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Journal of Operational and Strategic Analytics
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Journal of Operational and Strategic Analytics (JOSA)
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ISSN (print): 2959-0094
ISSN (online): 2959-0108
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2026: Vol. 4
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Journal of Operational and Strategic Analytics (JOSA) is an international, peer-reviewed, open-access journal focusing on analytical methods for data-driven decision-making in operational and strategic contexts. The journal publishes theoretical, methodological, empirical, and applied research in operations research, decision science, statistics, management science, computational methods, strategic management, business analytics, performance evaluation, and risk management. It also welcomes studies involving artificial intelligence, machine learning, the Internet of Things, blockchain, and other emerging technologies when they address clearly defined operational or strategic decision problems. JOSA connects rigorous analytical methods with practical applications in business, public institutions, government agencies, and society. Published quarterly by Acadlore, the journal releases issues in March, June, September, and December.

  • Professional Editorial Standards - All submissions are evaluated through a structured peer-review process involving independent reviewers and editorial assessment before acceptance.

  • Efficient Publication - The journal follows a defined review, revision, and production workflow to support regular, predictable publication of accepted manuscripts.

  • Open Access - JOSA is an open-access journal. All published articles are made available online without subscription or access fees.

Editor(s)-in-chief（1）
seyyed ahmad edalatpanah
Department of Applied Mathematics, Ayandegan Institute of Higher Education, Iran
s.a.edalatpanah@aihe.ac.ir | website
Research interests: Mathematical Programming; Operational Research; Numerical Modeling; Strategic Analytics; Decision Support Systems; Uncertainty Theories; Soft Computing

Aims & Scope

Aims

Journal of Operational and Strategic Analytics (JOSA) is an international, peer-reviewed, open-access journal devoted to the development and application of analytical methods for operational and strategic decision-making. The journal publishes research drawing on operations research, decision science, statistics, management science, computational methods, and related disciplines to examine problems faced by businesses, public institutions, government agencies, and society.

Operational and strategic decisions increasingly involve large or heterogeneous datasets, multiple and conflicting objectives, uncertain conditions, complex stakeholder relationships, and rapidly changing organisational environments. JOSA provides a forum for research that addresses these challenges through rigorous modelling, systematic analysis, empirical investigations, and well-supported decision frameworks. Particular attention is given to studies that connect analytical methods with clearly defined operational or strategic problems.

The journal welcomes theoretical, methodological, computational, empirical, and application-oriented contributions. Relevant approaches include optimisation, simulation, statistical analysis, forecasting, multi-criteria decision-making, efficiency and productivity analysis, machine learning, problem structuring methods, risk analysis, and decision support systems. The use of a particular method or technology alone does not establish suitability for JOSA; manuscripts should explain how the approach contributes to operational analysis, strategic planning, resource allocation, performance assessment, or decision-making.

JOSA is concerned with both the methodological foundations and practical consequences of analytics. Submissions are expected to state the decision problem clearly, justify the selected methods, describe data and assumptions transparently, and interpret results in relation to the organisational or societal context. Studies should extend beyond routine application of established techniques, descriptive reporting, or isolated algorithmic comparison and should provide a substantive methodological, empirical, or managerial contribution.

The journal also considers studies involving artificial intelligence, machine learning, the Internet of Things, blockchain, digital platforms, and other emerging technologies when these technologies form part of a clearly defined operational or strategic analytics problem. Manuscripts focused mainly on software development, technical system architecture, algorithmic performance, or general technology adoption without a meaningful decision or management dimension are generally outside the journal’s primary scope.

JOSA welcomes original research articles, review articles, short communications, and well-developed interdisciplinary studies. Special issues may be organized around focused topics that reflect important developments in operational and strategic analytics. Regardless of article type or application area, manuscripts are expected to demonstrate methodological transparency, analytical consistency, appropriate validation, and clear relevance to decision-making theory or practice.

