International Journal of Economics and Business Management Research
International Journal of Economics and Business Management Research aims to provide a global platform for high-quality research and scholarship in economics, business, and management. It seeks to foster intellectual exchange among academics, researchers, and practitioners by publishing innovative and impactful studies. IJEBMR is to foster a deeper understanding of economic theory, business management practices, and their practical applications across diverse sectors. IJEBMR welcomes all types of manuscripts meets the scope of the journal and the novel developments and innovations in Business and Economic management. The International Journal of Economics and Business Management Research serves as an important platform for scholars, practitioners, and policymakers to engage with emerging research.
A Study on Long-run Price Performance of Initial Public Offerings in India from 2012 – 2022
This study attempted to analyse the price performance of Initial Public Offerings in India from 2012 to 2022. This study employs data from the NSE website and analysed aftermarket performance of 236 IPOs. Data has been analysed using raw returns on the listing day, one year after the issue, two years after the issue and three years after the issue. The wealth relative index has been used to measure the extent of under-pricing of the issue. Analysis resulted that the issues are under-priced on the listing day and yielding good returns, but after two years and three years, the issues over performed.
Social Media Marketing in the Retail Sector: A study of Consumer Engagement
Social media marketing has transformed the retail landscape by enabling interactive and personalized communication between brands and consumers. This study examines the impact of key Social Media Marketing (SMM) dimensions, content quality, interactivity, personalization, influencer marketing, and electronic word-of-mouth (e-WOM) on consumer engagement in the retail sector. Using a structured questionnaire, data were collected from 283 respondents and analyzed through SPSS 28 and AMOS. The findings reveal that all SMM dimensions significantly influence consumer engagement, with e-WOM and content quality emerging as the strongest predictors. The study also confirms the mediating role of brand trust and customer satisfaction, with customer satisfaction exerting a stronger effect. Structural Equation Modeling (SEM) results indicate both direct and indirect relationships between SMM and engagement. The research highlights the importance of strategic, data-driven, and customer-centric social media practices for enhancing engagement and achieving competitive advantage in the dynamic retail environment.
AI-Driven Structural Modeling for Predicting Macroeconomic Shocks
Macroeconomic shocks such as financial crises, supply-chain disruptions, geopolitical events, and abrupt policy changes pose significant challenges to conventional forecasting models due to their nonlinear dynamics, structural breaks, and high-dimensional data environments. This paper proposes a hybrid AI-driven structural modeling framework that integrates the theoretical rigor of dynamic structural models with the predictive power of modern machine learning techniques. By embedding economic theory into AI architectures, the framework enhances shock identification, captures nonlinear transmission mechanisms, and improves early-warning capabilities for both anticipated and unanticipated macroeconomic disturbances. Using a combination of structural VARs, DSGE-based constraints, deep neural estimation, and reinforcement learning for policy simulation, the study demonstrates how AI can estimate latent structural parameters, detect regime changes, and model complex interactions across monetary, financial, and real-economy variables. Simulation experiments and empirical applications using global macroeconomic datasets indicate that AI-enhanced structural models significantly outperform traditional benchmarks in both accuracy and speed of shock prediction. The research contributes to the growing intersection of AI and macroeconomics by providing a systematic methodology for theoretical consistency, interpretability, and data-driven predictive power, offering new tools for policymakers, central banks, and financial institutions.
Digital HRM Practices and Their Influence on Workforce Productivity: Evidence from the IT Sector
This study examines the influence of digital Human Resource Management (HRM) practices on workforce productivity in the IT sector. Primary data was collected from 150 employees working in leading IT firms such as ABB, Oracle, Capgemini, Infosys, and Wipro using a structured questionnaire based on a 5-point Likert scale. Statistical tools including descriptive analysis, Cronbach’s alpha (0.882), correlation, and multiple regression were applied. The results reveal a strong positive relationship between digital HRM practices and workforce productivity (r = 0.672, p < 0.01). Regression analysis indicates that 52% of the variation in workforce productivity is explained by digital HRM dimensions (R² = 0.520). Among the variables, HR analytics (β = 0.271) and e-training (β = 0.246) have the highest impact. The findings highlight that digital HRM practices significantly enhance employee efficiency, engagement, and organizational performance in the IT sector.
