International Journal of Commerce, Accounting and Finance
DOI: 10.64823/ijcaf.2601005
The global proliferation of synthetic media—so-called deepfakes—and the parallel rise of organized disinformation have created urgent challenges for information integrity, public safety, and cultural preservation (Westerlund, 2019; Chesney & Citron, 2019). These issues disproportionately affect emerging democracies and culturally diverse societies where institutional resilience, media literacy, and technological infrastructure vary widely (Nyabola, 2018; GSMA, 2021). In Cameroon and other African states, manipulated media can threaten electoral integrity, inflame intercommunal tensions, and undermine cultural narratives when foreign-produced content or algorithmic biases misrepresent local traditions and identities (UNESCO, 2005; World Bank, 2020). Against this backdrop, mission-driven startups that combine AI capabilities with local cultural knowledge have emerged as potential defenders of truth and cultural sovereignty. CamShield AI (the subject of this case) is a Cameroonian venture developing AI tools to detect, flag, and mitigate deepfakes and disinformation while supporting community-led cultural digital archives and verification workflows.
By business model is meant “the logic and data that support a value-creation and value-capture mechanism” (Teece, 2010:7). By dynamic capabilities, we are referring to firm-level processes to sense opportunities/threats, seize opportunities, and transform organizational assets (Teece, Pisano & Shuen, 1997). Deepfake is a synthetic or manipulated audio/video content generated or altered by AI methods to falsely represent individuals or events (Westerlund, 2019). Disinformation is false or misleading information created and disseminated to deceive, often for political or financial motives. Cultural sovereignty refers to the capacity of a people or nation to control representation, narratives, and digital manifestations of their culture (UNESCO, 2005).
CamShield AI aims to address a complex socio-technical problem: how to design a sustainable business model that both captures sufficient value to remain financially viable and advances a social mission—combating deepfakes and disinformation while restoring and preserving cultural sovereignty in Cameroon and comparable contexts. This raises theoretical and practical questions about how a startup can align value propositions, customer segments, and capture mechanisms (Teece, 2010) in a resource-constrained market, how to build dynamic capabilities to adapt to rapidly evolving AI threats, and how to appropriate value given weak IP regimes and the public-good characteristics of information integrity. This paper is built on the hypothesis that a business model for CamShield AI that explicitly aligns a culturally grounded value proposition with prioritized customer segments (media, government, civil society, cultural institutions, diasporic platforms) and a mixed revenue architecture (tiered SaaS, API, public contracts, grants, and capacity-building services) can achieve internal coherence and sustainable value capture if complemented by strengthening dynamic capabilities and complementary assets (data, partnerships, reputation), thereby making the model difficult to replicate by generic global competitors (Teece, 2010; Teece et al., 1997). The aims of the study are to analyze CamShield AI’s business model using Teece’s 2010 framework., identify the key gaps and risks in value creation and capture, propose alternative business-model configurations and an implementation roadmap, recommend capability-building priorities to ensure adaptability and mission alignment.
The following research questions were formulated to investigate the above issues:
This paper contributes theoretically by operationalizing Teece’s framework in a mission-oriented African startup context, highlighting tensions between social impact and appropriability. Practically, it offers CamShield AI (and similar ventures) a structured set of recommendations for business-model refinement, capability development, and policy engagement. It also informs policymakers and funders on how to support local technological defenders of information integrity.
The study uses an exploratory single-case methodology combining theory-driven analysis with applied design thinking. The case of CamShield AI is examined via secondary literature on deepfakes, disinformation, business models, and African digital ecosystems (Teece, 2010; Westerlund, 2019; Nyabola, 2018), and it employs conceptual mapping to align business-model components with empirical constraints. Primary empirical data about CamShield AI were constructed from a mix of (a) developer-provided context (CamShield AI as a Cameroonian startup with mission above), (b) market and sector reports (GSMA, World Bank), and (c) academic literature on business models and deepfake technology. Where claims about CamShield AI’s operations are made, they are treated as illustrative, and recommendations are framed as hypotheses to be validated through fieldwork. The analysis proceeds in three steps following Teece’s methodological suggestions: (1) diagnostic mapping of existing business-model elements; (2) design of alternative configurations and prioritized experiments; (3) an implementation roadmap detailing capability-building, complementaries, and measurement. Each of the 20 findings below emerges from this analysis and links back to Teece’s conceptual constructs.
