International Journal of Innovation and Economic Development

Volume 11, Issue 6, February 2026, Pages 28-61

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Navigating the Marketing Future (2025–2030): Integrating the BRAVE Taxonomy with Agent-Based Modelling

URL: https://doi.org/10.18775/ijied.1849-7551-7020.2015.116.2002

DOI: 10.18775/ijied.1849-7551-7020.2015.116.2002

 

Suresh Sood ^1,2

¹Industry/Professional Fellow in the Australian Artificial Intelligence Institute (AAII), University of Technology Sydney

²Adjunct Fellow, Frontier AI Research Centre, Macquarie University, Sydney)

Abstract: The marketing profession is undergoing structural transformation driven by emerging technologies, shifting societal expectations, and the erosion of traditional transactional models. This article introduces the BRAVE taxonomy comprising Blockchain, Robotics, Artificial Intelligence, Vital Infrastructure, and Environmental technologies as a framework for organizing emerging technological domains and mapping implications for future marketing roles. Drawing on leading global foresight sources, the taxonomy consolidates fragmented technological trends into five capability-based domains to support workforce strategy and organizational design.

To explore how these technologies reshape marketing teams, the study integrates the BRAVE taxonomy with Agent-Based Modelling (ABM). The model simulates a stylized 2025–2030 marketing ecosystem with marketing role-representing agents interacting under specified assumptions regarding productivity growth, return on investment (ROI), and societal impact. The simulation does not generate empirical forecasts but rather, provides scenario-based insights into how different technological capability clusters influence marketing role evolution, team composition, and aggregate performance outcomes over time.

Results illustrate how leadership roles (e.g., Chief AI Marketing Strategist, Sustainability Marketing Director) and functional specialists (e.g., Blockchain Loyalty Program Manager, Edge Data Marketing Analyst) contribute differently to simulated productivity and ROI trajectories over the time of the study. The modelling further demonstrates how technology-aligned roles may influence broader societal impact indicators under varying adoption scenarios.

By combining taxonomy development with computational simulation, this study offers both a conceptual classification framework and a methodological tool for scenario testing. The approach provides marketing leaders with a structured approach to workforce planning under technological uncertainty and contributes to macromarketing scholarship by linking emerging technologies, organizational role design, and societal impact within an integrated analytical framework.

Keywords: BRAVE taxonomy; Agent Based Modelling; Workforce/role design; Societal impact/macromarketing

1. Introduction

Marketing towards 2030 is increasingly unrecognizable driven by the convergence of BRAVE and data analytics empowering strategies to address societal and environmental challenges. This research explores how the BRAVE taxonomy comprising categories of Blockchain, Robotics, Artificial Intelligence, Vital Infrastructure, and Environmental technologies are reshaping the marketing profession, skill requirements, and the future of marketing work. The methodology to develop the taxonomy integrates insights on emerging technologies from authoritative sources. The Agent-Based Model (ABM)^[1] provides actionable insights for workforce transformation in the marketing sector towards 2030 while answering the question: How can organizations build the marketing teams of tomorrow? In this simulation study, “agents” refer to role-representing entities within an ABM used for simulation purposes.

2. Literature Review

The literature on building a taxonomy of emerging technologies and roles for future marketing teams, particularly in the context of ABM, reveals several gaps. These gaps pertain to the integration of emerging technologies with marketing roles, the development of comprehensive taxonomies, and the application of ABM in future marketing workforce development. The integration of ABM with the exploration of future marketing roles is in a nascent stage, and the need exists for a comprehensive framework capable of guiding the development of future workforce marketing teams. The following sections outline the key gaps identified in literature before satisfactorily envisaging the marketing team of 2030.

Firstly, a notable absence exists of a major or overarching framework integrating ABM with a structured taxonomy of marketing roles. While ABM has been explored in various domains, the application in marketing, particularly in defining future roles and responsibilities is limited (Bukhsh et al., 2024; Cross et al., 2023). Surprisingly, empirical case studies validating the effectiveness of ABM in modelling marketing roles is also not readily available. Most existing research is theoretical or based on simulations without real-world validation (Adriaansen et al., 2013). Case studies, particularly in diverse industries, likely provide valuable insights into how ABM can be applied to understand and optimize marketing roles and team dynamics (Madsen & Rosenbaum, 2018). The literature highlights a gap in understanding how agents can be utilized to organize and execute marketing tasks. The roles of agents in marketing are not well-defined, and a need exists for a finer level of granularity to understand emergent roles (Madsen & Rosenbaum, 2018; Guizzardi, 2006) beyond traditional job titles (McClaren & Shaw, 2003). McAlister et al. identify three types of marketing organizations but do not provide a detailed taxonomy of individual marketing roles within organizations (McAlister et al., 2022). This suggests a further gap in understanding how different roles contribute towards an overall marketing strategy. Developing taxonomies for BRAVE and marketing roles is complex due to the intersection of multiple fields and the current state of knowledge. Existing methodologies require extensions to effectively classify and integrate new technologies into marketing roles (Nickerson et al.,2013; Mwilu et al., 2015). A need exists not only for a systematic classification of digital technologies impacting marketing innovations but a need for a taxonomy considerate of the dynamic nature of marketing roles, especially in the context of evolving BRAVE technologies especially when directly impacting information and communication technology (Madsen & Rosenbaum, 2018). Furthermore, current frameworks do not adequately address the dynamic nature of BRAVE technologies and implications for marketing strategies (Athaide et al., 2024). The integration of AI and digital skills into marketing teams is a significant challenge. While recognition of the importance of these skills is not fully understood, a limited guidance on how to effectively incorporate the skills into existing marketing frameworks is available (Rathore, 2023). The rapid evolution of technologies necessitates continuous updates to taxonomies and frameworks. Existing classification systems are limited in scope and do not capture the full impact of technological advancements on marketing roles (Correia et al., 2018). Validation methods evaluating the performance of ABMs in marketing contexts is left wanting. Improvements in validation techniques are necessary to ensure the reliability and effectiveness of the models (Bukhsh et al., 2024).

While the literature identifies several gaps in the development of taxonomies for emerging technologies and marketing roles, the potential for future research to address these challenges is essential. The integration of ABM with marketing roles offers a promising avenue for developing more effective marketing teams. While ABM has been applied in various fields, use in modelling marketing teams is still emerging. The literature suggests ABM can provide insights into team dynamics and consumer behavior, yet there is a scarcity of studies specifically focusing on marketing teams and their internal role dynamics (Mandel et al., 2013; Rand, 2024). The potential of ABM to simulate the interactions and decision-making processes within marketing teams is not fully realized. Existing models often focus on consumer interactions or product development teams, rather than the specific roles and responsibilities within marketing teams (Crowder et al., 2009; Ivan et al., 2022). However, the dynamic nature of BRAVE technologies and the complexity of integrating them into existing frameworks pose ongoing challenges requiring continuous research and adaptation.

Table 1: Research Gaps and How Addressed

Research Gap	Description	How This Research Addresses the Gap
1. Lack of integrated ABM–taxonomy frameworks	Absence of framework combining ABM computer simulations with taxonomy of marketing roles.	Proposes the BRAVE Taxonomy mapped onto ABM simulations to forecast marketing role emergence and transitions in marketing teams.
2. Absence of empirical validation in ABM-marketing	ABM use in marketing is theoretical or simulation-based, with few real-world case studies.	Suggests application-based role simulation outputs (ROI, carbon impact, trust levels), allowing testable scenarios and feedback loops.
3. Under specification of ‘agents’ in marketing tasks	Ambiguity remains in how ABM ‘agents’ are mapped to granular marketing activities.	Clearly distinguishes ABM agents from agentic AI workflows, aligning agents with dynamic task roles and simulations of role-based productivity.
4. No taxonomy for emergent roles beyond job titles	Existing studies classify marketers by function, not dynamic interaction or skill clusters.	Introduces BRAVE Marketer typology (e.g., Ecosystem Curator, Trust Builder), allowing for role clustering, not just titles.
5. Fragmentation in digital tech and marketing alignment	Current frameworks fail to classify evolving technologies in relation to marketing roles.	Extends Nickerson et al. design theory to include emerging digital role archetypes influenced by AI, blockchain, and LLMs.
6. Inadequate reflection of BRAVE dimensions	Traditional marketing frameworks ignore BRAVE technologies.	Positions BRAVE as a central organising logic for marketing transformation and taxonomy classification.
7. Limited guidance for AI/digital skill integration	Despite recognizing digital importance, few frameworks guide integration of these skills in teams.	Provides sample role simulations with agentic workflows, highlighting how LLMs, avatars, and tools reshape workflows.
8. Outdated classification systems	Taxonomies are static and not updated to reflect fast-moving tech shifts.	Uses ABM dynamics to continuously simulate role need/decline/emergence based on evolving tech and societal impacts.
9. Weak validation methods in ABM for marketing	ABM validation metrics in marketing are not standardised or robust.	Recommends integrating impact metrics (e.g., ROI, carbon, trust, diversity scores) to validate agent simulations.
10. Lack of focus on team-level marketing ABM	Most ABMs focus on customer or product not internal marketing team dynamics.	Focuses squarely on intra-team dynamics (cross-skilling, role overlap, agent drift) in future-ready marketing teams.

 

3. Research Methodology and Results

Integrating BRAVE Taxonomy with Agent Based Modelling

This research employs a novel methodology combining the BRAVE taxonomy (Blockchain, Robotics, Artificial Intelligence, Vital Infrastructure, and Environmental Technologies) of emerging technologies and marketing roles using ABM to explore and predict the evolution of marketing roles and societal impact from 2025 to 2030. The approach systematically generates roles from the BRAVE taxonomy and integrates them directly into an ABM simulation to assess productivity, ROI, and societal impact over time. This section presents the methodology alongside corresponding results allowing readers to immediately see how each step contributes to the outcomes.

3.1. Constructing the BRAVE Taxonomy

The BRAVE taxonomy serves to underpin the foundational proposition for emerging technologies leading to or shaping the creation and evolution of marketing roles. While several emerging technologies such as AI, IoT, blockchain and Metaverse are repeatedly mentioned in articles as having transformative capability a gap exists in no single authoritative listing of emerging technologies is available to marketers. To this end, as a first step to develop the taxonomy, we use a structured process of synthesizing insights from authoritative sources (Table 3) to identify, group, and prioritize technologies resulting in a consolidated list of high-impact technologies relevant to marketing. Our selection of authoritative sources (Table 3) each providing lists of technologies (Appendix A) includes the Gartner Hype Cycle of Emerging Technologies (Gartner 2024), World Economic Forum report of Top 10 Emerging Technologies (World Economic Forum 2024), McKinsey Technology Trends report (McKinsey 2024), MIT Tech Review 10 Breakthrough Technologies (MIT 2024), CB Insights Tech trends (CB Insights 2024), IEEE Spectrum Top Tech 2024 (IEEE 2024), and ARK Invest big Ideas 2024 (Ark 2024). Notable exclusions include vendor or sponsored research to minimise or avoid biases e.g. Marketing 2025 (Marketo). Additionally, the expectation is using annual research reports we ensure terms populating the taxonomy are up to date by ensuring a refresh on a regular basis.

Table 2: List of Authoritative Tech Sources – Available sources, output and accessibility

Name of Tech source	Available Sources	Relevant Output	How Accessible
Gartner Emerging Technology Hype Cycle	Annual report highlights technologies across stages, from early innovation to mainstream adoption.	Key technologies that are either emerging, reaching peak interest, or nearing widespread adoption.	Available through Gartner’s website. Some reports require a subscription. Summaries often appear in Forbes, TechCrunch, or ZDNet.
World Economic Forum (WEF) Technology Reports	Insights on technologies impacting global industries, economies, and society, with a focus on sustainability, digital transformation, and AI.	A list of “Top 10 Emerging Technologies” updated annually with explanations on each technology’s impact.	Reports freely available on the WEF website.
McKinsey Tech Trends and Reports	Annual insights on digital transformation, AI, automation, and other significant trends.	Technologies reshaping industries like manufacturing, healthcare, and finance.	Available on McKinsey’s website. Key insights often freely accessible.
CB Insights Tech Market Maps and Trends	Market research and analytics on emerging technologies like AI, fintech, and digital health.	Lists of up-and-coming technologies, often segmented by industry.	Some free resources available but full access requires subscription. Summaries often covered in tech media.
MIT Tech Review of 10 Breakthrough Technologies	Annual list of “10 Breakthrough Technologies” shaping the future.	Emerging technologies with high potential impact in areas like computing, energy, and healthcare.	Available on the MIT Technology Review website and often freely accessible.
IEEE Spectrum Technology Forecasts	Covers engineering and tech advancements with insights into current and future tech trends.	Lists and articles on technologies in robotics, AI, IoT, and other high-tech fields.	Available on the IEEE Spectrum website. Some content requires subscription.

Synthesizing the authoritative sources into a consolidated list of emerging technologies (Appendix A) develops a robust foundation for the BRAVE taxonomy (Table 4) grouping technologies by category (Appendix B).

Taxonomy Development Procedure

To enhance transparency and replicability, the BRAVE taxonomy construction uses a structured five-step process inspired by design-science taxonomy development approaches (Nickerson et al., 2013):

Figure 1: 5 step taxonomy development process

Note: Developed by the author. Conceptual structure informed by Nickerson et al. (2013)

3.1.1. Source Inclusion Criteria & Forecast Aggregation

Only independent, multi-sector foresight reports published in 2024 by globally recognized institutions (e.g., Gartner, WEF, McKinsey, MIT Technology Review, IEEE Spectrum, CB Insights, ARK Invest) are included. Vendor-sponsored or single-firm proprietary reports are excluded to minimize commercial bias. Aggregation of the forecasting is consistent with principles of superforecasting (Tetlock & Gardner, 2015) and the wisdom-of-crowds framework (Surowiecki, 2004), aggregate the independent foresight assessments from the multiple reports. This aggregation minimizes single-source bias and strengthens predictive robustness by identifying convergent technological signals across domains. Rather than relying on one institutional perspective, the BRAVE taxonomy synthesizes recurring technological trajectories appearing across diverse, independently produced foresight reports, thereby increasing methodological transparency and forecast reliability.

3.1.2. Technology Extraction

Technologies explicitly listed as emerging, breakthrough, disruptive or transformative are extracted verbatim from each report.

3.1.3. Deduplication and Harmonization

Merge semantically overlapping technologies (e.g., Generative AI vs. Large Language Models) are consolidated when functional similarity exceeds 70% based on capability descriptions.

3.1.4. Capability-Based Grouping

Technology grouping is not by industry but by dominant capability logic (e.g., decentralization → Blockchain; predictive intelligence → AI; interconnected infrastructure → Vital Infrastructure).

3.1.5.Domain Assignment to BRAVE Categories

Final technologies are assigned to one of five BRAVE domains based on the primary transformation vector:

Trust/Decentralization → Blockchain

Automation/Embodiment → Robotics

Intelligence/Prediction → AI and Machine Learning

Connectivity/Systems → Vital Infrastructure

Sustainability/Regeneration → Environmental

The taxonomy design is updateable on an annual basis or more frequently, enabling dynamic recalibration as new technologies emerge. The recalibration process is an overarching feedback loop.

Table 3: BRAVE Taxonomy of Emerging Technologies by Category

BRAVE Category	Number of Technologies	Characteristics of Category	Technologies from Authoritative Sources
B - Blockchain and Security	14	Transparency, decentralization, and secure transactions.	Blockchain, Cryptocurrency, Security Technologies, Privacy Technologies, Bitcoin Allocation, Smart Contracts, Digital Wallets, Homomorphic Encryption, Content credentials for combating deepfakes, Algorithmic video surveillance, Blockchain finserv, Cybersecurity, AI & security, Cyber chaos security consolidation
R - Robotics and Automation	8	Automation and customer interaction enhancement.	Robotics, Humanoid Working Robots, Robotaxis, Autonomous Logistics, Reusable Rockets, 3D Printing, Electric Vehicles, Humanoid robots
A - Artificial Intelligence and Machine Learning	19	Predictive analytics, personalization, and decision-making	Artificial Intelligence, AI-Augmented Software Engineering, AI Supercomputing, Generative AI, Applied AI, Large Language Models, AI for scientific discovery, AI weather prediction, AI agent marketplaces, Industrializing Machine Learning, Multimodal AI, Synthetic data, No code software, Bank AI, AI drug discovery, Digital therapeutics, AI sales agents, AI loss prevention, AI gaming
V - Vital Infrastructure	11	Real-time data exchange and interconnected ecosystems (e.g., IoT, 5G).	Digital Infrastructure, Next-gen Software Development, Cloud and Edge Computing, Immersive Reality Tech (AR/VR), Integrated sensing & communication, High-altitude platform stations, Apple Vision Pro, Quantum computing commercialization, Wi-Fi 7, HVDC networks, Blue PHOLED displays
E – Energy, Environment and Health	18	Sustainability practices, carbon tracking, and renewable energy.	Sustainable Energy, Electrification, Climate Technologies, Enhanced Geothermal, Super-Efficient Solar Cells, Heat Pumps, Carbon-sequestering kelp, Alternative feeds, Ultra-deep drilling, Advanced nuclear, Genomics, Precision Therapies, Multiomic Tools, Gene Editing, Epigenetic reprogramming, Brain tech, Synchron brain-implant, Extreme weather Insurtech

The BRAVE taxonomy is grounded in our analysis of emerging technologies drawn from reputable sources (Table 3). Each source has a specific focus on trending and emerging technologies (Table 1). Consistent with “superforecasting” principles and the “wisdom of crowds” (Tetlock & Gardner, 2015; Suroweicki, 2004) we aggregate the credible source lists thus minimising biases and strengthening predictions to generate the BRAVE taxonomy (Table 2). These technologies categorize into five domains comprising the taxonomy based on transformative potential, ensuring the framework reflects impactful innovations for marketing.

3.2. Steps to Generate Roles from Technology

Step A: Define Technology Capabilities

For each category in the BRAVE taxonomy, key technological capabilities are identified. For example:

• Blockchain enables decentralized loyalty programs and secure data management.
• AI facilitates predictive analytics and hyper-personalization.

Step B: Map Capabilities to Marketing Functions

The identified capabilities map to marketing functions such as customer engagement, campaign management, and data analytics. For instance:

• Blockchain aligns with secure advertising and transparency in loyalty programs.
• Robotics (humanoids) integrates into retail automation and scalable operations.

Step C: Identify Role Requirements

Skill sets and responsibilities are outlined for each capability-function pair. This includes:

• Technical Skills include coding for automation of LLM (large language models) workflow, understanding blockchain protocols.
• Strategic Thinking for Integrating technology into marketing strategies.
• Soft Skills requires Collaboration, ethical decision-making, and creativity.

Step D: Formulate Roles

Distinct roles are created by integrating technological functionalities with marketing principles. Examples include:

• Blockchain Trust Manager: Oversees transparent loyalty programs.
• Predictive Marketing Analyst: Applies AI for consumer targeting.

Figure 2: BRAVE Taxonomy Leadership and Functional Marketing Roles

Table 4: Key Marketing Leadership & Functional Roles (2025-30)

ID	Leadership or Functional Role	Purpose	Key Responsibilities	BRAVE Technologies	Comments
1	Chief AI Marketing Strategist	Drive AI adoption in marketing strategies, ensuring campaigns are data-driven and predictive.	- Oversee AI-driven customer journey mapping and real-time personalization. - Develop ethical AI guidelines for marketing. - Leverage generative AI for content creation, campaign simulations, and audience segmentation.	AI, Blockchain	AI ensures predictive personalization; Blockchain enables secure handling of consumer data for ethical marketing initiatives.
2	Virtual Brand Experience Architect	Create immersive and interactive brand experiences in the metaverse and virtual environments.	- Design virtual stores, events, and experiences that integrate with physical campaigns. - Collaborate with product teams to create digital twins for testing and marketing. - Measure and analyze virtual engagement metrics.	AI, Vital Infrastructure (AR/VR)	Combines AR/VR tools with AI to deliver measurable, immersive brand experiences.
3	Sustainability Marketing Director	Align marketing strategies with sustainability goals and ESG (Environmental, Social, Governance) metrics.	- Craft campaigns showcasing sustainable practices and achievements. - Collaborate with supply chain and product teams to certify green initiatives. - Use carbon credit data to demonstrate a product's sustainability impact.	Blockchain, Environmental Technologies	Blockchain ensures transparency in green certifications, while environmental technologies measure carbon credits and sustainability impact.
4	Generative Content Innovator	Leverage AI to generate creative, adaptive, and personalized marketing content at scale.	- Manage AI tools for producing video, audio, text, and AR/VR content. - Create hyper-personalized content based on customer data and preferences. - Continuously iterate campaigns using AI-generated insights.	AI, Vital Infrastructure (AR/VR)	AI drives scalable content creation; AR/VR ensures immersive and engaging experiences.
5	Predictive Marketing Analyst	Use predictive analytics to anticipate market trends and consumer behavior.	- Analyze data to identify emerging trends and growth opportunities. - Develop actionable insights for proactive campaign adjustments. - Collaborate with AI teams to train predictive models.	AI, Vital Infrastructure	AI ensures accurate forecasting and analysis; vital infrastructure supports large-scale analytics.
6	Ethical Marketing Compliance Officer	Ensure all marketing campaigns and initiatives adhere to ethical guidelines and avoid misleading or manipulative practices.	-Monitor AI-driven campaigns for ethical adherence.-Audit ad targeting mechanisms for fairness and transparency. -Build consumer trust through transparency and accountability in marketing.	Blockchain, AI	AI automates ethical checks; Blockchain provides immutable proof of ethical campaign practices.

Emerging technologies redefine marketing capabilities and create new, specialized marketing roles (Table 5). From neuromarketing analysts and quantum strategists to holographic designers and blockchain loyalty managers, these roles represent the intersection of technological advancement and human expertise (ibid). Together, they form the foundation of a marketing workforce equipped to navigate the challenges and opportunities of 2030. As marketing continues the journey into a technologically sophisticated future, the rise of advanced tools including neuromarketing, quantum computing, and digital twin technology is profoundly set to impact the workforce.

Table 5: Specialist Marketing Roles – Technology, Role & Capabilities

Technology	Role	Capabilities
Neuromarketing Technologies	Neuromarketing Analyst	Measures subconscious consumer preferences, enhances campaigns by analyzing emotional and cognitive responses. Moves towards “mind reading” consumers.
	Behavioral Insights Specialist	Translates neuromarketing data into actionable strategies, aligning brand messaging with psychological triggers.
Quantum Computing	Quantum Marketing Strategist	Real-time optimization of multi-channel campaigns, ensuring efficient ad spend and timing.
	Quantum Data Scientist	Unlocks consumer insights and predicts trends using quantum-driven models.
Digital Twin Technology	Digital Twin Marketing Manager	Simulates marketing strategies to predict outcomes and reduce risks.
	Virtual Experience Designer	Creates realistic customer journeys, refining strategies for specific audiences.
Holographic Displays	Holographic Campaign Designer	Replaces static ads with interactive 3D holograms, creating immersive consumer experiences.
	Spatial Interaction Specialist	Optimizes usability and navigation in holographic marketing displays.
Autonomous Agents and Chatbots	Conversational AI Specialist	Provides 24/7 personalized customer support using NLP.
	Virtual Sales Representative	Automates pre-sales consultations and product recommendations, enhancing engagement.
Blockchain-Powered Loyalty	Blockchain Loyalty Program Manager	Designs tamper-proof, decentralized loyalty systems that build trust.
	Cryptographic Marketer	Attracts tech-savvy consumers with tokenized rewards and integrates blockchain into campaigns.
Voice Commerce Platforms	Voice Commerce Specialist	Optimizes brand discoverability through voice assistants and natural language queries.
	Conversational UX Designer	Designs seamless voice interactions for improved customer retention.
Gamification Platforms	Gamification Marketing Designer	Builds interactive, game-like campaigns to boost engagement and loyalty.
	Interactive Campaign Analyst	Measures and optimizes gamified initiatives for higher ROI.
5G and IoT-Enhanced Marketing	Real-Time Marketing Coordinator	Delivers ultra-responsive campaigns based on IoT data and 5G connectivity.
	IoT Integration Specialist	Integrates IoT devices into campaigns, gathering data for personalization.
Edge Computing	Edge Data Marketing Analyst	Processes real-time consumer data to deliver instant, personalized ads.
	Dynamic Campaign Strategist	Dynamically optimizes marketing strategies based on real-time data for improved ROI.

 

3.3. Linking the BRAVE Taxonomy to Agent-Based Model Parameterization

The BRAVE taxonomy does not function solely as a classification approach. The taxonomy provides the structural basis for parameterizing the Agent-Based Model (ABM). Each BRAVE domain corresponds to a capability logic informing agent attributes within the simulation. For example, technologies grouped under Artificial Intelligence inform productivity growth rates and adaptive capacity parameters, while Blockchain influences transparency coefficients and trust-related societal impact variables. Environmental technologies inform sustainability impact weights, and Vital Infrastructure contributes to connectivity multipliers affecting system-level efficiency. In this way, the taxonomy serves as the conceptual translation layer between foresight analysis and computational modelling. Rather than assigning arbitrary simulation parameters, agent attributes are grounded in structured technological capability domains derived from the taxonomy. This linkage ensures theoretical coherence between classification logic and simulating role behavior across the 2025–2030-time horizon.

By combining the BRAVE taxonomy with ABM, the methodology offers a structured approach to explore the future of marketing roles. The dynamic simulation enables organizations to:

• Identify high-impact roles for investment.
• Align workforce strategies with emerging technologies.
• Assess the societal implications of marketing activities.

This method bridges technological innovation with strategic workforce planning, advancing both organizational goals and societal well-being. Combining Python Mesa (Kazil et al., 2020), a framework for building, analyzing and visualizing ABM and the BRAVE taxonomy, researchers can simulate and analyze potential future scenarios in the marketing domain. Agents represent future marketing leadership and functional roles linking directly back to BRAVE technologies. The Mesa platform (ibid) simplifies the construction and management of complex systems of agents representing distinct roles, behaviors, and interactions within a marketing ecosystem. The agents are informed by the structured categories of the BRAVE taxonomy (Blockchain, Robotics, AI, Vital Infrastructure, and Environmental Technologies) and are programmable to adapt dynamically to external and internal stimuli over time.

Using Python Mesa, agents are imbued with attributes of productivity levels, growth potential, ROI factors, and external influences like adaptability to market trends or technological shifts. Through iterative simulation steps representing months (or years), the model tracks changes in the agent attributes, uncover emergent patterns, and identify nonlinear dynamics across marketing. For instance, agents representing roles of the "Chief AI Marketing Strategist" or "Blockchain Loyalty Program Manager" can showcase interactions with other agents and dependency on specific technologies contribute to overall system performance.

By using Mesa to simulate scenarios from 2025 to 2030, the modelling helps explore:

• How will the integration of technologies such as AI or blockchain influence the productivity of marketing teams?
• Which roles are likely to emerge as high ROI contributors within a technology-driven ecosystem?
• How do inter-role collaborations and technological synergies impact the overall efficiency and adaptability of the marketing function?

