and infrastructure, often overlooking the social and behavioral dimensions that influence financial decision-making. This study analyzes the interplay of social norms, trust, financial literacy, and behavioral biases in shaping individuals' engagement with formal financial systems. Using a mixed-methods approach—including survey data, focus group discussions, and secondary literature—the research highlights how cultural attitudes, gender roles, peer influence, and perceived risks impact financial inclusion outcomes, especially in underserved communities. The study finds that addressing behavioral barriers is as essential as expanding physical and digital access to financial services. Policy recommendations include the design of inclusive financial literacy programs, community-driven trust-building initiatives, and the integration of behavioral economics into financial product development.

Approaches To the Digital Transformation of Traditional Business Processes.

2025-07-14T10:28:00+00:00

The article provides a detailed account of approaches applied to the digital transformation of traditional business processes. In the context of a rapid technological shift, such transformations become indispensable for the survival and competitiveness of economic actors. However, despite a proliferation of publications, both academic and practitioner literature remain fragmented in their definitions of the nature of transformational steps, their scope, and the organizational mechanisms involved. The objective of this paper is to undertake a critical analysis of the conceptual foundations of digital transformation and to identify the primary directions that underpin the rethinking and reconfiguration of established operational models. Special attention is given to juxtaposing strategic, institutional, and industry‐applied approaches, as well as to exploring the tensions between normative rhetoric and the empirical feasibility of these changes. A typology of the approaches under review is presented, key limitations and barriers are delineated, and the author’s position on the novelty of processes for the digital reconfiguration of business architecture is articulated. The scientific and practical value of this work lies in systematizing diverse viewpoints on the topic and interpreting them through an interdisciplinary lens. The material set forth will be of use to scholars in management, digital economics, organizational theory, and applied informatics, as well as to consulting professionals and business architects engaged in facilitating digital transformations.

AI in HR: Impact of Artificial Intelligence on Transforming Human Resources

2025-07-12T02:55:46+00:00

The article examines the impact of artificial intelligence on the transformation of human resource management functions, analyzing the practices of embedding AI modules in the Oracle Fusion Cloud HCM platform and assessing their economic and strategic effects. Against the backdrop of rapid growth in AI penetration into business processes and active participation of HR units in the selection of AI solutions, the relevance of this study is determined by the need to optimize recruitment, retention and development of personnel, as well as to free up to 12 hours of working time per week for strategic tasks. The novelty of the work lies in its comprehensive approach, combining an overview of industry surveys (McKinsey, Engagedly, SHRM), analysis of Oracle technical documentation (Dynamic Skills, Skills Nexus, Activity Centers, Fusion HCM Analytics), and corporate case studies (Carv, Candidate, Forrester-TEI, Adecco). Data have been synthesized concerning the level of HR-task automation, the architecture of Oracle’s unified object model, and the contributions of pre-trained AI agents in recruiting processes, employee performance appraisal, and benefits management. The main findings demonstrate that AI implementation in HR ensures a significant reduction in routine operations (81% of respondents consider automation a priority), improvement of employee experience (73%), decrease in time-to-hire (by up to 70% through automated interview scheduling) and enhanced accuracy of candidate selection (a 14% increase in diversified responses). Using the Dynamic Skills module creates a “live” competency inventory, Activity Centers prompt the “next best action,” and the Digital Assistant and other chatbots return up to one hour per day to employees. Additionally, the author has proposed the Set-up Extractor Tool for automating the migration of Oracle HCM Cloud configurations, eliminating the risks of manual copying and version conflicts. The article will be helpful to HR service leaders, HR-technology implementation specialists, and digital transformation consultants.

Use of Digital Tools for Sales Management in The Retail Business.