Findings .
3.1 From the perspective of multi-stakeholder pain, the layered and asymmetrical needs of CamShield AI face a heterogeneous set of customer pain points. Media organizations need fast, reliable detection integrated into newsroom workflows to avoid reputational damage; governments seek tools for electoral security and public communication integrity; civil-society fact-checkers require low-cost verification tools and training; cultural institutions desire provenance tools to protect and curate digital cultural assets; private firms want brand-protection against synthetic attacks. These segments manifest asymmetrical urgency and ability to pay: governments and large broadcasters can pay more but may pose governance risks (e.g., censorship), while civil society and cultural institutions have mission alignment but limited budgets (Nyabola, 2018; Teece, 2010). CamShield’s model must therefore reconcile diverse needs without diluting mission.
3.2 Value proposition must combine detection with cultural context Detection alone is necessary but insufficient. CamShield’s unique value proposition is the combination of technical detection and culturally contextualized interpretive analytics: recognition of local languages, cultural markers, and metadata that indicate misrepresentation of cultural artifacts. This duality reduces false positives common in tools trained on Global North datasets and increases trust among local stakeholders (Westerlund, 2019; Floridi et al., 2018).
3.3 Proprietary datasets as critical appropriability asset A defensible appropriability regime for CamShield requires proprietary datasets—annotated local-language media, cultural-heritage metadata, and labeled examples of local deepfakes. These datasets are costly to build but confer sustained advantages because they are hard to replicate and are sensitive to local cultural knowledge (Teece, 2010; Zott et al., 2011). Ethical stewardship and community ownership models are essential to avoid extractive practices.
3.4 Mixed revenue architecture is necessary because pure grant or donor dependence undermines sustainability; pure commercial approaches may conflict with mission. CamShield should adopt a hybrid revenue architecture: tiered SaaS for media and enterprises, API-based licensing for tech partners, enterprise/custom integration for government and telecoms, fee-for-service training and consulting, and targeted grants for public-good research and cultural digitization (Amit & Zott, 2001; Osterwalder & Pigneur, 2010).
3.5 Pricing must balance affordability and sustainability; it must be calibrated to perceived value (reputational risk avoided, legal costs prevented) and customers’ willingness to pay. For smaller civil-society entities and cultural organizations, subsidized or freemium tiers (basic detection and manual verification queues) are appropriate; for broadcasters and government agencies premium SLAs and integrated workflows justify higher prices. Transparent social-impact pricing (cross-subsidy) can sustain mission alignment (Teece, 2010).
3.6 If CamShield can become a platform aggregating verified media, flagged deepfakes, and community annotations, network effects would emerge: with more users providing more signals and labeled data, improving detection and strengthening the value proposition for all users. But realizing such effects requires robust governance and data-sharing agreements that respect cultural sovereignty and data privacy (Zott et al., 2011).
3.7 With complementary assets, local partnerships matter more than IP. In contexts with weak IP enforcement, complementary assets—distribution channels, regulatory allies, media partnerships, cultural institutions, local civil-society networks—are more critical than patents. These assets help with market access, legitimacy, and scaling while reducing imitation risk (Teece, 2010).
3.8 Dynamic sensing is a continuous requirement because deepfake techniques evolve rapidly; thus, CamShield needs active sensing mechanisms: threat intelligence pipelines, partnerships with global detection labs, and community reporting systems to capture new manipulative patterns in local contexts. Sensing must be resourced to avoid technological obsolescence (Teece et al., 1997).
3.9 Seizing requires flexible commercialization pathways. To monetize innovations rapidly, CamShield must have pre-configured commercial channels—ready-made SaaS offerings, partner revenue shares with platforms and ISPs, and prepared tender templates for public contracts. Quick-to-deploy products increase first-mover advantages and create learning loops (Chesbrough, 2007; Teece, 2010).
