Interdisciplinary Systems for Global Management
Interdisciplinary Systems for Global Management (ISGM) is a peer-reviewed journal dedicated to exploring the intersection of systems thinking, technology, and management in addressing complex global challenges. ISGM fosters the exchange of ideas that transcend traditional boundaries, encouraging holistic perspectives on economic, environmental, and social management.
Artificial Intelligence Applications in Sustainable Reverse Supply Chains: A Systematic Review, Gap Analysis, and Future Research Agenda
Artificial intelligence (AI) is increasingly reshaping sustainable reverse supply chains (SRSCs) by enhancing product return management, remanufacturing, recycling, recovery, and reintegration processes, while strengthening circular economy implementation and sustainability performance. Escalating regulatory pressures, resource scarcity, climate-related disruptions, and rising reverse logistics complexity have intensified the demand for intelligent, adaptive, and data-driven systems capable of operating effectively under uncertainty. However, despite growing scholarly attention, research on AI-enabled SRSCs remains fragmented across technological, operational, and managerial domains, limiting theoretical integration and a comprehensive understanding of system transformation. This study presents a systematic literature review of peer-reviewed publications from 2000 to early 2026 to synthesize and critically evaluate the evolution of AI applications in SRSCs. Using a structured review protocol and thematic synthesis, it develops a multidimensional taxonomy and a mechanism-based conceptual framework explaining how AI enhances decision intelligence, coordination efficiency, operational resilience, and sustainability performance across reverse supply chain systems. Findings reveal a structural shift from cost-centric reverse logistics toward predictive, adaptive, and sustainability-oriented closed-loop systems characterized by real-time visibility, dynamic optimization, and autonomous decision support. AI significantly improves return flow forecasting accuracy, recovery efficiency, waste reduction, and resilience under uncertainty and disruption. However, the literature remains unevenly developed, particularly in relation to governance structures, cross-functional integration, and methodological standardization. Key research gaps are identified in AI governance, explainability, interoperability, scalability, and social sustainability integration. Importantly, generative AI and large language models (LLMs) emerge as a nascent but largely underexplored frontier with strong potential to transform knowledge-intensive decision-making, coordination mechanisms, and adaptive control in reverse supply chains. In response, the study proposes a future research agenda across six interrelated dimensions to advance intelligent, resilient, and sustainability-oriented closed-loop supply chain systems.
Copyright (c) 2026 Attia Gomaa (Author) https://creativecommons.org/licenses/by/4.0
Model and Empirical Research on Passengers' Travel Mode Choice in the Context of Competition between High-Speed Rail and Air Express Lines
This study investigates passengers’ travel mode choice under competitive conditions between high-speed rail (HSR) and air express services. It identifies and analyzes the key factors influencing passengers’ preference for HSR versus air express, and develops a quantitative model to estimate market share. A structured questionnaire was designed to capture passengers’ socio-economic attributes, latent preferences, and perceptions of travel mode characteristics. Over 300 valid responses were collected. Employing the Multinomial logit (MNL) model and choice probability framework, the analysis adopts the maximization of passengers’ comprehensive utility as the underlying decision criterion. Grounded in utility theory, a six-dimensional utility function is formulated-encompassing safety and reliability, time efficiency, economic cost, service frequency, convenience, and comfort-and the corresponding utility coefficients are calibrated via maximum likelihood estimation. Model results indicate that time efficiency, economic cost, and safety and reliability are the most significant determinants of passengers’ travel mode choice. Finally, the choice probabilities for HSR and air express services are computed. The findings offer an evidence-based reference for transportation policy formulation and strategic decision-making by industry stakeholders.