2025-07-09T12:01:35+00:00

This article substantiates the necessity of transitioning to an integrated digital sales ecosystem as a key factor of competitiveness. The relevance of the study is determined by the rapid growth of electronic commerce and the approach of the online channel share to 20% in global retail, which renders traditional methods of sales management economically inefficient. The author emphasizes that digital transformation should be regarded not as a one-off project but as a continuously accelerating positive feedback loop requiring end-to-end integration of CRM, POS, BI, and ERP/OMS. The objective of the study is to systematize and analyze contemporary digital solutions applied to sales management in the retail business, as well as to identify the mechanisms of their interaction and their impact on key operational indicators. The methodological basis comprised a comparative analysis of reports by UNCTAD, Emarketer, McKinsey, Intellias, and leading industry research, as well as content analysis of practical case studies and statistical data. The theoretical part examines the four layers of the sales tech stack, while the empirical part provides examples of the implementation of AI modules, predictive analytics, and omnichannel platforms. The novelty of the research lies in the comprehensive consideration of the chain CRM → POS → BI → ERP/OMS as a single data loop that enables enterprises to achieve operational transparency of sales, responsiveness to demand, and process scalability. Additionally, current trends in marketing automation, SFA applications, and AR/VR solutions are analyzed, as well as the organizational and behavioral factors influencing the success of digital initiatives. Key findings: integration of digital tools ensures up to 65% reduction in revenue loss through AI demand forecasting and a 5–15% increase in revenues; omnichannel transforms the customer journey, increasing the average basket value and customer retention; the implementation stages (audit – pilot – phased migration) are critical for minimizing risks; the main barriers remain data fragmentation, employee resistance, cyber threats and the risk of vendor lock-in, overcoming which requires a systemic approach to training, security and data management. This article will be useful for executives of retail companies, IT directors, digital transformation consultants, and researchers in the field of retail.

Procurement Efficiency and Firm Competitive Advantage: Moderated Mediation Analysis of Unified Theory of Acceptance and Use of Technology: A Study in Ghana, Ashanti Region.

2025-07-03T11:17:37+00:00

This study explored how procurement practices relate to competitive advantage within organizations, using the Unified Theory of Acceptance and Use of Technology (UTAUT) to understand the role of technology in supply chain management. Researchers employed a quantitative approach, analyzing 245 responses from 100 regional universities using descriptive statistics and structural equation modeling (SEM) with SmartPLS software. The findings revealed a strong positive correlation between effective procurement methods and competitive advantage, leading to improved financial performance, return on investment, and profit margins. Regression analysis confirmed that efficient procurement strategically enhances economic performance. The UTAUT model highlighted that performance expectancy, effort expectancy, social influence, and facilitating factors influence the adoption and use of procurement technology. The study demonstrates how aligning procurement digitalization with the UTAUT framework can optimize sourcing, foster innovation, and boost overall profitability in supply chain management. Ultimately, this research contributes to a deeper understanding of the link between procurement practices and achieving a competitive edge in organizational supply chain management.

AI-Driven Demand Forecasting for Multi-Echelon Supply Chains: Enhancing Forecasting Accuracy and Operational Efficiency through Machine Learning and Deep Learning Techniques.

2025-07-15T09:49:48+00:00

Demand forecasting plays a crucial role in optimizing supply chain operations, particularly in multi-echelon supply chains where goods move through various stages, including manufacturers, wholesalers, and retailers. Traditional time-series models like ARIMA and SARIMA have been widely used for demand forecasting, but their limitations in handling complex, non-linear relationships and incorporating external factors such as promotions and weather events have led to the exploration of machine learning (ML) and deep learning (DL) techniques. This study evaluates and compares the performance of AI-driven demand forecasting models, including ARIMA, SARIMA, Random Forest (RF), Gradient Boosting Machines (GBM), and Long Short-Term Memory (LSTM) networks. The results demonstrate that the LSTM model outperforms traditional methods and other machine learning algorithms in terms of accuracy, as measured by lower MAE, RMSE, and MAPE values across all echelons of the supply chain (retailer, wholesaler, and manufacturer). The superior performance of LSTM highlights its ability to capture long-term dependencies and handle the complexity of multi-echelon supply chains. This study provides valuable insights into the effectiveness of AI-driven forecasting models for real-world supply chain applications, particularly in managing dynamic demand patterns and optimizing operations.