JOSA is published quarterly by Acadlore. Manuscripts considered suitable after editorial screening undergo structured peer review to assess their originality, methodological soundness, analytical depth, and clarity of presentation.

Key features of JOSA include:

  • The journal focuses on analytical approaches to operational and strategic decision problems in business, organisational, governmental, and societal settings.

  • It covers both methodological research and practical applications, provided that the connection between the analytical approach and the decision context is clearly established.

  • Contributions may employ quantitative, qualitative, computational, or integrated methods, with particular attention to transparent assumptions and appropriate validation.

  • The journal considers emerging technologies when they support operational analysis, strategic planning, performance assessment, risk management, or decision support.

  • Interdisciplinary studies are welcomed when they offer a clear contribution to operational or strategic analytics rather than merely applying a standard method in a new sector.

Scope

JOSA welcomes original research articles, review articles, short communications, theoretical studies, and well-documented empirical or computational investigations in areas including, but not limited to, the following:

  • Operational and Strategic Analytics: Development and application of analytical methods for operational planning, resource allocation, process improvement, capacity management, and long-term strategic decision-making.

  • Strategic Planning and Management: Analytical studies of strategy formulation, implementation, evaluation, organisational positioning, competitive priorities, and the alignment of strategic objectives with operational capabilities.

  • Business Analytics and Management: Use of analytical methods in business planning and management, including market analysis, customer and consumer behaviour, supply and demand assessment, organisational performance, and evidence-based managerial decisions.

  • Problem Structuring Methods: Methods for identifying, representing, and structuring complex or poorly defined decision problems involving multiple stakeholders, competing objectives, and different perspectives.

  • Knowledge and Information Management: Analytical approaches to the acquisition, organisation, sharing, and use of knowledge and information in support of organisational learning, operational coordination, and strategic decision-making.

  • Decision Analytics and Decision Support Systems: Models, frameworks, and systems supporting individual, group, and organisational decision-making, including multi-criteria decision analysis, preference modelling, expert systems, and data-supported decision processes.

  • Data-Driven Analysis: Quantitative, qualitative, statistical, and computational approaches that use data to explain, predict, or support operational and strategic decisions, with attention to data quality, model validity, and interpretation.

  • Digitalisation and Emerging Technologies: Examination of how artificial intelligence, machine learning, the Internet of Things, blockchain, digital platforms, automation, and related technologies influence operations, organisational strategy, and decision processes.

  • Accounting and Quantitative Finance: Analytical methods for accounting, budgeting, investment decisions, financial planning, portfolio analysis, risk assessment, corporate performance, and other operational or strategic financial problems.

  • Health and Tourism Management: Application of operational and strategic analytics to healthcare systems, hospitals, public health services, tourism organisations, hospitality operations, destination management, and service planning.

  • Project and Risk Management: Analytical approaches to project selection, scheduling, resource allocation, monitoring, uncertainty assessment, resilience planning, and the identification and management of operational or strategic risks.

  • Complexity and Uncertainty Management: Methods for decision-making under uncertainty, ambiguity, incomplete information, dynamic conditions, and complex interdependencies, including fuzzy, stochastic, probabilistic, robust, and scenario-based approaches.

  • Performance Measurement and Efficiency Analysis: Assessment of organisational, operational, financial, environmental, and service performance using productivity analysis, benchmarking, efficiency measurement, key performance indicators, and related analytical tools.

  • Innovative Applications of Decision Science: New or interdisciplinary applications of decision science in industrial, commercial, governmental, environmental, social, and other emerging contexts, provided that they make a clear contribution to operational or strategic analytics.