Copyright (c) 2026 Min Feng, Dongchen Zhang, Jingyun Wu (Author) https://creativecommons.org/licenses/by/4.0
Management 4.0 in Smart Manufacturing: A Systematic Review, Gap Analysis, and Future Roadmap
Management 4.0, driven by digital transformation and Industry 4.0 technologies, is reshaping organizational structures, managerial paradigms, and value creation mechanisms in smart manufacturing systems. Enabled by the convergence of artificial intelligence, cyber-physical systems, advanced analytics, and interconnected digital infrastructures, manufacturing organizations are increasingly transitioning toward adaptive, autonomous, and data-driven management models that transform decision-making, coordination, and operational governance. Despite growing scholarly attention, the Management 4.0 domain remains fragmented and under-theorized. Existing literature exhibits limited conceptual integration, inconsistent empirical validation, and a lack of scalable implementation frameworks. In particular, the socio-technical complexity of integrating intelligent systems with human decision-making across organizational transformation, workforce evolution, governance redesign, and sustainability (including ESG integration) remains insufficiently addressed. To address these gaps, this study conducts a systematic review of more than 200 peer-reviewed publications (2011-2026), supported by structured gap analysis. Grounded in socio-technical systems theory and the dynamic capabilities perspective, it reframes Management 4.0 as an integrated organizational transformation paradigm rather than a set of isolated digital initiatives. The synthesis develops a consolidated conceptual framework structured into four interdependent capability domains: technological infrastructure, organizational architecture, managerial and leadership capabilities, and governance systems. Three critical gaps are identified: absence of a unified theoretical foundation, limited empirical validation of transformation pathways, and insufficient understanding of human-AI socio-technical integration in smart manufacturing contexts. Finally, a ten-step implementation roadmap is proposed, encompassing digital maturity assessment, strategic alignment, digital architecture design, capability development, process reengineering, governance and ethics, innovation and risk management, customer-centric transformation, performance measurement, and knowledge management integration.
The Influence of Diverse Boards on the Corporate Social Responsibility of Indian Companies
Diversity on board is essential to the successful application of CSR initiatives. Businesses can enhance their decision-making procedures and better serve stakeholder demands by cultivating diverse leadership. The study analyses the impact of diversity of boards of directors on Indian companies' corporate social responsibility performance of 540 companies listed on the National Stock Exchange of India from April 2012 to March 2021. The data relating to CSR has been extracted from the Bloomberg database and other variables have been taken from the CMIE Prowess database. Built on a fixed effect panel regression analysis, this study discovers that the board diversity of Indian enterprises has a mixed impact on their CSR performance. The study also analyses the effect of board diversity on the environment, social and governance scores separately and presents the findings of the study. The paper explains how a diverse board affect corporate social responsibility in the Indian setting, where CSR spending and the elements of board diversity like the presence of women directors and independent directors is mandated by the law. The paper also makes suggestions and recommendations based on the findings.
Copyright (c) 2026 Deepa Sharma (Author) https://creativecommons.org/licenses/by/4.0
Impact of Digital Marketing on the Performance of Smes (A Case Study of Selected Smes in Ikeja, Lagos State)
The study examined the effect of digital marketing on the performance of SMES (a case study of selected SMES in Ikeja, Lagos State). The study adopted a descriptive research design. The population of the study included all employees of SMES at Ikeja, Lagos State. A total of 158 respondents were selected using the Quota sampling technique. Hypotheses were tested using the Pearson correlation coefficient to determine whether to accept or reject the null hypothesis. For the first hypothesis, the correlation between Social Media Marketing and Performance of SMES is 0.617, which shows a strong positive correlation. The p-value (0.000) is less than 0.01, confirming this correlation is statistically significant. Also, for hypothesis two, the correlation between Email Marketing and the SME performance is 0.815, showing a very strong positive correlation. The p-value (0.000) is less than 0.01, indicating that this correlation is statistically significant as well. The study recommends that businesses should leverage digital marketing strategies to enhance their performance by creating a well-defined digital marketing strategy that aligns with their business goals and target audience, and establish a strong presence on relevant social media platforms where their target audience is active.
