The business model bank: conceptualizing a database structure for large-sample study of an emerging management concept

The business model represents an increasingly important management concept. However, progress in research related to the concept is currently inhibited from inconsistencies in terms of formalizing and therewith also empirically measuring the business model concept. Taking this as a starting point, this paper offers a conceptualization for building a scalable database to rigorously capture large samples of business models. The following contributions are made: First, we suggest a concept for dimensions to be modeled in the database. Second, we discuss issues critical to the scalability of such an endeavor. Third, we point to empirical and simulation-based studies enabled by the population of such a database. Considerations for theory and practice are offered.

💡 Research Summary

The paper addresses a fundamental bottleneck in business‑model (BM) research: the lack of a unified, scalable way to capture and measure BM characteristics across large samples of firms. While the BM concept has become central to strategy, entrepreneurship, and innovation studies, scholars have been hampered by divergent definitions, ad‑hoc measurement instruments, and case‑study‑centric data that limit generalizability. To overcome these obstacles, the authors propose the “Business Model Bank” – a conceptual architecture for a comprehensive, extensible database that can store standardized BM information for thousands of organizations. The contribution unfolds in three interrelated parts.

First, the authors develop a dimensional framework that translates the qualitative elements of the BM canvas into a set of discrete, codified variables. The framework includes nine core dimensions: value proposition, customer segments, channels, customer relationships, core activities, core resources, core partners, revenue model, and cost structure. Each dimension is broken down into sub‑attributes (e.g., revenue model types such as subscription, transaction, advertising) and is paired with metadata describing its definition, measurement unit, permissible value range, and coding scheme. This systematic taxonomy ensures that data collected from diverse industries and geographic contexts can be harmonized under a common schema.

Second, the paper tackles the technical and organizational challenges of building a database that can scale to “big‑BM” data. The authors outline an automated ETL (extract‑transform‑load) pipeline that ingests data from multiple sources: annual reports, investor presentations, patents, corporate websites, social‑media feeds, and structured surveys. Advanced natural‑language‑processing (NLP) techniques are recommended for extracting BM attributes from unstructured text, while rule‑based parsers handle structured disclosures. To guarantee data quality, a two‑tier validation system is introduced: a “Data Quality Score” that evaluates completeness and consistency, and a “Source Reliability Index” that rates the trustworthiness of each origin. When conflicting records arise, priority rules based on source reliability and timestamp are applied, and all changes are logged for version control, enabling longitudinal tracking of BM evolution.

Third, the authors illustrate the research agenda that becomes feasible once the Business Model Bank is populated. With a large‑scale, cross‑sectional dataset, scholars can conduct clustering analyses to discover emergent BM archetypes, employ structural equation modeling to test causal pathways among BM dimensions, and apply machine‑learning algorithms (e.g., random forests, gradient boosting) to predict firm performance outcomes such as revenue growth or market share based on BM configurations. Moreover, the database supports simulation studies: researchers can model hypothetical policy shocks (e.g., regulatory changes) or technological disruptions (e.g., AI adoption) and observe how firms reconfigure their BM components in a dynamic systems framework. These capabilities dramatically expand the external validity of BM research beyond the limited scope of traditional case studies.

Beyond academic contributions, the paper emphasizes practical implications. Managers can use the standardized BM repository for competitive benchmarking, identifying gaps in their own model relative to industry peers, and informing new‑venture ideation. Investors may incorporate BM similarity metrics into due‑diligence processes, while policymakers could monitor sector‑wide BM shifts to assess the impact of regulatory initiatives. The authors argue that the Business Model Bank will serve as a shared infrastructure that fosters collaboration across disciplines (strategy, marketing, finance, innovation) and geographies, enabling meta‑analyses and cross‑country comparisons that have previously been impossible.

In conclusion, the article provides a clear, actionable blueprint for transitioning BM research from fragmented case narratives to a data‑driven discipline. By defining a rigorous dimensional taxonomy, proposing robust data‑integration and quality‑control mechanisms, and outlining a rich agenda of empirical and simulation‑based studies, the authors lay the groundwork for a “big‑data” era in business‑model scholarship. If implemented, the Business Model Bank could become a cornerstone resource that accelerates theory development, enhances managerial decision‑making, and informs evidence‑based policy in the rapidly evolving landscape of modern business.