Articles
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Engineering project credit-risk governance requires regulators and project participants to coordinate institutional controls with digital supervision capabilities. Yet decision-makers often lack a structured basis for identifying the factors that should receive priority and for judging how institutional and technological interventions may perform over time. This study investigates the causal structure of engineering project credit risk and examines the policy implications of alternative governance interventions. An online questionnaire collected 86 complete responses covering 33 predefined directional relationships among 13 factors organised under the Technology–Organization–Environment (TOE) framework. Full-precision mean scores were analysed using the Decision-Making Trial and Evaluation Laboratory (DEMATEL), Interpretive Structural Modeling (ISM), and Matrix of Cross-Impact Multiplications Applied to Classification (MICMAC). An exploratory system dynamics (SD) model was then used to compare the baseline, institutional-response, technology, and combined scenarios. Robustness was examined through alternative response coding, threshold sensitivity tests, and 1,000 bootstrap resamples. The results showed that insufficient credit verification by supervision units, environmental and resource compliance risk, and lagging credit-management methods were the three most prominent factors. The ISM analysis placed environmental and resource compliance risk at the root of the four-level hierarchy, while MICMAC classified four factors as independent drivers. All bootstrap samples retained the same three leading factors, and alternative coding preserved the complete prominence ranking (Spearman’s $\rho$ = 1.000). In the exploratory simulation, the technology intervention produced a credit index of 39.09 at time 20, compared with 4.06 under the baseline scenario. The institutional-response intervention showed no clear long-horizon advantage. At time 50, the combined scenario produced a value of 33.73, only slightly higher than the technology-only value of 33.42. These findings indicate that engineering project credit-risk governance should prioritise verifiable supervision, interoperable monitoring, and timely credit-management processes. The integrated framework provides a transparent basis for intervention prioritisation and lifecycle governance, while the simulation results should be interpreted as policy experiments rather than industry forecasts.

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Multi-tier supply chains are exposed to operational disruptions that can spread through interconnected supplier–customer relationships. Existing risk assessments often rank individual firms or links without considering whether these relationships form continuous routes of concentrated vulnerability. This study investigates critical-path identification as a decision-analytics problem in which relationship-level performance and network structure are considered jointly. A two-phase framework was developed by integrating the Criteria Importance Through Intercriteria Correlation (CRITIC) method with evolutionary path optimization. In the first phase, CRITIC was used to derive objective weights for 14 operational performance criteria and to calculate a criticality index for each supplier–customer link. In the second phase, two optimization models were formulated to identify the path with the highest cumulative criticality and the path with the highest average link criticality subject to a minimum path length. The framework was applied to an automotive supply chain consisting of 19 enterprises and 35 directed links. The cumulative model identified a five-link path with a total criticality of 3.274 and an average criticality of 0.655, whereas the average-criticality model identified a four-link path with a total criticality of 2.738 and an average of 0.684. Both models selected the same initial relationship but produced different subsequent routes. The results indicate that link-level rankings alone cannot identify the most critical continuous route because path selection also depends on connectivity, topological position, and the optimization objective. The framework provides a reproducible basis for prioritizing supplier relationships, directing monitoring resources, and selecting risk-mitigation measures across multi-tier supply networks.

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This study evaluates whether public-sector health financing and selected governance dimensions are associated with longevity in South Asia. The analysis uses a balanced panel of 168 country-year observations for Bangladesh, India, Maldives, Nepal, Pakistan, and Sri Lanka over 1996–2023. Life expectancy at birth (LE) is modelled against domestic general government health expenditure (HE_GDP), control of corruption (CC), political stability and absence of violence/terrorism (PS), and government effectiveness (GE).Pooled ordinary least squares (OLS), feasible generalized least squares (FGLS), panel-corrected standard errors (PCSEs), and Driscoll–Kraay standard errors are reported to assess the stability of the estimates under different error structures. Diagnostic testing indicates heteroskedasticity and cross-sectional dependence, while the test for first-order serial correlation is not statistically significant. In the FGLS specification, a one-percentage-point increase in government health expenditure as a share of gross domestic product (GDP) is associated with 1.2514 additional years of life expectancy ($p <$ 0.01). CC and political stability also show positive, statistically significant coefficients of 2.9629 and 2.3174, respectively. GE is negative but not statistically significant in the FGLS model (-1.3262, $p$ = 0.131), and its significance is sensitive to the estimator used. Across the robustness specifications, the central pattern remains that health financing, corruption control, and political stability are important correlates of longevity. The findings support a policy approach in which budget expansion is accompanied by institutional accountability and a stable environment for implementation.

