The Real Market in Scholarly Publishing

The Real Market in Scholarly Publishing is No Longer Content. It is Data.

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Table of Contents

Introduction

For decades, scholarly publishing revolved around a comforting assumption. Content was the asset. Academic journals were the gatekeepers. Libraries paid for access. Prestige flowed through impact factors and brand recognition. The publisher that controlled the journal controlled the market.

That logic made sense in a world defined by scarcity. Print runs were finite. Digital access was restricted. Paywalls created economic leverage. The PDF sat at the center of the business model.

That world is fading.

Today, content is still produced in staggering quantities. Articles, datasets, preprints, monographs, conference proceedings. But the economic center of gravity has shifted. The real strategic asset in scholarly publishing is no longer the article itself. It is the structured data built around it. Metadata. Citation graphs. Usage analytics. Funding linkages. Institutional performance metrics. Research intelligence dashboards.

Content is now the visible layer. Data is the infrastructure underneath.

This is not a minor adjustment in revenue strategy. It is a structural transformation in where power accumulates, how value scales, and what becomes defensible in the long term.

If you work in scholarly publishing and still think primarily in terms of journals and access models, you are looking at the surface. The deeper market has moved.

When Content Was the Core Asset

To understand the shift, it helps to remember how stable the old model felt.

In the subscription era, journals were scarce goods. Libraries paid substantial fees for bundled access. Individual scholars depended on institutional subscriptions. Publishers negotiated multiyear agreements that generated predictable revenue. Content ownership equaled bargaining power.

Even after the transition from print to digital, the logic remained intact. The format changed, but the scarcity remained. Access was controlled. Publishers priced access according to prestige, citation impact, and disciplinary demand.

Editorial excellence and brand identity mattered deeply. A high-impact journal commanded loyalty. Libraries rarely canceled top-tier titles because doing so would disadvantage their researchers. Content created leverage.

The more prestigious the journal portfolio, the stronger the pricing position.

This system rewarded scale and reputation. It also rewarded exclusivity. The fewer alternatives available, the more valuable each subscription became.

Then two forces disrupted the equation. Open access and digitization.

Open Access and the Erosion of Scarcity

Open access changed the optics and the economics.

As more articles became freely accessible, the logic of paywalls weakened. Funders began mandating public access. Governments emphasized transparency. Institutional repositories expanded. Preprint servers grew. Researchers shared PDFs directly.

The immediate debate centered on who pays instead of who reads. Article processing charges replaced subscription fees in many contexts. Transformative agreements reshaped library budgets. Discussions became heated and ideological.

But underneath the ideological battles, something quieter happened. Content became less scarce.

When millions of articles are freely available online, access itself loses some of its strategic power. The PDF becomes abundant. Discoverability, analytics, and contextualization become more valuable than mere possession.

At the same time, the volume of scholarly output continued to grow. More than six million research articles are expected to be published in 2026. Global research production now exceeds several million articles annually, and the curve remains upward. More content means more metadata, more citations, more usage signals, and more funding acknowledgments.

In other words, more data.

Open access did not eliminate value. It shifted where value accumulates.

The Rise of Research Intelligence

Look at how major players in the industry describe themselves today. They talk less about publishing and more about analytics, decision tools, and research intelligence. That linguistic shift reflects a deeper strategic repositioning.

An article is a finished product. An analytics platform is an ongoing service.

An article has limited direct monetization once published. An analytics dashboard can be licensed annually to institutions, embedded into strategic planning, and integrated into performance evaluation systems.

Research intelligence products promise to answer institutional questions such as:

  • Which departments are growing in impact?
  • Which collaborators increase citation visibility?
  • Which funding streams align with our strengths?
  • How does our output compare globally in specific fields?

These insights are derived from structured datasets that connect authors, affiliations, funding bodies, citation networks, and usage patterns. Once an institution relies on such a system to guide hiring, promotion, or funding strategy, the provider becomes embedded in governance processes. The relationship extends far beyond journal subscriptions.

Content feeds these systems. But the system, not the article, becomes the economic engine.