Does Sustainability Report Give more Value Relevance Rather Than Integrated Report? : A Study among G-20 Countries
The changing nature of the business environment has seen an increase in awareness on the importance of nonfinancial data when valuing companies and looking at their long-term sustainability. This is due to the argument that traditional reporting methods did not meet all stakeholders' demands of information. The more business entities disclose nonfinancial information, the better it is for reducing market issues and increasing capital market efficiency. The sustainability report and the integrated report, is a revolutionary reporting structure that aims to combine financial and nonfinancial data into a single cohesive report. However, it should be noted that simply creating sustainability or integrated reporting does not ensure greater value for the organization. It is critical that sustainability and integrated reporting go beyond simply delivering information and data, that demonstrate value-relevance by drawing a direct line between a company's sustainability initiatives and its financial performance. This research is designed to examine the value-relevance of integrated and sustainability through a comparison of integrated reports to sustainability reports and assess what effect of these reports has on the investment decisions of their stakeholders. Using of the value-relevance model by Ohlson to assess the relevance of disclosed information to market valuation, of which sample uses companies from G20 that report Integrated Report on IIRC as community member of GRI from 2020-2024. The results indicate that value-relevance for integrated reports is higher than sustainability reports, which means Integrated report can improve the value-relevance of accounting data, but unable to enhance the earnings per share. Integrated reporting indeed offering stakeholders more insight into the financial health and valuations of their assets.
Optimal Portfolio Construction Using Modern Portfolio Theory and Sharpe’s Single Index Model: Evidence from NSE Listed Companies
This study focuses on the construction of an optimal portfolio using Modern Portfolio Theory (MPT) with reference to the top 20 market capitalization companies listed on the National Stock Exchange (NSE). The analysis is based on secondary data collected from the Bloomberg database for the period January 2021 to December 2025. Key financial metrics such as mean return, standard deviation, and beta were computed to evaluate the risk-return profile of selected stocks. The findings reveal that Bajaj Finance (20.3%) and SBI (19.2%) offer higher returns but are associated with higher risk, whereas Nestle and HUL demonstrate lower volatility with stable returns. The Excess Return to Beta Ratio indicates that Reliance (10.00), HDFC Bank (9.39), and Bajaj Finance (9.23) provide superior risk-adjusted performance. A cutoff rate (Ci) of approximately 0.0110 was determined using Sharpe’s Single Index Model, and only stocks exceeding this threshold were included in the optimal portfolio. The final portfolio comprises diversified stocks such as Reliance, HDFC Bank, TCS, Infosys, and Bajaj Finance. The study concludes that a systematic and quantitative approach enhances portfolio efficiency by optimizing the trade-off between risk and return.
Impact of Economic Indicators on Stock Market Performance in India
This study examines the relationship between key economic indicators and stock market performance in the context of the National Stock Exchange (NSE) of India. Using quarterly data over a five-year period, the research investigates the impact of Gross Domestic Product (GDP) growth, inflation rate, interest rate, and foreign exchange rate movements on the Nifty 50 index. Employing econometric tools such as correlation analysis, regression modeling, and time-series techniques, the study explores both short-run and long-run dynamics between macroeconomic variables and market performance. The findings reveal that GDP growth and foreign exchange rates significantly influence stock returns, while inflation and interest rates exhibit a mixed and often lagged effect. These results offer valuable insights for investors, policymakers, and market analysts seeking to understand the interplay between macroeconomic fundamentals and capital market movements in India.