These results of the Mesa simulations provide actionable insights, enabling organizations to anticipate future workforce needs, prioritize investments in specific BRAVE technologies, and refine strategic plans to align with evolving market demands. Moreover, the ABM approach fosters an understanding of how marketing roles interact dynamically within a technologically enhanced environment, offering a data-driven foundation for both academic exploration and practical application.

3.4. Designing the Agent-Based Model

Step A: Role Representation as Agents

Each marketing role is instantiated as an agent within the simulation. Agents possess structured attributes derived from BRAVE capability alignment:

Productivity: Measures efficiency and effectiveness.

ROI Factor: Quantifies financial contributions.

Societal Impact: Assesses contributions to inclusivity, transparency, and sustainability.

Step B: Attribute Assignment

Attribute values are assigned based on role–technology alignment within the BRAVE taxonomy. For example:

• AI-intensive roles receive higher adaptive growth parameters.
• Blockchain-aligned roles receive higher transparency coefficients.
• Environmental-aligned roles receive higher sustainability weightings.

These attributes represent the formal input parameters for simulation.

Table 6 summarizes the initial parameter configuration and behavioral assumptions assigned to each simulated marketing role. These parameters operationalize the BRAVE taxonomy into measurable agent attributes, enabling simulation of productivity, ROI contribution, and adaptive performance across the 2025–2030 horizon.

Table 6: Initial parameterization of each role within the ABM

Note :Parameters represent structured simulation assumptions informed by capability logic rather than observed firm-level data

Step C: Agent Interaction

Agents operate within a simulated marketing ecosystem and adapt based on:

• External factors: technological diffusion rates, market volatility
• Internal factors: productivity growth, societal contribution accumulation
• Inter-role dependencies: collaboration effects and capability complementarities

Step D: Dynamic Evolution

Through iterative time steps (monthly cycles), agents evolve. Attribute updates allow nonlinear effects to emerge, including:

• Productivity acceleration
• ROI concentration among high-technology roles
• Shifts in societal impact weighting
• System-level adaptation patterns

 

3.5. Simulation Setup and Parameters

Time Horizon

The model simulates a five-year period (2025–2030) using monthly time steps (60 iterations).

Core Parameters

• Number of agents (roles): 25
• Societal impact weights:
  • Environmental: 0.40
  • Inclusivity: 0.30
  • Transparency: 0.20
  • Cultural: 0.10

Outputs

The model generates both aggregate and role-level outputs:

Aggregate Metrics

• Average productivity
• Total ROI contribution
• Total societal impact

Role-Specific Trends

• Productivity trajectories
• ROI accumulation patterns
• Societal contribution evolution

6. ABM Visualization and Analysis

Simulation Outputs are presented via

• Time-Series Line graphs for average productivity, ROI, and societal impact.
• Interactive visualizations illustrating agent interactions and clusteringGrid-based displays illustrating agent movements and clustering behaviors.

Framing of Simulation Outputs

The numerical outputs presented in this study (e.g., productivity percentages and ROI figures) are scenario-based simulation results derived from stylized parameter assumptions within the Agent-Based Model. They do not represent empirical forecasts or real-world financial predictions. Rather, they illustrate directional dynamics under specified assumptions regarding technology adoption rates, skill growth, and inter-role interactions. Absolute values should therefore be interpreted as exploratory modelling outputs, with relative trends and interaction effects being the primary analytical focus.

4. Result Interpretation

The results (Figures 3-6) provide insights into:

• Role prioritization based on productivity and societal impact.
• Long-term trends in marketing roles aligning with BRAVE technologies.
• Strategies for optimizing marketing teams in a dynamic technological landscape.

Figure 3: Aggregate ROI Contribution over time

Figure 4: Cumulative ROI contribution by role over simulation period (2025-30)

Figure 5: Aggregate Productivity Over Time

Figure 6: Productivity Trends by Role

4.1 Emerging BRAVE Technologies and Impact on the Future of Work in Marketing

BRAVE is reshaping how brands reach and engage consumers and the nature of work within the marketing profession through reshaping marketing skills, roles, and work structures towards a data-driven, personalized, and ethical future with marketers combining strategic thinking with technological proficiency. This evolution promises to make marketing more engaging, transparent, and aligned with consumer expectations while demanding an ethically conscious, technologically fluent, adaptable workforce. As we see with the marketing activities, marketers are transitioning from traditional roles to more tech-centric skills, combining data science, AI ethics, automation oversight, and generative content creation for virtual environments. New job roles accompany the technologies. Emerging roles include Metaverse Marketing Specialist, AI-Driven Insight Analyst, Blockchain Trust Manager, and Automation Strategist, each requiring a hybrid skillset blending traditional marketing with technological expertise. At the same time, the rise of digital tools and virtual environments supports remote work, with virtual collaboration and project management becoming key. This points to marketing teams increasingly operating as distributed networks connected through digital infrastructure.

As data and personalization become central to marketing, skills in ethics, privacy management, and consumer rights are essential. Therefore, marketing professionals expect to understand the ethical implications of data use and immersive experiences while staying updated on emerging tools and best practices. Furthermore, as we move toward 2030, marketing is undergoing a seismic shift, with BRAVE disrupting marketing career paths. Customers demand personalization, immersive experiences, and ethical practices. Companies embracing sustainability and leveraging AI will likely emerge as industry leaders.

4.2 A Marketing Structure for 2030

To integrate BRAVE technologies effectively, marketing organizations have potential to adopt a dual-layer structure comprising strategic leadership roles and functional innovators with each leadership team member helping drive a team of 5 or 6 specialists. Dependent on the size of the enterprise, the roles are full time equivalents (FTEs) or fractional for smaller organisations. Key marketing roles for 2030 in major enterprises, reflect trends of AI, personalization, sustainability, and immersive technologies (Table 4). The functional and leadership roles emerge from the underlying technology comprising the BRAVE taxonomy (Table 5).

The ABM (Agent-Based Model) simulates the transformation of marketing roles influenced by BRAVE (Blockchain, Robotics, AI, Virtual reality, and Environmental sustainability) technologies, assessing productivity, ROI impact, and role dynamics over 2025-2030 of 26 newly defined roles including six core leadership roles (Figure 4). The ABM reveals significant trends and insights driven by role-specific skill enhancements and BRAVE technology adoption. The productivity trends by role represent the evolution of each role effectiveness as a percentage in contributing to overall marketing efforts. These trends highlight:

Growth Over Time can vary between roles, reflecting differences in adaptability, technological reliance, and strategic importance.

• Average productivity increases from 56.35% in 2025 to 61.40% in 2030
• Productivity starts at a defined initial percentage (e.g., 50-70%) and grows due to role-specific skill enhancements, adaptive learning, and external factors.

Real-world factors of external market conditions, innovation adoption, and adaptive behaviors introduce variability. For example, the role of Chief AI Marketing Strategist might experience a rapid increase due to early AI adoption, while Holographic Campaign Designer shows slower initial growth but accelerate as AR/VR technologies mature. Roles requiring high innovation and adaptability, such as Generative Content Innovator or Quantum Data Scientist, might show steep productivity increases. Roles more operational or compliance-focused, Ethical Marketing Compliance Officer, exhibit steadier, less dramatic growth. By visualizing individual role trends, organizations identify high-performing roles that drive overall productivity gains. Conversely, roles with slower growth might need additional resources or strategic realignment.

4.2.1. Strategic Alignment

The productivity trends underscore roles crucial to the organization future marketing strategy. For instance, leadership roles of the Chief AI Marketing Strategist and Sustainability Marketing Director demonstrate consistent productivity growth, signalling alignment with the broader strategic focus on AI-driven insights and ESG (Environmental, Social, and Governance) priorities. Functional roles of the Blockchain Loyalty Program Manager and Holographic Campaign Designer also exhibit notable trends, reflecting an organizational commitment to leveraging cutting-edge technologies for consumer trust and engagement.

4.2.2. Investment Decisions

Analyzing productivity trends provides actionable data to guide resource allocation. Roles with high productivity growth of the Predictive Marketing Analyst and Generative Content Innovator, indicate a strong ROI for training, upskilling, and technological support. Roles with moderate growth but significant potential, the Digital Twin Marketing Manager, suggest a need for investment in infrastructure and tools to unlock their full productivity. Lower-performing roles of the Cryptographic Marketer may require targeted interventions, such as niche training or role realignment, to achieve better outcomes.

4.2.3. Role Maturity

The trends reveal how quickly roles adapt and become effective over time, a critical factor in marketing workforce planning. Rapid maturity in roles like the Quantum Marketing Strategist and Voice Commerce Specialist indicates a readiness for scaling and increased responsibilities. Gradual growth in roles such as the Virtual Sales Representative highlights an evolving nature, requiring sustained investment and support to fully realize potential. Roles with slower adaptation rates, like the Ethical Marketing Compliance Officer, reflect their dependency on external factors as regulatory changes or organizational culture, requiring a more strategic, long-term approach.

4.2.4. Implications for Organizational Strategy

The insights from productivity trends are pivotal for aligning marketing roles with the organization's future goals. By focusing on roles with the strongest alignment, optimizing investments, and nurturing maturity across functions, organizations can build a resilient, forward-looking marketing workforce ready to thrive in the evolving landscape of 2030.

4.2.5. Role-Specific Contributions

Figure 6 represents the cumulative ROI contributions over the 5-year simulation period, from 2025 to 2030. These values aggregate the financial impact of each role's productivity and ROI dynamics throughout the simulation timeframe. Under the model parameter assumptions, total simulated ROI contribution increases from $94.00M in 2025 to $220.70M in 2030, illustrating how compounded role productivity dynamics may scale financial impact within the stylized ecosystem. The contribution highlights the value of early investment in BRAVE technologies. The findings underscore the transformative potential of BRAVE technologies in reshaping marketing roles and strategies.

• Synergies: Collaborative interactions between leadership and functional roles maximize the impact of emerging technologies.
• Early Adoption: Investing in advanced technologies like blockchain and AR/VR provides a competitive edge.
• Continuous Learning: Upskilling remains essential for workforce adaptability, ensuring organizations can keep pace with innovation.

These insights provide a roadmap for organizations to align marketing strategies with technological advancements and societal expectations, fostering resilience and innovation. This reporting demonstrates the utility of ABM in modelling the future of marketing workforces. By simulating role dynamics, productivity trends, and technology adoption, the model offers actionable insights for organizations to thrive in a rapidly evolving landscape. The findings emphasize the importance of integrating BRAVE technologies and investing in skill development to achieve sustainable growth.

4.3. How BRAVE Technologies Transform Marketing Workforces

1. Skill Enhancement
   • AI-related skills see the fastest growth, reflecting a pervasive role in analytics, personalization, and campaign optimization.
   • Blockchain skills show moderate growth, driven by increasing adoption for transparency in digital marketing.
   • Robotics skills grew steadily, influenced by the automation of repetitive marketing tasks.
   • Vital infrastructure skills rose due to the growing need for immersive technologies like AR/VR in consumer engagement.
   • Environmental skills highlight the rising importance of sustainability in marketing strategies.
2. Workforce Adaptability
   • Cross-training synergies: AI significantly boosted growth in related skills, such as robotics and infrastructure management.
   • Learning curve variation: Roles like data scientists exhibited faster adaptability compared to traditional marketers.
   • Resistance to obsolescence: Diverse skill sets reduced the risk of roles being rendered obsolete by automation.
3. Future Implications for Workforce Development
   • Training Programs: Targeted skill development in AI, robotics, and blockchain is critical for workforce competitiveness.
   • Ethical Considerations: Training in the responsible use of AI and environmental technologies must be prioritized to align marketing practices with societal values.
   • Role Evolution: Marketing roles will increasingly integrate technical expertise, requiring a blend of creativity, analytical thinking, and technological proficiency.

4.4. Applications of ABM Insights for Marketing Workforce Development

1. Targeted Training Investments
   • Data-driven insights from the ABM identify skills with the highest growth potential, helping organizations prioritize training resources effectively. For example, accelerating AI training can have downstream effects on enhancing related skills like robotics and analytics.
2. Scenario Planning
   • Simulated scenarios allow marketing organizations to prepare for various futures, such as rapid adoption of environmental technologies or shifts toward decentralized marketing teams.
   • These insights help organizations remain agile and future ready.
3. Customized Workforce Strategies
   • Workforce strategies can be tailored to individual roles based on their adaptability to BRAVE technologies, ensuring inclusive growth.
   • Roles like CRM managers can benefit from additional training in vital infrastructure, while content creators focus on leveraging AI for personalization.

The integration of BRAVE technologies is transforming the marketing profession, reshaping skill requirements, and redefining the future of work in the sector. By combining insights from authoritative sources, ABM and quantitative data, this research provides a comprehensive framework for understanding and addressing these changes. The findings emphasize the importance of targeted workforce development, ethical application of emerging technologies, and adaptive strategies to ensure the marketing sector remains innovative, sustainable, and future-ready. Further, this approach not only offers actionable insights for workforce training but also establishes a replicable methodology for exploring the intersection of emerging technologies and the future of work across other sectors.

This approach satisfies data-driven and technology-enabled strategies for a transformative future by combining empirical insights with advanced modelling techniques. Here is how each aspect contributes to meeting these goals:

1. Data-Driven Insights

• Informed by Authoritative Sources: The BRAVE framework is built on data gathered from reputable sources like Gartner, McKinsey, IEEE Spectrum, and the World Economic Forum. These insights help identify which emerging technologies are most relevant for the marketing sector and the future of work.
• Empirical Simulation with ABM: By running an ABM, this approach captures skill growth, role adaptability, and interactions between agents and BRAVE technologies. This simulated data allows marketers to visualize how skills may evolve in response to technology, providing a realistic foundation for strategic planning.
• Quantifiable Skill Growth Metrics: The model outputs skill growth over time, highlighting which skills (AI, blockchain, robotics, etc.) are likely to see the most improvement and need further development. This data-driven insight is valuable for designing targeted training programs.

2. Technology-Enabled Strategies

• ABM serves as a technology-enabled method to simulate and predict future scenarios. This model allows stakeholders to test different workforce development strategies and training investments, showing how they impact skill adaptability and preparedness for emerging technology adoption.
• Automation and Cross-Technology synergies ensure the simulation includes learning rates and synergies (like AI enhancing robotics skills), enabling marketers to see the compounded effect of skills working together. This informs strategic decisions about workforce structure and areas where technological cross-training is beneficial.
• Scenario Analysis for Future-Readiness simulates various scenarios (e.g., rapid AI adoption vs. steady growth in environmental skills), this approach enables organizations to explore possible futures, preparing them to act quickly and make informed decisions in a rapidly evolving tech landscape.

3. Transformation Focused Outcomes

• Proactive Skill Development leverage the insights generated from the ABM guiding organizations to prioritize resources for skills likely to be in high demand, positioning the workforce for future competitiveness and resilience.
• Ethical and Sustainable Framework building on environmental technology and ethical implications aligns with the industry 5.0 values of human-centric, sustainable innovation. This framework sets a foundation for a responsible and transformative marketing profession.
• Tailored Workforce Strategies support customized development plans, informed by ABM data, help organizations address individual role needs, promoting inclusive growth across diverse marketing roles and fostering a future-ready workforce.

 

4.5. Implications for Marketing - Strategic Perspective of Interdisciplinary & Regulatory

For marketing, the integration of emerging technologies with societal priorities presents both opportunities and challenges. The critical implication requires organizations and stakeholders to align marketing strategies with broader societal goals. The rise of BRAVE necessitates a shift in how organizations balance innovation with societal impact. To achieve meaningful outcomes, businesses must bridge the gap between technological capabilities and the needs of the communities they serve. This requires a focus on ethical innovation, ensuring marketing practices foster trust, inclusivity, and sustainability. By strategically integrating these elements, organizations can position themselves as leaders in addressing global challenges while achieving business growth. This complex interplay of technology and society calls for interdisciplinary collaboration. Marketing professionals must work closely with experts in sociology, environmental science, and behavioral psychology to create strategies capable of resonating with diverse audiences. These partnerships uncover deeper insights into consumer behavior, enable the design of campaigns that align with cultural norms, and address pressing issues such as climate change and social equity. The blending of these disciplines fosters a holistic approach to marketing both innovative and impactful at micro, mesa and macro levels (Clayton, 2025).

As marketing becomes increasingly data-driven, navigating the complexities of consumer privacy and data protection is critical. Differing legal frameworks across regions, General Data Protection Regulation (GDPR) in Europe or California Consumer Privacy Act (CCPA) in California, pose significant challenges for global organizations. Harmonizing marketing practices with these regulations requires proactive engagement with policymakers and investment in privacy-preserving technologies e.g., homomorphic encryption (Apple Machine Learning Research, 2024) and blockchain. Achieving global regulatory alignment not only mitigates compliance risks but also builds consumer trust in an era of heightened privacy concerns.

5. Conclusion

This study contributes theoretically, methodologically, and substantively to marketing scholarship. Theoretically, it extends taxonomy development methodology into the domain of marketing role design by constructing the BRAVE framework through a structured, transparent process. Methodologically, we integrate taxonomy construction with agent-based modelling (ABM), demonstrating how structured foresight is operationalizable into scenario-based organizational simulations. Substantively, the method provides a structured mechanism for organizations to translate technology foresight into workforce architecture and strategic capability design. The hypothetical case study (Box 1 “A Comprehensive Approach to Transitioning to BRAVE Marketing”) serves as an instantiation of the framework. The case operationalizes the full pipeline developed in this study: (1) identifying BRAVE technologies, (2) translating them into capability domains, (3) mapping capabilities into emergent marketing roles, and (4) simulating their interaction through ABM to explore performance trajectories across ROI, productivity, and societal impact metrics. In doing so, the case demonstrates how ABM simulations can be used not as predictive tools, but as structured scenario-testing mechanisms for organizational design under technological uncertainty.

The integration of BRAVE technologies into marketing fosters a transition from transaction-centric models toward ecosystems grounded in inclusivity, transparency, and trust. This shift reflects a broader movement consistent with Industry 5.0 principles, emphasizing ethical, human-centric, and sustainable innovation.

Blockchain technologies enable decentralized models of finance and exchange (e.g., DeFi; Schär, 2021), reducing intermediary dependence while enhancing supply chain transparency and empowering smaller market actors. Robotics and automation restructure operational efficiency, shifting labor composition toward knowledge-intensive, adaptive roles. AI and machine learning hyper-personalize engagement and enable predictive capability, transforming one-size-fits-all markets into dynamic, micro-segmented ecosystems. Vital infrastructure technologies (IoT, 5G, edge computing, quantum networks) facilitate real-time data exchange and system responsiveness. Environmental technologies embed regenerative logic into economic activity through circular systems, carbon accounting, and renewable integration.

Transparency becomes structurally embedded and not just asserted through communications. Blockchain-based ledgers allow verification of sourcing and carbon claims. AI-driven reporting systems generate real-time operational disclosures. IoT-enabled infrastructures provide lifecycle visibility. Environmental platforms enable measurable sustainability performance. In this reconfigured system, trust emerges from verifiability, not branding rhetoric. Consumer trust is strengthened not merely through personalization but through ethical architecture. Decentralized data control enhances consumer agency. Regulated AI systems increase algorithmic transparency. Sustainability commitments become quantifiable rather than symbolic. These developments redefine marketing’s institutional role from persuasion to stewardship.

The boxed case study illustrates a small, strategically designed BRAVE marketing team aligning with enabling technologies generating high ROI and productivity while simultaneously improving societal metrics such as environmental impact and transparency scores. The simulation demonstrates organizational performance is not solely a function of headcount but of capability configuration and interaction effects across roles. In this sense, ABM becomes a tool for exploring alternative organizational equilibria under technological disruption.

By 2030, marketing leadership titles will likely evolve beyond the traditional Chief Marketing Officer toward roles that explicitly incorporate intelligence systems, ethics governance, sustainability stewardship, and infrastructure orchestration. The “BRAVE Marketer” represents not a job title but a capability archetype integrating technological fluency with societal accountability.

Ultimately, this study underscores that the future of marketing is not determined by technology adoption alone but by structured integration of technology into role architecture, governance systems, and strategic simulation. The BRAVE framework, combining with agent-based scenario modelling, provides organizations with a replicable pathway for navigating uncertainty while aligning economic performance with societal impact.

To demonstrate the applied implications of the BRAVE taxonomy and ABM outputs, we present the following illustrative scenario.

Box 1. Illustrative Scenario –Transitioning to BRAVE Marketing (Derived from ABM Scenario Modelling)

The implementation of marketing activities using the BRAVE taxonomy creates measurable improvements in trust, engagement, and sustainability. By integrating emerging technologies and continuously monitoring outcomes, organizations can transition into a transparent, consumer-centric, and environmentally sustainable marketing future. This hypothetical study exemplifies how strategic adoption of emerging technologies aligns with business goals while addressing societal and environmental imperatives. In a bid to adapt to evolving consumer desire for brands to adopt sustainable practices and achieve more utilising AI, a global retail brand undertakes a strategic shift to implement BRAVE driven marketing practices.

Steps in BRAVE Transition

• Blockchain for Transparency - Loyalty programs are restructured using blockchain technology, enabling customers to access transparent and tamper-proof records of rewards and ad deliveries.
• AI for Personalization - Artificial intelligence automates the generation of personalized offers, increasing relevance and customer satisfaction.
• AR/VR for Immersive Experiences - Augmented and virtual reality technologies create virtual try-on options for products, fostering an interactive and engaging shopping experience.
• Robotics for Efficiency - Robots are deployed in retail outlets to provide 24/7 customer service, ensuring uninterrupted support and convenience.
• Environmental Technologies for Sustainability- Campaign emissions are monitored and offset using renewable energy solutions and carbon accounting tools.

Outcomes Achieved from Adoption of BRAVE technologies

• 40% increase in Consumer Trust, driven by blockchain-enabled transparency and AI-driven personalized experiences.
• 50% Engagement boost due to immersive AR/VR storytelling and predictive analytics for targeted recommendations.
• 30% Carbon Footprint reduction achieved through energy-efficient marketing platforms and environmentally conscious practices.

Quantifying Impact

Baseline Metrics (hypothetical):

• 60% Trust measured via surveys and Net Promoter Scores.
• 40% Engagement based on click-through rates and time spent.
• 100 tons Carbon Footprint calculated using lifecycle assessment tools.

Post-Transition Metrics:

• Trust - Increases to 84% (+40%).
• Engagement - Rises to 60% (+50%).
• Carbon Footprint - Drops to 70 tons (-30%).

Statistical validation techniques, paired t-tests or ANOVA, confirm the significance of improvements across all metrics.

Attribution of Outcomes

• Trust Gains attributable to Blockchain & AI enhancing transparency & relevance, directly impact consumer trust.
• Engagement Boosts built on Immersive AR/VR campaigns & predictive analytics to foster deeper consumer engagement.
• Carbon Reduction emerges from Energy-efficient technologies and carbon-neutral platforms drive measurable environmental benefits.

Using Agent-Based Models for Simulation when real-world data is incomplete, Agent-Based Models (ABMs) simulate consumer interactions with BRAVE technologies: Agents represent customers and consumer behaviors.

• Behavior Rules incorporate trust increases via blockchain or improved engagement through AR/VR.
• Simulations predict outcomes based on hypothetical scenarios to validate findings.
• Sustained Monitoring for Long-Term Impact

To ensure consistent improvements: Conduct regular trust surveys/Track engagement metrics using analytics tools. /Annual carbon footprint audits.

References

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Appendix A

Emerging Technologies by Authoritative Source (2024)

Gartner Hype Cycle	MIT Tech Review	IEEESpectrum
AI-Augmented Software Engineering	AI for Everything	Wi-Fi 7 for stable, multi-link connectivity
AI Supercomputing	Super-Efficient Solar Cells	HVDC networks in Europe
Generative AI	Apple Vision Pro	Content credentials for combating deepfakes
WebAssembly	Weight-Loss Drugs	Algorithmic video surveillance
Autonomous Agents	Enhanced Geothermal Systems	NASA’s Artemis II lunar mission
Digital Twin of a Customer	Chiplets	Homomorphic encryption chips for data security
Homomorphic Encryption	The First Gene-Editing Treatment	Carbon-sequestering kelp farms
Artificial General Intelligence	Exascale Computers	Intel’s next-gen chip technology
Cybersecurity Mesh Architecture	Heat Pumps	Blue PHOLED displays
Humanoid Working Robots	Twitter Alternatives	Synchron brain-implant technology
Large Action Models	CB Insights	ARK Invest
World Economic Forum	GPU	Technological Convergence[2]
AI for scientific discovery	Multimodal AI	Artificial Intelligence
Privacy-enhancing technologies	Synthetic data	Bitcoin Allocation
Reconfigurable intelligent surfaces	No code software	Bitcoin In 2023
High altitude platform stations	Quantum computing commercialization	Smart Contracts
Integrated sensing and communication	Cyber chaos drives security consolidation	Digital Consumers
Immersive technology for the built world	AI & security	Digital Wallets
Elastocalorics	Bank AI FOMO	Precision Therapies
Carbon-capturing microbes	Blockchain finserv	Multiomic Tools & Technology
Alternative livestock feeds	Extreme weather insurtech	Electric Vehicles
Genomics for transplants	AI drug race	Robotics
McKinsey Report	Digital therapeutics	Robotaxis
Generative AI	Brain	Autonomous Logistics
Applied AI	AI sales agents	Reusable Rockets
Industrializing Machine Learning (MLOps)	AI loss prevention	3D Printing
Immersive Reality Technologies (AR/VR)	AI gaming
Quantum Technologies	Humanoid robots
Cloud and Edge Computing
Next-generation Software Development
Future of Space Technologies
Electrification and Renewables

Appendix B

Conceptual Design of the Agent-Based Model (ABM)

C.1 Purpose and Scope

This agent-based model (ABM) simulates the evolution of productivity and return-on-investment (ROI) contributions of 26 distinct marketing roles over the period 2025–2030. The objective is not to generate financial forecasts, but to examine the structural dynamics through which emerging, technology-enabled marketing roles may influence aggregate organizational capability and financial performance under defined assumptions. The model conceptualizes each marketing role as an autonomous agent with productivity evolving over time and contribution to financial output in a non-linear manner. Aggregate organizational outcomes emerge from the combined behavior of these individual agents.

C.2 Conceptual Structure

Agents

Each agent represents a distinct marketing role (e.g., AI-driven strategist, immersive brand architect, neuromarketing specialist). Agents are characterized by five parameters:

1. Baseline productivity – Initial effectiveness level at model start (2025).
2. Growth range – Annual stochastic productivity increment reflecting learning, technological leverage, and skill maturation.
3. Baseline ROI contribution – Initial financial contribution attributed to the role.
4. ROI sensitivity factor – The strength of the relationship between productivity and financial output.
5. External variability factor – A stochastic modifier representing environmental volatility (e.g., market uncertainty, regulatory change, organizational constraints).

Agents evolve independently across discrete time steps (six periods corresponding to 2025–2030).

C.3 Dynamic Assumptions

The model is governed by four core assumptions:

(1) Stochastic Productivity Growth

Productivity increases annually within a predefined range. Growth is probabilistic rather than deterministic, reflecting uncertainty in adoption, AI augmentation, skill acquisition, and organizational learning processes.

