Digital business model innovation: scale development and validation

Purpose This study aims to develop and validate a scale measuring Digital Business Model Innovation (DBMI) within organisations. The goal is to provide a reliable and comprehensive tool for assessing the extent of DBMI in practice. Design/methodology/approach The paper follows a rigorous scale development process, which includes item generation, scale development, and purification. Validity and reliability tests were conducted using survey data from two sample sets of 485 and 459 SMEs in India. Findings The study identifies a multidimensional conceptualisation of DBMI, consisting of Value Proposition Innovation, Revenue Model Innovation, Customer Engagement Innovation, Operational Model Innovation, Ecosystem and Partnership Innovation, Organisational Innovation, and Technological Innovation. The developed scale demonstrates strong convergent and discriminant validity, confirming its usefulness as a measurement tool. Research limitations/implications This study advances the understanding of DBMI by providing a solid conceptual framework and a reliable measurement scale. However, the cross-sectional design limits the ability to establish causal relationships, suggesting the need for longitudinal studies to explore the long-term effects of DBMI. Practical implications Practitioners can use the scale to assess their organisation’s DBMI level, benchmark against industry peers, and identify areas for improvement. The scale’s multidimensional nature enables the design of targeted interventions and more efficient resource allocation for successful DBMI. Social implications A higher DBMI level can enhance organisational efficiency, customer satisfaction, and value creation, contributing to broader societal benefits such as economic growth, job creation, and innovation. Originality/value This study contributes to the literature by developing a reliable measurement scale and offering a comprehensive conceptualisation of DBMI. The mixed-methods approach, combining quantitative and qualitative data, strengthens the findings and provides valuable insights for scholars, practitioners, and policymakers interested in DBMI.