Effects of Cashless Policy and Transportation on Market Mix: A Case Study of Meat Sellers in Ogbomoso, Oyo State, Nigeria
The impact of the cashless policy on the performance of SMEs in Nigeria has been a subject of concern among scholars and policymakers. The study therefore examined the effect of cashless policy and transport fares on the marketing mix of meat sellers in Ogbomoso, Oyo State. A random sampling technique was adopted to select 119 from the active meat sellers for the study using a structured questionnaire to obtain primary data. Multiple regressions were used to determine the extent of the contribution of each independent variable to the subject. Stress is the most emotional effect of the cashless policy at 37.5%, followed by depression, anger, and anxiety. Raised on the result of the physical effect of cashless policy, headache has the highest percentage of 34.2%, body pain (30.8%), sleeplessness (18.3%), and fatigue (12.5%). The major social effect of the cashless policy and transportation was harassment (39.2%). The result of the economic effect of the cashless policy and transportation shows (30.8%) of financial loss among others.
Impact of Corporate Governance Practices on Earnings Per Share of Industrial Goods Companies Listed in the Nigeria Stock Exchange
The study was carried out to investigate the impact of corporate governance practices on earnings per share of industrial goods companies listed on the Nigerian Stock Exchange. Specifically, the study examines the relationship between the corporate governance variables board size, board independence, board diligence, and the earnings per share of industrial goods companies listed on the Nigerian Stock Exchange. The study was anchored on the agency theory and collected secondary data from the audited financial statements and annual reports of randomly selected listed industrial goods companies. Regression analysis was used to analyze the collected data via the Statistical Package for Social Sciences (SPSS) version 23 to verify the relationships between the corporate governance variables and earnings per share. Findings of the study showed a positive and significant relationship between board size, board independence, board diligence, and earnings per share. The study therefore concludes that corporate governance practice has an impact on the earnings per share of industrial goods companies listed in Nigeria and recommends, among others, that firm should encourage a reasonable board size, a larger percentage of independent directors on the board of the firm, and finally, enforce attendance to board meetings by board members.
Integrated Palm Oil Production Systems: Navigating Complexity from Upstream Cultivation to Downstream Value Creation in the Sustainability Era
This review of qualitative literature addresses the complexity of oil palm (Elaeis guineensis) as an integrated upstream to downstream production system. The analysis synthesizes recent scholarly evidence (2020-2025) on how vertical and horizontal integration mechanisms, silvopastoral livestock-crop co-management strategies, and supply chain governance arrangements interact with ecological carrying capacity and economic profitability imperatives. It also identifies three foundational dimensions of complexity: structural multidimensionality relating to economic, ecological and institutional domains; actor heterogeneity giving rise to asymmetric power relations and issues of value distribution; and emergent system properties that cannot be predicted from its individual components. Critical insights show that integrated systems, especially livestock–crop co-management and Special Economic Zone clustering, provide pathways in this direction by reducing chemical dependence. There are diversification of farmer livelihoods and capture value downstream, but significant barriers remain. These issues include issues of fragmented governance across RSPO, ISPO and MSPO certification schemes, mechanisms for smallholder inclusion being inadequate as well as unresolved trade-offs between profitability driven by expansion while at the same time fulfilling ecological conservation mandates. The synthesis feasibility itself illustrates how the downstream industrialisation strategy of Indonesia, which aims for 200 derivative products by 2030 generates opportunities to add value but also risks further marginalising seven million small holders who account for 40% supply (Aquila et al., 2023). The review closes with evidence-based policy recommendations that highlight consolidated governance frameworks, differentiated smallholder support mechanisms and landscape-level integration ‘sustainable intensification’ approaches that reconcile productivity goals with sustainability imperatives.