3.10 Transforming organizational architecture for scale is necessary. As CamShield scales, it must transition from a founder-centric, product-development organization to a modular structure—core R&D for models and datasets, a product team for delivery, partnerships/government affairs, and community-engagement units. This transformation supports sustained innovation and prevents capability atrophy (Teece et al., 1997).
3.11 Appropriability through reputation and trustworthiness are also necessary. In misinformation domains, reputational capital—credibility with media, civil-society endorsements, recognized provenance standards—can be a powerful appropriability mechanism. Trust reduces customer search costs and switching propensity in absence of strong legal IP (Floridi et al., 2018; Teece, 2010).
3.12 Ethical governance is a competitive asset; through demonstrable governance —transparent detection methods, redress mechanisms, community oversight, and cultural rights protections— it becomes a differentiator in a market where major players may be opaque. Ethical design reduces risk of co-option by bad actors and increases institutional willingness to partner (EU HLEG, 2019).
3.13 Strategic choice on government contracts is important because government contracts provide revenue and scale but can threaten mission if used for censorship or surveillance. CamShield must construct decision rules and contractual safeguards (purpose limitations, auditability) before entering state procurement (Teece, 2010).
3.14 Diaspora as early-adopter market is critical because diasporic communities and cultural platforms (e.g., digital heritage aggregators) are high-value early adopters who value cultural sovereignty and may be willing to pay for provenance services that protect cultural representation abroad. These markets also help build initial revenue and international credibility (Nyabola, 2018).
3.15 Integration with media workflows is critical for adoption. Tools that do not integrate into newsroom CMSs, social-media monitoring dashboards, or broadcast pipelines will face adoption friction. API-first design and lightweight plugins increase adoption speed, create switching costs, and embed CamShield into customer routines (Osterwalder & Pigneur, 2010).
3.16 Data governance and community-benefit models reduce extraction risk Given cultural sovereignty concerns, data collection must be governed with community consent, benefit-sharing, and options for local data stewardship. Models such as data trusts, co-ownership, and local repositories increase legitimacy and long-term data sustainability (UNESCO, 2005).
3.17 Operational cost structure is dominated by data and compute The main cost drivers are dataset curation (annotation, collection), model training compute, and maintenance. Cost optimization strategies include model distillation for edge deployment, federated learning to minimize central compute, and hybrid cloud-edge architectures to lower recurring costs (Westerlund, 2019).
3.18 Risk of commoditization and the need for layering services show that generic detection capabilities are likely to be commoditized as major cloud providers integrate anti-manipulation tools. CamShield’s defense is to layer culturally specific services (provenance, archives, advisory) and to maintain unique datasets and partnerships (Teece, 2010).
3.19 Measurement: impact and business KPIs must be dual-tracked. CamShield should track both commercial KPIs (MRR, CAC, LTV, churn) and social-impact metrics (incidents mitigated, cultural assets preserved, community reach). Dual-tracking helps balance mission-market tensions and communicates outcomes to funders and partners (Amit & Zott, 2001).
3.20 Policy engagement is a strategic capability. Proactive engagement with regulators, standards bodies, and cultural institutions shapes the rules of appropriability and market structure. CamShield must cultivate policy expertise to influence procurement standards, digital-content labeling norms, and cultural-heritage protections (UNESCO; EU guidelines).
Teece (2010) emphasizes the importance of clearly identifying customer segments and configuring the customer architecture accordingly. CamShield’s heterogeneity necessitates explicit segment prioritization: initial focus on media organizations and diasporic platforms (higher urgency and manageable sales cycles), while building subsidized offerings for civil society and cultural institutions. This sequencing exploits willingness-to-pay and builds credibility for later public contracts. Teece argues that differentiation arises from unique application of technology to customer needs. CamShield’s local-language and cultural features are coherence-building elements in the business model—integrating what is created (detection) with who benefits (local stakeholders). This reduces imitation because cultural expertise and annotated datasets are tacit assets. Teece identifies appropriability regimes as central to value capture. In weak-IP environments, proprietary datasets act as quasi-IP: costly to replicate, embedded in organizational learning, and essential for model performance. Ethical collection is both moral imperative and strategic necessity to maintain community trust.