Copyright (c) 2026 Linus Izediuno Okafor, Ikenna Theodore Nnoli, Oladoyin Aderoju Kiladejo, Oluwaseun Deborah Olawunmi, Balikis Ayomide Adeyemi, Zachariah Millenvodjo Godonu (Author) https://creativecommons.org/licenses/by/4.0
Digital Entrepreneurship and Online Digital Marketing Strategies for SMEs: Technology-Organization-Environment (TOE) Model
The rapid expansion of the digital economy has created a new investment landscape defined by digital entrepreneurship. This study investigates the profound impact of online digital marketing strategies on Nigerian small and medium-sized enterprises (SMEs), highlighting how effective adoption enhances revenue generation and customer engagement. Evidence from the research shows that many SMEs experienced significant increases in turnover following the integration of digital marketing tools, underscoring their transformative potential. Guided by the Technology-Organization-Environment (TOE) framework, the study demonstrates how SMEs can strategically embrace digital activation to strengthen competitiveness and achieve sustainable growth. A random sample of 450 SMEs was drawn from a population of 500 businesses in the study area, with 90 percent of responses deemed valid for analysis. Findings reveal that 52 percent of SMEs reported annual revenues increase between 3million and 5million Naira after adopting digital marketing, compared to lower earnings prior to digital marketing implementation. The results confirm that leveraging technology, organizational readiness, and environmental adaptation through the TOE framework provides SMEs with a structured pathway to optimize digital entrepreneurship, expand market reach, and secure long-term resilience in the evolving digital economy.
Technology-Organization-Environment (TOE) Framework
Copyright (c) 2026 Inimfon Bassey Bassey, David Taiwo Oyekunle, Eboesomi Esther Bunmi, Godwin Nse Ebong, Equitozia May Eyeregba, Williams Ogochukwu Ugbomeh, Okwudili Matthew Ugochukwu (Author) https://creativecommons.org/licenses/by/4.0
Content Analysis of Media-Based Public Health Communication in Global Humanitarian Crises
This study applied Technology–Organization–Environment (TOE) framework, together with ANT, through the lens of Design Science Research Methodology (DSRM) to examine the fragmented methodological landscape in media-based public health communication during global humanitarian crises. The technological dimension encompasses digital platforms and data analytics; the organizational dimension highlights leadership readiness and institutional structures; and the environmental dimension captures socio-political contexts and cultural norms. ANT complements TOE by uncovering the interplay between human and non-human actors in shaping communication flows. A chi-square analysis was performed to empirically assess the frequency and reliance on different information sources among media channels communicators, revealing significant variation across regions and method types. The resulting framework provides a structured yet adaptable pathway for future research and policy development, supporting more coherent and context-sensitive public health communication strategies in crisis settings.
Technology–Organization–Environment (TOE)
Copyright (c) 2026 Lateef Olawale Fatai, Eric Emmanuel Uche, Praise Akinsipe, Esther Emarobebh Muojekwu, Blessing Adeni Amakuro, Okwudili Matthew Ugochukwu (Author) https://creativecommons.org/licenses/by/4.0
Green Lean Six Sigma for Smart and Sustainable Manufacturing: A Comprehensive Review and Strategic Framework in the Industry 4.0–5.0 Era
The transition toward smart, sustainable, and human-centric manufacturing is reshaping how organizations integrate operational excellence, environmental responsibility, and digital transformation. Green Lean Six Sigma (GLSS)—which synergizes Lean waste minimization, Six Sigma’s data-driven methodology, and green manufacturing principles—has emerged as a robust approach for improving sustainability and operational performance. However, its strategic integration within the evolving Industry 4.0–5.0 landscape remains conceptually fragmented and insufficiently structured. This study presents a comprehensive review of GLSS and its convergence with advanced digital technologies, including the Internet of Things, cyber-physical systems, artificial intelligence, robotics, cloud computing, blockchain, additive manufacturing, and big data analytics. The review synthesizes key implementation drivers, performance impacts, organizational and technological challenges, and critical research gaps, with particular emphasis on sustainability integration, digital maturity, human–technology collaboration, and the progression toward resilient, circular, and human-centric manufacturing systems. Building on these insights, the study proposes an integrated strategic GLSS framework tailored to the Industry 4.0–5.0 context. The framework extends the traditional DMAIC methodology by embedding circular economy principles, sustainability-oriented performance metrics, digital intelligence, and human-centric innovation across all phases. Structured around five interrelated pillars—Technological Enablement, Human-Centric Management, Sustainable Materials and Processes, Policy and Governance Alignment, and Operational Integration—the framework provides a structured roadmap for deploying GLSS to achieve eco-efficient, socially responsible, and high-performance smart manufacturing systems.
