Value Proposition Experimentation Procedures in Digital Business Models

A Systematic Literature Review

Abstract

Business model experimentation reduces uncertainty by converting assumptions about customers, value propositions, and value capture mechanisms into testable hypotheses. This article consolidates value proposition experimentation procedures for digital business models reported in the literature. A systematic review guided by the PRISMA method was conducted using Scopus and Web of Science. The initial search returned 625 records. After refining the terms, 40 records were obtained. Screening removed 15 duplicates, one unavailable document, and four documents belonging to excluded typologies. Four additional studies were incorporated through a complementary search, resulting in 24 documents. The analysis identified 11 experimentation models, organized into the macro-stages of experimentation planning and design, operational cycle and learning test, and strategic validation and market transition. The most frequent evidence sources were documentary research, case studies, interviews, and literature reviews. The techniques included content analysis, descriptive statistics, structural equation modeling, cluster analysis, simulation, and supporting frameworks. The results show convergence around an iterative logic, but limited consolidation and insufficient detail in quantitative approaches.