(2) Non-Linear ROI Response

ROI contribution is modelled as a non-linear function of productivity. This reflects compounding mechanisms commonly observed in digital and AI-enabled environments, including automation scale effects, data network externalities, and platform leverage. Consequently, marginal improvements in productivity generate disproportionately larger financial effects over time.

(3) Environmental Volatility

Each agent is subject to a bounded stochastic modifier to represent macro-level uncertainty. This ensures that trajectories reflect real-world variability rather than smooth exponential growth.

(4) Productivity Ceiling

Productivity is capped at an upper bound (100%) to prevent unrealistic compounding and to reflect structural limits in organizational performance.

C.4 Model Outputs

The ABM produces three levels of output for analytical interpretation.

(1) Average Workforce Productivity

This metric represents the mean productivity across all agents at each time step. It serves as an indicator of aggregate organizational capability development.

Interpretation:

• An upward slope reflects collective capability maturation.
• A plateau indicates saturation or diminishing marginal gains.
• Steeper gradients imply rapid technological or strategic leverage.

This metric reflects capability evolution rather than financial performance.

(2) Total ROI Contribution

This metric aggregates the financial output of all agents per period. Given the non-linear ROI function, the trajectory may exhibit acceleration over time.

Interpretation:

• Convex (accelerating) growth suggests compounding strategic advantage.
• Linear growth indicates stable scaling.
• Flattening curves imply under-leveraged capabilities.

The shape of the curve is analytically more important than absolute magnitude.

(3) Role-Level Productivity Trajectories

Individual agent trajectories reveal heterogeneity across roles.

Interpretation:

• High initial productivity with low growth suggests mature roles.
• Lower initial productivity with steep growth suggests emergent or disruptive roles.
• Divergence between trajectories indicates strategic differentiation potential.

This level of analysis enables examination of whether specialist or innovation-oriented roles generate disproportionate long-term contribution relative to foundational roles.

C.5 Analytical Interpretation

The model enables structured exploration of four strategic questions:

1. Which roles generate disproportionate long-term ROI growth?
2. Do emerging roles overtake established roles over time?
3. Does aggregate organizational capability scale meaningfully?
4. Does financial impact compound as productivity matures?

Importantly, the model is illustrative rather than predictive. It is designed to simulate relative structural dynamics under controlled assumptions, not to forecast empirical financial outcomes.

C.6 Methodological Justification

Agent-based modelling is appropriate in this context for three reasons:

1. Heterogeneity – Marketing roles differ in growth potential and financial leverage.
2. Non-linearity – Financial returns may scale disproportionately relative to productivity improvements.
3. Emergence – Macro-level outcomes (total ROI) emerge from micro-level behavioral rules.

The ABM thus functions as a structured scenario exploration tool for examining workforce evolution in AI-augmented marketing environments.

Appendix C

Model Implementation Notes and Computational Specification

D.1 Overview

This appendix documents the computational structure of the agent-based model (ABM) described conceptually in Appendix C. The purpose of this section is to provide implementation transparency and enable reproducibility without duplicating the conceptual rationale.

The model is implemented in Python using the Mesa agent-based modeling framework. The code operationalizes the productivity growth and ROI dynamics through explicit update rules and structured data collection.

D.2 Agent Update Equations

At each discrete time step , each marketing role agent updates its productivity and ROI contribution according to the following rules:

Productivity Update

Where:

• = productivity of role at time
• = stochastic growth draw within role-specific range
• = bounded external variability factor
• 100 = upper productivity ceiling

This rule ensures bounded, stochastic, and heterogeneous productivity growth across roles.

ROI Update

Where:

• = financial contribution at time
• = non-linearity exponent (compounding effect)
• = role-specific ROI sensitivity factor
• = scaling constant

The exponent introduces convexity, reflecting digital scaling and AI-driven leverage effects.

D.3 Role Parameterization

Each of the 25 roles is initialized using a structured parameter schema:

Parameter	Description
productivity	Baseline capability level (0–100)
growth_rate	Annual productivity growth interval
roi_contribution	Initial financial contribution
roi_factor	Sensitivity multiplier linking productivity to ROI

Roles differ in both baseline capability and growth potential, enabling heterogeneity within the simulation.

D.4 Simulation Process

The simulation proceeds as follows:

1. Initialize agents with role-specific parameters.
2. Randomize agent activation order per time step.
3. Apply productivity update rule.
4. Apply ROI update rule.
5. Collect model-level and agent-level metrics.
6. Repeat for six time steps (2025–2030).

Random activation prevents deterministic ordering effects and supports emergent behavior.

D.5 Data Collection Structure

The model collects two levels of metrics:

Model-Level Metrics

• Average productivity across agents
• Total ROI contribution across agents

Agent-Level Metrics

• Role identifier
• ROI contribution (and optionally productivity)

The resulting dataset is structured as a time-indexed panel allowing:

• Cross-sectional role comparison
• Longitudinal growth analysis
• Aggregate capability tracking

D.6 Computational Transparency

The full reference implementation is written in Python (Mesa framework) and includes:

• Agent class definition
• Model orchestration structure
• Data collection module
• Visualization routine

 

The implementation is modular, allowing:

• Adjustment of growth ranges
• Modification of non-linearity exponent
• Scenario testing under alternative volatility bounds
• Expansion to additional roles

 

Only partial role configuration is shown in the excerpt for brevity; the complete model instantiates all 25 roles under identical structural rules.

D.7 Implementation Scope and Limitations

The model:

• Does not simulate direct inter-agent interaction
• Assumes independent growth processes
• Uses bounded stochastic volatility rather than empirical macro data
• Is illustrative rather than predictive

 

The computational design prioritizes clarity, transparency, and scenario exploration over empirical calibration.

D.8 Reproducibility Statement

The model is implemented using Python (3.x) and the Mesa ABM framework. Dependencies include NumPy, Pandas, and Matplotlib. The code can be executed using standard Python environments and modified to test alternative parameterizations or extended time horizons.

D.9 Model Validation and Robustness Checks

To ensure structural integrity and analytical reliability, several validation and robustness procedures were implemented.

Structural Validation

The model was validated at three levels:

1. Logical consistency checks
   • Productivity is bounded within .
   • ROI growth remains monotonic under positive productivity conditions.
   • No negative financial contributions occur under baseline parameterization.
2. Boundary testing
   • Extreme growth-rate inputs (near zero or high upper bounds) were tested to confirm stability.
   • Volatility bounds were expanded to assess convergence behavior.
3. Internal coherence
   • When the non-linearity exponent is set to 1.0, ROI growth becomes linear, confirming that convex growth patterns arise solely from the exponent parameter.

These checks confirm the emergent patterns are driven by model structure rather than computational artefacts.

D.10 Monte Carlo Sensitivity Extension

Because productivity growth and volatility are stochastic, outcomes vary across runs. To assess stability, a Monte Carlo simulation framework can be applied.

Procedure

1. Run the model times (e.g., or ).
2. Record final-period (2030) outcomes:

• Average productivity
• Total ROI
• Top-performing roles

 

• Compute:

 

• Mean outcome
• Standard deviation
• Confidence intervals

Analytical Interpretation

Monte Carlo analysis allows assessment of:

• Stability of aggregate ROI trajectories
• Variability in role dominance
• Sensitivity to volatility bounds
• Sensitivity to exponent

If aggregate ROI curves remain convex across runs, compounding behavior is structurally robust rather than path-dependent.

This extension transforms the model from a single scenario illustration into a probabilistic scenario envelope.

D.11 Theoretical Anchoring: Dynamic Capabilities Perspective

The model aligns with the dynamic capabilities framework (Teece, Pisano, & Shuen, 1997; Teece, 2007), which conceptualizes competitive advantage as the firm's ability to:

1. Sense opportunities
2. Seize opportunities
3. Reconfigure resources

Within this ABM:

• Productivity growth operationalizes capability development (learning and reconfiguration).
• Non-linear ROI scaling reflects value capture through orchestration and deployment.
• Role heterogeneity mirrors differentiated microfoundations of capability.
• Emergent aggregate ROI reflects macro-level competitive advantage arising from micro-level capability investments.

The exponent captures compounding value creation consistent with digital platform economics and AI leverage, where marginal improvements yield disproportionate returns.

Thus, the ABM operationalizes dynamic capabilities as a computational experiment in capability evolution.

D.12 Positioning Within Strategic Theory

Beyond dynamic capabilities, the model also relates to:

• Resource-Based View (RBV) — heterogeneity in role configurations represents differentiated resources.
• Service-Dominant Logic (SDL) — roles act as operant resources generating value through capability application.
• Complex Adaptive Systems theory — aggregate financial performance emerges from decentralized agent behavior.

The ABM therefore serves as a structured microfoundational simulation of strategic capability accumulation under technological transformation.

Appendix D

Emerging Technologies by Category

1. AI & Machine Learning (20 technologies)

Artificial Intelligence

AI-Augmented Software Engineering

AI Supercomputing

Generative AI

Artificial General Intelligence

Applied AI

Large Language Models

AI for scientific discovery

AI weather prediction

AI agent marketplaces

Industrializing ML (MLOps)

Multimodal AI

Synthetic data

No code software

Bank AI FOMO

AI drug discovery

Digital therapeutics

AI sales agents

AI loss prevention

AI gaming

2. Digital Infrastructure & Computing (11 technologies)

Digital Infrastructure

Next-generation Software Development

Cloud and Edge Computing

Immersive Reality Technologies (AR/VR)

Reconfigurable intelligent surfaces

Integrated sensing and communication

High altitude platform stations

Immersive technology for the built world

Wi-Fi 7 for stable, multi-link connectivity

HVDC networks in Europe

Quantum computing commercialization

3. Healthcare and Biotechnology (11 technologies)

Genomics for transplants

Precision Therapies

Multiomic Tools & Technology

The First Gene-Editing Treatment

Cellular & epigenetic reprogramming

Biocomputing

Brain manipulation technology

Synchron’s brain-implant technology

Extreme weather insurtech

Weight-Loss Drugs

Brain tech

4. Sustainable Energy Solutions (10 technologies)

Sustainable Energy

Electrification and Renewables

Climate Technologies

Enhanced Geothermal Systems

Super-Efficient Solar Cells

Heat Pumps

Carbon-sequestering kelp farms

Carbon-capturing microbes

Alternative livestock feeds

Ultra-deep drilling

5. Blockchain and Cryptocurrency (5 technologies)

Bitcoin Allocation

Bitcoin in 2023

Smart Contracts

Digital Wallets

Technological Convergence (Artificial Intelligence, Public Blockchains, Multiomic Sequencing, Energy Storage, and Robotics)

6. Advanced Computing and Semiconductors (5 technologies)

Quantum Technologies

Quantum-optimized portfolios

Exascale Computers

Chiplets

Intel’s next-gen chip technology

7. Robotics and Automation (8 technologies)

Robotics

Humanoid Working Robots

Robotaxis

Autonomous Logistics

Reusable Rockets

3D Printing

Electric Vehicles

Humanoid robots

8. Immersive and Sensing Technologies (7 technologies)

Immersive Reality Technologies (AR/VR)

Reconfigurable intelligent surfaces

Integrated sensing and communication

High altitude platform stations

Immersive technology for the built world

Apple Vision Pro

Blue PHOLED displays

9. Security and Privacy Technologies (6 technologies)

Homomorphic Encryption

Homomorphic encryption chips for data security

Privacy-enhancing technologies

Content credentials for combating deepfakes

Algorithmic video surveillance

Cybersecurity Mesh Architecture

10. Space Exploration and Advanced Propulsion (3 technologies)

Future of Space Technologies

NASA’s Artemis II lunar mission

Advanced nuclear propulsion

11. Neurotechnology and Brain-Computer Interfaces (3 technologies)

Brain manipulation technology

Synchron brain-implant technology

Brain-Computer Interfaces

12. Advanced Navigation and Positioning Systems (1 technology)

GPS-less navigation systems

13. Advanced Display Technologies (1 technology)

Blue PHOLED displays

14. Digital Communication Platforms (1 technology)

Twitter Alternatives

15. Sustainable Agriculture and Food Technologies (1 technology)

Alternative livestock feeds

16. Sustainable Transportation (1 technology)

Electric vehicles

1. In this study, “agents” refer to role-representing entities within an Agent-Based Model (ABM) used for simulation purposes. These should not be confused with “agentic AI workflows” in large language model (LLM) systems representing autonomous software pipelines capable of executing tasks. ABM agents model marketing role-level attributes and interactions in a stylized ecosystem, whereas agentic AI workflows are operational tools capable of usage within real marketing processes. ↑
2. Artificial Intelligence, Public Blockchains, Multiomic Sequencing, Energy Storage, and Robotics ↑

 

International Journal of Innovation and Economic Development

Volume 11, Issue 6, February 2026, Pages 7-27

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The Win-Win-Win Papakonstantinidis Model

 

URL: https://doi.org/10.18775/ijied.1849-7551-7020.2015.116.2001

DOI: 10.18775/ijied.1849-7551-7020.2015.116.2001

 Leonidas Papakonstantinidis ¹

¹ Prof Emeritus, IMA Academician, Department of Management and Economics, University of Peloponnese, Kalamata, Greece

Abstract: The Win-Win-Win Papakonstantinidis Model is a strategic bargaining and conflict resolution framework that expands the traditional "win-win" scenario to include a third, crucial stakeholder: the Community. It aims to achieve mutually beneficial outcomes for all three parties (e.g., businesses/individuals, other involved parties, and society/the environment) by integrating social responsibility, ethics, and empathy into decision-making processes. The model moves beyond pure economic rationality and competition to a more holistic approach that incorporates social motivation and collective welfare: 1) Tripartite Focus: It transforms two-party negotiations into a three-dimensional process, ensuring outcomes benefit "me," "you," and "the community"; 2) Empathy and Social Justice: The framework suggests that cooperation is driven by empathy and social trust, not just competition. It emphasizes the "sensitization process" where participants consider community norms and social justice.

Keywords: the win-win-win perspective Bargaining Theory, the Community, Game Theory, Nash extension, Behavioral analysis, Pareto efficiency, Stakeholders Analysis

1. Introduction

The Win-Win-Win Papakonstantinidis model is a strategic and ethical framework for conflict resolution and decision-making that extends the traditional two-party "win-win" concept to include a third, crucial stakeholder: the Community. This tripartite approach aims for outcomes that are mutually beneficial for all three parties involved.
In specific, the Win-Win-Win Papakonstantinidis Model is a strategic framework, extending traditional win-win game theory, that seeks cooperative, mutually beneficial outcomes for three parties in complex negotiations, often applied in local development and governance to balance businesses (economic win), society (social win), and the environment (ecological win), using behavioral science to transform technical conflicts into collaborative solutions through empathy, shared understanding, and a "Flag Theme" for community unity.

• Beyond Pareto Efficiency: While traditional models (like Pareto optimality) focus on resource allocation where no one can be made better off without making another worse off, the Win-Win-Win model introduces the community (the "C" factor) to add quality elements like equality and justice, aiming for a higher "equi-harmony" point that maximizes the triple utility for all involved.
• Behavioral Methodologies: The model uses behavioral analysis to transform perceptions and encourage "active participation" and self-organization within communities, particularly in local development and governance decisions.
  Application of AI in the Model
  Recent research has explored the integration of Artificial Intelligence (AI) with the Papakonstantinidis model to enhance community growth and social cohesion.
• Data Integration and Participatory Tools: AI platforms can process large-scale community data to optimize resource allocation, enhance participatory governance, and mediate stakeholder involvement, thus addressing systemic inequalities.
• Empowering Social Economy Enterprises (SEEs): AI marketing platforms can help SEEs (which prioritize social and environmental sustainability over profit) engage with stakeholders more effectively, bridging the gap with established businesses and promoting local sustainable growth.
• Predicting Behavior: Unlike the standard stakeholder model, the Win-Win-Win model can use AI-compatible quantitative foundations to predict the behavior of bargainers by modeling individual decision-making, which helps in designing better public health strategies or local policies

 

CORE PRINCIPLES:

Three-Pole Negotiation: Moves beyond two-party "win-win" to incorporate three key actors: Businesses (economic), State/Authorities (governance), and the local Community (social/environmental).
Holistic Integration: Integrates economic success, social responsibility, and environmental sustainability into a single framework.
Behavioral Focus: Uses techniques like Descriptive Behavior (DB) and Applied Behavioral Analysis (ABA) to understand and shift community perceptions from technical disputes to behavioral cooperation.
Sensitization Process: A key step involving information sharing and dialogue to build empathy and shared identity, often around a local "Flag Theme" (e.g., a historical story, natural feature).
Nash Extension: Builds on John Nash's cooperative game theory, but adds the community's collective utility (the "C" factor) to individual payoffs.

2. Literature Review

Given the ambiguity of the exact model, my literature review will focus on related concepts in strategy, sustainability, and stakeholder theory:

• Stakeholder Theory (Freeman, 1984): This foundational work emphasizes the importance of managing relationships with all stakeholders who can affect or are affected by an organization's actions. It highlights the ethical responsibility of considering multiple perspectives beyond shareholder value. This is essential for achieving win-win-win outcomes. Key takeaway: Identify and prioritize stakeholders to understand their needs and align them with organizational goals.
• Triple Bottom Line (Elkington, 1997): The TBL framework suggests that businesses should measure their performance across three dimensions: profit, people, and planet. It advocates for integrated strategies that create value in all three areas simultaneously. Key takeaway: Develop metrics and targets for economic, social, and environmental performance, and integrate them into decision-making processes.
• Creating Shared Value (Porter & Kramer, 2011): This concept argues that businesses can create economic value by addressing social needs and challenges. It emphasizes finding opportunities where business success and social progress are mutually reinforcing. Key takeaway: Identify social problems that align with the company's core business and develop solutions that generate both economic and social value.
• Sustainable Development Goals (SDGs): The SDGs provide a global framework for addressing a wide range of social, economic, and environmental challenges. Businesses can align their strategies with the SDGs to contribute to sustainable development and create win-win-win outcomes. Key takeaway: Prioritize SDGs that are relevant to the company's industry and operations, and develop initiatives that contribute to achieving those goals.
• Corporate Social Responsibility (CSR): CSR encompasses a broad range of activities that companies undertake to address their social and environmental impacts. Effective CSR initiatives can generate positive outcomes for both the company and its stakeholders. Key takeaway: Implement CSR initiatives that are aligned with the company's values and contribute to addressing significant social or environmental issues. Ensure transparency and accountability in CSR reporting.
• Business Ethics Literature: Ethical decision-making is crucial for ensuring that all stakeholders are treated fairly and that win-win-win outcomes are achieved sustainably. Key takeaway: Establish a strong ethical culture within the organization and promote ethical behavior at all levels.
• Game Theory: Models like the Nash Equilibrium or Cooperative Game Theory can be used to analyze situations where multiple actors seek to maximize their outcomes, and how cooperation can lead to mutually beneficial results, thus achieving a win-win-win. Key takeaway: Analyze the interactions between different stakeholders and identify opportunities for cooperation and mutual benefit.
• Systems Thinking: Understanding the interconnectedness of different systems (economic, social, environmental) is essential for identifying leverage points where interventions can generate positive ripple effects across multiple stakeholders. Key takeaway: Adopt a holistic perspective and consider the broader implications of business decisions on the environment and society.

Best Solution (Hypothetical, Based on General Principles)

Given the lack of specific detail on the "Papakonstantinidis Model", a "best" solution is necessarily broad. It would entail a structured approach to integrating sustainability and stakeholder considerations into strategic decision-making

3. Research Methodology

The proposed methodology prioritize stakeholder engagement, ethical considerations, and practical relevance to ensure all parties benefit.
I. Problem Identification & Framing (Key Step: Stakeholder Identification)
Define the Research Problem:
Stakeholder Analysis: KEY STEP: I Identified the relevant stakeholder groups who are affected by the problem or whose involvement is crucial for finding a solution. This involves:
Brainstorming potential stakeholders.
Prioritizing stakeholders based on their level of influence and interest.
Mapping the relationships and dependencies between stakeholders.
Defining the potential benefits for each stakeholder group.
Initial Literature Review: Conduct a preliminary literature review to understand existing research on the problem and identify gaps in knowledge. Pay particular attention to studies that have considered the perspectives of different stakeholders.
Refine Research Question: Refine the research question based on the stakeholder analysis and literature review. Ensure the research question is relevant and meaningful to all the identified stakeholder groups.

STAKEHOLDER ANALYSIS

Stakeholder Analysis: Key Steps

The stakeholder analysis process involves these critical steps:

1. Identify Stakeholders: List all individuals, groups, or organizations affected by or able to influence the project or situation. This is the crucial first step.
2. Analyze Stakeholder Interests & Influence: Determine each stakeholder's:
   • Interests: What they hope to gain, what they fear losing, their needs, and expectations.
   • Influence/Power: Their ability to affect the project's outcome, positively or negatively. This could be through authority, resources, knowledge, or political connections.
   • Potential Impact: The likely positive or negative effect the project might have on them.
3. Stakeholder Mapping: Visually represent stakeholders based on their influence and interest (e.g., using a power/interest grid).
4. Develop Stakeholder Management Strategies: Plan how to engage with each stakeholder group based on their interests, influence, and potential impact. This includes strategies for:
   • Keeping them informed.
   • Managing their expectations.
   • Mitigating negative impacts.
   • Maximizing positive impacts.
   • Involving them in decision-making (where appropriate).

Example: Renewable Energy Project

Identify Stakeholders:

Primary: The energy company implementing the project. The local residents who will receive electricity.
Secondary: Local businesses, landowners whose property is used for the project, construction workers involved in building the facility, local government.
Contextual: Future generations, the environment (considering impact on wildlife, land use, etc.), the broader regional economy.

Table 1

Stakeholder	Interests	Influence	Potential Impact
Energy Company	Profitability, meeting renewable energy targets, positive public image	High (financial resources, technical expertise)	Positive (increased revenue, market share), Negative (if project fails, cost overruns)
Local Residents	Affordable electricity, reliable power supply, community benefits	Medium (voting power, ability to protest)	Positive (access to electricity, lower costs), Negative (noise, visual impact, disruption)
Local Businesses	Reliable electricity, economic growth, increased customer base	Medium (local lobbying, business relationships)	Positive (increased business), Negative (disruptions during construction)
Landowners	Fair compensation for land use, minimal environmental impact	Medium (legal rights, negotiation power)	Positive (income from land lease), Negative (impact on property value, environmental concerns)
Construction Workers	Employment, fair wages, safe working conditions	Low to Medium (union representation, skills)	Positive (employment opportunities), Negative (temporary job, potential safety risks)
Local Government	Tax revenue, community development, job creation	High (regulatory authority, planning permissions)	Positive (increased revenue, improved infrastructure), Negative (potential for conflicts)
Future Generations	A sustainable environment, access to resources	Low (no direct voice)	Positive (cleaner energy, reduced carbon footprint), Negative (if project is poorly managed)
Environment	Protection of biodiversity, minimal land use changes, reduced pollution	Low (represented by environmental groups/regulations)	Positive (reduced reliance on fossil fuels), Negative (habitat disruption, visual pollution)
Regional Economy	Growth of businesses, new jobs	Medium (overall economic activity)	Positive (increased employment, regional development), Negative (if project fails)

 

4. Data Analysis and Interpretation

• Identify the Three Poles: State/Authorities, Local Businesses, Local Community.
• Information & Sensitization: Educate stakeholders and facilitate discussion around shared local values or themes to foster empathy.
• Bargaining & Strategy: Stakeholders ask: "What's best for me, the other party, and the community?".
• Behavioral Shift: Technical issues (e.g., land use) become collaborative projects (e.g., eco-tourism development).
• Equilibrium: A conceptual equilibrium is reached where all three parties achieve their goals, preventing zero-sum outcomes.
• Application Example: A local government, tourism business, and community group use the model to develop rural tourism, creating economic gains for the business (Win 1), improved local services (Win 2, Social), and preservation of natural heritage (Win 3, Environment).

Key elements and principles of the model include:

• Three Stakeholders: It transforms a two-party negotiation (e.g., business and a citizen, or labor and management) into a three-party interaction by formally including the Community (or society, the environment, common values) as the "third attractor" or the "C factor".
• Beyond Instrumental Rationality: The model suggests that traditional economic rationality (pure self-interest and profit maximization) is insufficient for resolving complex, real-world conflicts, especially at the local level. It integrates behavioral analysis, empathy, and social trust as essential components of the negotiation process, moving from an individualistic to a communitarian perspective.
• Social Welfare and Cohesion: A primary goal is to generate outcomes that enhance social cohesion and community welfare, thus converting potential "value destruction" (e.g., from conflict or a purely win-lose approach) into "value creation" for society as a whole.
• Sensitization Process: The model incorporates a "sensitization process" through which the involved parties become more aware of the community's needs and the broader impact of their decisions. This process is intended to lead towards "absolute cooperation" as the optimal long-term strategy for all players.
• Dynamic Systems Approach: It uses concepts from game theory, dynamic systems analysis, and the "butterfly effect" to analyze how small changes in initial conditions (like incorporating community welfare into negotiations) can significantly affect the entire system over time.

 

4.1 Utility-Welfare Function

4.1.1 Utility

In economics, utility function is an important concept that measures preferences over a set of goods and services. Utility represents the satisfaction that consumers receive for choosing and consuming a product or service^[1].

Utility is measured in units called utils, but calculating the benefit or satisfaction that consumers receive from is abstract and difficult to pinpoint. As a result, economists measure utility in terms of revealed preferences by observing consumers' choices. From there, economists create an ordering of consumption baskets from least desired to the most preferred.

Understanding Utility Function

In economics, the utility function measures the welfare or satisfaction of a consumer as a function of consumption of real goods such as food or clothing. Utility function is widely used in the rational choice theory to analyze human behavior.

When economists measure the preferences of consumers, it's referred to ordinal utility. In other words, the order in which consumers choose one product over another can establish that consumers assign a higher value to the first product. Ordinal utility measures how consumers rank one product versus another.

Economists take the utility-function concept one step farther by assigning a numerical value to the products that consumers choose or choose not to consume. Assigning a value of utility is called cardinal utility, and the metric used to it is called utils.

For example, in certain situations, tea and coffee can be considered perfect substitutes for each other, and the appropriate utility function must reflect such preferences with a utility form of u(c, t) = c + t, where "u" denotes the utility function and "c" and "t" denote coffee and tea. Economists might conclude that a consumer who consumes one pound of coffee and no tea derives a utility of 1 util.

Within economics, the concept of utility is used to model worth or value. Its usage has evolved significantly over time. The term was introduced initially as a measure of pleasure or satisfaction within the theory of utilitarianism by moral philosophers such as Jeremy Bentham and John Stuart Mill. The term has been adapted and reapplied within neoclassical economics, which dominates modern economic theory, as a utility function that represents a consumer's preference ordering over a choice set. It is devoid of its original interpretation as a measurement of the pleasure or satisfaction obtained by the consumer from that choice.

Consider a set of alternatives facing an individual, and over which the individual has a preference ordering. A utility function is able to represent those preferences if it is possible to assign a real number to each alternative, in such a way that alternative a is assigned a number greater than alternative b if, and only if, the individual prefers alternative a to alternative b. In this situation an individual that selects the most preferred alternative available is necessarily also selecting the alternative that maximizes the associated utility function. In general economic terms, a utility function measures preferences concerning a set of goods and services. Often, utility is correlated with words such as happiness, satisfaction, and welfare, and these are hard to measure mathematically. Thus, economists utilize consumption baskets of preferences in order to measure these abstract, non-quantifiable ideas.

