Business, Economics and Management

Abstract: This study examines whether gender diversity in corporate leadership is associated with financial performance in the Serbian hotel sector. Drawing on the gender profitability paradox, it investigates whether female leadership serves as a strategic organizational resource or whether persistent structural barriers constrain its effects. The empirical analysis is based on a sample of 575 hotels operating in Serbia. Financial performance is assessed using Return on Equity (ROE), revenues, profitability margins, and asset turnover. Independent-samples t-tests examine differences between male- and female-led hotels, analysis of variance evaluates the association between female board representation and financial performance, and multiple regression analysis assesses the relationship between leadership gender and operational efficiency while controlling for hotel size, labor intensity, and hotel maturity. The results indicate that hotels headed by women achieve significantly higher ROE despite operating at a smaller revenue scale. Hotels with at least one female board member also exhibit significantly higher ROE. Regression analysis shows that leadership gender is not significantly associated with operational efficiency once firm characteristics are accounted for. Findings suggest that the relationship between gender diversity and hotel performance is multidimensional and context-dependent, emphasizing the importance of evaluating organizational performance through multiple complementary financial indicators.

Abstract: The EU farm structure is subject to significant changes, which is seen not only to reduction of their number but also in transformation of their characteristics from family-type farming unto agri-business type. There are different causes behind those trends and evolution and technological progress along with general economic growth, which provides better conditions for employment and remuneration of the working force are some of the reasons. Retaining the farm number is crucial for rural employment, maintaining biodiversity, and supporting farm sustainability as well. It is noted that between 1967 and 1997, the number of farms fell by 42% in the founding EU countries, which means for a period of 30 years, whereas for EU-27 only between the last two censuses 2010 – 2020 years, the farm number declines by 25% from 12 million to 9 million. Looking at the picture across the EU, it is ascertained that it is not an issue or perspective of some member states but rather an omnipresent process. The purpose of this study is to project the EU farm number to 2030 year applying a proportional extrapolation scenario model and to evaluate the influence from factors acting at three territorial levels – regional (NUTS 2), national (countrywide) and overarching (EU level). The assumptions are that on different territorial levels there are various factors affecting and impacting farm number and the goal is not to identify particularly which are those but to reveal at which territorial level those factors are designated. It is deemed that when member states’ trends in farm number resemble the EU average trend, it might be accepted that designation of factor influence is from the higher rather than national level and vice versa. Hence, the overarching level is considered to be EU level but the development in common direction mostly implies the influence of factors from general essence rather than specific national or regional origin. The results from the study demonstrate the farm number in EU-27 will decrease till 2030 by 16% in the status-quo scenario. The results from the pessimistic scenario testifies for a greater drop in EU-27 farm number by 33% compared to 2020, while the optimistic scenario indicates a likely slight enhancement by just 1%. Territorial analysis reveals the weight of overarching territorial factors, which are commonly admitted for EU level is evaluated to be 61%, the weight of national level and regional NUTS 2 factors can be allocated to 39%. Within the EU countries, there are divergences concerning the distribution of the territorial impact, which is explicated in the study.

Abstract: This paper examines firm performance through the lens of complexity theory and configurational analysis, arguing that effective business strategies require understanding multiple causal recipes rather than seeking universal prescriptions. By analyzing how firm demographics, external orientation, internal orientation, and firm performance outcomes interact, the study demonstrates how different combinations of ingredients produce high performance only in specific contexts. The analysis reveals that performance drivers in B2B industries differ fundamentally from those in B2C industries, and that high performance requires strategic alignment specific to industry type, firm size, market orientation, and organizational design. Drawing on configurational theory and recent research in organizational heterogeneity, this work challenges the "one-size-fits-all" management paradigm and provides practical guidance for executives seeking to understand which strategic recipes work best for their specific organizational contexts and market environments.

Abstract: Growing consumption of packaged products through retail and e-commerce is increasing the amount and complexity of packaging waste entering urban waste systems. The challenge is especially important in rapidly growing cities where limited source separation, collection constraints, fragmented recovery activities, and dependence on informal recycling can restrict material circularity. This study develops a qualitative system dynamics framework for circular urban packaging waste management, using Dhaka, Bangladesh, as the principal developing-city context and Chicago, USA, as a limited structural reference. Published literature and secondary evidence are synthesized to characterize relationships among waste generation, consumer separation, collection, reverse logistics, recycling capacity, and secondary-material recovery. A causal loop framework identifies four reinforcing and two balancing feedback loops, and four structured what-if conditions—current trajectory, worst case, moderate transition, and best case—are examined qualitatively. The analysis indicates that isolated interventions may shift system bottlenecks rather than resolve them. A coordinated transition requires packaging prevention, reliable source separation, integration of formal and informal reverse flows, aligned sorting and recycling capacity, and viable demand for recovered materials. The framework provides a theoretical basis for coordinated urban circular-economy policy and future quantitative system dynamics modeling.