Digital Transformation Readiness Models for Small and Medium Enterprises
Digital transformation has become a critical driver of competitiveness and sustainability for Small and Medium Enterprises (SMEs) in an increasingly digital economy. However, many SMEs struggle to successfully implement digital technologies due to limited resources, skills gaps, and inadequate strategic planning. This study examines digital transformation readiness models as tools for assessing the preparedness of SMEs to adopt and integrate digital technologies effectively. The purpose of this study is to analyze existing digital transformation readiness models and evaluate their applicability to SMEs, with particular attention to organizational, technological, and environmental factors influencing digital adoption. A qualitative research methodology was employed, involving an extensive review of existing literature, academic journals, and industry reports related to digital transformation, readiness assessment, and SME digital maturity frameworks. The key findings reveal that most readiness models emphasize core dimensions such as leadership and strategy, technological infrastructure, employee digital skills, organizational culture, and process integration. The study also finds that SME-specific readiness models are more effective than generic digital maturity models, as they account for financial constraints, managerial influence, and external environmental pressures faced by SMEs. Furthermore, the results indicate that SMEs with higher readiness levels experience smoother digital adoption, improved operational efficiency, and greater competitive advantage. In conclusion, digital transformation readiness models provide a structured approach for SMEs to evaluate their current digital capabilities and identify critical gaps before embarking on digital transformation initiatives. The study recommends that SMEs adopt tailored readiness models to guide strategic decision-making, reduce implementation risks, and enhance long-term business performance in the digital era.
Digital Technology and the Changing Nature of the Workplace on the Performance Effectiveness of the Banking Sector in Bayelsa State of Nigeria
This study examines the impact of digital technology on the changing nature of the workplace and its influence on performance effectiveness in the banking sector. It specifically investigates the extent of workplace digitalization, employee upskilling needs, cybersecurity challenges, data privacy concerns, and operational efficiency improvements. As the banking industry navigates technological transformation, it faces issues such as cybercrime, digital skill gaps, and customer dissatisfaction due to system failures. The study highlights the necessity of balancing digital innovation with personalized services and effective problem-solving mechanisms. The Theory of Reasoned Action (TRA) underpins the investigation by linking employee and customer intentions to behavior in the digital workplace. Recommendations include investing in cybersecurity infrastructure, enhancing staff training, and adopting customer-centric strategies to optimize digital transformation in Nigerian banks.
Innovationology and the End of Scarcity: A Post-Disciplinary Science of Abundance
Innovationology is a post-disciplinary scientific framework that conceptualizes innovation as a universal generative force governing the evolution of economic, technological, and social systems. This article demonstrates that sustained innovation systematically reduces marginal costs in energy, cognition, and digital information, thereby generating empirically observable post-scarcity regimes. By integrating complexity theory, endogenous technological change, and system dynamics, Innovationology establishes a formal relationship between innovation intensity, cost deflation, and abundance formation. Empirical evidence from renewable energy transitions, artificial intelligence and labor substitution, and digital replication validates this model. Simple but powerful equations link innovation to expanding productive capacity and collapsing marginal costs, offering testable predictions for the emergence of abundance-driven economies. The analysis shows that scarcity is not an ontological property of economic systems but a historically contingent condition that is progressively dissolved by innovation-driven scaling dynamics.
Neurocomplexity Leadership Theory: A Cognitive–Systemic Model for Decision-Making Under High Uncertainty
This article introduces Neurocomplexity Leadership Theory (NLT), a new theoretical framework integrating neuroscience, complexity science, and leadership studies to explain how strategic decision-making functions under conditions of volatility, uncertainty, and system instability. Moving beyond psychological trait accounts, NLT conceptualises leadership as an emergent cognitive–systemic process grounded in neural network dynamics and nonlinear organisational feedback loops. Drawing from predictive processing, large-scale brain network theory, and adaptive systems science, NLT argues that decision quality emerges through the co-evolution of neural plasticity, uncertainty management, affective regulation, and contextual entropy absorption. The theory challenges traditional perspectives that view leadership attributes as stable traits or rational cognitive choices, proposing instead that effective leadership arises from the dynamic integration of cognitive networks capable of reorganising information under stress and unpredictability. The article synthesises empirical evidence from cognitive neuroscience, organisational studies, and social complexity to formalise a conceptual apparatus capable of explaining why some leaders thrive under uncertainty while others collapse. It concludes by outlining the scientific implications of neurocomplexity for education, leadership development, and institutional design