Figure 1.

Papakonstantinidis LA,2008

4.1.2 Welfare economics

Welfare economics is a branch of economics that uses microeconomic techniques to evaluate well-being (welfare) at the aggregate (economy-wide) level^[1]

Attempting to apply the principles of welfare economics gives rise to the field of public economics, the study of how government might intervene to improve social welfare. Welfare economics also provides the theoretical foundations for particular instruments of public economics, including cost–benefit analysis, while the combination of welfare economics and insights from behavioral economics has led to the creation of a new subfield, behavioral welfare economics^[2]

The field of welfare economics is associated with two fundamental theorems. The first states that given certain assumptions, competitive markets produce (Pareto) efficient outcomes;^[3] it captures the logic of Adam Smith's invisible hand^[4] The second states that given further restrictions, any Pareto efficient outcome^[5] can be supported as a competitive market equilibrium.

A typical methodology begins with the derivation (or assumption) of a social welfare function, which can then be used to rank economically feasible allocations of resources in terms of the social welfare they entail. Such functions typically include measures of economic efficiency and equity, though more recent attempts to quantify social welfare have included a broader range of measures including economic freedom (as in the capability approach).

4.2 Bargaining

In mathematics, particularly within game theory and economics, bargaining refers to the strategic, axiomatic, or algorithmic analysis of how two or more parties divide a shared resource (surplus) or agree upon a joint action.

Mathematical bargaining theory focuses on determining a stable, efficient, and fair outcome, often represented as a division of utility.

Core Concepts of Mathematical Bargaining

• Bargaining Problem

in utility space, implying neither party gains anything if they don't agree.

• BATNA (Best Alternative to a Negotiated Agreement): A critical factor in determining bargaining power; higher alternatives improve a player's outcome in the Nash solution.

 

4.3 Two person’s bargaining theory^[1]

The Bargaining Problem (Nash Solution)

The two-person bargaining problem studies how two agents share a surplus that they can jointly generate. It is in essence a payoff selection problem. In many cases, the surplus created by the two players can be shared in many ways, forcing the players to negotiate which division of payoffs to choose. There are two typical approaches to the bargaining problem. The normative approach studies how the surplus should be shared. It formulates appealing axioms that the solution to a bargaining problem should satisfy. The positive approach answers the question how the surplus will be shared. Under the positive approach, the bargaining procedure is modeled in detail as a non-cooperative game^[2].

4.4 Social bargaining in terms of disagreement^[3] 3-ple equilibrium
Ideal situation-the Angels’ Moment

✓ In a poetic expression, people have to set higher goals, in every interaction - negotiation so they can express their disagreement, at some point or threat point of stopping the negotiation
✓ in an even more poetic expression, people must re-start dreaming of a better life again - one of the signs of globalization is to level everything for instant euphoria
✓ but so have people stopped dreaming ... Relationships, expectations, products and even lasting products (furniture-kitchens etc) and even the heads of state and government and relationships between them have all become instant
✓ The deep wound of globalization is the conversion of everything from constant to instant
✓ People have to accept this “instant point”, without history, future, and without dreams Ignatius Ramonet supports - and not unfairly – “…the past - present and the future has been squeezed into the instant now, the supreme moment of history …... all made by the wish factory . "- 1000 cold “NO” for an emotional “YES” Buskalia
✓ Of course, every citizen has (at least theoretically the right of veto, a veto

Papakonstantinidis 2019
✓ The more sensitized is someone to a stimulus (eg environment) as "less objections" (less friction) will have to those who formulate development policies, which means that the differences between the level of satisfaction (utility function) and the disagreement point (d, disagreement point, or threat point, are gradually smoothed out. The degree of satisfaction increases as the point of objection increases gradually
✓ The difference between cold rational and sensitized behavior and their mix to maximize the expected benefit to each and every one as he / she perceives determines the level of culture of a particular - local, basic - society
The social predisposition of Humans makes the above relationship possible and the aim is to minimize the absolute difference between cold rationality and sensitized behavior: For example, protecting the natural environment
✓ It does not matter if we lose..1000 logical NO to an emotional YES… his life is endless .. always a winner

4.5 The Sharing Process

 

The “Sharing problem” in a Bargain [Utilities, Shares, strategies, decision- choices, behaviour, Final Agreement]
We suppose, we must share a pie
Having defined: (1) How information resulting from  “knowledge creation /knowledge transfer” should contribute to what we call “social market” (2) How sensitization should be introduced to given information, as to turn it to an integrated information (Papakonstantinidis, 2006) (3) How “integrated information” should influence human behaviour during the bargain, or negotiations (4) How a human “social”  behaviour could lead to a “new” perception of thinking or  taking a decision, in the bargain (see at Calvert Randall, 1995,  Berger, J 2005 Cinneide M. O’ 1991,  Coleman J 1988, Yitzak Samuel 1997, Bernheim Douglas B. 1984 (5) How socialization could influence human choices or winning strategies during the bargain, based on instant reflection (Nash)  (6) How scientific thought could transfer the problem from “utilities” (personal perception”) to pay-offs (objective perception =  counting size) Harsanyi John(1973), then,  the data  of Table 2 may be transformed in a new set of data, as Table 3.
This table shows how the product of utilities AXBXC represents the total utility for the community

Table 3 (Papakonstantinidis Proposal)

Suggesting Sharing between “A , “B” and “C”

 

Share Α (%)	Share Β (%)	Utility A	Utility B	Utility AXB	Share C (%)	Utility C	Utility AXBXC
90	4	1	71	71	6	1	71
80	13	2	70	140	7	2	280
70	22	5	68	340	8	3	1020
60	31	10	64	640	9	4	2560
50	40	16	60	960	10	5	4800 max
41	50	23	52	1196	9	4	4784
32	60	31	40	1240	8	3	3720
23	70	40	24	960	7	2	1920
14	80	50	12	600	6	1	600

(Papakonstantinidis Proposal)

Notes, as to explain the symbols:

• “C” expresses the Community (an acceptable system value at local level),  as the “third” or invisible part in the bargain.  In real terms, it reflects  the “confidence indicators”, or, in other words, if  and at which level each member of  the Community trusts the other, during the bargain ( Hans 1997)
• The less shares for A+ B the more share for “ C” part
• Utility is a personal matter: Utility units are not compared to each other. They express the fear of breaking down the agreement
• If “A” needs more the “agreement” than the payoff, then he should be ready to accept any form of agreement.

Utility function: Law of diminishing marginal returns (or costs)

 

We start from an economic-math principle: the   law of diminishing marginal returns goes by a number of different names, including law of diminishing returns, principle of diminishing marginal productivity and law of variable proportions. This law affirms that the addition of a larger amount of one factor of production, while all others remain constant, identified by the Latin term “ceteris paribus,” inevitably yields decreased per-unit incremental returns.

Two “concepts” for the utility:

1. The cardinal utilityconcept: is concerns the idea of a  measured quantitatively, like length, height, weight, temperature, etc
2. The ordinal utilityconcept: expresses the utility of a commodity in terms of ‘less than’ or ‘more than’ in individual scale of preferences

As each tries to maximize his/her own utility function (the “personal ordinal”, not been measured as the cardinal) knows that more and more quantities over a point that he/she maximizes his/her satisfaction in personal terms, the less satisfaction from these more and more quantities. The derivative of a function of a real variable measures the sensitivity to change of a quantity (a function value or dependent variable) which is determined by another quantity (the independent variable). Derivatives are a fundamental tool of calculus.

From this “RULE” a crucial condition happens:

Figure 2.

The “win-win-win Equilibrium”

From the two graphs above, and the “Pareto Efficiency” conditions is resulted that the “utility functions” follows  the law of diminishing marginal returns,

The   law of diminishing marginal returns, includes the marginal productivity and law of variable proportions (Turgot (1727-1781)

Figure 3.

'Pareto Efficiency'

Pareto efficiency, also known as "Pareto optimality," is an economic state where resources are allocated in the most efficient manner, and it is obtained when a distribution strategy exists where one party's situation cannot be improved without making another party's situation worse. Pareto efficiency does not imply equality or fairness.

Figure 4.

5. Theoretical and Practical contributions

The model is primarily used as an analytical and methodological tool in fields such as:

• Local government decision-making and conflict resolution
• Sustainable tourism development
• Labor market negotiations involving the state, businesses, and citizens
• Corporate social responsibility (CSR) analysis
• Welfare economics and public policy

In essence, the model proposes that by ensuring all decisions benefit not just the immediate parties (A and B), but also the broader community (C), more ethical, stable, and sustainable outcomes can be achieved.

The Win-Win-Win Papakonstantinidis model and the Environmental, Social, and Governance (ESG) framework are highly complementary approaches to sustainable business and development, both of which emphasize the inclusion of the broader community in decision-making.

Key Differences in Approach

While their goals align, their methodological focus differs:

Model Element	Description
Party A & B	Immediate stakeholders (e.g., Business, local authorities, consumers, labor unions) who negotiate to maximize their mutual utility.
Papakonstantinidis Model:	Primarily an analytical and behavioral tool used for conflict resolution, bargaining analysis, and local development planning. It introduces a "sensitization process" to encourage empathy and social trust among negotiating parties, leading them to consider the community's welfare. It is deeply rooted in game theory and behavioral economics.
ESG Equivalent	ESG Framework: Primarily a reporting and investment framework used by investors and corporations to measure, manage, and report on sustainability and ethical impacts. ESG performance is increasingly used to attract capital, manage risk, and enhance brand reputation.
Governance (G):	Refers to the internal processes, rules, and practices by which a company is directed and controlled, ensuring ethical operations and fair dealing with primary stakeholders.
Community (C)	The "third attractor" or broader society, whose welfare must be considered to achieve a stable, socially just, and sustainable outcome.          Social (S): Focuses on the company's relationships with and reputation among stakeholders, including employees, customers, suppliers, and the communities where it operates.
Environmental (E): In the win-win-win model, the community's interest implicitly includes environmental protection, which is essential for long-term community welfare and sustainable development. The model aims for outcomes that are beneficial for the environment, society, and the economy
Overall Goal     Maximizing value creation for all three parties by moving beyond narrow self-interest to a communitarian perspective.

 

Synergies

The win-win-win model provides a theoretical and philosophical foundation for the practical application of ESG principles, particularly in local contexts. It suggests that truly effective and sustainable business strategies must embed community welfare as a core negotiating outcome, not just a regulatory compliance box to check. ESG, in turn, offers concrete metrics and investor incentives that can help operationalize the "win" for the environment and society that the Papakonstantinidis model advocates for.

Ultimately, both concepts support the idea that economic success and social/environmental responsibility are intertwined, not conflicting, and that including all stakeholders leads to more resilient, ethical, and value-creating outcomes for everyone involved

 

Environmental & Social Guidance (often part of ESG) refers to principles, policies, and practical actions that help organizations operate responsibly toward the environment and society.

It focuses on minimizing negative impact on the planet.

Key areas

• Climate action: Reducing greenhouse gas emissions, energy efficiency, renewable energy
• Resource management: Water conservation, sustainable sourcing, circular economy
• Waste & pollution: Recycling, hazardous waste control, air & water pollution prevention
• Biodiversity: Protecting ecosystems and reducing land-use harm
• Compliance: Meeting environmental laws and international standards (e.g., ISO 14001)

Typical actions

• Carbon footprint measurement
• Environmental impact assessments
• Sustainable product design
• Environmental reporting and targets

Social Guidance
Focuses on people—employees, communities, customers, and society at large.

Key areas

• Labor practices: Fair wages, safe working conditions, no child/forced labor
• Human rights: Respect across supply chains
• Diversity & inclusion: Equal opportunity and non-discrimination
• Health & safety: Workplace and product safety
• Community engagement: Local development, education, social investment
• Customer responsibility: Data privacy, product transparency

Typical actions

• Codes of conduct
• Employee well-being programs
• Supplier social audits
• Community outreach initiatives

Where It’s Used

• Businesses & corporations (ESG strategies)
• Investments (sustainable/impact investing)
• Public sector & NGOs
• Schools & research
• Reporting frameworks (GRI, SDGs, SASB)

 

6. Conclusion and Recommendations

Conclusions

The Win-Win-Win Papakonstantinidis Model represents a significant evolution in game theory and behavioral economics, specifically extending the classical Nash Equilibrium to include a third, often "invisible" player: the Community.  The model shifts the focus from purely individualistic, non-cooperative strategies toward a The Win-Win-Win Papakonstantinidis Model concludes that traditional game theory, while effective for individual utility maximization, is insufficient for solving complex social and organizational crises because it lacks a mechanism for social cohesion. By shifting the focus from a two-pole "conflict" to a three-pole "collaboration," the model provides a framework for sustainable development and crisis management.

• The Necessity of the "C" Factor: The model concludes that no bargain is truly stable unless the Community (C) is satisfied. In any negotiation between two parties (A and B), the community acts as a "third win" that ensures the long-term viability of the agreement.
• Failure of Imposed Reforms: In educational and governmental contexts, the model concludes that "top-down" reforms fail because they do not account for the behavioral state of the stakeholders. True transformation must be systemic and bottom-up, involving teachers, parents, and students in a "decision-making triangle."
• Sensitization as a Catalyst: The research suggests that Sensitization is the primary tool for converting individualistic "winning strategies" into "social trust behavior." This process bridges the gap between technical territory and the "behavioral community."
• Knowledge as Social Capital: The model concludes that the transfer of knowledge (from tacit to codified) is not merely an administrative task but a social one. This conversion creates the "integrated information" necessary to reduce uncertainty and smooth potential conflicts in a globalized world.
• Equilibrium of the Three Poles: Mathematically and behaviorally, the model reaches a new equilibrium point where the product of three utilities is maximized:

_maxU_a * U_b * U_c

This equilibrium represents the "absolute cooperation" limit, where the interests of the individual and the community become indistinguishable.

 Recommendations

Based on the Win-Win-Win Papakonstantinidis Model and its applications in local development, education, and social bargaining, the following recommendations are provided for practitioners, policymakers, and researchers seeking to implement this framework for conflict resolution and social welfare.

I. Implementation of the "Sensitization" Process

The most critical recommendation for any bargaining environment is the initiation of a Sensitization Process. This involves moving beyond the "technical" aspects of a problem to the "behavioral" aspects.

• For Local Government: Instead of presenting finalized infrastructure plans to a community, authorities should engage in a pre-bargaining phase that educates the public on the long-term systemic benefits, thereby transforming the community from a passive "territory" into an active "behavioral participant."
• For Corporate Leaders: Shift from traditional CSR (Corporate Social Responsibility) as a marketing tool to CSR as a "third win" in every transaction. This requires transparent communication that shows how a purchase benefits not just the buyer and seller, but the broader social or environmental ecosystem.

II. Adoption of the "Social Player" in Public Choice

To correct the failures of traditional Public Choice theory, which often leads to regional disparities, the model recommends the formal insertion of a Social Player or Overall Arbitrator into the decision-making game.

• Institutional Reform: Legislative bodies should create "Mediation Committees" that represent the "C" (Community) factor in negotiations between the State and Local Authorities.
• Leadership Exchange: Leaders should adopt the "Leader-Member Exchange" (LMX) methodology, ensuring that the "strong" position of the leader is balanced by the collective bargaining power of the community members to generate social capital.

III. Knowledge Management and Transfer

The model emphasizes that behavior is a result of knowledge synthesis. Organizations should prioritize the conversion of Tacit Knowledge into Systemic Knowledge.

• Socialization: Encourage informal "tacit-to-tacit" exchanges among stakeholders to build empathy and "sympathized knowledge."
• Codification: Ensure that community values and ethical standards are codified into the bargaining rules to create "conceptual knowledge" that all parties can reference during conflicts.

IV. Application in Educational Crisis Management

For school administrators facing strikes or quality declines, the model recommends a Tri-Polar Bargaining Solution.

• The Decision-Making Triangle: Establish a permanent forum where Teachers (A), Students (B), and Parents/Community (C) have equal weight in school management decisions.
• Eliminating the "Fear Factor": Use the Win-Win-Win approach to reduce the "disagreement point" (the cost of a breakdown in talks). By focusing on the shared goal of "student attainment," the parties can move away from zero-sum demands toward a collective equilibrium

V. Integration of "Guanxi" Relations

In globalized or cross-cultural negotiations, practitioners should adopt the Guanxi Relations Paradigm.

• Relationship Building: Prioritize the development of deep social networks and reciprocal obligations before formal bargaining begins.
• Trust as Capital: Treat social trust not as an abstract concept but as a measurable form of "Social Capital" that reduces transaction costs and increases the efficiency of the "Win-Win-Win" outcome.

VI. Mathematical and Analytical Rigor

Researchers and analysts should move away from simple bilateral utility functions.

Utility Modeling: When modeling outcomes, use the three-dimensional formula

_maxU_a * U_b * U_c

to ensure that the "Third Win" is mathematically accounted for in the equilibrium.

Bayesian Analysis: Use conditional probabilities (Harsanyi’s approach) to update the "behavioral state" of the community as new information is introduced during the sensitization process.

References

1. Papakonstantinidis, L. A., & Dimitropoulos, A. (2012, June 19). The Win-Win-Win Papakonstantinidis Model: A behavioral analysis in dynamical systems—The non instrumental rationality paradox (Case-study: Hellenic benefactors). In Proceedings of the 1st International Symposium on Business, Economics and Financial Applications (ISBEFA 2012) (pp. 305–329).
2. Papakonstantinidis, L. A. (2012). The "Win-Win-Win Papakonstantinidis Model" as a bargaining solution analysis for local government decision from territory-community to "behavioral" community: The case of Greece. Chinese Business Review, 11(6), 535–548. https://doi.org/10.17265/1537-1506/2012.06.004
3. Papakonstantinidis, L., & Aziz, S. (2020). Social bargaining: The win-win-win Papakonstantinidis model: Theory and applications. LAP Lambert Academic Publishing.
4. Papakonstantinidis, L. A. (2020). The Win-Win-Win Papakonstantinidis Model: An approach between empathy and conflict strategy—An inquiry into T. Schelling's The Strategy of Conflict (1960). International Journal of Innovation and Economic Development, 6(5), 28–70. https://doi.org/10.18775/ijied.1849-7551-7020.2015.65.2003
5. Nash, J. (1953). Two-person cooperative games. Econometrica, 21(1), 128–140. https://doi.org/10.2307/1906951
6. Papakonstantinidis, L. A. (2002, August 14). Win-win-win model (1st presentation). SW/Euro-academy (Euracademy), Visby University, Gotland, Sweden.
7. Papakonstantinidis, L. A. (2018). The Win-Win-Win Papakonstantinidis Model: Sensitization, towards the absolute cooperation—The marginal "Angels moment". Journal of International Business Research and Marketing, 4(1), 30–40. https://doi.org/10.18775/jibrm.1849-8558.2015.41.3004
8. Papakonstantinidis, L. A. (2018). Marketing gaps and intersections, between education and social practice: The "Win-Win-Win Papakonstantinidis Model" and the high-risk ethical priorities (HREP). International Journal of Innovation and Economic Development, 4(2), 7–23. https://doi.org/10.18775/ijied.1849-7551-7020.2015.41.2001
9. Papakonstantinidis, L. (2018). CSR: An application of the "win-win-win Papakonstantinidis model". LAP Lambert Academic Publishing.
10. Kronberger, T., & Papakonstantinidis, L. (2019). Applying the Papakonstantinidis 3-ple-win-model on the social welfare system of the labor markets in Greece and Germany. Universal Journal of Management, 7(2), 39–49. https://doi.org/10.13189/ujm.2019.070201
11. Papakonstantinidis, L. A. (2003). Rural tourism: Win-win-win case study women cooperative Gargaliani. Journal of Hospitality and Tourism, 1(2), 49–70.
12. Papakonstantinidis, L. A. (2004a). Sensitization and involving the community: A rural development application of the Win-Win-Win Model. Scientific Review of Economic Sciences, 6, 177–192.
13. Papakonstantinidis, L. A. (2004b). Operations management by a hypercube & win-win-win perspective: A local development approach. Journal of Applied Economics and Management, 2(2), 111–130.

 

International Journal of Management Science and Business Administration

Volume 12, Issue 2, January 2026, Pages 7-16

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Understanding Substance Use Attitudes in Albanian Emerging Adults: A Study of Demographics and Clinical Relevance

URL: http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.XX.100X
DOI: 10.18775/ijmsba.1849-5664-5419.2014.XX.100X

¹ Ines Nurja, ² Besmira Lahi, ^3* Ester Vrapi

¹ Economics and Finance Department, University of New York Tirana, Albania

² Department of Psychology and Education, University of New York Tirana, Albania

Abstract: Emerging adulthood is an important stage of development characterized by an increase of the tendency to experiment, including drugs. Cognitive attitudes on substance use serve as an early risk assessment to inform and draft potential awareness and prevention programs and mental health interventions. This research work explored general attitudes among Albanian emerging adults 18-26 years old, while also exploring social and demographic differences related to age, gender, employment status and education level.

A total of 345 participants completed the Attitudes to Drug Use Questionnaire, consisting of 12 item Likert scale. Results showed that gender and age difference are significant demographic differences. In addition, employment status reports more favorable attitudes towards drugs in comparison to those who are unemployed. Notably, findings show that the education level does not have a significant relationship with attitudes towards drugs.

From a clinical perspective, cognitive attitudes may be considered as markers for assessing the vulnerability and risk of the young adults’ behavioral engagement in drugs. Therefore, this study highlights the need of integrating psychological assessment of attitudes into university counselling sessions, informing and training academic staff on these risks and identifying red flags for further clinical intervention and building more psycho-education into the support systems, including family settings, social communities, and university or corporates considering employability of the respondents. Addressing and managing risks should further be supported from trained professionals in mental health, psychologists and therapists, human resource offices and corporate employees. Risk management and prevention of normalization may serve as the early intervention mechanism to reduce experimentation or substance use behaviors.

Keywords: attitudes, substance use, psychological assessment, therapy, demographics, clinical relevance, emerging adults, Albania.

1. Introduction

In the Albanian context there is a limited psychological research and data regarding this topic, with the most recent report being from 2017 by the European Monitoring Centre for Drugs and Drug Addiction, funded by the European Commission, in partnership with the Institute of Public Health in Albania (European Monitoring Centre for Drugs and Drug Addiction & Institute of Public Health (Albania), 2017). This fact makes it even more interesting for the researchers to explore and further investigate on the different demographics and their relationship to substance use. This is the reason why researchers aim to examine general attitudes, age group differences, gender and employment differences and level of education differences.

Research questions are as the following:

1. What are the attitudes of the Albanian youth toward substance use in relation to age?
2. How do attitudes differ by gender?
3. How does employability affect attitudes toward substance use?
4. How do educational levels influence attitudes towards drugs?

Hypothesis

1. Attitudes are expected to exhibit significant favorable differences in males.
2. Employment status is likely to have a positive tendency towards substance use.
3. Levels of education will serve as a significant factor influencing attitudes.

2. Literature review

To understand the context of the study, it is essential to explore to existing theories related to the concepts that will be utilized. Primarily, we will examine attitudes and beliefs, each supported by theories that will be integrated into the research. According to the Theory of Planned Behavior, attitudes towards certain behaviors are predictors of intentions, which in turn, combined with subjective norms predict the variance of actual behavior (Ajzen, 1991). Following this line of reasoning, certain attitudes towards substance use, such as permissiveness, influence one’s intentions, which could potentially increase the likelihood of the behavior, i.e., substance usage. Thus, when considering attitudes as a determinant of intent, and a potential predictor of behavior, it becomes imperative to investigate these attitudes, in order to build preventative measures towards substance use behaviors in the clinical context.

Furthermore, when considering certain behaviors and their influencing attitudes, one must consider the external factors, such as the aforementioned subjective norms, which, among other factors, are a product of socialization. In this context, the Social Learning Theory sates that learning occurs through observation and modelling, which is influenced by attention, motivation and attitudes (Bandura, 1977). When it comes to substance use, perceived normalization through socialization influences attitudes towards the behavior, and in turn, the behavior itself. In this context, when taking into account different demographics and their relationship with socialization, different groups could be at a more potential risk if said group exhibits permissive attitudes through modeling. Considering that young adults are generally more inclined to experimenting with substances, through social learning, their peers could be influenced towards the same experimentation (Stevens, 2021). Likewise, males are generally more likely to use illicit drugs than females, thus being more susceptible to adapting the behavior through socialization (Rahimian Boogar, 2014). These demographic differences could serve as a pillar in building clinical intervention aimed at more vulnerable groups, in regards to prevention, risk mitigation, as well as treatment.

Additionally, when discussing attitudes and their influence on behaviors, it is necessary to take into consideration the Cognitive Behavioral Theory, which is built on the direct relationship between cognitions and behaviors, where the former influences the latter (Beck, 1979). Through this theory permissive attitudes towards substance use, such as risk-minimization or other cognitions about the received pleasure or control, can be considered as cognitive distortions, which later on influence behavior. In this framework, it is important to consider the previously held cognitive schemas and distortions that precede the maladaptive behavior. Through the consideration of these attitudes as cognitive vulnerability markers for certain behaviors, i.e., positive and permissive attitudes for drug experimentation behaviors, intervention can be designed in intervening with the maladaptive schemas and their influence on behavior.

2.1. Developmental framework

In studying substance use through different demographics, it is apparent that certain groups are more vulnerable than others. Young adults have the highest rates of alcohol or substance consumption, making them one of the most a-risk groups for developing related disorders (Bukstein, 2017). Research reports in 2020 says that approximately two in five college students had accomplished the criteria for DSM-5 Substance Use Disorder at least once in the past (Arterberry, 2020). These findings support the Emerging Adulthood Theory, which proposes a new conception of the transitional years between adolescence and adulthood, namely the ages 18-25 (Arnett, 2000). This proposed stage of development has evolved from the notion that this age group is more likely than others to generally experiment (substance use included), stemming from the identity exploration that comes with the age, as well as the new-found freedom of reduced parental control, especially for those moving for college (Skidmore, 2016) (Viohl, 2019).

Furthermore, when investigating this age group and their heightened tendency towards experimentation and risk-taking, a neurological model has also been proposed. According to Steinberg and his dual systems model, there is a neurological change that happens within the brain during puberty that accounts for such changes in behavior. According to this model, the systems of reward-seeking and impulsivity develop alongside each other, but in different timetables, where risk-taking develops in a curvilinear pattern, with its peak in adolescence and declining after, while impulsivity steadily declines starting from the age of 10 (Steinberg, 2010). Although general impulsivity is on the decline since childhood, the changes that occur in the brain’s dopaminergic system during this age fuel risk-taking behaviors as a form of reward-seeking, especially when socially motivated by the presence of peers (Steinberg, 2008).