Abstract: This study investigates how external green pressures and internal adaptive capabilities are converted into sustainable performance through green product development (GPD) in resource-constrained culinary micro, small, and medium-sized enterprises (MSMEs). A cross-sectional survey of 383 owner-managers of culinary MSMEs in East Java, Indonesia, was analyzed using partial least squares structural equation modeling (PLS-SEM) with 5,000 bootstrap resamples. The results indicate that customer pressure significantly influences GPD (β = 0.241, p < 0.001), while green dynamic capability has the strongest effect on GPD (β = 0.426, p < 0.001). Environmental regulation shows a positive but nonsignificant effect on GPD (β = 0.101, p = 0.065). Furthermore, GPD positively affects sustainable performance (β = 0.166, p = 0.007). GPD significantly mediates the effects of customer pressure (β = 0.040, p = 0.031) and green dynamic capability (β = 0.071, p = 0.010) on sustainable performance, whereas environmental regulation directly influences sustainable performance (β = 0.361, p < 0.001) without a significant mediated effect. The model explains 42.3% of GPD and 30.1% of sustainable performance variance. This study develops a pressure-capability-conversion framework, demonstrating that green product development serves as a key mechanism for transforming environmental demands and organizational capabilities into sustainable value creation.

Abstract: Algorithmic management is embedded in distributed software development, yet its implications for shared cognition and technical outcomes remain unclear. Drawing on team cognition, this study examines algorithmic management, team mental models, supportive leadership, and technical delivery performance. Data were obtained from 324 professionals nested in 72 software development teams participating in Horizon Europe projects. Team-level path analyses linked survey measures to six GTmetrix indicators. Algorithmic management was positively associated with team mental models and associated with more favorable values on four of the six GTmetrix indicators; Total Size and First Contentful Paint were not significant. The interaction between algorithmic management and supportive leadership was statistically significant but opposite to H3: supportive leadership weakened the association between algorithmic management and team mental models. Team mental models did not significantly predict any technical indicator, and none of the bootstrapped indirect effects was significant; thus, H4 and H5 were unsupported. Theoretically, the findings delineate the limits of the proposed linear algorithmic management-team mental models-performance explanation rather than establish a new mechanism. Practically, algorithmic systems and supportive leadership should be configured as differentiated but jointly designed sources of coordination: systems provide visibility and standardized signals, whereas leaders provide interpretation, exception management, and relational support.

Abstract: Green energy vehicles (GEVs) are increasingly central to low-carbon energy transitions, but research remains fragmented across vehicle technologies, battery systems, critical materials, circularity, and sustainable supply chains. This bibliometric and thematic review maps the intellectual structure and evolution of GEV research using 904 English-language Scopus articles and reviews published during 2015–2025. Performance analysis, reference co-citation, author–keyword co-occurrence, longitudinal thematic analysis, and cluster-guided content synthesis were combined. The corpus accumulated 34,955 citations, and annual output rose from 13 publications in 2015 to 287 in 2025 (CAGR ≈ 36.3%). The intellectual base is strongly battery-centered, linking critical-material bottlenecks, recycling, second-life use, closed-loop systems, and circular business models. Six research streams were identified, spanning supply-chain and policy optimization, circular economy, critical-mineral security, battery life-cycle assessment, low-carbon mobility, and closed-loop recovery. Circular-economy and sustainable-supply-chain themes became markedly more prominent after 2019, while critical-mineral security regained attention in 2022–2025. The international dimension is framed primarily through material sourcing, battery value chains, resilience, and cross-border circularity. The study proposes a technology–resources–energy systems–governance–sustainability framework and priorities for integrating bibliometrics with energy-system, material-flow, trade, and geopolitical-risk data.