Information and Communication Technology for Efficient Operations Management: A Comprehensive Review, Research Gaps, and Strategic Framework
The rapid evolution of Information and Communication Technology (ICT) is transforming operations management (OM), driving efficiency, agility, and competitiveness. Traditional systems—such as Enterprise Resource Planning (ERP), Supply Chain Management (SCM), and Decision Support Systems (DSS)—are increasingly enhanced by Industry 4.0 technologies, including artificial intelligence, the Internet of Things, blockchain, and digital twins. Yet, ICT adoption in OM remains fragmented, challenged by technological limitations, organizational misalignment, workforce skill gaps, and systemic vulnerabilities. This study presents a comprehensive review of ICT applications in operations management, with emphasis on production planning, logistics, quality management, and performance optimization. The challenges of ICT adoption are classified into four dimensions: technological, organizational, human-centric, and systemic. Despite ICT’s transformative potential, critical gaps remain, including the absence of integrated performance evaluation frameworks, limited incorporation of sustainability and ethics, and insufficient adaptability of ICT models across diverse industrial and socio-economic contexts. To address these shortcomings, the study introduces a strategic framework structured around five pillars: technological infrastructure, organizational alignment, human-centric development, systemic and ethical integration, and continuous improvement. This framework positions ICT as a catalyst for operational excellence, resilience, and sustainable value creation, while bridging theoretical foundations with practical applications to accelerate digital transformation in operations management.
Information and Communication Technology (ICT)
Copyright (c) 2025 Attia Hussien Gomaa (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence (AI) Meets Blockchain: Transforming Industries for the Next Digital Era
Artificial Intelligence (AI) and blockchain are among the most transformative technologies of the last decade, with AI excelling in adaptive decision-making and predictive analytics, and blockchain ensuring secure, transparent, and tamper-resistant data management. This manuscript investigates their convergence as a catalyst for innovation across industries such as cybersecurity, supply chain management, financial services, and decentralized marketplaces. A structured framework is proposed—spanning use case identification, system design, deployment, and expert collaboration—to guide the development of AI–blockchain solutions. Quantitative findings demonstrate improvements of up to 35% faster threat detection in cybersecurity applications, 20% greater efficiency in supply chain logistics, and significant cost reductions in cross-border transactions. Furthermore, the integration fosters transparent peer-to-peer marketplaces, enhancing trust and accountability. Emerging trends—including decentralized AI governance, Web3-driven business models, and quantum-ready blockchain protocols—are highlighted as opportunities for building resilient digital ecosystems. By presenting both methodological insights and measurable outcomes, this study positions AI–blockchain integration as a strategic foundation for the next era of digital transformation, providing actionable guidance for researchers, developers, and industry leaders.