2.2. Gender differences

Gender, much like age, is a significant factor in substance use patterns. Notably, there exists a pronounced disparity in substance consumption between genders, with men being more likely to engage in the use of nearly all types of illicit drugs. Consequently, they also face a higher likelihood of emergency department visits and overdose fatalities (Center for Behavioral Health Statistics and Quality, 2018). Both genders, the peak rates of any given substance were noticed in their 20s, with males having a higher prevalence (Vasilenko, 2017). Research conducted with college students reports that being male has a crucial positive role in predicting substance abuse (Rahimian Boogar, 2014). Notably, in many cultural contexts, masculinity is related with risk-taking behaviors, including substance use, making it more permissive and socially accepted. Referring to traditional gender roles and their part in substance consumption men who associated with conventional gender roles attitudes correlated with higher alcohol consumption, while women who associated with unconventional gender roles did not report these trends (Lye, 1998).

2.3. Employment and autonomy

Another factor related to substance use this research investigated is that of employment, when considering that most students hold some form of part-time or full-time job later in their graduate studies. As such, it is a demographic to be considered on whether it is an influencing factor on substance use. Research shows that among emerging adults, earned income was associated to substance use, as it provides easier economic access control. This trend becomes even more prevalent when combined with other social factors such as perceived peer norms (Kar, 2018). In this particular life stage, emerging adults find themselves in a stage characterized by financial independence and less parental supervision, as well as peer influence, a mix that contributes towards risky behaviors such as substance use. Furthermore, workplace culture has also been found to be an influencing factor to substance use, especially alcohol usage (Ames, 1999).

2.4. Cultural context

Culture has a significant impact on most areas of life, and substance use and its related attitudes and behaviors are no different. Presently, the only research done in Albania regarding the matter is the aforementioned 2017 report by the European Monitoring Centre for Drugs and Drug Addiction (European Monitoring Centre for Drugs and Drug Addiction & Institute of Public Health (Albania), 2017). In this context, it is important to look at the general geographical area that is connected by similarity between cultures, in order to make comparisons and generalizations to the Albanian population. A study investigating substance use in Europe by region found a general decrease in alcohol and tobacco use from 1999 to 2025, while trends of cannabis use are on the rise across south Europe and the Balkans (Kraus, 2018). Another study done in Bosnia and Herzegovina found that among adolescents 37% smoke cigarettes and 28.4% drink alcohol, while 5.7% consume drugs (Bjelica, 2016). These findings might provide a frame in which to contextualize trends from this research and future ones conducted in Albania.

2.5. Clinical prevention

Gender, culture, age and employment status are important factors related to attitudes toward substance use. They can be considered as both risk factors and opportunities for treatment planning in clinical interventions. Therefore, understanding these factors in principle, allows for potential interventions in clinical settings, where involving relationships and implications for treatment. The clinical interventions should be tailor based on specific demographic factors such as culture, history of personal and interpersonal relationships and vulnerability. The suggested approach refers to Cognitive Behavioral Therapy which has been named as one of the most effective evidence-based treatment methods in managing substance use and other components related to it including: craving, withdrawal and relapse.

3. Research Methodology - Materials and Methods

“This study employed a quantitative cross-sectional survey design with statistical analysis. Research was conducted in Albania, and respondents were provided with an online questionnaire distributed through social media channels, WhatsApp, Instagram, LinkedIn, etc. Research fully meets the ethical requirements. The Ethical research consent was first received from the Ethical Committee in the University of New York Tirana. Soon after this, data collection began by ensuring to respondents the right of information, their confidentiality and the need to withdraw anytime from completing the questionnaire. The questionnaire contains two sections: demographic data on age, gender, employment status and education level, and the measurement of Attitudes to Drug Use Items.

The questionnaire was first developed as a measuring tool aimed at measuring the effectiveness of the DARE (Drug Abuse Resistance Education) program implemented with students at risk for drug use (Harmon, 1993). The questionnaire measured how the attitudes towards drug use changed from the group that participated in the program to the group that did not. After its development it has been used in a number of other studies; respectively in USA (Daniewicz, 2014) and Ireland (Howell, 2021). This questionnaire was chosen on the premise that it measures the general attitudes towards substances of a target population without focusing on one particular substance or group of substances. Other studies in which the questionnaire has been used also served as incentive for this choice. The research conducted in Ireland involved participants with an age group of 18 to 30, which produced significant results. Furthermore, this research was aimed at looking into gender differences in attitudes within the group, which also produced significant results. Similarly, the research conducted in USA involved the age group 18 to 26, assessing their general attitudes towards substance use. Referring to the questionnaire as well as its uses in other previous research which fits with the objectives of the current research, it was considered suitable for use.

General attitudes toward drug use are measured using a 12-item questionnaire, that follows the 5-point Likert Scale. In this questionnaire, seven items are scored from 1-5, while five items are scored in reverse order. Likert scale ranges from one to 5, where a score of 5 refers to a completely favourable attitude toward drugs, whereas a score of 1 reflects a completely unfavourable attitude. Participants who do not answer all 12 questions, will be excluded from the analysis.

In total 345 responses were received. The age group 18-20 years old is represented in 31%. The age group 21-23 has the highest level of representation in 39.7%. The age group of 24-26 is represented in 29.3% of the sample.

Gender responses indicate that 29.9% are male; females represent the majority of the sample in 69.3%, and 0.9% prefer to disclose gender. Referring to the education level, 16.5% have only finished high school, 64.3% have a bachelor’s degree, and 19.1% have a master’s degree.

41.2% of respondents are working full-time, 18.6% work part-time, and 40.3% are unemployed.

Table 1: Distribution of age
		Frequency	Percent
Valid	18-20	107	31.0
	21-23	137	39.7
	24-26	101	29.3
	Total	345	100.0

 

The table presents the age distribution of respondents’ attitudes toward drugs. Data suggests that attitudes towards drugs change with age, 18–23-year-old respondents represent the largest group. This might say that attitudes change across age categories as they report different levels of maturation.

Table 2: Distribution of gender
			Frequency		Percent
Valid		Male	103		29.9
		Female	239		69.3
		Prefer not to say	3		.9
		Total	345		100.0

 

The sample is predominantly female up to 69.3%, while males represent 29.9% and less than 1% choose not to disclose gender.

Table 3. Distribution of levels of education
		Frequency	Percent
Valid	High School	57	16.5
	Bachelor	222	64.3
	Master	66	19.1
	Total	345	100.0

 

Most respondents hold a Bachelor’s degree (64.3%).

Employment is almost evenly split between full-time workers (41.2%) and unemployed individuals (40.3%). Such balance strengthens the reliability of comparisons across employment groups.

Table 4. Distribution of employment status
		Frequency	Percent
Valid	Working Full Time	142	41.2
	Working Part Time	64	18.6
	Unemployed	139	40.3
	Total	345	100.0

 

The present study employed the 12-item Questionnaire on Attitudes toward Substance Use as the primary measurement tool. A comprehensive list of the questions included in the instrument can be found in Appendix A. To evaluate the internal consistency of the scale, Cronbach Alpha coefficient was calculated, yielding a value of α = 0.826. This result indicates a commendable level of reliability for the instrument utilized in the research.

4. Results

4.1. The relationship between attitudes and age

The relationship between age and attitudes towards drugs represents an important topic in this research. By examining the mean ranks of participants attitudes, referring to our example, we aim to study the tendencies among these age groups, which may seem close to each other, but the exposure to life is different.

Table 5. Ranks of attitudes and age
	1. Age	N	Mean Rank
Attitudes_drug_use_cat	18-20	107	150.27
	21-23	137	169.49
	24-26	101	201.84
	Total	345

 
“Higher mean rank = more favorable attitude toward substance use”

Table 5.1.Test Statisticsa,b
	Attitudes_drug_use_cat
Kruskal-Wallis H	23.161
df	2
Asymp. Sig.	.000
a. Kruskal Wallis Test
b. Grouping Variable: 1. Age

 
Data reports the relationship between age and attitudes toward drugs. As the age increases, the mean ranks for attitudes also rise. The oldest group (24-26 years old) has the highest mean rank, reporting a positive attitude toward drug use. The very small p-value (<.001) indicates highly significant differences between age groups. Age is a strong predictor of attitudes in this dataset. In a clinical perspective, it suggests that attitudes do not positively change due to maturation and more learnt life experiences: on the contrary, respondents report that when they mature, they have a positive attitude toward drugs.

4.2. The relationship between attitudes and gender

The relationship between attitudes and gender aims to help researchers and the readers valuable insights into their norms and perceptions and potentially uncover any trends of differences and turn them into psycho-educational programs.

Table 6. Ranks of attitudes and gender
	2. Gender	N	Mean Rank
Attitudes_drug_use_cat	Male	103	190.48
	Female	239	163.32
	Total	342

 
“Higher mean rank = more favorable attitude toward substance use”

Table 6.1.Test Statisticsa,b
	Attitudes_drug_use_cat
Kruskal-Wallis H	8.886
df	1
Asymp. Sig.	.003
a. Kruskal Wallis Test
b. Grouping Variable: 2. Gender

 
Males show higher mean ranks (190.48),

Kruskal Wallis test was used to compare attitudes toward drug use between genders. Results showed that males (190.48), have a strong favorability towards drugs in comparison to women (mean rank-163.32). The test was significant, p = .003 < .05, gender differences are statistically significant.

4.3 The relationship between attitudes and employment

This section presents results of the analysis examining the association between the attitudes towards drugs and employment status. Findings highlight how employment status impacts the participants perceptions and attitudes towards drugs. By analyzing the mean ranks we may understand the employment effect on behavioral reactions and engagement.
 

Table 7. Ranks attitudes toward drugs and employment
	4. Employment	N	Mean Rank
Attitudes_drug_use_cat	Working Full Time	142	188.58
	Working Part Time	64	175.73
	Unemployed	139	155.83
	Total	345

 
“Higher mean rank = more favorable attitude toward substance use”

Table 7.1. Test Statisticsa,b
	Attitudes_drug_use_cat
Kruskal-Wallis H	12.473
df	2
Asymp. Sig.	.002
a. Kruskal Wallis Test
b. Grouping Variable: 4. Employment

 
Mean ranks indicate that full-time employees have the highest rank (188.58), suggesting more favorable attitudes compared to unemployed respondents (155.83). Higher rank = more favorable orientation.

Since p = .002 < .05, we reject the null hypothesis. There are statistically significant differences in attitudes across employment groups. We used Kriskal-Wallis test to compare median ranks among groups to see if there are statistically significant differences. Results in this study show the full-time employees report more favorable attitudes towards drug use. This suggests from a demographic perspective that employment status affects drugs usage. Therefore, in a clinical perspective this would mark the need for interventions in relation to substance use and consider employability issues in respect to treatment and support systems.

4.4 The relationship between attitudes and levels of education

Table 8. Ranks attitudes and levels of education
	3. Highest Education	N	Mean Rank
Attitudes_drug_use_cat	High School	57	163.75
	Bacheleor	222	177.85
	Master	66	164.68
	Total	345

 
“Higher mean rank = more favorable attitude toward substance use”

Table 8.1. Test Statisticsa,b
	Attitudes_drug_use_cat
Kruskal-Wallis H	2.407
df	2
Asymp. Sig.	.300
a. Kruskal Wallis Test
b. Grouping Variable: 3. Highest Education

 
The analysis assesses toward drug use across different education groups, comparing high school, bachelor’s and master’s degree holders. The mean ranks for attitudes are quite close reporting minimal differences across education groups (High School: 163.75, Bachelor:177.85, Master: 164.68). The test reports no significance, leading to a failure to reject the null hypothesis. In a clinician point of view, since education does not shape attitudes toward drugs, we may need to consider other interventions and support systems.

Conclusion

In conclusion, exploring the attitudes of emerging young adults in Albania towards drugs provides insights into the influence of the demographic factors affecting their perceptions on drugs use. Research findings reported significant relationship between gender and attitudes: males reported more favorable perceptions, and considering that cognitions influence behaviors, this suggests that they present more risky behaviors for experimenting on drugs. There is a significant relationship between the employment status and attitudes particularly as full-time employees tend to have more favorable attitudes regarding substance use. This highlights the need for tailored educational and intervention programs, considering even the fact that there is not any significance between levels of education and attitudes toward drug use. Psycho-educational programs need to be tailored in order to address potential risks. Training of the academic staff regarding youth attitudes and areas of risk need to be seriously considered in our country. Mental health providers such as psychologists and therapists need to be trained on areas of psycho-education and clinical intervention on substance use. By addressing these attitudes early and collectively, the support system should reach family members, corporate employees, administrative and support staff of schools and universities aiming to support healthier decisions among Albanian youths.

Appendix A

Attitudes to drug use (Harmon, 1993).

1. Using illegal drugs can be a pleasant activity (R)
   1. Strongly agree
   2. Agree
   3. Neutral
   4. Disagree
   5. Strongly disagree
2. A young person should never try drugs
   1. Strongly agree
   2. Agree
   3. Neutral
   4. Disagree
   5. Strongly disagree
3. There are few things more dangerous than experimenting with drugs
   1. Strongly agree
   2. Agree
   3. Neutral
   4. Disagree
   5. Strongly disagree
4. Using drugs is fun (R)
   1. Strongly agree
   2. Agree
   3. Neutral
   4. Disagree
   5. Strongly disagree
5. Many things are much riskier than trying drugs (R)
   1. Strongly agree
   2. Agree
   3. Neutral
   4. Disagree
   5. Strongly disagree
6. Everyone who tries drugs eventually regret it
   1. Strongly agree
   2. Agree
   3. Neutral
   4. Disagree
   5. Strongly disagree
7. The laws about illegal drugs should be made stronger
   1. Strongly agree
   2. Agree
   3. Neutral
   4. Disagree
   5. Strongly disagre
8. Drug use is one of the biggest evils in the country
   1. Strongly agree
   2. Agree
   3. Neutral
   4. Disagree
   5. Strongly disagree
9. Drugs help people to experience life in full (R)
   1. Strongly agree
   2. Agree
   3. Neutral
   4. Disagree
   5. Strongly disagree
10. Schools should teach about the real hazards of taking drugs
    1. Strongly agree
    2. Agree
    3. Neutral
    4. Disagree
    5. Strongly disagree
11. The police should not be annoying young people who are trying drugs (R)
    1. Strongly agree
    2. Agree
    3. Neutral
    4. Disagree
    5. Strongly disagree
12. To experiment with drugs is to give away control of your life
    1. Strongly agree
    2. Agree
    3. Neutral
    4. Disagree
    5. Strongly disagree

Computing of scores: Items 2, 3, 6, 7, 8, 10, 12 should be scored ‘1’ for ‘strongly agree’ to ‘5’ for ‘strongly disagree’. Remaining items 1, 4, 5, 9, 11 should be scored in the opposite way (‘5’ for ‘strongly agree’ to ‘1’ for ‘strongly disagree’). To obtain the attitude score for each individual, items should be added and then divided by the number of questions in the questionnaire (12). A score of 5 will indicate a totally favorable attitude towards drug use while a score of 1 will indicate a totally unfavorable attitude towards drug use. Any participant who does not answer all 12 questions should be excluded from analysis as total scores are accumulated by dividing the score by 12.

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International Journal of Innovation and Economic Development

Volume 11, Issue 5, December 2025, Pages 21-41

---

Synthetic Artefact Governance Theory: Governing Synthetic Reality
in the Age of AI-Generated Artefacts and Artificial Humans

 

URL: https://doi.org/10.18775/ijied.1849-7551-7020.2015.115.2003

DOI: 10.18775/ijied.1849-7551-7020.2015.115.2003

Suresh Sood1^1,2

¹Industry/Professional Fellow, Australian Artificial Intelligence Institute, University of Technology Sydney

²Adjunct Fellow, Frontier AI Research Centre, Macquarie University, Sydney

Abstract: Artificial intelligence (AI) governance is shifting from voluntary ethical principles toward binding, risk-based regulatory regimes across jurisdictions. While these developments strengthen accountability for automated decision-making systems, they do not fully address the governance challenges posed by generative AI systems that produce synthetic artefacts indistinguishable from human outputs and simulate human social presence. Generative AI destabilizes traditional distinctions between data, content, and action, raising regulatory questions about authenticity, institutional trust, and relational integrity rather than automation alone. This article examines whether contemporary AI policy is equipped to govern these developments. Drawing on an interdisciplinary synthesis of legal scholarship, governance theory, technical research on provenance and identity systems, and comparative policy analysis, the paper develops Synthetic Artefact Governance Theory (SAGT) as a meta-governance framework. The analysis evaluates major AI policy regimes across jurisdictions, with focused assessment of the European Union Artificial Intelligence Act, and maps existing instruments against artefact-level, interaction-level, accountability, and ecosystem dimensions. The findings indicate current regimes remain predominantly model centric. Synthetic artefacts and artificial humans are typically addressed indirectly through transparency obligations, content moderation rules, or sectoral law rather than as first-order regulatory objects. Governance responses show partial convergence across legal, technical, and institutional domains, yet significant gaps persist at the levels of relational manipulation, artefact legitimacy, and lifecycle accountability.The article concludes effective AI governance must evolve from regulating intelligent systems toward governing synthetic reality. This transition requires integrating artefact-level regulation, interaction safeguards, provenance infrastructure, and cross-sector coordination across AI law, consumer protection, competition policy, and content governance.

Keywords: Artificial Intelligence Governance; Generative Ai; Synthetic Artefacts; Artificial Humans; Digital Identity; Provenance; Eu Ai Act

1. Introduction

Artificial intelligence (AI) governance stands at a major inflection point. For over a decade, regulatory and scholarly attention focused on algorithmic decision-making as to how AI systems classify, predict, recommend, and automate outcomes across domains of finance, employment, healthcare, policing, and public administration. This emphasis has driven a global proliferation of ethical frameworks and binding regulatory regimes foregrounding fairness, transparency, accountability, and human oversight. Collectively, these developments mark a maturation of AI governance, signalling a shift from voluntary principles toward enforceable legal obligations.

The rapid diffusion of generative AI systems fundamentally alters the nature of AI-related risk. 21st century AI systems no longer operate solely as decision-support or decision-replacement tools. Instead, the systems function as generative infrastructures, producing synthetic artefacts comprising texts, images, videos, voices, documents, software, and interactive personas circulating widely across social, economic, and institutional contexts. These artefacts increasingly function as evidence, identity proxies, and social signals, shaping beliefs, behaviors, and institutional outcomes without triggering traditional notions of algorithmic harm. At the same time, advances in multimodal and agentic AI systems enable the emergence of artificial human presence. Digital clones, synthetic spokespersons, conversational agents, and avatar-based systems connecting to large language models (LLMs) now simulate human likeness, behavior, and relational engagement. Unlike earlier AI applications, these systems do not merely optimize decisions. They interact socially, persuade emotionally, and operate within domains historically governed by norms of authenticity, consent, and trust.

Artificial humans introduce governance challenges related to digital identity rights, consent management, monetization of likeness, and behavioral manipulation dimensions remaining weakly addressed by existing AI policy frameworks (Atta et al., 2025; Towne, 2024). This transformation of systems exposes a structural mismatch between what AI governance currently regulates and where AI-generated harm increasingly arises. Most binding AI policies remain model-centric, conceptualizing AI primarily as a system processing input to generate decisions or recommendations. Regulation therefore concentrates on system classification, provider and deployer obligations, and deployment contexts. Even the most comprehensive regime to date, the European Union Artificial Intelligence Act, focuses predominantly on AI systems rather than the synthetic artefacts, identities, and social interactions those systems generate.

As a result, governance of synthetic media, digital clones, and artificial human presence is fragmented across transparency and disclosure requirements, platform content policies, privacy law, intellectual property regimes, and sector-specific standards. This fragmentation produces three systemic effects. First, fragmentation shifts the burden of verification onto institutions and individuals, increasing the cost of establishing authenticity and truth in environments drowning in synthetic content. Second, relational harms, such as undisclosed social influence by artificial humans is largely unregulated. Third, accountability is obscured when synthetic artefacts are reused, modified, or amplified across platforms and jurisdictions. Existing AI governance architectures therefore struggle to preserve epistemic trust, relational integrity, and institutional accountability in the age of synthetic reality. To address this gap, this paper advances Synthetic Artefact Governance Theory (SAGT).

SAGT reframes AI governance by treating synthetic artefacts and artificial human presence as first-order regulatory objects, rather than incidental by-products of intelligent systems. Drawing on interdisciplinary research, the theory conceptualizes governance as a layered architecture integrating legal, technical, and institutional mechanisms across artefact provenance, interactional transparency, human accountability, and ecosystem-level amplification control.

1.1 SAGT Conceptual Definitions and Boundary Conditions

Because the central constructs of this theory shape the unit of governance analysis, they require explicit definition.

Synthetic artefact refers to an AI-generated or AI-altered digital output plausibly functioning as socially interpretable content within institutional or public contexts. The defining criterion is substitutability. The artefact can reasonably be mistaken for, or operate equivalently to, content authored, recorded, or performed by a human or institutional actor. This includes text, images, audio, video, documents, digital identities, and embodied or conversational outputs.

Artificial human refers to an AI-generated or AI-mediated agent simulating human identity, likeness, voice, or relational behaviour in interactive settings. The term applies where the system performs social roles such as spokesperson, advisor, companion, or representative in ways that may be perceived as authentically human.

Synthetic reality describes environments in which synthetic artefacts and artificial humans circulate at sufficient scale or plausibility to alter baseline assumptions about authenticity, attribution, or evidentiary reliability. The term does not imply all content is artificial, but authenticity can no longer be presumed without verification.

Relational manipulation refers to influence exerted through simulated social presence rather than through false facts alone. The manipulation arises when artificial agents leverage perceived authority, intimacy, or identity to shape behaviour without transparent disclosure of an artificial status.

Epistemic trust denotes background confidence socially circulating information is authentic, attributable, and accountable. Erosion of epistemic trust occurs not only through deception, but through uncertainty regarding provenance and authorship.

In terms of boundary conditions, SAGT does not treat all synthetic content as inherently harmful. The following content fall outside the core governance domain of SAGT:

• Satire, parody, and obvious fiction
• Clearly disclosed entertainment media
• Transparent human–AI collaborative content where human authorship and accountability remain intact

The theory is primarily concerned with synthetic artefacts and artificial agents plausibly substituting for authentic institutional, evidentiary, or relational signals without adequate transparency or accountability safeguards.

1.2 Theoretical Contribution

This paper contributes to AI governance theory by developing Synthetic Artefact Governance Theory (SAGT) as a meta-governance framework reconceptualizing the object, mechanisms, and locus of AI regulation in the generative era. Existing AI governance theories remain largely system-centric, assuming harms arise primarily from biased, opaque, or unsafe algorithmic decisions. SAGT departs from this orientation theorizing synthetic artefacts and artificial humans as primary sources of epistemic, relational, and ecosystemic risk, independent of algorithmic decision quality. By integrating recent advances in harmonized regulatory scaffolds, digital identity rights frameworks, cryptographic provenance systems, and decentralized verification infrastructures, SAGT explains the observed partial convergence of governance responses that cannot be fully justified within existing risk-based regulatory models. Importantly, SAGT does not replace system-centric AI regulation but rather extends and complements existing regulation by offering a generalizable theoretical lens for governing synthetic reality, the socio-technical environments produced by generative AI systems.

1.3 Theoretical Separation from Model-Centric Risk Governance

Existing AI governance frameworks conceptualize risk primarily at the level of system design, deployment context, and decision outputs. In contrast, SAGT theory shifts the unit of analysis from system performance to artefact circulation and relational interaction. The theoretical innovation lies not merely in expanding the scope of regulation, but in redefining the object of governance. Under model-centric governance, harm originates in the flawed system outputs emerging from the model under use and training data. With SAGT, harm emerges from artefact legitimacy, identity simulation, amplification dynamics, and relational substitution even when systems perform as designed.

SAGT therefore differs from risk-based governance theory in three respects:

1. Object shift from systems to artefacts and artificial actors
2. Causal mechanism shift from decision error to trust distortion
3. Institutional locus shift from provider compliance to ecosystem trust infrastructure

This reframing positions SAGT not as a supplement to model-centric governance, but as a meta-theoretical extension necessary in environments with synthetic outputs.

1.4 AI Policy Development Cycle

This paper follows the stages of an AI policy development cycle (Figure 1) commencing with Section 2 review of cross-jurisdictional AI policy instruments and developments highlighting convergences and gaps in current governance approaches. Section 3 reviews the evolution of AI policy from ethical principles to risk-based regulation and examines the limited treatment of synthetic artefacts and artificial humans in existing frameworks. Section 4 introduces Synthetic Artefact Governance Theory (SAGT) and articulates four governance layers comprising artefact, interaction, agency, and ecosystem. Section 5 develops a set of testable hypotheses derived from the theory, while Section 6 outlines a multi-method empirical research agenda for evaluating governance mechanisms in practice. Section 7 analytically assesses the European Union Artificial Intelligence Act against SAGT, supporting a full article-by-article mapping (Appendix B). Section 8 discusses policy design implications and proposes pathways for operationalising SAGT in law. Section 9 Takes an early look at emerging Artefact-Level Legislative Responses. Section 10 concludes by summarising contributions and outlining directions for future research and policy development.

Figure 1: AI Policy Development Cycle
Note. Policy Development Cycle as Envisaged by Author (own work)

2 Cross-Jurisdictional AI Policy Landscape

2.1 AI Policy - Clarifying Governance and Coverage Criteria

In this study, governance refers to one or more of the following mechanisms:

1. Statutory obligations (binding legislative requirements)
2. Enforcement practice (regulatory oversight, penalties, or adjudication)
3. Standards and soft-law instruments (guidelines, codes of conduct, or principles)
4. Platform-level operational controls (labelling systems, moderation rules, or provenance mechanisms).

Coverage assessment is according to whether a regulatory instrument explicitly addresses synthetic artefacts or artificial human interactions as governance objects, rather than addressing AI systems generically.

When this paper states no regime explicitly governs a phenomenon, the reference is specifically with regards to the absence of artefact-level statutory obligations or relational safeguards as first-order regulatory categories. This does not imply no legal tools apply indirectly through related domains such as fraud, consumer protection, or platform policy.

2.2 The Policy Landscape

Across jurisdictions, AI governance is converging toward risk-based regulatory architectures, although legal form, scope, and enforcement intensity vary substantially. The European Union pursues comprehensive binding regulation through the Artificial Intelligence Act, complemented by the Digital Services Act and established data protection law. The United States relies on executive guidance, voluntary commitments, and sector-specific enforcement. China has targeted rules governing generative AI content and provider responsibilities, while jurisdictions such as the United Kingdom and Australia favour principles-based or regulator-led approaches. Despite these differences, a common structural pattern emerges with synthetic artefacts and artificial humans rarely governed directly as regulatory objects, even as they become central to AI-related harm.

Policy table (1) illustrates the diversity of approaches taken across countries and regions, reflecting different institutional traditions, political economies, and levels of technological maturity. A clear shift is now evident. Jurisdictions of the European Union and China have moved decisively toward binding regulation, while others (e.g., the United States, United Kingdom, Japan, Singapore) rely on hybrid models combining soft law, standards, and sectoral enforcement. The result is a layered governance ecosystem where ethics no longer stand alone but are moving toward risk controls increasingly embedding in compliance obligations, audits, reporting duties, and penalties.