Decentralized Intelligence: Designing AI-Enabled Governance for the 21st Century
This paper presents a novel conceptual framework for public governance termed Decentralized Intelligence Governance (DIG), which reconceptualizes the state as an adaptive, intelligence-driven system. In DIG, authority, institutional knowledge, and administrative control are redistributed across artificial intelligence (AI) systems, blockchain networks, and participatory data infrastructures, enabling real-time, transparent, and adaptive decision-making. Traditional Weberian bureaucracies, designed for industrial-era predictability, are increasingly incapable of managing contemporary complexity characterized by rapid technological change, polycentric societal demands, and transboundary crises. Drawing on sociotechnical systems theory, complexity governance, and African digital transformation literature, this study argues that DIG offers a structural solution to bureaucratic fragility, particularly in emerging African economies. The paper outlines the theoretical underpinnings, structural design, and potential applications of DIG while addressing the ethical, social, and infrastructural considerations critical for implementation. By integrating AI, blockchain, and distributed intelligence within public administration, DIG represents a paradigm shift that moves governance from hierarchical control toward algorithmic networks capable of dynamic adaptation and participatory legitimacy
Effect of Information Technology on Inventory Management in Oil and Gas Sector, South-South Nigeria
The study was on the effect of information technology on inventory management in the oil and gas sector, South-South Nigeria. Literatures were reviewed according to the variables identified. The design adopted for the study was a survey research design. The study employed a sample of 322 employees selected from a population that comprised all the selected oil and gas firms in South-South, Nigeria, through a stratified random sampling technique. The instrument used for data collection was a questionnaire. The split-half reliability method was used to determine the reliability coefficient of the data collection instrument. Experts from both industry and academia ascertained the face and content validity of the instrument. The results of the analysis using the Multiple regression model show that there is a significant effect of information technology (use of software, Artificial Intelligence, Network Administration, and Cyber security) on inventory management in the oil and gas sector in South-South, Nigeria. This means that the four variables of information technology (use of software, Artificial Intelligence, Network Administration, and Cyber security) predicted inventory management in the oil and gas sector in South-South, Nigeria. Based on these findings, it was recommended among others that Regulatory authorities and industry stakeholders should support these efforts by providing guidelines, funding incentives, and training programs that promote the effective use of information technology across the oil and gas sector.
The Impact of Artificial Intelligence on Business Strategy Formation in the Digital Economy
Through AI, companies in the digital economy can make better decisions, handle their operations more efficiently and build up their competitiveness. This paper explores the various ways AI impacts business strategies by linking information from data to common strategic guidance. It analyzes many industries by explaining how technology such as machine learning, natural language processing and robotics process automation shape strategy, market studies, customer interaction and new ideas. Important case studies demonstrate how AI is effectively utilised in business strategies, guiding companies seeking to succeed in the digital age. Matters involving ethics, data privacy and complicated system integration are included in these debates. The combination of scholarly sources and case studies in the study outlines the main strategy aspects of AI and recommends an approach for adopting it well in business strategy. According to the findings, AI improves a company’s ability to respond fast, but it has to fit into what the organization aims for and its environment if it is to succeed.
The Evolution of Entrepreneurship in the Digital Economy: A Cross-National Analysis
The Digital economy is recently changing the way of global entrepreneurship dramatically. With the evolution of digital technologies, including the Internet of Things (IoT), blockchain, Artificial Intelligence (AI), and big data analytics, novel forms of entrepreneurial ventures have emerged, altering the way firms build and run their businesses, as well as the foundation of the economy. In this paper, we present a comprehensive cross-national analysis of how entrepreneurship has evolved in response to the digital economy. The study of macroeconomic trends, policy implications, technological enablers and sociocultural factors in driving digital entrepreneurship across countries is explored. Using empirical data, case studies and comparative metrics, this study reveals key patterns of entrepreneurial activity that are affected by digital transformation. To categorise their digital readiness and entrepreneurial maturity, we examine how digital infrastructure affects startup success rates in different nations. The paper proposes that digital entrepreneurship is greatest in situations where innovation ecosystems, regulatory frameworks, digital literacy and access to capital combine. What's more, digital entrepreneurs not only are reshaping the core of traditional business sectors, but are also addressing social challenges through scalable, tech-enabled models. Policy recommendations and future research directions are outlined in the conclusion of this paper.