Volatility Clustering and Market Sentiment: A Quantitative Assessment of Bitcoin and Ethereum's Reaction to Macroeconomic Announcements.

2025-07-12T03:19:47+00:00

This article investigates the phenomenon of volatility clustering in the cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH), through empirical time-series analysis. The study employs quantitative methods, including GARCH modeling, to identify persistent patterns in the price fluctuations of the two leading digital assets. The analysis is based on trading data over an extended period, encompassing both phases of high market turbulence and periods of relative stability. Adopting an interdisciplinary approach that integrates behavioral finance, econometrics, and financial market theory, particular attention is given to identifying autocorrelation, memory effects, and the structure of market shocks. The findings demonstrate that volatility clustering in BTC and ETH significantly differs from similar phenomena in traditional financial markets, largely due to their speculative nature, asset novelty, and the influence of both institutional and retail participants. The identified patterns enhance risk profiling for crypto assets and may be applied in hedging strategies, automated trading algorithm development, and investment portfolio optimization. Additionally, the study highlights the importance of accounting for both micro- and macroeconomic factors influencing market behavior. The article is intended for researchers in digital finance, risk managers, analysts, investors, and anyone examining unstable assets in conditions of high uncertainty and a rapidly changing informational landscape.

The Study of Determinant Factors of Customer Satisfiction with Industrial Products in Helmand Province, Afghanistan

2025-07-11T06:02:25+00:00

This research aims to evaluate customer satisfaction with industrial products in Helmand province, Afghanistan, and identify which dimensions and factors of the marketing mix have the most significant impact on customer satisfaction. Sixty-five questionnaires were gathered from customers who visited industrial production companies within the past three days to collect data. The collected data was analysed using SPSS 26.0 and OLS and correlation techniques. The findings indicate that all dimensions of marketing (7Ps) have a positive and significant relationship with customer satisfaction. Among the variables, price was identified as the most influential factor affecting customer satisfaction compared to other variables. Based on the model, the obtained R Square is 0.451, which means that the independent variables can explain 45.1% of the variance in the dependent variable (customer satisfaction). Overall, the study's results show that all independent variables significantly impact the dependent variable

Strategies for the Implementation of Digital Dispatch Platforms in Small Trucking Companies.

2025-07-05T08:50:33+00:00

This article examines the issue of enhancing the operational resilience of small trucking companies under conditions of high rate volatility, driver shortages, and tightening regulatory requirements. The relevance of the study is determined by the extremely low level of digital readiness in the sector against the backdrop of the rapid growth of the global digital freight market. The aim of the work is to identify strategic approaches that allow enterprises with limited IT budgets and a shortage of qualified personnel to successfully implement digital dispatch platforms and record measurable economic benefits. The novelty of the study lies in the development of a systematic, phased implementation methodology: from the pilot launch of basic telematics to full integration with external accounting systems and payment modules. A unified roadmap is proposed, including the selection of model tariffs, mechanisms for engaging champions among drivers and dispatchers, as well as a recommended set of five key KPIs (ETA accuracy, empty‐run ratio, fleet utilization, driver idle time, and customer satisfaction) for regular performance monitoring. The most significant findings demonstrate that phased deployment of cloud solutions with open APIs and monthly payment minimizes capital expenditure and reduces operational risks, while microlearning modules and continuous KPI analysis accelerate personnel adaptation and ensure a sustainable effect: up to 9% fuel savings, 15% reduction in accident‐related costs, improved ETA accuracy, reduced unplanned downtime, and increased fleet profitability. Integration with ELD, accounting, and freight marketplaces creates conditions for continuous improvement and scalability. The article will be useful for managers of small fleets, IT consultants, and experts in the digital transformation of transport companies.