Abstract: Background: Artisanal and small-scale gold mining (ASGM) enterprises in Zimbabwe generate a continuous stream of geological, operational, processing and financial data through the ordinary course of daily work, yet almost none of this data is captured, integrated or analysed in a way that supports decision-making, producing a paradox in which mines possess data but not information, and information but not intelligence. Objective: This paper proposes a Big Data Analytics framework for artisanal and small-scale mining (the ASM-BDA Framework) that converts routinely generated, fragmented records into actionable geological, operational and business intelligence, and sets out a protocol for testing it empirically. Approach: The framework is developed conceptually from the knowledge-discovery-in-databases literature, the big-data “seven Vs”, and mineral resource classification standards, and is illustrated, not yet empirically tested, using the geology, structural evaluation and historical records of Unit 5 Gold Mine near Kwekwe. A mixed-methods research design is proposed, combining quantitative analysis of production and geological data across a sample of fifteen to thirty small-scale gold mines with semi-structured interviews of owners, geologists and technical service providers. Contribution: The paper contributes a conceptual framework, a set of testable propositions on resource sterilisation, and a practical, piloted-but-not-yet-fielded data-collection protocol for data-driven decision support in a sector that has, to date, been studied mainly through the lenses of informality, finance and formalisation policy. No empirical results are reported in this version of the manuscript.

Abstract: This mixed-methods study examines the pedagogical efficacy of AI-driven leadership simulations in undergraduate leadership education. The author served as course instructor. Twenty-nine students completed six sequential simulations paired with a reflective writing assignment over one six-week summer term. Qualitative thematic analysis reveals three primary outcomes: (1) simulations produced characteristic dip-then-climb learning trajectories consistent with productive failure frameworks, (2) personalized career profiles functioned as "mirrors with teeth" surfacing gaps between self-perception and demonstrated behavior, and (3) students underwent epistemological shifts from certainty-seeking to ambiguity tolerance. Quantitative self-report data indicates significant increases in adaptive leadership orientation (17% to 62%), listening-first approaches (24% to 76%), and comfort with uncertainty (21% to 66%). Findings suggest that consequence-driven simulation environments, when paired with individualized feedback and structured reflection, can accelerate the transition from declarative leadership knowledge to enacted judgment, particularly by surfacing relational skill gaps among analytically strong students.

Abstract: Highly regulated banking institutions continue to experience governance breakdowns, accountability gaps, compliance failures, and large-scale regulatory remediation programs despite substantial investments in risk management, artificial intelligence (AI), and control frameworks. This paper makes two contributions. First, it presents an exploratory qualitative research design for studying how governance structures, accountability mechanisms, organizational change management, and workforce effectiveness influence regulatory remediation outcomes in large financial institutions. Second, it synthesizes recent AI-driven Regulatory Technology (RegTech) architectures from the literature into a unified six-layer reference architecture that integrates data ingestion, AI/ML processing, RegTech automation, governance and accountability, organizational change management, and outcome feedback. The proposed architecture is grounded in recent studies of AI-enabled compliance in banking and is evaluated against key performance indicators including detection rate, false positive rate, compliance cost reduction, remediation cycle time, regulatory findings, workforce effectiveness, and governance maturity. The proposed research design employs semi-structured interviews with banking professionals and analysis of publicly available regulatory documents, using purposive sampling and thematic analysis. The paper proposes a conceptual framework, a technical architecture, and a research methodology that together bridge established business administration theory with applied managerial practice in highly regulated banking environments.

Abstract: This developmental paper investigates how simulation-based learning mechanisms translate across diverse management courses. Preliminary data from a summer 2026 pilot (n=29, single leadership course) suggested that consequence-driven simulations paired with personalized feedback can expose gaps between students’ conceptual knowledge and enacted judgment. Building on these findings, we propose a developmental process in which consequential simulation experiences create productive struggle, struggle makes performance-self-perception discrepancies visible, and credible feedback paired with structured reflection supports mental-model revision and adaptive judgment. The current study scales this design across six courses in Fall 2026 and seven courses in Spring 2027, examining whether this process generalizes beyond leadership education to functional areas including human resources, employment law, organizational behavior, ethics, organizational development, and training and development. We further examine whether task characteristics such as ambiguity, rule boundedness, and relational complexity shape when simulated struggle becomes productive. Data collection is underway in Fall 2026; we present the research design, theoretical framework, and preliminary summer 2026 findings that inform the expanded implementation. This work examines how AI-enabled pedagogies may close theory-practice gaps while surfacing challenges around feedback credibility, equity of access, and the boundaries of simulation fidelity across management sub-disciplines.