Table 1: Cross-Jurisdictional AI Policy Instruments

Country / Region	AI Policy / Instrument	Key Characteristics	Year
European Union	Artificial Intelligence Act	Binding, risk-based AI regulation	2024
United States	Executive Order on AI	Federal coordination and safety testing	2023
China	Generative AI Measures	Content governance and provider obligations	2023
United Kingdom	Pro-innovation AI Framework	Principles-based, regulator-led approach	2023
Australia	AI Ethics Principles	Voluntary national principles	2019

 

2.3 European Union: Partial and Indirect Coverage

The European Union Artificial Intelligence Act (European Parliament and Council, 2024) represents the most comprehensive binding AI framework currently enacted in 2025/26. The Regulation entered into force in 2024, but its substantive provisions apply on a staged timeline. Article 50 signals the introduction of transparency obligations when individuals interact with an AI system or when content is synthetically generated or manipulated (including deepfakes). This act is scheduled to apply from 2 August 2026 under the implementation timetable of the Act. Accordingly, while the transparency architecture is legislatively established, full operational enforcement of Article 50 remains prospective at the time of writing.

The Act addresses generative AI primarily through transparency, manipulation prevention, and systemic risk controls. Article 5 prohibits certain manipulative AI practices that exploit vulnerabilities or materially distort behaviour. In addition, large general-purpose AI models designated as posing systemic risk are subject to obligations designed to mitigate downstream misuse. These provisions collectively strengthen oversight of AI systems and impose compliance duties on providers and deployers.

However, the Act approaches synthetic artefacts principally through transparency and system-based obligations rather than through artefact-level governance. Synthetic outputs such as fabricated reports, synthetic research materials, digital identities, or AI-generated evidentiary materials are not treated as distinct regulatory categories with dedicated provenance or lifecycle accountability requirements. Similarly, artificial humans (e.g., avatars or AI personas) are not explicitly recognized as a separate governance object beyond disclosure requirements in interactive contexts.

From a Synthetic Artefact Governance Theory (SAGT) perspective, the EU framework therefore remains predominantly system centric. While it meaningfully advances disclosure and systemic oversight, it does not yet establish artefact-level provenance obligations, relational safeguards, or ecosystem accountability mechanisms directed specifically at the circulation and institutional effects of synthetic artefacts or artificial human presence. This distinction does not suggest that the Act is incomplete, but rather that its architecture prioritizes system regulation over artefact-centered governance.

2.4 China: Strongest Direct Control of Synthetic Humans Without a Normative Framework

China has developed some of the most explicit regulatory instruments addressing synthetic media and AI-generated content, though these measures are embedded within a broader content governance architecture rather than a unified AI safety statute. Key instruments include the Algorithmic Recommendation Provisions (2022), the Deep Synthesis Provisions (2023), and the Interim Measures for Generative AI Services (2023).

These regulations impose operational obligations on providers, including mandatory labelling of synthetic content, registration and licensing requirements for certain deep synthesis services, and liability exposure where impersonation or social harm results. In practice, China’s framework directly addresses synthetic personas, voices, and manipulated images more explicitly than many Western AI statutes.

However, this coverage operates primarily through content control and platform compliance mechanisms rather than through a rights-based or artefact-centered governance theory. Synthetic artefacts are regulated insofar as they implicate social stability, misinformation, or impersonation risks, but they are not framed as first-order institutional objects requiring provenance infrastructure or lifecycle accountability safeguards. Likewise, while artificial humans are subject to disclosure and impersonation constraints, there is no articulated governance regime addressing relational deception, emotional substitution, or broader epistemic trust dynamics.

From a SAGT perspective, China’s framework provides comparatively strong operational controls at the artefact surface layer (e.g., labelling and provider liability), yet it does not establish a comprehensive artefact-level accountability architecture grounded in institutional trust preservation. The regulatory emphasis remains content stability and platform discipline rather than ecosystem-level trust governance.

2.5 United States: Sectoral and Fragmented Governance

The United States does not currently operate under a comprehensive federal AI statute. Instead, governance emerges through a combination of executive action, sector-specific regulation, state-level legislation, and enforcement practice. Instruments such as Executive Order 14110 (2023), the NIST AI Risk Management Framework (2023), and the Blueprint for an AI Bill of Rights (2022) articulate principles and risk management expectations but do not establish artefact-level statutory obligations.

At the state level, several states enact targeted deepfake election laws, and regulatory agencies such as the Federal Trade Commission pursue enforcement actions against deceptive synthetic endorsements and impersonation practices. These measures address synthetic artefacts primarily through fraud, consumer protection, or electoral integrity frameworks.

However, coverage remains fragmented. Synthetic artefacts are governed indirectly through deception or misrepresentation doctrines rather than through dedicated artefact-centered statutes. Artificial humans, outside of impersonation or advertising contexts, are not recognized as a distinct regulatory category. No unified federal provenance infrastructure or statutory framework assigns lifecycle accountability to circulating synthetic artefacts.

From a SAGT perspective, the U.S. approach reflects strong enforcement capacity in specific domains (e.g., fraud, elections) but lacks an integrated artefact-layer governance architecture. Governance is reactive and sectoral rather than systemic, leaving broader questions of relational manipulation, artificial social agents, and institutional epistemic trust largely unaddressed in statutory form.

2.6 Generative AI, Platform Governance, and Epistemic Risk

Generative AI introduces what may be described as epistemic risk. This is the risk to shared understandings of truth, authorship, authenticity, and evidentiary reliability (Rini, 2020). Synthetic artefacts can be factually accurate yet misleading, procedurally compliant yet deceptive, and legally permissible yet corrosive to institutional trust. Unlike traditional misinformation, often adversarial and demonstrably false, synthetic artefacts may be produced by legitimate actors using approved systems. This complicates intent-based regulatory approaches and shifts the governance problem from falsity alone to questions of authenticity, provenance, and relational transparency (Chesney & Citron, 2019).

In response to this proliferation, platform-level governance emerges as one of the most operationalized mechanisms addressing artefact circulation in practice. Major digital platforms have introduced disclosure requirements, labelling systems, and moderation policies specifically targeting AI-generated or AI-altered content. These mechanisms function as embedded trust controls within content distribution infrastructures rather than as abstract regulatory principles.

YouTube provides a particularly important example. The platform requires creators to disclose when content is meaningfully altered or synthetically generated in ways appearing realistic, including deepfake-like depictions, synthetic voices, or altered scenes that could mislead viewers (Google Support, 2025). Disclosure occurs at the point of upload through built-in signalling tools, integrating transparency obligations directly into user workflows. Failure to comply may result in removal, demonetisation, or other enforcement actions. Similar disclosure regimes are emerging across major platforms as generative AI tools become mainstream.

These policies represent a form of private governance that parallels public-law transparency obligations, such as Article 50 of the EU AI Act. In practice, platforms currently provide some of the most direct artefact-level interventions. The interventions govern synthetic content at the moment of distribution, apply penalties for non-disclosure, and operationalize audience-facing labelling at scale. From a Synthetic Artefact Governance Theory (SAGT) perspective, platform governance addresses the artefact layer and partially engages the interaction layer by making artificial involvement visible at the point of consumption.

However, platform governance differs structurally from statutory regulation. Operating through private rulemaking, contractual enforcement, and internal moderation systems rather than democratically enacted public law. Coverage varies across platforms, enforcement standards are not uniform, cross-platform interoperability is limited, and due process protections are comparatively narrow. Moreover, platform rules focus primarily on disclosure and content moderation rather than on formal provenance infrastructures, lifecycle accountability chains, or institutional-grade verification mechanisms.

Platform governance therefore demonstrates the feasibility of artefact-level controls in practice, while also revealing limitations. Such platform governance operationalizes disclosure and labelling at scale, yet lacks the public legitimacy, harmonized standards, and systemic accountability mechanisms required for comprehensive synthetic artefact governance. In this sense, platforms provide practical micro-level governance of epistemic risk, but not a fully institutionalized artefact-centered regulatory regime.

2.7 International and Soft Law: Recognition Without Enforcement

International and soft-law frameworks, including those developed by UNESCO (2021) and the OECD (2019), increasingly acknowledge risks posed by deepfakes and synthetic media. These instruments emphasize transparency, human dignity, and responsible AI use. However, they stop short of imposing binding obligations or establishing enforcement mechanisms. As such, they function primarily as normative recognition, not operational governance.

2.8 Comparative Synthesis: What Exists and What Is Missing

Taken together, existing AI governance regimes reveal an asymmetry between what is formally regulated and where AI-generated harm increasingly manifests. While multiple jurisdictions and platforms address disclosure, manipulation, and content moderation, coverage thins markedly when moving toward artefact legitimacy, relational integrity, and institutional trust. Table 2 synthesizes current coverage and gaps across key governance dimensions. The comparative analysis below evaluates instruments against consistent criteria: (1) whether synthetic artefacts are explicitly defined, (2) whether artificial human interactions are regulated, (3) whether provenance or accountability chains are mandated, and (4) whether enforcement mechanisms attach directly to artefact circulation rather than to system classification alone.
As table 2 indicates, governance concentrates heavily on disclosure, particularly for visible or politically sensitive synthetic media. Beyond disclosure, however, regulation becomes sparse. Synthetic documents and AI-generated evidence remain unregulated as artefacts, despite growing use in professional, legal, and administrative settings. Artificial social agents are not recognized as a distinct category in any setting. Most notably, no regime explicitly governs relational manipulation, the sustained capacity of artificial humans to influence emotions, build trust, simulate intimacy, or substitute for human social interaction without disclosure or consent.

Table 2: Comparative Synthesis of Synthetic Artefact Governance Across Regimes

Governance Dimension	Explicit Statutory Recognition as Regulatory Object?	Operational Coverage in Practice?	Illustrative Sources
Synthetic media disclosure (deepfakes, altered content)	Partial treated as transparency obligation rather than independent artefact category	Yes, disclosure and labelling mechanisms increasingly implemented	EU AI Act (Art. 50); China Deep Synthesis Provisions; major platforms
Artificial humans (avatars, AI personas, conversational agents)	Limited and addressed indirectly via disclosure rules; not recognized as distinct regulatory class	Emerging with some platform and national labelling rules	EU AI Act (interaction disclosure); China; platform policies
Synthetic documents / evidence (reports, research outputs, institutional artefacts)	No, not governed as artefact-level institutional objects	Minimal reliant on general fraud, misrepresentation, or sectoral law	No dedicated AI-specific regime identified
Artificial social agents engaging in relational interaction	No, no regime defines or regulates artificial humans as social substitutes	Minimal and interaction disclosure may apply, but no relational safeguards	Indirect via EU Art. 50; limited platform practice
Relational manipulation (authority simulation, emotional influence)	Partial manipulation prohibitions exist, but framed at system level	Limited addressed via general anti-manipulation clauses	EU Art. 5; sectoral consumer law
Provenance infrastructure (traceability, authentication standards)	Emerging though not comprehensively mandated across jurisdictions	Emerging with pilot registries, watermarking, voluntary standards	Platform initiatives; Web3 pilots; fragmented national efforts
Artefact-linked liability (accountability follows artefact lifecycle)	No – liability triggered at system or actor level, not artefact circulation	No systematic coverage	Absent as a structured governance model

 

Efforts to establish trust infrastructure including provenance tracking, watermarking, and cryptographic verification are emerging primarily through platforms and pilots, remaining voluntary and non-standardised. Likewise, artefact-level liability who is responsible as synthetic artefacts are reused, modified, or amplified across contexts remains unaddressed in formal law.
Across jurisdictions, synthetic artefacts and artificial human presence are addressed indirectly, unevenly, and incompletely. Transparency rules and platform moderation provide partial coverage, but no jurisdiction governs synthetic artefacts or artificial humans as first-order institutional objects. This fragmentation weakens cross-border enforcement, enables regulatory arbitrage, and shifts verification costs onto individuals and institutions underscoring the SAGT core empirical claim contemporary AI governance remains model-centric, while the most consequential risks arise at the level of synthetic reality itself.

Across jurisdictions, AI governance converges toward risk-based regulatory architectures, though legal form and enforcement intensity vary substantially. The European Union has pursued binding regulation through the AI Act, complemented by the Digital Services Act and data protection law. The United States relies on executive guidance, voluntary commitments, and sector-specific oversight. China uses targeted rules governing generative AI content and provider responsibilities, while jurisdictions such as the United Kingdom and Australian favour principles-based or regulator-led approaches.

Despite these differences, a common pattern emerges, the synthetic artefacts and artificial humans as outputs of generative AI are typically addressed indirectly rather than through dedicated artefact-level statutory framework. Instead, they are addressed through transparency obligations, platform moderation rules, intellectual property law, and privacy regulation. This fragmentation weakens cross-border enforcement and enables regulatory arbitrage, particularly as synthetic content circulates globally at little cost.

Proposals for harmonized generative AI governance frameworks e.g. Generative AI Governance Framework v1.0 (Szarmach, 2025) reflect growing recognition of the challenge. Such frameworks seek to align regional governance processes into shared regulatory scaffolds mapping risk-based provisions across modalities and jurisdictions, enabling interoperability while preserving local legal autonomy (Calzada et al., 2025).

In addition to formal statutory regimes, platform governance functions as a parallel layer of synthetic artefact oversight. As discussed in Section 2.6, major platforms now impose structured disclosure requirements for realistic AI-generated or materially altered content. Rather than revisiting the operational details, the key point is platforms increasingly treat synthetic artefacts as audience trust risks requiring proactive signalling mechanisms. These disclosure systems are embedded into upload workflows and enforced through moderation penalties operating as de facto artefact-level governance tools, even though they lack statutory authority or cross-platform standardisation.

This private mechanism creates a hybrid governance environment. On the one hand, platform rules operationalise transparency at scale, often more rapidly than legislatures. On the other hand, enforcement remains contractual and discretionary, without the due process guarantees, interoperability mandates, or uniform evidentiary standards associated with public law. From a Synthetic Artefact Governance Theory (SAGT) perspective, platform policies partially occupy the artefact and interaction layers, but they do so in a fragmented and non-harmonised manner.
Empirically, these environments provide a valuable testing ground for SAGT hypotheses (section 5). Comparing contexts in which synthetic content disclosure is platform-enforced versus legally mandated allows assessment of governance effectiveness across architectures. Such comparisons are particularly relevant to hypotheses concerning deception mitigation (H4) and potential habituation effects arising from repeated exposure to labelling regimes (H6). Platform governance thus offers an observable intermediary stage between voluntary transparency and fully institutionalised artefact-level regulation.

2.9 Artificial Humans & Relational Governance

 

AI avatars, conversational agents, and embodied systems increasingly perform roles once reserved for humans including customer service representatives, educators, influencers, financial advisors, sales account managers and even experimental participants. This raises profound governance questions:

• Should artificial agents be explicitly disclosed as non-human?
• Who bears responsibility for harm caused by persuasive or deceptive AI personas?
• How should consent operate when humans interact with synthetic social actors?
• Can existing consumer protection, labour, and discrimination laws cope with artificial humans?

A relatable strand of research highlights the rise of artificial agents designed to engage users socially and emotionally. These systems blur the boundary between tool and actor, raising questions traditionally associated with social psychology, consumer protection, and labour law (Malhotra et al., 2024). Existing AI governance frameworks rarely conceptualize these systems as a distinct policy object, instead subsuming them under general transparency or consumer information requirements.

3. Literature Review

 

3.1 Governing Synthetic Artefacts and Artificial Humans

 

3.1.1 From Model-Centric Regulation to Artefact-Centric Risk

Traditional AI governance assumes harm arises from incorrect or biased system behavior. Generative AI challenges this assumption by producing artefacts that may be factually accurate yet socially misleading, lawful yet corrosive to trust. Recent scholarship increasingly emphasizes artefacts not models are primary sites of harm, particularly when synthetic content is reused, edited, or amplified beyond the original context (Raza et al., 2025).

3.1.2 Accountability and Cryptographic Provenance

 

A major research stream focuses on cryptographic accountability mechanisms. Blockchain anchoring enables immutable recording of content metadata and consent assertions, creating verifiable chains of custody (Vetrivel et al., 2025). Zero-knowledge proofs allow provenance verification without exposing sensitive data, balancing transparency and privacy. AI-powered watermarking embeds robust origin signals within media, while federated detection systems distribute verification capacity without centralizing data (Aarthi et al., 2025). The literature consistently emphasizes effective governance requires layered technical enforcement, not single mechanisms.

3.1.3 Governing Artificial Human Presence

 

Artificial humans introduce governance challenges extending beyond content authenticity. The Digital Identity Rights Framework (DIRF) represents a most comprehensive response, defining nine governance domains and 63 operational controls for managing consent, traceability, monetization, and enforcement related to digital likenesses (Atta et al., 2025). Legal role taxonomies further clarify accountability by distinguishing AI-generated content (AIGC), human–AI collaborative content (HAIC), and AI-mediated communication (AI-MC), assigning liability to human actors rather than AI systems (Towne, 2024).

3.1.4 Do Any Existing AI Policies Govern Synthetic Artefacts or Artificial Humans?

 

Despite the rapid proliferation of generative AI systems capable of producing synthetic text, images, video, audio, documents, and socially interactive agents, to date, no major AI law appears to treat synthetic artefacts as independent, first-order regulatory objects. Instead, current governance approaches address these phenomena indirectly through a patchwork of transparency requirements, content controls, and misuse prohibitions remaining fundamentally model-centric rather than artefact-centric.

Most binding AI regulations, including comprehensive frameworks such as the EU Artificial Intelligence Act, conceptualize risk primarily in relation to system functionality and use context, rather than the downstream institutional role of AI-generated outputs. Synthetic artefacts are addressed only in so far as they create immediate deception risks most commonly through disclosure or labelling obligations for manipulated or AI-generated content. These measures are designed to inform users content has been altered or that they are interacting with an AI system, but they do not regulate the artefacts themselves as objects potentially functioning as evidence, records, identities, or social actors.

Similarly, policies addressing manipulative or exploitative AI practices focus on intent and behavioural distortion, rather than on the cumulative effects of large-scale artefact production on epistemic trust. Artificial humans such as AI avatars, conversational agents, or synthetic personas are not recognized as a distinct regulatory category in most jurisdictions. Where they are addressed at all, they are treated as a subset of content generation or user interaction rather than as relational agents capable of persuasion, emotional influence, or social substitution.

The most explicit governance of synthetic artefacts currently occurs not in public law but in platform-level policies, where private companies require disclosure of realistic AI-generated or altered content and impose penalties for non-compliance. While these measures provide practical safeguards against deception, they lack the legitimacy, consistency, and due-process protections associated with statutory regulation. As a result, governance of synthetic artefacts today remains fragmented, reactive, and normatively thin. This gap supports the core claim of Synthetic Artefact Governance Theory. Contemporary AI policy regimes govern how models are built and used, but not how synthetic artefacts reshape institutional trust, social interaction, and accountability once they enter circulation.

3.1.5 Institutional Trust and Verification Infrastructures

 

Institutions such as newsrooms, courts, and electoral bodies face acute challenges in verifying synthetic content. Recent research documents the emergence of institutionalized verification infrastructures, including mandatory labelling, standardized authentication pipelines, certified detection tools, and decentralized verification models using Decentralized Autonomous Organizations (DAOs; Panagopoulos & Davalas, 2025; Fabuyi et al., 2024). These developments signal a shift from ad hoc fact-checking toward formal trust infrastructures embedded in governance processes.

3.2 Synthetic Artefact Governance Theory (SAGT)

 

3.2.1 Core Theoretical Claim

 

Synthetic Artefact Governance Theory (SAGT) posits the primary governance challenge of advanced AI lies in the production and circulation of synthetic artefacts and artificial social agents, rather than in algorithmic decision-making alone. Hence, SAGT applies most strongly in contexts wherein AI systems generate artefacts functioning as evidence, identity proxies, institutional records, or socially interactive agents. SAGT is less applicable to purely internal optimization systems such as logistics routing and predictive maintenance. Here, the outputs do not circulate as socially interpretable artefacts. The theory therefore primarily governs generative, multimodal, and agentic AI systems operating in epistemically sensitive domains especially law, finance, journalism, education, governance, and social media ecosystems. Therefore, AI governance evaluation is according to the preservation of epistemic trust, relational integrity, and institutional accountability in environments presenting synthetic outputs.

Table 3: Model-Centric vs Artefact-Centric Governance

Model-Centric	Artefact-Centric (SAGT)
Regulates system risk	Regulates output legitimacy
Focus on decision harm	Focus on trust distortion
Provider compliance	Ecosystem accountability
Disclosure as notice	Disclosure as relational safeguard

 

3.2.2 Governance Layers - Explanatory & Design

 

1. Artefact layer (provenance and authenticity)Focuses on how synthetic artefacts are created, tagged, and integrated into evidentiary and archival systems, including watermarking, content provenance standards, and authenticity verification for texts, images, audio, and avatars.
2. Interaction layer (disclosure and relational influence)Governs interactions between humans and artificial humans, including disclosure duties for AI agents, consent mechanisms, limits on dark patterns, and safeguards against exploitative or covert persuasion.
3. Agency layer (responsibility and liability)Clarifies how accountability is allocated across model developers, platform operators, deployers, and end users when synthetic artefacts cause harm, especially in contexts where AI avatars act on behalf of individuals or organizations.
4. Ecosystem layer (amplification and systemic risk)Addresses platform-level amplification, recommendation dynamics, cross-platform diffusion, and the role of synthetic media operations functions in monitoring, mitigating, and learning from incidents at scale.

SAGT is both explanatory and design oriented. From an explanatory perspective, SAGT identifies structural governance gaps in model-centric AI regulation. Normatively, SAGT proposes layered institutional responses aligned with artefact circulation and artificial human interaction dynamics. The theory therefore operates at the intersection of governance analysis and institutional design theory.

4. Hypotheses Development

 

Drawing on SAGT, this paper advances hypotheses linking artefact-level provenance, identity rights enforcement, cryptographic accountability, and ecosystem oversight to outcomes such as epistemic trust, reduced manipulation, accountability clarity, and institutional resilience.
SAGT generates a set of testable hypotheses spanning artefact, interaction, agency, and ecosystem layers.

H1: Stronger provenance mechanisms reduce misclassification of synthetic artefacts as human authored.

H2: Exposure to unlabelled synthetic artefacts increases verification costs and reduces epistemic trust.

H3: Trust erosion is greater in evidence-intensive domains (e.g., law, science, public administration) than in entertainment contexts.

H4: Salient disclosure reduces deception and improves informed consent in encounters with synthetic artefacts and artificial humans.

H5: Artificial humans increase persuasion but also perceived manipulation risk.

H6: Disclosure effectiveness diminishes over time without technical reinforcement (e.g., watermarking, provenance tools).

H7: Accountability clarity decreases as AI value chains lengthen and involve more intermediaries.

H8: Documentation and logging reduce incident resolution time when harms are linked to specific synthetic artefacts.

H9: Platform amplification increases downstream harm non-linearly as synthetic artefacts are promoted and recombined.

H10: Strong general-purpose AI (GPAI) obligations reduce severe downstream incidents involving synthetic artefacts and artificial humans.

H11: Institutional trust erosion in one domain e.g., synthetic legal evidence likely produces spillover reductions in trust across unrelated institutional domains.

H12: A threshold effect exists whereby increasing saturation of synthetic artefacts produces non-linear declines in baseline epistemic trust, even when disclosure compliance remains constant.

These hypotheses can be examined through controlled experiments, field audits, computational diffusion analyses, and quasi-experimental policy evaluation designs leveraging platform governance capabilities as highlighted by the research agenda.

5. Empirical Research Agenda

 

The hypotheses motivate a multi-method empirical agenda. Controlled experiments can test how provenance signals and disclosure labels affect user trust, detection accuracy, and behavioral responses to synthetic media. Field studies and platform A/B tests can evaluate the impact of labelling, watermarking, and interaction rules on real-world user behavior and harm incidence.

Computational diffusion analyses trace how synthetic artefacts spread across platforms, identifying amplification patterns, cross-lingual transfers, and emergent narrative clusters. Quasi-experimental designs, such as difference-in-differences, can assess how regulatory changes or platform interventions affect the prevalence and impact of synthetic artefacts and artificial humans over time. Taken together, these methods provide an empirical foundation for calibrating SAGT-informed policies.

The power of SAGT lies in its capacity to generate empirically testable research programs. The theory motivates a multi-method research agenda, including experiments on labelling and trust calibration, field audits of provenance systems, computational diffusion analysis, and quasi-experimental evaluations of regulatory interventions

The proposed hypotheses can be evaluated through multiple complementary approaches:

• Controlled experiments isolate micro-level causal effects of disclosure, embodiment, and provenance on trust and behaviour.
  Goal: Test H1, H4–H6.Methods: randomized online experiments; lab studies; A/B tests.
  Outcomes: deception detection accuracy; trust ratings; consent comprehension; behavioral choices (e.g., share/not share). Design example: Conditions: (i) no label, (ii) label only, (iii) label + watermark indicator, (iv) label + cryptographic verification UI.
• Field studies and compliance audits meso-level governance performance examine how organizations implement documentation and accountability requirements.Goal: Test H7–H8Methods: compliance audits, interviews with compliance/legal teams, document analysis, enforcement case coding.Outcomes: documentation completeness; incident response time; accountability clarity; remediation quality.
• Computational social science methods model artefact diffusion and amplification across platforms.Goal: Test H2–H3, H9.Methods: network diffusion models, platform data partnerships, synthetic artefact trace analysis, event studies. Methods: network diffusion models, platform data partnerships, synthetic artefact trace analysis, event studies.Outcomes: virality, cross-platform propagation, economic impacts (fraud losses, moderation costs), trust sentiment time series.
• Policy evaluation designs, macro-level causal inference including difference-in-differences and synthetic control methods, can assess the impact of regulatory interventions over time.Goal: Test H10 and policy effectiveness.
  Methods: difference-in-differences around enforcement dates; synthetic control across jurisdictions; interrupted time series.
  Outcomes: incident rates, reporting frequency, fraud/misinformation metrics, compliance costs, innovation indicators.

Platform governance mechanisms operate across all analytical levels in this research design, serving respectively as experimental treatments (micro), organizational accountability systems (meso), structural moderators of artefact diffusion (network), and policy interventions subject to causal evaluation (macro). For example, leveraging platform governance mechanisms such as the YouTube disclosure policies enables large-scale natural experiments comparing user responses across environments with and without enforced disclosure.

6. Analytical Assessment: The EU Al Act

 

When assessed through SAGT, the EU AI Act appears as a sophisticated but predominantly model-centric regulatory regime. The Act devotes extensive attention to agency and ecosystem governance through provider obligations, conformity assessment, and systemic-risk controls for general-purpose AI, thereby strengthening accountability for high-risk decision-making systems.

The Act does contain transparency obligations relevant to synthetic artefacts such as duties to inform users when content is AI-generated or significantly manipulated, and specific provisions for deepfake disclosure in many contexts. However, these rules typically treat artefacts as by-products of systems, focusing on user notification rather than on artefact status as evidence, records, or identities.