Organizational Agility in Crisis Management: A Study of Business Continuity During Global Pandemics
The recent unprecedented disruptions to business and regular daily activities due to global pandemics (especially COVID-19) have brought the question of organizational agility to the forefront of many crisis management and business continuity discussions. The paper discusses the strategies of agile organizations, leadership, and processes that enable companies to tackle the complexities and uncertainties that are presented by such crises. The paper is based on recent empirical findings, as well as cross-sectoral case studies, and discusses the shift from traditional, reactive continuity planning to proactive resilience approaches. Among the major enablers observed are digital transformation, agile leadership, decentralized decision-making, and adaptive risk management, which help an organization to react quickly and efficiently to the changing threats. The results indicate that those organizations that had adopted agility as part of their organizational culture, where flexibility, innovation and stakeholder engagement were prioritized were in a better position to continue operating, safeguarding the health of their workforce, and re-model their businesses when the pandemic struck. The paper develops a systematic model of combining technological innovation, scenario-based planning, and collaborative governance as the foundations of strategic resilience. Finally, the study notes that organizational agility is no longer considered a tactical advantage, but a premise supporting the maintenance of business continuity and long-term resilience in an unstable, uncertain world. These lessons provide practical recommendations to business leaders and policymakers to create stronger/more flexible organizations with a better chance to prosper in the event of future systemic shocks.
Blockchain Integration in Supply Chain Management: A Multi-Level Framework for Enhancing Transparency and Traceability
Blockchain technology is being seen more and more as the answer to problems related to transparency and traceability in supply chains today. The data used in these systems tends to be incomplete, and several inefficiencies exist, resulting in a high risk of fraud. Therefore, there has to be a strong structure that unites everyone and secures all aspects of the process. The Multi-Layered Blockchain Architecture combines public and private blockchains to ensure strong data integrity, security and interoperability among members of the supply chain network. Role-Based Access Control (RBAC) and Cross-Blockchain Oracles are integral to the system, enabling precise control over who accesses data and facilitating seamless communication between different chains. Smart contracts streamline jobs such as buying supplies, managing stocks and checking rules, thus lessening paperwork and mistakes by staff members. Because the ledger cannot be changed, it becomes easier to track shipments, verify origins and source goods responsibly in pharmaceuticals, car manufacturing and farming. Although AI plays a significant role, it must navigate obstacles such as high implementation fees, difficulties in connecting to older systems, and changes in laws and regulations. Research evidence indicates higher efficiency, lower spending, and improved risk management, particularly for large international supply chains. Enhanced predictive analytics and swift decision-making occur when the framework supports the Internet of Things (IoT) and Artificial Intelligence (AI). In the future, emphasis will likely be placed on making supply chains more scalable, sustainable, and open to collaboration between various industries, which should make the system more robust and resilient.
Assessing the Economic Viability of Renewable Energy Subsidies through a Multi-Criteria Decision-Making Framework
Even though renewable energy subsidies help the transition to more sustainable energy faster, some still argue that they are not economically sensible because of pressing concerns such as budget cuts, market disruptions and their effect on the planet. A Multi-Criteria Decision-Making (MCDM) process is employed in this study to evaluate the effectiveness of renewable energy subsidies, taking into account economic, environmental, technical, and social aspects. Using AHP and CoCoSo, the analysis ranks key factors such as costs, the time required to recover the investment, the project's ability to limit carbon emissions, and potential employment opportunities. Results of case studies in India and Serbia indicate that solar and wind power are most important due to their low cost and ability to be applied on a wide scale. Financial factors like upfront costs and returns mainly decide subsidies, yet the benefits to the environment and society in the long run are a good reason to continue subsidizing such projects. The concept highlights the need to pick technologies that can balance short-term advantages in growth with goals for a better, more sustainable future. Many policy suggestions focus on adjusting who gets subsidies, improving how markets set prices and increasing collaborations with other countries to address trade disagreements. This method for MCDM provides policymakers with a clear tool that relies on data to develop robust renewable plans that both align with global environmental goals and meet local needs.