Abstract: Against the backdrop of intensifying global uncertainties and digital transformation, Chinese manufacturing enterprises face structural contradictions where traditional efficiency-oriented supply chain management is becoming obsolete. This study proposes an "SR-SC-IT" analytical framework to enhance supply chain resilience by integrating a dual-dimensional approach of "external perception" and"internal adaptation." The methodology repositions Porter's Five Forces model to identify external shocks and extends the Strategic Alignment Model (SAM) to analyze internal resilience, forming a "Resilience Five Forces Framework." Furthermore, the study constructs an indicator system of "External Risk Perception—Internal Capability Adaptation—Transformation Gradient Alignment," incorporating a five-level maturity assessment mechanism ranging from the Initial Response Level to the Intelligent Ecosystem Level. The framework provides a systematic tool for measuring resilience and diagnosing shortcomings across maturity levels. This research contributes to the field by theoretically transforming competitive analysis models into environmental shock frameworks and offering manufacturing enterprises a practical, graded approach to identifying risk sources and formulating digital transformation strategies.

Abstract: We propose a decision-making framework, methodologically based on the Analytic Hierarchy Process (AHP), designed specifically for hybrid organizations whose activities are project-based. We adapt the AHP (a multi-criteria decision-making model) to the context of decision-making in hybrid organizations by including a preliminary screening stages (project identification and economic assessment) that ensures the financial viability of candidate projects. We also use artificial intelligence to improve the methodological robustness of the decision-making process. Our methodological adaptation of the AHP facilitates complex decision-making processes in hybrid organizations by enabling simultaneous analyses of multiple objectives and criteria and by using artificial intelligence to operationalize the AHP-based decision-making process, making the method accessible to most hybrid organizations, regardless of size. Although the proposed AHP-based method is solidly grounded in theory, the robustness and accuracy of its results ultimately depend on the quality of the information used, and the inclusion of biased or incomplete information may compromise the validity of the final ranking of alternatives. Our proposal contributes to the professionalization of decision-making in hybrid organizations, reduces reliance on intuitive approaches, enables all relevant information to be considered, and mitigates any possible biases of decision-makers.

Abstract: Retail media refers to retailer-operated online and offline media environments in which brands place advertisements and retailers use first-party transaction data to measure and attribute advertising outcomes. By 2026, spending in North America will rise by 17.8% year-on-year to $71.09 billion, and by 2030, it is expected to reach $142.07 billion and account for 23.9% of the total U.S. digital ad spend. In-store digital media is one of the fastest-growing types of this. The attribution rules for this format are defined independently by each network; attribution windows vary substantially, from seven days after a view to same-day click attribution, and performance metrics range from total sales to gross merchandise value (GMV).The network cannot determine whether the same consumer has been identified by another network, and according to industry surveys, the performance data of the same advertising campaign across different networks may differ by as much as 15 per cent. Seventy-five per cent of the advertisers are unaware of how to determine an additional effect, and only 15 per cent believe their organisation can measure such changes. As the number of networks run by brands has increased from six to eleven, the total self-reported incremental effects across networks have been consistently higher than the actual incremental effects at the brand level; in-store digital media is understated under the last-click attribution model because it lacks a click signal. This study develops a computational calibration and budget-reallocation framework that preserves the original attribution rules of individual retail media networks while mapping their self-reported effects onto a unified incremental scale. Geographic holdout experiments are used as attribution-independent anchors, and network-specific conversion relationships are estimated through a hierarchical Bayesian model implemented by parallel NUTS sampling. Cross-network repeated exposure is encoded in a sparse user–network–time matrix using anonymized identifiers, duplicated increments are corrected through vectorized contribution reallocation, and monotonic cubic-spline response curves are fitted for constrained budget optimization at 1% budget increments. The computational dataset contains nine retail media networks, 42 brands, and 317 advertising campaigns; all cross-platform records are organized in 24-hour aggregation windows, with a maximum association window of seven days.The in-store calibration anchor contains 23 cities, 133 stores, and 23,901 ad-slot weeks, with the spatial-recognition error radius controlled within 20 m to provide a high-precision offline reference for computational calibrationComputational evaluation shows that the mean absolute cross-network discrepancy decreases from 14.8% to 3.2% after Bayesian calibration. Duplicate reach accounts for 27.4% of total reach, including 21.3% from users reached by exactly two networks and 6.1% from users reached by three or more networks. For in-store digital media, the normalized incremental effect increases from 0.583 under last-click attribution to 0.90 after Bayesian calibration and 0.96 after overlap correction, while the median unit-budget return rank improves from 6 to 3. Under a fixed total budget, the constrained reallocation algorithm predicts a 12.6% increase in brand-level incremental response, including 7.9 percentage points associated with reallocating budget toward complementary in-store reach and 4.7 percentage points associated with redistribution among online networks. The resulting workflow provides a reusable computer-side procedure for cross-platform data standardization, Bayesian metric calibration, sparse-matrix overlap correction, marginal-response estimation, and constrained budget optimization, thereby improving the reproducibility of cross-network incremental-effect measurement and allocation analysis.