Data Compounds While Content Plateaus

Content is finite. Each article enters the archive and competes for attention. Some become influential. Many remain obscure. Economically, most articles generate limited direct revenue after publication.

Data behaves differently.

Every new article strengthens the citation graph. Every new funding acknowledgment enriches grant intelligence datasets. Every affiliation update improves author disambiguation systems. Every download contributes to usage analytics.

As datasets expand, their value increases. Network effects emerge. Predictive models improve with scale. Historical depth becomes a competitive advantage. A comprehensive citation database spanning decades cannot be easily replicated.

This compounding effect creates defensibility. A new journal can be launched within months. Reconstructing a large, clean, longitudinal dataset of global research activity is far more difficult.

The asset shifts from individual pieces of content to the relationships between them.

In a data-driven environment, relationships are everything.

Artificial Intelligence Accelerates the Shift

Artificial intelligence did not invent the data market in scholarly publishing. It intensified it.

AI systems require structured, machine-readable inputs. Clean metadata. Standardized identifiers. Citation networks. Funding links. Author affiliations. Peer review timelines.

The better the structure, the better the output.

Imagine a dataset that integrates citation patterns across disciplines, maps collaboration networks, links funding to outcomes, and tracks usage by geography. Such a dataset can power manuscript triage systems, reviewer recommendation engines, literature mapping tools, and grant intelligence platforms.

The PDF alone cannot support these functions. Structured data can.

AI turns metadata from a technical requirement into strategic infrastructure. Organizations that control rich, high-quality datasets gain leverage in building or licensing AI-driven tools. Those that only produce content risk becoming suppliers to larger data ecosystems.

The value chain shifts upward.

Platformization and Ecosystem Control

Scholarly publishing is increasingly organized around platforms rather than isolated products. A platform connects multiple stakeholders and extracts value from interactions among them. In research ecosystems, these stakeholders include authors, reviewers, editors, institutions, and funders.

Submission systems capture workflow data. Author profile systems link publications to identities. Institutional dashboards integrate performance metrics. Funding databases connect grants to outputs. Citation indexes map influence.

When these components are integrated, the result is a comprehensive research data ecosystem.

Control of such an ecosystem provides several advantages. It generates granular behavioral data. It allows cross-disciplinary analysis. It embeds the provider into institutional decision-making. It increases switching costs for clients who depend on longitudinal data continuity.

This is a fundamentally different level of influence than controlling access to a journal.

The organization that maps the research ecosystem occupies a central position within it.

The Open Access Paradox

There is an irony worth highlighting.

Open access sought to democratize knowledge. In many respects, it has succeeded. Readers face fewer paywalls. Articles are more widely shared. Public access has expanded.

Yet the intelligence layer built on top of that content remains largely proprietary.

Citation databases are commercial assets. Usage analytics are controlled by platform providers. Institutional benchmarking systems are licensed products. Evaluation dashboards are subscription services.

In other words, the surface layer of scholarship has opened. The infrastructure layer has consolidated.

Universities may celebrate open access to articles while simultaneously allocating significant budgets to research analytics subscriptions. The invoice did not disappear. It changed category.

The debate about openness is moving from content availability to data governance.

Evaluation Culture and Metric Dependency

Modern research environments rely heavily on metrics. Citation counts, field weighted impact scores, collaboration indices, funding success rates, and altmetric signals influence hiring, promotion, and funding decisions.

These metrics are not abstract. They are derived from structured datasets maintained by specific providers. The methodology used to normalize fields, count citations, or assign institutional credit shapes outcomes.

When evaluation systems depend on proprietary data infrastructures, the providers of those infrastructures gain structural influence over academic incentives.

This does not require malicious intent. It is an economic reality.

If a university’s strategic plan incorporates a specific analytics platform, replacing that platform becomes difficult. Historical comparability matters. Benchmarking consistency matters. Internal workflows adapt to available metrics.

The infrastructure provider becomes part of the governance architecture.

That level of integration is far more durable than a journal subscription.

