Towards Sustainable Innovative Business Models

Towards Systematic Sustainable Business Model Innovation: What Can We Learn from Business Model Innovation

This research aims to understand how sustainable business model innovation (SBMI) can learn from business model innovation. For this, first, we conducted a bibliometric analysis to evaluate the relationships between business model innovation and SBMI literature. After this, we performed a systematic literature review to create a comprehensive framework for managing SBMI. The bibliometric analysis showed that the SBMI stream grew quickly and significantly in recent years, evolving into a separated new research stream, which does not leverage recent business model innovation advancements. Through the performed analyses, we were able to discuss critical gaps in the SBMI literature and shed light on possible pathways to solve these gaps through lessons learned from business model innovation. We depicted five critical gaps for managing SBMI; (1) the need to understand the sustainable business model as a wicked problem, in which SBMI leads to “better than before” solutions calling for systematic SBMI, (2) the poor definition of distinctive dimensions of dynamic capabilities for SBMI, (3) the lack of studies exploring the role of open innovation for improving the SBMI process, (4) the lack of tools supporting SBMI implementation and (5) the need to explore game-changing, competitive advantages of SBMI. The findings of this study contribute to guiding future research on SBMI, which can be a basis for further efforts towards sustainable development.

Explainable Ontology-Based Intelligent Decision Support System for Business Model Design and Sustainability

Background: Case-Based Reasoning (CBR) is a problem-solving paradigm that uses knowledge of relevant past experiences (cases) to interpret or solve new problems. CBR systems allow generating explanations easily, as they typically organize and represent knowledge in a way that makes it possible to reason about and thereby generate explanations. An improvement of this paradigm is ontology-based CBR, an approach that combines, in the form of formal ontologies, case-specific knowledge with domain one in order to improve the effectiveness and explanation capability of the system. Intelligent systems make daily activities more easily, efficiently, and represent a real support for sustainable economic development. On the one hand, they improve efficiency, productivity, and quality, and, on the other hand, can reduce costs and cut waste. In this way, intelligent systems facilitate sustainable development, economic growth, societal progress, and improve efficiency. Aim: In this vision, the purpose of this paper is to propose a new generation of intelligent decision support systems for Business Model having the ability to provide explanations to increase confidence in proposed solutions. Findings/result: The performance results obtained show the benefits of the proposed solution with different requirements of an explanatory decision support system. Consequently, applying this paradigm for software tools of business model development will make a great promise for supporting business model design, sustainability, and innovation.