Copyright (c) 2025 Zarif Bin Akhtar (Author) https://creativecommons.org/licenses/by/4.0
Data Saturation Reliability Theory: A Framework for Optimising AI Input Feeds
Artificial Intelligence (AI) systems increasingly rely on large and diverse data streams to support accurate, adaptive, and context-aware decision-making. However, beyond a certain point, adding new data can lead to diminishing or even negative returns due to redundancy, noise, and bias, a phenomenon known as data saturation. This paper introduces the Data Saturation Reliability (DSR) framework, a conceptual framework that optimises AI input feeds by balancing data volume, quality, and reliability. Drawing on principles from information theory, machine learning, and data governance, the DSR framework formalises saturation thresholds, signal-to-noise ratio assessment, temporal relevance, and dynamic feedback mechanisms as key factors for sustainable AI performance. By linking marginal information gain to input reliability, the DSR framework provides strategies to mitigate risks of over-saturation, bias propagation, and operational inefficiencies, while improving predictive accuracy and adaptive learning. The framework prioritises quality over quantity, encouraging intelligent curation of inputs rather than indiscriminate data collection. Applications include high-stakes fields such as healthcare diagnostics, financial forecasting, autonomous systems, and large-scale natural language processing, where real-time decision accuracy and reliability are vital. The paper highlights opportunities for empirical validation, cross-domain adaptation, and integration of DSR principles into AI lifecycle management and governance. Ultimately, the framework promotes shifting from “more data equals better performance” towards an optimal data balance that ensures operational effectiveness and ethical responsibility in AI deployment.
Copyright (c) 2025 Michael Mncedisi Willie, Siyabonga Jikwana, Lesiba Arnold Malotana, Zwanaka James Mudara (Author) https://creativecommons.org/licenses/by/4.0
Transforming Manufacturing from Industry 4.0 to Industry 6.0: A Comprehensive Review, Gap Analysis, and Strategic Framework
Industry 6.0 represents a pivotal advancement in the evolution of manufacturing—surpassing the automation and cyber-physical integration of Industry 4.0 and the human-centric ethos of Industry 5.0. It is founded on the principles of consciousness, circularity, and resilience, positioning manufacturing as a catalyst for human flourishing, ecological regeneration, and adaptive intelligence. This study offers a comprehensive review of more than 90 peer-reviewed publications that explore the transition from Industry 4.0 to 6.0, synthesizing key conceptual foundations, enabling technologies, and strategic imperatives. It proposes a strategic framework for implementing Industry 6.0, anchored in core principles of conscious intelligence, ethical design, and global equity. The framework is organized around five strategic pillars: technological convergence, ethical governance, inclusive transformation, organizational agility, and sustainability. These are supported by cross-cutting enablers such as innovation ecosystems, human-centric competencies, and coherent policy frameworks. The study further presents a phased roadmap for implementation, outlines expected outcomes—including empathetic artificial intelligence and regenerative industrial systems—and illustrates real-world applicability through forward-looking scenarios. Together, these elements provide a visionary yet actionable blueprint for policymakers, industry leaders, and researchers to co-create ethical, resilient, and sustainable industrial futures guided by moral intelligence, empathy, and planetary stewardship.
Managing Data Saturation in AI Systems: A Cross-Domain Framework Integrating Human Insights and Algorithmic Verification
Artificial Intelligence (AI) systems are increasingly relied upon for real-time decision-making, yet high data volumes often introduce noise, inefficiencies, and performance degradation. Drawing inspiration from data saturation concepts in qualitative research, this study examined how excessive or unfiltered data compromises AI system effectiveness. The study aimed to develop a cross-domain conceptual framework that integrated human insights with algorithmic verification to manage data saturation and ensure timely, accurate, and trustworthy AI outputs. A conceptual and analytical approach was employed, combining an extensive literature review with case analyses of high-volume navigation platforms, including Waze and Google Maps. The study investigated adaptive filtering, contextual relevance scoring, hybrid verification, and temporal decay mechanisms to identify generalisable strategies for managing data saturation in AI systems. Findings show that adaptive filtering, prioritisation of contextually relevant information, hybrid verification combining human input and automated data, and temporal decay mechanisms can potentially mitigate data saturation. Lessons from Waze and Google Maps highlighted the importance of balancing human participation with computerised processes, enhancing system performance and user trust. Managing data saturation proves to be a critical operational challenge for AI systems, not merely a research consideration. The proposed framework provides a structured approach for integrating human insights and algorithmic verification, improving decision accuracy, responsiveness, and reliability across domains. The study recommends implementing adaptive data management strategies that combine human and algorithmic inputs, regularly evaluate system performance under high data loads, and adopt context-aware prioritisation techniques. Further research is suggested to explore empirical validation of the framework across diverse AI applications.