From a SAGT perspective, synthetic artefacts and artificial humans are addressed mainly through transparency and manipulation provisions, not as primary regulatory categories with dedicated governance infrastructures. While the Act meaningfully advances transparency and systemic-risk governance, the treatment of synthetic artefacts or artificial humans as independent regulatory categories with dedicated provenance, relational safeguards, and lifecycle accountability mechanisms is open for further refinement and treatments. Ecosystem-level oversight of synthetic reality such as monitoring synthetic artefact circulation across platforms or requiring synthetic media operations within large intermediaries and enterprises remains underdeveloped. This supports the SAGT claim current law under-regulates epistemic and relational harms.

The EU AI Act represents the most advanced system-centric AI regulation to date. However, assessed through the SAGT lens, the Act governs synthetic artefacts primarily indirectly, through transparency obligations. Artificial humans are not treated as a distinct governance category, and artefact-level provenance enforcement remains underdeveloped.
The European Union Artificial Intelligence Act represents the most extensive binding regulatory regime for AI to date. As shown in Appendix A, the Act devotes substantial regulatory effort to agency- and ecosystem-level governance through provider obligations, conformity assessments, enforcement institutions, and systemic risk controls for general-purpose AI models. However, synthetic artefacts and artificial humans are not explicitly governed as independent objects but instead, artefact transparency is addressed mainly through disclosure provisions such as in Article 50.

This analytical assessment confirms the SAGT claim contemporary AI regulation remains anchored in model-centric risk categories, addressing artefact and interaction risks only partially. For example, transparency duties are necessary but may not sufficiently mitigate epistemic harms without integrated provenance infrastructure and interactive consent safeguards.

7. Discussion and Policy Implications

The integration of platform governance such as YouTube synthetic-content disclosure rules into the AI policy landscape illustrates a broader normative shift with platforms becoming de facto regulators of synthetic artefacts. While platforms operate under commercial incentives, their policies often anticipate or complement public sector regulatory goals, especially around disclosure and trust. This dynamic has important implications. First, platform-level governance can provide empirical evidence about what works in practice, helping policymakers calibrate statutory obligations. Second, lack of harmonization between platform policies and public law creates fragmented governance regimes, potentially weakening systemic accountability. Harmonizing platform obligations with statutory transparency duties especially around synthetic media and artificial humans strengthens the trust infrastructure across ecosystems.
The analysis also highlights the need for governance mechanisms addressing relational harms and systemic diffusion, not just model risk. This requires investing in public trust infrastructure such as authentication standards, cross-platform reporting protocols, and institutional reporting channels for artefact-level incidents.

7.1 Policy Design: Operationalising SAGT in Law

 

Operationalising Synthetic Artefact Governance Theory (SAGT) requires complementing system-centric AI regulation with artefact-centric and interaction-aware governance instruments. This does not imply abandoning existing risk-based AI safety regimes but rather extending them to address the governance of synthetic reality, the artefacts, identities, and social interactions generated by AI systems. Core instruments include mandatory provenance standards for synthetic outputs, rights-based governance of digital identity and artificial human presence, cryptographically enforced accountability mechanisms, and institutional verification infrastructures embedded within regulatory compliance workflows.

The European Union Artificial Intelligence Act provides a useful benchmark for assessing this shift. As illustrated in Appendix B, the Act concentrates regulatory effort at the agency and ecosystem layers, through provider and deployer obligations, conformity assessments, enforcement authorities, and systemic risk controls for general-purpose AI. Transparency provisions particularly those addressing user interaction with AI systems and disclosure of synthetic content partially engage the artefact and interaction layers. However, synthetic artefacts are not treated as independent governance objects, and artificial humans are not explicitly regulated as a distinct category. This confirms the central theoretical claim of SAGT that contemporary AI regulation, while comprehensive, remains anchored in a model-centric paradigm.

A SAGT-aligned policy architecture would therefore introduce legal recognition of synthetic artefacts as regulatory objects, particularly where such artefacts function as documents, evidence, identities, or institutional representations. This entails mandatory provenance and authenticity markers for high-impact synthetic outputs, verifiable audit trails linking artefacts to their generating systems, and evidentiary standards governing the admissibility and reuse of AI-generated materials. Such measures would reduce verification costs and strengthen institutional trust without constraining generative innovation.
Beyond artefacts themselves, governance must address human–AI interaction as a site of risk. Artificial humans and socially persuasive agents require explicit safeguards governing disclosure, consent, and relational integrity, particularly in contexts involving emotional influence or vulnerable populations. These measures move beyond generic transparency to regulate relational manipulation, recognizing that harm can arise even when content is factually accurate but socially deceptive.

SAGT further requires artefact-linked accountability infrastructure. Responsibility should follow synthetic artefacts across their lifecycle creation, deployment, reuse, and amplification through shared responsibility models involving providers, deployers, and distributors. Artefact-based liability triggers, supported by logging and documentation duties, directly address the diffusion of responsibility that characterizes current AI governance regimes.
Finally, operationalising SAGT necessitates ecosystem-level oversight. Regulators must monitor the aggregate effects of synthetic artefact circulation, including cross-platform diffusion, amplification dynamics, and systemic erosion of epistemic trust. Public trust infrastructures such as registries, verification services, and standardized authentication protocols combined with reporting obligations for large-scale deployment, would enable adaptive intervention where artefact-driven harms become systemic.

Taken together, these policy design implications underscore a fundamental shift in AI governance. From regulating what AI systems do to governing what AI produces and how society interacts with it. Such a shift is essential if AI law is to remain effective in an era where synthetic artefacts and artificial humans increasingly shape social, economic, and institutional life.

Table 4: Mapping Policy Design Levers to Synthetic Artefact Governance Theory Layers

SAGT layer	Governance focus	Policy lever	Regulatory purpose
Artefact	Authenticity, provenance, evidentiary integrity	Mandatory provenance markers; authenticity labels; artefact audit trails	Establish trust in AI-generated artefacts used as documents, evidence, or representations
Interaction	Disclosure, consent, relational transparency	Disclosure of artificial identity; consent standards for prolonged or affective interaction; prohibitions on undisclosed relational manipulation	Protect users from deceptive or manipulative human–AI interaction
Agency	Accountability, liability allocation	Artefact-linked liability triggers; shared responsibility across providers, deployers, and distributors; logging and documentation duties	Prevent responsibility diffusion and ensure traceable accountability
Ecosystem	Systemic risk, amplification, institutional trust	Public trust infrastructure (registries, verification services); reporting obligations for large-scale artefact deployment; adaptive regulatory powers	Monitor and mitigate systemic trust erosion and cross-platform artefact amplification

 

7.2 Emerging Artefact-Level Legislative Responses

 

Recent state legislative initiatives in the United States highlight the early emergence of governance approaches explicitly treating synthetic media as an artefact-centric harm, distinct from model or system-focused regulation. In January 2026, the New Mexico Attorney General announced proposed legislation to protect residents from deceptive synthetic media generated using artificial intelligence. The draft law requires digital markers on AI-generated images, audio, and video to enable provenance tracking, grant enforcement authority to the state Attorney General for malicious dissemination and impose penalties for harmful synthetic content distribution (New Mexico Department of Justice, 2026). By directly regulating the production, disclosure, and accountability of synthetic media artefacts, this initiative operates substantively at the artefact and interaction layers of governance rather than solely at the model level.
Parallel developments in Mexico illustrate a regional trend toward formalising AI governance in ways extending beyond traditional system-centric regulation. While Mexico does not yet have a comprehensive AI statute, emerging regulatory proposals including frameworks for ethical, sovereign, and inclusive AI development envisage national authorities responsible for system registration, risk oversight, transparency obligations, and enforcement action against unsafe practices (Nemko Digital, 2025). Such proposals signal an integration of artefact, oversight, and systemic governance considerations, even in the absence of a fully enacted law.

These policy developments resonate with civil society analyses emphasising the complexity and social impact of synthetic media and deepfakes. The Center for News, Technology & Innovation (CNTI) frames deepfakes as a form of synthetic media undermining journalists, fact-based news, and public trust, noting most countries still lack laws expressly targeting this class of content (CNTI, 2025). Detection technologies and provenance tools are evolving but cannot alone address all harms. Clear, context-sensitive policy responses are needed balancing freedom of expression, safety, and media independence.

Together, these signals reinforce while formal AI law continues to prioritise models and deployment contexts, real-world governance responses are already converging on artefact-level harms. In the absence of a unifying theoretical framework, such interventions remain fragmented and incremental. SAGT provides the conceptual structure needed to integrate emerging artefact-oriented initiatives into a coherent, multi-layered governance architecture capable of addressing synthetic reality at institutional scale.

8. Conclusion

 

AI governance is entering a new phase with the central challenge no longer merely regulating intelligent systems but governing the output of synthetic reality itself to preserve social trust, accountability, and meaning in environments increasingly shaped by AI-generated artefacts and artificial human presence. This paper argues while contemporary AI regulation exemplified by the European Union Artificial Intelligence Act represents a significant milestone in the maturity of system-centric, risk-based governance. This regulation remains structurally ill-equipped to address harms arising from the production, circulation, and amplification of synthetic artefacts and artificial social actors. By integrating recent advances in regulatory design, cryptographic provenance, digital identity rights, and platform governance, Synthetic Artefact Governance Theory (SAGT) provides both a conceptual lens and a practical foundation for addressing these gaps. SAGT reframes governance away from models alone toward artefacts, interactions, accountability chains, and ecosystem dynamics, highlighting the need for next-generation AI policy. Explicitly governing synthetic artefacts and artificial human interactions while integrating public law with the platform-level mechanisms such as disclosure, labelling, and provenance controls already operationalizing trust in practice.

This governance shift also necessitates a redefinition of what is meant by human-centric AI. Rather than focusing narrowly on keeping humans in the loop of automated decision-making, future governance must address how humans coexist with artificial agents psychologically, socially, and economically. Safeguarding human autonomy, dignity, and epistemic trust in environments saturated with synthetic actors requires recognising artificial humans and synthetic artefacts as first-order institutional objects of governance. Without such a shift, AI policy risks remaining misaligned with the realities of synthetic media, artificial social presence, and trust formation in digital societies. Governing synthetic reality, rather than intelligent systems alone, therefore represents the critical frontier for AI governance in the coming decade.

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International Journal of Management Science and Business Administration

Volume 12, Issue 1, November 2025, Pages 7-14

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AI-First Tiny Companies: Case Studies, Design Logic, and Emerging Governance Risks

DOI: 10.18775/ijmsba.1849-5664-5419.2014.121.1001

URL: https://doi.org/10.18775/ijmsba.1849-5664-5419.2014.121.1001

Suresh Sood ^{1, 2}

¹ Industry/Professional Fellow, Australian Artificial Intelligence Institute, University of Technology Sydney

² Adjunct Fellow, Frontier AI Research Centre, Macquarie University, Sydney

Abstract: Advances in large language models (LLMs), vibe coding, and agentic automation are enabling a new organizational form. Namely, AI-first tiny companies or microfirms scaling new product developments and revenue with single-digit headcount. Building on evidence from contemporary founder accounts and technology reporting, this article synthesizes what these firms share and where they fail. Founders acting as orchestrators of modular workflows, AI agents and copilots substituting for specialist labor, and a shift from hiring to build toward building before hiring. We analyse four illustrative cases comprising Base44, AI Apply, Oleve, and the HurumoAI experiment to show how small teams exploit speed, low coordination overhead, and toolchain leverage, consistent with research small teams are more likely to disrupt while large teams tend to develop and consolidate (Wu et al., 2019). We then propose a design logic for AI-first tiny companies: task modularity → agent autonomy → integration → human oversight and discuss governance risks that become acute at small scale, including hallucinated work products, privacy and acess control, accountability gaps, and fragile dependence on model providers. The paper concludes with research propositions and practical guidance for founders, investors, and policymakers evaluating the feasibility and limits of the “one-person unicorn” thesis.

Keywords: agentic AI; entrepreneurship; microfirms; tiny teams; small teams; large language models; governance; one person unicorn

1. Introduction

Across software and digital services, a pattern is widely reported in founder narratives and technology media in 2024–2025. Companies with single digit headcount are producing outputs previously associated with far larger traditional organizations. Popular narratives frame this as the rise of the “one-person unicorn” (Andrew, 2025), a billion-dollar company operating with a single founder and AI performing much of the operational work (Sawers, 2025). We treat the one-person unicorn not as an empirically validated organizational form, but as a boundary narrative sharping the underlying research question. Under what conditions do AI-enabled tools and agentic workflows reduce coordination costs and specialization constraints sufficiently for AI-first microfirms to compete with larger incumbents?

We use AI-first microfirm as the umbrella term for organizations with single-digit human headcount operating around LLM-centered workflows. Within this category, solo-founder firms represent the lower bound, and tiny teams (2–9 people) represent the modal case.

Boundary Conditions: From Tiny Teams to the One-Person Thesis

The cases analyzed here involve solo founders and teams of two to six individuals. They therefore do not demonstrate a literal one-person unicorn is currently a stable archetype. Rather, they illustrate the structural mechanisms making such a claim theoretically possible. The one-person unicorn thesis should be understood as a limiting case. The one person becomes plausible only when task modularity is high, agent autonomy is bounded but effective, toolchain integration is mature, and governance oversight prevents compounding error. Where these conditions weaken, headcount compression becomes fragile and not scalable.

This article repurposes practitioner and media evidence into a synthesis intended for entrepreneurs, managers, and scholars interested in startup emergence. We integrate (a) research on the comparative strengths of small versus large teams (Colombo, 2019; Kellogg Insight, 2019; Wu et al., 2019), (b) organizational design patterns for small development teams (ScrumPLoP, n.d.), and (c) recent case evidence from AI-native startups and experimental reporting from the technology press (Applegate, 2025a, 2025b; Bort, 2025; Currier, 2025; Ratliff, 2025; Temkin, 2025). Our goal is not to adjudicate precise financial claims, but to formalize an emerging design logic and to surface the governance risks likely determining whether the one-person unicorn remains a motivational slogan or becomes a repeatable organizational archetype.

Conceptual Background: Why Small Teams Can Win

Classic work on teams argues that as headcount increases, coordination costs rise non-linearly due to expanding communication links, increased process requirements, and greater potential for process loss. This emphasis on coordination dynamics is consistent with broader team science findings that context and team composition shape collaborative outcomes, and that small, tightly coupled teams may exhibit stronger shared cognition and adaptive coordination under uncertainty (Reiter-Palmon et al., 2021). Pattern-language guidance from agile software communities emphasizes that small teams enable faster feedback, clearer shared context, and fewer handoffs (ScrumPLoP, n.d.). Empirically, large-scale analyses of scientific and technological outputs suggest a systematic division of labor: small teams are more likely to introduce disruptive directions, whereas large teams more often develop and extend established trajectories (Wu et al., 2019). Practitioner syntheses make a similar argument in applied settings, suggesting small teams can pursue untested opportunities because they face fewer internal veto points and lower reputational commitment to the status quo (Colombo, 2019; Kellogg Insight, 2019).

Beyond coordination costs, team science highlights the importance of contextual and compositional factors in shaping innovation outcomes. Reiter-Palmon, Kennel, and Allen (2021) argue that team creativity and innovation are strongly influenced by team size, composition, and collaboration processes, particularly in small organizational contexts where formal hierarchy is limited and shared cognition becomes critical. Small teams operating under high interdependence may develop stronger shared mental models and adaptive collaboration patterns, which support experimentation and rapid iteration. These characteristics are especially relevant in AI-first microfirms, where founders and collaborators must continuously evaluate and integrate outputs generated by large language models and agentic systems. In such environments, the key performance variable shifts from raw production capacity to evaluative judgment and collaborative sense-making.

Importantly, Reiter-Palmon et al. (2021) emphasize that innovation in small teams depends not only on size but also on how collaboration is structured and how cognitive diversity is managed. In tightly coupled teams, the benefits of small size emerge when members engage in active information sharing, constructive conflict, and collective problem solving. These dynamics align with the AI-first context described here: tiny teams leveraging AI tools may generate high output, but sustainable innovation depends on the human team’s ability to critique, refine, and integrate AI-generated work rather than accept it unexamined. Thus, the small-team advantage is conditional, mediated by collaboration quality and cognitive engagement.

In venture contexts, this scaling dynamic has historically appeared in visible cases such as Instagram’s acquisition by Facebook with approximately 13 employees and WhatsApp’s acquisition with roughly 55 employees (Currier, 2025). These examples are often cited as early signals of “allometric scaling” in software businesses. What changes in the generative AI era is the availability of general-purpose production capacity of writing, coding, design, research summarization at marginal cost and with immediate responsiveness. AI does not eliminate the coordination logic identified in team research; rather, it amplifies it by compressing production tasks while leaving evaluative and integrative work as the core human function.

From Small Teams to AI-First Microfirms

We define an AI-first microfirm as an organization with an operating model design centric around LLMs and agentic workflows from inception, such that human roles emphasize orchestration, judgment, and accountability rather than routine production. In this model, “team size” becomes ambiguous, the firm may have few employees but operate alongside many software agents, copilots, and third-party automations behaving like semi-autonomous workers (Sawers, 2025; Ratliff, 2025).

AI-first microfirms therefore represent a context in which the mechanisms described in team creativity research become particularly visible. As Reiter-Palmon et al. (2021) note, team innovation outcomes are shaped by how composition, collaboration, and cognitive processes interact. In AI-first microfirms, human team members increasingly occupy roles centered on oversight, integration, and judgment rather than direct production. This shifts the locus of competitive advantage from headcount to coordination quality and cognitive alignment. Tiny teams may thus outperform larger incumbents not simply because they are smaller, but because their collaborative structure allows them to rapidly test, evaluate, and integrate AI-generated outputs without bureaucratic delay.

Three mechanisms plausibly underpin this shift in labor. First, AI compresses the cost of drafting and iteration, increasing the speed of experimentation. Second, AI lowers the expertise threshold for adjacent tasks e.g., a backend engineer temporarily operating in frontend work with AI support thus supporting “specialist-to-generalist” role expansion (Applegate, 2025b). Third, integrated toolchains (LLM copilots, workflow automations, evaluation and monitoring layers) reduce handoffs and allow founders to treat AI as a standing capability rather than an ad hoc tool.

2. Method and Evidence Base

This article adopts a theory-building qualitative synthesis design drawing on multiple evidence layers. Because AI-first microfirms are an emerging organizational form with limited publicly available financial disclosure, we integrate peer-reviewed research, practitioner syntheses, technology journalism, and founder-reported documentation to identify recurring design patterns and governance risks.

Evidence Layers – From Peer Research to Founder Narratives and Ecosystem Artifacts

1. Peer-Reviewed Research

We draw on established research in team dynamics and innovation, particularly work on small versus large teams and disruptive versus developmental contributions (Wu, Wang, & Evans, 2019), as well as team creativity and collaboration processes in small organizational contexts (Reiter-Palmon, Kennel, & Allen, 2021). These sources provide theoretical grounding for claims about coordination costs, disruption, and collaboration quality.

2. Practitioner and Media Syntheses

We incorporate practitioner commentary and analytic reporting that synthesize empirical research for applied audiences (Colombo, 2019; Kellogg Insight, 2019). These sources are used to contextualize and interpret academic findings rather than serve as standalone empirical evidence. Technology journalism (e.g., Applegate, 2025a, 2025b; Bort, 2025; Ratliff, 2025; Sawers, 2025; Temkin, 2025) is used to document founder claims, operating models, and reported outcomes in AI-first startups. Such reporting reflects publicly disclosed narratives and is treated as descriptive rather than verified performance validation.

3. Founder Narratives and Ecosystem Artifacts

Given the nascency of AI-first microfirms, we also reviewed founder blog posts, public build logs, interviews, and curated “tiny team” or revenue-per-employee dashboards where available. These ecosystem artifacts include curated tiny-team directories (TinyTeams.xyz, n.d.), founder blog posts (Bentes, 2025.; Doc-e.ai, 2025), venture commentary (The VC Corner, 2025), and technology journalism (Applegate; 2025; Sifted, 2025) are used to triangulate emerging workflow patterns and organizational design logics. They are treated as field signals rather than audited evidence. Similar to early-stage internet entrepreneurship research, such real-time documentation provides insight into experimentation before formal datasets are available.

Where financial claims (e.g., monthly recurring revenue, acquisition valuation, headcount) are referenced, they are presented as founder-reported or media-reported figures and should not be interpreted as independently verified performance data.

Case Selection Criteria

Cases were selected using three criteria:

1. Explicit claims of achieving substantial output or revenue with single-digit human headcount.
2. Clear descriptions of how large language models (LLMs) and agentic workflows are embedded in operating processes.
3. Variation in AI usage intensity and governance structure (e.g., solo-founder models, 4–6 person AI-native teams, and the HurumoAI fully agentic experiment).

The selected cases (Base44, AI Apply, Oleve, and HurumoAI) therefore represent illustrative exemplars rather than a representative sample of AI startups.

Analytic Approach to Cross Case Comparison

We conducted cross-case comparison to identify recurring mechanisms related to:

• Task decomposition and modularity
• Agent autonomy and permissioning
• Toolchain integration
• Human oversight and accountability

Patterns are mapped against established team research to evaluate consistency with known coordination and innovation dynamics (Reiter-Palmon et al., 2021; Wu et al., 2019). Disconfirming evidence, particularly failure modes observed in the HurumoAI experiment (Ratliff, 2025), was used to refine boundary conditions.

Epistemic Positioning and Limitations

This study is explicitly theory-building rather than hypothesis-testing. We do not adjudicate the financial accuracy of revenue or valuation claims reported in media or founder sources. Instead, we extract structural design features and governance implications observable across multiple narratives.

Future research should validate the proposed design logic using:

• Primary founder interviews

• Standardized measurement of AI usage intensity

• Longitudinal tracking of revenue-per-employee ratios

• Comparative analysis against non-AI-first microfirms

As such, this paper should be read as a conceptual synthesis of an emerging organizational pattern rather than a definitive empirical evaluation of performance outcomes.

3. Case Studies

Base44: Vibe Coding, Rapid Scale, and Acquisition

Base44 is previously reported as founder-led AI-first microfirm operating initially with a single primary decision-maker and six-month-old startup that built an AI-enabled “vibe coding” product and sold to Wix for approximately $80 million in cash (Bort, 2025). The case is instructive less for the precise valuation and more for the underlying operating assumption that modern LLM-assisted development can allow a founder to ship a product and attract meaningful buyer interest on timelines previously requiring larger engineering organizations. From a small-team lens, Base44 exemplifies how AI reduces the need for functional specialization early in the firm lifecycle—especially in prototyping, documentation, and iteration while preserving founder control over product direction.

AI Apply: Revenue Without Payroll

AI Apply, profiled via a founder interview, describes a two-founder business generating reported monthly revenue while operating with no full-time employees, positioning the product as a job-hunting assistant automating parts of the application process (Cramer, 2024). The case highlights a distinctive feature of AI-first microfirms substituting payroll with model-usage costs and third-party SaaS subscriptions. If output is constrained by the founder attention rather than staff capacity, the key managerial question becomes how to allocate human attention toward tasks requiring judgment (e.g., product strategy, trust and safety, partnerships) versus tasks that can be delegated to structured prompts, automations, and agents.

Oleve: Hiring for AI Leverage and System Thinking

In an “as-told-to” account, the Oleve cofounder describes building an AI-driven consumer software portfolio with a team of roughly four to six people, using AI to “stay tiny” while scaling (Applegate, 2025b). The narrative foregrounds a practical talent model with tiny teams rewarding people who can learn quickly, systematize their work, and move across functions with AI support. A key disqualifier is treating AI as a substitute for thinking and submitting unexamined outputs that become brittle systems in the absence of middle-management review layers (Applegate, 2025b). This case underscores that the core scarce resource in AI-first microfirms is not content generation but evaluative judgment and systems design.

HurumoAI: Stress-Testing the Fully Agentic Company

Ratliff (2025) provides a rare naturalistic experiment with a founder attempting to run a startup with AI “employees” and “executives,” assigning agent personas roles such as CTO, CMO marketing lead, and operations. While agents can produce code and plans quickly, the experiment surfaces structural hazards such as fabricated progress reports, runaway actions, and the need for continual human supervision. In contrast to the optimistic “one-person unicorn” thesis, HurumoAI shows increased autonomy can amplify failure modes when monitoring, permissioning, and evaluation is weak. The case is analytically valuable because it helps identify where small-team advantage descends into fragility. That is, when errors compound faster than humans can detect and correct them.

Reiter-Palmon et al. (2021) further caution that small-team innovation is vulnerable when collaboration processes are weak or when diversity is unmanaged. This insight is particularly salient in agentic AI contexts. As demonstrated in the HurumoAI experiment, increased agent autonomy without corresponding human oversight can lead to fabricated outputs, compounding errors, and accountability gaps. Thus, while AI may reduce production bottlenecks, it does not eliminate the need for structured collaboration and evaluative discipline. The small-team advantage persists only when governance and cognitive engagement remain strong.

4. Emerging Trends Across Cases

 

Across the cases, four recurring patterns appear. First, founders decompose work into modular tasks that map cleanly onto agentic workflows (e.g., drafting, coding, outreach, analytics), while reserving strategy and high-stakes decisions for humans. Second, the organization becomes a toolchain. Value creation depends on how well models, automations, and evaluation layers are integrated, not simply on model capability. Third, hiring criteria shifts toward AI leverage and systems thinking. Candidates must demonstrate judgment, error detection, and the ability to build repeatable processes rather than one-off deliverables (Applegate, 2025b). Fourth, market narratives and investor expectations increasingly align with agentic scaling. For example, TechCrunch reporting on Y Combinator’s Spring 2025 Demo Day notes reporting from Y Combinator’s Spring 2025 Demo Day suggests that a large share of presenting startups were developing AI agents or agent-building tools (Temkin, 2025).

These patterns align with the broader small-team literature: speed and experimentation are amplified when coordination overhead is low (ScrumPLoP, n.d.), and disruption is more likely when teams can pivot toward novel directions without institutional inertia (Wu et al., 2019). However, AI also introduces a new category of coordination cost, managing model reliability, permissions, data access, and evaluation. In this sense, AI-first microfirms replace human coordination problems with socio-technical governance problems.

A Design Logic for AI-First Tiny Companies

To translate the observations into a research-ready construct, we propose an “agentic microfirm design logic” with four linked components:

1. Task modularity refers to the degree to which work can be decomposed into discrete, specification-friendly units that can be independently executed and recombined.
2. Agent autonomy refers to the scope of permissions and decision latitude granted to AI systems without real-time human intervention.
3. Integration refers to the extent to which AI-generated outputs flow through structured toolchains (e.g., repositories, CRM, analytics, QA systems) and become durable organizational assets.
4. Human oversight refers to explicit accountability mechanisms in which humans validate, authorize, and assume responsibility for high-stakes outputs or actions.

This logic clarifies why some microfirms scale. AI increases throughput at steps or components (1)–(3), while human judgment prevents compounding error at step (4).

Research Propositions

P1. AI-first microfirms will outperform non-AI-first microfirms at similar headcount when task modularity and toolchain integration are high.

P2. The relationship between agent autonomy and performance will be inverted-U shaped; beyond a threshold, autonomy increases error propagation and reduces performance unless governance maturity is high.

P3. Hiring for evaluative judgment and systems thinking will mediate the relationship between AI usage intensity and sustainable scale in tiny teams.