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New product selection is a strategic decision problem in which market opportunities must be considered alongside financial requirements, technological capabilities, and production constraints. Existing fuzzy multi-criteria decision-making (MCDM) methods can rank product alternatives under uncertainty, but they do not always maintain consistency between the priority classes assigned to evaluation criteria and their final weights. This study develops a fuzzy decision-analytics approach that links Pareto-based ABC classification with dynamic criterion weighting for strategic new product prioritization. Linguistic assessments from nine experts were represented by triangular fuzzy numbers (TFNs) and combined with quantitative data to evaluate eight candidate products against ten market, economic, technological, and production-related criteria. The criteria were first assigned to ABC classes, after which class-specific scaling factors were calculated to preserve their relative importance within each class while enforcing the required weight order across classes. The resulting weights were then used to rank and classify the candidate products. The procedure maintained the specified priority structure among the criteria. Transport racks obtained the highest overall decision value of 0.825 and were assigned to Class A, followed by excavator buckets with a value of 0.769 and excavator chassis with a value of 0.704. Six products were placed in Class B, while quick couplers ranked last with a value of 0.611 and were assigned to Class C. The results indicate that integrating ABC classification directly into criterion weighting provides a consistent basis for product ranking and priority grouping. The proposed approach supports manufacturing managers in aligning new product decisions with strategic objectives, existing production capabilities, and resource constraints.

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Perishable agricultural supply chains face significant uncertainties in production yield, market demand, and availability of resources. This paper presented a multi-period, multi-echelon, and multi-product mathematical model for a perishable agricultural supply chain network comprising farms, cold storage facilities, and wholesale markets. The model determined optimal allocation of cultivation area, harvest timing, duration of storage, and product flow distribution to minimize total supply chain costs, including production, transportation, storage, and import costs. A key innovation is the integration of interval programming to handle uncertainty in production costs, transportation rates, and water availability. Three metaheuristic algorithms including particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA) were employed to solve the nondeterministic polynomial (NP)-hard problem. A comprehensive case study of three major perishable crops, i.e., potato, onion, and tomato, across 31 Iranian provinces and 24 candidate cold storage facilities attested to the effectiveness of the model. Results demonstrated that PSO outperformed GA and SA in solution quality by achieving a total cost of 64.14 trillion Rials, with 93.7% of costs attributed to production, 5.2% to transportation, and 1.1% to storage. The model reduced final product prices to one-third of market averages. Sensitivity analyses revealed that production cost was the most sensitive parameter, with a 100% increase triggering economically viable imports. Water availability reduction below 10% of baseline rendered domestic production infeasible. The model achieved 19.9% average water savings (1.062 billion cubic meters annually) through optimized cultivation patterns. The proposed framework provided agricultural policymakers with a quantitative tool for balancing domestic production, investment in storage capacity, and import decisions under uncertainty.

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The spatiotemporal spread of plant pathogens introduces substantial uncertainty into agricultural production systems, thereby complicating timely disease management and resource allocation. An information-theoretic framework was developed to quantify structural uncertainty in pathogen transmission by integrating stochastic population dynamics with system entropy. A finite agricultural field containing a homogeneous crop population was represented as a two-state system, in which individual plants existed in either a healthy or an infected state. Pathogen transmission was modeled as a birth-death process, while the probability distribution of the infection state was characterized using a binomial formulation under the assumed population structure. On this basis, system entropy was introduced as a quantitative measure of structural uncertainty. It was demonstrated that entropy followed a characteristic inverted-U-shaped trajectory, increasing during the early stages of disease propagation and reaching a maximum at an intermediate level of infection intensity, where uncertainty and system volatility were greatest. As pathogen spread approached saturation, entropy progressively decreased. This entropy maximum was shown to define a critical operational threshold at which surveillance, treatment, and resource deployment can be implemented with the greatest expected effectiveness. Beyond operational decision support, the proposed framework establishes a quantitative basis for evaluating long-term risk mitigation strategies. Reductions in entropy achieved through the deployment of disease-resistant crop varieties, improved biosecurity measures, optimized field infrastructure, or enhanced monitoring systems can be directly interpreted as reductions in structural uncertainty, thereby providing measurable indicators for minimizing disease-related economic losses, improving intervention efficiency, and strengthening the resilience of agricultural production systems and supply chains. By establishing a rigorous connection between stochastic epidemiological dynamics and information theory, the proposed framework provides a generalizable analytical foundation for uncertainty-aware agricultural management and data-driven decision-making under pathogen risk.

