Developing high-performing business models
Abstract/Contents
- Abstract
- Strategy and innovation scholars point to the rising importance of business models for firm performance. While prior research offers some insight into high-performing business models, it largely focuses on established firms with stable business models. Yet, this overlooks how entrepreneurial ventures develop and scale novel business models. This dissertation addresses this gap with three closely linked studies. The first is a comprehensive review of prior literature linking business models to firm performance. The second uses a novel theory-building methodology — machine learning and multiple cases -- to fit revenue models (i.e., value capture) with underlying activities (i.e., value creation) in high-performing business model configurations. The third is a longitudinal multiple-case theory-building study that unpacks how entrepreneurs build scalable business models in nascent markets. Jointly, the studies in this dissertation offer rich theory regarding how entrepreneurs can effectively develop and scale novel business models. Overall, this research contributes to literature on strategy, innovation, and entrepreneurship, as well as to practice
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Tidhar, Ron |
|---|---|
| Degree supervisor | Eisenhardt, Kathleen M |
| Thesis advisor | Eisenhardt, Kathleen M |
| Thesis advisor | Byers, Thomas (Thomas H.) |
| Thesis advisor | Eesley, Charles |
| Thesis advisor | Katila, Riitta |
| Degree committee member | Byers, Thomas (Thomas H.) |
| Degree committee member | Eesley, Charles |
| Degree committee member | Katila, Riitta |
| Associated with | Stanford University, Department of Management Science and Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Ron Tidhar |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Ron Tidhar
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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