Abstract: This study investigates the centralized CBI concept in CBI Value co-creation. Centralized CBIs are formed by multi-corporations to facilitate venturing, innovation and co-creation. The study expands on earlier study in CBI value co-creation via the development of a synergetic approach blending cognitive processes and value co-creation. A conceptual framework and scenarios are developed for the central CBI based on thematic analysis on cases of CBIs grounded in data and cognitive generation application to different organizational combinations. It is posited that Parent Companies(PCs) and other organizations (CBIs, UBIs) form centralized CBIs in a Value Co-Creation Ecological System which contains several Meta-Cognitive Processes (Collaboration, Business Restructuring, Communication, Ecological Advantage Function & Adaptation, Opportunity Perception with Strategic Adaptability. Predictive analysis with ML models are also conducted for the proof of concept using the scenarios. An algorithm and an app (Centralized CBI) are thereafter developed to ease the assessment of Centralized CBI formation. This study further expands the CBI theme across multi-industry collaboration with inter-sectorial co-creation efforts using different scenarios, the MMSST (Mixed Methods Strong Structuration Theory) and Cognitive Generation as the core conceptual theories.

Abstract: Purpose: This review explores how artificial intelligence (AI) and AI-enabled digital transformation (AI-DT) may reshape the executive role of the Chief Financial Officer (CFO). It treats the CFO as a member of the top management team and as a strategic, operational, and governance actor rather than only as the leader of the finance function. Design/methodology: An integrative review synthesizes 22 publications identified through targeted searches, review through the CFO lens by one of the authors, publisher and DOI verification, and backward and forward citation searching. One author conducted the identification, initial screening, extraction, and coding; the second author independently audited every inclusion, classification, extraction, and claim–source decision. All differences were resolved by consensus. The final hierarchy contains two direct role-transformation studies, one direct executive-relationship study, five CFO antecedent-and-outcome studies, and fourteen contextual finance-function studies. Findings: The strongest direct evidence does not support simply replacing traditional finance tasks with AI-related strategic tasks. Instead, the reviewed publications suggest a cumulative rebalancing across seven dimensions: (1) responsibilities and mandate, (2) decision rights and authority, (3) strategic influence, (4) executive relationships, (5) competencies and capabilities, (6) professional identity, and (7) governance and accountability. Evidence is strongest for digitalization, analytics, automation, and multi-technology transformation; it is more limited for AI-specific research and very limited for generative or agentic AI. Four provisional configurations—AI Value Strategist, Transformation Catalyst, Digital Steward and Governor, and Efficiency-focused Finance Operator—are presented as emerging but testable propositions rather than validated job types. Originality/value: The review clearly distinguishes between direct insights into the CFO’s role and those that originate from the broader finance and organizational context or are related but less robust. It specifies which technologies are involved and develops a multilevel research agenda covering AI investment, AI-related value creation, human–AI work allocation, and responsible corporate governance.

