Drivers of Sustainable Business Model Innovations: An Upper Echelon Theory Perspective

Article


Dhir, A., Khan, S. J., Islam, N., Ractham, P. and Meenakshi, N. 2023. Drivers of Sustainable Business Model Innovations: An Upper Echelon Theory Perspective. Technological Forecasting and Social Change. 191 (Art. 122409). https://doi.org/10.1016/j.techfore.2023.122409
AuthorsDhir, A., Khan, S. J., Islam, N., Ractham, P. and Meenakshi, N.
Abstract

This study explores the factors that drive the adoption of sustainable business model innovations (SBMIs). In this mixed-method (qualitative and quantitative) study, we draw on upper echelon theory to identify the factors that have led firms to switch from conventional products and processes to sustainable business innovation. This study of senior managers uses qualitative data to understand the mechanisms adopted by top management to make the switch to SBMIs. Data was gathered from 285 middle managers to empirically validate the theoretical model. The study concludes that in the top management team (TMT), ambidextrous learning has a positive association with the firm's decision to adopt SBMIs. However, TMT diversity and university-industry collaboration are positively associated with ambidextrous learning by top management and, subsequently, the adoption of SBMIs. Our findings also suggest that transformational leadership positively moderates the association between TMT diversity and ambidextrous learning. However, the impact on the relationship between collaboration and ambidextrous learning is negative.

JournalTechnological Forecasting and Social Change
Journal citation191 (Art. 122409)
ISSN0040-1625
Year2023
PublisherElsevier
Publisher's version
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.1016/j.techfore.2023.122409
Publication dates
PrintJun 2023
Online11 Mar 2023
Publication process dates
Accepted08 Feb 2023
Deposited13 Mar 2023
Copyright holder© 2023 The Authors
Permalink -

https://repository.uel.ac.uk/item/8vv92

Download files


Publisher's version
  • 31
    total views
  • 32
    total downloads
  • 6
    views this month
  • 13
    downloads this month

Export as

Related outputs

Data sharing for business model innovation in platform ecosystems: From private data to public good
Kazantsev, N., Islam, N., Zwiegelaar, J., Brown, A. and Maull, R. 2023. Data sharing for business model innovation in platform ecosystems: From private data to public good. Technological Forecasting and Social Change. 192 (Art. 122515). https://doi.org/10.1016/j.techfore.2023.122515
Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda
Zirar, A., Ali, S. I. and Islam, N. 2023. Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda. Technovation. 124 (Art. 102747). https://doi.org/10.1016/j.technovation.2023.102747
Theorizing the relationship between the digital economy and firm productivity: The idiosyncrasies of firm-specific contexts
Sun, Z., Zhao, L., Kaur, P., Islam, N. and Dhir, A. 2023. Theorizing the relationship between the digital economy and firm productivity: The idiosyncrasies of firm-specific contexts. Technological Forecasting and Social Change. 189 (Art. 122329). https://doi.org/10.1016/j.techfore.2023.122329