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Binary chronic obstructive pulmonary disease healthy control (COPD–HC) classification results in the Exasens dataset may be inflated by age and smoking-related structure. This exploratory secondary complete-case audit examined whether sensor-derived salivary dielectric response features provide internally predictive information beyond age, recorded sex/gender, and smoking status. The public Exasens dataset contained 399 records, of which 100 had complete salivary response measurements. Demographic only, salivary-response only, and combined feature sets were evaluated for binary COPD–HC and four-class label classification, using repeated stratified cross validation, limited overlap sensitivity analyses, calibration summaries, and SHapley Additive exPlanations (SHAP) based audits of model behavior. No independent external validation cohort was available. In the complete-case COPD–HC analysis, demographic only Logistic Regression achieved a balanced accuracy of 0.955 whereas the best salivary- response only model achieved 0.56. In the four-class complete-case task, combined Gradient Boosting achieved a balanced accuracy of 0.68 and a receiver operating characteristic–area under the curve (ROC–AUC) of 0.881. This represents exploratory internal separation, with performance strongest for chronic obstructive pulmonary disease (COPD) and healthy control (HC), moderate for infection-related cases, and weak for asthma. Matching and overlap weighting analyses did not support an independent COPD–HC contribution from salivary-response features, primarily because common support for age and smoking was poor. These findings should be interpreted as a secondary and hypothesis-generating dataset audit rather than validation of clinical prediction model. Transportability was uncertain under demographic shift or different salivary-response missingness structures. External validation, richer clinical metadata, and improved demographic and smoking overlap across groups are required before any diagnostic, screening, triage, risk-prediction, or clinical decision support could be considered.

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Personnel selection represents one of the most important tasks of human resource management, while the selection of engineering personnel is of particular interest to the industrial manufacturing sector. This study dealt with the problem of selecting a quality engineer based on applications submitted in response to a job advertisement announced by a company that manufactured industrial equipment. The aim of the research is to demonstrate the robustness of a hybrid Multi-Criteria Decision-Making (MCDM) model based on the integration of the CRiteria Importance Through Intercriteria Correlation (CRITIC) method and the Square-Root based Evaluation Method (SREM). Such a model aims to reduce subjectivity in the decision-making process and serves as a supporting tool for company management in the personnel selection process. The results obtained from the case study demonstrated that the proposed model objectively and reliably identified the most suitable candidate, while taking into account different performance characteristics. The solution proved to be highly stable, since the sensitivity analysis revealed that even in the case where all criteria had equal importance, the best candidates remained at the top of the ranking list. Furthermore, this research indicated that the proposed CRITIC–SREM model could probably be a standard decision-support tool for decision-makers in personnel selection and the handling of similar problems.

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Rice is a strategic food commodity, and its supply chain involves farms, mills, distribution centres, and markets. This study presented a multi‑objective mathematical model for a rice supply chain that minimized total costs (economic objective), soil erosion caused by water used in cultivation (environmental objective), and total water consumption. A real case study from Iran including four major rice‑producing regions was analysed under three water availability scenarios. To handle uncertainty in rainfall and irrigation water, stochastic programming was applied and the multi‑objective model was solved using extended goal programming. Sensitivity analyses examined changes in water availability, objective function weights, and import limits. Results demonstrated that under water‑scarce conditions, production in arid regions (e.g., Khuzestan) was not economically or environmentally viable, leading to increased imports. The model provides a practical tool for policymakers balancing food security, cost, environmental sustainability, and water conservation.