A mixed revenue model aligns with Teece’s notion that business-model components must be mutually reinforcing. Cross-subsidization (commercial tiers funding public-good work) helps reconcile mission-market tension, but requires clear governance to avoid mission drift. Teece emphasizes that pricing must reflect value capture opportunities and appropriability. Social-impact pricing is consistent with mission-driven models but must be tested through experiments (A/B pricing, pilot contracts) to ensure viability. Network effects can transform CamShield from a tool provider to a platform of provenance and verification. However, network effects require investments in governance and incentives for contributions, or they risk collapse due to low-quality reporting.
Complementary assets over formal IP aligns with Teece’s claim that complementary assets—distribution, branding, relationships—often determine appropriation more than patents. For CamShield, partnerships with broadcasters, cultural institutions, and telecos are defensible assets. Sensing as a dynamic capability is a must because the rapid evolution of deepfake tech demands structured sensing—continuous scanning for new modalities, adversarial testing, and community-sourced reports. Such sensing capabilities are investments in the firm’s long-run adaptability (Teece et al., 1997). Seizing possibilities through commercialization readiness mean that seizing capabilities—quick productization and contractual readiness—enable CamShield to translate sensing into revenue before competitors. Pre-configured packages, and developer-friendly APIs, reduce friction and create early scaling opportunities. Transforming capabilities require CamShield to reorganize as it scales; Teece underscores reconfiguration as essential for sustaining advantage. Governance structures that preserve mission and enable commercialization are necessary to navigate scaling tensions.
Reputation can be considered as an appropriability mechanism. In sensitive fields where trust is a scarce resource, reputation functions as a powerful asset. Certification, partnerships with trusted NGOs, and transparency in methods increase switching costs for customers considering alternatives. Ethical governance reduces risk and unlocks partnerships Robust ethical frameworks (EU HLEG; Floridi et al., 2018) not only mitigate reputational harms but also become market differentiators that attract partners sensitive to rights and cultural sovereignty. Conditionality of government contracts is important because Teece warns that complementary assets can be double-edged. Government contracts can scale reach but require contractual clauses that protect against misuse. CamShield should develop procurement templates and legal counsel capacity to negotiate those safeguards. Diaspora as strategic bridge market means that diasporas often have higher purchasing power and seek cultural authenticity; serving them can produce revenue and create international proof points. This matches Teece’s prescription to identify where to compete for advantage.
Embedding solutions into workflows creates switching costs and increases customer lifetime value. CamShield should prioritize product integrations early to secure persistent revenue streams. Ethical data governance is a strategic necessity because data governance mechanisms that embed community benefit create both moral legitimacy and strategic barriers to imitation. They align with Teece’s emphasis on complementary assets rooted in local knowledge.
Teece notes that cost structure is a key element of the business model; for AI firms, compute and data dominate. Optimizing for resource efficiency (model compression, federated approaches) is a strategic priority. Commoditization risk are linked to layered services because when core detection becomes commodities, layered services (cultural provenance, advisory) preserve margins. This is consistent with Teece’s view that value capture depends on appropriation beyond the technology itself.
Teece insists that measurement must validate the business-model logic. Dual KPIs provide governance visibility on both bottom-line sustainability and mission delivery, informing strategic trade-offs. Policy engagement shapes the competitive arena Proactive policy engagement influences the appropriability regime and market rules. This aligns with Teece’s recognition that firms should shape institutions and complementary assets to sustain advantage.
Applying Teece’s framework reveals that CamShield AI’s potential hinges on aligning a culturally grounded value proposition with prioritized customer segments and a hybrid revenue model, protected by proprietary local datasets, partnerships, reputation, and governance mechanisms. Dynamic capabilities—sensing evolving threats, seizing commercialization opportunities, and transforming organizational structures—are essential for adaptability. Ethical, community-centered data governance and strategic policy engagement are critical to securing both mission integrity and long-term value capture. Practical recommendations include:
Suggested areas for future research