Copyright (c) 2025 Michael Mncedisi Willie (Author) https://creativecommons.org/licenses/by/4.0
Identifying and Prioritizing Barriers to Implementing a Strategic Innovation Ecosystem Based on Corporate Sustainability in the Food Industry (Including a Case Study)
Strategic innovation leading to corporate sustainability is a difficult, continuous and challenging process in business. Corporate sustainability plays an important role in establishing a balance between social, economic and environmental goals, as well as improving the competitive position by using opportunities; therefore, many businesses seek to innovate their processes and actions in line with corporate sustainability. The main objective of this research is to identify and prioritize the obstacles to the implementation of a strategic innovation ecosystem based on corporate sustainability in the food industry (case study: confectionery and chocolate sector). The present research is of an applied-developmental purpose, descriptive in terms of data collection and quantitative in terms of the nature of the data; and it includes four main parts. In the first part, using the research literature, the obstacles to the implementation of a strategic innovation ecosystem based on corporate sustainability were identified. To obtain the indicators, after reviewing the theoretical literature, 3 main criteria and 8 sub-criteria were identified, which were finalized after two stages of surveying supply chain experts in Shahd Arang Food Industries Company using the fuzzy Delphi method. Finally, 3 suitable criteria for intra-organizational barriers, 3 suitable criteria for extra-organizational barriers, and 2 criteria for environmental barriers were identified. Then, the fuzzy Savaray method was used to weight and prioritize the criteria. The results of the research showed that the criterion of lack of networking and strategic communication with a weight of 0.249 has obtained the first rank. The lack of internal and external stakeholders with a weight of 0.2 and the lack of a purposeful organizational culture with a weight of 0.160 have obtained the second and third ranks, respectively. Finally, the influence of each factor has been investigated using a one-sample t-test. Based on the results of this method, it was shown that intra-organizational, extra-organizational and environmental barriers are effective barriers in the implementation of the strategic innovation ecosystem based on corporate sustainability in the food industry. It is also worth noting that the sub-components of intra-organizational, extra-organizational and environmental barriers also have a significant impact on the implementation of the strategic innovation ecosystem based on corporate sustainability in the food industry. Considering the barriers to the implementation of the strategic innovation ecosystem based on corporate sustainability in this study, managers and owners of various industries and businesses can take steps to select appropriate factors and benefit from its benefits, according to the findings of this study.
Copyright (c) 2025 Mohammad Taghipour, Seyedeh Zahra Hoseini Mavini (Author) https://creativecommons.org/licenses/by/4.0
Coping Mechanisms and Privacy Concerns in Digital Health on Social Media
Background: Digital platforms, particularly social media sites like TikTok, have emerged as influential tools for health communication and peer support. However, they present challenges, including the possible spread of misinformation, privacy concerns, and limited professional oversight. Aim: This review investigates the coping mechanisms patients and healthcare providers adopt to manage the risks and challenges encountered on social media platforms within digital health environments. Methods: A scoping review was conducted using PubMed, Google Scholar, and Scopus literature. Studies were selected based on relevance, quality, and methodological rigour, focusing on strategies addressing misinformation, privacy protection, and professional accountability. Findings: Digital platforms facilitate health information sharing and community support but also expose users to risks such as unverified content and privacy violations. A lack of consistent verification mechanisms for healthcare professionals and insufficient regulatory frameworks increases users’ vulnerability to misinformation and unethical practices. Conclusion and Recommendations: To improve safety and outcomes in digital health spaces, the review recommends stricter social media guidelines for healthcare providers, increased efforts to improve digital health literacy, and enhanced privacy protections. Strengthening regulatory oversight is essential to ensure the credibility and safety of health information shared online.