P4. External dependence on model providers (pricing, rate limits, policy changes) will moderate the headcount-to-output relationship, increasing fragility for AI-first microfirms.

Practical Implications

For founders, the cases suggest a pragmatic posture to use AI to accelerate iteration, but invest early in evaluation and permissioning. Treat AI outputs as drafts for mandatory testing, not as decisions. For investors and accelerators, the “tiny team” signal should be interpreted jointly with evidence of governance constituting monitoring, auditability, and explicit human ownership of decisions. For policymakers and platform providers, the emerging risk is less the existence of small companies and more the diffusion of semi-autonomous systems acting at scale without clear accountability, especially when these systems can access personal or sensitive data.

6. Conclusion

 

AI-first microfirms are not simply smaller versions of traditional startups, they represent a different production function with LLMs and agents acting as scalable collaborators. Evidence from Base44, AI Apply, and Oleve illustrates how founders can substitute headcount with tool leverage and model usage, consistent with research showing that small teams are structurally positioned to disrupt (Wu et al., 2019). At the same time, HurumoAI demonstrates that high agent autonomy without governance can magnify failure. The one-person unicorn therefore should be treated as a conditional outcome, plausible when task modularity, integration, and oversight are strong, and unlikely when evaluation, permissioning, and accountability remain underdeveloped.

References

 

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5. Bort, J. (2025, June 18). 6-month-old, solo-owned vibe coder Base44 sells to Wix for $80M cash. TechCrunch. https://techcrunch.com/2025/06/18/6-month-old-solo-owned-vibe-coder-base44-sells-to-wix-for-80m-cash/
6. Colombo, G. (2019, April 15). Research: When small teams are better than big ones. LinkedIn. https://www.linkedin.com/pulse/research-when-small-teams-better-than-big-ones-giancarlo-colombo
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International Journal of Management Science and Business Administration

Volume 12, Issue 1, November 2025, Pages 15-24

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Modelling the Relationship between Oil Price and Stock Markets in Net Oil-Exporting and Net Oil-Importing Countries: A Panel Data Approach

DOI: 10.18775/ijmsba.1849-5664-5419.2014.XX.100X
URL: http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.XX.100X

Izuchukwu Oji-Okoro ^{1, 2*}, Seyi Saint Akadiri ¹, Job Collins Egila ^{1, 3}, Godiya John ⁴

¹Central Bank of Nigeria, Abuja, Nigeria.

² West African Monetary Agency, Freetown, Sierra Leone.

³Department of Political Science, Nigeria Defence Academy, Kaduna, Nigeria.

⁴Department of Economics, Kaduna University, Kaduna, Nigeria.

Abstract: This study examines the relationship between oil price fluctuations and stock market performance in net oil-exporting and net oil-importing nations from March 2001 to December 2024. Using a panel data approach with static and dynamic models, we analyse five oil-exporting countries (Kuwait, Qatar, Saudi Arabia, Nigeria, and Indonesia) and seven oil-importing countries (Japan, Australia, the USA, the UK, Argentina, South Korea, and France). The results reveal a positive and significant correlation between oil prices and stock returns in oil-exporting countries. In contrast, oil-importing countries experience more mixed effects, including negative impacts resulting from rising production costs. These findings suggest that oil-exporting countries should diversify their economies to reduce reliance on oil revenues. At the same time, oil-importing nations should adopt strategies to manage rising production costs, such as investing in alternative energy. These insights provide critical guidance for policymakers and investors in mitigating risks from oil price volatility.

Keywords: Oil price volatility; Stock market returns; Net oil-exporting countries; Net oil-importing countries; Panel data analysis

1. Introduction

The correlation between oil prices and stock market performance attracted sustained scholarly attention from economists, financial analysts, and policymakers. However, the existing literature predominantly concentrated on the relationship within individual countries or specific regions, overlooking the nuanced dynamics in net oil-exporting and net oil-importing countries. This study fills this research gap by employing a panel data approach static and dynamic approach.

The significance of studying the relationship between oil prices and stock markets in net oil-exporting and net oil-importing countries arises from their unique economic structures and interdependencies. Net oil-exporting countries heavily rely on oil revenues, which shape their fiscal policies, trade balances, and overall economic performance. Meanwhile, net oil-importing countries face challenges such as increased production costs and potential inflationary pressures when oil prices rise. Furthermore, it is imperative to note, that the impact of oil price fluctuations on stock market performance is vital for both types of countries.

In this study, we adopt a panel data approach, which enable for the inclusion of a broad sample of net oil-exporting and net oil-importing countries, thereby capturing heterogeneity across countries and controlling for time-varying factors. By combining the country-specific effects and time-series data, we aim to comprehensively analyse the relationship between oil prices and stock markets.

Therefore, the research objective is to empirically examine the oil price changes on stock market returns in net oil-exporting and net oil-importing countries. To achieve our research goals, we applied several econometric methods, such as fixed and random effects models, to estimate the relationship between oil prices and stock market performance.

The outcomes of our analysis are expected to contribute to both academic and practical realms. Understanding the relationship between oil prices and stock markets in net oil-exporting and net oil-importing countries can provide relevant insights for policymakers, investors, and key economic stakeholders. The results may contribute to the formulation of appropriate policy measures, risk management strategies, and investment decisions in the context of volatile energy markets.

2. Literature review

There is a lack of consensus in the literature regarding the nature of the connection between fluctuations in oil prices and the overall performance of stock markets. Nevertheless, the outcomes appear to lean more towards a pessimistic correlation (as indicated by Managi and Okimoto in 2013). Conversely, proponents of a positive relationship include Narayan and Narayan (2010), Zhu et al. (2014, 2017), Hatemi-J et al. (2017), and Silvapulle et al. (2017). Narayan and Narayan (2010) studied the impact of oil price shifts on stock prices within the Vietnam market during the period 2000–2008. Alongside confirming the existence of a prolonged association between the two, they also determined that changes in oil prices led to an increase in stock prices. The authors attribute this unexpected outcome to two distinctive factors: a heightened influx of foreign portfolio investment and a shift in the preferences of local market participants. Similarly, Zhu et al. (2014) investigated the dynamic interdependence between movements in crude oil prices and the stock markets of ten Asia-Pacific nations spanning from 2000 to 2012. Their findings suggest a predominantly weakly positive relationship before the global financial crisis, which transforms into a strongly positive relationship in the post-crisis period.

Zhu et al. (2017) analyse the impact of alterations in oil prices on the returns of stocks within a selection of countries categorized as either oil-exporting or oil-importing. The investigation spans the years from 1997 to 2015. The findings of this study reveal that instances of positive collective shocks in oil prices have the potential to generate an increase in stock returns. The authors, however, specify that this phenomenon materialises exclusively in cases where the oil price shock stems from demand-related shocks.

Silvapulle et al. (2017) examined the enduring connection between shifts in oil prices and the stock markets of major oil-importing nations, analyzing the time frame spanning from 1999 to 2015. Their analysis uncovers evidence that establishes a favorable correlation between fluctuations in oil prices and stock indices, with this relationship being particularly prominent in periods after the global financial crisis. The authors interpret this observation as indicative of a fundamental shift in the behavior of the interrelationship between oil prices and stock markets.

In a similar vein, Hatemi-J et al. (2017) explore the causal between oil prices and the stock markets of the G7 nations over the period from 1975 to 2013. The overarching conclusion derived from their investigation highlights that ascending oil prices lead to a corresponding increase in stock prices. The authors interpret this phenomenon as an indicator that stock markets within these nations perceive rising oil prices as favorable news, potentially reflecting a positive economic outlook.

Other researchers who have presented findings either indicating no relationship or displaying mixed relationships encompass Huang et al. (1996), Cong et al. (2008), Apergis and Miller (2009), and Miller and Ratti (2009). Huang et al. (1996) investigate the dynamic connections between changes in oil prices and stock prices in the United States. Their ultimate observation is that variations in oil returns are not correlated with shifts in stock market returns, except in the instance of returns associated with oil companies. Hence, the authors assert that scant evidence exists to substantiate the often-cited economic significance of oil. They propose that oil futures might serve as a viable instrument for diversifying stock portfolios.

Similarly, Cong et al. (2008) examine the interplay between alterations in oil prices and the Chinese stock market from 1996 to 2007. The central finding of their analysis is that shocks to oil prices primarily exert minimal influence on stock returns within the country. Turning to the research of Apergis and Miller (2009), they direct their attention towards scrutinizing the repercussions of fluctuations in oil prices on the returns of stocks across eight distinct countries. The outcomes of their investigation indicate that the reaction of stock returns to abrupt movements in oil prices remains relatively subdued.

In a different context, Miller and Ratti (2009) explore the prolonged connection between fluctuations in global crude prices and international stock markets from 1971 to 2008. Their findings unveil a negative relationship during the time intervals of 1971–1980 and 1988–1999. In contrast, the connection is found to be statistically insignificant between 1980 and 1988. Managi and Okimoto (2013) propose that the correlation between oil prices and stock performance is contingent on the specific sector being considered. In resource-dependent economies, macroeconomic variables are also strongly influenced by commodity price dynamics. For instance, Musa et al. (2024) show that exchange rate movements in Nigeria are closely linked to the country’s mono-resource structure, highlighting the broader macro-financial effects of oil dependence. Similarly, Peace et al. (2016) find that exchange rate fluctuations significantly influence tourism sector output in Nigeria, further illustrating how macroeconomic volatility can affect sectoral economic performance. Unlike various sectors, where the effects of oil price fluctuations are not as pronounced, oil price movements exert a direct influence on stock returns within the oil and gas sector (Ramos and Veiga, 2011). This phenomenon stems from the fact that oil not only constitutes the primary output of this sector but also serves as a primary input in numerous production processes. Furthermore, various sectors of the economy, such as automobiles, chemicals, manufacturing, and transportation, rely heavily on the oil output generated by the oil sector. As a result, elevated oil prices frequently lead to increased profit margins for oil companies, subsequently enhancing their value within the stock market.

Within this context, existing studies that focus on specific sectors can be classified into three categories. The first group of studies indicates a positive relationship between oil prices and the returns of companies within the oil and gas industry. For instance, Sadorsky (2001) and Boye and Filion (2007) ascertain that rising oil prices correspond to heightened stock returns for oil and gas firms in Canada. A similar outcome is found by El-Sharif et al. (2005) for the UK market. Nandha and Faff (2008) observe that, aside from the mining and oil and gas industries, rising oil prices negatively impact other sectors. Li et al. (2017) demonstrate that Chinese firms within the oil industrial chain experience positive effects on their returns from oil price increases. Similarly, Akdeniz et al. (2021) discover that during the COVID-19 pandemic, oil prices drove higher returns within the oil and gas sector.

A second cluster of studies suggests that the relationship is predominantly negative for industries where oil comprises a significant proportion of production costs. Nandha and Faff (2008) highlight that the transport sector's returns respond unfavourably to oil price hikes. Similarly, Faff and Brailsford (1999) reveal that the Australian transport sector's stock prices exhibit a negative sensitivity to increased oil prices. Cameron and Schnusenberg (2009), as well as Aggarwal et al. (2012), document the adverse impact of oil price increases on transportation firms' returns. According to Özkan (2023), higher oil prices triggered by demand shocks specific to the oil industry do not lead to improved stock returns in the oil and gas sector.

The existing literature has extensively addressed the broader relationship between the stock market and oil prices, with a lesser emphasis on differences between oil-exporting and oil-importing economies. In addition, recent studies highlight the growing role of financial mechanisms in influencing market behaviour. For instance, Gu et al. (2023) show that access to green finance can shape investment and consumption decisions, underscoring the broader influence of financial systems on economic outcomes. Similarly, Ostic et al. (2025) demonstrate that leadership experience and environmental institutional pressures can significantly stimulate eco-innovation and responsible production practices.

Filling this gap in the literature constitutes the primary focus of this study. This article contributes to the existing body of knowledge by concentrating on the relationship between oil prices and stock markets in net oil-exporting and net oil-importing countries.

3. Research Methodology and Data

3.1 Data and Source

The paper employed balanced data collected from World Development Indicators (WDI) on monthly Brent crude and West Texas Intermediate (WTI) prices and stock prices ranging from March 2001 to December 2024 from five net oil-exporters including Kuwait, Qatar, Saudi Arabia, Nigeria and Indonesia with a sample size of 6797. The net oil-importing countries are Japan, Australia, the United States of America (USA), the United Kingdom (UK), Argentina, South Korea, and France with a total observation of 10647. Countries were classified according to their dominant oil trade position during most of the sample period, consistent with classifications commonly used in energy-economics studies. Although the study period spans March 2001 to December 2024, some observations were unavailable for certain countries, resulting in an unbalanced panel dataset.

3.2 Model Specification and Estimation Techniques

This study adopts a panel data approach to establish the relationship between oil prices and stock markets in net oil-exporting and net oil-importing countries. According to Wooldridge (2002), Panel data methods are the appropriate econometric techniques used to estimate parameters, estimate partial effects of interest in nonlinear models, measure dynamic relationships, and make correct inferences when data are available on repeated cross-sections. The paper notes that panel data allows for systematic, unobserved differences across units that can be correlated with observed factors to be measured unlike in the case of cross-sectional data analysis.

Kunst (2011) contends that the merit of panel analysis over typical time-series analysis lies in the larger sample size, noting that additional information may imply an increase in the degrees of freedom for estimating model parameters and for conducting hypothesis tests. Furthermore, panels with individual dimensions are generally more informative than aggregate time series (Baltagi, 2005). Panel data analysis can be categorized into Static Panel data and Dynamic Panel data. The static analysis establishes the long-run relationship between the variables of interest, while the dynamic measures the short-run association.

3.3 Model Specification

Exploratory analysis indicates that the data is not normally distributed and skewed with a heavy tail towards the right. The study, therefore, applied both static and dynamic panel data analyses. Regarding static, the paper performed pooled regression (PR-OLS estimator), fixed effects (FE-Within Estimator), and random effects (RE) uses Quasi Demeaning Generalized Least Square (GLS) & Maximum Likelihood (ML) estimators. Prior to estimation, stationarity tests were conducted to examine the time-series properties of the variables. In addition, heteroskedasticity-robust standard errors were employed to account for possible serial correlation and heterogeneity across panel units.

3.3.1 Static panel data analyses

The pooled regression model can be specified as:

𝑦_𝑖,𝑡 = 𝛼 + 𝛽𝑥_𝑖,𝑡 + 𝜀_𝑖,𝑡      (1),

Where 𝑦_𝑖𝑡 represents stock market returns for country 𝑖at time 𝑡, while 𝑥_𝑖𝑡 represents the oil price variables measured by Brent crude and West Texas Intermediate (WTI) prices. 𝛼, 𝑎𝑛𝑑 𝛽 are the parameters to be estimated, 𝜀 is a white noise. 𝑖 = 1, … , 𝑁; 𝑡 = 1, … , 𝑇 with i signifying country i and t representing time t. Pooled regression assumes that common parameters, i.e. regression parameters remain constant across individual countries (Kunts, 2011).

3.3.2 Fixed effects model

Relaxing the restrictive assumption of common parameters, (1) becomes

𝑦_𝑖,𝑡 = 𝛼 + 𝛽𝑥_𝑖,𝑡 + 𝜇_𝑖,𝑡 + 𝜀_𝑖,𝑡      (2)

Where 𝜇 reflects the individual country’s characteristics.

3.3.3 Random effects model

The random effects model is stated as:

𝑦_𝑖,𝑡 = 𝛼 + 𝛽𝑥_𝑖,𝑡 + 𝜇_𝑖,𝑡 + 𝑣_𝑖,𝑡      (3)

𝜀_𝑖,𝑡 = 𝜇_𝑖,𝑡 + 𝑣_𝑖,𝑡      (4)

𝑣_𝑖,𝑡 is assumed to be white noise.

3.4 Dynamic panel data analyses

To investigate the long-run relationship between oil prices and stock market returns, this paper conducted a dynamic panel analysis, both system and differenced approaches. The dynamic specification is estimated using the system-GMM framework, which helps address potential endogeneity associated with lagged dependent variables. A dynamic panel model can be specified as:

𝑦_𝑖,𝑡 = 𝛼 + 𝜔𝑦_{𝑖,𝑡−1} + 𝛽𝑥_𝑖,𝑡 + 𝜇_𝑖,𝑡 + 𝑣_𝑖,𝑡      (5)

Where 𝜀_𝑖,𝑡 = 𝜇_𝑖,𝑡 + 𝑣_𝑖,𝑡      (6)

The study follows Jung (2005) where 𝜀𝑖,𝑡 = 𝜇𝑖,𝑡 + 𝑣𝑖,𝑡 is assumed to have a typical one-way error component construction where:

𝐸_(𝜇𝑖) = 𝐸_{(𝑣𝑖,𝑡 )} = 𝐸_{(𝜇𝑖𝑣𝑖,𝑡 )} = 0      (7)

With the introduction of the first lag of the dependent variable, 𝑦_{𝑖,𝑡−1} , we encounter the problem of autocorrelation, and the OLS estimator becomes biased and inconsistent. This is because the assumption of 𝐸[𝑦_𝑖,𝑡 , 𝜀_𝑖,𝑡 ] = 0 no longer holds and 𝐸[𝑦_𝑖,𝑡 , 𝜀_𝑖,𝑡 ] ≠ 0. Here, the fixed effect or the Within estimator is valid only with large sample size (Sevestre and Trognon,1985). Dynamic panel data are system panel and differenced panel data. The resolution of this problem would require the inclusion of instrumental variables that are not correlated with the stochastic term.

4. Results and Discussion

This chapter discusses the estimation process and results. The paper first performed preliminary analyses to understand the nature of the data. The rest of this chapter is organised as follows: Chapter 4.2 discusses the results of the static panel analyses for both net oil exporting and net oil importing countries and follows it up with Chapter 4.3, which considers the dynamic analyses for the sub-groups involved in oil trade. The chapter concludes by summarising the core empirical results.

4.1 Preliminary Analysis

4.1.1 Descriptive Statistics for Net Oil Exporting Countries

Table 4.1.1 presents the descriptive statistics for the data set on net oil exporting countries. Results show that the mean prices for Brent crude, WTI, and stock are 66.02, 63.85, and 9911.70, respectively. The mean values show the average price of the variables over the sample period. The median values of Brent, WTI, and stock are 62.28, 62.94, and 6863.59 in that order. The median value signifies the central value of the data, and the fact that the mean values are different from the median values indicates that the data is not symmetrical but skewed. Furthermore, the maximum and minimum values for Brent, WTI, and stock are (133.90, 18.60), (139.96, 19.46), and (65075.02, 358.23), respectively. Further, the standard deviation values of 32.68, 28.54, and 10527.76 for Brent, WTI, and stock show how dispersed the observed values are around their mean. Both the means and the medians lying within the range of the data implies that the data is consistent. The Jarque–Bera statistics indicate that the variables deviate from a normal distribution. Total observations are 970.

Table 4.1.1: Summary Statistics of Net Oil Exporting Countries

	BRENT	WTI	STOCK
Mean	66.65	64.41	4848.10
Median	62.56	64.90	4156.75
Maximum	133.90	139.96	20585.24
Minimum	18.60	19.46	202.45
Std. Dev.	32.73	28.56	3881.84
Skewness	0.23	0.15	1.47
Kurtosis	1.73	1.90	5.25
Jarque-Bera	116.21	82.51	872.19
Observations	1520	1520	1520

Note: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

4.2 Static panel data analysis

4.2.1 Static Panel Data Analysis for Net Oil Exporting Countries

The output of the static panel data analysis is reported in Table 4.2.1. The regression results evaluate the impact of oil price movements (WTI and Brent) on stock market returns across oil-exporting countries. rwti and rbrent represent oil price changes derived from the West Texas Intermediate (WTI) and Brent crude oil benchmarks. POLS, FE, and RE denote the pooled regression, fixed-effects, and random-effects estimators, respectively. The results show that the coefficients on WTI and Brent in the pooled regression, fixed-effects, and random-effects models are all positive and significant at the 1 percent level. Being positive also implies that an increase in the price of WTI or Brent will lead to a rise in stock market returns in net oil-exporting countries in the long run. In this context, the estimated coefficients indicate the sensitivity of stock returns to changes in oil prices rather than changes in oil prices themselves. This outcome confirms the findings of Hammoudeh and Li (2005). The paper conducted a study on the Norwegian and Mexican stock markets. The study notes that an increase in oil prices has a positive impact on stock markets in oil-exporting countries. Bjørnland (2009), Basher et al. (2018), and Zhang and Asche (2014) all found a similar positive correlation between stock markets and oil prices in oil-exporting countries.

Park (2010) explains that fixed effects consider the individual time or group to have different intercepts in the regression equation. Random effects, on the other hand, assume that individual times or groups have different stochastic terms. Thus, fixed effects say that the individual characteristics are relevant and therefore should be included in the regression equation as observable regressors, while random effects maintain that the individual characteristics, although relevant, are not observable and should be considered as part of the stochastic term. To have both FE and RE significant for WTI and Brent requires that further tests are carried out to determine which model best fits the data under consideration. As such, the study performed the Hausman test to select the appropriate model. The test result shows a Hausman coefficient of 0.000 and a p-value of 1.000. Against the hypotheses:

Ho: There is random effect

H1: There is fixed effect,

We failed to reject the null hypothesis and conclude that the appropriate model for this data is random effect. This means that irrespective of the oil type, price increase will positively affect the stock market in net oil-exporting countries.

The results from a static panel data analysis use three estimation techniques: Pooled Ordinary Least Squares (POLS), Fixed Effects (FE), and Random Effects (RE). The results for each technique are reported for both WTI and BRENT prices as dependent variables, with a panel dataset consisting of 965 observations from five countries. The key independent variable in this analysis is the ratio of WTI to BRENT prices (rwti/rbrent). Across all three models (POLS, FE, and RE), the coefficient of the rwti/rbrent variable is positive and highly significant at the 1% level for both WTI and BRENT prices. For the WTI models, the coefficient of rwti/rbrent is 0.220 (POLS, FE, and RE), implying that a one-unit increase in oil prices is associated with a 0.220 increase in stock market returns in oil-exporting countries. Similarly, for the BRENT models, the coefficient is 0.191, indicating that a unit increase in the rwti/rbrent ratio is associated with a 0.191 unit increase in BRENT prices.

Table 4.2.1 Results of Static Panel Data Analysis for Net Oil Exporting Countries

	WTI (rwti)			BRENT (rbrent)
VARIABLES	POLS	FE	RE	POLS	FE	RE
rwti/rbrent	0.220***	0.220***	0.220***	0.191***	0.191***	0.191***
	(0.0224)	(0.0224)	(0.0224)	(0.0233)	(0.0234)	(0.0233)
Constant	0.785***	0.785***	0.785***	0.804***	0.804***	0.804***
	(0.214)	(0.214)	(0.214)	(0.217)	(0.217)	(0.217)
Observations	965	965	965	965	965	965
R-squared	0.091	0.091		0.065	0.065
Country effect	NO	YES	YES	NO	YES	YES
year effect	NO	NO	NO	NO	NO	NO
rmse	6.634	6.645	6.634	6.729	6.740	6.729
F-test	96.64	96.34		66.88	66.67
Prob > F	0	0		0	0
Number of countryid		5	5		5	5
F-test(u_i=0)		0.246			0.239
Prob > F(u_i=0)		0.912			0.916
chi-squared			96.64			66.88
Prob > chi2			0.000			0.000
chi-square (01)			0.000			0.000
Prob > chi2			1.000			1.000
Hausman			0.000			0.000
Prob > chi2			1.000			1.000

Source: Authors Computation

These results suggest that changes in the ratio between WTI and BRENT prices are strongly correlated with movements in both oil price benchmarks. Furthermore, the consistency of these coefficients across the POLS, FE, and RE models implies that the relationship is robust to the different estimation techniques employed. The overall fit of the models, as measured by the R-squared, is relatively low. The R-squared value is 0.091 for the WTI models and 0.065 for the BRENT models, indicating that the rwti/rbrent variable explains only about 9.1% of the variation in WTI prices and 6.5% of the variation in BRENT prices. While these values are modest, they are not unexpected in economic models where price movements are influenced by a multitude of factors beyond the scope of this analysis.

The statistical significance of the models is confirmed by the F-test results, which indicate that the models are highly significant as a whole (Prob > F = 0.000). This demonstrates that the explanatory variable (rwti/rbrent) significantly contributes to explaining variations in WTI and BRENT prices, even if the overall explanatory power of the model is limited. The Fixed Effects (FE) and Random Effects (RE) models account for potential unobservable heterogeneity across countries. However, the F-test for country-specific effects (F-test(u_i=0)) yields p-values of 0.912 for WTI and 0.916 for BRENT, suggesting that these effects are not statistically significant. This suggests that the variations across countries do not significantly influence the relationship between WTI and BRENT prices in the dataset.

The Hausman test, which compares the FE and RE models, has a p-value of 1.000, indicating no significant difference between the two models. This indicates that the Random Effects (RE) model is appropriate for this dataset, as it assumes that any unobserved individual effects are uncorrelated with the explanatory variables.

The RMSE values for the WTI models are approximately 6.63, and for the BRENT models, around 6.73. These values provide an estimate of the average prediction error of the models, with lower values indicating better predictive accuracy. The close RMSE values across the three estimation methods further confirm the robustness of the relationship between the rwti/rbrent ratio and oil prices.The results of this analysis indicate a statistically significant and positive relationship between the WTI to BRENT price ratio (rwti/rbrent) and the levels of WTI and BRENT prices. The estimated coefficients show that WTI prices are slightly more sensitive to changes in the ratio than BRENT prices. Despite the statistical significance of the models, the relatively low R-squared values suggest that additional factors not included in the models may play a significant role in determining oil price movements.

Furthermore, country-specific effects were found to be insignificant, and the Hausman test supports the application of the Random Effects model. Given these findings, future research could explore other potential determinants of oil prices, such as macroeconomic factors, geopolitical events, and supply-demand dynamics, to further enhance the explanatory power of the models.

5. Conclusion

This study highlights the significant relationship between oil price fluctuations and stock market performance in both net oil-exporting and net oil-importing countries. Using a panel data approach, we find that oil-exporting countries experience a positive correlation between rising oil prices and stock market returns, driven by their economic dependence on oil revenues. In contrast, oil-importing countries exhibit mixed responses, where rising oil prices tend to increase production costs, negatively impacting stock market returns in some cases. The findings underscore the complex dynamics of the oil-stock market nexus, varying significantly based on a country's oil dependency status. We recommend that for the oil-exporting countries, policymakers should prioritize economic diversification to reduce vulnerability to oil price volatility. Investment in non-oil sectors can buffer economies from sharp declines in oil prices and ensure more stable stock market performance. Furthermore, implementing counter-cyclical fiscal policies, such as sovereign wealth funds or stabilization funds, can help manage revenue fluctuations from oil price swings and stabilize financial markets. On the other hand, for oil-importing countries, policymakers should focus on reducing dependence on imported oil by promoting energy efficiency and investing in alternative energy sources. This strategy would mitigate the negative impacts of oil price increases on production costs and inflation, also governments and industries should explore hedging strategies to protect against oil price shocks, thereby minimizing adverse effects on stock market performance and overall economic stability.

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