Abstract: The perishable agro-product supply chain in Bangladesh faces numerous barriers that contribute to food spoilage, economic losses, and reduced supply chain efficiency. Identifying and understanding the interrelationships among these barriers is essential for developing a resilient and sustainable supply chain. This study aims to identify, prioritize, and analyze the critical barriers affecting the perishable agro-product supply chain in Bangladesh. A three-phase methodology integrating the Delphi method, Best-Worst Method (BWM), and Interpretive Structural Modeling (ISM) was employed. Initially, barriers were identified through an extensive literature review and expert consultation. Subsequently, BWM was applied to rank the barriers based on their relative importance, while ISM was utilized to examine their hierarchical relationships and driving-dependence structure. The findings revealed 24 significant barriers affecting the supply chain. BWM results indicated that lack of government support for adopting green supply chain management (GSCM), product freshness depletion, informal payments to local influential groups and law-enforcement agencies, lack of support and guidance from regulatory authorities, and climate change are the most critical barriers. ISM analysis further demonstrated that lack of government support, lack of support and guidance from regulatory authorities, and climate change are the key driving barriers that significantly influence other supply chain obstacles. A nine-level hierarchical structure was developed to illustrate the interrelationships among the identified barriers. The proposed framework provides valuable insights for policymakers, practitioners, and supply chain managers to formulate effective risk mitigation strategies, enhance supply chain resilience, reduce food wastage, and promote sustainable agro-product supply chain management in Bangladesh.

Abstract: The voluntary carbon market (VCM) certifies and trades greenhouse gas mitigation and removal outcomes generated outside compliance markets, yet concerns about additionality, measurement, reporting and verification (MRV), double counting and credit quality continue to constrain market credibility. Stringent integrity requirements may simultaneously increase the cost and expertise burden borne by new climate-tech firms and carbon mitigation project developers. This study examines how institutional requirements for environmental integrity are connected to the market entry and value creation of these actors. A SciVal-based research landscape analysis is combined with a qualitative analysis of legal, policy and market-operation documents, and the European Union, Singapore and the Republic of Korea are compared functionally as cases with different institutional contexts and stages of development. The comparison shows that integrity requirements simultaneously constitute market-access conditions and entry requirements, that entry burdens are addressed not by relaxing quality standards but by entry-enabling conditions such as standardization, technical and advisory support, burden mitigation, collective participation and early-stage finance, and that market participation is converted into economic value only where revenue realization, price valuation, revenue predictability and commercial scaling operate. On this basis, an Integrity–Entry–Value (IEV) structure is proposed as a conditional rather than linear account of VCM formation, offering conditional policy implications for late-forming markets.

Abstract: UNESCO Global Geoparks are recognized as instruments for territorial development. However, limited research explains how emerging geoparks can transform distinctive territorial assets into sustainable and inclusive tourism destinations. This study examines the Kütralkura UNESCO Global Geopark in Chile and asks how its tourism potential can be activated by addressing governance gaps, territorial capacity constraints, and destination visibility. Adopting a pragmatic approach, we analyzed an open-ended survey of 32 tourism ecosystem actors across the Geopark. Thematic analysis identified four interrelated dimensions shaping tourism development. First, sustainable territorial competitiveness depends on strategically leveraging geo-natural capital, biocultural heritage, and territorial identity as tourism assets. Second, strategic destination governance requires multilevel coordination and sustained vertical institutional commitment. Third, territorial tourism capacity highlights the need to balance tourism development and visitor use with environmental conservation and local capabilities. Fourth, destination activation and visibility involve negotiating local and international branding while strengthening residents’ geoheritage literacy. The findings demonstrate that tourism potential in emerging geoparks depends not only on resource endowment but also on governance, territorial capacity, and destination activation. The study contributes practical insights for geopark management and proposes landscape management as an integrated approach to sustainable tourism development in emerging economies.

Abstract: Initial Coin Offerings (ICOs) have become an important blockchain-based fundraising mechanism; however, ICO white papers vary considerably in structure, content, and disclosure quality, limiting transparency, comparability, and informed investor decision-making. Because white papers often serve as the primary source of project information during fundraising, the absence of standardized disclosure practices represents a significant challenge. Existing literature has examined ICOs from perspectives including tokenomics, governance, disclosure quality, investor behaviour, and fundraising performance, yet little attention has been devoted to developing a harmonized disclosure framework for ICO white papers. To address this gap, this study proposes a harmonized ICO white paper framework developed through a multi-stage research process. A corpus of the ten highest-funded pure ICO white papers was analysed using content analysis, and the findings were compared with academic literature and regulatory guidance to identify both commonly disclosed and externally recommended disclosure elements. The study resulted in a harmonized framework comprising nineteen disclosure sections organized into five major components that integrate empirical industry practice with best-practice recommendations. The contribution of this paper is the development of an evidence-based harmonized ICO white paper framework that provides a standardized disclosure structure to enhance transparency, comparability, and informational completeness within blockchain fundraising ecosystems.