The Strategic Vulnerability of Smaller Publishers

Here is where the shift becomes uncomfortable.

University presses and smaller publishers often prioritize editorial rigor, disciplinary service, and ethical standards. These are essential foundations of scholarship. They should not be compromised.

But excellence in content production does not automatically translate into control over data infrastructure.

If a small publisher’s content flows into large citation indexes and analytics platforms without retaining structured oversight of metadata or analytics layers, it effectively supplies raw material to someone else’s data economy.

The asymmetry grows over time. Larger entities accumulate cross-disciplinary datasets. Smaller entities focus on individual titles.

Without a data strategy, smaller publishers risk being positioned as content providers within a system whose strategic value lies elsewhere.

This does not mean every press must build a global analytics platform. It does mean that ignoring metadata quality, interoperability, persistent identifiers, and structured data exports is shortsighted.

Data awareness is no longer optional.

Commoditization of Content

Another difficult truth is that high-quality scholarly content, while indispensable, is increasingly abundant.

Preprint servers expand rapidly. New journals launch continuously. AI-assisted writing tools accelerate drafting. Global research investment increases output volume.

When supply rises, differentiation shifts.

Brand and prestige remain powerful signals. However, at scale, individual articles compete in an environment saturated with alternatives. Discoverability and contextualization become as important as publication.

In contrast, comprehensive datasets that map relationships across millions of outputs remain scarce and defensible.

You can replicate a journal model. Replicating decades of integrated citation and funding data is a different challenge.

The competitive moat moves from content ownership to relational data control.

What a Data-Aware Publisher Should Consider

A publisher that recognizes this shift treats metadata as a strategic asset rather than a compliance task. Affiliations are standardized carefully. Funding information is captured consistently. Persistent identifiers for authors and institutions are integrated into workflows. Structured abstracts and machine-readable formats are prioritized.

Interoperability becomes central. APIs and structured exports allow flexible integration. Participation in shared standards increases resilience.

Internal analytics capabilities matter as well. Submission trends, reviewer performance, decision timelines, and citation trajectories can inform strategic decisions.

Exploration of AI tools should be grounded in data readiness. Automated reviewer matching, topic clustering, and trend analysis depend on clean underlying information.

The objective is not to mimic multinational analytics corporations. It is to ensure that the publisher is not invisible in a data-dominated ecosystem.

Governance, Ethics, and Control

As data becomes central, governance questions intensify.

Who controls citation graphs? Who defines field normalization? Who owns usage data generated by publicly funded research? How transparent are the methodologies behind widely used metrics?

If research evaluation increasingly depends on proprietary infrastructures, transparency and accountability become critical issues.

Open citation initiatives and community-driven metadata standards offer partial counterbalances. The next major reform movement in scholarly communication may focus less on article access and more on data openness and infrastructure governance.

The stakes are high. Metrics influence incentives. Incentives influence research behavior. Data infrastructures shape those metrics.

Control of data is not merely a business question. It is a systemic one.

Conclusion

Scholarly publishing has not abandoned content. Articles, books, and peer review remain the foundation of academic knowledge. Editorial integrity and intellectual rigor still matter profoundly.

But content alone is no longer the core strategic asset.

The real market has shifted toward structured data, analytics, and research intelligence. Citation networks. Funding linkages. Usage metrics. Institutional dashboards. AI-ready datasets. These elements define the new economic center of gravity.

Open access expanded the availability of content while inadvertently increasing the value of large-scale data aggregation. Analytics platforms embedded themselves into institutional governance. Artificial intelligence amplified the importance of structured information.

The organizations that thrive in this environment will not only publish. They will map research ecosystems, analyze relationships, power decision tools, and build trusted data infrastructures.

If you operate in scholarly publishing today, the essential question is not simply how to attract better manuscripts. It is how to position your organization within a data driven ecosystem.

What metadata do you control? How interoperable are your systems? Where does your content flow? Who builds the intelligence layer on top of your output?

Content may still be visible. Data is where leverage accumulates.

Ignoring that shift is comfortable. It is also risky.

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