Description:
Purpose: This paper explores the challenges sustainable business model (SBM) studies may face related to the information gathered during the data collection process, and elaborates on how transdisciplinary research can help to overcome these challenges. Our contribution is based on the theoretical lens of information asymmetry. Design/Methodology/Approach: This paper uses a qualitative methodology based on a transdisciplinary program that aims to support disadvantaged communities in Bolivia. The program started 3 years ago, with as objective to help vulnerable communities through transdisciplinary intervention in 6 projects, viz. 1. Social vulnerability, 2. Integrated water management, 3. Food security, 4. Indigenous rights, 5. Productive development and 6. Transversal. In addition to our experience in the program, we have conducted 57 interviews and 10 focus group discussions with vulnerable entrepreneurs and relevant stakeholders, alongside numerous on-site observations. Findings: The findings of our study illustrate that SBM research can face information asymmetry issues such as lack of access to, lack of understanding of and lack of trust in the information provided. We also show how transdisciplinary research helps to bridge such issues of trust, understanding and information availability. Based on our research, we propose 5 suggestions to scholars who wish to adopt transdisciplinary research in their study of SBMs: (i) understand the context, (ii) adapt to the context, (iii) develop relationships of trust, (iv) be flexible with your research focus and (v) systematically present to other disciplines and non-academic actors. Originality/Value: Our contribution is based on the theoretical lens of information asymmetry and argues that a transdisciplinary approach is necessary to accumulate fundamental knowledge on SBMs. Such an approach constitutes a rather sophisticated research methodology that can help us embrace the complexity of sustainable business models and find practical solutions for their scalability.