Open Access
Research article
Strategic Value Relevance of Environmental, Social, and Governance Disclosure: Re-Examining the Ohlson Valuation Framework in the Nigerian Capital Market
samuel abiodun ajayi ,
elizabeth adeola julius ,
olalekan adebola kolawole ,
grace oyefunke ajagbe ,
oguntuase sunday isaac
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Available online: 04-08-2026

Abstract

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Sustainability disclosure increasingly influences investment decisions and market valuation in global capital markets. Traditional valuation frameworks primarily rely on accounting fundamentals such as earnings and book value, while the growing importance of environmental, social, and governance (ESG) information has expanded the role of non-financial indicators in strategic investment analysis. Empirical evidence regarding the value relevance of ESG disclosure in emerging markets, particularly within the Nigerian capital market, remains limited. This study investigates the relationship between ESG disclosure and firm market value using an extended Ohlson valuation framework. Annual firm-level data obtained from companies listed on the Nigerian Exchange Group were analyzed using panel regression techniques, including fixed-effects estimation and dynamic generalized method of moments (GMM) analysis. The study further examined the individual effects of ESG disclosures and evaluated the moderating role of governance quality in strengthening the value relevance of environmental and social disclosure. The results showed that ESG disclosure positively affected share prices, indicating that sustainability information contributed to investors’ valuation decisions. Governance disclosure exhibited the strongest and most consistent positive effect on market value, while social disclosure remained positively significant and environmental disclosure demonstrated a weaker but positive influence. The interaction analysis further revealed that governance quality strengthened the positive effects of environmental and social disclosure on share prices. These findings indicate that governance mechanisms improve the credibility and valuation relevance of sustainability information in emerging capital markets. This study extends the Ohlson valuation framework by integrating ESG dimensions and governance interaction effects within a data-driven market valuation model. The findings provide practical insights for corporate managers, investors, regulators, and policymakers seeking to enhance strategic sustainability reporting and improve long-term market confidence in emerging economies.

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Environmental sustainability remains a major challenge in the era of digital transformation and global financial integration. The increasing adoption of artificial intelligence (AI) technologies and the expansion of financial globalization continue to reshape economic systems and influence ecological outcomes. This study investigates the dynamic relationships among private AI investment, financial globalization, and environmental sustainability in the United States within the Load Capacity Curve (LCC) framework and from a strategic analytics perspective. Annual time-series data from 1990–2019 were employed. Economic growth, technological innovation, and urbanization were incorporated as additional determinants of environmental sustainability measured by the load capacity factor (LCF). Unit root procedures were conducted, and the Autoregressive Distributed Lag (ARDL) bounds testing framework was applied to estimate both long-run equilibrium relationships and short-run dynamics. Robustness analysis was further performed using alternative cointegration estimators. The results showed that a long-run equilibrium relationship existed among the variables. A U-shaped relationship between income and environmental sustainability was identified, supporting the LCC hypothesis. Private investment in AI positively affected ecological capacity, suggesting that AI-related investment contributed to environmental improvement through resource optimization and efficiency gains. Financial globalization and technological innovation negatively affected environmental sustainability, implying that uncontrolled financial expansion and non-green technological activities intensified ecological pressure. Urbanization demonstrated a positive long-run contribution to ecological sustainability. The robustness analysis produced consistent findings. The results indicate that AI-related investment can serve as a strategic instrument for balancing technological development and ecological objectives. This study provides evidence that integrating strategic analytics with sustainability assessment improves understanding of the environmental implications of digital transformation. The findings offer practical decision support for policymakers seeking to align technological investment and global financial integration with long-term sustainability goals.
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